Novel car-following model incorporating steady-state control effect in intelligent transportation systems

The usage of information and communication technology in the intelligent transportation system (ITS) has provided drivers with a vast amount of valuable traffic information. To characterize driver behavior and vehicle interactions in the ITS environment by extensively harnessing vehicle-to-vehicle (V2V) data from arbitrary number of adjacent vehicles, we propose an enhanced car-following model incorporating the steady-state control effect. Our proposed model aims to expound on how V2V data influences the car-following dynamics of a connected and autonomous vehicle (CAV) platoon, thus shedding light on its intrinsic mechanisms for enhancing travel smoothness and operational flexibility. This proposition is discussed through a rigorous analysis involving both analytical and numerical methodologies. Initial findings from linear stability analysis indicate a marked enhancement in anti-interference capabilities compared to conventional autonomous vehicle platoons. Specific phenomena such as traffic bottlenecks and spontaneous instability are subsequently investigated by employing the reductive perturbation method and Hopf bifurcation theory. The results demonstrate that bottlenecks are effectively mitigated and subdued in the enhanced model, as evidenced by soliton solutions with reduced amplitude and increased wave velocity. The enhanced CAV platoon demonstrates a broader adjustable range for expected headway, thereby mitigating the instability risks stemming from Hopf bifurcation. Numerical simulations are then conducted under varied V2V usage scenarios in a circular road setting, exhibiting the detailed steady-state control performance across diverse real-world traffic conditions. Our findings of the enhanced model reveal the inherent mechanism in which V2V communication information prevents the potential CAV platoon instabilities and alleviates the traffic congestion on roadways, and can be served as a fundamental principle in the future era of intelligent driving.