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High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks

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  • Osorio, Carolina

Abstract

This paper considers high-dimensional offline calibration problems for large-scale simulation-based network models. We propose a metamodel simulation-based optimization (SO) approach. The proposed method is formulated and validated on a simple synthetic toy network. It is then applied to a high-dimensional case study of a large-scale Singapore network. Compared to two benchmark methods, a derivative-free pattern search method and the SPSA method, the proposed method improves the objective function estimates by two orders of magnitude. Moreover, this improvement is achieved after only 2 simulation runs. Hence, the proposed method is computationally efficient.

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  • 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.
    • Handle: RePEc:eee:transb:v:124:y:2019:i:c:p:18-43
    DOI: 10.1016/j.trb.2019.01.005
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    References listed on IDEAS

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    1. Moshe Ben-Akiva & Haris N. Koutsopoulos & Constantinos Antoniou & Ramachandran Balakrishna, 2010. "Traffic Simulation with DynaMIT," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 363-398, Springer.
    2. Carolina Osorio & Krishna Kumar Selvam, 2017. "Simulation-Based Optimization: Achieving Computational Efficiency Through the Use of Multiple Simulators," Transportation Science, INFORMS, vol. 51(2), pages 395-411, May.
    3. 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.
    4. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    5. Bruce Ankenman & Barry L. Nelson & Jeremy Staum, 2010. "Stochastic Kriging for Simulation Metamodeling," Operations Research, INFORMS, vol. 58(2), pages 371-382, April.
    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. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Other publications TiSEM e49ba0fc-853c-4a13-b564-d, Tilburg University, School of Economics and Management.
    8. Carolina Osorio & Michel Bierlaire, 2013. "A Simulation-Based Optimization Framework for Urban Transportation Problems," Operations Research, INFORMS, vol. 61(6), pages 1333-1345, December.
    9. 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.
    10. 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.
    11. 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.
    12. Yang, Yudi & Fan, Yueyue & Wets, Roger J.B., 2018. "Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 192-211.
    13. 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.
    14. Hazelton, Martin L., 2008. "Statistical inference for time varying origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 542-552, July.
    15. Kleijnen, Jack P.C. & Beers, Wim van & Nieuwenhuyse, Inneke van, 2010. "Constrained optimization in expensive simulation: Novel approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 164-174, April.
    16. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.
    17. Yang, Yudi & Fan, Yueyue, 2015. "Data dependent input control for origin–destination demand estimation using observability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 385-403.
    18. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
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    3. Xu, Guangming & Zhong, Linhuan & Hu, Xinlei & Liu, Wei, 2022. "Optimal pricing and seat allocation schemes in passenger railway systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).

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