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A Stochastic Model for the Temporal Distribution of Traffic Demand---The Peak Hour Problem

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  • Attahiru Sule Alfa

    (University of New South Wales, Australia)

  • Do Le Minh

    (New South Wales Institute of Technology, Australia)

Abstract

One of the suggested techniques for reducing peak period traffic congestion is to flex work hours. If one is to predict the effect this work hour change shall have on the congestion, it is essential to understand how the peak traffic demand develops. However, there seem to be no existing theories explaining this process. In this paper, therefore, a mechanism by which commuters select their departure time from home is suggested and, based on this, a method is proposed for predicting the temporal distribution of traffic demand during the peak period. This should lead to the understanding of the effects of flexed work hours on the congestion. The model developed in this paper is based on the assumption that the commuters wish to arrive at work at a particular time without being delayed for too long in the system. A commuter therefore attaches cost to delays and also to early and late arrivals at work. This model proposes an iterative procedure by which to obtain the distribution of a commuter’s departure time from home, assuming that he tries to minimize the total cost.

Suggested Citation

  • Attahiru Sule Alfa & Do Le Minh, 1979. "A Stochastic Model for the Temporal Distribution of Traffic Demand---The Peak Hour Problem," Transportation Science, INFORMS, vol. 13(4), pages 315-324, November.
    • Handle: RePEc:inm:ortrsc:v:13:y:1979:i:4:p:315-324
    DOI: 10.1287/trsc.13.4.315
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    Cited by:

    1. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1999. "Information and time-of-usage decisions in the bottleneck model with stochastic capacity and demand," European Economic Review, Elsevier, vol. 43(3), pages 525-548, March.
    2. Watling, David, 1996. "Asymmetric problems and stochastic process models of traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 30(5), pages 339-357, October.
    3. Watling, David, 1999. "Stability of the stochastic equilibrium assignment problem: a dynamical systems approach," Transportation Research Part B: Methodological, Elsevier, vol. 33(4), pages 281-312, May.
    4. Liu, Ronghui & Van Vliet, Dirck & Watling, David, 2006. "Microsimulation models incorporating both demand and supply dynamics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(2), pages 125-150, February.
    5. Richard Arnott, 1986. "Information and Time-Of-Use Decisions in Stochastically Congestable Facilities," Discussion Papers 788, Northwestern University, Center for Mathematical Studies in Economics and Management Science.

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