Intelligent Traffic Congestion Prediction System Based on ANN and Decision Tree Using Big GPS Traces

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Intelligent Traffic Congestion Prediction System Based on ANN and Decision Tree Using Big GPS Traces

Elleuch, Wiam / Wali, Ali / Alimi, Adel M.

Abstract Road traffic jam is one of the major problems related to transportation field in big cities around the globe. The main purpose of our article is providing drivers with an intelligent system to predict the congestion states of the roads. In this paper, we present the architecture of our intelligent congestion prediction system based on the ANN and the data fusion. Our system does not take into account only the historical GPS data but also the real time unpredictable events which have impacts on traffic jams such as accidents. ANN has demonstrated its efficiency in forecasting traffic congestion. The fusion of predicted congestion state, the real time GPS information and the anomalous events using decisional tree has more improved the results. A real time mobile application is provided to drivers in order to help them to discover the traffic state of their destination. The model has been evaluated and validated using big GPS datasets gathered from vehicles circulating in very crowded urban city in Tunisian territory.