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Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns

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  • Barabino, Benedetto
  • Di Francesco, Massimo
  • Mozzoni, Sara

Abstract

This paper investigates punctuality at bus stops. Although it is typically evaluated from the point of view of bus operators, it must also account for users, as required in recent service quality norms. Therefore, evaluating punctuality at bus stops is highly important, but may also be a complex task, because data on both bus arrivals (or departures) and users must be taken into account and processed. Data on buses can be collected by Automatic Vehicle Location (AVL) systems, but several challenges must be addressed in order to use them effectively. Passengers data at bus stops cannot be derived from AVL, but they can be used to derive passenger patterns and need to be integrated into processed AVL data. This paper proposes a new punctuality measure defined as the fraction of passengers who will be served within an acceptably short interval after they arrive. A method is proposed to determine this measure: it provides (i) several rules to handle AVL collected data, (ii) a procedure integrating processed AVL data and potential passengers’ patterns and (iii) a hierarchical process to perform the punctuality measure on each bus route direction of a transit network, as well as for every bus stop and time period. The paper illustrates the experimentation of this method on more than 4,000,000 data of a real bus operator and represents outcomes by easy-to-read control dashboards.

Suggested Citation

  • Barabino, Benedetto & Di Francesco, Massimo & Mozzoni, Sara, 2015. "Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 84-95.
  • Handle: RePEc:eee:transa:v:75:y:2015:i:c:p:84-95
    DOI: 10.1016/j.tra.2015.03.012
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    References listed on IDEAS

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    1. Chen, Xumei & Yu, Lei & Zhang, Yushi & Guo, Jifu, 2009. "Analyzing urban bus service reliability at the stop, route, and network levels," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(8), pages 722-734, October.
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    3. Philippe H. J. Marguier & Avishai Ceder, 1984. "Passenger Waiting Strategies for Overlapping Bus Routes," Transportation Science, INFORMS, vol. 18(3), pages 207-230, August.
    4. F McLeod, 2007. "Estimating bus passenger waiting times from incomplete bus arrivals data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(11), pages 1518-1525, November.
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    Cited by:

    1. Ulrik Berggren & Carmelo D’Agostino & Helena Svensson & Karin Brundell-Freij, 2022. "Intrapersonal variability in public transport path choice due to changes in service reliability," Transportation, Springer, vol. 49(6), pages 1517-1547, December.
    2. Benedetto Barabino & Nicola Aldo Cabras & Claudio Conversano & Alessandro Olivo, 2020. "An Integrated Approach to Select Key Quality Indicators in Transit Services," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 1045-1080, June.
    3. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    4. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    5. Ramli, Muhamad Azfar & Jayaraman, Vasundhara & Kwek, Hyen Chee & Tan, Kian Heong & Lee Kee Khoon, Gary & Monterola, Christopher, 2018. "Improved estimation of commuter waiting times using headway and commuter boarding information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 217-226.

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