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Optimal Time-Varying Pricing for Toll Roads Under Multiple Objectives: A Simulation-Based Optimization Approach

Author

Listed:
  • Xiang He

    (International Aviation Division, Institute of Air Transport, China Academy of Civil Aviation Science and Technology, Beijing 100028, China)

  • Xiqun (Michael) Chen

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Chenfeng Xiong

    (Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland 20742)

  • Zheng Zhu

    (Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland 20742)

  • Lei Zhang

    (Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland 20742)

Abstract

The determination of the pricing for tolled facilities always involves consideration of multiple objectives, e.g., efficiency, safety, pollution, reliability, and economy. Simulations are widely used to evaluate the performance of transportation systems as to the various objectives in response to different travel demand management policies. However, transportation simulation is usually associated with high computation costs and non-closed-form objective functions. This paper builds a simulation based optimization framework, and uses surrogate models to approximate the true simulation function. A significant amount of computation time can be saved with this method. To solve real world application problems, we develop infill strategies for multiobjective and constrained optimization problems. Using DynusT as the simulator, we optimize the toll rates for a five-segment toll road in Maryland, and successfully update the Pareto front based on initial samples for the multiobjective optimization problem. The method works even more efficiently for the constrained optimization problem. By adjusting the toll rates for the five segments, the network-wide average travel time can be reduced by 20% compared to the currently implemented toll scheme: A total of 22,250 hours can be saved in travel time for all network users in the three-hour morning peak period. Also, the toll revenue is increased by 50% compared to the baseline case.

Suggested Citation

  • Xiang He & Xiqun (Michael) Chen & Chenfeng Xiong & Zheng Zhu & Lei Zhang, 2017. "Optimal Time-Varying Pricing for Toll Roads Under Multiple Objectives: A Simulation-Based Optimization Approach," Transportation Science, INFORMS, vol. 51(2), pages 412-426, May.
  • Handle: RePEc:inm:ortrsc:v:51:y:2017:i:2:p:412-426
    DOI: 10.1287/trsc.2015.0661
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    References listed on IDEAS

    as
    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. Carolina Osorio & Kanchana Nanduri, 2015. "Energy-Efficient Urban Traffic Management: A Microscopic Simulation-Based Approach," Transportation Science, INFORMS, vol. 49(3), pages 637-651, August.
    3. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    4. Raghu Pasupathy, 2010. "On Choosing Parameters in Retrospective-Approximation Algorithms for Stochastic Root Finding and Simulation Optimization," Operations Research, INFORMS, vol. 58(4-part-1), pages 889-901, August.
    5. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    6. Yan, Hai & Lam, William H. K., 1996. "Optimal road tolls under conditions of queueing and congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 319-332, September.
    7. Chiu, Yi-Chang & Zhou, Liang & Song, Houbing, 2010. "Development and calibration of the Anisotropic Mesoscopic Simulation model for uninterrupted flow facilities," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 152-174, January.
    8. Yang, Hai & Bell, Michael G. H., 1997. "Traffic restraint, road pricing and network equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 31(4), pages 303-314, August.
    9. Carolina Osorio & Michel Bierlaire, 2013. "A Simulation-Based Optimization Framework for Urban Transportation Problems," Operations Research, INFORMS, vol. 61(6), pages 1333-1345, December.
    10. Carolina Osorio & Linsen Chong, 2015. "A Computationally Efficient Simulation-Based Optimization Algorithm for Large-Scale Urban Transportation Problems," Transportation Science, INFORMS, vol. 49(3), pages 623-636, August.
    11. Eglese, R. W., 1990. "Simulated annealing: A tool for operational research," European Journal of Operational Research, Elsevier, vol. 46(3), pages 271-281, June.
    12. Yang, Hai & Huang, Hai-Jun, 1998. "Principle of marginal-cost pricing: how does it work in a general road network?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(1), pages 45-54, January.
    13. Yang, Hai & Zhang, Xiaoning & Meng, Qiang, 2004. "Modeling private highways in networks with entry-exit based toll charges," Transportation Research Part B: Methodological, Elsevier, vol. 38(3), pages 191-213, March.
    14. Hai Yang, 1999. "System Optimum, Stochastic User Equilibrium, and Optimal Link Tolls," Transportation Science, INFORMS, vol. 33(4), pages 354-360, November.
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