title: Differences in Factors Affecting Various Crash Types with High Numbers of Fatalities and Injuries in China.
abstract: OBJECTIVE
  Road traffic crashes that involve very high numbers of fatalities and injuries arouse public concern wherever they occur. In China, there are two categories of such crashes: a crash that results in 10-30 fatalities, 50-100 serious injuries or a total cost of 50-100 million RMB ($US8-16m) is a "serious road traffic crash" (SRTC), while a crash that is even more severe or costly is a "particularly serious road traffic crash" (PSRTC). The aim of this study is to identify the main factors affecting different types of these crashes (single-vehicle, head-on, rear-end and side impact) with the ultimate goal of informing prevention activities and policies.
  
  
  METHODS
  Detailed descriptions of the SRTCs and PSRTCs that occurred from 2007 to 2014 were collected from the database "In-depth Investigation and Analysis System for Major Road Traffic Crashes" (IIASMRTC), which is maintained by the Traffic Management Research Institute of the Ministry of Public Security of China (TMRI). 18 main risk factors, which were categorized into four areas (participant, vehicle, road and environment-related) were chosen as potential independent variables for the multinomial logistic regression analysis. Comparisons were made among the single-vehicle, head-on, rear-end and side impact crashes in terms of factors affecting crash occurrence.
  
  
  RESULTS
  Five risk factors were significant for the six multinomial logistic regression models, which were location, vertical alignment, roadside safety rating, driver distraction and overloading of cargo. It was indicated that intersections were more likely to have side impact SRTCs and PSRTCs, especially with poor visibility at night. Overloaded freight vehicles were more likely to be involved in a rear-end crash than other freight vehicles. Driver distraction is an important risk factor for head-on crashes, while vertical alignment and roadside safety rating are positively associated with single-vehicle crashes.
  
  
  CONCLUSIONS
  Based on the findings, promising measures were proposed to prevent each type of SRTC and PSRTC, which governmental or regulatory agencies could employ to plan strategies to reduce SRTCs and PSRTCs and support lifesaving policies.
authors.affiliation: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China.|Traffic Management Research Institute of the Ministry of Public Security, Wuxi, Jiangsu, China.
authors.forename: Yikai
authors.identifier: http://orcid.org/0000-0002-4737-4371
authors.initials: Y
authors.lastname: Chen
authors.affiliation: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.affiliation: Centre for Accident Research and Road Safety- Queensland, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.
authors.forename: Mark
authors.initials: M
authors.lastname: King
authors.affiliation: School of Transportation, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Jie
authors.initials: J
authors.lastname: He
authors.affiliation: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China.
authors.forename: Jianxun
authors.initials: J
authors.lastname: Ding
authors.affiliation: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Shi
authors.affiliation: Traffic Management Research Institute of the Ministry of Public Security, Wuxi, Jiangsu, China.
authors.forename: Changjun
authors.initials: C
authors.lastname: Wang
authors.affiliation: Traffic Management Research Institute of the Ministry of Public Security, Wuxi, Jiangsu, China.
authors.forename: Pingfan
authors.initials: P
authors.lastname: Li
mesh_terms: D000063:Accidents, Traffic / Q000191:economics / Q000523:psychology / Q000706:statistics & numerical data*; D002681:China; D016208:Databases, Factual; D006801:Humans; D008962:Models, Theoretical; D012307:Risk Factors; D012449:Safety