Greater Toronto and Hamilton Area with its hub at Union Station in. Toronto. Local streets: Paved city street with no demarcated lanes of motor vehicle traffic; car ...
Enabling cycling access to rail stations: Prioritizing and bridging unsafe connections The development and testing of a 4-Step Bike-Rail cycling corridor identification tool to improve cycling access to rail stations in Toronto, Canada.
Sibel Sarper Master’s Thesis Master of Science in Resource Efficiency in Architecture and Planning (REAP)
HafenCity Universität Master of Science in Resource Efficiency in Architecture and Planning (REAP) Master’s Thesis
Enabling cycling access to rail stations: Prioritizing and bridging unsafe connections The development and testing of a 4-Step Bike-Rail Cycling Corridor Identification Tool to improve cycling access to rail stations in Toronto, Canada.
Author: Sibel Sarper Supervisors: Prof. Dr.-Ing. Wolfgang Dickhaut and George Liu, M.Sc.
© Copyright by Sibel Sarper 2018
Abstract The level of bicycle-rail integration in Toronto presents an
condition, the lack of safe cycling connectivity was predicted to be
unrealized sustainable mobility potential. Currently only 1% of rail
harming the GO rail’s cycling access rates. As a result, a “Bike-
passengers access the GO rail network by bicycle. In contrast, there
Rail cycling corridor identification tool” was developed to assess
is demonstrated potential based on other European cities, namely
the current level of safe cycling connectivity from commuters’
Amsterdam, which has reached 43% cycling access mode share to
homes to the GO rail station, identifying whether gaps in safe
rail. There is much to learn from cities that have reached high
cycling exist, then applying a method of identifying where
cycling mode share so this master’s thesis draws on some best
infrastructure improvements have the highest potential of enabling
practices and lessons learned and pulls them into the Toronto
cycling access. Using an underperforming GO rail station as a case
context. Rail on its own suffers from weaknesses in providing a
study, the tool was applied to Kipling GO station. Results showed
convenient and flexible door-to-door connection that cycling is
there are indeed no safe cycling routes connecting commuters from
able to overcome. In combination, bicycle-rail integration provides
their home to the station, particularly on the routes that were
a competitive rival to substitute car journeys of comparable
modelled to carry the highest commuter volume, helping explain
distances. Suburban rail with station spacing between 4 km and 10
why the station may have unrealized latent cycling demand.
km are ideally suited for cycling as an access mode, considering cycling is the most competitive at distances between 1 and 5 km.
i
The outcome of the analysis helped prioritize what cycling infrastructure upgrades on which corridors demonstrate the most
However, the built form of the suburbs causes travel from homes
potential of opening up cycling access. The tool can objectively
to destinations to be pushed to high-speed arterial roads which have
quantify the gain on cycling investment, specifically the savings
been designed to optimize vehicular flows, providing little space
from attracting ridership without having to supply additional, free
and accommodation for cyclists within the public realm. Most
parking to customers. With parking capacity at its peak at GO rail
suburban rail stations are located adjacent to major arterials,
stations, the rail agency, Metrolinx is unable to provide parking to
creating a hazardous journey for connecting to the station on bike.
keep pace with ridership growth. Thus, the rail agency gains from
Since the majority of the population is traffic risk-intolerant, with
integrating bicycle-rail since growing cycling access essentially
only about 1% of the population comfortable riding in any road
translates into increased ridership at low access-service cost.
Acknowledgements Foremost, I would like to express my sincere gratitude to my
Additionally, many thanks to Raul Kalvo for the responsive
principal supervisor Prof. Dr.-Ing. Wolfgang Dickhaut for the
assistance with the Urban Network analysis toolbox that helped me
unwavering support of my study and research. I truly appreciate
put the pieces together.
you always making the time to meet; your immense knowledge and motivation was the most valuable guidance I could have hoped for. A special thanks to my thesis supervisor George Liu at the Eindhoven University. From start to finish you were an endless source of inspiration, patience and support. I could not have asked for a better mentor. Thanks for sharing your cycling planning insights and guiding me in the right direction with tough questions. It is amazing how one small idea snowballed into this thesis! I would also like to thank Reuben Briggs from the University of Toronto Data Management Group for the patience and careful
My sincere thanks to Deutschlandstipendium for the financial support and to DAAD for financing our unforgettable project and trip to Cairo. A big thank you to all my friends and colleagues from the REAP program at HCU, it has been an amazing two years and I am so grateful to have met you all. I will never forget the inspiring discussions, the great team work and most of all the fun and good times we have had on this journey together! My dearest family, thanks for the help and support with any path I take, especially my mother for taking the time to proofread.
guidance in helping navigate the Transportation Tomorrow Survey databases. The data analysis is a key element of my paper and I
Finally, the most special thanks to my husband Philipp for giving
appreciate your support in making this thesis possible.
me your unconditional support and love along the way.
_____________________________________________________
Cover photo source: S-F / Shutterstock.com
ii
Table of Contents Abstract ....................................................................................................................................................................................................... i Acknowledgements .................................................................................................................................................................................... ii List of Figures............................................................................................................................................................................................ vi List of Tables ............................................................................................................................................................................................ vii List of Appendices................................................................................................................................................................................... viii List of Abbreviations ............................................................................................................................................................................... viii Definitions ................................................................................................................................................................................................. ix PART A: INTRODUCTION AND RESEARCH DESIGN........................................................................................................................1 1 Introduction ............................................................................................................................................................................................2 1.1 Toronto’s Urban Mobility Challenges.............................................................................................................................................2 1.2 Geographic Context .........................................................................................................................................................................2 1.3 Opportunities for Suburban Rail .....................................................................................................................................................4 1.4 Opportunities for station access by bicycle .....................................................................................................................................6 1.5 Problem Statement...........................................................................................................................................................................7 1.6 Research Questions .........................................................................................................................................................................8 1.7 Structure and Methodology .............................................................................................................................................................9 PART B: THEORY ...................................................................................................................................................................................13 2 How to make rail competitive to the car ..............................................................................................................................................14 2.1 Factors that affect propensity of rail use .......................................................................................................................................14
iii
2.2 Access or rail service improvement, which is the most cost-effective investment?..................................................................... 14 2.3 Review of access mode options .................................................................................................................................................... 15 2.4 Opportunities for bicycle-rail combination .................................................................................................................................. 19 PART C: THE TORONTO CONTEXT ................................................................................................................................................... 22 3 Cycling to GO Stations ........................................................................................................................................................................ 23 3.1 Existing Conditions ...................................................................................................................................................................... 23 3.2 GO Policy Context........................................................................................................................................................................ 23 3.3 GO rail expansion ......................................................................................................................................................................... 25 PART D: MANUAL................................................................................................................................................................................. 27 4 How to improve the cycling-rail combination ..................................................................................................................................... 28 4.1 Variables that Influence Cycling Uptake to GO stations in the GTHA........................................................................................ 28 4.2 Importance of connectivity – the key to enabling cycling uptake ................................................................................................ 28 4.3 Acceptable level of detour ............................................................................................................................................................ 30 4.4 Transport choices and how to make behaviour change happen.................................................................................................... 31 5 How to encourage cycling to stations in practice ................................................................................................................................ 33 5.1 Urban Structure............................................................................................................................................................................. 33 5.2 Infrastructure Interventions .......................................................................................................................................................... 34 6. Proposed cycling infrastructure .......................................................................................................................................................... 46 6.1 Measures for Quiet Streets............................................................................................................................................................ 46 6.2 Measures for Fast, Busy Streets.................................................................................................................................................... 48 PART E: THE 4-STEP BIKE-RAIL CORRIDOR PLANNING TOOL ................................................................................................. 54
iv
7 Where should cycling infrastructure be prioritized to most effectively improve rail access?..............................................................55 7.1 Modelling Process .........................................................................................................................................................................55 7.2 Data Used ......................................................................................................................................................................................55 7.3 Creating a building-level commuter population dataset: How the number of commuters in each residential home is calculated ...............................................................................................................................................................................................................58 7.4 Defining potentially cyclable trips to GO rail stations ..................................................................................................................59 7.5 Bike-Rail cycling corridor identification tool procedure ..............................................................................................................60 PART F: CASE STUDY APPLICATION ................................................................................................................................................63 8 Case study station selection ..................................................................................................................................................................64 8.1 Kipling GO station: Bike-Rail cycling corridor identification tool application ............................................................................64 8.2 Existing Travel Conditions and impact on travel downtown from cycling access improvements................................................80 8.3 Current trips that could be made by GO rail transport from Kipling GO (within its 3.5 km cycling catchment area) .................82 PART G: CONCLUSION AND DISCUSSION .......................................................................................................................................85 9 Summary and Conclusions ...................................................................................................................................................................86 9.1 Results and answers to research questions ....................................................................................................................................86 9.2 Concluding remarks.......................................................................................................................................................................89 9.3 Reflections .....................................................................................................................................................................................90 9.4 Limitations of research ..................................................................................................................................................................91 9.5 Outlook and further research .........................................................................................................................................................92 10 Appendices .........................................................................................................................................................................................94 11 References ........................................................................................................................................................................................113
v
List of Figures Name
Page
Figure 17: Bikeway safety comparison between user preferences and observed safety performance
41
Figure 18: Cycling access mode share in Amsterdam
42
Figure 19: Copenhagenize bicycle planning guide.
44
Figure 20: Low stress cycling city
45
Figure 21: The Relationship of Traffic Speed and Volume to Types of Cycling Facilities.
46
Figure 1: Downtown employment as a proportion of City employment is increasing.
3
Figure 2: Conceptual comparison between the transport options available at the city or regional scale available during peak commuting hours in an urban area
4
Figure 3: Comparing the speed of different travel modes for different trip distances
6
Figure 22: higher vehicle speeds require longer stopping times,
47 47
Figure 3: Overview of research questions and report structure
9
Figure 23: Higher vehicle speed increases likelihood of cyclist and pedestrian fatalities in a collision.
Figure 5: Building-level modelling procedure
10
Figure 24: Components of a protected bikeway
48
Figure 4: Conceptual comparison between transport feeder modes
15
Figure 25: relative levels of exposure a person cycling has based on the type of bicycle facility
50
Figure 5: Combining rail with cycling as an access mode.
20
Figure 26: Protected Intersection
50
Figure 6: The competition for a home-work journey
21
Figure 27: increased cyclist awareness to the motor vehicles
50
Figure 7: Average GO Access Distance in Toronto from the GO Transit Commuter Survey
23
Figure 28: Copenhagen intersection design elements
51 52
Figure 8: GO access mode share
24
Figure 29: Example of raised protected bikeway crossing at a minor street intersection in Delft, Netherlands
Figure 9: GO rail parking supply and ridership growth
24
Figure 30: Example of bending out a protected bikeway at an intersection
53
Figure 10: The destination GO stations from commuters’ home in the GTHA
25 Figure 31: 4-Step bike-to-rail cycling corridor identification tool process
56
Figure 13: Four Types of Cyclists.
29 31
Figure 32: Volumetric procedure for calculating number of units (households in apartments and multi-unit homes
58
Figure 14: Force Field Analysis. Figure 15: Pyramid for successful public space for cyclists
37
Figure 33: Financial district, city centre
60
Figure 16: Relative risk of injury for different bikeway types
40
Figure 34: Betweenness analysis, data import.
65
vi
vii
Figure 35: Closest facility tool results
65
Figure 36: Betweenness analysis results, visualized in ArcMap
66
Figure 37: Betweenness analysis results (catchment area), visualized in ArcMap
67
Figure 38: Route stress level identification
68
Figure 39: Route stress level identification (reclassified)
69
Figure 40: Spatial intersection: High commuter volume and stress levels
Figure 52: Number of commuters who live within 3.5km (network-based distance) of each Toronto GO station. Dat
80
Figure 53: Average mode share over all Aggregate Dissemination Areas (ADAs) around the Kipling GO Cycling Catchment. Data Source: TTS data, 2011
81
82
70
Figure 54: Average mode share over all Aggregate Dissemination Areas (ADAs) around the Kipling GO Cycling Catchment. Data Source: TTS data, 2011
82
Figure 41: Infrastructure phasing prioritization
74
Figure 55: Trips made from Kipling GO to Downtown destination -egress distance (TTS, 2011)
Figure 42: Bloor St W near Martin Grove Rd existing conditions
75
Figure 56: Egress distances from Kipling GO rail station
83
Figure 43-44: One-way raised protected bikeway (NACTO, 2018)
76
Figure 45: Mountable curb separation between bikeway and sidewalk.
76
Figure 46: Bloor St W near Martin Grove Rd cross section (existing conditions)
List of Tables Table 1: Summary and statistical influences in cycling uptake
28
77
Table 2: BiTiBi six building blocks for improving bicycle-rail and associated barriers
34
Figure 47: Bloor St W near Martin Grove Rd cross section (proposed)
77
Table 3: Bike-rail intervention options.
35
78
Table 4: The type and function of roads in a sustainably safe road traffic system
37
Figure 48: Bloor Street West (near Renforth Dr) – existing conditions
Table 5: Cycling route requirements.
39
Figure 49: Bloor Street West (near the West Mall) cross section– existing conditions
79
Table 6: Top cycling deterrents and motivators
40
Figure 50: Bloor Street West (near the West Mall) cross section– proposed
79
Table 7: Road function then speed determines infrastructure type
45
Figure 51: Location of Kipling GO and downtown Toronto (PD1)
80
Table 8: Classification of Toronto’s cycling streets.
46
Table 9. Summary of Research about Cycling Distance to Access Transit
59
Table 10 bikeable roads.
61
List of Abbreviations
Table 11: high and low-stress link classification
62
Bike-Rail: combining cycling to rail stations
Table 12 GO Stations with 10 Largest Negative Residuals
64
Table 13: Bikeway stress level summary
70
Table 14: Description of cost-benefit variables
72
BSM - Bus, streetcar, metro CBA - Cost Benefit Analysis
Appendix 1 - Buffer separation types.
93
CROW - Acronym of the Information and Technology Platform for Transport, Infrastructure and Public space, a Dutch non-profit collaboration between government and businesses that produces the CROW-publication 261 “Handboek verkeersveiligheid” (“Road safety manual”)
Appendix 2– Four Types of Cyclists by Proportion of Population
94
GTHA – Greater Toronto and Hamilton Area
Appendix 3 – Pyramid for successful public space for cyclists
95
MIT - Massachusetts Institute of Technology
Appendix 4 – TTS Zone data
96
Appendix 5 – CoGran Procedure
98
Appendix 6 – Modelling the number of units in multi-unit buildings or apartment: Volumetric calculation procedure
100
Rhino3D – Rhinoceros is a commercial 3D computer graphics and computer-aided design (CAD) application software developed by Robert McNeel & Associates,
Appendix 7 – Residential land use and building footprints
102
Appendix 8 – Betweenness analysis results from Rhino3D (area)
103
Appendix 9 – Betweenness analysis results from Rhino3D (station access)
104
Appendix 10 – Cost-Benefit values used
105
Appendix 11 - Mode share data in all Aggregate Dissemination Areas (ADAs) around the Kipling GO Cycling Catchment.
108
Appendix 12 – GO rail commuter trips – Access Map for Kipling GO and Egress Maps for Union Station
109
Appendix 13 – Neighbourhood Improvement Areas
111
List of Appendices
TAZ – Traffic Analysis Zone TTC – The Toronto Transit Commission is a public transport agency that operates bus, subway, streetcar, and paratransit services in Toronto TTS – Transportation Tomorrow Survey UNA toolbox – Urban Network Analysis toolbox for Rhino3D Units: m: metre(s) km: kilometre(s) cm: centimetre(s) mm: millimetre(s)
viii
Definitions
including pedestrians, cyclists, joggers, and skaters. Also known as an off-street path.
Arterial Road: The primary function of this road class is to support traffic movement of all vehicle types. Some property access control
Electric bicycles: The general term for power-assisted bicycles,
is provided and carries an estimated 8,000 to +20,000 vehicles per
namely pedelecs (i.e. electric bikes that are propelled with physical
day. Speed limits are between 50 to 60 km/h. Sidewalks are provided
strength up to a speed of 25 km/hr. Pedelecs differ very little from
on both sides of the road for pedestrian safety. High priority of winter
conventional bicycles in how they are operated.) and e-bikes (i.e.
maintenance. Also referred to as a major street.
bicycles with electric motors that can be ridden without pedalling, meaning entirely electrically powered).
Bicycle boulevards: Streets with low motorized traffic volumes and speeds, designated to give bicycle travel priority. Bicycle Boulevards
Planning District 1 (PD1): Covers the downtown district and is a
use signs, pavement markings, speed and volume management
common analysis area for both land use planning and transportation
measures to discourage through trips by motor vehicles and create
planning by the City of Toronto.
safe, convenient bicycle crossings of busy arterial streets.
Protected bikeway: Paved path meant for cyclists to use alongside
GO Train: A regional, public commuter rail system serving the
major streets, separated by a physical barrier (e.g., a curb or bollards)
Greater Toronto and Hamilton Area with its hub at Union Station in
or grade-separated. Also known as cycle tracks (separation types
Toronto.
described in appendix 1)
Local streets: Paved city street with no demarcated lanes of motor
Traffic calming: Measures intended to slow traffic and discourage
vehicle traffic; car parking may be allowed or not
cut-through in residential neighbourhoods, with the goal of enhancing
Multi-use trail: A path physically separated from motor vehicle traffic (at least on straightaways between intersections) by an open space or barrier and either within a park, public right of way or easement, which accommodates two-way non-motorized users
ix
pedestrian
and
cyclist
safety
and
improving
neighbourhood liveability. Traffic calming features may also contribute to the aesthetic quality of the streetscape. Measures may include speed humps or bumps, traffic circles, traffic diverters, medians, or street width restrictions.
x
PART A: INTRODUCTION AND RESEARCH DESIGN
PART A: INTRODUCTION AND RESEARCH DESIGN
1
1 Introduction 1.1 Toronto’s Urban Mobility Challenges
encourage people to choose cycling and walking for their trips is the key to slashing emission levels and creating healthy, liveable cities. However, muscle power can only get you so far. Many of
The United Nations estimates that urban areas are currently home
our trips require a distance too far to undertake by foot or cycling
to 54 percent of the world’s population, a figure that is predicted to
alone. In Toronto, the average commuting distance is 10km
reach 66 percent by 2050 (United Nations, 2014). Growing
(Yawar, 2016), well above the 5km trip length threshold that has
urbanization provides both opportunities and challenges from the
been found to be a strong motivator for cycling (Winters et al.,
perspectives of transportation mobility, climate change and air
2010), and a competitor to the car (City of Toronto, 2012). For trips
quality. Higher population densities generally support greater
above 5km, a suitable, more sustainable alternative to the car needs
levels of public transport and can make destinations more easily
to be found that will rival the speed and door-to-door convenience
accessible by walking or cycling, thus providing increased mobility
of driving.
while discouraging the usage of motor vehicles. At the same time, urbanization leads to higher competition for space on existing
1.2 Geographic Context
roadways and tends to increase surface transportation congestion.
Toronto is the capital of Ontario and the economic powerhouse of
Therefore, as the number of travel kilometres increase in cities, the
Canada. It has a population of over 2.7 million people and with the
severity of congestion and air pollution grow at an increasing pace.
suburban population greater than its urban population, car culture
Transportation accounts for more than 41% of Toronto’s total greenhouse gas emissions (City of Toronto, 2017). To keep our cities healthy and liveable, modes of transport that move the most people with the least amount of pollution should be encouraged. Shifting commuters from their cars to sustainable and active forms of transportation can address these issues by reducing the amount of pollution emitted and providing positive socioeconomic and health benefits to the population. Creating urban environments that
has often dominated the City’s planning agenda (Yawar, 2016). Its downtown centre is home to the country’s most important financial and business centre (City of Toronto, 2018a). Since Toronto’s central business district is located in the centre of downtown, it is a big draw for commuters from the Greater Toronto and Hamilton area (GTHA) commuting from the periphery of Toronto. Toronto has the highest job density of all cities within the GTHA (approximately 511,200 jobs within 21.4km2) (City of Toronto, 2016a). This pattern of monocentric commuting is causing
2
immense strain on the city’s road network, further exacerbated by
The GTHA is Canada’s largest metropolitan area with a population
the high proportional increase of employment downtown compared
of 6,574,140 in 2011 (Statistics Canada, 2011) and draws a large
to the rest of the city (Figure 1). Historically, the city has attracted
number of workers with different skills, education and income
high public transport patronage due the high employment
levels. The GTHA is growing and projected to expand from 6.8
concentration and limited parking supply (and therefore associated
million in 2011 to 10.1 million by 2041 (Hemson Consulting,
high parking costs) (Metrolinx, 2012).
2011). This surge of commuters will add stress to the already congested commuting corridors within Toronto, especially during the commuting hours when traffic is at its peak. Another major reason behind the rapid increase of population in the GTHA is the attraction of immigrants that find this region desirable due to its versatility, ethnic mix, and job opportunities. With the increasing population trend and limited availability of housing, the demand for affordable housing has increased rapidly, giving rise to suburbs which has resulted in longer commuting distances and increased congestion during peak hours.
Figure 11: Downtown employment as a proportion of City employment is increasing. Source: Metrolinx (2015b)
Although the city is highly self-contained with 81% of resident workers commuting within Toronto, it also attracts an additional 37.9% inbound commute from other towns and cities in the GTHA (Yawar, 2016). The towns and cities sending the highest number of commuters to Toronto based on the total residents from these areas are Richmond Hill (45%), Vaughan (46%), Markham (51%), Ajax (52%) and Pickering (55.5%) (Yawar, 2016).
3
In the GTHA, the Toronto Board of Trade (2013) estimates congestion is costing the region an estimated $11 billion annually, with approximately 5 million tons of carbon dioxide (CO2) emitted per year due to fuel wasted from inefficient vehicle operation due to congestion. The concentration of both emission sources and exposed populations in urban areas creates a significant challenge for protecting human health from the effects of poor air quality. Efforts to reduce congestion through road building frequently fail to keep pace with transportation demand; they are likely to generate
additional vehicle traffic due to the phenomenon of induced demand, which further worsens air quality and creates more demand for road building (Pike, 2010). Overall, the road and transit infrastructure has been unable to keep pace with the commuting lifestyle of the region’s residents, leading to unprecedented levels of congestion; the sprawl has increased commuting times by locating residents ever further from their places of employment. Therefore, travel times are steadily get longer, resulting in Toronto
High levels of congestion are reportedly costing the
Figure 25: Conceptual comparison between the transport options available at the city or regional scale available during peak commuting hours in an urban area, including: rail, BSM (bus, streetcar, metro) and car. Interpretation by Author based on: (Bertolini et. al., 2003; Kager, Bertolini, and Te Brömmelstroet 2016; Litman, 2002). Icons from The Noun Project (2018).
Toronto region between $6 billion and $11 billion a
*Costs are measured per passenger kilometre (Litman, 2002)
year in lost productivity (Sorensen & Hess, 2015).
Note: The criteria speed, flexibility and reliability were reported by Metrolinx as the top needs of GO customers
residents having the highest commute time in Canada and among the highest in North America.
Toronto’s population growth has led to major
private-sector investments in both housing and offices, yet in the suburbs much of that development is in locations poorly served by transit. As a result, both congestion and travel times will continue to worsen unless major investments in public transport are made.
1.3 Opportunities for Suburban Rail The GO rail network is a high-speed, regional-level commuter rail system connecting Toronto to towns and cities within the GTHA. The operations and planning are managed by Metrolinx, the provincial agency responsible for transport planning integration in the GTHA (Metrolinx, 2015a). Figure 2 illustrates a comparison between the transport options at the city or regional scale available
4
during peak commuting hours in an urban area, namely rail, BSM
environmentally attractive and reducing the sensitivity to passenger
(bus, streetcar, metro) and car. The GO rail network ranks the best
load variances (Chan et. al. 2013). Overall, rail is the cleanest of
in regards to environmental impacts, speed, spatial reach and
the mass transport modes in land transportation and requires
reliability; making it the most preferable alternative to the car.
significantly less space to move the same amount of car passengers.
