In recent years, frequent traffic accidents have become an important factor threatening people’s travel safety. Its high data bring unprecedented challenges to the public security organs, especially in the situation of constantly changing traffic space environment, and the case space of different types of accidents usually shows different rules and characteristics. Based on real traffic accident data and machine learning technology, analyzing traffic accident cases from the perspective of time and space can reveal the distribution law and the causes behind traffic accidents in a scientific and profound way, so as to formulate different prevention strategies according to different types of traffic accidents and make relevant departments respond to traffic accidents in a more directional and targeted way. In 2003, Kuanmin Chen and the others obtained the distribution characteristics of traffic accidents in time and space through simple statistical methods, explored the causes, and proposed countermeasures [1
] to improve road traffic safety. In 2009, T-K Anderson used kernel density estimation and k
-means clustering method to extract road section with frequent traffic accidents [2
]. In 2012, Wenhao Yu, Tinghua Ai, and others used the planar kernel density estimation method expands to the network space as the network kernel density estimation method to extract and visualize the clustering distribution area of event points under network constraints [3
], providing a reference for the analysis of traffic accident spatial and temporal distribution pattern. In 2016, Jinyan Tan and others used space-time GIS technology to identify [4
] urban road black spots by comprehensively considering the number, spatial location, severity, and other factors of traffic accidents. In 2016, K Z Htut and others directly used the nuclear density estimation method in ArcGIS spatial analysis tool to identify the clustered sections of expressway accidents and grade their severity [5
]. The study used a variety of techniques to explore the characteristic of spatial distribution of traffic accidents, but the analysis of the integration of time and space of traffic accidents is insufficient, the analysis scale is relatively macro, and most of them took a city as the research object to discuss the spatial distribution law of traffic accidents, and the application of intelligent machine learning technology is not deep enough. It also ignores the fact that multiple traffic accident spaces are similar in the time of occurrence, which leads to the weakening of the research on the spatial differentiation pattern of traffic accidents. In recent years, only a few studies have divided the traffic accident space. In 2015, Zhenhong Wang analyzed the time distribution, spatial distribution, and crowd distribution characteristics of traffic accidents [6
]. In 2014, Lian Xie, Chaozhong Wu, Nengchao Lu, Yan Gao, and others proposed an improved DBSCAN clustering algorithm for the identification of road sections with multiple traffic accidents [7
] and studied the application of this algorithm in the identification of spatial distribution characteristics of traffic accidents by taking highway traffic accidents as an example. Clustering analysis is an important means of space-time analysis of traffic accidents. Based on the time series as the axis and the traffic accident law of the grassroot traffic police brigade as the research object, the integrated application of time series analysis technology and cluster technology, this study explore the space differentiation for the traffic accident, to explore the space of different types of traffic accident in time and space to carry out targeted analysis of different law of both accidents, put forward aiming at all kinds of traffic accident prevention strategies of the space, so as to greatly improve the working efficiency of the traffic police department.
In Section 2
, the data sources are introduced and simply analyzed, and the analysis method based on WaveCluster is explained in Section 3
. Spatial differentiation of traffic accidents and empirical analysis is proposed in Section 4
. Finally, a conclusion is obtained in section 5.