Comparative Safety and Stability Assessment of Autonomous Vehicle Lane-changing Strategies in Mixed Traffic at Urban Expressway Weaving Section

With the rapid development of autonomous driving technology, the influence of autonomous vehicles on traffic safety and stability has received major attention. This study aims to evaluate the effects of autonomous vehicle lane-changing strategies and penetration rates on the mixed traffic safety and stability at urban expressway weaving sections. A mixed traffic environment including autonomous vehicles (AVs) and human-driven vehicles (HDVs) was simulated with SUMO. Two AV lane-changing strategies were set by adjusting the parameters of LC2013 lane-changing model. Safety levels were assessed using conflict numbers and Time-to-Collision (TTC), and traffic stability was evaluated based on the spatial distribution of average and standard deviation of speed. The results indicated that when the penetration rate of autonomous vehicles exceeded 30%, there was a significant improvement in the safety and stability of the mixed traffic improved significantly at the weaving section. Furthermore, when the penetration rate reached 100%, the safety and stability of the autonomous vehicles outperform those at the 0% penetration rate. Particularly, autonomous vehicles leveraging more proactive lane-changing strategies led to a more safe and stabilized traffic flow at the weaving section. This study provides valuable insights for the deployment and traffic management of autonomous vehicles at the urban expressway weaving sections.