title: Investigation of work zone crash casualty patterns using association rules.
abstract: Investigation of the casualty crash characteristics and contributory factors is one of the high-priority issues in traffic safety analysis. In this paper, we propose a method based on association rules to analyze the characteristics and contributory factors of work zone crash casualties. A case study is conducted using the Michigan M-94/I-94/I-94BL/I-94BR work zone crash data from 2004 to 2008. The obtained association rules are divided into two parts including rules with high-lift, and rules with high-support for the further analysis. The results show that almost all the high-lift rules contain either environmental or occupant characteristics. The majority of association rules are centered on specific characteristics, such as drinking driving, the highway with more than 4 lanes, speed-limit over 40mph and not use of traffic control devices. It should be pointed out that some stronger associated rules were found in the high-support part. With the network visualization, the association rule method can provide more understandable results for investigating the patterns of work zone crash casualties.
authors.affiliation: College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China; MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. Electronic address: wjx206@gmail.com.
authors.forename: Jinxian
authors.initials: J
authors.lastname: Weng
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Jia-Zheng
authors.initials: JZ
authors.lastname: Zhu
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Xuedong
authors.initials: X
authors.lastname: Yan
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China. Electronic address: leakeliu@163.com.
authors.forename: Zhiyuan
authors.initials: Z
authors.lastname: Liu
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D017278:Air Bags; D000465:Algorithms; D057225:Data Mining; D000066448:Driving Under the Influence / Q000706:statistics & numerical data; D004777:Environment; D005260:Female; D006801:Humans; D008297:Male; D008824:Michigan / Q000453:epidemiology; D012622:Seat Belts; D013672:Technology; D014887:Weather; D014947:Wounds and Injuries / Q000453:epidemiology*
keywords: Association rules; Casualty; Data mining; Data visualization; Work zone