A Comprehensive Analysis of Machine Learning and Deep Learning Approaches for Road Accident Prediction

Abstract

Road accidents pose a substantial concern for all individuals. On a daily basis, a substantial number of precious lives are tragically lost due to vehicular collisions. The research conducted on road accident detection and prevention involves the utilization of various datasets to predict potential scenarios that may result in road accidents. Nevertheless, a significant obstacle that arises during the development of computer-vision models aimed at identifying traffic attributes in road accidents is the scarcity of available datasets. The dataset lacks diverse factors contributing to road accidents, which could lead to leveraging for training deep-learning models on a broad scale. This work seeks to give an in-depth examination of road accident databases, implementing machine-learning techniques, and use of Deep Learning (DL) algorithms on road accident datasets.  This research examines the methods of Machine Learning (ML) and DL algorithms that are utilized in the process of creating road accident projections, as well as their relevance to the data sets that are being taken into consideration.