Lag and duration of leader–follower relationships in mixed traffic using causal inference

Abstract

This study presents comprehensive analysis of car-following behavior on roads, utilizing Granger causality and transfer entropy techniques to enhance the validity of existing car-following models. It was found that most leader–follower relationships exhibit a delay in lateral movement by 4–5 s and last for short periods of around 3–5 s. These patterns are exhibited for all types of relationship found in the dataset, as well as for followers of all types. These findings imply that lateral movement reactions are governed by a different set of rules from braking and acceleration reactions, and the advantage in following lateral changes is short-lived. This also suggests that mixed traffic conditions may force drivers to slow down and calibrate reactions, as well as limiting the speed advantage gained by following a leader. Our methods were verified against random sampling as a method of selecting leader–follower pairs, decreasing the percent error in predicted speeds by 9.5% using the optimal velocity car-following model. The study concludes with a set of recommendations for future work, including the use of a diversity of car-following models for simulation and the use of causation entropy to distinguish between direct and indirect influences.