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A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

Author

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  • Linsen Chong

    (Civil and Environmental Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Carolina Osorio

    (Civil and Environmental Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

This paper addresses large-scale urban transportation optimization problems with time-dependent continuous decision variables, a stochastic simulation-based objective function, and general analytical differentiable constraints. We propose a metamodel approach to address, in a computationally efficient way, these large-scale dynamic simulation-based optimization problems. We formulate an analytical dynamic network model that is used as part of the metamodel. The network model formulation combines ideas from transient queueing theory and traffic flow theory. The model is formulated as a system of equations. The model complexity is linear in the number of road links and is independent of the link space capacities. This makes it a scalable model suitable for the analysis of large-scale problems. The proposed dynamic metamodel approach is used to address a time-dependent large-scale traffic signal control problem for the city of Lausanne. Its performance is compared to that of a stationary metamodel approach. The proposed approach outperforms the stationary approach. This comparison illustrates the added value of providing the algorithm with analytical dynamic problem-specific structural information. The performance of a signal plan derived by the proposed approach is also compared to that of an existing signal plan for the city of Lausanne, and to that of a signal plan derived by a mainstream commercial signal control software. The proposed method can systematically identify signal plans with good performance.

Suggested Citation

  • Linsen Chong & Carolina Osorio, 2018. "A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems," Transportation Science, INFORMS, vol. 52(3), pages 637-656, June.
    • Handle: RePEc:inm:ortrsc:v:52:y:2018:i:3:p:637-656
    DOI: 10.1287/trsc.2016.0717
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    References listed on IDEAS

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    Cited by:

    1. Tay, Timothy & Osorio, Carolina, 2022. "Bayesian optimization techniques for high-dimensional simulation-based transportation problems," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 210-243.
    2. Hyoshin (John) Park & Ali Haghani & Song Gao & Michael A. Knodler & Siby Samuel, 2018. "Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies," Service Science, INFORMS, vol. 52(6), pages 1299-1326, December.
    3. Tian, Qingyun & Wang, David Z.W. & Lin, Yun Hui, 2021. "Service operation design in a transit network with congested common lines," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 81-102.
    4. David Schmaranzer & Roland Braune & Karl F. Doerner, 2020. "Population-based simulation optimization for urban mass rapid transit networks," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 767-805, December.
    5. Liu, Renming & Jiang, Yu & Seshadri, Ravi & Ben-Akiva, Moshe & Azevedo, Carlos Lima, 2024. "Contextual Bayesian optimization of congestion pricing with day-to-day dynamics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    6. Mo, Baichuan & Koutsopoulos, Haris N. & Shen, Zuo-Jun Max & Zhao, Jinhua, 2023. "Robust path recommendations during public transit disruptions under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 82-107.
    7. David Schmaranzer & Roland Braune & Karl F. Doerner, 2021. "Multi-objective simulation optimization for complex urban mass rapid transit systems," Annals of Operations Research, Springer, vol. 305(1), pages 449-486, October.
    8. Xiao Chen & Carolina Osorio & Bruno Filipe Santos, 2019. "Simulation-Based Travel Time Reliable Signal Control," Transportation Science, INFORMS, vol. 53(2), pages 523-544, March.
    9. Fu, Quanlu & Wu, Jiyan & Wu, Xuemian & Sun, Jian & Tian, Ye, 2024. "Managing network congestion with link-based incentives: A surrogate-based optimization approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    10. Yong Lin, 2023. "Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    11. Osorio, Carolina & Punzo, Vincenzo, 2019. "Efficient calibration of microscopic car-following models for large-scale stochastic network simulators," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 156-173.
    12. Osorio, Carolina, 2019. "High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks," Transportation Research Part B: Methodological, Elsevier, vol. 124(C), pages 18-43.

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