Road Accident Data Collection Systems in Developing and Developed Countries: A Review

The road accidents trigger major financial loss and casualties to the individual as well as the state as a whole. The intelligent safety systems are developed to provide all road users with a safe transport system. This approach acknowledges the sensitivity of individuals to extreme injury in road accidents and recognizes the need for the system for improvement. To establish a proper system for road accident prevention, records from prior accidents play a key role in the evaluation and prediction of the accident, damage, and consequences. Therefore, this study was performed to evaluate and comparing existing practices in developing and developed countries for collecting road accident data. Moreover, the manual and digital approaches of data collection are highlighted. Keeping this in mind, this review provides an overview of how developing countries currently collect their data and their data dissemination methods to extract such useful information, which could prove beneficial in deciding the road safety programs for the well-being of end-users

Vehicle Driving Risk Prediction Based on Markov Chain Model

A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state