Using Spatio-Temporal Constraints to Quantify Transit ...

Using Spatio-Temporal Constraints to Quantify Transit Accessibility: Case Study of a Potential Transit Oriented Development Location in West Valley, Utah

Ivana Tasic* Graduate Research Assistant Civil and Environmental Engineering University of Utah 110 Central Campus Dr., Rm. 1650 Salt Lake City, Utah 84112 Phone: (801) 585-5859 Fax: (801) 585-5860 E-mail: [email protected] *Corresponding Author Xuesong Zhou Associate Professor School of Sustainable Engineering and the Built Environment Arizona State University Tempe, AZ 85287, USA Email: [email protected] Milan Zlatkovic Research Assistant Professor Civil and Environmental Engineering University of Utah 110 Central Campus Dr., Rm. 1650 Salt Lake City, Utah 84112 Phone: (801) 585-5859 Fax: (801) 585-5860 E-mail: [email protected]

Word Count: 5499 + 8 (8 Figures +0 Tables) = 7499

Prepared for the Transportation Research Record 2014 Revised and Submitted on March 11, 2014

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ABSTRACT Accessibility emerges as the transportation performance measure that emphasizes the benefits of the transportation system users, capturing more than the speed of travel. Transit accessibility shows how easy it is for an individual to travel to a desired destination using public transit. However, in order for transit to be considered as an option in mode choice at all, there has to be a feasible transit route leading from given origin to desirable destination within the available time frame. This paper uses spatial and temporal constraints, and a set of transit features that impact access to transit, to develop a conceptual framework for transit accessibility measurements in the potential Transit Oriented Development (TOD) location in West Valley City, Utah. As this network develops more transit friendly features, both temporal and spatial accessibility indicators will provide useful information on the opportunities the users can reach using transit. The proposed methodology builds upon the traffic and transit data from the case study network, and uses an open source tool to perform transit accessibility measurements by calculating the number of accessible transit stops from each origin. The methodology considers network features, acceptable walking time, available time budget, transit schedule variability and spatial constraints as impact factors in accessibility measurements. The goal of the paper is to establish a feasible set of transit accessibility indicators that would be used for both the case study street network and transit service modifications into a transit friendly and eventually a TOD environment.

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INTRODUCTION Transit is a unique mode of transportation because of the way it is constrained in terms of space and time. In terms of space, it requires transit stop facilities and special road design treatments, while in terms of time, transit follows specifically scheduled timetables. These spatial and temporal components determine the accessibility of public transit system. Transit accessibility indicates how easy it is for an individual to reach a desired destination using public transit. It is important for the existing transit riders, as an indicator of the service quality; and for the potential riders as well, as it might be a factor in their mode choice (1). And while current policy makers still use transport system metrics that are mobility oriented, partially because they are the most available, these performance metrics are excluding some crucial components of urban transportation systems (2). Accessibility emerges as the measure that captures more than the speed of travel, emphasizing the benefits of the transportation system users. It relates to both transportation and land use, as it quantifies how many destinations an individual can reach using the given mode of transport within the available time (3). In the era when sprawling communities and car culture are a serious threat to urban environments, improving accessibility becomes more and more important for the cities (4). Access to transit is a precondition for all the efforts taken towards multimodal transportation systems. Whether an individual will use transit or not, depends on many factors, including their value of time and available time budget, transit fare price, and ratio of car/transit utility (5). However, in order for transit to be considered as an option in mode choice at all, there has to be a feasible transit route leading from given origin to desirable destination within the available time frame. This paper uses spatial and temporal constraints, and a set of transit features that impact access to transit, to develop a conceptual framework for transit accessibility measurements in the potential Transit Oriented Development (TOD) location in West Valley City, Utah. The case study network is chosen because using accessibility related performance metrics is particularly relevant for TOD environments. As this network develops more transit friendly features, both temporal and spatial accessibility indicators will provide useful information on the opportunities the users can reach using transit. Planning transportation network and systems for a TOD could result in social, environmental and financial benefits. The increase in non-motorized mode users and transit ridership are two most direct effects of TOD. Other TOD impacts include opportunities for community development and mix of land uses, reduced number of private vehicle users that leads to lower traffic volumes, and potentially lower delays and improved safety (6, 7, 8). The proposed methodology builds upon the traffic and transit data from the case study network, and uses an open source tool to perform transit accessibility measurements by calculating the number of accessible transit stops from each transportation analysis zone (TAZ) centroid as a defined origin. The methodology considers network features, acceptable walking time, available time budget, transit schedule variability and spatial constraints as impact factors in accessibility measurements. The goal of the paper is to establish a feasible set of transit accessibility indicators that would be used for both the case study street network and transit service modifications into a transit friendly and eventually a TOD environment. The following section of this paper introduces the previous research on transit accessibility measurements and accessibility measurements in general. The Methodology section explains the approach and tools used to perform transit accessibility measurements on the given

