Use of Spatiotemporal Constraints to Quantify Transit Accessibility Case Study of Potential Transit-Oriented Development in West Valley City, Utah Ivana Tasic, Xuesong Zhou, and Milan Zlatkovic 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 met rics 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 to the transportation sys tem users. Accessibility relates to transportation and land use as it quantifies how many destinations an individual can reach by using the given mode of transport within the available time (3). In the era when sprawling communities and the car culture are a serious threat to urban environments, improving accessibility becomes more and more important for cities (4). Access to transit is a precondition for all efforts taken toward multi modal transportation systems. Whether people will use transit or not depends on many factors, including their value of time and available time budget, transit fare price, and the ratio of car-to-transit utility (5). However, for transit to be considered as an option in mode choice at all, there has to be a feasible transit route leading from the given origin to the desirable destination within the available time frame. This paper uses spatial and temporal constraints and a set of transit features that affect access to transit to develop a conceptual frame work for transit accessibility measurements in the potential transitoriented development (TOD) location in West Valley City, Utah. The case study network is chosen because using accessibility-related per formance metrics is particularly relevant for TOD environments. As this network develops more transit-friendly features, temporal and spatial accessibility indicators will provide useful information on the opportunities users can reach by using transit. Planning a transportation network system for a TOD could result in social, environmental, and financial benefits. The increase in non motorized mode users and transit ridership are the two most direct effects of a TOD. Other TOD effects include opportunities for com munity 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 (6–8). The proposed methodology builds on the traffic and transit data from the case study network and uses an open source tool to per form 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 net work features, acceptable walking time, available time budget, transit schedule variability, and spatial constraints as impact factors in acces sibility measurements. The goal of this paper is to establish a feasible set of transit accessibility indicators that would be used for the case
Accessibility emerges as the transportation performance measure that emphasizes the benefits to transportation system users and captures more than the speed of travel. Transit accessibility shows how easy it is for an individual to travel to a desired destination by using public transit. However, for transit to be considered as an option in mode choice at all, there has to be a feasible transit route leading from a given origin to a desirable destination within the available time frame. This study used spatial and temporal constraints and a set of transit features that affected access to transit to develop a conceptual framework for transit accessibility measurements in a potential transit-oriented development (TOD) location in West Valley City, Utah. As this network develops more transit-friendly features, temporal and spatial accessibility indicators will provide useful information on the opportunities that users can reach by using transit. The proposed methodology was based on traffic and transit data from the case study network and used an open source tool to perform transit accessibility measurements by calculating the number of accessible transit stops from each origin. The methodology considered network features, acceptable walking time, available time budget, transit schedule variability, and spatial constraints as impact factors in accessibility measurements. The goal of the study was to establish a feasible set of transit accessibility indicators that would be used for both the case study street network and transit service modifications to transform the network into a transit-friendly and eventually a TOD environment.
Transit is a unique mode of transportation because of the way it is constrained in regard to space and time. In regard to space, transit requires transit stop facilities and special road design treatments, while in regard to time, transit follows specifically scheduled time tables. These spatial and temporal components determine the accessi bility of the public transit system. Transit accessibility indicates how easy it is for an individual to reach a desired destination by using public transit. It is important for existing transit riders as an indica tor of the service quality, and for the potential riders as well, as it I. Tasic and M. Zlatkovic, Department of Civil and Environmental Engineering, University of Utah, Room 1650, 110 Central Campus Drive, Salt Lake City, UT 84112. X. Zhou, School of Sustainable Engineering and the Built Environment, Arizona State University, Engineering G-Wing, 501 East Tyler Mall, Tempe, AZ 85287. Corresponding author: I. Tasic,
[email protected]. Transportation Research Record: Journal of the Transportation Research Board, No. 2417, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 130–138. DOI: 10.3141/2417-14 130
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study street network and transit service modifications to develop the network 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 section on methodology explains the approach and tools used to perform transit accessibility measure ments on the given case study network. Following that section are the results of accessibility measurements and a discussion. The final section of the paper introduces conclusions along 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 devel oped in the existing research. Cumulative or opportunity accessibil ity 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 dis tance, 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 avail able choices (9, 13, 14). The composite accessibility measure incor porates temporal constraints in addition to spatial constraints for a more complex measurement approach (15–17). The best accessibil ity measurement method should be chosen according to the purpose and a situation that requires such measurement (3). Space–Time Accessibility Measures As public transit has unique characteristics among other modes because of 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 an individual’s behav ior (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 sig nificant effect on an individual’s ability to reach desired destinations, the STP-based accessibility measurement methods were updated to account for that finding (15–17). The STP-based measures incor porate 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 relation to temporal constraints or maximum available travel time, and static and dynamic traveler delay (18). Further deployment of these measures is demonstrated in the section on methodology. Transit Accessibility Public transit is considered to be a feasible travel choice when tran sit stops are accessible to and from trip origins and destinations (spatial coverage) and when transit service is available at times that
