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Efficient calibration of microscopic car-following models for large-scale stochastic network simulators

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  • Osorio, Carolina
  • Punzo, Vincenzo

Abstract

This paper proposes a simulation-based optimization methodology for the efficient calibration of microscopic traffic flow models (i.e., car-following models) of large-scale stochastic network simulators. The approach is a metamodel simulation-based optimization (SO) method. To improve computational efficiency of the SO algorithm, problem-specific and simulator-specific structural information is embedded into a metamodel. As a closed-form expression is sought, we propose adopting the steady-state solution of the car-following model as an approximation of its simulation-based input-output mapping. This general approach is applied for the calibration of the Gipps car-following model embedded in a microscopic traffic network simulator, on a large network. To this end, a novel formulation for the traffic stream models corresponding to the Gipps car-following law is provided.

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  • 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.
    • Handle: RePEc:eee:transb:v:119:y:2019:i:c:p:156-173
    DOI: 10.1016/j.trb.2018.09.005
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    References listed on IDEAS

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    1. Cascetta, Ennio & Nguyen, Sang, 1988. "A unified framework for estimating or updating origin/destination matrices from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 437-455, December.
    2. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
    3. Carolina Osorio & Michel Bierlaire, 2013. "A Simulation-Based Optimization Framework for Urban Transportation Problems," Operations Research, INFORMS, vol. 61(6), pages 1333-1345, December.
    4. Zhang, Chao & Osorio, Carolina & Flötteröd, Gunnar, 2017. "Efficient calibration techniques for large-scale traffic simulators," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 214-239.
    5. Ennio Cascetta & Domenico Inaudi & Gérald Marquis, 1993. "Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts," Transportation Science, INFORMS, vol. 27(4), pages 363-373, November.
    6. Osorio, Carolina & Nanduri, Kanchana, 2015. "Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 520-538.
    7. 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.
    8. Carolina Osorio & Jana Yamani, 2017. "Analytical and Scalable Analysis of Transient Tandem Markovian Finite Capacity Queueing Networks," Transportation Science, INFORMS, vol. 51(3), pages 823-840, August.
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    Cited by:

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    2. Vittorio Astarita & Ciro Caliendo & Vincenzo Pasquale Giofrè & Isidoro Russo, 2020. "Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    3. Kumarage, Sakitha & Yildirimoglu, Mehmet & Zheng, Zuduo, 2023. "A hybrid modelling framework for the estimation of dynamic origin–destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    4. Borja Alonso & Vittorio Astarita & Luigi Dell’Olio & Vincenzo Pasquale Giofrè & Giuseppe Guido & Marcella Marino & William Sommario & Alessandro Vitale, 2020. "Validation of Simulated Safety Indicators with Traffic Crash Data," Sustainability, MDPI, vol. 12(3), pages 1-22, January.

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