Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach.

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-vehicle-road system at the mesoscale level, integrating the characteristics of heterogeneous driving behavior. We developed a traffic flow simulation model for a three-lane urban arterial road segment and used rule-based algorithms to simulate vehicle following, lane changing, and collision behavior. A visualization simulation program was developed using NetLogo software to observe and analyze vehicle behavior. The findings revealed that collision events were the leading cause of traffic bottlenecks, impacting the continuity and coherence of traffic flow. Even after the collision event subsided and congestion eased, the average vehicle speed could not fully recover to its original level, reaching only about 70% of the maximum vehicle speed. Aggressive drivers exhibited distinct speed control strategies compared to conservative and ordinary drivers. This study demonstrates the effectiveness of multi-agent modeling in capturing the relationship between traffic bottlenecks and collision events, highlighting the influence of dynamic traffic events on driving behavior. [ABSTRACT FROM AUTHOR]