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Simple Decentralized Feedback Strategies for Route Guidance in Traffic Networks

Author

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  • Yannis Pavlis

    (Dynamic Systems and Simulation Laboratory, Technical University of Crete, 73100 Chania, Greece)

  • Markos Papageorgiou

    (Dynamic Systems and Simulation Laboratory, Technical University of Crete, 73100 Chania, Greece)

Abstract

The paper presents a feedback route guidance strategy for complex, meshed traffic networks. Essential components of the strategy are simple, decentralized control laws of the bang-bang, P, or PI types that may be designed based on trial-and-error. Simulation investigations demonstrate the efficiency of the proposed strategy for two example networks under several scenarios of demand and incident conditions. Feedback route guidance, though exclusively based on measurable instantaneous travel times (no predictions, no demand nor origin–destination information are provided), is shown to equalize experienced travel times along any couple of used alternative routes in the network, and to considerably reduce travel delays compared to the no-control case.

Suggested Citation

  • Yannis Pavlis & Markos Papageorgiou, 1999. "Simple Decentralized Feedback Strategies for Route Guidance in Traffic Networks," Transportation Science, INFORMS, vol. 33(3), pages 264-278, August.
    • Handle: RePEc:inm:ortrsc:v:33:y:1999:i:3:p:264-278
    DOI: 10.1287/trsc.33.3.264
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    Citations

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    Cited by:

    1. Mingzhou Jin & Burak Ekşioğlu, 2010. "Optimal routing of vehicles with communication capabilities in disasters," Computational Management Science, Springer, vol. 7(2), pages 121-137, April.
    2. Peeta, Srinivas & Zhou, Chao, 2006. "Stochastic quasi-gradient algorithm for the off-line stochastic dynamic traffic assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 40(3), pages 179-206, March.
    3. Pi, Xidong & Qian, Zhen (Sean), 2017. "A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 710-732.
    4. Paz, Alexander & Peeta, Srinivas, 2009. "Information-based network control strategies consistent with estimated driver behavior," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 73-96, January.
    5. Dinopoulou, Vaya & Diakaki, Christina & Papageorgiou, Markos, 2006. "Applications of the urban traffic control strategy TUC," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1652-1665, December.
    6. Spiliopoulou, A. & Kontorinaki, M. & Papamichail, I. & Papageorgiou, M., 2018. "Real-time route diversion control at congested freeway off-ramp areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 90-105.
    7. Du, Lili & Han, Lanshan & Chen, Shuwei, 2015. "Coordinated online in-vehicle routing balancing user optimality and system optimality through information perturbation," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 121-133.

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