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case study network. Following this section are results of accessibility measurements with the discussion. The final section of the paper introduces conclusions with the future research plans for this study. LITERATURE REVIEW While there is an agreement among researchers on how to define accessibility, finding an appropriate way to measure it remains a challenge (9, 10). Several types of accessibility measures are developed in the existing research. Cumulative or opportunity accessibility measures evaluate accessibility as a total number of opportunities that can be reached within the defined distance or travel time from a given origin (9, 11). Gravity-based accessibility measures use distance, travel time and travel costs to assign weights to destinations or the activity locations (9, 12). Utility-based accessibility measures calculate accessibility as a probability of making a travel choice depending on its utility and relative to the utilities of all other available choices (9, 13, 14). The composite accessibility measure incorporates temporal constraints in addition to spatial constraints for a more complex measurement approach (15, 16, 17). The best accessibility measurement method should be chosen based on the purpose and a situation that requires such measurement (3). Space-Time Accessibility Measures As public transit has unique characteristics among other modes, due to its spatial and temporal constraints, using composite space-time accessibility measures is appropriate for developing transit accessibility indicators. One of the most powerful techniques for space-time accessibility measurements is the space-time prism (STP). The STP-based accessibility measures determine a “feasible set of locations for travel and activity participation”, considering spatial and temporal constraints that affect individual’s behavior (15). Some earlier STP-based accessibility measures had the disadvantage of treating travel time as static rather than dynamic. After empirical research proved that temporal constraints have a significant impact on individual’s ability to reach desired destinations, the STP-based accessibility measurement methods have been updated to account for this (15, 16, 17). The STP-based measures incorporate the spatial distribution of destinations, uncertainty of origin and destination choices, travel time variability as a consequence of transportation network configuration, time needed to participate in various activities at various destinations, destination availability in terms of temporal constraints or maximum available travel time, static and dynamic traveler delay (18). Further deployment of these measures is demonstrated in the Methodology section. Transit Accessibility Public transit is considered to be a feasible travel choice when transit stops are accessible to and from trip origins/destinations (spatial coverage), and when transit service is available at times that one wants to travel (temporal coverage) (2, 19). Transit accessibility determines the attractiveness of transit as a mode choice. How accessible transit stops are depends on whether the transit users are walking, biking or driving to their nearest stop. The primary factor affecting pedestrian access is distance to transit stops. Based on the assumed average walking speed of about 4ft/s, 5 minutes of walking to transit stops is considered to be acceptable in urban areas, or about quarter of a mile in terms of walking distance (20, 21). Location and spacing between transit stops have a significant impact on transit service performance and users satisfaction, as they not only ensure reasonable accessibility but influence travel time as well (18, 22).

