Modeling Heterogeneity for Car-following Risk Evaluation Under Truck Movement Block

Abstract:

Based on the movement bottleneck and traffic flow theory， a "truck movement block" effect model was constructed to analyze occurrence mechanism and the car-following behavior. Then， the UAV was selected to collect the vehicle driving video under the truck movement block， and the high-precision car-following trajectory was extracted by trajectory software. Last， the car-following risk assessment methods and grading standards that considers both the possibility and the severity of the risk was proposed. And the random parameter ordered probit model （RP-ORP） was established to assess the car-following risk， and the elasticity coefficient was introduced to analyze the magnitude and direction of the impact on car-following risk. This finding demonstrates that truck movement block includes two processes： car-following and overtaking， which dynamically affects the traffic stability. the RP-ORP model has a better fit than the FP-ORP model， and can realize the probability prediction that different car-following levels under certain conditions. Five variables significantly affect the car-following risk level， such as truck longitudinal acceleration， car-following distance， car-following time， average car-following speed difference， radical driver， among the variables of car-following time and radical driver have random parameter characteristics.