Detection and prediction of traffic accidents using deep learning techniques

Road transportation is a statutory organ in a modern society; however it costs the global economy over a million lives and billions of dollars each year due to increase in road accidents. Researchers make use of machine learning to detect and predict road accidents by incorporating the social media which has an enormous corpus of geo-tagged data. Twitter, for example, has become an increasingly vital source of information in many aspects of smart societies. Twitter data mining for detection and prediction of road accidents is one such topic with several applications and immense promise, although there exist challenges related to huge data management. In recent years, various approaches to the issue have been offered, but the techniques and conclusions are still in their infancy. This paper proposes a deep learning accident prediction model that combines information extracted from tweet messages with extended features like sentiment analysis, emotions, weather, geo-coded locations, and time information. The results obtained show that the accuracy is increased by 8% for accident detection, making test accuracy reach 94%. In comparison with the existing state-of-the-art approaches, the proposed algorithm outperformed by achieving an increase in the accuracy by 2% and 3% respectively making the accuracy reach 97.5% and 90%. Our solution also resolved high-performance computing limitations induced by detector-based accident detection which involved huge data computation. The results achieved has further strengthened confidence that using advanced features aid in the better detection and prediction of traffic accidents.