Universiti Teknologi Malaysia Institutional Repository

Road traffic crash severity classification using support vector machine

Mohamed Radzi, Nor Haizan and Birgin, Isah Sani and Mustaffa, Noorfa Haszlinna (2017) Road traffic crash severity classification using support vector machine. International Journal of Innovative Computing, 7 (1). pp. 15-18. ISSN 2180-4370


Official URL: https://ijic.utm.my/index.php/ijic/article/view/13...


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.

Item Type:Article
Uncontrolled Keywords:Classification, road crash, support vector machine, severity injuries
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:80349
Deposited By: Fazli Masari
Deposited On:10 May 2019 15:16
Last Modified:10 May 2019 15:16

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