Abstract
In recent decades, pre-predicting the roadway accidents is essential for real time traffic incident management that effectively minimizes the environmental pollution, traffic congestion and secondary incidents. Currently, the traffic data are available in thousands of public and private datasets and also generates terabytes of data each year. Though, it is infeasible to manage the huge datasets by utilizing traditional software and hardware. It is therefore essential that an automated system to predict road accidents is developed. The present review paper investigates the researches done on road accident prediction, particularly for urban roads under heterogeneous traffic conditions. It also explores the problems faced in existing works by researchers. This review paper helps researchers achieve a better solution for the current problems faced by heterogeneous traffic conditions when it comes to urban road accident prediction. The findings demonstrate that the operating speed and the disparities between the speed restrictions and the operating speed are the key factors influencing the accident frequency rate.
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