Along with its advantages, there are disadvantages to the GO rail
By ensuring a high availability and usage of rail services,
network that need to be overcome to make it a desirable transport
encouraging rail use will help Toronto and Canada achieve its
mode for the user like the door-to-door connection and flexibility.
energy/environmental targets.
The following is a summary of rail’s positive impact on society and the strengths that make it an advantageous mode of transport to encourage.
Safety: Railways have the best safety performance among land transit modes. In 2015, two fatalities on the GO railway system (one crossing and one from trespassing) occurred in the entire
Environmental-friendliness: Rail transport is responsible for only
GTHA (TSB, 2015) compared to over 65 fatalities from road
9% of Canada’s total transport greenhouse gas emissions,
collisions in Toronto (City of Toronto, 2015a).
compared to 86% coming from the road section (Statistics Canada, 2017). Compared to private automobiles, diesel commuter trains were found to emit 5.5 times lower emissions per passenger-mile (for an occupancy of 1.4 passengers/car and 223 passengers/train). However, the emission savings by diesel rail are very sensitive to passenger loads and distances travelled. For an occupancy rate of
Encouraging investment: Rail transport is an important tool to encourage investment in the suburbs, serving a dual purpose: providing better mobility options for existing residents and employers,
and
encouraging
new
local
investment
and
intensification that will lead to improved services, walkability, and liveability in areas that need it the most.
1.6 passengers/car and 66 passengers/train, a car consumes 2.7
5
times more energy; cars emit 3.1 times more greenhouse gases and
Socioeconomic benefits: Access to frequent, fast and affordable
emit 8.5 times more pollutants than a diesel heavy rail train per
transit is crucial for equity and social cohesion. There are many
passenger-miles travelled (Chester & Hovarth, 2008; Chan et. al.
people in Toronto who can not afford to own a car and many more
2013). When the diesel commuter rail system is electrified, annual
who stretch their resources to do so. As the cost of living increases
emissions are decreased by 98%, making rail transport even more
and housing affordability decreases, low-income residents are
being pushed to areas of the city that are not as well-served by transit, raising the potential of social exclusion as more people are unable to afford to participate in activities due to the high cost of travel. The key competitor to the car: Rail transport is the strategic sector, on which the success of the efforts to shift the balance [from private to public modes] will depend” (CEC, 2001; Givoni et. al. 2007). In other words, rail has a key potential in encouraging people to shift from motor vehicles to more sustainable forms of transport, helping achieve environmental and health objectives.
Figure 3: Comparing the speed of different travel modes for different trip distances* Source: City of Toronto (2012), adapted from Dekoster and Schollaert (1999).
1.4 Opportunities for station access by bicycle
*The modes depicted in the figure do not all start at 0 minutes because of the
Travel time, particularly for commuters, is one of the most
time required at the “start-up” of some of the modes. For example, train trips
important determinants of transport mode choice, balanced
require ~22 minutes to arrive at the station, purchase a ticket and board the
against cost, comfort and safety (Tyrinopoulos et. al., 2013).
train. Alternatively, there is no “start-up time” for walking, which starts at 0.
For trips 5 km or less in an urban area, cycling is competitive in travel time to the car (Figure 3).
rates requires a “package of many different, complementary interventions” ranging from infrastructure, marketing, supportive
Combining the advantages of cycling with the speed of rail helps
land-use planning, and restricted car use (Pucher, Dill & Handy,
create a seamless door-to-door journey that can strategically rival
2010). Since safety is ranked as the number one deterrent for more
the speed and door-to-door convenience of driving. The bicycle-
people to cycle and existing cyclists to cycle more (Tfl, 2014;
rail synergy therefore holds the key potential to shift people from
Cycling Embassy of Great Britain, 2018), the cycling routes to rail
motor vehicles to more sustainable forms of transport with the least
stations needs to offer safe, low-stress connectivity in order to
amount of pollution. However, substantially increasing cycling
attract the widest possible segments of the population and
6
encourage people to cycle more. During a cycling trip,
driveways that break up the sidewalks and increase the exposure to
encountering a break in the perception of safety is enough to deter
car-human conflicts.
the majority of the population from cycling as only 1% of the population are comfortable cycling in all road conditions (Geller, 2012). To go beyond a 1% mode share, routes between people’s desired origins and destinations need to feel safe for cycling. In areas where safety is compromised, the risks need to be mitigated
Many suburban rail stations are located adjacent to major arterials with speed limits of 50 km/hr and higher that have unsafe cycling conditions but are also surrounded by a dense network of residential streets with speed limits of 40 km/h and lower with acceptably safe cycling conditions.
with cycling infrastructure that upgrades the unsafe breaks to a level of safety that satisfies the safety prerequisites of most of the
Therefore, the lack of connectivity of safe cycling infrastructure
population; enabling more people to be able to make the choice to
within the cycling catchment area around GO rail stations is
cycle. Thus, more demographic market segments are able to be
anticipated to be harming the potential of cycling access and
targeted, making cycling promotions and marketing more effective
causing the current low cycling access rates. To increase the station
in inducing behaviour change toward cycling. Otherwise, efforts to
cycling access rates to stations and in turn effectively attract more
promote cycling will not reach enough people for it to be as
rail passengers, the connections to rail stations from commuters’
effective.
homes need to be convenient, direct and safe.
The built form of the suburbs causes travel from homes to
1.5 Problem Statement
destinations to be pushed to high-speed arterial roads which have been designed to optimize vehicular flows with little space and accommodation for cyclists or pedestrian within the public realm (Relph, 2014). Arterial roads create hazardous environments for pedestrians and cyclists due to their character: long block lengths and wide road widths causing limited opportunities for safe crossing; often large and complex intersections that are difficult to navigate by foot or bike; expansive parking lots and abundance of
7
With average commuting distances in Toronto at 10km, many people commute to a workplace that is too far to travel by cycling or walking alone (Yawar, 2016). For trips over 5 km, driving tends to be the default option since its flexibility, speed and convenience makes it attractive for door-to-door long-distance travel. During rush hour, the station to station time for rail travel is often much quicker than car travel, yet going from front door to station is a barrier for many commuters (Metrolinx, 2016). In order to shift
commuters from their car to rail for trips over 5km, a flexible, affordable, practical, seamless, and safe solution is needed to bridge the distance between the front door and the rail station.
4. Which cycling investments are the most effective in encouraging cycling access to rail stations and how can these improvements be prioritized?
1.6 Research Questions This master’s thesis is structured around one main overarching question that is supported by five sub-questions. Together, these questions aim to investigate the current state and future potential of the bicycle-rail combination in Toronto.
Main Question
5. How can locations of cycling improvements be identified in order to effectively improve station access for existing and potential suburban rail users? To date, there is a minimal but growing amount of published material that comprehensively documents the knowledge of how bicycling can best be integrated with rail transport and
How can bicycle-rail integration be improved to enable and
methodologies for systematically approaching this synergy. To
encourage cycling access to rail stations in suburban areas?
help fill the literature gap in bicycle-rail integration, the objective of this master’s thesis is to develop and test a bike-rail cycling
Sub-Questions
corridor identification tool that enables and encourages cycling access to rail station. The tool is used to investigate the safe cycling
1. Why is cycling as an access mode beneficial to regional rail? 2. How can the bicycle-rail combination be made more attractive to commuters?
connectivity of important routes to rail stations and identifies where cycling infrastructure is needed to maximize access to GO stations, balanced against the estimated amount of investment required. Then based on a review of the latest cycling infrastructure design best practices, the most suitable types of cycling infrastructure are
3. What is contributing to the low cycling mode share
proposed to bridge any unsafe gaps along important routes to
of suburban rail?
stations. The tool is universally applicable to suburban residential rail stations and helps efficiently prioritize cycling infrastructure
8
investment, especially in areas that have little or no existing safe cycling infrastructure.
1.7
Structure
and
Methodology This master’s thesis represents a review of recent and informative sources of literature on how to shift commuters from personal vehicles to an alternative, competitive mode of transportation that is more sustainable:
the
bicycle-rail
combination. Since the union of these two transport modes creates a synergy that improves access to suburban rail and has proved to be successful
internationally,
this
thesis will look into how to enhance this synergy in Toronto. Since safe routes between home to rail stations is predicted to be lacking in the Toronto
suburbs,
a
modelling
procedure is proposed to serve as a
9
Figure 51: Overview of research questions and report structure
tool to help identify where and what kind of cycling infrastructure
is proposed to help prescribe the type of infrastructure needed for
is needed to maximize rail access improvement, balanced against
each road context to create safe cycling routes specific for Toronto.
infrastructure investment costs. Figure 4 provides an illustration of the thesis structure.
Part E introduces the 4-step Bike-Rail corridor planning tool which helps investigate the safe cycling connectivity of important
Part A provides an overview of the geographic context and the
routes to rail stations and identifies where cycling infrastructure is
mobility challenges that are faced by Toronto and the opportunity
needed to maximize access to GO stations, balanced against the
suburban rail provides. Based on this, the research questions and
estimated amount of investment needed. A review of literature
objectives are derived. The structure and methods are also
helps identify and describe the factors that are important to consider
presented in this part.
for the tool, helping develop the modelling process.
Part B presents a literature review of the landscape of bicycle-rail
Modelling dataset creation
integration and the key factors and interventions that make the
To create a building-level commuter population dataset that will be
bicycle-rail synergy more attractive are covered. This helps create
used as the basis of the modelling for the tool, the modelling
a knowledge document that informs the objectives and approach of
procedure in figure 5 is developed. This needs to be done since it
the modelling process to develop the 4-step Bike-Rail corridor
is important to identify where buildings that are classified as
planning tool
apartments or multi-unit dwellings are located since they create
Part C provides a background on the existing situation of the
more commuter traffic on the roads they front on to than single
bicycle-rail integration in Toronto, the supporting policy context
family homes.
and future rail objectives.
Step 1: Use ArcMap geospatial mapping software to collect and
Part D builds on the information from the previous chapters to
prepare the road and residential building datasets for the modelling.
compile a state-of-the-art Manual based on a review of literature
All of the 3D massing building dataset and road classification
and international best practices to guide how the bicycle-rail
shapefiles were accessed from the City of Toronto’s Open Data
combination can be made more attractive. A package of
Portal (City of Toronto, 2018c). The number of units in each
infrastructure interventions drawn from international best practice
building are modelled using a volumetric method. ArcMap does
10
Step 2: CoGran is a command line tool that uses dasymetric areal interpolation for combining data from different spatial granularity. The tool is used to take the estimated commuter population in each Transportation Tomorrow Survey zone (TTS, 2011) and distribute it to the residential buildings located in each zone, weighed by the number of units in each building. This process creates a dataset that estimates the commuter population in each residential building so the commuter volumes on the routes of travel from each building to the GO station can be more accurately calculated. Step 3: ArcMap is then used to transform the buildinglevel commuter population estimate dataset to a form that can be utilized by the Urban Network Analysis (UNA) toolbox for Rhinoceros3D (Rhino3D) (CityFormLab, 2015).
The Bike-Rail cycling corridor identification tool method Figure 5: Building-level modelling procedure
Next, the estimated commuter population dataset is used in the modelling tool by following these steps:
not have the ability to transform shapefiles to geojson so QGIS
11
geospatial mapping software is used to transform the residential
Step 1: Run the betweenness analysis tool from the Urban
building dataset from shapefile to geojson so it can be used in
Network Analysis (UNA) toolbox for Rhino3D created by
CoGran.
MIT. The UNA toolbox has an analysis feature called
betweenness and this is used to perform the commuter
Part F applies the 4-step Bike-Rail corridor planning tool
route volume simulation between residential buildings and
developed in part E and demonstrates it on an underperforming GO
the GO rail station within the station’s cycling catchment
rail station in Toronto in terms of cycling access. A cost-benefit
area. The tool does this by simulating how many times
analysis is then applied to select a route that has the highest cost-
each segment of the road network will be crossed by
to-increased access ratio to the GO station, weighed against
commuters taking the shortest-distance path to their closest
infrastructure investment. Then cycling infrastructure that is the
GO station from their home, weighted by estimated
most appropriate to bridge the unsafe gaps along the selected
household commuter population.
corridor, improving perceived and operational safety, is proposed.
Step 2: After exporting the results from Rhino3D to
Part G summarizes the overall findings to the research questions
ArcMap, a spatial intersection is performed to identify
and a reflection on further research.
which of the most high-volume routes in the model have unsafe routes. This is done by identifying the routes that have safe (low-stress) connections and those that are unsafe (high-stress connections). Next, the important routes are overlayed with unsafe routes to identify important, unsafe gaps in the network
In the end, a logical model is created that looks like a geographic framework but at its core is an economic decision-making framework. This provides flexibility in choosing project options that quantify the gain on investment so there is a clear understanding of the financial trade-off from choosing one infrastructure project over another. Since cycling infrastructure
Step 3: A cost-benefit analysis is applied to identify which
planning is very political, with diverse stakeholder involvement, it
of the most important (high-volume) routes to the station
provides the ability to pivot from one project to another while still
that have the most potential of unlocking GO station access
being able to reach overall goals.
when made safer, balanced against cost. Step 4: Proposing cycling infrastructure based on international best practice on the unsafe sections of road, bridging safe cycling access to the GO station.
12
PART B: THEORY
PART B: THEORY
How to encourage mode shift from car to rail by making rail more competitive.
How to encourage mode shift from car to rail by making rail more competitive
13
2 How to make rail competitive to the car
indicate that the accessibility of the railway station can be a factor in determining whether rail is chosen as a travel alternative (Hine
2.1 Factors that affect propensity of rail use To rival the end-to-end trip time of driving to a destination, the
et. al., 2000; Wardman et. al., 2000; Wardman et. al., 2000; Krygsman et al. 2004).
travel time savings of taking rail can only be realized if the access
2.2
to and from the station is as fast and efficient as possible and there
most cost-effective investment?
is a good level and quality of rail service provided. Martens (2007)
Access or rail service improvement, which is the
found that the time and inconvenience related to trips to and from
The paper by Brons et. al (2009) further investigates the factors
rail stops substantially reduces the attractiveness of rail in relation
affecting the propensity of rail use by looking into the effect
to the private car. By creating a seamless rail journey, rail can be
individual improvements to either accessibility or rail service had
made more attractive and user friendly which in turn will attract more passengers. Brons et. al. (2009) describes these as key factors
on the aim of increasing rail ridership (mainly by attracting passengers from the private car), balanced against limited
affecting the propensity of rail use. In the past, to increase ridership,
resources. In other words, discovering whether improvements to
efforts were typically centred on improving the level of rail service;
accessibility or upgrades to rail service were most effective at
the focus was mainly on wider network coverage, lower travel
attracting customers per dollar investment. Using a Dutch rail
times and higher service reliability. These such efforts focus on the
network as a case study, the researchers analyzed the Dutch
actual rail journey. By using similar means, ridership can also be increased by improving the access to rail services by means of
Railways customer satisfaction survey to first establish how important passengers perceive the access to the rail station in
covering a wider geographical area with access services, lower
relation to their overall satisfaction with the rail journey. Second,
travel times to the railway station and better quality of service at
to determine rail use in different parts of the rail network, they
the interchange point when transitioning between the feeder mode
investigated the balance between characteristics of the service, the
and the rail station. Research about access to railway stations,
access to it and the population served. They found that when the
multimodal public transport and on the inconvenience associated
overall goal is to increase rail ridership, improving station access
with the need to change vehicles during a journey (transfer) all
was the most cost-effective and could substitute for improving and
14
expanding services on the rail network; especially on peripheral parts of the system (Brons et al., 2009; Chan 2016). A study by Krygsman et. al. (2004) further supported this finding and suggested that investing in station access or egress (e.g. improving accessibility, customer satisfaction or improve the complete door-to-door journey’s reliability) rather than the rail journey can be more effective in increasing ridership at a lower cost. While common practice by rail operators is to mainly focus on the rail journey alone (Brons et al., 2009), these studies demonstrate how improvements to access hold a great potential in economically attracting new ridership. Currently, 92.4% of GO rail trips have Union Station as
Figure 65: Conceptual comparison between the transport feeder modes of walking, cycling, local feeder transport and personal motor vehicle during peak commuting hours in an urban area. Interpretation by Author based on: (Bertolini et. al., 2003; Kager, Bertolini, and Te Brömmelstroet 2016; Litman, 2002). Icons from The Noun Project (2018).
their destination (Metrolinx, 2016a) and once arriving, almost none make use of an automobile to reach their final destination (Chan, 2016). Therefore, this thesis focusses
15
*Speed is based on an accesses distance of up to 5 km from the station since 87% of existing GO passengers in Toronto live within 5 km of their home station (Metrolinx, 2015) **Costs are measured per passenger kilometre (Litman, 2002)
on improving access from home-to- rail-station part of the
transport. Figure 6 summarizes and compares the local transport
journey since it holds the most potential in shifting people from
feeder options to rail during peak commuting hours in an urban
their car to more sustainable, active forms of transport.
area. The criteria speed, flexibility and reliability were reported by
2.3 Review of access mode options
Metrolinx as the top needs of GO customers (Metrolinx, 2017). The
This section reviews rail access mode options and evaluates each
in all three criteria but cycling suffers from one major disadvantage
mode based on each mode’s effectiveness when combined with rail
in the suburbs: the low perceived comfort and safety.
results show that both cycling and personal motor vehicle rank high
2.3.1 Walking
“The increasing interest stems from the fact that bike-and-
Walking as an access mode only covers a small section of the
ride can help solve a key weakness of public transport: the
intended catchment area of suburban rail stations. Conventional
accessibility of public transport stops.”
wisdom is that residents and employees will walk to a station within a distance of 800 metres and those further from the station will not (TCRP 2009). However, the large stop spacing of commuter rail, typically 5 kilometres or more apart, means that many people living along commuter rail corridors do not consider walking to be a practical access option. Further research indicates that people are willing to walk an average of 800 metres, or 10 minutes, and many will walk considerably farther to high quality rail transport especially where the pedestrian infrastructure and conditions are favourable (Martens 2007; Dantas 2005). Even in attractive walking environments, the main limitation to people walking to stations is a relatively slow speed of 4 km/h.
Recent publications have highlighted the potential of the little studied bicycle-rail combination (Kager, Bertolini, & Te Brömmelstroet, 2016; KiM, 2016; Scheltema, 2012; Singleton & Clifton, 2014). When cycling and rail are combined, a mutuallybeneficial partnership is made that can be very successful in nurturing the growth of both modes (Martens, 2004; Van Nes et. al., 2014, Kager and Harms, 2017). Due to rail’s higher level of service and speed compared to other forms of public transport, people will cycle further to reach rail stations (Flamm et. al., 2014), increasing the catchment distance and accessibility of the rail system (Semler & Hale, 2010). The demand placed on road infrastructure surrounding GO stations is reduced by shifting car
2.3.2 Cycling
access to cycling access while still supporting the wide station
Suburban rail with station spacing between 4 km and 10 km are
spacing of suburban rail. Thus, the rail sector also gains from
ideally suited for cycling as an access mode, considering cycling is
combining cycling-rail since growing cycling access essentially
the most competitive at distances between 1 and 5 km (Mitra et al.,
translates into increased ridership at low access-service cost,
2016). Although bicycle access at rail stations has been given low
mainly from the parking cost savings for rail agencies (QT 2006).
priority or has even been completely overlooked, some transit
While a bicycle can serve as a suitable mode of transport for both
agencies, including Metrolinx, are beginning to see benefits in
the access (from home) and egress (to destination) legs of a GO
increased bicycle access (and egress) mode share. As Martens (2007) states,
16
transit trip, in practice the bicycle is most frequently used for access
Based on research by the Danish government, every car kilometre
trips.
driven creates a net loss of 84 Euro cents whereas cycling creates a
Rietveld (2000) explains that notwithstanding the presence of a bicycle sharing scheme or the ease of being able to bring the bicycle on transit, passengers tend not to have access to a bicycle for their trip between their destination GO station and final destination;
(Copenhagenize, 2017). Both individual and societal benefits for cycling are more pronounced in urban areas where congestion is growing and space is limited.
describing this imbalance as an “asymmetry of availability and
In the time it takes a customer to walk 800m to a rail station, a
use.” Chan (2016) cited other international experiences to support
customer can bicycle 3.2km, effectively increasing the active
this assertion: in the Netherlands 47% of transport users access
transportation access shed area by 9 times (Givoni et al., 2007;
stations by bicycle while only 12% use a bicycle at egress (KiM,
Fleming, 2016). As an access mode that has the effect of increasing
2016); in Shanghai a study found 11% used a bicycle for access but
the catchment of rail stations, it has the secondary effect of
only 1% used a bicycle at egress (Pan et al., 2010); and a
animating the corridors cyclists take. Cyclists tend to make stops
comparative study of the Netherlands, UK, and Germany found
along their trip to and from the rail stations, also known as trip-
similar findings suggesting the share of egress trips are much lower
chaining, which supports the businesses along the routes cyclists
in all three cities (Martens, 2004).
take to the station. Studies have shown that routes with cycling
There are significant economic and societal benefits to encouraging more cycling to rail stations. Bicycle infrastructure is relatively cheap to construct and maintain; it requires little space, enhances
17
societal net profit of 24 Euro cents per kilometre cycled.
infrastructure increase business (NYC DOT, 2014). This means that over time, it will help businesses thrive and the cyclist and foot traffic will help liven and animate the corridors.
rather than detracts from the quality of public spaces, and has a low
“The use of the bicycle in access trips (at the home-end of a trip)
environmental impact. For the cost and space of building a single
and/or egress trips (at the activity-end of a trip) can substantially
car parking spot, at an estimated cost of $39,000, plus $200/year
reduce the door-to-door travel time of public transport trips. As a
maintenance and servicing (Marshall, 2015), three secure, card-
feedering mode, the bicycle is substantially faster than walking and
access bicycle cages holding a total of 78 bicycles can be built
more flexible than public transport due to its “continuous”
(Martin et. al., 2009).
character eliminating waiting and scheduling costs, suggesting
that the use of the bicycle in access and/or egress trips can help
for the public transport agency. The location of suburban rail
closing the “travel time gap” between car and public transport.”
stations tend to be in residential areas that have densities that are
(Martens 2007).
not transit-supportive (Semler & Hale, 2010). As a result, the
More interesting though is the question of station-area bicycle infrastructure – which has also traditionally been considered “someone else’s problem.” Improving facilities such as bike lanes around rail stations is typically a local government’s responsibility and would require the rail agency to co-operate with other stakeholders to construct valuable bike lanes or other on-street or off-street bicycle improvements (FHA, 1992). Despite the
service tends to be inconvenient, suffering from slow and infrequent service. Schedule and fare coordination between the feeder and rail service is of prime importance for feeder transit. Strategies for more effective feeder bus services identified in the literature include increased route efficiency and intelligent transportation systems (ITS) to provide customer information and scheduling (TCRP 2007; TCRP 2006).
perceived challenges, the research overwhelmingly demonstrates
2.3.4 Personal motor vehicle
that cycling-focused infrastructure improvements at the station and
GO rail passengers use cars to access rail stations either by driving
in the station area corresponds with an increase in bicycle access
and parking at the station (park-and-ride) or being dropped off by
mode share (TCRP 2005; Martens 2007).
another driver (kiss-and-ride). The car is a flexible and convenient
2.3.3 Local feeder transport
mode of transport since it is capable of door-to-door and long-
Public transport feeder service has the potential of extending the rail catchment area well beyond walking distance, especially for those who do not own a vehicle or the cost of parking is prohibitive. Feeder bus service also places less demand on the road network surrounding the station and is considered more environmentally
distance travel. This is the case in free-flow situations but due to this convenience and flexibility, demand for more parking and routes have driven up demand at GO stations, causing congestion and other negative externalities from tying to accommodate this demand.
friendly than car access (TCRP 2009). Many agencies, however,
Auto access is the dominant mode at suburban rail stations, made
find it difficult to provide feeder service that is both time
attractive against other modes by how GO stations and access
competitive against other modes for passengers and cost-effective
routes to the station have been designed. The access and services
18
have been planned to cater primarily to car-based access; parking
consuming, adding to the overall travel time (Planbureau voor de
is free for users at GO stations, with the cost of parking included as
Leefomgeving, 2014; Scheltema, 2012). This congestion can
part of fare, meaning the costs are subsidized by all passengers
impact both the immediate rail station area and spill-over into
whether they drive to the station or walk. Since car drivers do not
adjacent neighbourhoods. Considering ridership is projected to
bear the actual costs of driving to the station and access is designed
grow, trying to meet this increased demand with more parking
to be convenient for driving, driving is artificially made more
capacity is financially unsustainable (Metrolinx, 2016; Mitra et al.,
attractive against other modes. Enabled by cheap fossil fuel and
2016) and will negatively impact the liveability of the
subsidized parking, cars have low direct user charges due to the
neighbourhoods around the station with the increased vehicular
heavy subsidies for the car industry (Semler & Hale, 2010).
traffic.
