Analysis of Vehicle-Following Behavior in Mixed Traffic Conditions using Vehicle Trajectory Data

In mixed traffic streams without lane discipline, driving behaviors are complex and difficult to model. However, limited attempts have been made to study the characteristics of these maneuvers using trajectory data. This paper proposes a novel use of vehicle trajectory data to identify car-car and auto-car pairs in the following regime and the regime duration, classify pairs as strict and staggered following, and investigate the factors influencing the following vehicle's speed under different regimes in mixed traffic. Oblique trajectories and relative speed hysteresis plots are used to identify vehicle pairs in the steady-state following regime. Two new variables, oblique spacing (R) and the angle between the leader and the follower (theta), are proposed. Multiple linear regression models for the follower speed in strict and staggered following regimes are developed. The results show that cars exhibit following behavior more often than other vehicles. Also, while car-car pairs display both left and right staggered following, auto-car pairs predominantly demonstrate left staggered following. Regression analysis shows that the relationship between R and the speed of the following vehicle differs significantly when theta is close to 90 degrees than when it deviates from 90 degrees. The speed of followers is affected by leader and relative speeds. However, the relative speed has a smaller influence in both right and left staggered cases than strict follower cases. Finally, this study provides empirical evidence of qualitative and quantitative differences among following behaviors that can help in developing better microscopic traffic flow models for mixed traffic conditions.