A study on the car-following model for mountainous curves incorporating driving behavior characteristics

Abstract

This study addresses the high accident rate on mountainous highways, driven by complex road alignments, harsh climatic conditions, and heterogeneous driving behaviors, aiming to enhance the accuracy of car-following behavior modelling. Using a typical mountainous curved section in Yunnan Province as the test case, drone-collected vehicle trajectory data were filtered using Kalman filtering to reduce noise. Subsequently, a K-Means algorithm optimized by differential evolution classified driving behaviors into three categories: aggressive, conservative, and standard. This revealed significant differences in speed, acceleration, and headway between distinct driving styles. To characterize curve dynamics, this study introduced a curve-radius parameter to enhance the Intelligent Driving Model (IDM). It calibrated it according to the rules for the three driving styles using genetic algorithms. Validation through macro-level error analysis and micro-level trajectory comparisons demonstrated that the improved model significantly enhances prediction accuracy for curve-following behavior while effectively adapting to diverse driving characteristics. This study pioneers the integration of driving behavior heterogeneity with curve geometry characteristics, providing a theoretical foundation for traffic flow simulation, safety assessment, and intelligent driving system design on mountain roads. It holds significant engineering value for reducing the risk of following-distance accidents and optimizing traffic management in mountainous regions.