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one wants to travel (temporal coverage) (2, 19). Transit accessibil ity 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. On the basis of the assumed average walking speed of about 4 ft/s, 5 min of walking to transit stops is considered to be acceptable in urban areas, or about a quarter of a mile in walking distance (20, 21). Location and spac ing between transit stops have a significant effect on transit service performance and user satisfaction, as they not only ensure reasonable accessibility but also influence travel time (18, 22). Measuring “the ease of access” to transit services with respect to space–time con straints is important for evaluating 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. The 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 as a result of a denser street network and of mixed land use that provides more opportunities. This study develops a con ceptual framework for quantifying transit accessibility on the basis of spatiotemporal constraints. The network scenario is developed to reflect the transportation network and transit system on a future TOD location in West Valley City, Utah. The location is chosen on the basis of the Wasatch Choice for 2040 map of the potential TOD spots in the Salt Lake region of Utah, and it represents a future town center with the intersection of two bus rapid transit (BRT) lines. The case study network is shown in Figure 1. Network Description and Modeling The case study network has about 40 mi of road length, with a road way density of 20 mi of roadway per square mile. Each TAZ has about three to four intersections per acre. The network is character ized by many cul-de-sacs and three-way intersections, making it less connected despite its relatively good road density. A total of five transit lines traverse this network, one of which is a BRT line; another BRT line is planned to be finalized and opened by the end of 2015. 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 the indicators are flexible enough to be applied for future network modifications. The network was first created in VISUM, by using Google Earth maps and local metropolitan planning organization data for road way 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 geographic information system shapefiles for nodes, links, connectors, zones, and zone centroids. This base model, with transit data input, was used for further transit accessibility calcula tions. Transit accessibility is expressed through the number of transit stops reachable from each TAZ centroid with the use of walking and
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transit routes, constrained by spatial characteristics of the case study network and temporal dimension determined by transit service and traffic characteristics.
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 Net work Explorer for Traffic Analysis (NEXTA) software. NEXTA is an open-source graphical user interface that aims to facilitate the preparation, postprocessing, and analysis of transportation assign ment, simulation, and scheduling data sets. One advantage of NEXTA is that it facilitates importing transportation network data from both macro- and microsimulation environments. This benefit means that it has the ability to integrate with the previously built traffic and transit models. Loading transit data from Google and additional features for accessibility calculations are the 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 the transit schedules, a topic that 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 by using the algorithm integrated into NEXTA. This algorithm first identifies accessible bus trips by using the stop time records in the 15-min 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 acces sible stop times is counted along each trip as the indicator of acces sibility. Average measures across all origin activity locations are also considered. The data input and loading process with the output procedure demonstration is shown in Figure 2. Impact Factors
Google Transit Feed Data Structure Transit data were provided by the Utah Transit Authority (UTA) and loaded into the network through Google Transit Feed (22). The general transit feed specification from Google applied here includes the following: • Calendar that specifies when service starts and ends, including the days of the week that service is available; • Calendar dates with possible service exceptions; • Routes or groups of trips displayed to riders as a single service; • Shapes or rules for representing transit routes on the maps; • Stop times or arrival and departure times for each individual trip; • Stops or passenger pickup and drop-off points; and • Trips or sequences of stops for each route.
Evaluating public transit is always more complex than evaluating any other mode of transportation, and selecting adequate accessibil ity measures is also a challenge. It is, however, important to quantify changes in accessibility that different transit features could bring, to adjust travel forecasting models and make them more applicable to a transit-intensive environment. Accessibility provided through imple menting transit-friendly treatments will also affect other measures of effectiveness related to transit, such as level of service. For the case study network, factors that affect space–time con straints are shown in the conceptual framework in Figure 3. S ervice variability refers to the frequency of transit service and service span in general. Walking distance is the acceptable walking distance to transit stops. The available time budget defines the time that an indi vidual has to access activity locations from the given trip origin.
