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Measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data

Author

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  • Luyu Liu

    (The Ohio State University, USA)

  • Harvey J Miller

    (The Ohio State University, USA)

Abstract

The emergence of urban Big Data creates new opportunities for a deeper understanding of transportation within cities, revealing patterns and dynamics that were previously hidden. Public transit agencies are collecting and publishing high-resolution schedule and real-time vehicle location data to help users schedule trips and navigate the system. We can use these data to generate new insights into public transit delays, a major source of user dissatisfaction. Leveraging open General Transit Feed Specification (GTFS) and administrative Automatic Passenger Counter (APC) data, we develop two measures to assess the risk of missing bus route transfers and the consequent time penalties due to delays. Risk of Missing Transfers (RoMT) measures the empirical probability of missed transfers, and Average Total Time Penalty (ATTP) shows overall time loss compared to the schedule. We apply these measures to data from the Central Ohio Transit Authority (COTA), a public transit agency serving the Columbus, Ohio, USA metropolitan area. We aggregate, visualise and analyse these measures at different spatial and temporal resolutions, revealing patterns that demonstrate the heterogeneous impacts of bus delays. We also simulate the impacts of dedicated bus lanes reducing missing risk and time penalties. Results demonstrate the effectiveness of measures based on high-resolution schedule and real-time vehicle location data to assess the impacts of delays and to guide planning and decision making that can improve on-time performance.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:urbstu:v:58:y:2021:i:15:p:3140-3156
    DOI: 10.1177/0042098020919323
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    References listed on IDEAS

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    1. Peter Knoppers & Theo Muller, 1995. "Optimized Transfer Opportunities in Public Transport," Transportation Science, INFORMS, vol. 29(1), pages 101-105, February.
    2. 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.
    3. Martin Hilbert, 2016. "Big Data for Development: A Review of Promises and Challenges," Development Policy Review, Overseas Development Institute, vol. 34(1), pages 135-174, January.
    4. Guo, Zhan & Wilson, Nigel H.M., 2011. "Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 91-104, February.
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    Cited by:

    1. Jon Bannister & Anthony O’Sullivan, 2021. "Big Data in the city," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3061-3070, November.
    2. 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).
    3. Linnet Taylor, 2021. "The taming of chaos: Optimal cities and the state of the art in urban systems research," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3196-3202, November.

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