Lane-changing decision rule with the difference of traffic flow's variation in multi-lane highway for connected and autonomous vehicles

Abstract

Abstract Drivers are not far-sighted when they execute lane-changing manipulation. To address this issue, this study proposes a rule to improve vehicles' lane-changing decisions with accurate information of surrounding vehicles (e.g. time headway). More specifically, connected and autonomous vehicles (CAVs) change lanes in advance if they find severer flow reducing in the lanes, while CAVs should maintain the car-following state if the variations of traffic flow in all lanes have a similar trend. To illustrate the idea, this study first calibrates two classic car-following models and a lane-changing model, and then conducts numerical simulations to illustrate the short-sighted decision of drivers. The study incorporates the idea into a lane-changing decision rule by changing the lane-changing model's parameter, and conducts numerical tests to evaluate the effectiveness of the lane-changing decision rule in a multi-lane highway with a bottleneck. The results of this study indicate that the new lane-changing decision rule can substantially improve the throughput of the traffic flow, especially when the inflow exceeds the remaining capacity of the road. The lane-changing rule and results can bring insights into the control of CAVs, as well as the driver assistance system in connected vehicles.