Modeling Mixed Traffic Flow with Connected Autonomous Vehicles and Human-Driven Vehicles in Off-Ramp Diverging Areas

Abstract

This paper focuses on modeling mixed traffic flow that comprises human-driven vehicles (HV), adaptive cruise control (ACC) vehicles, and cooperative adaptive cruise control (CACC) vehicles in the off-ramp diverging area. The car-following behaviors of HVs, ACC vehicles, and CACC vehicles are modeled using an intelligent driver model (IDM), ACC car-following model, and CACC car-following model, respectively. The lane-changing behaviors of different types of vehicles in off-ramp diverging areas are modeled using the anticipatory lane change (ALC) model and the mandatory lane change (MLC) model. These models are important for describing the interaction among different types of vehicles in mixed traffic. The safety and efficiency of mixed traffic flow are analyzed by integrating the developed car-following models and lane-changing models in numerical simulation. A one-way, two-lane scenario is established for the simulation. The results reveal that when the proportion of CACC vehicles is about 0.6, the safety and general operating efficiency of mixed traffic flow in the off-ramp area deteriorate significantly. Increasing the conservative MLC zone length can improve the average speed of traffic flow. Guiding drivers in changing lanes is one way to improve the efficiency of traffic flow.