The problems with auto-based access approaches start with the fact
2.4 Opportunities for bicycle-rail combination
that on a per rider basis, park-and-ride is by far the most expensive
Growing rail ridership into the future means growing access; by
access mode, in terms of capital and opportunity cost, from the rail
promoting the seamless integration of rail and cycling, rail
agency’s point of view (Weant & Levinson 1998; Vuchic 2005;
ridership can grow using sustainable and cost-effective means, for
TCRP 2009). Dedicated land around the rail station must be
both the user and rail operator, attracting positive secondary
purchased for surface or structured parking, creating an opportunity
consequences that will improve the liveability of neighbourhoods.
cost from not being able to develop that land more intensively, which would create a revenue-generating opportunity. A meagre amount of these costs could be recovered through fares; in some cases, parking charges. Depending on the configuration and scale, station-area mixed-use development should be able to generate the same level of ridership, or more, compared to providing car parking (Fehr & Peers 2006; Semler & Hale, 2010).
For trips over 5km, in order to shift commuters from car to rail, the relative total end-to-end travel time and convenience of taking rail needs to rival the car. Independently, the rail network is the fastest, most reliable and cheapest mode of transport on a regional scale. Although there are clear advantages of rail transport, some fundamental weaknesses exist including poor door-to-door connection, flexibility and adaption. Since the criteria speed, access
The urban environment is plagued by congestion—especially in peak hours— which can make driving unreliable and time-
19
flexibility and reliability were reported by Metrolinx as the top
access
and
overcoming
the
weaknesses of the individual rail journey. As shown in figure 7, rail combined
with
the
seamless
integration of cycling helps overcome rail’s weaknesses. Since this shift from car to bike would be encouraged during commuting hours,
the
most
congested
and
polluted time, it would have the greatest impact on improving air quality,
easing
congestion
and
decreasing greenhouse gas emissions. As a result, encouraging cycling, particular to rail stations during Figure 79: Combining rail with cycling as an access mode. Interpretation by Author based on: (Bertolini et. al., 2003; Kager, Bertolini, and Te Brömmelstroet 2016; Litman, 2002). Icons from The Noun Project (2018).
commuting hours can serve as an important
strategy
*Costs are measured per passenger kilometre (Litman, 2002)
liveability
and
** The perceived comfort and safety of rail is achieved is the cycling connection is perceived as safe and relatively comfortable
emissions in cities.
to
increase
reduce
harmful
Note: The criteria speed, flexibility and reliability were reported by Metrolinx as the top needs of GO customers
needs of GO customers (Metrolinx, 2017), encouraging an access mode that is flexible, reliable and fast is crucial for encouraging rail
20
Figure 8 illustrates the access part of the rail journey to a
mode. This includes minimizing the travel time lost transitioning
destination and how cycling bridges the gap from home to GO
between modes by finding bicycle parking and getting to the rail
station compared to other access modes. Cycling enables a
platform as fast and conveniently as possible, thereby maximizing
convenient, adaptable door-to-door option within a reach that can
the attractiveness of the bicycle-rail combination.
capture the majority of access trips that are currently made to GO stations
(Metrolinx,
2016a).
Therefore,
the
cycling-rail
combination is a promising competitor to less sustainable forms of transport, and strengthening this marriage is the focus of this master’s thesis. An
important
aspect
to
consider in the bicycle-rail combination is the weakness of cycling’s comfort and safety in suburban areas. In order to attract the greatest proportion of GO passengers, the level of service
of
the
cycling
connection between peoples’ homes and the rail station must be as safe, comfortable and direct as possible to mitigate cycling’s weaknesses as a
21
Figure 92: The competition for a home-work journey, comparing the bicycle-rail combination to other (door-todoor) alternatives (Author’s own, 2017)
PART C: THE TORONTO CONTEXT
PART C: THE TORONTO CONTEXT
22
3 Cycling to GO Stations
GO Rail Station Access Plan sets a modest goal of achieving a 24% bicycle mode share by 2031, up from 1% today, citing that “the
3.1 Existing Conditions
low rates of cycling access suggest cycling is not regarded as a
A Metrolinx study found that most Toronto GO Transit passengers live quite close to their home station; 61% live within 3.5 km of the station they access and 87% within 5 km of their access station (Figure 9); distances that are more competitive cycling than by car in urban areas (Metrolinx, 2016a). If an average of 15 km/hr can be cycled in an urban, built-up area with traffic lights (Jensen et al. 2010), a distance of 3.5 km would only take 12 minutes by bicycle. Conversely, with a walking speed of about 4 km/hr a distance of ~0.8 km can be covered within 12 Figure 105: Average GO Access Distance in Toronto. Data from the GO Transit Commuter Survey (2016a)
minutes. Therefore, cycling can cover four times the distance of walking in the
same time and given the quadratic relationship between radius and area means that cycling can cover a 9 times larger catchment area than walking (Givoni et al., 2007; Fleming, 2016). This catchment distance is extended even further with an electric bike, which has been increasing in popularity. Despite this opportunity, the 2016
23
viable option for many GO Rail passengers” (Metrolinx, 2016b). International research shows that bicycle access mode shares up to 40 percent are attainable (Parsons Brinkerhoff 2009; Martens 2004; Herman et. al. 1993) with certain cities surpassing this trend, namely Amsterdam, with a mode share of 43% (KiM, 2016). Further, 75% of respondents with a bicycle in a Metrolinx rail commuter survey stated they would consider cycling from home to the rail station, if facilities were improved (Metrolinx, 2017), Indeed, a clear disparity exists between what customers want, the suggested potential and what Metrolinx deems attainable to achieve.
3.2 GO Policy Context Metrolinx is the provincial agency responsible for transportation planning for the Greater Toronto and Hamilton Area (GTHA). Current Metrolinx policy around GO Rail station parking and station access is heavily focused on cars, which has been based on current travel patterns (see figure 10). However, single occupancy vehicles require extensive parking facilities and pickup/drop-off areas which cause congestion around the rail station area (Metrolinx, 2016b).
Despite significant parking expansion, there continues to be parking pressure at many stations; approximately 85% of station parking lots are at or near capacity.
The
increasing parking Figure 133: GO access mode share. Source: (Metrolinx, 2016b).
demand is resulting in higher levels of
illegal parking and increased congestion on local roads. If current station access patterns remain unchanged into 2031, GO rail stations would need up to 80,000 additional parking spaces to accommodate the more than doubling of ridership that is projected. At an estimated cost of $39,000 per parking spot plus about $200 a
Figure 119: GO rail parking supply and ridership growth Source: (Metrolinx, 2016b)
accommodated through unlimited parking expansion (Figure 11). Therefore, shifting investment to more sustainable forms of transport will be required to accommodate and encourage ridership growth in a sustainably and financially responsible way.
year in maintenance (Marshall, 2015), these levels of parking
Metrolinx has outlined several strategic directions in a recently
expansion would be financially unsustainable; would significantly
released Discussion Paper for the Next Regional Transportation
limit the ability to achieve provincial intensification targets around
Plan (Metrolinx, 2016b). As an important first step, Metrolinx has
GO stations; would severely add to congestion around GO stations;
proposed a strategy of promoting active transportation for short
and would not align with municipal and provincial planning
trips. Further, there is also a crucial need for the planning and
objectives (Metrolinx, 2016b). Given the projected ridership
investment of all municipal and regional transit expansion projects
forecast and parking constraints, this growth can no longer be
to include an active transportation plan that prioritizes walking and
24
cycling connections to transit. Second, as the GTHA communities continue to grow within the provisions set out in the Growth Plan, the GO-transit corridors have evolved as spines that have guided much of this growth in the suburban municipalities (Mitra et. al., 2016). As a result, an opportunity has emerged where Metrolinx can play a key role as an urbanization facilitator by utilizing the land development potential around transit stations and creating dense, mixed use, and complete communities. In turn, station-area intensification will increase the number of residents living next to the station, driving up GO ridership without having to supply additional parking. Adjacent development can also prove to be advantageous for transit users, as it can create additional retail and
3.3 GO rail expansion GO transit’s Regional Express Rail (RER) project is a massive transformation of the GO rail service which will support the development of new stations throughout the network and bring improvements to every rail corridor such as more rush hour rail trips, more two-way service and all-day service (Metrolinx, 2016b). The increase of GO service through the transition to RER is anticipated to contribute to substantial ridership growth and changes to the patterns of station usage, including an increase in off peak users and reverse commuters, moving away from the current monocentric pattern of commuting to Union Station (figure 12) and towards polycentric growth. Since the spatial reach of cycling
employment spaces and provide the population to support them; supporting the two-way rapid transit plans for the GO line. By carefully designing the communities and streets near major transportation nodes, significant improvements can be made to the active transportation mode share to GO stations. Building on this need for cycling connections to transit to be included in all municipal and regional transit expansion projects, this thesis develops a tool to assist in prioritizing where and what type of cycling infrastructure would have the most impact in achieving active transport access objectives. Figure 146: The destination GO stations from commuters’ home in the GTHA (Metrolinx, 2016a)
25
around GO stations is able to cover at least 61% of current access trips (Metrolinx, 2016a), improved cycling access will help capture these trips and ease the demand for car parking and car access to the station. Therefore, enabling access for all commuters within the catchment area of GO station is important; not just on those travelling to Union station. Focusing on commuter movement is key since the supply of parking during the weekday peak hour is an urgent limiting factor to expanded rail travel in the GTHA. This is when Toronto’s congestion is at its peak and suffers from the greatest socio-economic costs and pollution. Therefore, this thesis will help improve rail station access, supporting GO expansion and the evolution of the GO rail system to a rapid-transit corridor. This in turn will support the Province’s emphasis on more sustainable modes of transport, land use intensification and reurbanization helping to achieve provincial intensification targets around GO stations.
26
PART D: MANUAL
PART D: MANUAL A guide for improving the bike-rail combination.
A guide to improving the cycling-rail combination
27
4
How to improve the cycling-rail
characteristics and found the following had a statistically
combination 4.1
significant influence on active transportation access to GO:
Variables that Influence Cycling Uptake to GO
Union GO station. Considering all variables that are shown to
Mitra et. al. (2016) explored current cycling behaviour and opportunities for cycling growth in the GTHA by analyzing variables that influence cycling uptake such as: trip length, sociocharacteristics,
population density, proportion of population 25-54, number of GO parking spaces, number of zero-car households, and minutes to
stations in the GTHA
demographic
Chan (2016) explored several population and neighbourhood
other
people
cycling,
built
environment, and neighbourhood characteristics. With regard to
influence cycling rates, there are only two factors which in the short term, the cities in the GTHA or the rail operator can influence: increasing the presence of cycling facilities and reducing trip lengths. In practice, this translates to improving access connections to and from rail stations so they can be made faster and easier.
the built environment characteristics, the model results indicated a positive correlation association between the presence of cycling
4.2
facilities and expected incidences of cycling trips for commuting
cycling uptake
purposes (Table 1).
Importance of connectivity – the key to enabling
In order to attract the widest possible segment of the population, low-stress connectivity is a crucial element of a cycling network. A 2010 Transport for London (TfL) study found that “safety, traffic and lack of facilities are the greatest barriers” to uptake amongst existing infrequent cyclists, and also that: “For all groups, including frequent cyclists, safety was the most significant barrier to cycling in general and for specific trips. This suggests that, in order to realise the remaining potential from
Table 1: Summary and statistical influences in cycling uptake Source: (Mitra et. al, 2016)
existing frequent cyclists, practical measures to increase safety and
28
improve the provision of facilities will be the most effective.” (Tfl,
based on their tolerance for traffic stress and found that the majority
2010)
of the population is traffic risk-intolerant, with only about 1% of
These findings were again confirmed by a TfL (2014) in a report
the population is comfortable riding in any condition, regardless of the presence of cycling infrastructure (Figure 13). This statistic
about Attitudes to Cycling that found that:
highlights that the majority of people will not attempt to cycle until “Safety concerns remain the key deterrent to cycling, far more so
conditions achieve a safety level that enables them to cycle. To help
than concerns about lack of fitness or cycling ability”
explain the cycling conditions that would be suitable for the three
with 80% of those surveyed ranking safety as their number one
categories of people who would consider cycling, an example of
barrier.
road environments is included in Appendix 2 (SFCTA, 2015). Most people who are new to cycling in the road environment are timid;
Further, safety was also identified as the main reason why respondents were cycling less; the main barrier cited to cycling to school more often. Even amongst those who were already cycling, safety was the number one reason for not cycling more, with “no significant difference” in attitude between regular and occasional cyclists (Cycling Embassy of Great Britain, 2018). Thus, safety and connectivity are crucial aspects of a bicycling network and should
this is obvious for those who have ever cycled with someone new to cycling or who have tried to cycle for the first time themselves. A cyclist can face many sources of conflict in the road environment. These conflicts include watching out for rightturning vehicles or a door opening into their path. A single bad experience can shatter someone’s confidence and they may never ride again out of fear for their safety (Pinder, 2017). This is why
be a priority in network planning. Therefore, direct routes need to be provided between people’s origins and destinations that do not require cyclists to use roads that exceed their tolerance for traffic stress. On streets that have high-stress characteristics such as high traffic speeds and volumes, providing separation between cyclists and vehicles in order to create a low-stress cycling environment is needed. Geller’s (2012) research classified the population
29
Figure 13: Four Types of Cyclists. Source: Geller, 2012
removing sources of conflict and danger by separating cyclists from unsafe road conditions and mitigating points of bicycle-car conflict is so important; by adding protected bikeways along highstress links, cycling is made as safe as possible, encouraging a greater number of people to cycle.
4.3 Acceptable level of detour Cyclists have a limited willingness to go out of their way to find a lower-stress bike route. If the shortest route that avoids high-stress links involves too much detour, many cyclists will not consider that route acceptable. One study (Winters et. al. 2010) of non-
Like many North American cities, cyclists in Toronto, particularly
recreational cyclists in Vancouver, B.C., found that 75 percent of
in the suburban residential areas, find it impossible to get to where
cyclist trips were within 10 percent of the shortest distance possible
they want to go without riding on roads with unacceptably high
on the road network, and 90 percent were within 25 percent; (They
traffic stress. This is typical in the suburban residential
found virtually identical results for automobile trips). This small
neighbourhoods in Toronto where most of the GO rail stations are
level of average detour is consistent with a 1997 study of bicycle
located. There are many quiet, residential streets that are
commuters (Aultman-Hall et. al. 1997). However, they also found
comfortable, low-stress links; however, to get from the
that people were more likely to go out of their way to take a route
neighbourhood pockets to the GO rail station, high-stress links
with more green cover and more bicycle-actuated signals. Broach,
along arterials need to be crossed (Relph, 2014). If the current state
Glebe, and Dill (2011) found that commuting cyclists in Portland,
of the bicycling network around GO stations is such that only a
Oregon were willing to add 16 percent on average to their trip
small fraction of the work or school trips can be made without using
length to use a bike path, and to add 11 percent to use a low-stress
high-stress links, it should not be surprising that the share of bicycle
route using local streets (a “bike boulevard”). For non-commuting
access to GO stations is on average only 1% (Metrolinx, 2016b). In
cyclists, those figures are 26 percent and 18 percent, respectively.
order for the GO rail system to go beyond a 1% access mode share, cycling infrastructure that continuously provides a high level of safety to attract a wider amount of the population, namely the “enthused and confident” and “interested but concerned” in Geller’s classification (2012) is vital.
Overall, these statistics affirm the necessity that if a bicycling network is to attract the widest possible segment of the population, its most fundamental attributes should be low-stress connectivity; providing routes between people’s origins and destinations that do not require cyclists to use links that exceed their tolerance for
30
traffic stress, and that do not cause excessive detour (Mekuria et al., 2012).
4.4
Transport choices and how to make behaviour
change happen We need to step back and understand that a cyclist is merely a human riding a bicycle with well-established emotions, attitudes,
Figure 14: Force Field Analysis. Source: Kaminski, 2011
worries and behavioural habits that have been moulded over years
driving and restraining forces; these forces can be positive, urging
through family, societal and cultural influences. Whether one
us toward a behaviour, or negative, propelling us away from a
chooses to cycle or not is highly influenced by attitudes that
beneficial behaviour (illustrated in figure 14).
manifest into behaviours and form into habits. Kurt Lewin, a German-American psychologist, developed several ideas in the 20th century that became central to modern phycology; particularly how you induce people to change their behaviour. Kurt Lewin’s Change Management Theory, is a time-tested, easily applied field theory that is often considered the epitome of change models,
31
As an example, he explains that you can see this by the speed you choose to drive at; there’s an equilibrium between being in a rush, feeling tired, and worried about the police stopping you. Using this principle of behaviour, there are many human behaviours that can be described in this way.
suitable for personal, group and organizational change (Kaminski,
For change to happen from the status quo, the equilibrium must be
2011). He describes that people’s behaviour is strongly driven by
upset – either by adding conditions favourable to the change or by
two main external forces; there are driving forces that drive you
reducing restraining forces. His insight is that if you want a change
into a particular direction (e.g. incentives, pressure from a
in behaviour from the status quo, there is one good way to do it and
supervisor, social demands) and there are restraining forces (e.g.
one bad way to do it: the good way is by diminishing the restraining
apathy, prohibitive cost, technology illiteracy) (Kaminski, 2011)
forces, not by increasing the driving forces. This is profoundly non-
which are preventing you from taking a particular path. The notion
intuitive; traditionally, in most cases we try to change behaviour
that Lewin offers is that behaviour is an equilibrium between the
through a mishmash of driving forces such as arguments, incentives
and threats (Kaminski, 2011). Diminishing the restraining forces is
safety performance, like collision rate). Since safety is ranked by
a completely different approach. Instead of asking how I can get
80% of people as their number one reason for not cycling or not
him/her to do it, it starts with a question of why isn’t s/he doing it
cycling more (Tf, 2014), it helps bring some insight into why
already. Then you can go one by one systematically and find out
there is low cycling mode share in the suburban residential areas
what you can do to get that person to move. It turns out that in order
of Toronto and accordingly, the GO rail system. For most people,
to make things easier, the person’s environment needs to be
it is almost impossible to cycle in the suburbs or to connect to a
controlled to make things easier and overcome the real restraining
GO station without exposing yourself to high-stress situations.
forces discouraging them from acting in an intended or desired way. If more success in modifying behaviour is found by reducing restraining forces, then why are most public campaigns focused on the promotion of the benefits? Human beings may have more of an authoritarian complex about driving things rather than taking the time looking at the situation from the individual’s point of view. By looking deeply into the underlying individual’s environment, only then can the real retraining forces be found; by using interventions to lessen those restraining forces, only then can successful and effective behaviour change happen. This is not
We can apply Lewin’s theory and encourage greater rates of cycling by making the experience of cycling safer (perception and observed safety), easier and more enjoyable, lessening the key restraining force in people’s environment and encouraging them to bicycle. As this approach is grounded in effective intervention principles from behaviour change psychology, this tactic is expected to be more effective than the traditional methods of promoting and advertising the positive effects of cycling, in particular the health benefits, since these are driving forces, not restraining forces.
really a natural approach since when humans want things done, they tend to drive them with arguments, and threats.
In conclusion, tactics for encouraging behaviour change from one habit, for example encouraging commuters to switch from driving
Looking at the environmental factors that underlie the restraining forces that discourage people from cycling, a clear trend emerges: safety. The measure of safety can be separated into two aspects (Teschke 2012): safety defined as the perception of safety (i.e. how safe something “feels”) and the other observed safety (i.e. the
to cycling to work every day, requires the help of tools, approaches and intervention theories from a diverse range of fields. By synthesizing some important aspects from the fields of psychology, transport planning and economics, a holistic, interdisciplinary an approach to effective cycling infrastructure planning can be
32
developed. Therefore, this thesis aims to bridge these diverse but
safety risk through the intervention of cycling infrastructure where
interconnected fields of research to develop an interdisciplinary
it is needed the most, the key restraining force discouraging
approach to cycling planning.
people from making the choice to cycle is lessened, thus encouraging cycling uptake as an access mode to rail stations.
5
How to encourage cycling to stations in
practice
The built neighbourhood form has an important impact on
The body of knowledge on bicycle-rail integration is relatively small; however, as demonstrated by the growing research and transportation initiatives, interest in this form of multimodal transportation is growing. Literature recommends that a “package of many different, complementary interventions” ranging from infrastructure to marketing; supportive land-use planning, and restricted car use are needed to substantially increase cycling access to rail stations (Pucher et. al., 2010). These areas of intervention can be grouped into three main categories: policy and promotions,
5.1 Urban Structure
enhancements
to
the
urban
structure
and
neighbourhood form around rail stations, and infrastructure interventions.
transportation patterns (Krizek, 2003). Population density, land use mix, dedicated cycling facilities (i.e. bicycle lanes and protected bikeways) and safer streets (i.e. roads with lower speed limits) are all associated with increased cycling uptake (Damant-Sirois and ElGeneidy, 2015; Saelens et al., 2003; Mitra et. al., 2016). Consequently, reduced dependence on car mobility will be achieved when the following conditions are met: daily trip needs are located close to home (e.g. shopping and schools); the road network and geometry is designed for slower traffic (bike and foot) and is therefore made less convenient for a car; and accessibility of locations outside the neighbourhood (including the main streets and places for shopping) discourage car use (Meurs & Haaijer, 2001). The reduction in car use is greatest when it occurs in a
Since safety is the vital prerequisite to enable and encourage the
densely built-up, mixed use community. The mixed-use design
greatest number of people to bike, a change in environmental
reduces daily trip lengths by locating more destinations close to
conditions is shown to be the most effective in inducing behaviour
homes; the reduction of travel distances is strongly correlated with
change. This thesis focuses on infrastructure-based safety
higher rates of cycling.
interventions that will push people toward cycling. By mitigating
33
5.2 Infrastructure Interventions Encouraging cycling as a feeder mode to rail requires its own toolbox of measures for seamless bicycle-rail integration. Researchers have grouped these measures into four areas: enabling bicycles to be brought on transit; providing safe and convenient bike parking; connecting stations to existing cycling routes; and offering public bike share near stations and major destinations (Bachand-Marleau et. al., 2011). The EU-coordinated BiTiBi project, based on Dutch best practice, describes six building blocks for good bicycle-rail integration. A description of the building blocks and the barriers they aim to overcome are found in table 2. Both building block number one (providing bike parking) and building block number six (safe cycling infrastructure to the station) refer to improving station access. Table 2: BiTiBi six building blocks for improving bicycle-rail and
Metrolinx already recognizes the importance of providing bike
associated barriers, derived from (BiTiBi, 2017)
parking and on-going work is happening to add more bike parking
(2017); RDG Cycle-rail Toolkit (2016); Sustrans Cycle and rail
at stations (Metrolinx, 2016b). What is neglected, however, is how
Integration Manual (2014b) and the FTA Manual on pedestrian and
cyclists reach the rail station and access the bike parking. The
bicycle connections to transit (2017). Table 3 provides some
cycling access journey to the station starts from commuters’ homes
solutions for enhancing the bicycle journey to the station provided
and the part of the trip on rail station property if just one segment
as excerpts from these guidelines.
of the overall trip. Like all rail passengers, cyclists want direct, convenient and safe Some examples of ways to enhance station access from different
routes to the station with minimal delay. A pleasant, separate, car-
guidelines were selected from four guidelines: BiTiBi guidelines
free route helps to increase the number of cyclists. Therefore routes
34
should be direct, convenient, safe, well signed links to and from residential areas and employment centres within an easy cycling distance
(3-5km)
(Sustrans,
2014b). Even a small barrier at any point along the chain from home, to the station and to the final destination can disrupt potential cycling-rail activity. For example, minimizing detours, waiting time, and rescheduling is crucial to making this form of integration work (Keijer & Rietveld, 2000). This is why quality, convenient, and secure bicycle parking at stations
is
important;
priority
location near access routes and near departure platforms minimizes the delay when transitioning from cycling to rail. The BiTiBi (2017) guidelines provide excellent design and
placement
guidelines
Table 3: Bike-train intervention options. Source: Leferink (2017)
for
bicycle parking based on Dutch best practice.
