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Enhancing Mixed Traffic Stability with TD3-Driven Bilateral Control in Autonomous Vehicle Chains

Author

Listed:
  • Kan Liu

    (School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Pengpeng Jiao

    (School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Weiqi Hong

    (School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Yue Chen

    (School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

Abstract

This study presents a TD3-driven Bilateral Control Model (TD3-BCM) aimed at improving the stability of mixed traffic flows in autonomous vehicle (AV) chains. By integrating deep reinforcement learning, TD3-BCM optimizes control strategies to reduce traffic oscillations, smooth speed and acceleration fluctuations, and enhance overall system performance. Stability analysis shows that TD3-BCM effectively suppresses traffic fluctuations, with system stability improving from 1.132 to 1.182 as AV penetration increases. At an AV penetration rate of 40%, TD3-BCM surpasses both Cooperative Adaptive Cruise Control (CACC) and traditional Bilateral Control Model (BCM) approaches in terms of traffic efficiency, safety, and energy use—raising trailing vehicle speed by 12.6%, shortening average headway by 19.0%, increasing Time-to-Collision (TTC) by 87.3%, and lowering fuel consumption by 14.8%. When AV penetration reaches 70%, fuel savings rise to 19.7%, accompanied by further improvements in both traffic stability and safety. TD3-BCM provides a scalable and sustainable solution for intelligent transportation systems, particularly in high-penetration AV environments, by significantly enhancing stability, operational efficiency, and road safety.

Suggested Citation

  • Kan Liu & Pengpeng Jiao & Weiqi Hong & Yue Chen, 2025. "Enhancing Mixed Traffic Stability with TD3-Driven Bilateral Control in Autonomous Vehicle Chains," Sustainability, MDPI, vol. 17(11), pages 1-30, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4790-:d:1662450
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    References listed on IDEAS

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    5. 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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