Modeling Mixed Traffic Flow of Human, Connected, Autonomous, and Connected-Autonomous Vehicles in a Freeway Merging Area

Freeway merging areas are frequent sources of traffic bottlenecks, but connected and autonomous technologies may offer solutions for optimizing merging operations and reducing impediments to the flow of traffic. In this study, we develop a mixed traffic flow model for merging areas with four types of vehicles: human-driven vehicles (HVs), connected vehicles (CVs), autonomous vehicles (AVs), and connected autonomous vehicles (CAVs). There are two parts to the model. First, the concepts of autonomous penetration (Pa) and connected penetration (Pc) are introduced to characterize vehicle demands, and the extent to which the different proportions of vehicle types impact the traffic flow in the merging area is examined. Second, the driving behaviors of the vehicles are modeled to capture the various distinctions between them. In addition, two critical features, CAV degradation and merging control strategies under the connected environment, are incorporated. The SUMO platform was utilized to model the mixed traffic flow and conduct simulation experiments on the impacts of Pa, Pc and merging control strategies. The results show that (1) increasing Pa consistently enhances merging area performance at any level of Pc, especially when Pc=1; (2) when Pa$\geq$0.7 and Pc$\leq$0.7, the operational performance of mixed traffic flow decrease with increasing Pc; (3) when Pa$\leq$0.2, the implementation of a merging control strategy reduces the operational performance of the merging area with increasing Pc; and (4) the utilization of a merging control strategy enhances the performance of the merging area when Pa$\geq$0.3 and Pc$\geq$0.8, and the enhancement increases significantly with Pa.