Freeway Merging Area Lane Changing Advisory Optimization Under Connected Vehicles Environment

Abstract:

The on-ramp merging area is the bottleneck of the freeway system， the breakdown of which will trigger many problems such as the deterioration of traffic congestion， traffic oscillation， and increase in accident rate. Currently most studies aim to improve the operation of merging area by adjusting the merging behavior of on-ramp vehicles or controlling the speed of the mainline vehicles. This research， on the other hand， focus more on the potential of mainline vehicles in lifting the traffic throughput by dynamically controlling the upstream lane flow distribution. Specifically， a lane changing advisory optimization model under the connected vehicles （CV） environment is proposed， in which the lane changing decision （change to the left/right lane or keep the current lane） of each individual CV is optimized to reduce the disruption caused by the merging vehicle and thus improve the efficiency. The optimization model is formulated as a mixed integer linear programming，which is independent of the critical density derived from fundamental diagram. Based on traffic simulation software VISSIM， a real-time evaluation platform is developed， in which the proposed lane changing advisory method is implemented and further validated under various traffic demand combinations and CV penetration rate. Trajectories from all the lanes show that this method could effectively reduce the conflict of merging vehicles. The averaged traffic delay analysis results reveal that the proposed method can significantly improve the operation efficiency if the average traffic flow level is near the critical flow of traffic breakdown， i.e.， 1 550 –1 800 veh·h^-1 per lane. In such scenarios， the average traffic delay could be decreased by 10 %–50 %. Furthermore， the sensitivity analysis on CV penetration rate show that at a with lower CV penetration rate of 0.2–0.5， the proposed method is capable of reducing traffic delay.