Emerging connected autonomous vehicle (CAV) technologies provide an opportunity to the vehicle motion control to improve the traffic performance. This study simulated and evaluated the CAV-based speed and lane-changing (LC) control strategies at the expressway work zone in heterogeneous traffic flow. The control strategies of CAV are optimized by the multi-layer control structure based on model predictive control. The heterogeneous traffic flow composed of human-driven vehicles and CAVs is constructed based on cellular automata by the proposed Expected Distance-based Symmetric Two-lane Cellular Automate (ED-STCA) LC model and CAV car-following model. The six control strategies composed of variable speed limits (VSL), LC and their coordinated control strategies are experimented. The average travel time and throughput are selected to assess the advantages and disadvantages of each strategy under each combination of vehicles’ arrival rates and CAV mixed ratios. The numerical results show that: (i) the effect of the control strategy on the traffic is not obvious under free flow, and the control strategy may worsen the traffic under medium traffic. (ii) Early lane-changing control (ELC) is better than late lane-changing control (LLC) under medium traffic, and LLC is better under heavy traffic. (iii) ELC + VSL is the best choice under heavy traffic and the mixed rate of CAVs is high. The simulation results obtained in the paper would provide some practical references for transportation agencies to manage the traffic in work zone under networking environment in the future.
Publication Date: August 2020
Source URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Connected Vehicles; Lane changing; Mathematical Models; Traffic Flow; Traffic Simulation; Traffic Speed; Variable Speed Limit Systems; Vehicle Following; Work Zones