Grouping mechanisms of vehicles in heterogeneous traffic with weak lane discipline: A single-site observational study focusing on leader–follower relations

Abstract

The widespread adoption of automobiles has accelerated global economic growth and improved daily convenience; however, the increase in the number of automobiles has led to severe traffic congestion, especially in developing countries with two-dimensional (2D) mixed traffic. In these mixed traffic conditions, each vehicle type exhibits distinct behaviors, which influence both microscopic and macroscopic traffic characteristics. Previous studies have shown that the composition or sequence of vehicle types in one-dimensional mixed traffic shapes traffic characteristics. Although the local composition and collective dynamics of motorcycles in 2D mixed traffic have been extensively investigated, an analysis that accounts for the dynamics of all vehicle types remains scarce. This study aims to detect “LF-groups (Leader–Follower groups)” in which vehicles tend to maintain leader–follower relationships in various traffic situations and identify the reasons for such group formation. Using video traffic observations on a single road segment in Mumbai, India, leader–follower estimation, graph mining techniques, and statistical comparisons, we enumerated all LF-groups formed in different traffic situations and their tendencies with respect to the compositions of vehicle types. As a hypothesis-generating result, our findings suggest that LF-group formation and nonformation in each traffic situation can be explained by the following three factors: similarity in speed, acceleration, and deceleration (maneuver similarity); surrounding space within the focusing combination of vehicle types (spatial confinement); and surrounding space for vehicles outside the focusing combination of vehicle types (permeability). The interaction among these factors across all vehicle type combinations leads to the formation of LF-groups with multiple vehicle types. Moreover, our findings offer a novel perspective: mixed traffic comprises not only groups but also “collections” of adjacent vehicles that continue to travel closely while their leader–follower relationships keep changing, as well as residual vehicles. Our findings may facilitate the active creation or elimination of LF-groups to improve 2D mixed traffic flow.