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Effects of Connected Autonomous Vehicles on the Energy Performance of Signal-Controlled Junctions

Author

Listed:
  • Yiqing Wen

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

  • Yibing Wang

    (Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China)

  • Zhao Zhang

    (School of Transportation Science and Engineering, Beihang University, Beijing 100191, China)

  • Jiaxin Wu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

  • Liangxia Zhong

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

  • Markos Papageorgiou

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Dynamic Systems and Simulation Laboratory, Technical University of Crete, 73100 Chania, Greece)

  • Pengjun Zheng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

Abstract

This study proposes an optimal control method for connected autonomous vehicles (CAVs) through signalized intersections to reduce the energy consumption of mixed human-driven vehicles (HDVs) and CAV traffic. A real-time optimal control model was developed to optimize the trajectory of each CAV by minimizing energy consumption during the control period while ensuring traffic efficiency and safety. The control conditions of the CAVs were analyzed under different driving scenarios considering the impact of signal phase timing and preceding vehicles. Additionally, a method is proposed for CAVs to guide other vehicles directly and reduce the energy consumption of the entire signalized intersection. Simulation experiments using MATLAB and SUMO were conducted to evaluate the performance of the proposed method under various traffic conditions, such as different levels of saturation, market penetration rates (MPRs), and the green ratio. The performance was measured using average energy consumption and an average time delay. The results show that the proposed method can effectively reduce vehicle energy consumption without compromising traffic efficiency under various conditions. Moreover, under traffic saturation, the proposed method performs better at a high MPR and green ratio, especially at 40–60% MPR.

Suggested Citation

  • Yiqing Wen & Yibing Wang & Zhao Zhang & Jiaxin Wu & Liangxia Zhong & Markos Papageorgiou & Pengjun Zheng, 2023. "Effects of Connected Autonomous Vehicles on the Energy Performance of Signal-Controlled Junctions," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5672-:d:1105835
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    References listed on IDEAS

    as
    1. Zhao, Jing & Knoop, Victor L. & Wang, Meng, 2020. "Two-dimensional vehicular movement modelling at intersections based on optimal control," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 1-22.
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    Cited by:

    1. Tao Li & Baoli Gong & Yong Peng & Jin Nie & Zheng Wang & Yiqi Chen & Guoquan Xie & Kui Wang & Honghao Zhang, 2023. "Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data," Energies, MDPI, vol. 16(17), pages 1-24, August.

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