Calibration of Vehicle-Following Model Parameters Using Mixed Traffic Trajectory Data

Abstract

A number of models for car following have been proposed for homogeneous traffic and some of these have been modified or adapted to represent mixed traffic conditions in India. Vehicle-following behavior under mixed traffic is both complex and challenging and cannot be adequately captured by conventional lane-based following models and their variants. For example, the behavior of a subject vehicle in a mixed traffic condition depends on the behavior of lead vehicle as well as the influence of neighboring vehicles. Most existing models are based on the longitudinal spacing and the relative speed of the lead and the subject vehicles. However, the vehicular interactions also depend on the lateral movements such as lateral spacing and lateral speed. Furthermore, the response of the subject vehicle also depends on the type of vehicles involved and their maneuvers in the surrounding space. This study aims to address some of these gaps in the existing vehicle-following models for mixed traffic. Mixed traffic trajectory data collected from the mid-block section of a six-lane divided urban arterial road in Chennai city were used for this study. From the data set, leader–follower vehicle pairs identified based on three different methods: influence area method, headway method, and video data tracking are compared, and the most suitable method is chosen for further analysis. Variation of driver behavior due to different factors such as follower’s speed, relative spacing, lateral position, vehicle types, and following behaviors were examined. Calibration and validation of the models were done for different leader–follower vehicle pairs. The results show that the model parameters vary with not only by subject vehicle type, but also by leader–follower pairs. In addition, there is a significant effect of factors such as lateral position of vehicles and types of following behaviors. This study will find application in developing more realistic mixed traffic simulation models by including these factors.