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Measuring “the ease of access” to transit services in terms of space-time constraints is important for evaluation of the existing services, travel demand forecasts, and decision making related to transportation investments and land use development (19, 21). This study applies space-time accessibility measures to transit in dynamic traffic conditions. It accounts for the impact factors that will be relevant as the case study network develops into a TOD environment. Developed methodology uses an open source tool that easily integrates with different traffic and transit data sources. METHODOLOGY The TOD by definition involves more accessibility for public transit passengers, due to denser street network and mixed land use that provides more opportunities. This study develops a conceptual framework for quantifying transit accessibility based on spatio-temporal constraints. The network scenario is developed to reflect transportation network and transit system on a future TOD location in West Valley City, Utah. Location is chosen based on Wasatch Choice for 2040 map of the potential TOD spots in Salt Lake Region, and it represents a future town center with the intersection of two Bus Rapid Transit (BRT) lines. Case study network is given in the Figure 1. Network Description and Modeling The case study network has about 40 miles of road length, with roadway density of 20 miles of roadways per mile squared. Each TAZ has about 3-4 intersections per acre. Network is characterized with a lot of cul-de-sacs and 3-way intersections, which make it less connected despite its relatively good road density. A total of five transit lines traverses this network, one of which is BRT, while another BRT line is planned to be finalized and opened by the end of 2015. In order to support the future TOD environment, the network is likely to be denser and better connected in the future with modified transit service. This study presents a set of accessibility indicators for the current state of this network, but they are flexible enough to be applied for the future network modifications. Network was firstly created in VISUM, using Google Earth maps and local MPO data for roadway lengths, functional classification, link speeds, and node design. Intersection delay data are based on signal timing plans from the local Department of Transportation and they were obtained from microsimulation models for current conditions. These inputs were converted to GIS shapefiles for nodes, links, connectors, zones and zone centroids. This base model, with transit data input, was used for further transit accessibility calculations. Transit accessibility is expressed through the number of reachable transit stops from each TAZ centroid, using both walking and transit routes, constrained by spatial characteristics of the case study network and temporal dimension determined by transit service and traffic characteristics. Google Transit Feed Data Structure Transit data were provided by the UTA, and loaded into the network through Google Transit Feed (23). The general transit feed specification from Google applied here includes:  Calendar that specifies when service starts and ends, including the days of the week when service is available  Calendar dates with possible service exceptions  Routes or groups of trips displayed to riders as single service  Shapes or rules for representing transit routes on the maps

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  

Stop times or arrival and departure times for each individual trip Stops or passenger pick up and drop off points Trips or sequences of stops for each route All these files are in text format and loaded together with base network shapefiles. Particularly important for our accessibility measurements are stop time records, which include a sequence of stops along each trip. Each stop time record contains required data such as trip identification, arrival and departure time, stop identification, and stop sequence. These data were used for the accessibility measurements. Accessibility Measurements and Shortest Path Algorithm Accessibility measurements were based on network data shapefiles and transit data feed from Google. Both data sets prepared and adjusted in the way previously described, were loaded into NEXTA (Network Explorer for Traffic Analysis) software. NEXTA is an open-source GUI that aims to facilitate the preparation, post-processing and analysis of transportation assignment, simulation and scheduling datasets. One of the advantages of NEXTA is that it facilitates importing transportation network data from both macro and microsimulation environments. This means that it has the ability to integrate with our previously built traffic and transit models. Loading transit data from Google and additional features for accessibility calculations are most recent specifications of the software. Together with the case study network, a regional transportation and transit network is loaded to enable calculations to all available transit stops. Network TAZ centroids were defined as origins, while transit stops represent destinations. Accessibility can be calculated from each defined origin or from all origins, and accounts for time variability of transit schedules which will be discussed later. Accessibility is expressed through a number of reachable destinations from each origin for variable space and time constraints. For each defined set of constraints a shortest path was calculated using the algorithm integrated into NEXTA. This algorithm first identifies accessible bus trips using the stop time records within the 15 minutes waiting time from the departure time at the origin and within the acceptable walking distance from the origin activity location. Then it identifies stop time records reachable from the origin of each trip within the defined time budget constraints. The number of accessible stop times is counted along each trip as the indicator of accessibility. Average measures across all origin activity locations are also considered. The data input and loading process with the output procedure demonstration are given in the Figure 2. Impact Factors Evaluating public transit is always more complex than any other mode of transportation, and selecting adequate accessibility measures is also a challenge. It is however important to quantify changes in accessibility that different transit features could bring, in order to adjust travel forecasting models and make them more applicable to transit-intensive environment. Accessibility provided through the implementation of transit-friendly treatments will also impact other measures of effectiveness related to transit, such as Level of Service. For the case study network, factors that impact space-time constraints are given in the conceptual framework in the Figure 3. Service variability refers to the frequency of transit service and service span in general. Walking distance is the acceptable walking distance to transit stops. Available time budget defines the time that individual has to access activity locations from the given trip origin. Transit speeds will differ between BRT lines and regular transit lines. Spatial constraints refer to the destination or activity location type. Activity location

