Scholarlink

<p>Vehicle-to-vehicle (V2V) communication technology plays a pivotal role in intelligent transportation systems by enabling real-time information exchange to optimize traffic flow. This study introduces a novel car-following model that incorporates the “backward-looking effect" (BLE), backward-forward velocity feedback coefficient (BFV Coefficient), and optimal velocity deviation (OVD) feedback coefficient. Through linear and nonlinear stability analyses, supported by numerical simulations, the model’s impact on traffic flow stability is investigated. Key findings indicate that reducing BLE improves traffic stability by minimizing abrupt accelerations and decelerations, reducing congestion and accident risks. Increasing the OVD feedback Coefficient enhances stability by ensuring minimal velocity deviations, while the BFV coefficient stabilizes traffic by balancing backward and forward interactions. The simulation results confirm the theoretical analysis, showing that optimized feedback coefficients and reduced BLE lead to smoother and more stable traffic flow, mitigating stop-and-go waves. These insights emphasize the potential of V2V communication to improve traffic efficiency and road safety.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Influence of Backward-Forward Velocity and Backward-Looking Effect on Traffic Flow Dynamics with Optimal Velocity Deviation in V2V Communication

  • 期刊论文

  • 发表日期：

    2026年6月19日

查看全文
  • Darshana Yadav,
  • Vikash Siwach,
  • Sahil Kajal

摘要

Vehicle-to-vehicle (V2V) communication technology plays a pivotal role in intelligent transportation systems by enabling real-time information exchange to optimize traffic flow. This study introduces a novel car-following model that incorporates the “backward-looking effect" (BLE), backward-forward velocity feedback coefficient (BFV Coefficient), and optimal velocity deviation (OVD) feedback coefficient. Through linear and nonlinear stability analyses, supported by numerical simulations, the model’s impact on traffic flow stability is investigated. Key findings indicate that reducing BLE improves traffic stability by minimizing abrupt accelerations and decelerations, reducing congestion and accident risks. Increasing the OVD feedback Coefficient enhances stability by ensuring minimal velocity deviations, while the BFV coefficient stabilizes traffic by balancing backward and forward interactions. The simulation results confirm the theoretical analysis, showing that optimized feedback coefficients and reduced BLE lead to smoother and more stable traffic flow, mitigating stop-and-go waves. These insights emphasize the potential of V2V communication to improve traffic efficiency and road safety.