A modified social force model for high-density through bicycle flow at mixed-traffic intersections

Abstract

• A modified social force model for high-density through bicycle flow is proposed. • A dynamic boundary model is established to present the dynamic dispersion extent. • Behavior force model with the decision process is developed to capture interactions. • Provide a simulation model to evaluate bicycle operation precisely at intersections. Mixed-traffic intersections with high-density bicycle traffic flow are one of the most common bottlenecks in the urban traffic network. Due to the shared space and frequent interactions among heterogeneous road users, through bicycle flow shows an obvious tendency of lateral dispersion, resulting in a reduction in traffic efficiency and safety at intersections. A precise evaluation of its impacts on intersection efficiency and safety is necessary, while the microscopic simulation model is a useful tool to fulfill the need. However, existing microscopic simulation models of through bicycles simplify cyclists’ behaviors and interactions with other road users, and cannot reproduce the lateral dispersion with dynamic variations accurately. To address the shortcomings, this paper proposes a modified social force model to simulate the through bicycle flow at mixed-traffic intersections. A dynamic boundary model and the behavior force model embedded with the decision process are introduced. The dynamic boundary model captures the dynamic characteristics of the lateral dispersion for different cycles under various traffic context. The behavior force model composes four interactive behaviors, i.e. freely moving, following, overtaking, and merging behavior, and rule-based behavior decision models are integrated with force-based approaches to represent interactions and bicycle dynamics. The proposed model has been calibrated based on 743 through bicycle trajectories at a signalized intersection in Shanghai, China. The simulation results indicate that the proposed model is more capable of reproducing the realistic motion features of through bicycles, and show higher simulation accuracy than the original social force model.