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Modeling public-transit connectivity with spatial quality-of-transfer measurements

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  • Hadas, Yuval
  • Ranjitkar, Prakash

Abstract

Improving public-transit connectivity is one of the most vital tasks in transit-operations planning. A poor connection can cause some passengers to stop using the transit service. This work analyzes the performance of public-transit networks in terms of the attributes involved with coordination and connectivity. These attributes are primarily concerned with passenger transfers, and include ride, wait and walk times and type of transfers made, that is, with street-crossing, sidewalk, non-walk and one-leg trip. Based on these attributes, transit-connectivity measures are established as a tool to evaluate the pros and cons of each defined zone of transit lines from a connectivity perspective. The zone of transit lines can be associated with small or large transit networks from which two types of analyses can be made: (i) detecting the inefficiency of connectivity-related issues for the zone, and (ii) comparing the measures between different zones to arrive to the conclusion of which zone is more worthy of improvements. This will allow, for instance, the comparison between cities and between zones in the city. In this work a model is developed to integrate spatial and non-spatial data for the construction of a public-transit network spatial repository, which in turn, is used to classify transfers, and calculate the developed connectivity measures. A case study in Auckland, New Zealand, demonstrates the benefits of the model and connectivity measures.

Suggested Citation

  • Hadas, Yuval & Ranjitkar, Prakash, 2012. "Modeling public-transit connectivity with spatial quality-of-transfer measurements," Journal of Transport Geography, Elsevier, vol. 22(C), pages 137-147.
    • Handle: RePEc:eee:jotrge:v:22:y:2012:i:c:p:137-147
    DOI: 10.1016/j.jtrangeo.2011.12.003
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    Cited by:

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    3. Luyu Liu & Harvey J Miller, 2021. "Measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3140-3156, November.
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    7. Hadas, Yuval & Gnecco, Giorgio & Sanguineti, Marcello, 2017. "An approach to transportation network analysis via transferable utility games," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 120-143.
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    9. Ceder, Avishai & Chowdhury, Subeh & Taghipouran, Nima & Olsen, Jared, 2013. "Modelling public-transport users’ behaviour at connection point," Transport Policy, Elsevier, vol. 27(C), pages 112-122.
    10. Liu, Chengliang & Duan, Dezhong, 2020. "Spatial inequality of bus transit dependence on urban streets and its relationships with socioeconomic intensities: A tale of two megacities in China," Journal of Transport Geography, Elsevier, vol. 86(C).
    11. Nkurunziza, Alphonse & Zuidgeest, Mark & Brussel, Mark & Van den Bosch, Frans, 2012. "Spatial variation of transit service quality preferences in Dar-es-Salaam," Journal of Transport Geography, Elsevier, vol. 24(C), pages 12-21.
    12. Chowdhury, Subeh & Ceder, Avishai (Avi), 2016. "Users’ willingness to ride an integrated public-transport service: A literature review," Transport Policy, Elsevier, vol. 48(C), pages 183-195.
    13. Zhou, Yaoming & Wang, Junwei & Sheu, Jiuh-Biing, 2019. "On connectivity of post-earthquake road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 1-16.
    14. Sharma, Ishant & Mishra, Sabyasachee & Golias, Mihalis M. & Welch, Timothy F. & Cherry, Christopher R., 2020. "Equity of transit connectivity in Tennessee cities," Journal of Transport Geography, Elsevier, vol. 86(C).
    15. Zhou, Yueer & Li, Linbo & Zhang, Yahua, 2023. "Location of transit-oriented development stations based on multimodal network equilibrium: Bi-level programming and paradoxes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    16. Ingvardson, Jesper Bláfoss & Nielsen, Otto Anker, 2018. "How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas," Journal of Transport Geography, Elsevier, vol. 72(C), pages 50-63.
    17. Zhenbao Wang & Dong Liu & Shihao Li & Shuyue Liu & Huiqing Li & Ning Chen, 2023. "Analyzing the Impact of Decreasing Out-of-Vehicle Time of Public Transportation Travel on Accessibility to Tertiary Hospitals," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    18. Wanke, Peter & Barros, C.P. & Figueiredo, Otávio, 2016. "Efficiency and productive slacks in urban transportation modes: A two-stage SDEA-Beta Regression approach," Utilities Policy, Elsevier, vol. 41(C), pages 31-39.
    19. Pu, Han & Li, Yinzhen & Ma, Changxi, 2022. "Topology analysis of Lanzhou public transport network based on double-layer complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    20. Chia, Jason & Lee, Jinwoo (Brian), 2020. "Extending public transit accessibility models to recognise transfer location," Journal of Transport Geography, Elsevier, vol. 82(C).
    21. Moyano, Amparo & Moya-Gómez, Borja & Gutiérrez, Javier, 2018. "Access and egress times to high-speed rail stations: a spatiotemporal accessibility analysis," Journal of Transport Geography, Elsevier, vol. 73(C), pages 84-93.
    22. Dimitrov, Stavri Dimitri & Ceder, Avishai (Avi), 2016. "A method of examining the structure and topological properties of public-transport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 373-387.
    23. Hu, Xinlei & Huang, Jie & Shi, Feng, 2019. "Circuity in China's high-speed-rail network," Journal of Transport Geography, Elsevier, vol. 80(C).
    24. Jinjoo Bok & Youngsang Kwon, 2016. "Comparable Measures of Accessibility to Public Transport Using the General Transit Feed Specification," Sustainability, MDPI, vol. 8(3), pages 1-13, March.

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