Literature DB >> 30553431

Association rule analysis of factors contributing to extraordinarily severe traffic crashes in China.

Chengcheng Xu1, Jie Bao2, Chen Wang3, Pan Liu4.   

Abstract

INTRODUCTION: This study aimed to investigate the contributing factors to serious casualty traffic crashes and their interdependency in China. Serious casualty crashes are defined as crashes that lead to more than 10 deaths.
METHOD: The data of serious casualty crashes between 2009 and 2013 were obtained from the Annual Report for Road Traffic Accidents published by the Ministry of Public Security of China (MPSC). Descriptive statistics were used to illustrate the characteristics of serious casualty crashes in terms of road user behavior, vehicle conditions, geometric characteristics, and environmental conditions. The association rule mining technique was further applied to identify sets of crash contributory factors that often occur together in serious casualty crashes.
RESULTS: The results showed that serious casualty crashes are a result of complex interactions between road user behavior, vehicle factors, road geometric characteristics, and environmental factors. Association rule analysis revealed the reasons for the occurrence of serious casualty crashes in different circumstances, based on which potential policy implications for preventing serious casualty crashes were identified. PRACTICAL APPLICATIONS: The results of this study can provide transportation agencies with useful insights for understanding why serious casualty crashes occur and developing effective policy initiatives and engineering countermeasures to reduce the fatalities and injuries of serious casualty crashes.
Copyright © 2018 National Safety Council and Elsevier Ltd. All rights reserved.

Keywords:  Association rules; Crash characteristics; Crash contributing factors; Serious casualty crashes; Traffic safety policy

Mesh:

Year:  2018        PMID: 30553431     DOI: 10.1016/j.jsr.2018.09.013

Source DB:  PubMed          Journal:  J Safety Res        ISSN: 0022-4375


  3 in total

1.  Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

Authors:  Chen Wang; Lin Liu; Chengcheng Xu; Weitao Lv
Journal:  Int J Environ Res Public Health       Date:  2019-01-25       Impact factor: 3.390

2.  Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera.

Authors:  Chuanyun Fu; Hua Liu
Journal:  PLoS One       Date:  2020-03-04       Impact factor: 3.240

3.  Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016.

Authors:  Guofa Li; Yuan Liao; Qiangqiang Guo; Caixiong Shen; Weijian Lai
Journal:  Int J Environ Res Public Health       Date:  2021-01-28       Impact factor: 3.390

  3 in total

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