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Multiclass dynamic system optimum solution for mixed traffic of human-driven and automated vehicles considering physical queues

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  • Ngoduy, Dong
  • Hoang, N.H.
  • Vu, H.L.
  • Watling, D.

Abstract

Dynamic traffic assignment (DTA) is an important method in the long term transportation planning and management processes. However, in most existing system optimum dynamic traffic assignment (SO-DTA), no side constraints are used to describe the dynamic link capacities in a network which is shared by multiple vehicle types. Our motivation is based on the possibility for dynamic system optimum (DSO) to have multiple solutions, which differ in where queues are formed and dissipated in the network. To this end, this paper proposes a novel DSO formulation for the multi-class DTA problem containing both human driven and automated vehicles in single origin-destination networks. The proposed method uses the concept of link based approach to develop a multi-class DTA model that equally distributes the total physical queues over the links while considering explicitly the variations in capacity and backward wave speeds due to class proportions. In the model, the DSO is formulated as an optimization problem considering linear vehicle composition constraints representing the dynamics of the link capacities. Numerical examples are set up to provide some insights into the effects of automated vehicles on the queue distribution as well as the total system travel times.

Suggested Citation

  • Ngoduy, Dong & Hoang, N.H. & Vu, H.L. & Watling, D., 2021. "Multiclass dynamic system optimum solution for mixed traffic of human-driven and automated vehicles considering physical queues," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 56-79.
    • Handle: RePEc:eee:transb:v:145:y:2021:i:c:p:56-79
    DOI: 10.1016/j.trb.2020.12.008
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    Cited by:

    1. Chapala, Sai Bharath Kumar & Nair, Preetha & Sreekumar, M. & Bhavathrathan, B.K., 2024. "A dynamic traffic assignment framework for policy analysis in cities with significant share of two-wheelers," Transport Policy, Elsevier, vol. 147(C), pages 125-139.
    2. Yao, Zhihong & Li, Le & Liao, Wenbin & Wang, Yi & Wu, Yunxia, 2024. "Optimal lane management policy for connected automated vehicles in mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    3. Zhang, Fang & Lu, Jian & Hu, Xiaojian & Meng, Qiang, 2023. "A stochastic dynamic network loading model for mixed traffic with autonomous and human-driven vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    4. Ngoduy, Dong & Nguyen, Cuong H.P. & Lee, Seunghyeon & Zheng, Zuduo & Lo, Hong K., 2024. "A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    5. Wang, Zihao & Xing, Chen & ZHU, WENXING & Ma, Xiaolong, 2024. "Modeling dedicated lanes for connected autonomous vehicles with poly-information uncertainties and electronic throttle dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    6. Wang, Guanfeng & Jia, Hongfei & Feng, Tao & Tian, Jingjing & Wu, Ruiyi & Gao, Heyao & Liu, Chao, 2024. "Modelling the dual dynamic traffic flow evolution with information perception differences between human-driven vehicles and connected autonomous vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    7. Sun, Mingmei, 2023. "A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).

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