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MPO Travel Model and Google Maps
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FIGURE 2 Traffic and transit data input and shortest path procedure (MPO 5 metropolitan planning organization; DOT 5 department of transportation).
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FIGURE 3 Transit accessibility measurements—conceptual framework.
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Definitions and Assumptions
Transit speeds will differ between BRT lines and regular transit lines. Spatial constraints refer to the destination or activity location type. Activity location can be fixed or final when the entire time budget is used to reach the destination or flexible and intermediate. Transit accessibility is expressed through the number of destina tions 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. To represent the time variability aspect of transit accessibility, incremental change of accessibility measured with each change in control variables is also introduced.
Walking distance: twalk = 0.05; 0.10; 0.15;0.20; 0.25 mi
Concept for Accessibility Measurements and Performance Measures
Results and Discussion
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Spatial constraints: zonal access distribution
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 shown in Figure 4. The total number of accessible transit stops, not only spatially but also in regard to temporal availability of transit service, was aggregated in 15-min time periods over the course of a weekday, from 06:00 to 22:00. Only results for one origin are presented to provide better visualization. The service schedule is presented as dependent on time, while other variables are kept constant. The assumed constant acceptable walking distance in this case is 0.25 mi, or equivalent to 5 min of walking time. The results show that transit accessibility, dependent only on schedule vari ability and network features, is more consistent during the morning peak (06:00 to 09:00) than in the afternoon. Although the variability of the number of stops that can be reached with transit or nonmotor ized modes is higher in the afternoon, the average accessibility is better in the afternoon as well. This finding is a very good indica tor of changes that transit schedules will need to undergo to sup port a transit friendlier environment. Again, a reminder from the literature: recommendations for TOD transit service frequencies are 15 min or less in areas similar to the one analyzed here (22). What the simplest analysis also indicates here is how specific transit is in regard to accessibility when compared to other modes, because the impact of schedule variability makes accessibility to transit more time-dependent than in other modes. Although spatial distribution of stops is constant, the service frequency clearly affects transit accessibility to vary within each period of the day (morning peak,
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Number of Accessible Transit Stops
One of the most utilized space–time constrained accessibility mea surement methods is based on Miller’s interpretation of STP appli cation 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 individual sequence of trips and spatiotemporal con straints, 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 space and time. In the case of public transit, travel times are affected not only 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 des tination 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 relate specifically to transit mode. Instead of using a simple, radius-based service coverage, an actual transportation network is used here, with assigned volumes, ade quate 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 sta tions. The following are calculations and assumptions adapted from Wu and Miller for different space–time constraints and applied to compute the number of accessible transit stops (15, 16):
Service variability: tt = 15-min frequency versus regular lines
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off-peak, and 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 recommend up to one-quarter-mile distance acceptable from the pedestrian’s standpoint (21). While ranges from 0.05 to 0.25 mi of walking dis tance are analyzed, four representative values are given in Figure 5, since the walking distance of 0.05 mi 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 walk ing distances, there are more points at which transit stops are not accessible at all. This finding is also not surprising, since the ana lyzed network has many disconnected links or cul-de sacs, which decrease the number of potential paths to transit. The field inspec tion of the study network confirmed the limited number of accept able pedestrian paths. What limits accessibility even more than the network features is the service schedule, which causes the acces sibility to drop to zero when smaller walking distances are consid ered. With the lower values of the acceptable walking distances, not only does the area of spatially accessible transit stops decrease, but the number of stop times depending on the service frequency also decreases as well. The lowest transit service headway in this area is 15 min, so if the acceptable walking distance is equivalent to a walk time of less than 15 min, the chances of being able to use transit also decrease. This temporal dimension of transit accessibil ity determined by schedule characteristics is what causes the values in Figure 5 to be zero in a nonrandom manner. Figure 5 also shows that different curves in the upper ranges (0.20- and 0.25-mi walk distances) have similar patterns, and simi lar patterns are shown in the lower ranges of walking distance as well. This similarity is first related to the relationship between the acceptable walking distance to transit and transit accessibility in regard to space and time. Second, the way the curves change dur ing the entire day period shows the direct relationship with transit characteristics in the study network area. As the network continues to be modified toward a more transit supportive pattern, it is likely that there will be more routing options
