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A multi-scale control framework for urban traffic control with connected and automated vehicles

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  • Guo, Qiangqiang
  • Ban, Xuegang (Jeff)

Abstract

Urban traffic control is multi-scale in both temporal and spatial domains, which is of great importance to improve the quality and efficiency of our transportation system. In this paper, we propose a general multi-scale modeling and control framework for urban traffic control with connected and automated vehicles (CAVs). We apply the proposed multi-scale framework to explicitly model and solve the two-scale signal-vehicle coupled control (SVCC) problem under the environment of full penetration of CAVs. To solve the SVCC problem, we propose a model predictive control (MPC) scheme and develop a stability analysis method based on the concept of consistency of key states between the two scales. We show numerical results of the SVCC model and comparisons with some benchmark methods. We also discuss how to extend the SVCC model to the mixed traffic flow and tri-scale/multi-scale urban traffic control problems.

Suggested Citation

  • Guo, Qiangqiang & Ban, Xuegang (Jeff), 2023. "A multi-scale control framework for urban traffic control with connected and automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:transb:v:175:y:2023:i:c:s0191261523001121
    DOI: 10.1016/j.trb.2023.102787
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    References listed on IDEAS

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    1. Guo, Qiangqiang & Ban, Xuegang (Jeff), 2020. "Macroscopic fundamental diagram based perimeter control considering dynamic user equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 87-109.
    2. Siriorn Pitanuwat & Hirofumi Aoki & Satoru Iizuka & Takayuki Morikawa, 2020. "Development of Hybrid-Vehicle Energy-Consumption Model for Transportation Applications—Part I: Driving-Power Equation Development and Coefficient Calibration," Energies, MDPI, vol. 13(2), pages 1-20, January.
    3. Ramezani, Mohsen & Haddad, Jack & Geroliminis, Nikolas, 2015. "Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 1-19.
    4. 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.
    5. Zhou, Xuesong & Mahmassani, Hani S., 2007. "A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework," Transportation Research Part B: Methodological, Elsevier, vol. 41(8), pages 823-840, October.
    6. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    7. Stephen M. Robinson, 1980. "Strongly Regular Generalized Equations," Mathematics of Operations Research, INFORMS, vol. 5(1), pages 43-62, February.
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    Cited by:

    1. Cui, Shaohua & Xue, Yongjie & Gao, Kun & Wang, Kai & Yu, Bin & Qu, Xiaobo, 2024. "Delay-throughput tradeoffs for signalized networks with finite queue capacity," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    2. Yuncheng Zeng & Minhua Shao & Lijun Sun, 2023. "Network-Level Hierarchical Bottleneck Congestion Control Method for a Mixed Traffic Network," Sustainability, MDPI, vol. 15(23), pages 1-27, November.
    3. Guo, Qiangqiang & Ban, Xuegang (Jeff), 2024. "Network multiscale urban traffic control with mixed traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 185(C).

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