| Issue |
MATEC Web Conf.
Volume 325, 2020
2020 8th International Conference on Traffic and Logistic Engineering (ICTLE 2020)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 6 | |
| Section | Design and Development of Traffic Information System | |
| DOI | https://doi.org/10.1051/matecconf/202032501005 | |
| Published online | 22 October 2020 | |
Identification of potential traffic accident hot spots based on accident data and GIS
1 Hongge Zhu, China Highway Engineering Consulting Corporation, Beijing University of Technology, China
2 Yuntong Zhou, Beijing Engineering Research Center of Urban Transport Operation Guarantee; Beijing University of Technology Beijing, China
3 Yanyan Chen, Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
a Corresponding author: 2047768025@qq.com
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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