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Spatio-temporal analysis of on-demand transit: A case study of Belleville, Canada

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  • Sanaullah, Irum
  • Alsaleh, Nael
  • Djavadian, Shadi
  • Farooq, Bilal

Abstract

The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11) was substituted with the ODT providing a real-time ride-hailing service. We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019. The independent and combined effects of the demographic characteristics (population density, working-age, and median income) on the ODT trip production and attraction levels were studied using GIS and the K-means machine learning clustering algorithm. The results indicate that ODT trips demand is highest for 11:00 pm–11:45 pm during the weekdays and 8:00 pm–8:30 pm during the weekends. We expect this to be the result of users returning home from work or shopping. Results showed that 39% of the trips were found to have a waiting time of smaller than 15 min, while 28% of trips had a waiting time of 15–30 min. The dissemination areas with higher population density, lower median income, or higher working-age percentages tend to have higher ODT trip attraction levels, except for the dissemination areas that have highly attractive places like commercial areas. For the sustainable deployment of ODT services, we recommend (a) proactively relocating the empty ODT vehicles near the neighbourhoods with high level of activity, (b) dynamically updating the fleet size and location based on the anticipated changes in the spatio-temporal demand, and (c) using medium occupancy vehicles, like vans or minibuses to ensure high level of service.

Suggested Citation

  • Sanaullah, Irum & Alsaleh, Nael & Djavadian, Shadi & Farooq, Bilal, 2021. "Spatio-temporal analysis of on-demand transit: A case study of Belleville, Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 284-301.
    • Handle: RePEc:eee:transa:v:145:y:2021:i:c:p:284-301
    DOI: 10.1016/j.tra.2021.01.020
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    References listed on IDEAS

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    Cited by:

    1. Nael Alsaleh & Bilal Farooq & Yixue Zhang & Steven Farber, 2021. "On-Demand Transit User Preference Analysis using Hybrid Choice Models," Papers 2102.08256, arXiv.org, revised Aug 2023.
    2. HUO, Zhengqi & YANG, Xiaobao & LIU, Xiaobing & YAN, Xuedong, 2024. "Spatio-temporal analysis on online designated driving based on empirical data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
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    4. Alsaleh, Nael & Farooq, Bilal, 2021. "Interpretable data-driven demand modelling for on-demand transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 1-22.
    5. Bürstlein, Johanna & López, David & Farooq, Bilal, 2021. "Exploring first-mile on-demand transit solutions for North American suburbia: A case study of Markham, Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 261-283.
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    7. Klar, Ben & Lee, Jinhyung & Long, Jed A. & Diab, Ehab, 2023. "The impacts of accessibility measure choice on public transit project evaluation: A comparative study of cumulative, gravity-based, and hybrid approaches," Journal of Transport Geography, Elsevier, vol. 106(C).
    8. Alsaleh, Nael & Farooq, Bilal & Zhang, Yixue & Farber, Steven, 2023. "On-demand transit user preference analysis using hybrid choice models," Journal of choice modelling, Elsevier, vol. 49(C).

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