Vehicle Road Accident Prediction Model Along Federal Road FT050 Kluang-A/Hitam-B/Pahat Route Using Excess Zero Data

Abstract

Traffic accidents have become a major socio-economic problem in Malaysia and are considered the primary cause of mortality. Over 60 percent of these fatal accidents occurred on rural roads. Nearly half of all fatalities took place on federal roads and over a quarter happened on state roads. It is also estimated that about 2 percent of the country’s Gross Domestic Product (GDP), or approximately RM 9 billion, is lost through road accidents. Several models therefore have been developed for modelling the occurrence of accidents, but most of these models have plenty of deficiencies. The following study focuses on stochastic regression models, such as Poisson, Negative Binomial, Zero- Inflated Poisson and Zero-Inflated Negative Binomial with excess zero outcomes on the response variables. Furthermore, R-statistical programming is used to specify the regression relationship with a sophisticated result. The method used consider as updating approach in predicting potential road accidents, which can also produce an accuracy probability of hazardous locations. Based on road accident data collected over a five-year period from 2010 to 2014 at Federal Road F0050: Kluang- A/Hitam-B/Pahat in Johor, Malaysia, results of this study show that Zero Inflated model performed better, in terms of the comparative criteria based on the AIC value.