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Title	Road Traffic Forecasting Using A Real Data Set In Morocco
ID_Doc	46755
Authors	Slimani N.; Slimani I.; Amghar M.; Sbiti N.
Year	2020
Published	Procedia Computer Science, 177
DOI	http://dx.doi.org/10.1016/j.procs.2020.10.020
Abstract	Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart cities. This work is in line with this perspective and aims to solve this problem. The proposed methodology plans to forecast day-by-day traffic stream using three different models: the Multilayer Perceptron of Artificial Neural Networks (ANN), the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Support Machine Regression (SMOreg). Using those three models, the forecast is realized based on a history of real traffic data recorded on a road section over 42 months. Besides, a recognized traffic manager in Morocco provides this dataset; the performance is then tested based on predefined criteria. From the experiment results, it is clear that the proposed ANN model achieves highest prediction accuracy with the lowest absolute relative error of 0.57%. © 2020 The Authors. Published by Elsevier B.V.
Author Keywords	Artificial neural networks; MLP; Road traffic forecasting; SARIMA; SMOreg; Statistical forecasting

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