A Heterogeneous Visual Imaging Model for Analyzing Impact of Vehicle Type on Car-Following Dynamics

Heterogeneity is an essential characteristic in car-following behaviors, which can be defined as the differences between the car following behaviors of driver/vehicle combination under comparable conditions. This paper proposes a Visual Imaging Model (VIM) with relaxed assumption on a driver’s perfect perception for 3-D traffic information and uniform reaction to vehicles with different sizes in most existing car following models. The proposed model can generate greater stimuli to the followers from the leading vehicles with larger back sizes (i.e. defined as vehicle width×vehicle height) and short distance to the following vehicles, but less changes in stimuli for the distant leading vehicles under various back sizes. The US101 NGSIM data set containing vehicle type/size information is used to evaluate the proposed model at the levels of single trajectory pair and vehicle types. The calibration and validation results show the promising performance of the proposed model in describing heterogeneous car-following behavior. In this study, it is also found from US101 NGSIM data set that in relatively high velocity range, the following gap distance for car following truck (C-T) is greater than that for car following car (C-C), while in low velocity range, C-T has a smaller spacing than C-C. The phenomenon can also be reproduced by the proposed model.