Stochastic modeling of the impact of meteorological conditions on road traffic accidents

Meteorological conditions have become one of the major factors that influence the frequency and severity of motor vehicle collisions in urban environments. In Kuwait, more than 60,000 accidents occur each year, and about 500 people are killed annually on the roads. This paper is intended to investigate the impact of meteorological conditions on traffic accidents in Kuwait. Stochastic models are developed to analyze and examine the influence of meteorological conditions on the level of road accidents. Normal and lognormal probability densities and their associated cumulative density functions are used to model the meteorological conditions in four different seasons. The results indicate that the most influential meteorological condition that causes accidents is temperature during the fall, spring, and winter seasons. In the summer, wind speed is identified as the most influential factor that accounts for the increased road accidents, with temperature as the second highest meteorological condition affecting accidents. Wind speed and humidity are also found to have significant influence on accident level, following temperature in the fall and winter seasons, respectively. Correlation analyses were also applied and supported the findings obtained using stochastic analyses. The results of this study may help local authorities to reduce the number of accidents and help save people lives.