Abstract
During the past years, there were so many researches focusing on traffic prediction and ways to resolve future traffic congestion; at the very beginning, the goal was to build a mechanism capable of predicting the traffic for short-term; meanwhile, others did focus on the traffic prediction using different perspectives and methods, in order to obtain better and more precise results. The main aim was to come up with enhancements to the accuracy and precision of the outcomes and get a longer-term vision, also build a prediction’s system for the traffic jams and solve them by taking preventive measures (Bolshinsky and Freidman in Traffic flow forecast survey 2012, [1]) basing on artificial intelligence decisions with the given predictions. There are many algorithms; some of them are using statistical physics methods; others use genetic algorithms… the common goal was to achieve a kind of framework that will allow us to move forward and backward in time to have a practical and effective traffic prediction. In addition to moving forward and backward in time, the application of the new framework allows us to locate future traffic jams (congestions). This paper reviews the evolution of the existing traffic prediction’s approaches and the edge given by AI to make the best decisions; we will focus on the model-driven and data-driven approaches. We start by analyzing all advantages and disadvantages of each approach to reach our goal in order to pursue the best approaches for the best output possible.
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Soussi Niaimi, BE., Bouhorma, M., Zili, H. (2022). The Evolution of the Traffic Congestion Prediction and AI Application. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_2
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