Identifying Factors that Influence the Patterns of Road Crashes Using Association Rules: A case Study from Wisconsin, United States

被引:29
|
作者
Yu, Shuai [1 ]
Jia, Yuanhua [1 ]
Sun, Dongye [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
基金
中国国家自然科学基金;
关键词
traffic safety; significant factor; association rules; Apriori; DRIVER-INJURY SEVERITY; TRAFFIC ACCIDENTS; SINGLE-VEHICLE; ALGORITHM; IDENTIFICATION; GEOMETRICS; PREDICT; MODELS; SET;
D O I
10.3390/su11071925
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Road traffic injury is currently the leading cause of death among children and young adults aged 5-29 years all over the world. Measures must be taken to avoid accidents and promote the sustainability of road safety. The current study aimed to identify risk factors that are significantly associated with the severity in crash accidents; therefore, traffic crashes could be reduced, and the sustainable safety level of roadways could be improved. The Apriori algorithm is carried out to mine the significant association rules between the severity of the crash accidents and the factors influencing the occurrence of crash accidents. Compared to previous studies, the current study included the variables more comprehensively, including environment, management, and the state of drivers and vehicles. The data for the current study comes from the Wisconsin Transportation crash database that contains information on all reported crashes in Wisconsin in the year 2016. The results indicate that male drivers aged 16-29 are more inclined to be involved in crashes on roadways with no physical separation. Additionally, fatal crashes are more likely to occur in towns while property damage crashes are more likely to occur in the city. The findings can help government to make efficient policies on road safety improvement.
引用
收藏
页数:14
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