35
5.2.1
SWOV Sustainable Safety principles and Vision
Zero – the basis of safe cycling route design The Sustainable Safety vision of road safety underlies the safe road design of the road system in the Netherlands. The vision places humans as the centre of safe road planning and design, under the premise that the road environment and rules must be adapted to the limitations of human capacity. In a sustainably safe road traffic
speed/road geometries should be made through accident assessment and intervention implementation. The strategy requires abandoning the traditional economic model where road safety is provided at reasonable cost and safety is balanced against mobility – meaning the system accepted a certain number of collisions and fatalities. In Vision Zero, no loss of life is acceptable (Wadhwa, 2001).
system, everything is aimed at preventing collisions from occurring
To achieve a sustainably safe traffic system, the success lies in the
and if the crash is unavoidable, the severity of the crash must be
systematic and consistent application of the three sustainable safety
minimized (SWOV, 2005). The system acknowledges that people
principles: functionality; homogeneity; and predictability. This
are not machines; humans, inherently prone to making errors of
classification is established for each road and clear design
judgement. Around 95% of collisions are caused by some sort
guidelines are created to link the design of roads to the intended
of human error (NHTSA, 2015). Therefore, any sort of traffic
function (SWOV, 2005). This produces a road network with three
system that mixes humans and the exposure to death should have
categories, as described in Table 4. Each road or street may only
precautions in place to mitigate serious injury or death, especially
have one function. For example, a distributor road should not give
from those who are at the wheel of a car that has the potential of
direct access to houses, shops, or offices). The homogeneity of the
causing the most harm.
road system is meant to avoid significant differences in speeds,
This human-centred approach to safe road design has been established in Vision Zero strategies, originally initiated in Sweden. At the strategy’s core, it specifies that road systems should be designed so that crash energy cannot exceed human tolerance, shifting the responsibility of serious injuries and fatalities to the failure of the road system rather than the road user (Wadhwa,
driving directions, and mass. When incompatible traffic types mix, then the preference is to segregate the modes to reduce or eliminate the risk exposure, or if not possible, force motorized traffic to drive slowly, thereby mitigating the severity of the collision. Finally, it is important that the behaviour demanded of road users in each road type is legible and what they may expect from other road users is
2001). This means as accidents happen, appropriate adjustments to
36
clear. This is accomplished through consistent design measures applied to each road type that is recognizable (SWOV, 2005).
Figure 15: Pyramid for successful public space for cyclists. Source: Scheltema (2012)
providing space where people live, shop, work, meet, view the streetscape) function. When “Movement” is considered to be the priority, then segregated cycling facilities are likely to be required, whereas if “Place” dominates, then spaces are likely to be shared, Table 4: The type and function of roads in a sustainably safe road
and vehicle flows and speeds are accordingly restricted (Scheltema,
traffic system (EU, 2018)
2012). Scheltema (2012) adapted Maslow’s hierarchy of needs and
Therefore, as a principle for safe cycling infrastructure design, the
applied it to how a successful public space can be created for
intended functionality of the road should correspond to the design
cyclists (figure 15).
of cycling infrastructure that is appropriate for each road type in a
This pyramid presents the idea that the top conditions can only be
given context. This can be defined by whether the street has a
met when fundamental condition (safety) and precondition
“Movement” (i.e. the purpose of getting people and vehicles from
(directness) have already been established. This concept lead to the
one place to another) or “Place” (i.e. the purpose of a street in
37
development of a tool to assess the strengths and weaknesses
cohesion, directness, safety, comfort and attractiveness. Table 5
cycling routes to rail stations (appendix 3) in the BiTiBi project
describes the core principles in detail and recommended
(2017).
considerations to meet the requirements, as suggested by CROW
Overall, within this sustainably safe context of road design, the appropriate cycling infrastructure to fit each road context can be designed. The next sections of this thesis give guidance on the best practice cycling infrastructure design.
(2016); Transport for London (2014); and Sustrans (2014a). An exemplary cycling network will satisfy each of these principles; safety and directness are both fundamental preconditions before comfort, cohesion and attractiveness are met (Scheltema, 2012). Accordingly,
5.2.2 Best Practice cycling infrastructure design Updated guidance from CROW (2016) – the world’s most authoritative manual on bikeway design based on 40 years of European experience and adopted by the Dutch– identifies the
the
design
of
cycling
infrastructure
must
fundamentally have characteristics that make it safe from the aspect of “actual” road safety measured by the ability to prevent collisions or near misses and “perceived” safety identified by how safe cyclists feel safer (Teshke et. al., 2012).
following factors as key to making a successful bikeway network: Core principles for routes used by cyclists Principle
Summary
Details • link all potential origins and destinations
Cohesion
How connected is the network in terms of its concentration of
• be continuous and recognizable
destinations and routes? Infrastructure should be easy to understand
• offer consistent standard of
and follow for all users.
protection throughout • be properly signed • include well located cycle parking
Directness and Accessibility
Does the network provide direct and convenient access to destinations? Cycle routes should be as direct as possible, while being logical, and
• be based on desire lines • result in minimal detours or delays • provide a positive advantage in terms
38
Safety
avoiding unnecessary obstacles and delays to a journey. Planning
of directness and priority over motor
routes as part of a network is key.
traffic
Does the network provide routes that minimize risk of injury, danger,
• be safe and perceived as safe
and crime? Infrastructure should help to make cycling safer and
• provide personal security
address perceptions of cycling being unsafe, particularly at junctions.
• limit conflict between cyclists and
Space is an important consideration when considering safety
pedestrians and other vehicles • be smooth, non-slip, well maintained, drained and free of debris • have sufficient width for the
Comfort
Does the network appeal to a broad range of age and ability levels and
level of use
is consideration given to user amenities? Surfaces which cyclists ride
• have easy gradients
on should be fit for purpose, enable smooth riding and be well
• be designed to avoid complicated
constructed and maintained
manoeuvres • enable cyclists to maintain momentum • minimise impacts of noise, spray and headlight dazzle from other traffic • be attractive and interesting • integrate with and complement their
Attractiveness
Infrastructure should add to the attractiveness of the public realm while
surroundings
not contributing to unnecessary street clutter,
• contribute to good urban design • enhance personal security • be well maintained
Infrastructure should accommodate all types and experiences of cyclist Adaptability
and should be designed taking into account an increase in cyclists in the future
• Where substantial increases in cycling are expected, consideration should also be given to the adaptability of infrastructure to accommodate large increases in use
Table 5: Cycling route requirements. Adapted from (CROW, 2017b; Transport for London, 2014; Sustrans, 2014a)
39
Route Type Teschke, from the University of British Columbia, completed an opinion survey of 1,400 people in the Metro Vancouver region and studied the types of bicycle facilities that people reported feeling safe using and the major factors that motivated or deterred cycling (Winters, 2011; ITE 2017).
The findings reveal that both men and women prefer cycling in facilities that are separated from traffic and on quiet (low-volume, low-speed) streets. Men are more willing to ride on busy streets than women, but do not prefer them; indicating that cycling route types that are separated or quiet appeal to everyone (ITE, 2017). As described in figure 16, protected bikeways are perceived as being safer and directly support the top motivators as well as mitigate the top deterrents to cycling (Table 6).
Table 6: Top deterrents and motivators. Source:(ITE, 2017)
Teschke and colleagues (2012) also used a case-crossover Figure 16: Relative risk of injury for different bikeway types Source: Kay Teschke et. al., 2012)
study with data from Vancouver and Toronto to compare the impact bicycle facility types have on the relative risk of injury. Their findings show that protected bikeways had about 1/10th of
40
the risk of cycling on a major street with parked
cars
infrastructure.
and Further,
no
bicycle
when
the
research team compared the route preferences to the observed safety (figure 17), with a few exceptions, what people perceive as being safer was also observed to be safer (Teschke et. al. 2012). Conversely, the following four route types carried significantly lower injury risk: major streets without parked cars and with bicycle lanes; local streets with no cycling infrastructure, local streets designated as cycling routes, and protected bikeways (Teschke et. al. 2012).
Therefore,
not
only
were
protected bikeways preferred by people, the associated route safety was found to be the highest of any bicycle facility. Their findings supported the routedesign approach of many Northern
41
Figure 17: Bikeway safety comparison between user preferences and observed safety performance (Source: adapted by ITE (2017) from Winters et. al., (2012)
European countries; designing quiet streets (low-speed, low-
busy, fast-moving streets (high-speed, high-volume) (Teschke et
volume) with mixed traffic and adding protected bikeways along
al. 2012).
Both perceived and actual safety are important aspects that affect a
majority of the population who does not cycle or not cycle regularly
cyclist’s propensity to cycle but are difficult to measure. By
would if they could minimize their exposure to car traffic and other
understanding the design approaches used by cities that have
road risks. This awareness affirms the need to provide low-stress
achieved the highest cycling rates, insight can be gained about what
bicycle routes between people’s desired origins and destinations.
types of cycling routes have the most demonstrated success in
These are low-stress bicycle facilities, including trails, low-traffic
mitigating safety concerns and driving cycling uptake. Therefore,
shared roadways (such as bicycle boulevards) and protected
this thesis covers the cycling design approaches that have been the
bikeways (bikeways that are separated either physically or spatially
most successful in the Netherlands and Copenhagen; cities that
from higher-volume roadways) (Portland, 2010). Emphasizing
have been able to achieve more than a 40% cycling mode share
development of this low-stress network of streets and trails
(Pucher, Dill et. al. 2010), with Amsterdam achieving a 43%
provides an effective strategy for advancing the crucial principles
cycling access mode share to rail stations (Figure 18) (KiM, 2016).
of cohesion, comfort, directness, safety and attractiveness commonly identified as international best practices for bikeway design. The successes of low-stress protected bikeways have been demonstrated in Toronto along cycling routes such as Bloor Street and Richmond/Adelaide, which have become among the city’s most popular bikeways. Bicycle boulevards have also proven to attract high numbers of riders of all ages and abilities due to the level of comfort they provide, the mobility function they serve and
Figure 18: Cycling access mode share in Amsterdam Source: Adapted from KiM (2016)
As Toronto’s bikeway network has expanded and ridership has grown, it has become increasingly clear that there are obstacles in place that are constraining cycling mode share growth from reaching Dutch or Danish levels. Research suggests that the
their proximity to where people live and travel (Portland, 2010). Location and impact on businesses Not only are protected bikeways good for encouraging cycling use, they are also good for businesses; findings from New York City have shown increases in retail sales for businesses along streets with protected bikeways by up to 49 percent two years after
42
construction as compared to 3 percent elsewhere (NYC DOT,
facility, and the maintenance equipment width (ITE, 2017). The
2014). Although, it is important to note that not all businesses
MassDOT (2015) and CROW (2017b) design guides provide
experience the impacts of bikeway interventions the same way;
recommended widths for protected bikeway facilities based on
there is a nuance between how all establishments perform after
bicycle traffic volumes and whether the facility is planned to
protected bikeways are installed compared to locally-serving
accommodate scooters or E-bikes. For cities that have snow
establishments such as restaurants and retail stores. The latest
accumulation in winter, it is important to consider the type and
research by Nelson Nygaard (2018) studied three case studies in
width of snow clearing equipment which may require wider
San Francisco over a 5-year period, looking at the impact of
facilities than necessary, particularly for one-way protected
dedicated bikeways on adjacent and surrounding businesses. The
bikeways.
findings show that dedicated cycling infrastructure had no significant impact on businesses overall but a significant, positive effect on locally-serving businesses (Nelson Nygaard, 2018). For example, a cycling route through a light-industrial corridor will not have great impact on the sales of the businesses along the street as compared to a retail corridor with customer-serving businesses. The design implications highlight the importance of being aware of the land use and mix of businesses when selecting the best dedicated bikeway alignment (Nelson Nygaard, 2018). To have the most positive effect on businesses, a bikeway corridor is best located adjacent to streets with a mix of local, customer-serving businesses.
cycling lane width wide enough for two side-by-side cyclists, ideally 2-2.5 m (minimum 1.7) in order to accommodate for social and safety aspects such as allowing for an adult and child to ride side-by side, room for overtaking, and space to anticipate movements or room for error. Using camera observations at 23 locations in the Netherlands and opinion surveys, CROW studied the lateral positioning of cyclists and events in dedicated bikeways. The study found that on roads over 5.8m, cycling lanes of at least 1.7 m lead to wider overtakes by cars, provided more distance to the road edge, allowing for more room for error and were better appreciated by cyclists CROW (2017b). Therefore it is shown that
Bikeway Width
wider cycle lanes improve objective and subjective cycle safety,
The width of the bikeway is typically dependent on three variables:
which are both aspects that are key for encouraging cycling use.
projected cycling volumes, whether it is a one-way or two-way
43
As a starting point, the CROW (2017b) manual recommends a
5.3.3 Overview of bicycle facility types Based on Danish best practise, the Copenhagenize design group created a bicycle planning guide that provides uniform recommendation about how the traffic speed on a roadway determines the infrastructure type selected (Figure 19): roads 1030 km/hr no separation is required; At 40 km/hr painted lines are recommended; 50-60 km/hr curb separated lanes necessary; and 70 km/hr and over the cycling lane must be fully separated by a median (Copenhagenize, 2013). There are about four types of infrastructure to choose from. Further, they recommend that cycling infrastructure should only be placed on the right side of parked cars and bi-directional cycling paths only off-street. This approach creates uniform and legible cycling infrastructure that makes all roads in the city low-stress cycling routes (figure 20). Alternatively, the CROW manual (2017b) recommends the approach of identifying the type of function the road is intended to
Figure
19:
Copenhagenize
bicycle
planning
guide.
Source:
(Copenhagenize 2013)
serve, then based on the road category specified, a cycling network
traffic is greater than 5,000 a day; this is when converting the street
category is selected to match the road function (Table 7). If the road
into a bicycle street or dedicated/separated infrastructure is
that is planned for cycling improvements is a low-volume, 30km/hr
recommended. Regarding distributor roads 50-60 km/hr, both
residential street that carries less than 750 cyclists a day, then
guides recommend curb or physical separation from the road
mixed traffic would be sufficient. This matches Copenhagenize’s
(unless only one lane each direction and low cycling volume, then
guide of providing mixed traffic on 30 km/hr roads. Where CROW
CROW advises a painted cycle lane can be a sufficient alternative).
departs from the Copenhagenize guide is when the 30km/hr road
Where both guides differ in this category is the design of the
carries a higher volume of cyclists and the volume of motorized
separation; in Copenhagen it is typically a raised curb, between the
44
The City of Toronto uses the Ontario Traffic Manual (OTM) Book 18 as a starting point for how the city’s cycling infrastructure is designed. The OTM (2013) Book 18 uses both speed and volume of motorized traffic as key factors influencing the design of cycling facilities (Figure 21). The main criticism of this guideline is that when motor vehicle speeds reach 50 km/hr, the provision of painted bicycle lanes is still suitable as a separation from motor vehicle traffic whereas in the Dutch and Danish guidelines, a physical curb or median separation is necessary. At speeds of 50 km/hr, the speed differential between cars and cycling makes riding next to cars without protection perceptively unsafe for the cyclist and is observed to be unsafe based on collision research (Teschke et al. 2012). Furthermore, the recommended cycling facility types is very broad, opening it up to greater subjective interpretation by designers which can result in insufficiently safe separation from
Figure 20: Low stress cycling city (Copenhagenize 2013)
level of the road bed and sidewalk; whereas, in the Netherlands it is fully separated with a median. Both guides agree that any road 70km/hr and over must be fully separated with a median and in the CROW manual, accommodate for mopeds. Table 7: road function then speed determines infrastructure type (CROW, 2017a)
45
Figure 21: The Relationship of Traffic Speed and Volume to Types of Cycling Facilities. Source: Adapted by the City of Toronto (2016) from OTM Book 18.
motor vehicle traffic and design of infrastructure that is not uniform
Toronto’s local, residential streets are set at either 30 or 40 km/hr
and therefore not legible to the public; making it disconcerting for
and the distributor roads are 50km/hr and higher. For simplicity and
both car drivers and cyclists.
consistency, this street type classification will be used to propose appropriate cycling measures based on Dutch and Danish best
6. Proposed cycling infrastructure
practise for each road function.
The City of Toronto Bike Plan and the City of Toronto Complete
6.1 Measures for Quiet Streets
Streets guidelines separate cycling streets into two categories: Fast,
Local roads serve as access to properties, requiring low speeds so
Busy Streets and Quiet Streets (City of Toronto, 2016b). Most of
road users can exchange movement between the road and properties. To match the road function, it is shown that 30 km/hr is
the
optimal
speed
for
supporting access and mitigating the severity of collisions if a collision Table 8: Classification of Toronto’s cycling streets. Source: City of Toronto (2016b)
does
occur.
Motor
vehicles speeds of 30 km/h and
46
under reduce the risk, severity and fatality rate in the event of a collision (Teshke et. al., 2012) (Figure 22). When speeds are increased even by 10 km/hr to 40 km/hr, the braking distance is almost doubled, greatly reducing the opportunity of crash avoidance. Research from Grenoble, France has shown that changing the speed limit from 40 km/hr to 30 km/hr has minimal
Figure 23: Higher vehicle speed increases likelihood of cyclist and pedestrian fatalities in a collision. Source: Cities safer by design (2015)
(Publie, 2015). Lower speed limits may even reduce congestion in some cases, as they reduce the incidence of bottlenecks. Reduced midblock acceleration also reduces the noise and emissions a vehicle emits. Vehicle emission rates increase with the acceleration Figure 22: higher vehicle speeds require longer stopping times, source:
rate so reducing acceleration rates reduces the amount of emissions
Cities safer by design (2015)
and noise a car produces (Bokare & Maurya, 2013). It has been shown that lowering the speed limit on major arterials reduced
impact on the flow rate and capacity of a road (Publie, 2015). A lower speed limit also has the added benefit of achieving more uniform speeds and reduce dangerous midblock acceleration, while adding little to overall journey times. Further, the research has shown that a speed limit of 30 km/hr rather than 50 km/hr only added 18 seconds of travel time between intersections 1 km apart
47
congestion by 10 percent during the first month of implementation, while fatalities also dropped significantly (Estadao, 2015). For vulnerable road users such as cyclists and pedestrians, a speed difference of 10 km/hr can have a big influence on the difference of minor or severe injury, or at worst, life or death.
Therefore, the design and geometry of roads need to be legible and
There are different design features that need to be considered for
communicate the desired speed, thereby serving to prevent
midblock and at intersections. The next sections describe these
speeding and increased collision risk. If the mean observed speed
contexts in more detail.
on a road exceeds the desired speed, new or improved traffic
6.2.1 Mid-Block Design
calming measures need to be put in place.
Components
6.2 Measures for Fast, Busy Streets
The components of a protected bikeway at mid-block locations
For Fast, Busy Streets, protected bikeways that provide full
include the bike lane, sidewalk buffer and sidewalk, street buffer,
separation from motor vehicles are the only suitable design; they
and street (figure 24).
have been shown to not only increase the perceived safety of people cycling, but also the observed safety (Teshke et. al., 2012). A protected bikeway combines the user experience of a separated path with the on-street infrastructure of a conventional bike lane. Although they come in many different form, they all share common elements, such as: providing a dedicated lane that is exclusive for cycling and separated from other road uses (i.e. motor
Protected bikeways may be one-way or two-way, and may be at street level, at sidewalk level, or at an intermediate level. If at sidewalk level, a curb or median separates them from motor traffic, while different pavement colour/texture separates the cycle track from the sidewalk (ITE, 2017). If at street level, they can be separated from motor traffic by raised medians, on-street parking, or bollards. By separating cyclists from motor traffic, protected
vehicle travel lanes, parking lanes, and sidewalks.). In situations where on-street parking is allowed, the protected bikeway is located curb-side (in contrast to bike lanes). The separation must be included midblock and be maintained through the intersections. (ITE, 2017)
Figure 24: Components of a protected bikeway.Source: Toole Design Group, MassDOT, 2015)
48
bikeways can offer a higher level of security than bike lanes and
physical protection. Therefore, a protected bikeway physically
are attractive to a wider spectrum of the public (ITE, 2017).
raised and separated from traffic or with a physical separation, preferably with a vertical object like a curb or planter, is the most
Buffer Types A 2015 study from Portland (McNeil, Monsere, & Dill, 2015)
safety requirements of new cyclists and attract them to cycling.
investigated the difference between buffer types (see appendix 1)
6.2.2 Safe Intersection Design
for descriptions) and how they affect people’s sense of the safety
Intersection safety for cyclists is a function of exposure to conflicts
and comfort bicycling. The paper used survey data collected from
with motor vehicles and the speed at which those conflicts occur;
multiple cities with newly-constructed protected bike lanes in
intersections with motor vehicle speeds of over 50 km/hr had twice
various configurations (both with and without physical protection)
the injury risk (Winters et. al., 2012). The MassDOT Separated
to study the perceived safety by current cyclists and of residents
Bike Lane Planning & Design Guide (2015) is a great resource and
living near the new facilities who could be potential cyclists.
provides a graphic illustrating the relative levels of exposure a
Findings suggest that residents felt buffers with a barrier created a
person cycling has based on the type of bicycle facility and design
stronger, therefore safer protection than facilities with buffers
of the intersection for different movements (Figure 25). The
containing flex posts or a parking strip. Around 71% of all residents
“vehicular cycling” methods of making turns from a conventional
indicated they agree somewhat or strongly that they would be more
bike lane has significantly higher exposure than the low exposure
likely to ride a bicycle if motor vehicles and bicycles were
scenario of a protected intersection design.
physically separated by a barrier, with 89% of the “interested but
In a protected bikeway, to increase drivers’ awareness of bicyclists
concerned” group in agreement (McNeil et al., 2015). In contrast,
in the cycle track, the stop line is usually moved back 2-5 m,
among current cyclists, a ~1m buffer with some type of vertical
allowing cyclists to wait as far up on the intersection as possible.
separation (e.g. plastic flexposts) were sufficient to make a positive
In addition, the protected bikeway can be dropped into a bike lane
difference in increasing comfort. Overall, both current bicyclists
about 5m before the intersection and incorporate a bike box.
and residents would feel comfortable riding on a busy commercial
Typically installed on the longest crossing leg or at offset
street (speed limit over 50 km/hr) if there was a bike lane with
49
effective at creating a sense of safety that is able to best satisfy the
6.2.2.1 Major Road Crossing a Major Road In the Netherlands, where protected bike lanes are common along major roads with parking,
a
commonly
used
type
of
intersection is the protected signalised intersection. Figure 26 is an example of the intersection and figure 27 demonstrates the increase of awareness as a motor vehicle.
Figure 26: Protected Intersection (BicycleDutch, 2014) Figure 25: relative levels of exposure a person cycling has based on the type of bicycle facility and design of the intersection for different movements. (Source: MassDOT, 2015)
intersection legs, with the bikeway coloured and marked as a solid marking through the intersection. The width of the markings should be 2.5m, according to the CROW (2017b) design guidelines and removing parking from intersections 5m prior to the intersection Figure 27: increased cyclist awareness
increases the visibility of cyclists in cycle tracks.
to the motor vehicles (MassDOT 2015)
50
In Copenhagen, along arterial roads that do not have car parking, there are three design elements that mitigate collision risk at intersections. These include: advance lights for cyclists, pulledback stop lines for cars and dedicated traffic lights for cyclists (Copenhagenize 2016). These features are illustrated and described in figure 28.
