Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

144 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)333-340
Journal / PublicationAccident Analysis and Prevention
Volume36
Issue number3
Publication statusPublished - May 2004

Abstract

A population-based case-control study was conducted to examine factors affecting the severity of single vehicle traffic accidents in Hong Kong. In particular, single vehicle accident data of three major vehicle types, namely private vehicles, goods vehicles and motorcycles, which contributed to over 80% of all single vehicle accidents during the 2-year-period 1999-2000, were considered. Data were obtained from the newly implemented traffic accident data system (TRADS), which was developed jointly by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. The effect of district, human, vehicle, safety, environmental and site factors on injury severity of an accident was examined. Unique risk factors associated with each of the vehicle types were identified by means of stepwise logistic regression models. For private vehicles, district board, gender of driver, age of vehicle, time of the accident and street light conditions are significant factors determining injury severity. For goods vehicles, seat-belt usage and weekday occurrence are the only two significant factors associated with injury severity. For motorcycles, age of vehicle, weekday and time of the accident were determined to be important factors affecting the injury severity. Identification of potential risk factors pertinent to the particular vehicle type has important implications to relevant official organisations in modifying safety measures in order to reduce the occurrence of severe traffic accidents, which would help to promote a safe road environment. © 2003 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Injury severity, Logistic regression models, Risk factors, Single vehicle accident