A Natural Lane Changing Decision Model For Mixed Traffic Flow Based On Extreme Value Theory

TL;DR

This paper introduces a novel lane changing decision model for connected automated vehicles in mixed traffic, utilizing extreme value theory to enhance safety and traffic efficiency through simulation analysis.

Contribution

It develops a new decision model based on extreme value theory for CAVs in mixed traffic, improving safety and efficiency over existing models.

Findings

Model effectively reduces traffic risk.

Significantly improves traffic efficiency.

Enhances safety in mixed traffic flow.

Abstract

With the high frequency of highway accidents,studying how to use connected automated vehicle (CAV) to improve traffic efficiency and safety will become an important issue. In order to investigate how CAV can use the connected information for decision making, this study proposed a natural lane changing decision model for CAV to adapt the mixed traffic flow based on extreme value theory. Firstly, on the bias of the mixed vehicle behavior analysis, the acceleration, deceleration, and randomization rules of the cellular automata model of mixed traffic flow in two lanes are developed. Secondly,the maximum value of CAV's lane change probability at each distance by extreme value distribution are modeled. Finally, a numerical simulation is conducted to analyze the trajectory-velocity diagram of mixed traffic flow, average travel time and average speed under different penetration rates of CAV. The result shows that our model can avoid the traffic risk well and significantly improve traffic efficiency and safety.