从真实数据挖掘行人与车辆的互动模式，揭示人类移动行为的深层规律。
Data-driven Exploration of Mobility Interaction Patterns
- 基于数据挖掘方法，直接从轨迹数据中发现个体间的潜在互动事件。
- 识别出持久存在的复杂互动模式及随时间演化的配置结构。
- 适用于人群仿真与应急响应建模，为动态行为模拟提供新思路。
理解个体移动行为及其对外部环境的反应，是构建物理层面人类动力学模型的关键。尤其需要捕捉个体存在对他人产生的影响。重要应用场景包括人群仿真与应急处置，其核心在于通过个体模拟反映群体行为，将他人作为上下文因素纳入考量。现有方法多依赖预设的行为模型，而本文提出一种从数据出发的方法，采用数据挖掘视角，搜索轨迹数据中可能体现个体间相互作用的移动事件，并在此基础上发现复杂的、持续性的模式及随时间演变的事件配置。该研究为理解个体移动交互机制提供了新洞见，有助于改进现有仿真模型。我们在两个真实案例（车辆与行人）上验证了该方法，进行了完整的实验评估，涵盖性能表现、参数敏感性及典型结果的解释。
原文摘要 · Abstract (English)
Understanding the movement behaviours of individuals and the way they react to the external world is a key component of any problem that involves the modelling of human dynamics at a physical level. In particular, it is crucial to capture the influence that the presence of an individual can have on the others. Important examples of applications include crowd simulation and emergency management, where the simulation of the mass of people passes through the simulation of the individuals, taking into consideration the others as part of the general context. While existing solutions basically start from some preconceived behavioural model, in this work we propose an approach that starts directly from the data, adopting a data mining perspective. Our method searches the mobility events in the data that might be possible evidences of mutual interactions between individuals, and on top of them looks for complex, persistent patterns and time evolving configurations of events. The study of these patterns can provide new insights on the mechanics of mobility interactions between individuals, which can potentially help in improving existing simulation models. We instantiate the general methodology on two real case studies, one on cars and one on pedestrians, and a full experimental evaluation is performed, both in terms of performances, parameter sensitivity and interpretation of sample results.
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