Figure 28: Copenhagen intersection design elements (Copenhagenize 2016)
51
6.2.2.2 Major Road Crossing a Local Road
collision incidence for cyclists was lower at intersections
In addition to protected intersections crossing major roads, there
that have raised bicycle crossings or other speed reduction
are designs that help mitigate risk when crossing local, access
measures for traffic turning into side roads.
roads. In reviewing available research from North America and
Therefore raised entry treatments can, when implemented
Europe, five primary treatments have been found to improve safety
in a suitable location, help reduce the speed of vehicles
at intersections and crossings (ITE, 2017; CROW 2017b; and TfL,
turning into a side road, thereby addressing some of the
2014):
risks at side road crossings. They can also be used to
1.
Application of pavement markings on a raised table at conflict zones at the entrance to a local street from a major road (figure 29). In general, Sustrans (2014b) recommend that designs should be on a raised table to reduce traffic speeds and include coloured surfacing (normally green in Canada) and cycle logos on the road
suggest
priority
for
cyclists
and
pedestrians
by
differentiating the crossing from the carriageway road surface.
2. Separate bicycle phasing (and leading intervals). This measure separates the conflicting vehicle and bicycle
surface to highlight the crossing both to drivers and to cyclists (Sustrans, 2011). They can also be used to suggest priority for
cyclists
and
pedestrians
by
differentiating the crossing from the carriageway
road
surface.
This
is
particularly important for improving safety at uncontrolled, stop and yield intersections which are found within residential neighbourhoods. Based on
Figure 29: Example of raised protected bikeway crossing at a minor street intersection in Delft,
findings by Schepers et. al. (2011)
Netherlands. Source: Tyler Golly (ITE, 2017)
52
movements by giving an opportunity for cyclists to safely
path of cyclists close to perpendicular, increasing the
clear the intersection first while providing dedicated time
visibility of cyclists and pedestrians by placing them in the
for the turning vehicles to complete their turn.
direct line of sight of drivers. In most circumstances, the
3. Bending the cycling crossing away from the adjacent street
safety benefits to cyclists of tighter geometry and the
with the bikeway coloured and raised if space and site
reduction in speed of turning motor vehicles outweighs the
conditions allow (Pedler and Davies, 2000). This approach
risk to cyclists that exists in relation to larger vehicles
allows queuing space for turning vehicles to yield to the
moving out to the centre of the carriageway to make a left
right-of-way of crossing cyclists. This design is
turn.
appropriate for both urban and suburban areas. CROW
5. Preventing parking and loading close to junctions (at least
(2017b) suggests a distance of between 2-5m from the
5m) helps maintain visibility at the side road crossing.
adjacent motor vehicle lane, although many applications
(Transport for London, 2014)
use a guiding principle of one car length (see figure 30). 4. Tightening the geometry of a side road by reducing the turning radius. This treatment causes vehicles to cross the
Figure 30: Example of bending out a protected bikeway at an intersection (Source: CROW, 2017b)
53
PART DEVELOPMENT OFPLANNING THE 4-STEP PARTE: E: THE 4-STEP BIKE-RAIL CORRIDOR TOOL BIKE-RAIL CORRIDOR PLANNING TOOL A guide to where and what type of cycling investment should be made
A guide to where and what type of cycling infrastructure investment should be made
54
7
Where should cycling infrastructure be
prioritized to most effectively improve rail access?
have unsafe cycling conditions and would therefore benefit the most number of commuters if cycling was made safer. The flow and pattern of commuter movements are modelled using a microscale transportation network model that identifies the shortestdistance-paths from each home to the closest GO rail station,
Since the lack of safe cycling access to GO rail stations is predicted to be the harming GO’s cycling access rates, a “Bike-Rail cycling corridor identification tool” is developed to assess the current level of safe cycling connectivity from commuters’ homes to the GO rail station and if gaps of safe cycling exist, propose a method of identifying where infrastructure improvements have the highest
weighted by the estimated commuter population in each residential home. The numbers of commuters are estimated by dasymetrically mapping the TTS zone commuter counts from the Transportation Tomorrow Survey and distributing them to the residential buildings found in each zone, weighted by the estimated number of units (households) in each building.
potential of enabling cycling access. The outcome of the analysis helps to prioritize cycling corridor upgrades based in which have
The modelling procedure has been informed by findings from the
the most potential of opening up cycling access.
“Theory” and “the Toronto Context” and is developed using the information described in the following sections: “Data Used” and
7.1 Modelling Process The Bike-Rail corridor planning tool is created by following a method that utilizes the following software programs: ArcMap,
55
“Defining potentially cyclable trips to GO rail stations”
7.2 Data Used
CoGran, QGIS and the Urban Network Analysis Toolbox by MIT
7.2.1 Travel Data
for Rhino3D. The tool, which is mapped out in Figure 31, focuses
Household travel data from the Transportation Tomorrow Survey
on developing a deeper understanding of which road sections
(TTS) was analyzed for this master’s thesis. The TTS is a series of
within each GO rail station catchment area are the most important
cross-sectional household travel surveys conducted in the Greater
connections to the rail station (capture the most amount of
Golden Horseshoe (GGH) region once every five years since 1986.
commuter traffic). This helps prioritize cycling infrastructure
The TTS collects retrospective travel behaviour data using a
investment by identifying which of these important sections of road
computer assisted telephone interviewing (CATI) method. The
Figure 31: 4-Step bike-rail cycling corridor identification tool process
56
TTS data is collected from fall until spring. The data is based on a
7.2.2 Open Street Map and Toronto Open data
5% sample of all households in the GTHA region (n = 160,000
All GIS datasets used in the analysis are available free for
households) (Data Management Group 2013). The most recent year
download and use. The following provides a brief explanation of
is 2011 that TTS data was available for so 2011 data was used in
each data set:
the analysis. The TTS data for Toronto was examined, specifically looking at the populations of employed persons and OriginDestination trip trend.
The 3D massing building dataset: a geospatial 3D ESRI shape / 3D CAD format file of building shapes for City of Toronto. The 3D Massing datasets provides a spatial representation of building
Since the TTS data is collected only from households with a home
footprints and the building height (EleZ attribute) (City of Toronto,
phone line, the survey is less representative of the youth and young
2018c).
adult population since many do not have a home phone (Mitra et. al. 2016). Despite its limitations, the survey offers the largest population-representative travel behaviour dataset of its kind in North America and has been widely used to inform transportation planning in the GTHA (Mitra et. al. 2016).
Toronto Road Centreline data: set of linear features representing streets, walkways, rivers, railways, highways and administrative boundaries within the City of Toronto. Each line segment is described with a series of attributes including a unique identifier, name, feature code, and address ranges (where applicable) (City of
For this paper, the TTS data for Toronto was examined to
Toronto, 2018b).
specifically look at the populations of employed persons and Origin-Destination trip trend. This included employed persons (part time and full-time) who do not work at home and students over the age of 18 to capture those who may be commuting to a post-secondary institution. This segment was selected to reflect the
57
Toronto Bikeways: A geospatial dataset that contains bikeway information appended to the enhanced Toronto Centreline file. The Toronto bikeways data contains bicycle lanes, signed bicycle routes and pathways. (City of Toronto, 2018c)
potential users of the GO rail system. This assumption is aligned
Zoning By-law: A geospatial dataset that contains information on
with the Metrolinx user survey which indicates 96% of GO
zoning bylaws that regulate the use, size, height, density and
customers were either employed full time, part time or are students
location of buildings on properties and affect every property in the
(Metrolinx, 2016a).
City. The Zoning layer was used to identify where residential
homes are located in the City (appendix 7) (City of Toronto,
counts need to be modelled at the building-level within the cycling
2018c). Since zoning sometimes does not reflect the existing use,
catchment area around each GO station. As a result, a dataset needs
the OSM building data was used to cross-reference the data for
to be created that contains residential building locations with
accuracy, in combination with Google Maps and Street View.
estimated commuters at each location so the network analysis can
OpenStreetMap (OSM): The OpenStreetMap project is a
be weighted by the commuter population at each address.
knowledge collective that provides user-generated street maps.
To make this dataset, Toronto’s Transportation Survey was used to
OSM follows the peer production model that created Wikipedia; its
identify the estimated number of “commuters” (full and part time
aim is to create a set of map data that's free to use, editable, and
employed persons, not working from home and students 18+)
licensed under new copyright schemes. The existing residential
(Appendix 4). Dasymetric areal interpolation was then used (see
buildings layer was used from this dataset to cross-verify whether
CoGran, appendix 5) to distribute the number of commuters in each
the buildings in areas classified as residential reflect the current use
TTS zone to the residential homes found within the limits of each
(OSM, 2018).
zone, weighted by the number of households (units) in each
7.3
Creating a building-level commuter population
building. Single family homes are coded as one household and the estimated number of households in each apartment and multi-unit
dataset: How the number of commuters in each
homes is modelled using a volumetric procedure illustrated in
residential home is calculated
figure 32 and described in detail in Appendix 6. This process
Variations in population along each street affects the commuter volume on each road segment. In other words, roads that have
creates a dataset that estimates the commuter population in each residential building so the commuter volumes on the routes of
higher-density housing will create more foot traffic than roads abutting lowdensity development. commuter
residential Thus, population
Figure 32: Volumetric procedure for calculating number of units (households in apartments and multi-unit homes
58
travel from each building to the GO station can be most accurately calculated.
7.4
Defining potentially cyclable trips to GO
rail stations 7.4.1 Catchment Area While distances under 5km have been found to be a strong motivator for cycling (Winters et al., 2010), international research has indicated that a smaller range appears to be appropriate for access to transit and that this range seems to reach a peak at about 3.5km (Chan, 2016). Based on various studies from North America, Asia and Europe, Chan (2016) identified a 3.5km catchment area around GO stations as a realistic distance people are willing to travel to access public
Table 9. Summary of Research about Cycling Distance to Access Transit (Chan, 2016)
transit (table 9). He mentions that although he used a Euclidean (“as the crow flies”) distance measure for his analysis,
R2 of a model using network distances was 0.724 compared to only
actual access distance may be better captured by network distance
0.707 for the model using fixed distances. Therefore, a 3.5km
as this provides information about the functional distance you
network-based catchment area around the GO rail stations is used
would really need to travel due to barriers (e.g. river, highway or
for this study.
railway track itself) and level of road connectivity (see Sorensen
59
and Hess 2015; Nelson et al. 2015; Guerra et al., 2012). A notable
7.4.2 Distance from downtown
difference in ridership estimation between the two methods could
Bachand-Marleau et. al. (2011) studied the travel circumstances in
be seen in the study of Gutiérrez et al. (Gutiérrez, 2008) where the
which individuals are most likely to desire and choose to combine
cycling and transit in Montreal, Canada. The authors found that
accommodation, this study focuses on prioritizing the stations that
opportunities are greatest for people living farther than 15 km from
are expected to have the most benefit from enhanced bike-rail
the city centre. This finding is in agreement with the research by
improvements. Therefore, only GO stations outside of a network-
Brons et al., 2009 which found that improving station access at the
based 15km from a central point in the downtown core (figure 33)
peripheral parts of the network was the most cost effective at
were selected for this study. The specific point was the approximate
increasing rail use.
centre of Toronto’s Financial District which is the intersection of
These results are useful in helping determine which GO rail stations
Bay St and King St, the biggest commuter draw.
have the most potential for bike-rail combination in Toronto.
7.5
Bike-Rail cycling corridor identification tool
Although every GO station should provide for cycling access
procedure A 4-step Bike-Rail cycling corridor identification tool is described that is expected to help identify where and what type of cycling infrastructure is needed to maximize access to GO stations from residential homes. Roads with high cycling potential are defined as segments that have the potential to carry the most commuters from their homes to the GO rail station within a realistically cyclable 3.5km network-based catchment area. This model captures the assumption that people 3.5 km away from the GO station are just as likely to travel to the station by bike as people who living closer. Based on the purpose of this model, the goal is to help enable everyone within a realistically cyclable distance (~10 minute bicycle ride) to be able to cycle to the station so the model analyzed all paths equitably.
Figure 33: Financial district, city centre. Source: Author’s Own,
Alternatively, a gravity model can be included as part of the
60
betweenness analysis; this is recommended as further research,
the road network will be crossed by commuters taking the shortest-
especially if a wider catchment area is being assessed (ie. 5km)
distance path to their closest GO station from their home, weighted
since the propensity to cycle reduces the farther you move away
by household population.
from a destination.
1) Identify the most travelled paths to the station by doing a
Unsafe links are roads that have high-stress characteristics such as
betweenness analysis between the rail station and the
high traffic speeds and volumes without dedicated or separated
residential homes located within their 3.5km cycling catchment
cycling infrastructure. By highlighting the unsafe links, they can be
areas using MIT’s Urban Analysis Toolbox for Rhino3D
strategically prioritized based on which has the most potential of
2) Export the path results to ArcMAP or QGIS for the next
increasing accessibility, weighed against the estimated amount of cycling infrastructure investment needed ($/m). The study only focuses on the access trip made at the “home” end since it holds the most potential of shifting people from their car to active forms of transport.
analysis step
Step 2: Cycling infrastructure gap identification Identify which of the well-travelled roads have gaps, meaning roads that expose cyclists to unsafe conditions (i.e. high-speed roads without cycling infrastructure).
In order to know where and what cycling infrastructure is needed 1) Create a network
to maximize access to GO stations from residential homes, the
dataset in ArcMap or
following 4-step tool is described:
QGIS that identifies
Step 1: Identify the most important connections from
bikeable roads (table
commuters’ home to the GO station
9) that have unsafe
Conduct a network analysis to identify the most important
(high-stress)
connections to the GO stations. These are roads that are the most
safe
travelled between commuters’ home and the closest GO station within its 3.5km cycling catchment area. This is done by using betweenness analysis to simulate how many times each segment of
61
(low
Table 10: Bikeable roads. Adapted from the
cycling
Toronto Road classification GIS shapefile
(table 10).
(City of Toronto, 2018b)
and stress) sections
of protected bikeways that can be built instead, which is anticipated to draw more ridership to the station. 1) Use an estimated cost for building protected cycling infrastructure to estimate the cost to upgrade the unsafe sections of road with protected bikeways. 2) Compare the estimated cost to upgrade each unsafe corridor of road against the number of estimated commuters that the corridor is able to carry to the station (access). Table 11: high and low-stress link classification **Red indicates high stress links. *Blue indicates low-stress links
Step 4: Cycling Infrastructure Design Proposal
2) Perform a GIS overlay with the betweenness analysis dataset
Based on best practices described in the “Manual” section, propose
results from step 1 with the unsafe cycling link network
appropriate cycling infrastructure design on the unsafe sections that
dataset described in step 2 (1). This will identify which of the
helps achieve the greatest amount of perceived and actual safety
most-travelled routes have unsafe cycling sections
Step 3: Cost/Benefit Analysis Perform a cost/benefit analysis to identify which sections of road have the most potential of unlocking GO station access balanced against cost. This will help identify the key improvements that need to be made to unlock station access to entire residential neighbourhoods. Since Metrolinx typically supplies free parking spaces at GO rail stations for commuters, this cost benefit analysis weighs the cost of providing commuter parking against the amount
62
PART
F:
CASE
STUDY
APPLICATION
PART F: CASE STUDY APPLICATION The application of the 4-Step Bike-Rail Cycling Corridor Planning Tool to a GO rail station Location modelling and recommended cycling infrastructure design
63
8 Case study station selection
negative residuals are expected to have significant latent demand for increased active transportation access. The results are
In order to select the Toronto GO rail station to apply the 4-step Bike-Rail cycling corridor identification tool to, the master’s thesis by Chan (2016) was used. Chan determined which station land-use and demographic characteristics help predict cycling access rates to GO rail stations in the GTHA. The results helped identify which GO stations are performing relatively well in active transportation access mode share and which stations seem to be underperforming given their underlying characteristics. The method used was a residual analysis at the individual station level. Stations with large
summarized in table 12 and the stations are ranked based on which have the largest negative residual, meaning the greatest potential of latent demand that is unrealized. Within Toronto, Kipling GO station ranks the highest in this aspect. Further, this station is located 15km outside the centre of Toronto’s Financial District, the city centre, so it is at a distance found to be most desirable for combining cycling and public transport (Bachand-Marleau et. al., 2011). This makes Kipling GO the most attractive station to use as a case study for testing the tool to determine whether there is a lack of safe cycling routes to the station, helping explain why the station is underperforming and what improvements could be made to best encourage cycling access.
8.1
Kipling GO station: Bike-Rail cycling
corridor identification tool application Step 1: Perform the Betweenness Analysis in Rhino3D using the Urban Network Analysis Toolbox by MIT. a)
Import the data (residential homes with building-level
commuter population estimation, roads and Kipling GO rail Table 12 GO Stations with 10 Largest Negative Residuals (Adapted
station location.
from Chan, 2016)
64
Rhino will automatically attach the origin and destination points to
UNA toolbox. A new layer is created that includes all residential
the closest network link (road) (Figure 34). The little blue squares
buildings within a 3.5 km network-based catchment and excludes
represent residential buildings and the red lines illustrate the road
all other buildings that fall outside this area (figure 35). Since each
section the building or GO station is connected to for the analysis.
home is modelled with an estimated number of commuters from
The feature connection location can be adjusted if they are
the Transportation Tomorrow Survey, by running UNA’s “closest
associated with the incorrect segment. The green square indicates
facility tool” the station’s market area for cycling is determined;
the GO station entrance
this is the number of commuters captured within a 3.5km networkbased catchment area of the GO station (commuter count is located at the centre of the orange area).
Kipling GO Figure 34: Betweenness analysis, data import.in Rhino3D visualized by Author
b) Identify all residential buildings located within a 3.5km network-based distance from the GO station that will be included in the analysis.
65
Select the Kipling GO station as the origin and all other residential
Figure 35: Closest facility tool results. Visualized in Rhino3D by
buildings as destinations and run the service area tool from the
Author
c) Run the Betweenness analysis tool. The results show the number of commuters who would cross along each segment of the road network (bikeable sections) using the shortestdistance path between home and the GO station, weighted by
the
population
of
commuters in each household within a 3.5km network-based catchment. The count on each segment is cumulative so all commuters within the 3.5km analysis area arrive at the road segment the GO rail station is connected to. This accounts for the big range of values within the map’s symbology. The result
betweenness from
analysis
Rhino3D
Figure 36: Betweenness analysis results (station-level), visualized in ArcMap
is
illustrated in Appendix 8 and 9. The analysis results around the GO rail station are useful for identifying which approach to the GO
station would draw the highest number of area commuters, helping identify which station entrance is the most important for access.
66
This information could be useful in planning the placement amenities
of or
including
cycling services, helping
approximate the amount of bicycle parking capacity needed at each station entrance.
The
station
entrance access modelling estimates that 38% of commuters within 3.5km of the station would use the west station access from Aukland Road and 62% would use the east access, from Kipling Avenue. c) Visualize Betweenness Analysis. In ArcMap, the natural breaks (Jenks) data classification method was used since it groups similar value
classes
together,
Figure 37: Betweenness analysis results (catchment area), visualized in ArcMap by Author
helping maximize the difference between classes (De Smith,
67
Goodchild and Longley, 2009). The estimated number of commuters each road segment is modelled to carry to the GO station is illustrated, identifying the importance of each segment within the road network as part of the journey to the station from commuters’ homes. Only four symbology categories were used to most clearly illustrate the road segments that would carry the highest commuter traffic.
Step 2: Connectivity Analysis Identify whether the routes that are the most important between the station and commuters’ homes are also safe, low-stress routes for cycling a)
Identify the routes that have
safe (low-stress) connections and
Figure 38: Low-stress and high-stress route identification, visualized in ArcMap by Author
those that are unsafe (high-stress connections). This is done using the parameters in section 7.5
speed, low-volume (less than 2,500 veh/day), and have a property
(table 11). The results (figure 38) show many local roads were
access function (City of Toronto, 2018b) making it acceptably safe
picked up under the definition “unsafe” even though they are low-
to cycle with mixed traffic. Since Toronto has a default statutory
68
speed limit in urban areas of 50 km/hr, if a road does not have a posted speed limit, it is set at 50 km/hr by default (City of Toronto, 2015b). Since these local roads are low-volume and fit the CROW (2016) category of permitting mixed traffic, for the purposes of this study, these roads will be classified as “safe”. Although it is out of the scope of this master’s thesis, these roads should be studied further as part of a sustainable safety strategy to ensure the intended function of the road matches the current traffic patterns. This could result in a speed limit change (to 30 km/hr is recommended), traffic calming or appropriate cycling infrastructure installed if the function of the road is upgraded to serve as both facilitating
Figure 39: Low-stress and high-stress route identification (reclassified), visualized in ArcMap by Author
traffic flow and property access instead of solely access. Since these sections of local road are part of the betweenness analysis, carrying commuter movement from
69
homes, they remain as part of the model but are classified as “N/A (not applicable): Local road, does not need to be upgraded but needs further study”. Accordingly, the analysis in part (a) is redone
to reflect the modified classification (figure 40). The results of the modified analysis are illustrated in Table 13 and a summary of the distribution of road kilometres by level of traffic stress is identified.
Table 13: Bikeway stress level summary
During the safety analysis classification, segments that were redundant paths or on dead end streets were found totalling 2 km of road segments; these were reclassified as N/A as well, so they can be investigated during future research with the N/A local roads. Figure 40: Spatial intersection: High commuter volume and stress levels, visualized in ArcMap by Author
b) Identify the most important routes and overlay with the safe/unsafe route dataset. Most important routes are identified by selecting the top 10% of road segments with the highest commuter traffic from the betweenness analysis. These
priority routes are then overlaid with the safe/unsafe dataset from step (a). Results indicate (figure 41) there are a total of 17.8 km of priority segments, of which only 26% (4.6 km) are along low-stress
70
routes. Most importantly, none of the important routes provide a
betweenness analysis would need to be re-run to reassess cycling
continuous, safe, low-stress connection to either of the GO station
reach of commuters.
entrances. The existing cycling network is illustrated to understand how the important routes fit within the context of the overall
Characteristics of the cost-benefit model: 1. New cycling infrastructure will induce a portion of drive
cycling network. The connectivity map of Kipling GO station shows that not only
and park rail passengers to cycle to the station. 2. The cost of building additional parking spaces is constant.
are these important routes are of benefit to the commuters in the area trying to access the station, but as well fills in some major gaps
3. The per kilometre cost of building additional bicycle infrastructure is constant.
in the overall existing cycling network. Not only would these routes encourage cycling to the GO station but also encourage residents
When considering the overall cost of facilitating station access as a
to incorporate cycling for their daily activities and errands,
trade-off between building car parking or building more protected
supporting the growth of the overall cycling mode share in the city.
cycling infrastructure, this cost-benefit model considers the 20 year infrastructure cost of station access if building cycling
71
Step 3: Cost-Benefit Analysis - identify which sections of road
infrastructure along weak links can cause 7% of peak-hour drive
have the most potential of unlocking GO station access
and park customers to switch to cycling (the “enthused and
balanced against cost.
confident” proportion of the population cycling based on Geller,
Given every street within the cycling catchment area facilitates
2012). Currently GO projects an increase from 1% to 2-4% in
commuter movement, it would be most desirable to build the whole
Kipling GO’s cycling access mode share by 2031 without plans for
concept of safe cycling routes by bridging all unsafe sections of
a connected network of safe cycling routes to the station. So it is
road with safe cycling infrastructure upgrades. This approach is
predicted a 7% increase in cycling access mode share to a total 8%
demonstrated in the following cost-benefit analysis and is based on
is realistically achievable. The benefits of other aspects of cycling,
the values and assumptions in Appendix 10. As needed, the
including the use of cycle infrastructure to reach other destinations,
formula can be adapted to analyze the cost-benefit results for
health benefits, and environmental benefits are excluded from this
building one or more cycling corridors on their own. The
model.
