Integrated deployment of dedicated lane and roadside unit considering uncertain road capacity under the mixed-autonomy traffic environment - Zhang, Fang; Lu, Jian; Hu, Xiaojian; Meng, Qiang (Transportation research. Part B: methodological, 2023)
•Integrate the deployment of roadside units and AV lanes on the road network.•Take into account the capacity uncertainty induced by different car-following scenarios and random fleet sequence.•Formulate the problem as a mathematical program with complementarity constraints, and approximate it with a mixed-integer linear programing model.•Numerical tests are performed to investigate the tradeoff between the two types of infrastructures. The rapid development of automated vehicle (AV) technologies enables us to explore how to update the urban road infrastructure to cater for the upcoming mixed-autonomy traffic with both AVs and human-driven vehicles (HVs). In this study, we aim to seek an optimal solution to integrate the deployment of AV-dedicated lane and roadside unit assisting automated driving subject to a limited budget by considering the route choice behaviors of AVs and HVs. An AV-dedicated lane segregates AVs from the mixed traffic to create a fully automated driving environment and eliminate disturbances from HVs. The deployment of roadside units assisting automated driving could overcome the connectivity gap for AVs and lower their headway when they follow an HV. We seek to develop a robust and optimal integrated deployment solution that accommodates the uncertain road capacity caused by the stochastic mixed AV and HV fleet sequence. We first establish the stochastic network equilibrium conditions for the mixed traffic, and formulate the integrated deployment problem as a mathematical program with complementarity constraints (MPCC). The MPCC is approximated by a mixed-integer linear programing (MILP) model, which allows existing algorithms for its global optimum. We further develop two interesting strategies, including domain reduction and breakpoint selection, to enhance the effectiveness of the MILP model. Numerical experiments are finally carried out to evaluate the feasibility of research methodology proposed in this study and find some valuable insights.