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for pedestrians. The TOD can reduce walking time at signalized inter sections, too, and thus can increase the potential time for walking within the available time budget, which is the variable discussed next. Transit service characteristics are expected to be modified as well. The impact of the available time budget on transit stops accessibility in the analyzed network is presented in Figure 6. Four representative values for the available time budget—30, 40, 50, and 60 min—are given for consideration as the time available for the end-to-end transit trip. Results show that the highest number of accessible transit stops for the given time budgets exists between 09:00 and 18:00. It is notice able that early-morning and late-evening time periods have less fre quent transit service and that the service is limited between 06:00 and 20:00. Patterns are related primarily to transit service characteristics and show how accessibility increases with the time budget. Because of space limitations, more detailed user information effects were not included in this paper. In general, available user informa tion would, however, affect the available user time budget. With high-quality information available, transit users would spend less time waiting and would have more time to spend in transit within their available time budgets. When one considers future development plans of the case study and the regional network, user information is an impact factor that should be considered for improving access to transit. The incremental change in measured transit accessibility with changes in the acceptable walk time and the available time budget is shown in Figure 7. The accessibility is almost constant when the acceptable walking time to a transit stop is 0.10 mi, correspond ing to about 2 min 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 min, the percentage of accessible transit stops becomes almost constant in all three cases of walking distance criteria presented here. A 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 Figure 7, it is apparent that the dif ference in accessibility for different values of the acceptable walk ing distance also increases as the available time budget increases. One hundred percent accessibility is constrained with the upper
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bound of the available time budget analyzed, which is 65 min of transit ride. The incremental change in accessibility is useful for the case of transit because it indicates the maximum number of stops that can be reached for the given set of conditions or impact factors. While previous results show the time variable aspect of transit accessibility, spatial accessibility for three time points and all defined origins during the daily service span is shown in Figure 8. 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-min available time budget and 0.25-mi acceptable walking dis tance values were adopted, these results indicate which TAZs will have to improve their access to transit in the future. It is surpris ing that the access to transit stops for some TAZs is higher during the midday period than during the morning and afternoon peak hour periods. This finding is probably related to the fact that transit lines in this area start to decrease their frequency after 13:00, so that 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 reach
able opportunities in the area. This network is relatively small, so the obvious inconsistency in the spatial distribution of transit acces sibility shows how network features can affect the transit service coverage even on a smaller scale.
Conclusions and Recommendations Measuring accessibility to transit is more challenging than measur ing accessibility to other modes of transportation. The reason is the number of impact factors that affect the ability of users to access transit, starting with network features and transit schedules and including 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 spatial and temporal constraints, exploring further the relation ships between factors that affect transit accessibility. The measures and impact factors presented here indicate how reachable activity
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l ocations are from different origins at different times, which is what users can relate to. Results show how access to transit varies temporally and spatially. Specific to the transit mode, service schedule variability significantly affects the changes in accessibility to transit during the course of a day. Adopted pedestrian criteria for the acceptable walking distances also show their impact, underscoring the need to improve existing network connectivity for future development. Considering quality transit service information for 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 min, 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 distribu tions shows inequality between different TAZs in the network. The diversity in the number of reachable transit stops is apparent even in a relatively small area. Provided measurement tools can serve as the indicators of critical areas that need improved access to transit. The spatial distribution of transit accessibility can also be related to mobility patterns by showing that areas with low transit access probably have low street network connectivity, and that private vehicles could be the dominant travel mode. The methodology proposed in this study relies on 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 a micro- and macrosimulation environment, allowing the exploration of relationships between impact factors con sidered in this study and spatiotemporal transit accessibility and pro viding a wide range of transportation performance measures at the same time. Criteria adopted for impact factors are validated through previous research and existing transportation guidelines. This study has several limitations. Because of the network size, this study does not consider transfers between different transit lines as an acceptable option from the users’ perspective. The reason for this omission 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 need for transfers in networks of similar sizes. Another reason for this omission 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 socioeconomic factors would broaden the prospects of this research as well. As the network develops into a TOD support ive environment it will provide the opportunities for before and after studies. These limitations and potential ways to advance the study are some of the future efforts as this research continues. Acknowledgments The authors thank the Utah Transit Authority and the Mountain–Plains Consortium for supporting this research. The authors especially thank Jinjin Tang for his help and work on the software problems during the study.
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