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Enhancing Model-Based Anticipatory Traffic Signal Control with Metamodeling and Adaptive Optimization

Author

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  • Wei Huang

    (School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518063, China
    Guangdong Key Laboratory of Intelligent Transportation Systems, Guangzhou 510275, China)

  • Yang Hu

    (School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518063, China
    Guangdong Key Laboratory of Intelligent Transportation Systems, Guangzhou 510275, China)

  • Xuanyu Zhang

    (School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518063, China
    Guangdong Key Laboratory of Intelligent Transportation Systems, Guangzhou 510275, China)

Abstract

Traffic signal control is one effective way to alleviate traffic congestion. Anticipatory traffic signal control determines signal settings from a network planning perspective, which takes into account the influence of travelers’ route choice response and triggers better equilibrium flow patterns for better network performance. For the route choice response, it is usually predicted by a response function known as traffic assignment model. However, the response behavior can never be precisely modeled, leading to a mismatch between the modeled and real traffic flow patterns. This model-reality mismatch generally contributes to suboptimal control performance and hence brings unexpected congestion in real-life traffic operations. This study aims to address the model-reality mismatch and proposes an effective anticipatory traffic control for real operations. A metamodel is introduced that serves as a surrogate of the unknown structural model bias. Then an iterative optimizing control scheme is applied to correct the model bias by learning from observations. By integrating the model-based control design with data-driven learning techniques, the metamodeling framework is able to enhance the control performance. Moreover, the analytical model bias formulation allows theoretical investigation of the model approximation error. To further improve the control performance, a joint traffic model parameter estimation is developed, hence achieving a better model calibration jointly with the model bias correction. The proposed control method is examined on a test network. Numerical examples confirm the effectiveness of the proposed method in improving control performance despite the model-reality mismatch. Comparison results show that the proposed method outperforms the traditional model-based control method and an improvement of 14.8% in total travel time is achieved in the example network.

Suggested Citation

  • Wei Huang & Yang Hu & Xuanyu Zhang, 2022. "Enhancing Model-Based Anticipatory Traffic Signal Control with Metamodeling and Adaptive Optimization," Mathematics, MDPI, vol. 10(15), pages 1-18, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2640-:d:873627
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    References listed on IDEAS

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    1. Ennio Cascetta, 2009. "Transportation Systems Analysis," Springer Optimization and Its Applications, Springer, number 978-0-387-75857-2, December.
    2. Yang, Hai & Meng, Qiang & Lee, Der-Horng, 2004. "Trial-and-error implementation of marginal-cost pricing on networks in the absence of demand functions," Transportation Research Part B: Methodological, Elsevier, vol. 38(6), pages 477-493, July.
    3. Tao Zhang & Yang Yang & Gang Cheng & Minjie Jin, 2020. "A Practical Traffic Assignment Model for Multimodal Transport System Considering Low-Mobility Groups," Mathematics, MDPI, vol. 8(3), pages 1-19, March.
    4. Zhu, Shanjiang & Levinson, David & Liu, Henry X. & Harder, Kathleen, 2010. "The traffic and behavioral effects of the I-35W Mississippi River bridge collapse," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 771-784, December.
    5. Zhou, Bojian & Bliemer, Michiel & Yang, Hai & He, Jie, 2015. "A trial-and-error congestion pricing scheme for networks with elastic demand and link capacity constraints," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 77-92.
    6. Kouvelas, Anastasios & Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Enhancing model-based feedback perimeter control with data-driven online adaptive optimization," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 26-45.
    7. Ennio Cascetta & Mariano Gallo & Bruno Montella, 2006. "Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment models," Annals of Operations Research, Springer, vol. 144(1), pages 301-328, April.
    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. Wang, Yibing & Papageorgiou, Markos & Messmer, Albert, 2008. "Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1340-1358, December.
    10. Meng, Q. & Yang, H. & Bell, M. G. H., 2001. "An equivalent continuously differentiable model and a locally convergent algorithm for the continuous network design problem," Transportation Research Part B: Methodological, Elsevier, vol. 35(1), pages 83-105, January.
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    Cited by:

    1. Krasimira Stoilova & Todor Stoilov, 2023. "Optimizing Traffic Light Green Duration under Stochastic Considerations," Mathematics, MDPI, vol. 11(3), pages 1-25, January.

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