Abstract
The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and characteristics of case occurrence in a space area has profound significance for the prevention of traffic accidents. Starting from the space-time angle and based on the traffic accident data, this article firstly carries out the wavelet decomposition of the incident data of time series to realize the problem optimization of sparse matrix and then studies the spatial differentiation pattern of traffic accidents through the k-means clustering method. And under the formed differentiation pattern, the spatial and temporal laws of the incident are deeply analyzed. Finally, accident causes based on vehicle information system data are analyzed. The results show that the traffic accident space in Beijing is divided into 5 categories, among which, the hot spot space is the area with large traffic volume, diverse driver quality, or the junction of urban and rural roads, and the vehicle information system distracting the driver’s attention is also the cause of accidents from a micro view through vehicle information system data.
Highlights
In recent years, frequent traffic accidents have become an important factor threatening people’s travel safety
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
K-means clustering was conducted based on the wavelet coefficients of the traffic police brigade after decomposition, and the conclusion of spatial differentiation pattern of traffic accidents was obtained
Summary
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.
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