Improvement and Characteristic Analysis of Car-Following Models in Mixed Traffic Flow Based on Empirical Datasets - Huanfeng Liu; Hanfei Wang; Ziyan Wu; Anning Song; Zishuo Zhang (IEEE Access)
To fully utilize the advantages of Connected and Automated Vehicles (CAV) in mixed traffic flow, this study focuses on investigating the car-following characteristics of mixed traffic flow. First, the potential car-following patterns in mixed traffic are analyzed, and an improvement to the Intelligent Driver Model (IDM) is made by incorporating dynamic response times. Next, based on the NGSIM and Gunter datasets, Genetic Algorithm (GA) are employed to calibrate and evaluate IDM parameters. Subsequently, an analysis of mixed traffic flow characteristics is conducted, including a sensitivity analysis of key parameters. Finally, the stability and safety of mixed traffic flow are analyzed. The results demonstrate that the improved numerical simulations closely match real-world data. CAV can enhance the stability of mixed traffic flow under certain conditions. The improved model provides a theoretical basis for analyzing future traffic flow characteristics.