Effect of vehicle groups on heterogeneous disordered traffic flow

TL;DR

This study examines how vehicle group formation influences traffic flow in heterogeneous, disordered conditions, revealing nonlinear effects of group proportions on flow rates and the importance of normalization methods.

Contribution

It provides new insights into the impact of vehicle group proportions on flow-density relationships using real-world data and multiple PCU estimation methods.

Findings

Moderate group proportions (30-60%) improve flow in medium/high densities.

High group proportions (>50%) skew traffic towards low or high densities.

Normalization methods significantly influence group dynamic interpretations.

Abstract

In heterogeneous disordered traffic, where various vehicle types operate without strict lane discipline, self-organized vehicle groups often emerge. While the formation of such groups has been recognized, their influence on macroscopic traffic dynamics remains unclear. This study investigates how the prevalence and composition of vehicle groups affect flow-density relationships in heterogeneous disordered traffic. Using trajectory data from real-world video observations, we apply three distinct Passenger Car Unit (PCU) estimation methods to construct flow-density diagrams that account for traffic heterogeneity. The analysis reveals that group proportions, i.e., the proportion of vehicles that are classified as belonging to groups, have a nonlinear and traffic-situation-dependent impact on flow characteristics. Specifically, moderate group proportions (30-60%) are associated with higher flow rates in medium- and high-density conditions, whereas proportions exceeding 50% correspond to skewed traffic distributions toward low- or high-density extremes. Comparisons between vehicle-count-based and PCU-based group proportions indicate that normalization methods significantly affect the interpretation of group dynamics, particularly when groups consist mainly of small-PCU vehicles such as motorcycles. Additionally, lower group proportions enhance flow under free-flow conditions, while the entropy-based analysis indicates that the association between entropy alone and speed is not consistently observed across traffic situations. By contrasting representative trends and extreme high-flow cases, the results further suggest that traffic under similar density and group-proportion conditions can exhibit low-efficiency and high-efficiency modes.