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can be the fixed or final, when the entire time budget is used to reach the destination, or flexible/intermediate. Transit accessibility is expressed through the number of destinations reachable from the defined origin within the given space-time constraints, and it is calculated through the number of accessible stop times loaded from the transit feed data. In order to represent the time variability aspect of transit accessibility, we also introduce incremental change of accessibility measured with each change in control variables. Concept for Accessibility Measurements/Performance Measures One of the most utilized space-time constrained accessibility measurement methods is based on Miller’s interpretation of STP application for transit accessibility calculations (18). The STP is a set of locations in space and time that are accessible to an individual, given the locations and duration of fixed activities, time budget, and transportation speeds. The STP-based accessibility measures account for both individual sequence of trips and spatio-temporal constraints, calculating the amount of space that an individual can reach at specific combinations of times and locations. The purpose of this study is to represent how real-time constraints imposed by network pattern, dynamic traffic conditions and transit supply affect the choice of activity accessible by transit users. Travel times to activity locations vary with both space and time. In the case of public transit, travel times are not only affected by traffic conditions, but also by the time needed to access the transit stop, waiting time which depends on familiarity with the timetable, potential stops and transfers, and the time needed to reach the destination from the final transit stop. The models developed here are based on the dynamic potential path calculations, but also account for pedestrian connectivity, transit stop accessibility, and schedule time variability as elements that specifically relate to transit mode. Instead of using a simple, radius based service coverage, we use actual transportation network with assigned volumes, adequate signal timing plans and calculated traffic delays to represent accessibility in realistic traffic conditions. Considering the network size, walk to transit is adopted as the mode used to access transit stations. Calculations and assumptions adapted from (15, 16) for different space-time constraints, applied to compute the number of accessible transit stops are as follows: 𝑀 = {𝑘 ∈ 𝑁|𝑇𝑘 = 𝑡𝑘𝑚 + 𝑡𝑡 + ∑ 𝑡𝑤𝑎𝑙𝑘 + 𝑡𝑤𝑎𝑖𝑡 + 𝑡𝑑 ≤ 𝑇} Definitions and Assumptions: 𝑀– 𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑐𝑐𝑒𝑠𝑠𝑖𝑏𝑙𝑒 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛𝑠 𝑘 𝑁 – 𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛𝑠 𝑇𝑘 – 𝑡𝑖𝑚𝑒 𝑛𝑒𝑒𝑑𝑒𝑑 𝑡𝑜 𝑒𝑎𝑐ℎ 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑘 𝑇 – 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑡𝑖𝑚𝑒 𝑏𝑢𝑑𝑔𝑒𝑡 𝑡𝑘 − 𝑡𝑖𝑚𝑒 𝑛𝑒𝑒𝑑𝑒𝑑 𝑓𝑜𝑟 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 𝑡𝑡 – 𝑡𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑡𝑟𝑎𝑛𝑠𝑖𝑡, 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑓𝑜𝑟 𝐵𝑅𝑇 𝑎𝑛𝑑 𝑟𝑒𝑔𝑢𝑙𝑎𝑟 𝑙𝑖𝑛𝑒𝑠 𝑡𝑤𝑎𝑙𝑘 − 𝑡𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑠𝑝𝑒𝑛𝑡 𝑤𝑎𝑙𝑘𝑖𝑛𝑔 𝑡𝑜 𝑜𝑟 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑡𝑟𝑎𝑛𝑠𝑖𝑡 𝑠𝑡𝑜𝑝 𝑡𝑤𝑎𝑖𝑡 − 𝑡𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑠𝑝𝑒𝑛𝑡 𝑤𝑎𝑖𝑡𝑖𝑛𝑔 𝑓𝑜𝑟 𝑡𝑟𝑎𝑛𝑠𝑖𝑡, (𝑑𝑒𝑝𝑒𝑛𝑑𝑖𝑛𝑔 𝑜𝑛 𝑓𝑎𝑚𝑖𝑙𝑖𝑎𝑟𝑖𝑡𝑦 𝑤𝑖𝑡ℎ 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒) 𝑡𝑑 − 𝑜𝑡ℎ𝑒𝑟 𝑑𝑒𝑙𝑎𝑦𝑠 𝑑𝑢𝑒 𝑡𝑜 𝑠𝑖𝑔𝑛𝑎𝑙𝑠, 𝑐𝑟𝑜𝑠𝑠𝑖𝑛𝑔 𝑡𝑖𝑚𝑒, 𝑎𝑛𝑑 𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟𝑠 Service Variability: 𝑡𝑡 = 15 min 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑣𝑠 𝑟𝑒𝑔𝑢𝑙𝑎𝑟 𝑙𝑖𝑛𝑒𝑠

