Citation

Rakotonirainy A, Chen S, Scott-Parker BJ, Loke SW, Krishnaswamy S. J. Transp. Saf. Secur. 2015; 7(4): 358-375.

Copyright

(Copyright © 2015, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2014.959585

PMID

unavailable

Abstract

Curves are a common feature of road infrastructure; however, crashes on road curves are associated with increased risk of injury and fatality to vehicle occupants. Countermeasures require the identification of contributing factors. However, current approaches to identifying contributors use traditional statistical methods and have not used self-reported narrative claim to identify factors related to the driver, vehicle, and environment in a systemic way. Text mining of 3,434 road-curve crash claim records filed between 1 January 2003 and 31 December 2005 at a major insurer in Queensland, Australia, was undertaken to identify risk levels and contributing factors. Rough set analysis was used on insurance claim narratives to identify significant contributing factors to crashes and their associated severity. New contributing factors unique to curve crashes were identified (e.g., tree, phone, oversteer) in addition to those previously identified via traditional statistical analysis of police and licensing authority records. Text mining is a novel methodology to improve knowledge related to risk and contributing factors to road-curve crash severity. Future road-curve crash countermeasures should more fully consider the interrelationships between environment, the road, the driver and the vehicle, and education campaigns in particular could highlight the increased risk of crash on road curves.
Keywords: driver distraction

Language: en