On the accuracy of schedule-based GTFS for measuring accessibility
Nate Wessel
University of Toronto
http://orcid.org/0000-0002-8996-7968
Steven Farber
University of Toronto Scarborough
http://orcid.org/0000-0002-3870-5984
DOI: https://doi.org/10.5198/jtlu.2019.1502
Keywords: Public Transport, Accessibility, GTFS, General Transit Feed Specification
Abstract
In this paper we assess the accuracy with which General Transit Feed Specification (GTFS) schedule data can be used to measure accessibility by public transit as it varies over space and time. We use archived Automatic Vehicle Location (AVL) data from four North American transit agencies to produce a detailed reconstruction of actual transit vehicle movements over the course of five days in a format that allows for travel time estimation directly comparable to schedule-based GTFS. With travel times estimated on both schedule-based and retrospective networks, we compute and compare a variety of accessibility measures. We find that origin-based accessibility even when averaged over one-hour periods can vary widely between locations. Origins with lower scheduled access tend to produce less reliable estimates with more variability from hour to hour in real accessibility, while higher access zones seem to converge on an estimate 5-15 percent lower than the schedule predicts. Such over- and under-predictions exhibit strong spatial patterns which should be of concern to those using accessibility metrics in statistical models. Momentary measures of accessibility are briefly discussed and found to be weakly related to momentary changes in real access. These findings bring into question the validity of some recent applications of GTFS data and point the way toward more robust methods for calculating accessibility.Author Biographies
Nate Wessel, University of Toronto
Nate Wessel is a PhD candidate in urban planning at the University of Toronto. His research interests include public transport, GIS, and cartography.Steven Farber, University of Toronto Scarborough
Steven Farber is an assistant professor in the Department of Human Geography at University of Toronto Scarborough. His research investigates the social and economic impacts of transportation and land use planning and policy.References
Allen, J. and S. Farber. 2018a. Generating measures of access to employment for Canada’s eight largest urban regions. Technical report, University of Toronto. URL https://osf.io/preprints/socarxiv/pvrd9/.
Allen, J. and S. Farber. 2018b. How time-use and transportation barriers limit on-campus participation of university students. Travel Behaviour and Society, 13:174–182.
Anderson, P. A., A. Owen, and D. M. Levinson. 2012. The time between: Continuously-defined accessibility functions for schedule-based transportation systems. Technical report, University of Minnesota.
Anselin, L. and D. A. Griffith. 1988. Do spatial effects really matter in regression analysis? Papers in Regional Science, 65(1):11–34.
Antrim, A., S. J. Barbeau, et al. 2013. The many uses of GTFS data–opening the door to transit and multimodal applications. Location-Aware Information Systems Laboratory at the University of South Florida, p. 4.
Bertini, R. and A. El-Geneidy. 2003. Generating transit performance measures with archived data. Transportation Research Record: Journal of the Transportation Research Board, 1841(1):109–119.
Boisjoly, G. and A. El-Geneidy. 2016. Daily fluctuations in transit and job availability: A comparative assessment of time-sensitive accessibility measures. Journal of Transport Geography, 52:73–81.
Conway, M. W., A. Byrd, and M. van Eggermond. 2018. Accounting for uncertainty and variation in accessibility metrics for public transport sketch planning. Journal of Transport and Land Use, 11(1).
Cui, M. and D. Levinson. 2018. Full cost analysis of accessibility. Journal of Transport and Land Use, 11(1).
Du, Q., V. Faber, and M. Gunzburger. 1999. Centroidal voronoi tessellations: Applications and algorithms. SIAM review, 41(4):637–676.
El-Geneidy, A., R. Buliung, E. Diab, D. van Lierop, M. Langlois, and A. Legrain. 2016a. Non-stop equity: Assessing daily intersections between transit accessibility and social disparity across the Greater Toronto and Hamilton Area (GTHA). Environment and Planning B: Planning and Design, 43(3):540–560.
El-Geneidy, A., D. Levinson, E. Diab, G. Boisjoly, D. Verbich, and C. Loong. 2016b. The cost of equity: Assessing transit accessibility and social disparity using total travel cost. Transportation Research Part A: Policy and Practice, 91:302–316.
El-Geneidy, A. M., J. Horning, and K. J. Krizek. 2011. Analyzing transit service reliability using detailed data from automatic vehicular locator systems. Journal of Advanced Transportation, 45(1):66–79.
Farber, S. and L. Fu. 2017. Dynamic public transit accessibility using travel time cubes: Comparing the effects of infrastructure (dis)investments over time. Computers, Environment and Urban Systems, 62:30–40.
Farber, S. and M. Grandez. 2016. Transit accessibility, land development and socioeconomic priority: A typology of planned station catchment areas in the Greater Toronto and Hamilton Area. Journal of Transport and Land Use.
