Modeling of low-risk behavior of pedestrian movement based on dynamic data analysis

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 iden-tification 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.

Automated summary

In this study, a pedestrian risk identification model was established by converting real-world video files into calculable data through video recognition technology. The combination of video recognition technology with machine learning was used to identify pedestrian states, classify different risk types, and locate potential crowd dangers. The proposed method significantly improved the accuracy of risk positioning compared to other methods.