Accident predictive model based on environmental factors at Federal Route, Malaysia

Abstract

In the world about 1.24 Million people die every year and 20-50 million sustain non-fatal injuries. Road tragic injuries estimated to be the eighth (8th) leading cause of death globally. In Malaysia about 18 deaths per day or 1 death every hour, which require serious attention in searching for preventive measures to minimize this problem. This study aims to investigate environmental factors that contribute to a higher potential of fatal accidents at Federal Route, Malaysia. The study attempted to identify the relationship among the severity of accidents and several identified environmental factors. 166 accidents reports were collected randomly based on serious collision and fatal of the Federal Roads in Peninsular Malaysia from year 2008 to 2015. Twenty eight variables were ranked according to the frequency. Then, Pareto analysis was used as a tool to select the most often contributing factors to the accidents severity. From the analysis, nine variables were then identified (78.4%) as the most contributing factors to the accidents. Logistic Regression was applied to develop accident predictive model based on data collected. It was expected that proactive measures can be taken by the respective authorities before the actual fatal accidents happen in the area under investigation.