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<p>This study investigates lane-changing strategy optimization for connected and autonomous vehicles (CAVs) in heterogeneous traffic systems to enhance traffic efficiency and orderliness, addressing challenges posed by heterogeneous traffic flows and variable CAV penetration rates. An enhanced Nagel-Schreckenberg (NaSch) model integrated adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) frameworks to simulate heterogeneous traffic. Relative entropy (Kullback–Leibler divergence) served as a novel metric for quantifying traffic orderliness. Two innovative lane-changing strategies—Conservative Aggregation Strategy (CSA) and Radical Aggregation Strategy (RDA)—were proposed, alongside a Lane Type Aggregation (LTA) approach for dedicated CAV lanes. CSA increased road capacity by 12.6%, while RDA achieved a 14.0% improvement under moderate CAV penetration (20–40%). However, excessive aggregation at high penetrations (60–80%) degraded efficiency, highlighting the importance of strategy calibration. Optimal CACC platoon size was determined as four vehicles for CSA, balancing stability and flow dynamics. LTA implementation reduced relative entropy and achieved a critical full-load density of 27 veh/km, optimizing lane utilization. Strategic lane aggregation and dedicated CAV lanes significantly enhance traffic order and efficiency, with penetration rate-specific recommendations: CSA/RDA for moderate adoption and LTA for higher CAV densities. Findings offer theoretical and practical guidance for deploying adaptive traffic management systems and CAV-exclusive infrastructure.</p>

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Exploration of Aggregation Strategies for Connected and Autonomous Vehicles with Entropy Reduction Consideration

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • Dehua Wu,
  • Tianxing Zhang,
  • Jingsong Liu

摘要

This study investigates lane-changing strategy optimization for connected and autonomous vehicles (CAVs) in heterogeneous traffic systems to enhance traffic efficiency and orderliness, addressing challenges posed by heterogeneous traffic flows and variable CAV penetration rates. An enhanced Nagel-Schreckenberg (NaSch) model integrated adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) frameworks to simulate heterogeneous traffic. Relative entropy (Kullback–Leibler divergence) served as a novel metric for quantifying traffic orderliness. Two innovative lane-changing strategies—Conservative Aggregation Strategy (CSA) and Radical Aggregation Strategy (RDA)—were proposed, alongside a Lane Type Aggregation (LTA) approach for dedicated CAV lanes. CSA increased road capacity by 12.6%, while RDA achieved a 14.0% improvement under moderate CAV penetration (20–40%). However, excessive aggregation at high penetrations (60–80%) degraded efficiency, highlighting the importance of strategy calibration. Optimal CACC platoon size was determined as four vehicles for CSA, balancing stability and flow dynamics. LTA implementation reduced relative entropy and achieved a critical full-load density of 27 veh/km, optimizing lane utilization. Strategic lane aggregation and dedicated CAV lanes significantly enhance traffic order and efficiency, with penetration rate-specific recommendations: CSA/RDA for moderate adoption and LTA for higher CAV densities. Findings offer theoretical and practical guidance for deploying adaptive traffic management systems and CAV-exclusive infrastructure.