Abstract
The problem of road traffic safety has been widely concerned in recent years. The identification of traffic accident hot spots can effectively improve the road traffic safety and let the traffic managers formulate targeted improvement measures and suggestions. The traditional identification method of accident hot spot does not consider the spatial attribute of the accident, so it has some limitations in the identification of traffic accident hot area. Therefore, this paper first proposes a method to identify the hot spot of traffic accidents based on geographic information system (GIS). The mathematical model and machine learning model are used to explore the correlation between traffic accidents and spatial characteristics from macro and micro aspects. Finally, taking Beijing as an example, the feasibility of the research method is proved by using the accident data of Beijing in 2015 and the geographic information of Beijing. The research results of this paper can realize the spatial effective transformation of accident records, comprehensively consider the micro and macro attributes of the accident itself, realize the automatic and efficient identification of the accident hot spot. In addition, the causality analysis results between each attribute and the distribution of accident hot spots can help decision makers to formulate safety and sustainable road strategies.
Highlights
The research significance of the text is as follows:(1) This paper uses ArcGIS software to study
ArcGIS is a complete geographic information system (GIS) software system based on industrial standards
The purpose of this paper is to find out the relationship between the severity of vehicle accidents and macro and micro factors, and to provide the basis for the identification of traffic accident hot areas
Summary
The research significance of the text is as follows:(1) This paper uses ArcGIS software to study. ArcGIS is a complete GIS software system based on industrial standards. It has the characteristics of comprehensive function, good scalability and user-defined flexibility. It can combine the traditional traffic accident database with the visualization and spatial analysis ability of GIS system, and use the traditional mathematical model algorithm and machine learning algorithm to find out the various attributes hidden behind the traffic accident data. The road type information in traffic accidents is shown in the following table: 3.2 Analysis of accident influencing factors. The firstclass highway and self -built road are related to the accident
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