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Practical modelling of trip re-scheduling under congested conditions

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  • Bates, John

Abstract

There is plenty of evidence that drivers may make small changes in their time of travel to take advantage of lower levels of congestion. However, progress in the practical modelling of such "micro" re-scheduling within peak period traffic remains slow. While there exist research papers describing theoretical solutions, techniques for practical use are not generally available. Most commonly used assignment programs are temporally aggregate, while packages which do allow some "dynamic assignment" typically assume a fixed demand profile. The aim of the paper is to present a more heuristic method which could at least be used on an interim basis. The assumption is that the demand profile can be segmented into a number of mutually exclusive "windows" in relation to the "preferred arrival time", while on the assignment side, independently defined sequential "timeslices" are used in order to respect some of the dynamic processes relating to the build-up of queues. The demand process, whereby some drivers shift away from their preferred window, leads to an iterative procedure with the aim of achieving reasonable convergence. Using the well-known scheduling theory developed by Vickrey, Small, and Arnott, de Palma & Lindsey, the basic approach can be described, extending from the simple "bottleneck", to which the theory was originally applied, to a general network. So far, insufficient research funds have been made available to test the approach properly. It is hoped that by bringing the ideas into the public domain, further research into this area may be stimulated.

Suggested Citation

  • Bates, John, 2007. "Practical modelling of trip re-scheduling under congested conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 788-801, November.
    • Handle: RePEc:eee:transa:v:41:y:2007:i:9:p:788-801
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    1. Han, S. & Heydecker, B.G., 2006. "Consistent objectives and solution of dynamic user equilibrium models," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 16-34, January.
    2. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-260, May.
    3. Small, Kenneth A, 1992. "Trip Scheduling in Urban Transportation Analysis," American Economic Review, American Economic Association, vol. 82(2), pages 482-486, May.
    4. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-479, June.
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    1. Xie, Litian & Olszewski, Piotr, 2011. "Modelling the effects of road pricing on traffic using ERP traffic data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 512-522, July.

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