Multiple-Factors Aware Car-Following Model for Connected and Autonomous Vehicles

The emergence of connected and autonomous vehicles (CAV) is of great significance to the development of transportation systems. This paper proposes a multiple-factors aware car-following (MACF) model for CAVs with the consideration of multiple factors including vehicle co-optimization velocity, velocity difference of multiple PVs, and space headway of multiple PVs. The Next Generation Simulation (NGSIM) dataset and the genetic algorithm are used to calibrate the parameters of the model. The stability of the MACF model is first theoretically proved and then empirically verified via numerical simulation experiments. In addition, the VISSIM software is partially redeveloped based on the MACF model to analyze mixed traffic flows consisting of human-driven vehicles and CAVs. Results show that the integration of CAVs based on the MACF model effectively improves the average velocity and throughput of the system.

Automated summary

The proposed multiple-factors aware car-following (MACF) model provides an effective solution for connected and autonomous vehicles (CAV) by considering vehicle co-optimization velocity and the impact of multiple factors to improve the performance of transportation systems. The model's stability is theoretically proven and empirically verified, showing that integrating CAVs based on the MACF model effectively improves the average velocity and throughput of the system.