(Eq. 1)

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Walking Distance: 𝑡𝑤𝑎𝑙𝑘 = 0.05; 0.10; 0.15; 0.20; 0.25 𝑚𝑖𝑙𝑒𝑠 Available Time Budget: 𝑇 = 30, 35, 40,45,50, 55, 60 𝑚𝑖𝑛 Spatial Constraints: Zonal Access Distribution RESULTS AND DISCUSSION This section presents the results and discussion of the results obtained for each impact factor described in the methodology. The impact of transit service variability on the accessibility of transit stops is given in the Figure 4. The total number of accessible transit stops, not only spatially but in terms of temporal availability of transit service as well, was aggregated in 15 minute time periods over the course of a weekday, from 6:00 to 22:00. Only results for one origin are presented to provide better visualization. Service schedule is presented dependent on time, while other variables are kept constant. The assumed constant acceptable walking distance in this case is 0.25 miles, or equivalent to 5 minutes walking time. The results show more that transit accessibility dependent only on schedule variability and network features, is more consistent during the morning peak (6:00 to 9:00) than in the afternoon. Although variability of the number of stops that can be reached using transit or non-motorized modes is higher in the afternoon, the average accessibility is better in the afternoon as well. This is a very good indicator of changes that transit schedules will need to undergo to support transit friendlier environment. Again a reminder from the literature, recommendations for TOD transit service frequencies are 15 minutes or less in areas similar to the one analyzed here (22). What the simplest analysis also indicates here is how specific transit is in terms of accessibility when compared to other modes, because it is more time-dependent due to schedule variability impact. Although spatial distribution of stops is constant, the service frequency clearly affects transit accessibility to vary within each period of the day (morning peak, off-peak, afternoon peak) and during the entire typical weekday in general. Another impact factor analyzed here is the acceptable walking distance. Guidelines on the acceptable walking distance (21) recommend up to a quarter mile distance acceptable from pedestrian standpoint. While ranges from 0.05 miles to 0.25 miles of walking distance are analyzed, four representative values are given in the Figure 5, since the walking distance of 0.05 miles results in transit accessibility equal to zero. All other variables are kept constant. As expected, the access to transit stops becomes better as the acceptable walking distance increases. With lower acceptable walking distances, there are more points when transit stops are not accessible at all. This is also not surprising, since the analyzed network has many disconnected links or cul-de sacs, which decrease the number of potential paths to transit. The field inspection of the study network confirmed the limited number of acceptable pedestrian paths. What limits accessibility even more are not just the network features, but service schedule as well, causing the accessibility to drop to zero when smaller walking distances are considered. With the lower values of the acceptable walking distances, not only the area of spatially accessible transit stops decreases, but the number of stop times depending on the service frequency as well. The lowest transit service headway in this area is 15 minutes, so if the acceptable walking distance is equivalent to walk time lower than 15 minutes, the chances of being able to use transit also decrease. This temporal dimension of transit accessibility

