Improving car-following model to capture unobserved driver heterogeneity and following distance features in fog condition

The paper aims to develop an improved Fog-related Intelligent Driver Model (FIDM) that reproduces drivers' car-following behaviour features by taking into account unobserved driver heterogeneity in fog condition. A multi-user driving simulator experiment was performed, and a vehicle fleet consisting of nine vehicles was tested in different fog and speed limits conditions. The experimental results showed that the unobserved driver heterogeneity (the combination of intra-driver heterogeneity and inter-driver heterogeneity) tended to increase as the fog density decreased. The average following distance tended to increase with the decrease of fog density and increase of speed limit. Two indexes were proposed to verify the performance of the FIDM. The results showed that FIDM performed better in reproducing unobserved driver heterogeneity and average following distance compared to the current popular car-following models. This study contributes to an improved car-following model for better understanding traffic flow phenomena under foggy conditions.