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Schedule-Based and Autoregressive Bus Running Time Modeling in the Presence of Driver-Bus Heterogeneity

In: Computer-aided Systems in Public Transport

Author

Listed:
  • Rabi G. Mishalani

    (The Ohio State University)

  • Mark R. McCord

    (The Ohio State University)

  • Stacey Forman

    (TranSystems)

Abstract

Bus route running time represents a key element of transit performance. An understanding of running time behavior and the factors that influence it is essential for off-line planning and operations design purposes including fleet size planning, schedule design, and passenger travel time performance assessment. Such an understanding is also critical for realtime applications including bus operations control and passenger information systems. This paper focuses on developing models of running time and estimating them using field data. Two model structures are considered. The schedule-based model specifies the upcoming running time as a function of the most recent deviation from the schedule the bus has exhibited at the terminus. This model characterizes the situation where a late running bus attempts to catch up with the schedule and, hence, reflects an upcoming running time shorter than the target running time, and vice versa. The autoregressive model specifies the upcoming running time as a function of the most recent running time. This model characterizes one of two situations depending on the sign of the parameter estimate. On the one hand, when the most recent running time is longer than the mean, the upcoming running time would also be longer than the mean if the operation is dominated by exogenous factors that cause delays such as other traffic or weather. On the other hand, the upcoming running time would be shorter than the mean if the driver is capable of speeding up to reduce the delay in the operation. Irrespective of the model structure, the characteristics of the driver-bus pair may also influence the extent to which the upcoming running time will deviate from the target or the mean. To capture this potential heterogeneous phenomenon, the fixed effects formulation is adopted whereby driverbus pair dummy variables are included in the model. Field data are utilized in estimating the two types of models in the presence of driver-bus heterogeneity. In general, the schedule-based model is superior to the autoregressive model in describing running time behavior. Moreover, driver-bus heterogeneity is found to be a significant contributor to this behavior.

Suggested Citation

  • Rabi G. Mishalani & Mark R. McCord & Stacey Forman, 2008. "Schedule-Based and Autoregressive Bus Running Time Modeling in the Presence of Driver-Bus Heterogeneity," Lecture Notes in Economics and Mathematical Systems, in: Mark Hickman & Pitu Mirchandani & Stefan Voß (ed.), Computer-aided Systems in Public Transport, pages 301-317, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-73312-6_15
    DOI: 10.1007/978-3-540-73312-6_15
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    Citations

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    Cited by:

    1. Hadas, Yuval & Shnaiderman, Matan, 2012. "Public-transit frequency setting using minimum-cost approach with stochastic demand and travel time," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 1068-1084.
    2. 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).
    3. 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).
    4. 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.
    5. Weiya Chen & Xin Liu & Dingfang Chen & Xin Pan, 2019. "Setting Headways on a Bus Route under Uncertain Conditions," Sustainability, MDPI, vol. 11(10), pages 1-13, May.

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