Optimization of Lane-changing Advisory in Mixed Traffic of Connected Vehicles and Human-driven Vehicles at Expressway Bottlenecks

Lane closure due to events such as accidents creates bottlenecks on expressways. The mandatory lane changes of merging vehicles lead to congestion. As the cyber-physical system (CPS) develops, mixed traffic consisting of connected vehicles (CVs) and human-driven vehicles (HVs) has emerged. CVs can receive lane-changing (LC) advisories and complete merging to alleviate congestion. Most existing studies assume CVs can timely and exactly follow LC advisories, which is not realistic with human drivers. This study develops an LC advisory model for CVs, whose response time and compliance degrees are considered, at an expressway bottleneck with lane closure under mixed traffic of CVs and HVs. The LC strategies are optimized for CVs to minimize the total delay of CVs and HVs approaching the bottleneck. The constraints include the domains of decision variables, vehicle passing states at the bottleneck, vehicle kinematics, implementation of LC advisories, LC safety, the maximum number of LC manoeuvres, the minimum time interval between LC advisories, the potential LC CVs, and the evolution of vehicle states. The simulation-based method is applied to predict vehicle delay, which is formulated as an implicit function of the LC strategies for CVs. Genetic Algorithm (GA) is designed for solutions. The numerical studies validate the advantages of the proposed model. The sensitivity analysis shows that: 1) the critical CV penetration rate is 60%, below which the marginal benefits are significant with increasing CV penetration rates; and 2) the consideration of the response time and the compliance degree of CVs makes a great difference.