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

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 traffc flow in all lanes have a similar trend.To ilustrate the idea,this study frst 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 pa-rameter,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.