Pedestrian movements constitute a complex self-organizing system in which various potential risks and conflicts are introduced by the accumulation of randomness and inconsistency over time. Due to the lack of relevant data, knowledge of low-risk behavior identification and risk formation mechanisms in pedestrian movement is insufficient. In this study, we present probable risk indicators reflecting the consistency of a crowd’s state and establish a pedestrian risk identification model that provides an early warning method for safety and security management. A complete pedestrian movement risk identification process is summarized by converting real-world video files into calculable data via video recognition technology, making it possible to identify and obtain crowd risk information in real-time and locate abnormal phenomena. The process makes extensive use of hierarchical and machine learning methods for identifying pedestrian states, proposes quantified conflict-prone and congestion-prone indices to classify different risk types, and defines an improved “crowd risk” index for locating potential crowd dangers. The combination of video recognition technology with machine learning has apparent advantages in solving potential risks in pedestrian movement, particularly in terms of the risk identification rate, the effectiveness of the risk classification, and the accuracy of risk positioning. Results showed that the three-stage risk identification process could accurately determine congestion and conflicting risks and indicate the potential risk-prone in the crowd. The proposed method has significantly improved the output and description accuracy compared with other methods in terms of risk positioning.

行人运动构成了一个复杂的自组织系统，随着时间的推移，随机性和不一致性的积累会引入各种潜在的风险和冲突。由于缺乏相关数据，对行人运动中低风险行为识别和风险形成机制的认识不足。在这项研究中，我们提出了反映人群状态一致性的可能风险指标，并建立了行人风险识别模型，为安全管理提供了一种预警方法。通过视频识别技术，将真实世界的视频文件转化为可计算的数据，总结出一个完整的行人运动风险识别过程，使得实时识别和获取人群风险信息，定位异常现象成为可能。该过程广泛使用分层和机器学习方法来识别行人状态，提出量化的易发生冲突和易拥堵的指标来对不同的风险类型进行分类，并定义改进的“人群风险”指标来定位潜在的人群危险。视频识别技术与机器学习的结合在解决行人运动中的潜在风险方面具有明显的优势，特别是在风险识别率、风险分类的有效性和风险定位的准确性方面。结果表明，三阶段风险识别过程可以准确地判断拥堵和冲突风险，并指示人群中潜在的风险易发点。