Farber, S., M. Z. Morang, and M. Widener. 2014. Temporal variability in transit-based accessibility to supermarkets. Applied Geography, 53:149–159.
Fransen, K., T. Neutens, S. Farber, P. De Maeyer, G. Deruyter, and F. Witlox. 2015. Identifying public transport gaps using time-dependent accessibility levels. Journal of Transport Geography, 48:176–187.
Google. 2018. General Transit Feed Specification Reference. URL https://developers.google.com/transit/gtfs/reference/.
Lee, J. and H. J. Miller. 2018. Measuring the impacts of new public transit services on space-time accessibility: An analysis of transit system redesign and new bus rapid transit in Columbus, Ohio, USA. Applied Geography, 93:47–63.
Lee, Y.-J., K. Chon, D. Hill, and N. Desai. 2001. Effect of automatic vehicle location on schedule adherence for mass transit administration bus system. Transportation Research Record: Journal of the Transportation Research Board, 1760:81–90.
Ma, T. and G. Jan-Knaap. 2014. Analyzing employment accessibility in a multimodal network using GTFS: A demonstration of the Purple Line, Maryland. Technical report, University of Maryland, National Center for Smart Growth.
Mandelzys, M. and B. Hellinga. 2010. Identifying causes of performance issues in bus schedule adherence with automatic vehicle location and passenger count data. Transportation Research Record: Journal of the Transportation Research Board, 2143:9–15. URL https://doi.org/10.3141/2143-02.
Merlin, L. A. and L. Hu. 2017. Does competition matter in measures of job accessibility? Explaining employment in Los Angeles. Journal of Transport Geography, 64:77–88.
Michael, T. and T. Quint. 1999. Sphere of influence graphs in general metric spaces. Mathematical and Computer Modelling, 29(7):45–53.
Owen, A. and D. Levinson. 2015. Modeling the commute mode share of transit using continuous accessibility to jobs. Transportation Research Part A: Policy and Practice, 74:110–122.
Owen, A. and D. Levinson. 2016. Developing a comprehensive US transit accessibility database. In Seeing Cities rough Big Data, pp. 279–290. Springer.
Owen, A. and B. Murphy. 2018a. Access across America: Transit 2017. Technical report, University of Minnesota. URL http://hdl.handle.net/11299/199920.
Owen, A. and B. Murphy. 2018b. Temporal sampling and service frequency harmonics in transit accessibility evaluation. Transportation Research Record. URL https://trid.trb.org/view/1497217.
Pereira, R. H., D. Banister, T. Schwanen, and N. Wessel. 2018. Distributional effects of transport policies on inequalities in access to opportunities in Rio De Janeiro. SSRN Electronic Journal. doi: 10.2139/ssrn.3040844.
Stępniak, M., J. P. Pritchard, K. T. Geurs, and S. Goliszek. 2019. The impact of temporal resolution on public transport accessibility measurement: Review and case study in Poland. Journal of transport geography, 75:8–24.
Stewart, A. F. 2017a. Advancing accessibility: Public transport and urban space. Ph.D. thesis, Massachusetts Institute of Technology. URL http://hdl.handle.net/1721.1/111444.
Stewart, A. F. 2017b. Mapping transit accessibility: Possibilities for public participation. Transportation Research Part A: Policy and Practice.
Wessel, N. 2015. Discovering the space-time dimensions of schedule padding and delay om GTFS and real-time transit data. Master’s thesis, University of Cincinnati. URL http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445342602.
Wessel, N., J. Allen, and S. Farber. 2017. Constructing a routable retrospective transit timetable from a real-time vehicle location feed and GTFS. Journal of Transport Geography, 62:92–97. URL http://sausy.ca/wp-content/uploads/2017/11/retro-GTFS-paper.pdf.
Wessel, N. and M. Widener. 2016. Discovering the space–time dimensions of schedule padding and delay from GTFS and real-time transit data. Journal of Geographical Systems, pp. 1–15. ISSN 1435-5949. doi: 10.1007/s10109-016-0244-8. URL http://dx.doi.org/10.1007/s10109-016-0244-8.
Widener, M., S. Farber, T. Neutens, and M. Horner. 2015. Spatiotemporal accessibility to supermarkets using public transit: An interaction potential approach in Cincinnati, Ohio. Journal of Transport Geography, 42:72–83.
Widener, M. J., L. Minaker, S. Farber, J. Allen, B. Vitali, P. C. Coleman, and B. Cook. 2017. How do changes in the daily food and transportation environments affect grocery store accessibility? Applied Geography, 83:46–62.
Zervaas, Q. 2018. Transitfeeds.com. URL https://transitfeeds.com/.
Zygo, A. 2017. Measuring and Improving Seniors’ Access to Medical Facilities. Master’s thesis, University of Connecticut. URL https://opencommons.uconn.edu/gs_theses/1042