Cost-benefit model equation:
driving & parking
$ Yearly $ Yearly benefit of Net parking savings from benefit people who switch to cycling as access mode C= +ax
to cycling due to
$ Yearly cost of cycling infrastructure to connect weak links in 3.5 km station catchment area by -by
infrastructure improvements (based
on
GO
transit
ridership
projection) year 2031
Description of variables: Input for these variables are derived from projected station access
Let x =
customer).
appendix 10 for full list and description of variables. Let b =
Let C =
Net
benefit
of N/A
fix
N/A
peak
hour
km
weak 38 km
to links (calculated links using GIS)
3.5km
catchment area
cycle Let y =
Total number of 830
weak
within
infrastructure Let a =
km of protected 38 infrastructure
Input
building missing links
=
per peak hour GO $2,569/year
discount rate, infrastructure lifespan, and maintenance costs. See
Calculation
(Net $2,569/
1 parked vehicle 20years
cycling given safe infrastructure, estimated cost of parking spaces,
Description
Parking cost per $51,371
person (assuming Present Value) / year
demand by drive and park, willingness of people to switch to
Variable
by
(projected 58
GO customers)*0.07
customers within (cycling
mode
customers
Per year cost per N/A
$13,627/
km of protected
km/year
bikeway Table 14: Description of cost-benefit variables
3.5km catchment share who switch from increase)=58.1
72
The model assessed the net economic benefits of realizing the
Therefore, 201 passengers need to switch from driving to cycling
entire concept for the limits of the network-based cycling
in the 3.5 km access area by 2031 for the savings in parking spaces
catchment area around Kipling GO station using GO rail’s 2031
to equal the cost for building 38 km of missing links cycling
ridership forecast.
infrastructure.
Calculation for realizing the whole safe route concept:
Sensitivity analysis:
C= +ax -by
Sensitivity analysis was used to analyze the uncertainty surrounding the CBA results. The variables for the cost of cycling
= (58)(2569) - (38)(13627)
infrastructure, parking, cycling ridership switch from car to bike and discount rate were changed to a more conservative value see
= 149002 - 517826 = $-368,824/yr The results show there would be a net cost of $-368,824/yr if all 38km of unsafe routes were constructed with protected bikeways.
how it affected the net economic benefits. The results (appendix 10) show a range of 2-9% sensitivity in the values. Since the NPV changes by only a small amount (e.g. ±10 per cent change causes a ±3 per cent change in NPV), it implies that the uncertainty surrounding the variable is not very important and is not critical for
Next, the break-even point was calculated to determine the number of passengers switching from driving to cycling that would be needed to match parking construction costs: C = ax - by 0 = a(2569) - 517826 517826=a(2569) 517826/2569= 201
73
decision-making.
Step 4: Cycling Infrastructure Design
Proposal
-
propose
appropriate cycling infrastructure design on the unsafe sections of road There are 5 “important, unsafe” routes that were identified as part of the modelling. All were assessed based on how much commuter volume they are modelled to carry and whether there are suitable alternative routes that do not involve excessive detour. This analysis helped
determine
construction
the
phasing
infrastructure to
understand
which corridors would benefit the most from safe bikeway upgrades. Based on the assessment, Bloor Street West from the GO station to Renforth Rd was selected for the infrastructure design proposal since it is modelled to have high
Figure 41: Infrastructure phasing prioritization. Visualized in ArcMap by Author
commuter volumes and is an important route that does not have a
channelled over a bridge near the West Mall which is a barrier that
safe alternate route nearby. Commuters along Bloor St W are
limits alternate east-to-west access opportunities.
74
There are two different typical road profiles on Bloor Street so a different design for each section is proposed:
Existing Condition 1 – Bloor Street West near Martin Grove Road Existing conditions •
25m Right of Way (estimated using Google Maps)
•
15m
road
(estimated
using
Google Maps) •
4 lane, 2-way road
•
Major Arterial (distributor), 50 km/hr
•
Constraints: Narrow boulevard space next to road due to mature trees Figure 42: Bloor St W near Martin Grove Rd existing conditions (Google Maps, 2018)
Recommended design considerations Protected bikeway (grade-separated from the road surface, at sidewalk level).
-
Extend bikeway 0.6m into each boulevard to achieve a 1.8 m one-way path for each travel direction (bikeway width is less than 2m due to boulevard constraints)
-
Function: separating motorized traffic and bicycle traffic for the benefit of cyclists’ safety and comfort
Note: With new roadway construction a raised cycle track can be less expensive to construct than a wide or buffered bicycle lane
75
Design examples for both sections:
Figure 43: One-way raised protected bikeway (NACTO, 2018)
Figure 44: One-way raised protected bikeway (NACTO, 2018)
Figure 45: Mountable curb separation between bikeway and sidewalk in Eindhoven. Source: Author’s Own
76
Figure 46: Bloor St W near Martin Grove Rd cross section (existing conditions) Visualized by Author using Streetmix
Figure 47: Bloor St W near Martin Grove Rd cross section (proposed) Visualized by Author using Streetmix
77
Existing Condition 2 – Bloor Street West (near Renforth Drive) Existing conditions •
27m Right of Way (estimated using Google Maps)
•
15m
road
(estimated
using
Google Maps) •
4 lane, 2-way road
•
Major Arterial (distributor), 50 km/hr
•
Constraints: TTC transit stop in the boulevard
Recommended design considerations Protected bikeway (boulevard bikeway with curb separation, grade-separated from the sidewalk to reduce conflict with
Figure 48: Bloor Street West (near Renforth Dr) – existing conditions (Google Maps, 2018)
pedestrians). -
-
Function: separating motorized traffic and bicycle traffic for
For cyclist safety, the grade separation between the bikeway
the benefit of cyclists’ safety and comfort
and sidewalk should include a mountable curb separation
Extend bikeway 0.9m into each boulevard to achieve a 2 m
between bikeway and sidewalk (figure 45).
one-way protected bikeway for each travel direction. -
-
-
Consider adding trees in the boulevard to create a sense of
bikeway diverted around bus shelter to prevent conflict with
enclosure on the street, improving the attractiveness of the
transit riders boarding and alighting the bus.
public realm.
78
Figure 49: Bloor Street West (near the West Mall) cross section– existing conditions Visualized by Author using Streetmix
Figure 50: Bloor Street West (near the West Mall) cross section– proposed Visualized by Author using Streetmix
79
8.2
Existing Travel Conditions and impact on travel
downtown from cycling access improvements In this section, the effect of cycling access improvements on travel patterns around Kipling GO are investigated. The focus is on the impacts that are predicted to happen in Downtown Toronto, since this is where the vast majority of trips on the GO rail network are made and where the greatest negative externalities occur.
Kipling GO ridership forecast Metrolinx predicts the ridership forecast for people accessing Kipling GO from home is very low; an increase of only 200
Figure 52: Number of commuters who live within 3.5km (network-
people/day (800 today to 1000 passengers/day) by 2031 (Metrolinx
based distance) of each Toronto GO station. Data Source: (TTS, 2011)
access plan 2016b). The map in figure 51 shows the location of
Kipling GO in relation to Downtown Toronto. Figure 52 illustrates how many commuters live within the 3.5km cycling catchment area around each GO station. Currently, no free parking at Kipling GO exists like at other stations and no additional parking spots are planned (Metrolinx, 2016b); though there is nearby TTC parking at $6/day. Since 60% of people drive to the station, parking may be a limiting factor for GO ridership projections at this station, especially since other desirable access mode options with similar reach and flexibility are limited. So, by improving access to the station by bike, they would be drawing additional ridership to the GO system from the untapped number of trips made downtown by
Figure 51: Location of Kipling GO and downtown Toronto (PD1).
Kipling GO in relation to Downtown Toronto. .Currently, no free
car. Increased ridership created by improved access to the station
Visualized in ArcMap by Author
80
by cycling is not factored into GO’s current ridership projections.
How many commutes are made 3.5km around Kipling GO
Kipling GO is connected to a TTC station, part of the city’s local
station from home to downtown during a weekday?
subway system that is also able to take commuters to downtown Toronto. Therefore, improving access to GO will simultaneously improve access to TTC as well, sharing the rail ridership increase from the anticipated access improvements for cycling.
What is the mode share of commutes within 3.5km
9,176 based on the area the catchment area covers. To get a more statistically representative count, the trip count from TTS zones around Kipling are redistributed
using dasymetric areal
interpolation to arrive at an estimated trip count within the limits of the GO rail catchment area (Figure 54).
around Kipling GO station? Figure 53 illustrates the Average mode share over all Aggregate Dissemination Areas (ADAs) around the Kipling GO Cycling Catchment. Appendix 11 contains an extended map that illustrates the mode share within each ADA.
5%
Figure 53: Average mode share over all Aggregate Dissemination Areas (ADAs) around the Kipling GO Cycling Catchment. Data
Figure 54: Average mode share over all Aggregate Dissemination Areas
Source: Statistics Canada, 2011
(ADAs) around the Kipling GO Cycling Catchment. Data Source: TTS data, 2011. Visualized in ArcMap by Author
81
The map in appendix 12 tells us the Kipling GO rail station is
at Dutch levels to capture this proportion of trips. Therefore, by
currently only used for 9% of weekday trips to the downtown area
implementing the highest-quality and safest cycling infrastructure,
(PD1). Currently, 92.4% exit at Union station, 2.8% Exhibition.
the cycling environment can be made attractive, enabling cycling
94.3% of commuters work within 2.9km of their egress station
access to rail and nurturing the growth of cycling in the suburbs.
(Metrolinx, 2016a).
8.3
Therefore, more conservatively, it is anticipated that safe cycling
Current trips that could be made by GO rail
improvements to GO rail would be able to increase cycling access
transport from Kipling GO (within its 3.5 km cycling
mode share to 8%, capturing the “enthused and confident”
catchment area)
population. This would translate to a potential of switching of 217 car trips to cycling-GO rail trips. This is estimated to be the latent
If 9,178 trips from homes within Kipling GO’s cycling catchment
ridership that exists and could be realized if the whole concept of
area are made to Downtown Toronto during a typical weekday
cycling improvements are made to unsafe corridors in the Kipling
(Figure 54), and 52% of those trips are to destinations within a 1km
GO cycling catchment area. As a result, the strain of 217 one-way
walking distance from Union GO station (52%) (figure 55), then
car trips could be alleviated by the GO rail system, up to a potential
4,775 of those trips could potentially be made by GO Train. Since
of 2,078 as the cycling environment matures in the long-term.
the current mode share split is 65% for car (whether as driver or passenger), 3,104 potential one-way trips could potentially be switched from car to cycling-GO rail. Based on Geller’s (2012) research, he estimated that 33% of the general population is unwilling to bicycle even if high-quality bicycle infrastructure is in place, lowering the potential cycle access trip capture to 2,079 car trips. However, capturing the “interested but concerned” proportion of cyclists and getting up to a 60% cycling mode share would be a challenge; the cycling built
Figure 55: Trips made from Kipling GO to Downtown destination -
form, environment and attitudes towards cycling would need to be
egress distance (TTS, 2011)
82
The cost-benefit analysis indicated that a switch of 201 cyclists is
capacity, about 28,000 passengers per hour per direction (pphpd)
the break-even point for realizing the whole safe cycling route
(Metrolinx, 2015b). As congestion continues to grow on the TTC
concept with the savings from parking spaces. Since 217 current
Bloor/Yonge line during rush hour, taking the GO rail network will
car trips are expected to be diverted from driving to cycling with
become a faster, more attractive option helping ease the ridership
the bikeway improvements, the cost that would have been needed
load on the system during peak hours, especially if train frequency
to provide car parking would more than pay for the construction of
and the fares were reduced and made more competitive against the
38 km of missing links cycling infrastructure in the entire bikeway
TTC.
access area. Improvements to cycling access to GO will also improve access to the local Toronto Transit Commission (TTC) subway station (Islington station) that is connected to Kipling GO. Since trips to downtown Toronto can be made from Islington station as well, some of these trips are expected to be shared between the two public transport modes. This in part explains why Kipling GO seems to be underperforming in ridership given its underlying land-use and demographic characteristics. A trip on the TTC is cheaper than GO (a single cash fare is $3.25 for TTC and $5.60 for GO) but takes twice the trip time to Union station and requires a transfer to the TTC Bloor-Yonge line which is becoming increasingly congested during peak hours, especially in the morning peak; operating at least 11% over
83
Figure 56: Egress distances from Kipling GO train station. Visualized in ArcMap by Author
The other 48% of trips to downtown Toronto that have a destination
Therefore, given the number of trips currently made downtown and
outside the 1km walking distance from Union GO, 4,405 trips in
if the connection of cycling routes to the station from peoples’
total, can still be made by GO, or better yet, by TTC since it has
homes were improved and made safe, a higher proportion of people
greater public transport reach downtown (as illustrated in figure
who drive downtown would be encouraged to take the cycling-rail
56). If 65% of the remaining 4,405 trips from homes within Kipling
combination instead, exceeding GO projection numbers and
GO’s cycling catchment area are made by car, public transit (TTC
improving access to TTC without having to provide expensive
or GO) could capture 308 of these one-way trips in the short-term
parking or having it be the constraining factor for ridership growth.
and 2,643 in the long-term if the cycling environment achieves Dutch levels of improvement. However, for these mode switch scenarios to be possible, factors such as the capacity and service frequency of TTC and GO would need to be able to accommodate these trips. Overall, the current ridership projection by Metrolinx at Kipling GO appears to be quite low considering the overall number of trips that are made downtown from a distance that can be made by cycling to Kipling GO station then walking to their final destination when they exit at Union Station, the GO rail station downtown. Increased ridership from access improvements are not currently realized since cycling conditions are high-stress around the station, hence the 1% cycling access mode share. This research anticipates that without greatly-improved access to the station, the station forecast will remain low, constrained by current access limitations.
84
PART G: CONCLUSION AND DISCUSSION PART G: CONCLUSION AND DISCUSSION Answers to research questions, reflections and further research Answers to research questions, reflections and further research.
85
9 Summary and Conclusions 9.1 Results and answers to research questions
and expensive parking facilities as cars do. The GO rail system has historically supplied car parking to accommodate growing ridership. Despite significant parking expansion, parking capacity
The main target of this research was to understand how bicycle-rail
is currently at its peak; the GO rail transit agency is unable to keep
integration could be improved to enable and therefore encourage
pace with providing car parking to accommodate forecasted
more people to use a bicycle to access rail stations. In European
ridership growth. Continuing to supply car parking to
cities, the cycling-rail combination has demonstrated potential,
accommodate ridership growth would be financially unsustainable
namely in Amsterdam, where cycling access rates are 43% (KiM,
and would exacerbate negative station-area and neighbourhood
2016). However, current cycling levels to suburban GO stations in
impacts; limiting the access of other modes to the rail stations and
Toronto are nearly non-existent, leading to rail companies to
constraining the ability to grow ridership.
assume cycling is not a viable way to get to the rail station (Metrolinx, 2016b); further constraining the necessary investment into enabling cycling access and therefore the growth of cycling as a viable access mode. For cycling rates to grow, the right infrastructure needs to be in place; otherwise most of the population will not be able to have the choice to cycle.
The demand placed on road infrastructure surrounding GO stations can be reduced by shifting car access to cycling access while still supporting the wide station spacing of suburban rail. Since the spatial reach of cycling around GO stations is able to cover at least 61% of current access trips (Metrolinx, 2016a), improved cycling access will help capture these trips and ease the demand for car parking and car access to the station.
1. Why is cycling as an access mode beneficial to regional rail?
Rail on its own suffers from weaknesses in creating a fast, convenient and flexible door-to-door connection that cycling is
Bicycle-rail integration is a powerful, flexible and scalable strategy
able to overcome. In combination, bicycle-rail integration provides
for urban mobility. Suburban rail with station spacing between 4
a competitive rival to substitute car journeys of comparable
km and 10 km is ideally suited for cycling as an access mode,
distances. Thus, the rail sector also gains from combining bike-rail
considering cycling is the most competitive at distances between 1
since growing cycling access essentially translates into increased
and 5 km. Bicycles take up less space and do not require extensive
86
ridership at low access-service cost, mainly from the parking cost
the majority of the population from cycling since only 1% of the
savings for rail agencies.
population are comfortable cycling in all road conditions (Geller, 2012). Therefore, mitigating the unsafe moments during the
2. How can the cycling-rail combination be made
cycling experience is vital for making cycling more attractive and
more attractive to commuters?
encouraging people to cycle to rail stations.
There is much to learn from cities that have reached high cycling
3. What is contributing to the low cycling mode share
mode share; by drawing from some best practices and lessons
of suburban rail?
learned, the opportunities can be framed and applied within the context of Toronto. Looking at the environmental factors that underlie the restraining forces that discourage people from cycling, a clear trend emerges: unsafe cycling conditions; both by the perception of safety and observed safety. Safety is ranked by 80% of people as their number one reason for not cycling or not cycling more (TfL, 2014). To attain high levels of cycling access to rail stations, literature has made it clear that the road conditions between people’s desired origins and destinations need to be attractive for cycling; not just for the small proportion of the population who have a higher road risk tolerance, but for people of all ages and demographic backgrounds.
The level of bicycle-rail integration is inextricably linked to the low level of cycling in Toronto and the dominance of the car, enabled by cheap fossil fuel and subsidized parking. The access and services provided by the GO rail company have been planned to cater primarily to car-based access; the stations are located mostly along high-speed roads designed for the efficient flow of traffic, causing a trade-off that creates unsafe and undesirable routes for cycling. Parking is free for rail users at GO stations, with the cost of parking included as part of all fares, meaning the costs are subsidized by all passengers whether they drive to the station or walk. Since car drivers do not bear the actual costs of driving to the station and access is designed to be convenient for driving, driving is artificially made more attractive against other modes. Driving
87
Therefore low-stress connectivity is a crucial element of a cycling
and parking at rail stations is not scalable and by reducing the
network and any break in the perception of safety is enough to deter
incentives that encourage people to drive and instead investing into
making the cycling environment attractive, GO transit can create a
improved traffic calming measures need to be put in place to force
paradigm shift that incentivizes bicycle-rail and accordingly,
car drivers to slow down, enhancing pedestrian and cyclist safety.
reduces the attractiveness of driving and parking.
On high-speed, high-volume routes, protected bikeways that provide separation (continuously, mid-block and at intersections)
4. Which cycling investments are the most effective in encouraging cycling access to rail stations and how can these improvements be prioritized?
from motor vehicles are the only suitable design; they have been shown to not only increase the perceived safety of people cycling, but also the observed safety. Like all rail passengers, cyclists want direct, convenient and safe
Lewin’s behaviour change theory can be applied by understanding
routes to the station with minimal delay. Since the cycling journey
the environmental factors that restrain desired behaviour. Since
starts from home, every street that carries commuters between their
safety is ranked as the number one environmental restraint in
home and the rail station need to be acceptably safe. Many
cycling use, by making the cycling experience safer (perception
suburban rail stations are located adjacent to major arterials with
and observed safety), easier and more enjoyable, the key
speed limits of 50 km/hr and higher that have unsafe cycling
restraining force in people’s environment can be lessened and
conditions but are also surrounded by a dense network of
therefore encourage more people to cycle.
residential streets with speed limits of 40 km/h and lower with
As a principle of a safe cycling network, the intended functionality of roads should correspond to the design of cycling infrastructure that is appropriate for each road type. When “Movement” is considered to be the priority, then segregated cycling facilities are likely required, whereas if “Place” dominates, then space is more likely to be shared, and vehicle flows and speeds are accordingly restricted. For low-speed and low-volume residential streets, cycling in mixed traffic is acceptably safe. Only if the mean
acceptable cycling conditions. Therefore, these high-speed arterials need to be prioritized for cycling improvements since they are responsible for creating the unsafe, high-stress cycling conditions that break the safe cycling experience to the station and cause reduced cycling uptake. Since arterials have the intended use of “Movement,” cyclists need to be segregated from vehicular flows with protected cycling infrastructure to ensure a continuous, safe journey from home to the rail station.
observed speed on a road exceeds the desired speed, new or
88
5. How can locations of cycling improvements be identified in order to effectively improve station access for existing and potential suburban rail users?
corridor identification tool. The results showed that there were no safe, continuous cycling routes connecting people from their home to the station, especially along the important routes modelled to carry the highest volume of commuters. The lack of safe cycling
High-speed arterials with no dedicated cycling infrastructure are identified as the main source of reduced cycling uptake due to the reduced perceived and observed safety they cause. By assessing the
connectivity may help explain why unrealized latent cycling demand exists at the station given its underlying demographic, land use and rail station characteristics.
level of safe cycling connectivity between commuters’ homes and
The outcome of the Kipling GO station analysis demonstrated that
the rail station, any gaps of unsafe conditions can be identified so
the tool is successful in identifying the particular roads that require
they can be mitigated and cycling access accordingly improved.
infrastructure investment, identifying the amount of cycling
Using the Bike-Rail cycling corridor identification tool, commuters’ routes from home and the rail station within a realistic bikeable catchment area can be modelled. The road sections that are estimated to carry the highest volume of commuters accessing the rail station (most important sections) can be identified and if gaps in safe cycling exist along the most important routes, they can be prioritized for upgrades. This method effectively prioritizes
infrastructure upgrades that are needed to open up safe cycling access in the entire cycling catchment area. Since each corridor is ranked based on how much cycling access they enable, the tool quantifies that cycling access gain on investment, helping understand the financial trade-off of one infrastructure project over another.
9.2 Concluding remarks
cycling infrastructure upgrades by identifying where safe cycling infrastructure improvements have the highest potential of enabling the highest cycling access to the rail station, balanced against the cost estimated to upgrade each section of road.
Having objective data and analysis to inform decisions is especially important for cycling infrastructure planning since it is very political, with diverse stakeholder involvement. Public awareness of the issues can be better communicated and serve as an ongoing
The safe cycling connectivity of Kipling GO, an underperforming rail station in cycling access, was tested using the Bike-Rail cycling
89
means of engaging and sharing information with a wide audience.
Quantifying trade-offs helps to pivot from one project to another
Strategy's goal is to advance equity across all neighbourhoods,
while still being able to achieve overall goals.
especially ones identified as Neighbourhood Improvement Areas,
It would be insightful to use Kipling GO as a case study to test the results of dramatically mitigating all unsafe routes with appropriate cycling infrastructure. The rate of cycling access increase can be assessed; especially in the suburban residential areas where cycling typically captures low mode share. This would provide the opportunity to investigate the effects opening up cycling access
in aspects such as physical environment and infrastructure; social and human development; economic opportunity; governance; and health. By overlaying this data with neighbourhood improvement areas that intersect with the GO cycling catchment areas, the City of Toronto can prioritize improved cycling-GO access with an added lens of equity in the investment.
would have on mode share split and ridership forecasts.
9.3.2
Regarding the “important, safe routes,” identified in the analysis:
integration
These are routes that are low-volume, low-speed sections of road
Growing use and popularity of micro-transit, autonomous vehicles
that are modelled to carry a high volume of commuters to the GO
and ridesharing options like Uber and Lyft
station from commuters’ homes. Although they may not need
In literature, public transport feeder modes to rail stations have
cycling infrastructure upgrades, they serve as important
been found to be a direct competitor to bicycle-rail integration
wayfinding routes to the station. Accordingly, they would be
(Leferink, 2017). Kager, Bertolini, & Te Brömmelstroet (2016),
suitable for wayfinding elements and signage, helping advertise
however, oppose this by characterizing a good public transport
recommended low-stress routes to the GO station.
network as a backup system to bicycle-rail, which adds to the
9.3 Reflections
Future outlook and impacts on bike-rail
system’s robustness. For example, in the case of a flat tire or spontaneous detour to complete an errand, people can switch to the
9.3.1 Equity
bus to access the rail station or connect from the rail station to
Under the City of Toronto 2005 Toronto Strong Neighbourhoods
home. Since cycling is a more advantageous feeder option
Strategy, Priority Neighbourhoods for Investment were identified
compared to public transport or vehicular access, the increase in the
for focused City action and broader investment (Appendix 13). The
variety of vehicular options like ridesharing, micro-transit and autonomous vehicles for completing the access or egress trip from
90
the rail station will serve as back-up options to support the bicycle-
additional considerations in how cycling infrastructure is built and
rail combination. As a result, people who cycle to the station and
the design of parking facilities at rail stations. For example,
perhaps choose to not own a car would not be “bicycle-rail-
protected bikeways need to be sufficiently wide to allow for two
captives” and have alternate travel options to the rail station.
cyclists or e-bike rider to pass comfortably. Li et. al. (2017) recommends that e-bikes need more lateral space for riding as a
Growing popularity of Electric bikes
safety buffer, requiring a minimum bikeway width of 2.6m,
Already surging in popularity in Europe, the worldwide growth of
whereas CROW (2016) recommends a minimum of 2.8m. This is
electric bikes is increasing and is anticipated to spread to North
above the typical minimum 2m bikeway width design for protected
America (Wachotsch, 2014). Electric bikes offer many advantages
bikeways. Further, secure bicycle parking facilities with charging
and possible uses: they make it easier for people to travel longer
facilities are ideally needed for e-bikes, located at ground-level
distances, allow them to transport larger loads, and to more easily
since e-bikes are typically heavier than bicycles. Overall, these are
overcome natural obstacles such as hills and headwinds. Electric
some considerations that should be made to accommodate e-bike
bikes broaden the distance of activity of bike travel, especially for
access that would impact the planning of cycling access to rail
trips of distances between 5 and 20 km and the transport of cargo,
stations.
shopping and/or children (Wachotsch, 2014). This effectively has the ability to extend the cycling catchment distances of rail stations,
9.4 Limitations of research
enabling rail access to an even greater number of commuters.
