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Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

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  • Tao, Sui
  • Rohde, David
  • Corcoran, Jonathan

Abstract

Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy.

Suggested Citation

  • Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    • Handle: RePEc:eee:jotrge:v:41:y:2014:i:c:p:21-36
    DOI: 10.1016/j.jtrangeo.2014.08.006
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    Cited by:

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    12. Kandt, Jens & Leak, Alistair, 2019. "Examining inclusive mobility through smartcard data: What shall we make of senior citizens' declining bus patronage in the West Midlands?," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    13. Yu, Chang & He, Zhao-Cheng, 2017. "Analysing the spatial-temporal characteristics of bus travel demand using the heat map," Journal of Transport Geography, Elsevier, vol. 58(C), pages 247-255.
    14. Vergel-Tovar, C. Erik & Leape, Jonathan & Villegas Carrasquilla, Mónica & Peñas Arana, Maria Claudia & Toro Gonzalez, Daniel & Canon Rubiano, Leonardo & Salas Barón, Eliana & Martinez, Paulo, 2022. "Mapping the transit network of greater Cartagena with mobile phones: Coverage, accessibility, and informality," Journal of Transport Geography, Elsevier, vol. 105(C).
    15. Cong Liao & Teqi Dai, 2022. "Is “Attending Nearby School” Near? An Analysis of Travel-to-School Distances of Primary Students in Beijing Using Smart Card Data," Sustainability, MDPI, vol. 14(7), pages 1-12, April.
    16. Xie, Jiemin & Zhan, Shuguang & Wong, S.C. & Wen, Keyu & Qiang, Lixia & Lo, S.M., 2022. "High-speed rail services for elderly passengers: Ticket-booking patterns and policy implications," Transport Policy, Elsevier, vol. 125(C), pages 96-106.
    17. Zijia Wang & Hao Tang & Wenjuan Wang & Yang Xi, 2020. "The Pattern of Non-Roundtrip Travel on Urban Rail and Its Application in Transit Improvement," Sustainability, MDPI, vol. 12(9), pages 1-16, April.
    18. Jonathan Corcoran & Sui Tao, 2017. "Mapping spatial patterns of bus usage under varying local temperature conditions," Journal of Maps, Taylor & Francis Journals, vol. 13(1), pages 74-81, January.
    19. Enhui Chen & Zhirui Ye & Hui Bi, 2019. "Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    20. Pan Wu & Jinlong Li & Yuzhuang Pian & Xiaochen Li & Zilin Huang & Lunhui Xu & Guilin Li & Ruonan Li, 2022. "How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method," Sustainability, MDPI, vol. 14(15), pages 1-31, August.
    21. Weng, JianCheng & Yu, JiangBo & Di, XiaoJian & Lin, PengFei & Wang, Jing-Jing & Mao, Li-Zeng, 2023. "How does the state of bus operations influence passengers’ service satisfaction? A method considering the differences in passenger preferences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    22. An, Ran & Zahnow, Renee & Pojani, Dorina & Corcoran, Jonathan, 2019. "Weather and cycling in New York: The case of Citibike," Journal of Transport Geography, Elsevier, vol. 77(C), pages 97-112.

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