交通事故时空模式可视分析方法

doi:10.11896/j.issn.1002-137X.2019.04.003

Abstract

Abstract: With the advancement of urbanization and the rapid growth of urban population and vehicles,urban traffic accidents are increasingly frequent,which becomes a hot social concern.This paper took the traffic accident record data of Hefei City in the past ten years as the research object,used the visual analysis method to analyze the accident time and the place information in the traffic accident record data,explored the time and space pattern of the traffic accident,and constructed the traffic accident analysis system,so as to assist relevant departments to improve frequent traffic accidents.In this paper,the concept of road accident risk degree was put forward for the first time,and a new identification method of accident-prone road section was constructed based on multi-scale time statistical zigzag chart and periodic time statistical circle map.Compared with the traditional method,this method does not need to deal with the road segmentation,thus avoiding the impact of the quality of the segmentation on the recognition results.On this basis,this paper combined traffic accident data with urban road network data,and used visual analysis technology to build a visual analysis system of traffic accidents.This system can help relevant departments to understand the time pattern of the overall urban traffic accidents and single road and the accident-prone sections,and explore the accident-prone sections under the continuous time limit or cycle time limit.In addition to time conditions,the system can also identify accident-prone sections under different weather and other limited conditions,so that traffic police departments can make decision management through road accident risk under different circumstances,deploy rescue police forces reasonably and reduce accident hazards.The proposed system has important practical significance for mitigating and curbing the growth of traffic accidents,reducing and preventing road traffic accidents.It is also conducive to the scientific and effectivemana-gement of road traffic.

Key words: Black spot, Road accident risk degree, Road segmentation, Traffic accident, Visual analysis

CLC Number:

TP391.9

RAO Yong-ming, ZHANG Yan-kong, XIE Wen-jun, LIU Lu, LIU Xin-yue, LUO Yue-tong. Visual Analysis Method of Traffic Accident Spatial-Temporal Pattern[J].Computer Science, 2019, 46(4): 14-21.

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