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determined by schedule characteristics is what causes the values in Figure 5 to be zero in nonrandom manner. The Figure 5 also shows that different curves in the upper ranges (0.20 and 0.25 mi walk distance) have similar patterns, and similar happens in the lower ranges of walking distance as well. This is firstly related to the relationship between the acceptable walking distance to transit and transit accessibility in terms of both space and time. Secondly, the way these curves change during the entire day period shows the direct relationship with transit characteristics in the study network area. As the network continues to be modified towards a more transit supportive pattern, it is likely that there will be more routing options for pedestrians. The TOD can reduce walking time at signalized intersections too, and thus increase the potential time for walking within the available time budget which is the following variable discussed. Transit service characteristics are expected to be modified as well. The impact of the available time budget on transit stops accessibility within the analyzed network is presented in the Figure 6. Four representative values for the available time budget are given to consider 30, 40, 50, and 60 minutes available for end-to-end transit trip. The results show that the highest number of accessible transit stop for the given time budgets occurs between 9:00 and 6:00. It is noticeable that early morning and late evening time periods have less frequent transit service, and that the service is limited between 6:00 in the morning and 20:00 in the evening. Patterns are related primarily to transit service characteristics, and show how accessibility increases with time budget. Due to space limitations more detailed user information impacts were not included in this paper. In general context, available user information would however impact the available user time budget. With the quality information available for transit users, they would spend less time waiting and would have more time to spend in transit within their available time budgets. Considering the future development plans of the case study and the regional network, this is something that should be considered as a factor for improving access to transit. The incremental change in measured transit accessibility with changes of the acceptable walk time and the available time budget is given in the Figure 7. The accessibility is almost constant when the acceptable walking time to a transit stop is 0.10 miles, corresponding to about 2 minutes spent walking. The increase is noticeable as the acceptable walking distance becomes higher. Incremental change is higher also as the acceptable walking distance increases. However, after the available time budget reaches 45 minutes, the percentage of accessible transit stops becomes almost constant in all three cases of walking distance criteria presented here. Similar performance measure can be applied to each of the impact factors analyzed. This type of analysis indicates the relationship between different impact factors. In the case presented in the Figure 7, it is apparent that the difference in accessibility for different values of the acceptable walking distance also increases, as the available time budget increases. It should be noted that 100% accessibility is constrained with the upper bound of the available time budget analyzed, which is 65 minutes of transit ride. The incremental change in accessibility is useful for the case of transit since it indicates the maximum number of stops that can be reached for the given set of conditions or impact factors. While previous results show time variable aspect of transit accessibility, spatial accessibility for three different time points and all defined origins during the daily service span is given in the Figure 8. The results show that TAZs 726 and 728 have the highest access to transit, while TAZs 725 and 753 have no access to transit. Considering that the 30 minute available time

