Sustainable Mixed-Traffic Micro-Modeling in Intelligent Connected Environments: Construction and Simulation Analysis

Sustainable urban mobility necessitates traffic regimes that enhance operational efficiency and improve traffic safety and flow stability; the rise in intelligent connected vehicles (ICVs) provides a salient mechanism to meet this imperative. This paper aims to investigate the mixed traffic flow characteristics in an intelligent connected environment, using one-way single-lane, double-lane, and three-lane straight highways as modeling objects. Combining the different driving characteristics of human-driven vehicles (HDVs) and ICVs, a single-lane mixed traffic flow model and a multi-lane mixed traffic flow model are established based on the intelligent driver model (IDM) and flexible symmetric two-lane cellular automata model (FSTCAM). The mixed traffic flow in the intelligent connected environment is then simulated using MATLAB R2021a. The research results indicate that the integration of ICVs can improve the speed, flow, and critical density of traffic flow. The increase in the proportion of ICVs can reduce the congestion ratio and speed difference between front and rear vehicles at the same density. As the proportion of ICVs increases, the frequency of lane-changing for HDVs gradually increases, while the frequency of lane-changing for ICVs gradually decreases. The overall lane-changing frequency shows a trend of first increasing and then decreasing. In addition, with the continuous infiltration of ICVs, the area of road congestion gradually decreases, and congestion is significantly alleviated. The speed fluctuation of following vehicles gradually decreases. When the infiltration rate reaches a high level, vehicles travel at a stable speed and remain in a relatively steady state. The findings substantiate the potential of ICV-enabled operations to advance efficiency-oriented and stability-enhancing urban mobility and to inform evidence-based traffic management and policy design.