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A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities

Author

Listed:
  • Gu, Xiaoning
  • Chen, Chao
  • Feng, Tao
  • Yao, Baozhen

Abstract

Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions.

Suggested Citation

  • Gu, Xiaoning & Chen, Chao & Feng, Tao & Yao, Baozhen, 2024. "A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    • Handle: RePEc:eee:phsmap:v:639:y:2024:i:c:s0378437124001754
    DOI: 10.1016/j.physa.2024.129666
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    References listed on IDEAS

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    1. Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 193-211.
    2. 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.
    3. André Duarte & Camila Garcia & Grigoris Giannarakis & Susana Limão & Amalia Polydoropoulou & Nikolaos Litinas, 2010. "New approaches in transportation planning: happiness and transport economics," Netnomics, Springer, vol. 11(1), pages 5-32, April.
    4. Poulopoulou, Maria & Spyropoulou, Ioanna, 2019. "Active traffic management in urban areas: Is it effective for professional drivers? The case of variable message signs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 412-423.
    5. Wang, Jian & Peeta, Srinivas & He, Xiaozheng, 2019. "Multiclass traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 139-168.
    6. Tang, T.Q. & Shi, W.F. & Yang, X.B. & Wang, Y.P. & Lu, G.Q., 2013. "A macro traffic flow model accounting for road capacity and reliability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6300-6306.
    7. Guo, Yajuan & Yang, Licai & Hao, Shenxue & Gao, Jun, 2019. "Dynamic identification of urban traffic congestion warning communities in heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 98-111.
    8. Larsson, Torbjörn & Patriksson, Michael, 1995. "An augmented lagrangean dual algorithm for link capacity side constrained traffic assignment problems," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 433-455, December.
    9. Bahrami, Sina & Roorda, Matthew J., 2020. "Optimal traffic management policies for mixed human and automated traffic flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 130-143.
    10. Nie, Yu & Zhang, H. M. & Lee, Der-Horng, 2004. "Models and algorithms for the traffic assignment problem with link capacity constraints," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 285-312, May.
    11. Ji, Yuxuan & Geroliminis, Nikolas, 2012. "On the spatial partitioning of urban transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1639-1656.
    12. Jacob, Celine & Abdulhai, Baher, 2010. "Machine learning for multi-jurisdictional optimal traffic corridor control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(2), pages 53-64, February.
    13. Chunguang Liu & Xinyu Zuo & Xiaoning Gu & Mengru Shao & Chao Chen, 2023. "Activity Duration under the COVID-19 Pandemic: A Comparative Analysis among Different Urbanized Areas Using a Hazard-Based Duration Model," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    14. Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2016. "Clustering of heterogeneous networks with directional flows based on “Snake” similarities," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 250-269.
    15. Wang, Jian & Gong, Siyuan & Peeta, Srinivas & Lu, Lili, 2019. "A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 271-301.
    16. Zhu, Jing & Fan, Yingling, 2018. "Daily travel behavior and emotional well-being: Effects of trip mode, duration, purpose, and companionship," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 360-373.
    17. Leclercq, Ludovic & Ladino, Andres & Becarie, Cécile, 2021. "Enforcing optimal routing through dynamic avoidance maps," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 118-137.
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