Factors affecting the number of road traffic accidents in Kerman province, southeastern Iran (2015–2021)

Abstract

Road traffic accidents (RTAs) are among the top causes of mortality and disability globally, particularly in developing nations like Iran. In this study, RTAs were analyzed to develop precise predictive models for predicting the frequency of accidents in the Kerman Province (southeastern Iran) using the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modeling methods. The findings demonstrate that including factors regarding humans, vehicles, and elements of nature in the time-series analysis of accident records resulted in the development of a more reliable prediction model than utilizing only aggregated accident count. The understanding of safety on the road is increased by this research, which also offers a method for forecasting that utilizes a variety of parameters relating to people, cars, and the environment. The findings of this research are likely to contribute to lowering the incidence of RTAs in Iran.

Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review

“…To reduce the probability of automobile accidents, improving vehicle driving safety has become a common goal in both the automotive industry and academia. Vehicle driving safety is easily influenced by multiple factors, among which, the driver's condition is an important factor [6][7][8]. The driver's condition is affected by various aspects, including the driver's physiology, psychology, and emotions [9,10].…”