There are some limitations to this research, mainly focused on the
People can travel significantly further with the same amount of
modelling procedure used for the route analysis and include:
physical exertion, making cycling easier for people with lower
Estimating the commuter population along each road
physical fitness levels or different abilities (e.g. elderly and physically impaired individuals), attracting even more people to cycling from other, less sustainable modes of transport of comparable reach. Although e-bikes are not seen as a competitor to cycling, accommodating for the growing use of e-bikes will require
The population in each residential building is estimated, since data is not available that identifies household commuter population at the building level. So some assumptions were used to get an estimation of the population distribution across the catchment. Apartments and multi-unit buildings create more traffic flow onto
91
the street network compared to single-family homes, so it was
a detour ratio, however, since the goal of this modelling was to look
important to model population distribution as accurately and finely
at ways to make the bike-rail combination fast and seamless, the
as possible. The lowest granularity of commuter population
decision was made to look at the routes that would take the least
information is the TTS data, so associating the commuter
travel time. This way, the desire lines can be better illustrated and
population in each zone to the residential buildings helped more
subsequently
accurately distribute the commuter population into each possible
improvement that may not necessarily need to run along the
household. Since statistical information was available on average
shortest distance path since there are other variables that can help
household size, using this information in combination with
inform the optimal placement of bikeways. Some of these variables
modelling the estimated number of household in each building and
are described in this master’s thesis and include preferring corridor
knowing where residential buildings are located, the model tried to
alignment along routes that abut locally-serving businesses or
best represent the commuter distribution across the catchment area
measures identified by CROW that contribute to building an
with data that is available.
exemplary and attractive bikeway.
Therefore, some shortcomings of the modelling procedure can be
9.5 Outlook and further research
mitigated by acquiring better data that more accurately estimates the commuter population in each building. Some of these improvements are outlined in the “outlook and further research section.”
Shortest-distance path
accommodated
with
cycling
infrastructure
There are some recommendations for further research that were out of the scope of this master’s thesis but would be interesting to investigate to develop the Bike-Rail cycling corridor identification tool further and mitigate some of the limitations of the research. •
There are many positive social and economic impacts associated with increased cycling rates. Calculating the social
Next, a shortest-distance path analysis was used to identify commuters’ paths from home and the rail station. Studies have found that cyclists deviate up to 10% from the shortest-distance path, particularly when better-quality cycling infrastructure is
health and economic benefits from increased cycling is recommended to be added in the Cost-Benefit analysis. A few tools exist such as the Health Economic Assessment Tool (HEAT) for walking and cycling by WHO/Europe (HEAT,
present. The betweenness analysis does have the ability to specify
92
2018). The HEAT estimates the value of reduced mortality that
especially if a wider catchment area like 5 km is being
results from specified amounts of walking or cycling, in
assessed.
addition to accounting for the health effects from road crashes •
•
•
and air pollution, and effects on carbon emissions.
not factor in topography and delays caused by turns and traffic
Overlay cycling collision information after the perception of
lights which are found to impact the trip time and propensity to
safety step to capture the current collision trends along the
cycle. Therefore, investigating the impact of these variables on
routes and see whether the “unsafe, high-stress” classification
the catchment area could be further investigated and catchment
agrees with cycling collision information.
area modified accordingly. It is important to note the increase
Data availability, case studies and calibration: in order to
use of electric bikes can reduce the impact of topography,
improve the accuracy of the model, MPAC housing data
which would be important to consider as their popularity
information can be used to identify the exact number of units
increases.
in each multi-unit building or apartment building. This can help
•
more accurately estimate the number of commuters in each building. Due to privacy and propriety data ownership issues,
•
•
Assess whether there is a correlation between routes that this thesis identifies as unsafe with bicycle crash statistics
•
Investigate the potential of connecting GO stations, especially
this data is not accessible to the public and therefore this thesis
those with overlapping cycling catchment areas, with cycling
but if a good business case is made, it can be made available to
routes. This is expected to give cyclists better station choices
City of Toronto Staff for analysis.
as two stations which may be close in proximity may operate
It would be interesting to see what the results of the
on different rail lines. This station connectivity has potential in
betweenness analysis would be from adding a 10%-20%
serving as the backbone of cycling in the suburbs, aiming to
deviation from the shortest-distance path which is anticipated
create a dense network of safe cycling that compliments the
to better reflect cyclists’ route choices
minimum grid concept desired in downtown Toronto.
Alternatively, a gravity model can be included as part of the betweenness analysis; this is recommended as further research,
93
The 3.5km network-based catchment used in this thesis does
10 Appendices Appendix 1 - Buffer separation types.
Source: Nick Falbo, Alta Planning and Design (McNeil et al., 2015)
94
Appendix 2– Four Types of Cyclists by Proportion of Population Strong and fearless - mixed with traffic, in all conditions (1%)
Enthused and confident (7%)
Source: (SFCTA, 2015)
95
Interested but concerned (60%)
Appendix 3 – Pyramid for successful public space for cyclists
Source: BiTibi (2016)
96
Appendix 4– TTS Zone data
Data source: TTS, 2011 and visualized by Author in ArcMap
97
In the Transportation Tomorrow Survey (2011), in order to find the population of commuters (going to work or university/college) in each zone, each combination or student and employment category needs to be filtered to avoid double-counting since each category is not mutually-exclusive. For example, persons can be both part time student and part time employed. Any combination: part time work + fulltime student, part time student + full time work are both possible. Therefore the iDRS filters need to be specified for each category of employment and student by filtering based on the following combinations: Full time work (F) + not a student (O) Full time work (F) + part time student (P) Part time work (P) + part time student (P) Part Time work (P) + full time student (S) Part time work (P) + not a student (O) Not employed (O)+ [full time student (S) + 18-98 years of age] Not employed (O)+ [part time student (P) + 18-98 years of age] Home full time (H)+ part time student (P) Home Part time (J) + Full-time student (S) Home Part time (J) + part time student (P) Full time work (F) + full time student (S)
98
Appendix 5 - CoGran Procedure CoGran is a command line tool for combining data of different spatial granularity. CoGran allows a spatial re-organization of geodata containing quantitative (statistical) information due to the fact that this is often not given in identical spatial units (like postal code districts, wards, or urban districts). Based on five different methods correlations between e.g. election results and income can be visualized. Recommendations for choosing the most suitable method is found on the website: https://github.com/berlinermorgenpost/cogran Attribute Weighting was used since it weights the attribute value by an additional attribute of the target file. This analysis used the number of units in each apartment or home as the attribute weight so this method was the most suitable.
Illustration of the analysis process of the attribute weighting tool: For example, if there is a commuter population count of 8 people in zone 1 and there are 2 buildings with count of 2 units (households) each, this tool allocates a population of 4 people to each building
99
Analysis Steps:
•
Convert shapefile to GeoJSON using QGIS
•
Install cogran.js 1. To install cogran.js you need to clone the repository first. git clone https://github.com/berlinermorgenpost/cogran.git 2. Now you have to install all dependencies for the application. cd /path/to/cogran (the full directory name where corgan is located) ex. cd C:\Users\sibel\Documents\Mastersthesis\cogran-master npm install cd C:\Users\sibel\Documents\Mastersthesis\cogran-master 3. Command Line Execution examples to execute analysis: cogran
-d
-i
testdata\toronto\TTS_zones_qgis.geojson
-t
testdata\toronto\RESBUILDINGS_qgis.geojson
-o
testdata\toronto\attributeweight_ttszone_to_building.geojson --attr POP --weight Units --mode attributeWeighting
cogran
-d
-i
testdata\toronto\TTSzone1_json.geojson
-t
testdata\toronto\buildings1_json.geojson
-o
testdata\toronto\attributeweight_building1.geojson --attr WRK_STU --weight Units --mode attributeWeighting cogran
-d
-i
testdata/toronto/kipling_tts.geo-countjson.geojson
-t
testdata/toronto/kipling_catch_final.geojson
-o
testdata/toronto/kipling_dasy2.geojson --attr count
100
Appendix 6 – Modelling the number of units in multi-unit buildings or apartment: Volumetric calculation procedure Calculation Process:
The volume of each building is found by multiplying the footprint area with the average height, as found in the GIS 3D massing dataset. The following variables were used in the calculation: • • •
101
average unit size is 70 m2 and 3m high (City of Toronto, 2016c, prepared by Urbanation Inc, 2014) Net Floor Area of the private units in a residential condo building is typically 60% of the building’s GFA (therefore the NFA of common property in our building is 40% of the GFA) (AssetInsights, 2017) Average household size of 2.46 residents in all Toronto dwellings (City of Toronto, 2016c)
To prepare this dataset for use in Rhino 3D, the following approach is recommended in ArcMAP or QGIS:
a) Using the Network Analyst toolbox, run a network analysis to identify a 3.5km catchment area around each GO rail station in Toronto (Figure 51) b) Extract the buildings that fall within the 3.5km catchment area c) Overlay the City of Toronto zoning land use layer over the buildings to identify the ones that are classified as residential (Appendix 7) d) Data Cleaning: By using the City of Toronto property address dataset and Open Street map, add the buildings which have an existing residential use but are not found in the residential zoning areas. Delete the buildings that are not existing residential buildings but are found in the residential zoning category e) Model the number of units in the apartment buildings: using the City of Toronto 3D massing GIS data, calculate the GFA for each building. Based on the estimated net floor area, divide the building based on the average unit size to arrive an estimated number of units for each multi-family home and apartment. Assign the single-family homes as single units. f) Calibrate the estimated number of units by comparing it to the actual apartment unit data. 20 apartments were selected at random and the margin of error was reduced by adjusting the estimated net floor area value and average unit size g) Using CoGran, a dasymetric mapping tool (Appendix 6), distribute the number of commuters in each Transportation Tomorrow Survey zone to each building, weighted by the number of units in each building. These are employed persons, not working from home and students 18+ found in each Transportation Tomorrow Survey (TTS) zone (Appendix 4 for details)
102
Appendix 7 – Residential land use and building footprints
(Source: Data from City of Toronto, 2018c and visualized in ArcMap by Author)
103
Appendix 8 – Betweenness analysis results from Rhino3D (area)
(Visualization made in Rhino3D by Author)
104
Appendix 9 – Betweenness analysis results from Rhino3D (station access)
Appendix 10 –
(Visualization made in Rhino3D by Author)
105
Appendix 10 – Cost-Benefit values used The model assessed the net economic benefits of realizing the entire concept for the limits of the network-based cycling catchment area around Kipling GO station Key assumptions underpinning the model development were as follows: •
Period of analysis: a 20 year period of analysis was adopted. This period of analysis is consistent with the approach to appraisal of transport infrastructure (Davies et. al., 2017)
•
Economic life of cycling facility assets: a 50-year economic life was assumed for all new cycling facilities;
•
Cycling infrastructure maintenance costs: annual maintenance were estimated to be 2% of the capital expenditure required to construct each cycling facility over the evaluation period (Davies et. al., 2017)
•
Residual value: a residual value representing the remaining useful life of cycling facilities was added at year 21, the final year of the appraisal. (Davies, 2017). The remaining capital value is calculated by determining the percentage of useful life remaining beyond the analysis period, and multiplying that percentage by the construction cost for that component. The estimate of the remaining capital value at the end of the analysis period is then converted to a present value and subtracted from the initial capital cost (MnDOT, 2018).
•
Economic life of parking: Metrolinx estimates the useful life of parking lots at 20 years, amortized on a straight-line basis for 20 years (Metrolinx, 2015a)
•
As recommended by TCRB (2002), a 7% discount rate is the core rate in the analysis. A range between 5% to 8% is typically used for public investment and regulatory analyses.
•
lag factor of 1 year for maintenance costs since maintenance typically commences the year after construction
106
1. Parking spot: $39,000 and yearly maintenance and servicing of parking costs approximately $200 for parking garages, exclusive of realty taxes (Source: GO Transit’s Anne Marie Aikins, 2016). The cost to GO is the same regardless of whether customers pay the real cost of parking or if it is “free” (read – subsidized by non-parking customers). Total net present value (NPV) of $51,371 / 20 years = $2,569/year 2. Cost of protected bikeway: $320,000/km (Road to Health, 2012) note: The quote includes the cost of studies. Yearly maintenance and servicing estimated at $320,000 * 0.02 = $215/year maintenance. Total net present value (NPV): $322,549 Residual cost: is $322,549 * 0.6 (60% useful life left) = $193,529. Convert $193,529 to a present value and subtract from initial capital cost: $322,549 - $50,012 (present value of residual) = 272,537/ 20 years = $13,627/km/year. 3. The anticipated cycling mode share access increase from building missing links cycling infrastructure: 7%, the “enthused and confident” proportion of the population cycling (Geller, 2012). Currently GO projects an increase from 1% to 2-4% in Kipling GO’s cycling access mode share by 2031 without plans for a connected network of safe cycling routes to the station. So it is predicted a 7% increase in cycling access mode share to a total 8% is realistically achievable. 4. Ridership forecast in 2031 is estimated to increase from 800 daily riders to 1000 daily riders. Since 73% of GO riders live within 3.5km of the station, 730 riders are estimated to live within the catchment area. 5. Current access mode share within 3.5km catchment area: 1% cycle, 35% walk, 32% drive and park, 19% get dropped off by car, “ carpool, 8% take local transit, 3% other
107
Sensitivity Analysis
test Sensitivity
discount rate
cyclcing infrastructure
cycling rate
5% +
10% +
3%+
3%
6%
parking
10% 9%
2%
ATAP (2016)
108
Appendix 11 - mode share data in all Aggregate Dissemination Areas (ADAs) around the Kipling GO Cycling Catchment.
(Source: Data from Statistics Canada, 2011 and visualized by Author in ArcMap)
109
Appendix 12– GO rail commuter trips – Access Map for Kipling GO and Egress Maps for Union Station
Source: Metrolinx (2016a)
110
92.4% exit at Union station, 2.8% Exhibition, other - Trip patterns indicate monocentic commuting patterns, but as city densifies and 2-way service is offered, more polycentric patterns may emerge 94.3% work within 2.9km of their egress station Source: Metrolinx (2016a)
111
Appendix 13 – Neighbourhood Improvement Areas
Source: City of Toronto (2013)
112
11 References Asset Insights. (2017). Net floor area. Retrieved 15 December 2018, from http://www.assetinsights.net/Glossary/G_Net_Floor_Area.html Aultman-Hall, L., Fred H., and Baetz, B. (1997). Analysis of Bicycle Commuter Routes Using Geographic Information Systems: Implications for Bicycle Planning. Transportation Research Record 1578: 102–110. Australian Transport Assessment and Planning (ATAP). (2016). Australian Transport Assessment and Planning Guidelines. M4 Active Travel. Transport and Infrastructure Council. Retrieved 30 February 2018, from https://atap.gov.au/mode-specificguidance/active-travel/files/m4_active_travel.pdf Bachand-Marleau, J., Larsen, J., & El-Geneidy, A. (2011). MuchAnticipated Marriage of Cycling and Transit. Transportation Research Record: Journal of the Transportation Research Board, 2247(2247), 109–117. Https://doi.org/10.3141/2247-13 Bicycle Dutch. (2014). State of the Art Bikeway Design, or is it? . Retrieved 30 December 2017, from https://bicycledutch.wordpress.com/2011/04/07/state-of-theart-bikeway-design-or-is-it/ Bitibi. (2016). Quality level of infrastructure used by bitibi cyclists in 8 pilot locations. Retrieved 16 March 2018, from http://www.bitibi.eu/dox/bitibi_WP6_Infrastructure.pdf
113
Bitibi. (2017). Easy and energy efficient from door-to-door Bike+Train+Bike. Retrieved 23 March 2018, from Http://www.bitibi.eu/dox/D2_5_Guidelines_bitibi_May_2017 n.pdf Bokare, P. S., & Maurya, A. K. (2013). Study of Effect of Speed , Acceleration and Deceleration of Small Petrol Car on Its Tail Pipe. International Journal for Traffic and Transport Engineering, 3(4), 465–478. https://doi.org/10.7708/ijtte.2013.3(4).09 Broach, J., Gliebe, J., and Dill., J. (2011). Bicycle Route Choice Model Developed Using Revealed Preference GPS Data (paper presented at the 90th Annual Meeting of the Transportation Research Board, http://otrec.us/main/ document.php?Doc_id=858. Canadian Urban Institute (2011), The New Geography of Office Location and the Consequences of Business as Usual in the GTA, Toronto. Retrieved 23 March 2018, from https://www.toronto.ca/legdocs/mmis/2012/pg/bgrd/backgroun dfile-43264.pdf CEC) Commission of the European Communities (2001) White Paper - European transport policy for 2010: time to decide, Commission of the European Communities, COM(2001)370, Brussels. Retrieved 13 March 2018, from http://europa.eu.int/comm/off/white/ index_en.htm
Chan, S., Miranda-Moreno, L., and Patterson, Z. (2013). Analysis of GHG Emissions for City Passenger Trains: Is Electricity an Obvious Option for Montreal Commuter Trains? Journal of Transportation Technologies, 2013, 3, 17-29 http://dx.doi.org/10.4236/jtts.2013.32A003
City of Toronto. (2013). Staff Report: Toronto Strong Neighbourhoods Strategy 2020 Implementation. Retrieved 13 January 2018, from Https://www.toronto.ca/legdocs/mmis/2013/cd/bgrd/backgrou ndfile-59534.pdf
Chan, K. (2016). Latent Demand for Cycling-Transit Integration at GO Train Stations in the Greater Toronto and Hamilton Area. (Master Thesis). University of Toronto.
