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Measure dynamic individual spatial-temporal accessibility by public transit: Integrating time-table and passenger departure time

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  • Cheng, Shaowu
  • Xie, Bing
  • Bie, Yiming
  • Zhang, Yaping
  • Zhang, Shen

Abstract

The spatial–temporal accessibility of a transport system assesses the spatial–temporal constraints faced by individuals based on their fixed activities and the ability of the transport system to facilitate trading time for space in movement. Previous studies either measured the individual spatial–temporal accessibility of a general transport network or measured the cumulative-opportunity accessibility by public transit through an exclusive computation of the bottom of the full network time prism. By contrast, the current study measures the individual spatial–temporal accessibility by public transit by integrating timetable and passenger departure time and computing the full network time prism. A public transit network is modelled as a time-dependent weighted directed graph, wherein every single directed arc is associated with a time-dependent travel time to represent the linkage between two adjacent stops on a transit route. The time-dependent travel time assigned to arcs is determined according to timetables, which is particularly assigned to infinity when transit service is unavailable between two stops. A modified network potential path area (N-PPA) algorithm based on the time-dependent weighted directed graph is employed to produce a potential path area for the activity participation of an individual by public transit. The proposed methodology is applied as a case study to measure individual spatial–temporal accessibility using the Salt Lake City TRAX system. Results indicate that the outcome of the proposed methodology is sensitive to departure time of passengers. The results of this study provide suggestions on potential improvement of bus/rail line layout and timetables and may aid in trip planning of passengers.

Suggested Citation

  • Cheng, Shaowu & Xie, Bing & Bie, Yiming & Zhang, Yaping & Zhang, Shen, 2018. "Measure dynamic individual spatial-temporal accessibility by public transit: Integrating time-table and passenger departure time," Journal of Transport Geography, Elsevier, vol. 66(C), pages 235-247.
    • Handle: RePEc:eee:jotrge:v:66:y:2018:i:c:p:235-247
    DOI: 10.1016/j.jtrangeo.2017.12.005
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    References listed on IDEAS

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    1. Mavoa, Suzanne & Witten, Karen & McCreanor, Tim & O’Sullivan, David, 2012. "GIS based destination accessibility via public transit and walking in Auckland, New Zealand," Journal of Transport Geography, Elsevier, vol. 20(1), pages 15-22.
    2. G H Pirie, 1979. "Measuring Accessibility: A Review and Proposal," Environment and Planning A, , vol. 11(3), pages 299-312, March.
    3. Kondo, Katsunao & Kitamura, Ryuichi, 1987. "Time-space constraints and the formation of trip chains," Regional Science and Urban Economics, Elsevier, vol. 17(1), pages 49-65, February.
    4. Tijs Neutens & Tim Schwanen & Frank Witlox & Philippe De Maeyer, 2010. "Equity of Urban Service Delivery: A Comparison of Different Accessibility Measures," Environment and Planning A, , vol. 42(7), pages 1613-1635, July.
    5. Fayyaz, S. Kiavash & Liu, Xiaoyue Cathy & Porter, Richard J., 2017. "Dynamic transit accessibility and transit gap causality analysis," Journal of Transport Geography, Elsevier, vol. 59(C), pages 27-39.
    6. Xu, Wangtu (Ato) & Li, Yongling & Wang, Hui, 2016. "Transit accessibility for commuters considering the demand elasticities of distance and transfer," Journal of Transport Geography, Elsevier, vol. 56(C), pages 138-156.
    7. Nishii, Kazuo & Kondo, Katsunao, 1992. "Trip linkages of urban railway commuters under time-space constraints: Some empirical observations," Transportation Research Part B: Methodological, Elsevier, vol. 26(1), pages 33-44, February.
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    3. Jaller, Miguel & Qian, Xiaodong & Joby, Raina & Xiao, Runhua Ivan, 2023. "Optimizing Bikeshare Service to Connect Affordable Housing Units with Transit Service," Institute of Transportation Studies, Working Paper Series qt9mp4g0xz, Institute of Transportation Studies, UC Davis.

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