Investigating mixed traffic dynamics of pedestrians and non-motorized vehicles at urban intersections: Observation experiments and modelling

2025-10-05

AI Summary

Traditional collision-avoidance models fail in urban mixed-traffic intersections—where pedestrians and non-motorized vehicles (NMVs) coexist—due to unmodeled speed heterogeneity and dynamic multi-agent interactions. Method: Leveraging high-precision aerial trajectory data, we propose a Time- and Angle-Dependent Social Force Model (TASFM). It innovatively incorporates a Time-to-Collision (TTC)-based threshold and a velocity–angle-coupled tangential avoidance force to explicitly capture real-time conflict responses among multiple agents. TTC-driven force-field reconstruction and multi-scenario microscopic simulations enable realistic behavioral modeling. Contribution/Results: TASFM successfully reproduces emergent phenomena including lane self-organization, path deviation, and adaptive behaviors under three canonical conflict types. Validated at a high-density intersection in Shenzhen, it achieves a trajectory root-mean-square error of only 0.154 m, significantly improving hotspot identification accuracy for conflicts. The model provides a verifiable, physics-informed computational framework for infrastructure design and traffic management in mixed-traffic environments.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Modeling other Agents

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Urban intersections with mixed pedestrian and non-motorized vehicle traffic present complex safety challenges, yet traditional models fail to account for dynamic interactions arising from speed heterogeneity and collision anticipation. This study introduces the Time and Angle Based Social Force Model (TASFM), an enhanced framework extending the classical Social Force Model by integrating Time-to-Collision (TTC) metrics and velocity-angle-dependent tangential forces to simulate collision avoidance behaviors more realistically. Using aerial trajectory data from a high-density intersection in Shenzhen, China, we validated TASFM against real-world scenarios, achieving a Mean Trajectory Error (MTE) of 0.154 m (0.77% of the experimental area width). Key findings reveal distinct behavioral patterns: pedestrians self-organize into lanes along designated routes (e.g., zebra crossings), while non-motorized vehicles exhibit flexible path deviations that heighten collision risks. Simulations of three conflict types (overtaking, frontal/lateral crossing) demonstrate TASFM's capacity to replicate adaptive strategies like bidirectional path adjustments and speed modulation. The model provides actionable insights for urban planners, including conflict hotspot prediction and infrastructure redesign (e.g., segregated lanes), while offering a scalable framework for future research integrating motorized traffic and environmental variables. This work advances the understanding of mixed traffic dynamics and bridges the gap between theoretical modeling and data-driven urban safety solutions.

Problem

• Modeling pedestrian and non-motorized vehicle interactions at intersections
• Simulating collision avoidance using enhanced social force model
• Analyzing behavioral patterns to improve urban traffic safety

Innovation

• Enhanced Social Force Model with collision metrics
• Integrated Time-to-Collision and velocity-angle forces
• Validated using aerial trajectory data from intersections