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budget and 0.25 miles acceptable walking distance values were adopted, these results indicate which TAZs will have to improve their access to transit in the future. It is surprising that the access to transit stops for some TAZs is higher during the midday period than during the morning and the afternoon peak hour periods. This is probably related to the fact that transit lines in this area start to decrease their frequency after 13:00 in the afternoon, so noon is still a period of the highest available service frequency. Also noticeable is the inequality in access to transit among different TAZs, indicating the potential differences in the number of reachable opportunities in the area. This is a relatively small network, so the obvious inconsistency in spatial distribution of transit accessibility shows how network features can impact the transit service coverage even on a smaller scale. CONCLUSIONS AND RECOMMENDATIONS Measuring accessibility to transit is more challenging when compared to other modes of transportation. The reason is the number of impact factors that affect the ability of users to access transit, starting from network features and transit schedules, to acceptable walking distances and available time budget for transit trips. This paper presents an alternative approach for measuring transit performance through the accessibility of transit stops, considering both spatial and temporal constraints, exploring further the relationships between factors that impact transit accessibility. The measures and impact factors presented here indicate how reachable activity locations are from different origins in different times, which is what users can relate to. The results show how access to transit varies both temporally and spatially. Specific to transit mode, service schedule variability significantly affects the changes in accessibility to transit over the course of a day. Adopted pedestrian criteria for the acceptable walking distances show their impact too, and the need to improve the existing network connectivity for the future development. Considering quality transit service information for the users is recommended as one of the methods for accessibility improvements. The end-to-end transit trips should be shorter in the analyzed area, up to 45 minutes, because as the available time budget increases the number of the accessible transit stops remains the same. The case study network analysis of spatial accessibility distributions shows inequality between different TAZs in the network. Apparent is the diversity in the number of reachable transit stops even in a relatively small area. Provided measurement tools can serve as the indicators of critical areas that need improved access to transit. This again can also be related to mobility patterns, showing that areas with low transit access probably have low street network connectivity and private vehicles could be the dominant travel mode. The methodology proposed in this study relies both on the previous literature sources and the practical needs related to the case study network. An open source tool used for quantifying transit accessibility is applicable for networks of different sizes and integrates well with a wide range of data sources and modeling approaches. While there are many other commercial packages that calculate transit travel time, the tool used here has an interface with both micro and macrosimulation environment allowing the exploration of the relationships between impact factors considered in this study and spatio-temporal transit accessibility and providing a wide range of transportation performance measures at the same time. Criteria adopted for impact factors are validated through the previous research and the existing transportation guidelines. This study has several limitations. Due to the network size, it does not consider transfers between different transit lines as the acceptable option from the users’ perspective. The reason

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for this is that the analyzed network is considered to be the potential TOD spot, with higher transit line densities and frequencies, where transit should be easily accessible and without the needs for transfers in the networks of similar sizes. Another reason for this is the overall behavior of local transit users, who generally prefer transit trips that do not require any transfers. Further implementation of the described methodology on some other TOD locations would enable a valuable comparison and make this approach even more beneficial. The inclusion of socio-economic factors would broaden the prospects if this research as well. As the network develops into a TOD supportive environment it will provide the opportunities for before and after studies. These are some of the future efforts as this research continues. ACKNOWLEDGEMENTS The authors of this paper would like to thank Utah Transit Authority and Mountains Plain Consortium for supporting this research. The authors especially thank Dr. Jinjin Tang for his help and work on the software problems during the study. REFERENCES 1. Moniruzzaman, M. and A. Paez. Accessibility to Transit, by Transit, and Mode Share: Application of a Logistic Model with Spatial Filters. Journal of Transport Geography, Vol. 24, 2012, pp. 198-205. 2. Transit Capacity and Quality of Service Manual, 2nd Edition. Transportation Research Board of the National Academies, Washington, D. C. 2003. 3. Handy, S., and Niemeier, D.A. (1997). Measuring Accessibility: An Exploration of Issues and Alternatives. Environment and Planning A 29(7), 1175-1194. 4. Farber, S. and A. Paez. Running to Stay in Place: The Time-Use Implications of AutomobileOriented Land-Use and Travel. Journal of Transport Geography, Vol. 19, 2011, pp. 782-793. 5. Taylor, B.D. et al. Nature and/or Nurture? Analyzing the Determinants of Transit Ridership across US Urbanized Areas. Transportation Research Part A, in press, 2008. 6. Cervero, R., and G.B.Arrington. Vehicle Trip Reduction Impacts of Transit-Oriented Housing. Journal of Public Transportation, Vol. 11, 2008. 7. Arrington, G.B. New Transit Cooperative Research Program Research Confirms TransitOriented Developments Produce Fewer Auto Trips. ITE Journal, 2009, pp. 26-29. 8. Belzer, D. and G. Autler. Transit Oriented Development: Moving From Rhetoric to Reality. Prepared for the Brookings Institution, Center on Urban and Metropolitan Policy and the Great American Station Foundation, 2002. 9. Handy, S. and K. Clifton. Evaluating Neighborhood Accessibility: Issues and Methods Using Geographic Information Systems. Journal of Transportation and Statistics, September/December, 2001, pp. 68-78.

