城市交叉口行人与非机动车混合交通动力学研究：观测实验与建模
Investigating mixed traffic dynamics of pedestrians and non-motorized vehicles at urban intersections: Observation experiments and modelling
AI总结：
针对城市交叉口行人与非机动车混行的安全挑战，本研究提出融合TTC指标与切向力的TASFM模型，经深圳真实场景验证精度优异，可复现交通行为模式并为城市交通规划提供支撑。
摘要：
行人与非机动车混行的城市交叉口存在复杂的安全挑战，而传统模型未能解释由速度异质性和碰撞预判带来的动态交互作用。本研究提出了基于时间与角度的社会力模型（TASFM），这是对经典社会力模型（Social Force Model）的扩展增强框架，通过整合碰撞时间（Time-to-Collision, TTC）指标和与速度-角度相关的切向力，更真实地模拟避撞行为。我们利用中国深圳一处高密度交叉口的航拍轨迹数据，在真实场景下验证了TASFM的性能，其平均轨迹误差（Mean Trajectory Error, MTE）为0.154米，占实验区域宽度的0.77%。关键发现揭示了两类主体的 distinct 行为模式：行人会沿指定路线（如斑马线）自组织成队列行进，而非机动车则表现出灵活的路径偏移，提升了碰撞风险。针对超车、正面/侧向横穿三类冲突场景的仿真表明，TASFM能够复现双向路径调整、速度调节等自适应策略。该模型可为城市规划者提供冲突热点预测、基础设施重新设计（如设置隔离车道）等可落地的洞见，同时为未来整合机动车交通与环境变量的研究提供可扩展框架。本工作深化了对混合交通动力学的理解，填补了理论建模与数据驱动的城市安全解决方案之间的空白。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
