Dynamic zonal modeling and connectivity: Enhancing safety and efficiency in merging zones

Abstract

Urban expressways are critical components of transportation networks, playing a key role in improving traffic flow and mitigating congestion. This study investigates traffic flow simulations in urban expressway merging areas to ensure efficient, safe, and seamless driving for connected and autonomous vehicles (CAVs). The merging area is systematically divided into zones, with tailored car-following and lane-changing rules developed for CAVs and human-driven vehicles (HDVs) to capture their distinct behavioral characteristics. A CAV-oriented cellular automata (CA) model, integrating dynamic safety spacing and interconnectivity, is proposed to evaluate the effects of CAV penetration rates on traffic efficiency and safety. The results demonstrate that higher CAV penetration rates significantly enhance outflow volumes, particularly under medium to high traffic conditions. Analysis using the Average Travel Time (ATT) metric underscores substantial efficiency improvements facilitated by CAVs, while the Lane Change Time to Collision (LCTTC) metric highlights their contribution to improved safety in congested scenarios. Furthermore, increased CAV penetration rates effectively mitigate traffic flow disruptions caused by stochastic slowing behavior, enhancing overall system stability. The proposed model provides a comprehensive framework for analyzing lane-changing dynamics and operational risks in merging areas, offering valuable insights for traffic management and the strategic deployment of CAV technologies.