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10. Litman, T. Measuring Transportation – Traffic, Mobility and Accessibility. Victoria Transport Policy Institute, 2011. 11. Black, J., and M. Conroy. Accessibility measures and the social evaluation of urban structure. Environment and Planning, Vol. 9, No. 9, 1977, pp. 1013-1031. 12. Hansen, W. G. How Accessibility Shapes Land Use. Journal of the American Institute of Planners. Vol. 35, 1959, pp. 73-76. 13. Ben-Akiva, M. E., and S. R. Lerman. Disaggregate travel and mobility choice models and measures of accessibility. In Behavioral Travel Modelling, edited by D.A. Hensher and P. R. Storper, London, 1979, pp. 654-679. 14. El-Geneidy, A. and D. M. Levinson. Access to destinations: Development of accessibility measures. Report No. 2006-16, Minnesota Department of Transportation, 2006. 15. Wu, J. H., and H. J. Miller. Computational Tools for Measuring Space Time Accessibility Within Dynamic Flow Transportation Networks. Journal of Transportation and Statistics, Bureau of transportation Statistics, Volume 4, No. 2-3, September/December, 2001. 16. Miller, H.J. Measuring Space Time Accessibility benefits within Transportation Networks: Basic Theory and Computational Procedures. Geographical Analysis, Vol. 31, No. 1, 1999. 17. Kwan, M.-P. (1998) Space-time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point-based Framework. Geographical Analysis, Vol. 30, No. 3, 1998. 18. Miller, H. J. Modeling Accessibility Using Space Time Prism Concepts within Geographical Information Systems. International Journal of Geographical Information Systems, Vol. 5, No. 3, pp. 287-301. 19. Coffel, K. et al. Guidelines for Providing Access to Public Transportation Stations. TCRP Report 153. Transportation Research Board of the National Academies, Washnigton D.C. 2012. 20. A Guidebook for Developing Transit Performance Measurement System. TCRP Report 88. Transportation Research Board of the National Academies, Washnigton D.C. 2003. 21. AASHTO Guide for Planning, Design, and Operation of Pedestrian Facilities, 1st edition. American Association of State Highway Transportation Officials, 2004. 22. Google Transit Feed Data, https://developers.google.com/transit/gtfs/reference?hl=fr-FR . Accessed June 15, 2013.

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Frequencies During Time of Day Periods Route 6:00-9:00 9:00-12:00 12:00-15:00 15:00-18:00 18:00-21:00 4700 South 15 15 15 15 60 4100 South 15 15 15 15 60 4800 West 30 60 60 30 N/A 5600 W Lift 30 60 60 30 N/A MAX 35 South 15 15 15 15 15

FIGURE 1 Study network with Utah Transit Authority (UTA) transit lines, stops and service frequencies.

FIGURE 2 Traffic and transit data input and shortest path procedure.

Case Study Network

Space-Time Constraints

Network Shapefiles

Service Variability

Roadway Types



Walking Distance

Traffic Delays

Available Time Budget

Walking Times

User Information

Google Transit Feed

Spatial Constraints

Transit Accessibility



Number of Reachable Destinations Incremental Accessibility Change

FIGURE 3 Transit Accessibility Measurements - Conceptual Framework

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FIGURE 4 Transit accessibility weekday variations for time variable service schedule.

FIGURE 5 Transit accessibility weekday variations from 6:00 to 22:00 time of day as a function of the acceptable walking distance.

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FIGURE 6 Transit accessibility weekday variations from 6:00 to 20:00 time of day as a function of the available time budget.

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FIGURE 7 Transit accessibility increase as a function of the acceptable walking distance and the available time budget.

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Legend TAZ_TOD Transit_Stops_Accessible 0 - 50 51 - 100 101 - 150 151 - 200 201 - 250

FIGURE 8 Transit accessibility for different TAZs.