Car-following model in highway tunnel sections based on risk perception and psychological field theory

To accurately capture the varying characteristics of car-following behavior among different types of vehicles in highway tunnel sections, a car-following model is established, drawing inspiration from the theory of psychological field. This model posits that car-following behavior results from the combined influence of psychological driving forces and inhibitory forces acting on the driver. The psychological inhibitory forces stem from the risk information associated with the preceding and following vehicles, encompassing vehicle attributes such as mass and operational states like speed and acceleration. These risk factors are distilled into technical parameters like equivalent mass and psychological distance. By quantifying the psychological inhibitory forces and their effects through a driver's psychological field model, a psychological field-based car-following model is formulated. The model's parameters are calibrated using real-world trajectory data and the artificial bee colony algorithm, and its performance is benchmarked against existing general models. The findings reveal that this model enhances the representation of vehicle attributes (type, mass, handling performance), operational characteristics (speed, acceleration), and driver traits in influencing car-following behavior. It offers a more nuanced understanding of how various risk factors play out in the driver's psychological field, leading to a more precise depiction of car-following dynamics. The comparative analysis with existing models validates the effectiveness of this approach.