Road traffic crash severity classification using support vector machine

• Main Authors: Mohamed Radzi, Nor Haizan, Birgin, Isah Sani, Mustaffa, Noorfa Haszlinna
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2017
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/80349/1/NoorfaHaszlinnaMustaffa2017_RoadTrafficCrashSeverityClassification.pdf; http://eprints.utm.my/id/eprint/80349/; https://ijic.utm.my/index.php/ijic/article/view/134
• Institution: Universiti Teknologi Malaysia

Road traffic crash (RTC) is considered among the leading cause of death in many countries in the world and gives negative impact to the social and economic progress. In Nigeria, 13,583 RTC cases were reported in the year 2013 and this figure rising rapidly. Prediction on injuries severity and analysis on accident contributory factors is vital in order to improve either the road condition or the road safety regulation in attempt to reduce fatalities due to RTC. In this paper, a support vector machine model is developed to predict the road crash severity injuries using human, environment and vehicle contributory factors.