Optimal Differential Variable Speed Limit Control in a Connected and Autonomous Vehicle Environment for Freeway Off-Ramp Bottlenecks

When traffic congestion occurs on freeway off-ramp bottlenecks, the traffic state becomes complicated and changeable, which leads to increased vehicle travel time and decreased traffic safety and traffic efficiency. Variable speed limit (VSL) control is an effective method to improve traffic conditions, increase bottleneck throughput, improve traffic efficiency, and reduce emissions. Currently, there is an emerging trend of using connected and autonomous vehicle (CAV) technology to develop VSL control. This paper proposes an optimal differential variable speed limit (DVSL) control strategy under mixed CAVs and human-driven vehicles (HVs) environment for freeway off-ramp bottlenecks. The proposed DVSL control considers the characteristics of on-ramp, off-ramp, and mixed traffic flow (i.e., CAVs coexist with HVs). The proposed optimal DVSL control can describe and forecast the dynamics of traffic flow, and can set different speed limits across each lane with a multiple-objective function of total travel time (TTT) and total travel distance (TTD). A model predictive control (MPC) approach was utilized to optimize the DVSL control algorithm. The designed DVSL control was tested on a real-word freeway section with a simulated off-ramp bottleneck. The simulation results show that the proposed control strategy outperforms other existing methods in terms of improving the mobility of a freeway off-ramp bottleneck and maximizing the environmental benefits. Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the penetration rate (PR) of CAVs. The proposed methods form the basis of VSL control at off-ramp sections under mixed traffic environment.

Automated summary

This study proposes an optimal differential variable speed limit (DVSL) control strategy for freeway off-ramp bottlenecks under mixed connected and autonomous vehicle (CAV) and human-driven vehicle (HV) environment. The proposed strategy considers the characteristics of on-ramp, off-ramp, and mixed traffic flow, and sets different speed limits across each lane using a multiple-objective function. The model predictive control (MPC) approach is utilized to optimize the DVSL control algorithm. The simulation results demonstrate that the proposed control strategy outperforms existing methods in improving mobility and maximizing environmental benefits. Sensitivity analysis shows that the proposed strategy can improve performance with an increase in CAV penetration rate.