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Regularity-driven bus operation: Principles, implementation and business models

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  • Cats, Oded

Abstract

Service reliability is a key determinant of public transport performance. In the context of high-frequency urban lines, irregular service results with long waiting times, bunched vehicles, long delays, uneven passenger loads, poor capacity utilization and higher operational costs. Field experiments were conducted in Stockholm, Sweden, in order to test the feasibility and implications of a regularity-driven operation scheme designed to mitigate bus bunching and facilitated by a real-time control strategy. This paper investigates alternative service indicators and business models that could best support the long-term implementation of operation geared towards better regularity performance. A paradigm shift towards regularity-based service evidently requires the consideration of a series of measures along the service chain as it involves a paradigm shift in production planning, operations, control center and performance monitoring.

Suggested Citation

  • Cats, Oded, 2014. "Regularity-driven bus operation: Principles, implementation and business models," Transport Policy, Elsevier, vol. 36(C), pages 223-230.
    • Handle: RePEc:eee:trapol:v:36:y:2014:i:c:p:223-230
    DOI: 10.1016/j.tranpol.2014.09.002
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    References listed on IDEAS

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    Cited by:

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    3. Zhenzhong Yin & Bin Zhang, 2023. "Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles," Future Internet, MDPI, vol. 15(7), pages 1-18, June.
    4. Martínez-Estupiñan, Yerly & Delgado, Felipe & Muñoz, Juan Carlos & Watkins, Kari E., 2023. "Improving the performance of headway control tools by using individual driving speed data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    5. Dai, Zhuang & Liu, Xiaoyue Cathy & Chen, Zhuo & Guo, Renyong & Ma, Xiaolei, 2019. "A predictive headway-based bus-holding strategy with dynamic control point selection: A cooperative game theory approach," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 29-51.
    6. Gkiotsalitis, K. & Cats, O., 2021. "At-stop control measures in public transport: Literature review and research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    7. Cats, Oded & West, Jens & Eliasson, Jonas, 2016. "A dynamic stochastic model for evaluating congestion and crowding effects in transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 43-57.
    8. Gkiotsalitis, K. & Schmidt, M.E. & van der Hurk, E., 2021. "Subline frequency setting for autonomous minibusses under demand uncertainty," ERIM Report Series Research in Management ERS-2021-008-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Fadaei, Masoud & Cats, Oded, 2016. "Evaluating the impacts and benefits of public transport design and operational measures," Transport Policy, Elsevier, vol. 48(C), pages 105-116.
    10. Godachevich, Javiera & Tirachini, Alejandro, 2021. "Does the measured performance of bus operators depend on the index chosen to assess reliability in contracts? An analysis of bus headway variability," Research in Transportation Economics, Elsevier, vol. 90(C).

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