City of Toronto. (2015a). Safety Measures & Mapping. Retrieved 9 January 2018, from Https://www.toronto.ca/servicespayments/streets-parking-transportation/road-safety/visionzero/safety-measures-andmapping/?6=0&7=0&8=0&9=0&10=0&11=0&12=0&13=0& 14=0&15=1&16=0&fat=1&inj=0&sch=0&ear=0&sen=0&ret =0&bik=0&cyc=0&agg=0&ped=0&oad=0&chd=0&mtr=0
Chester, M., & Horvath, A. (2008). Environmental life-cycle assessment of passenger transportation: a detailed methodology for energy, greenhouse gas and criteria pollutant inventories of automobiles, buses, light rail, heavy rail and air v. 2. Retrieved 9 March 2018, from https://escholarship.org/uc/item/5670921q City of Toronto. (2012). Road to Health: Improving walking and cycling in Toronto. Retrieved 2 March 2018, from https://www.toronto.ca/legdocs/mmis/2012/hl/bgrd/backgroun dfile-46520.pdfCity of Toronto. (2016). Toronto Complete Streets. Cityformlab (2015). Urban Network Analysis Toolbox for Rhino3D. MIT. Retrieved 9 March 2018, from Http://cityform.gsd.harvard.edu/projects/una-rhino-toolbox City of Toronto. (2012). Road to Health: Improving walking and cycling in Toronto. Retrieved 11 January 2018, from https://www.toronto.ca/legdocs/mmis/2012/hl/bgrd/backgroun dfile-46520.pdf
City of Toronto. (2015b). Staff Report: Proposed 30 km/h Speed Limit Policy. Retrieved 10 January 2018, from Https://www.toronto.ca/legdocs/mmis/2015/pw/bgrd/backgrou ndfile-78246.pdf City of Toronto. (2016a). Toronto employment survey. Retrieved 9 February 2018, from https://www.toronto.ca/legdocs/mmis/2017/pg/bgrd/backgroun dfile-99543.pdf. City of Toronto. (2016b). Toronto Complete Streets. 23 February 2018, from Https://www.toronto.ca/services-payments/streetsparking-transportation/enhancing-our-streets-and-publicrealm/complete-streets/overview/ City of Toronto. (2016c). The bulletin: Housing Occupancy Trends 1996-2011. Retrieved 30 December 2017, from
114
https://www.toronto.ca/wp-content/uploads/2017/08/958cHousing-Occupancy-Trends-1996-2011-Bulletin.pdf City of Toronto. (2017). Transformto. 19 February 2018, from Https://www.toronto.ca/wp-content/uploads/2017/10/9028transformto-Conversation-Kit.pdf City of Toronto. (2018a). Business and Economic Development Toronto Facts - Your City | City of Toronto. Retrieved 1 March 2018, from https://www1.toronto.ca/wps/portal/contentonly?vgnextoid=41 e067b42d853410VgnVCM10000071d60f89RCRD&vgnextch annel=57a12cc817453410VgnVCM10000071d60f89RCRD City of Toronto. (2018b). Road classification system. 19 February 2018, from Https://www.toronto.ca/services-payments/streetsparking-transportation/traffic-management/road-classificationsystem/ City of Toronto. (2018c). Open Data Catalogue. 19 February 2018, from Https://www.toronto.ca/city-government/data-researchmaps/open-data/open-datacatalogue/transportation/#e4ec3384-056f-aa59-70f79ad7706f31a3 Copenhagenize. (2013). The Copenhagenize Bicycle Planning Guide. Retrieved 22 February 2018, from Http://www.copenhagenize.com/2013/04/the-copenhagenizebicycle-planning-guide.html
115
Copenhagenize. (2016). Three Design Elements for Safer Intersections. Retrieved 24 February 2018, from http://www.copenhagenize.com/2016/08/three-designelements-for-safer.html Copenhagenize. (2017). Copenhagenize design philosophies [presentation] 23 June 2017. Unpublished. COWI (2010) Economic evaluation of cycle projects – methodology and unit prices. City of Copenhagen. Retrieved 9 February 2018, from http://www.cyclingembassy.dk/2010/06/10/economic-evaluation-of-cycleprojects/ CROW. (2017a). Presentation Meet the Makers Cycle-lanes, cyclehighways and bicycle streets. 15 June 2017. Velo City. Unpublished. CROW (2017b). Design Manual for Bicycle Traffic (2016 update), vol. Record 25, National Information and Technology Platform for Infrastructure, Traffic, Transport, and Public Space, Netherlands Cycling Embassy of Great Britain. (2018). Wiki: Barriers to cycling. Retrieved 25 February 2018, from Https://www.cycling-embassy.org.uk/wiki/barriers-cycling Damant-sirois, G., and El-geneidy, A. M. 2015. Who cycles more? Determining cycling frequency through a segmentation
approach in Montreal, Canada. Transportation Research Part A Policy and Practice 77: 113–125. Dantas, L (2005) Improving Pedestrian and Bicycle Access to Selected Transit Stations Massachusetts Highway Department. Boston, MA Data Management Group. (2013). Transportation Tomorrow Survey. University of Toronto. Retrieved 24 December 2017, from Http://www.dmg.utoronto.ca/transportationtomorrowsurvey/ Davies, R., Rogers, A., Lawrence C., and Vardon, B. (2017). Economic Benefits of Cycling Infrastructure at the program level. TPM 2017 National Conference. Retrieved 10 March 2018, from https://www.aitpm.com.au/wpcontent/uploads/2017/08/Economic-Benefits-of-CyclingInfrastructure-Ben-Vardon-Robyn-Davies_TP8.pdf Dekoster, J., & Schollaert, U. (1999). Cycling: the way ahead for towns and cities. Directorate-general, I. (2008). European commission. Risk Management, (February), 1–25. https://doi.org/10.2903/j.efsa.2015.4206.OJ De Smith, M. Goodchild, M. And Longley, P. (2009). Geospatial Analysis - A comprehensive guide. Univariate classification schemes. Retrieved 4 February 2018, from Http://www.spatialanalysisonline.com/output/ Estadao. (2015). The state of S. Paulo. Retrieved January 18, 2018,
from http://sao-paulo.estadao.com.br/noticias/geral,numero-deacidentes-cai-30-apos-reducao-de-velocidade-nasmarginais,1746754 EU. (2018). Road Classification. Mobility and transport. Retrieved 13 February 2018, from Https://ec.europa.eu/transport/road_safety/specialist/knowledg e/road/designing_for_road_function/road_classification_en Fehr & Peers (2006) Optimizing Transit Ridership Through Balanced Investment in TOD and Parking San Francisco, CA (FHA) Federal Highway Administration (1992) National Bicycle and Walking Study: Case Study No. 9 – Linking Bicycle/Pedestrian Facilities to Transit, Federal Highway Administration. Washington, D.C. Flamm, B., & Rivasplata, C. (2014). Perceptions of BicycleFriendly Policy Impacts on Accessibility to Transit Services: The First and Last Mile Bridge, 100. Retrieved 13 January 2018, from http://transweb.sjsu.edu/PDFs/research/1104-bicyclepolicy-transit-accessibility-first-last-mile.pdf Fleming, S. (2016). How the Dutch do it: fewer train stations with bike-centric catchments, (blogpost) Retrieved 24 January 2018, from http://cycle-space.com/fiets-professor/ FTA. (2017). Manual on Pedestrian and Bicycle Connections to Transit. FTA Report No. 0111 Federal Transit Administration. Retrieved 3 January 2018 from
116
https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/researc h-innovation/64496/ftareportno0111.pdf Geller. (2012). Four Types of Cyclists. Retrieved 1 December 2018 from https://www.portlandoregon.gov/transportation/44597?a=2375 07 Givoni M. And Rietveld P. (2007) “The access journey to the railway station and its role in passengers' satisfaction with rail travel”, Transport Policy, 14: 357-365, http://dx.doi.org/10.1016/j.tranpol.2007.04.004 Guerra, Erick, Robert Cervero, and Daniel Tischler (2012). HalfMile Circle: Does It Best Represent Transit Station Catchments? Transportation Research Record: Journal of the Transportation Research Board 2276: 101-109. Gutiérrez, J. And García-Palomares, J. C., 2008. Distance-measure impacts on the calculation of transport service areas using GIS. Environment and Planning B: Planning and Design, 35(3), pp. 480- 503 Harris, M. A., Reynolds, C. C. O., Winters, M., Cripton, P. A., Shen, H., Chipman, M. L., … Teschke, K. (2013). Comparing the effects of infrastructure on bicycling injury at intersections and non-intersections using a case–crossover design. Injury Prevention, 19(5), 303–310. http://doi.org/10.1136/injuryprev2012-040561
117
Health Economic Assessment Tool (HEAT). (2018). Retrieved 20 January 2018 from Http://www.heatwalkingcycling.org/#homepage Hemson consulting. (2011). Based on Statistics Canada 2011 Census and 2011 National Household Survey. Retrieved March 15, from http://www.metrolinx.com/en/docs/pdf/board_agenda/2018030 8/20180308_BoardMtg_Draft_Final_2041_RTP_EN.pdf Herman, M Komanoff, C Orcutt, J Perry D (1993) Transportation Alternatives Bicycle Blueprint, Chapter 9: Bicycle and Transit Transportation Alternatives Horvath, A and Chester, M. (2008). Environmental Life-Cycle Assessment of Passenger Transportation: An Energy, Greenhouse Gas, and Criteria Pollutant Inventory of Rail and Air Transportation, University of California Transportation Center, UC Berkeley ITE. (2017). Protected Bikeways Practitioners Guide. Retrieved 11 March 2018 from https://trid.trb.org/view/1483671 Jensen, S., Underlien, C., Rosenkilde, and Niels J. Road safety and perceived risk of cycle facilities in Copenhagen. Retrieved 5 January 2018 from http://www.ecf.com/files/2/12/16/070503_ Cycle_Tracks_Copenhagen.pdf Kager, R., Bertolini, L., & Te Brömmelstroet, M. (2016). Characterisation of and reflections on the synergy of bicycles
and public transport. Transportation Research Part A: Policy and Practice, 85, 208–219. Https://doi.org/10.1016/j.tra.2016.01.015 Kager, R., & Harms, L. (2017). Discussion paper: Synergies from improved bicycle-transit integration Towards an integrated urban mobility system. Retrieved 4 December 2017, from https://www.itfoecd.org/file/16904/download?token=z4vbf7mZ Kaminski, J. (2011). Theory applied to informatics – Lewin’s Change Theory. CJNI: Canadian Journal of Nursing Informatics, 6 (1), Editorial. Retrieved 104 December 2017, from Http://cjni.net/journal/?P=1210 Keijer, M.J.N., and Rietveld, P. (2000). How do people get to the railway station? The Dutch experience. Transportation Planning and Technology 23, 215–235. Kim Institute for Transport Policy. (2016). Mobility Picture 2016. Retrieved 4 March 2017, from Https://www.rijksoverheid.nl/binaries/rijksoverheid/document en/rapporten/2016/10/24/mobiliteitsbeeld2016/mobiliteitsbeeld-2016.pdf Krizek, K.J. (2003) Residential Relocation and Changes in Urban Travel: Does Neighbourhood-Scale Urban Form Matter? Journal of the American Planning Association Vol.69, No.3. American Planning Association, Chicago, IL
Krygsman, S., Dijst, M., & Arentze, T. (2004). Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio. Transport Policy, 11(3), 265–275. Https://doi.org/10.1016/j.tranpol.2003.12.001 Leferink, T. (2017). Why cycle to the railway station? A station scanner based on factors that influence bicycle-rail use. (Master Thesis). TU Delft. Li, Y., Zhou, W., Nan, S., Wang, F. and Chen, K. (2017). Redesign of the cross-section of bicycle lanes considering electric bicycles. Proceedings of the Institution of Civil Engineers Transport 2017 170:5, 255-266 Litman, T. (2002). Transportation Cost and Benefit Analysis: Techniques, Estimates and Implications. Retrieved 14 December 2017, from Http://www.vtpi.org/tca/ Massdot (2015). Separated Bike Lane Planning & Design Guide. Retrieved 24 December 2017, from Https://www.mass.gov/lists/separated-bike-lane-planningdesign-guide. Martens, K. (2004). The bicycle as a feedering mode: Experiences from three European countries. Transportation Research Part D: Transport and Environment, 9(4), 281–294. Http://doi.org/10.1016/j.trd.2004.02.005 Martens, K (2007) Promoting bike-and-ride: The Dutch experience Transportation Research. Transportation Research Part A:
118
Policy and Practice, 41 https://doi.org/10.1016/j.tra.2006.09.010.
(4),
326-338.
Martin, S and den Hollander, J. (2009). Parkiteer – Secure bicycle parking at PT nodes in Melbourne. Retrieved 22 December 2017, from Http://atrf.info/papers/2009/2009_Martin_denhollander.pdf Marshall (2015). GO Transit and the high cost of “free” parking. Retrieved 24 December 2017, from Https://seanmarshall.ca/2015/11/12/go-transit-and-the-highcost-of-free-parking/. Mcneil, N., Monsere, C. M., & Dill, J. (2015). Influence of Bike Lane Buffer Types on Perceived Comfort and Safety of Bicyclists and Potential Bicyclists. Transportation Research Record: Journal of the Transportation Research Board, 2520, 132–142. Https://doi.org/10.3141/2520-15 Mekuria, M., Furth, P. G., & Nixon, H. (2012). Low-Stress Bicycling and Network Connectivity. Mineta Transportation Institute. Retrieved 4 December 2017, from http://transweb.sjsu.edu/PDFs/research/1005-low-stressbicycling-network-connectivity.pdf Metrolinx, (2012). Union station 2031 demands and opportunities study. Retrieved 4 December 2017, from Http://www.metrolinx.com/en/regionalplanning/projectevaluat ion/studies/Union_Station_2031_Study_EN.pdf
119
Metrolinx (2015a). Moving the region forward: 2014 -2015 annual report. Retrieved 28 December 2017, from http://www.metrolinx.com/en/aboutus/publications/Annual_Re port_2014-2015_EN.pdf Metrolinx. (2015b). Yonge Relief Network Study (YRNS). Retrieved 4 March 2018, from Http://www.metrolinx.com/en/docs/pdf/board_agenda/201506 25/2015-06-25_Yonge_Relief_Network_Study.pdf Metrolinx. (2016a). GO transit 2015 rail passenger survey report (final report). Internal report Metrolinx. (2016b). GO rail access plan. Retrieved 4 March 2018, from http://www.metrolinx.com/en/regionalplanning/projectevaluati on/studies/GO_Rail_Station_Access_Plan_EN.pdf Metrolinx. (2017). Bicycle Access at GO stations. Report on market research. Internal report. Meurs, H., & Haaijer, R. (2001). Spatial structure and mobility. Transportation Research Part D: Transport and Environment, 6(6), 429–446. Https://doi.org/10.1016/S1361-9209(01)000074 Mitra, R., Smith Lea, N., Cantello, I., & Hanson, G. (2016). Cycling behaviour and potential in the Greater Toronto and Hamilton area, School of Urban and Regional Planning Ryerson University, 51. Https://doi.org/10.13140/RG.2.1.2629.3203
MnDOT. (2018). Benefit-Cost Analysis for Transportation. Retrieved 9 March 2018, from Https://www.dot.state.mn.us/planning/program/benefitcost.ht ml National Association of City Transportation Officials (NACTO). (2017). Raised Cycle Tracks. Retrieved 11 March 2018, from Https://nacto.org/publication/urban-bikeway-designguide/cycle-tracks/raised-cycle-tracks/ Nelson Nygaard. (2018). Bicycles and business: A San Francisco examination in three case studies. Retrieved 3 March 2018, from Https://www.slideshare.net/otrec/bicycles-businesssuccess-a-san-francisco-examination Nelson, A., Miller, M., Eskic, D., Kim, K., Ewing, R., Liu, J., … Mumuni, Z. (2015). Do TODs Make a Difference? Transportation Research and Education Center, (December), 1– 40. Retrieved 2 March 2018 from http://pdxscholar.library.pdx.edu/trec_reports/7 NHTSA. (2015). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. Retrieved 5 March 2018 from Https://crashstats.nhtsa.dot.gov/Api/Public/viewpublication/81 2115 NYC DOT. (2018). Protected Bicycle Lanes in NYC. [ebook] Retrieved 15 March 2018 from http://www.nyc.gov/html/dot/downloads/pdf/2014-09-03bicycle-path-data-analysis.pdf [Accessed Feb 26, 2018].
(OTM) Ontario Traffic Manual. (2013). Book 18 Cycling facilities. Ontario Traffic Council. Retrieved 15 December 2018, from Http://www.otc.org/research/otm-book-18/ (OSM). Openstreetmap. (2018). Openstreetmap Foundation. Retrieved 9 March 2018 https://www.openstreetmap.org/#map=16/43.6356/79.5323&layers=T Pan, H., Shen, Q., & Xue, S. (2010). Intermodal Transfer Between Bicycles and Rail Transit in Shanghai, China. Transportation Research Record: Journal of the Transportation Research Board, 2144(-1), 181–188. Http://doi.org/10.3141/2144-20 Parsons Brinckerhoff (Australia) (2009) The provision and use of bicycle parking at Sydney region public transport interchanges New South Wales Premier‟s Council for Active Living. Sydney, NSW Pedler A and Davies. D (2000). Cycle track crossings of minor roads (TRL462). Retrieved 15 March 2018 Https://trl.co.uk/reports/TRL462 Pike, P.E. (2010). Congestion Charging: Challenges and Opportunities. International Council on Clean Transportation Retrieved 10 March 2018, from: http://www.theicct.org/sites/default/files/publications/congesti on_apr10.pdf
120
Pinder. (2017). Bike Lanes are Not for Cyclists – Beyond the Automobile. Retrieved 23 December 2017. From https://beyondtheautomobile.ca/2017/11/16/bike-lanes-are-notfor-cyclists/ Planbureau voor de Leefomgeving. (2014). Bereikbaarheid verbeeld. Den Haag: Uitgeverij PBL. Retrieved 11 December 2017, from http://www.pbl.nl/publicaties/bereikbaarheidverbeeld Portland. (2010). PORTLAND BICYCLE PLAN FOR 2030. Retrieved 2 March 2018 Https://www.portlandoregon.gov/transportation/44597?A=379 134
121
(QT) Queensland Transport (2006) Cycle Note: Cycling and Public Transport Queensland Government, Note C6. Brisbane, QLD RDG. (2016). Cycle-rail toolkit 2. Rail delivery group. Retrieved 14 December 2017, from Https://www.raildeliverygroup.com/files/Publications/201604_cycle_rail_toolkit_2.pdf Relph, E. (2014). Toronto: Transformations in A City and its Region. Retrieved 17 December 2017 from Http://www.torontotransforms.com/home-about-torontostransformations/chapter-7-infrastructure-and-polycentricity/
Publie. (2015). Grenoble speed study. Retrieved January 18, 2018, from https://www.grenoble.fr/actualite/75/103-ville-apaiseegrenoble-a-30-km-h-depuis-le-1er-janvier-2016.htm
Robyn D., Adam R., Craig, L. and Ben, V. (2017). Economic Benefits of Cycling Infrastructure. Retrieved March 8, 2018 from Https://www.aitpm.com.au/wpcontent/uploads/2017/08/Economic-Benefits-of-CyclingInfrastructure-Ben-Vardon-Robyn-Davies_TP8.pdf
Pucher, J., & Buehler, R. (2009). Integrating Bicycling and Public Transport in North America. Journal of Public Transportation, 12, 79–104. Retrieved 10 December 2017, from http://131.247.19.1/jpt/pdf/JPT12-3.pdf#page=82
Saelens, B. E., Sallis, J., and Frank, L. D. (2003). Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine 25(2): 80–91.
Pucher, J., Dill, J., & Handy, S. (2010). Infrastructure, programs, and policies to increase bicycling: an international review. Preventive Medicine, 50 Suppl 1, S106–25. Http://doi.org/10.1016/j.ypmed.2009.07.028
Scheltema, E.B. (2012). ReCYCLE City: Strengthening the bikeability from home to the Dutch railway station. Retrieved March 1, 2018, from https://repository.tudelft.nl/islandora/object/uuid:4ccd153c133e-4bc9-981c-0e03a4f425bc?collection=research
Schepers, J.P. Kroeze, P.A., Sweers W. & Wüst, J.C. (2011). Road factors and bicycle-motor vehicle crashes at unsignalised priority intersections. Retrieved 1 December 2018 from http://www.sciencedirect.com/science/article/pii/S0001457510 003350 School of Urban and Regional Planning Ryerson University, Mitra, R., Smith Lea, N., Cantello, I., & Hanson, G. (2016). Cycling behaviour and potential in the Greater Toronto and Hamilton area, 51. https://doi.org/10.13140/RG.2.1.2629.3203 Semler, C., & Hale, C. (2010). Rail Station Access - An assessment of options. ATRF 2010: 33rd Australasian Transport Research Forum, (October), 1–14. Retrieved 28 December 2017 from http://www.scopus.com/inward/record.url?eid=2-s2.084866241642&partnerID=tZOtx3y1
2017, from http://uttri.utoronto.ca/files/2015/03/Choices-forScarborough.pdf Spears, J., and T. Kalinowski (2010), Toronto commuting times worst of 19 major cities, study says, Toronto Star, March 30. Statistics Canada, (2011). The Canadian Population in 2011: Population Counts and Growth. Catalogue no. 98-310X2011001. Statistics Canada. (2017). Canada's 2016 greenhouse gas emissions reference case: Transportation Emissions (Mt CO 2 eq) https://www.canada.ca/en/environment-climatechange/services/climate-change/publications/2016greenhouse-gas-emissions-case/chapter-2.html
SFCTA. (2015). Bicycle strategy update. SFMTA Municipal Transportation Agency. Retrieved 18 December 2017 from Http://www.sfcta.org/sites/default/files/content/Executive/Mee tings/cac/2015/03%20Mar/Bicycle%20Strategy%20for%20C AC%203.25.15%20FINAL.pdf
Sustrans. (2011). Segregation of shared use routes (Technical Information Note No. 19 (TIN 19) http://www.sustrans.org.uk/sites/default/files/images/files/migr ated-pdfs/Technical per cent20note per cent2019 per cent20per cent20segregation per cent20of per cent20shared per cent20use per cent20routes.pdf
Singleton, P. A., & Clifton, K. J. (2014). Exploring Synergy in Bicycle and Transit Use: Empirical Evidence at Two Scales. Transportation Research Record: Journal of the Transportation Research Board, (2417). https://doi.org/10.3141/2417-10
Sustrans. (2014a). Sustrans Design Manual: Handbook for cyclefriendly design. Retrieved 8 February 2018 from http://www.sustrans.org.uk/sites/default/files/file_content_type /sustrans_handbook _for_cycle-friendly_design_11_04_14.pdf
Sorensen, A., & Hess, P. M. (2015). Choices for Scarborough: Transit, Walking, and Intensification in Toronto’s Inner Suburbs. University of Toronto, 1–42. Retrieved 8 December
Sustrans (2014b). Cycle and Rail Integration (draft). Retrieved 8 February 2018 from
122
https://www.sustrans.org.uk/sites/default/files/images/files/Ro ute-DesignResources/9_Cycle_Rail_Integration_09_12_14.pdf SWOV. (2005). Sustainable Safety in the Netherlands: the vision, the implementation and the safety effects. Contribution to the 3rd International Symposium on Highway Geometric Design, 26 June 2 July 2005, Chicago, Illinois http://citeseerx.ist.psu.edu/viewdoc/download?Doi=10.1.1.471 .4157&rep=rep1&type=pdf TCRP (2002). Report 78: Estimating the Benefits and Costs of Public Transit Projects: A Guidebook for Practitioners http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp78/guidebook/tcr p78.pdf TCRP (2006) Report 116: Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services, Transportation Research Board/Transit Cooperative Research Program, Transportation Research Board. Washington, D.C. TCRP (2007) TCRP Report 111: Elements Needed to Create High Ridership Transit Systems, Transportation Research Board, National Academy Press. Washington, D.C. TCRP (2009) TCRP Web-Only Document 44: Literature Review for Providing Access to Public Transit Stations, Transit Cooperative Research Program, Transportation Research Board, National Academy of Sciences, Washington, D.C.
123
TCRP (2012) Guidelines for Providing Access to Public Transportation Stations. Retrieved 18 February 2018 from http://www.reconnectingamerica.org/assets/Uploads/20120327 tcrprpt153.pdf Teschke, K., Harris, M. A., Reynolds, C. C. O., Winters, M., Babul, S., Chipman, M., … Cripton, P. A. (2012). Route infrastructure and the risk of injuries to bicyclists: A case-crossover study. American Journal of Public Health, 102(12), 2336–2343. https://doi.org/10.2105/AJPH.2012.300762 The Noun Project. (2018). Noun Project - Icons for Everything. Retrieved 8 February 2018, from Https://thenounproject.com/ Toronto Board of Trade (2013), A Green Light to Moving the Toronto Region: Paying For Public Transportation Expansion, Toronto. Retrieved 28 February 2018, from https://www.bot.com/portals/0/unsecure/advocacy/Discussion Paper_AGreenLight_March18_2013.pdf Toronto Public Health (2012). Road to Heatlh: Improving Walking and Cycling in Toronto. Retrieved 6 February 2018, from Https://www.toronto.ca/wp-content/uploads/2017/10/967bTPH-road-to-health-report.pdf (TfL) Transport for London (2010). Analysis of cycling potential: policy analysis research report. Retrieved 12 January 2018, from http://content.tfl.gov.uk/analysis-of-cycling-potential.pdf
(TfL) Transport for London (2014). London Cycling Design Standards. Retrieved 2 January 2018, from Https://tfl.gov.uk/corporate/publications-and-reports/streetstoolkit#on-this-page-1 (TSB) Transportation Safety Board of Canada. (2015). Federally regulated occurrences and casualties, by train operator (A-M), 2006-2015. Government of Canada. Retrieved 12 January 2018, from Http://www.tsb.gc.ca/eng/stats/rail/r13d0054/sl-lcr13d0054-a-m.asp#tbl-25 TTS (2011). Commuter population in each survey zone. Retrieved 12 November 2017, from Https://dmg.utoronto.ca/idrs/ttsform/Cros/trip/2011 Tyrinopoulos, Y., & Antoniou, C. (2013). Factors affecting modal choice in urban mobility. European Transport Research Review, 5(1), 27–39. Https://doi.org/10.1007/s12544-012-0088-3 United Nations. (2014). World urbanization prospects. Department of Economic and Social Affairs. Retrieved March 11, 2018 from https://esa.un.org/unpd/wup/publications/files/wup2014highlights.pdf Urbanation Inc., UrbanRental Q4-2014 Press Release. Van Nes, R., Hansen, I., & Constance, W. (2014). Duurzame Bereikbaarheid Randstad: Potentie multimodaal vervoer in stedelijke regio’s. Retrieved January 3 2018 from
http://dbr.verdus.nl/upload/documents/DBR_Notitie_10_Poten tie_Multimodaal_Vervoer.pdf Vuchic, V. (2005) Urban Transit: Operations, Planning and Economics. Wiley. Retrieved 28 November 2017, from https://www.researchgate.net/publication/234164009_Urban_ Transit_Operations_Planning_and_Economics Wachotsch, U., Kolodziej, A., Specht, B., Kohlmeyer, R. and Petrikowski, F. (2014). Electric bikes get things rolling: The environmental impact of pedelecs and their potential. Federal Environment Agency. Retrieved 12 March, 2018 from https://www.umweltbundesamt.de/sites/default/files/medien/3 78/publikationen/hgp_electric_bikes_get_things_rolling.pdf Wadhwa, L. C. (2001) Vision Zero Requires Five Star Road Safety System, paper presented at 2001: Road Safety, Research, Policing and Education Conference, 19–21 November 2001, Melbourne, Australia. Retrieved 12 December 2017, from http://www.monash.edu.au/cmo/roadsafety/abstracts_and_pap ers/135/lwrs2001revised.pdf accessed in February 2006 Weant, R & Levinson, H (1998) Parking, ENO Foundation for Transportation, Westport, CT. Retrieved 30 December 2017, from http://sbd.iuav.it/sbda/mostraindici.php?&EW_D=NEW&EW _T=TF&EW_P=LS_EW&EW=148609&EW_INV=AA_CIA_ 000019188& Winters M, Babul S, Becker HJ, Brubacher JR, Chipman M, Cripton P, Cusimano MD, Friedman SM, Harris MA, Hunte
124
G, Monro M, Reynolds CC, Shen H, Teschke K. (2012). Safe Cycling: How Do Risk Perceptions Compare with Observed Risk?. Canadian Journal of Public Health; 103(Suppl. 3):S42S47. Winters, Megan,, Kay Teschke, Michael Grant, Eleanor M. Setton, and Michael Brauer. (2010). How Far Out of the Way Will We Travel? Built Environment Influences on Route Selection for Bicycle and Car Travel. Transportation Research Record 2190: 1–10. Winters M, Davidson G, Kao, D, Teschke K. (2011). Motivators and deterrents to bicycling: comparing influences on decisions to ride. Transportation (2011) 38: 153. https://doi.org/10.1007/s11116-010-9284-y Yawar, S. (2016). An analysis of commuting distance and its controlling factors in the greater toronto-hamilton area. Master’s Thesis. McMaster University
125
