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.
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Acknowledgement
This work was carried out within the framework of a PhD MOBIDOC (PASRI program), funded by the EU and managed by the ANPR. The authors would like also to acknowledge the partial financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Elleuch, W., Wali, A., Alimi, A.M. (2017). Intelligent Traffic Congestion Prediction System Based on ANN and Decision Tree Using Big GPS Traces. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_47
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DOI: https://doi.org/10.1007/978-3-319-53480-0_47
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