Abstract
At present, China is in a period of steady development of highways. At the same time, traffic safety issues are becoming increasingly serious. Data mining technology is an effective method for analysing traffic accidents. In-depth information mining of traffic accident data is conducive to accident prevention and traffic safety management. Based on the data of Wenli highway traffic accidents from 2006 to 2013, this study selected factors including time factor, linear factor and driver characteristics as research indicators, and established the decision tree using C4.5 algorithm in WEKA to explore the impact of various factors on the accident. According to the degree of contribution of each variable to the classification effect of the model, various modes affecting the type of the accident are obtained and the overall prediction accuracy is about 80%.
Highlights
As a passage connecting the cities, the highway undertakes huge traffic flow
The j48 algorithm in WEKA was used to explore the distribution of accident types
Because this study aims to simulate the distribution of accident types under various conditions in traffic accidents, and the results of distribution types are discrete, a classification tree has been developed
Summary
As a passage connecting the cities, the highway undertakes huge traffic flow. In China, the highway mileage is long, reaching 136,500 kilometers at the end of 2017 [1]. With the vigorous development of the highway industry, the traffic safety problem has become increasingly prominent. The daily personal injury and property damage caused by traffic accidents in China is very serious. The urgent problem that needs to be solved in the field of transportation today is to reduce the number of accidents and reduce accident losses. In order to achieve this goal, in addition to strengthening infrastructure construction and optimizing management measures, we need to conduct research from the traffic accident itself to deeply understand the law of accidents
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