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Optimising traffic signal settings for periods of time-varying demand

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  • Han, Bin

Abstract

When traffic demands are different in successive time periods, the signal settings that are optimal for each individual period are only local solutions to the problem. These settings may be readjusted and the changes between them shifted in time so that the overall performance for those periods taken together is improved. A sequential optimisation technique has been developed to minimise the total junction delay over the successive periods taken together by searching for the optimal signal timings and the time-shifts subject to certain queue length considerations, and each traffic stream in the junction can be undersaturated or oversaturated during a time period. Some example calculations are given and the results show that such a technique can provide modest improvements in the junction control performance and give less delay than the existing methods that optimise only for individual periods.

Suggested Citation

  • Han, Bin, 1996. "Optimising traffic signal settings for periods of time-varying demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(3), pages 207-230, May.
    • Handle: RePEc:eee:transa:v:30:y:1996:i:3:p:207-230
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    References listed on IDEAS

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    1. Han, Bin, 1996. "A new comprehensive sheared delay formula for traffic signal optimisation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(2), pages 155-171, March.
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    Cited by:

    1. D’Acierno, Luca & Gallo, Mariano & Montella, Bruno, 2012. "An Ant Colony Optimisation algorithm for solving the asymmetric traffic assignment problem," European Journal of Operational Research, Elsevier, vol. 217(2), pages 459-469.
    2. J. Lei & Ü. Özgüner, 2002. "Optimal Decentralized Regulation for a Class of Flow Networks," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 659-684, December.
    3. Ennio Cascetta & Mariano Gallo & Bruno Montella, 2006. "Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment models," Annals of Operations Research, Springer, vol. 144(1), pages 301-328, April.

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