Traffic forecasting in Morocco using artificial neural networks

Abstract

Due to industrialization and the growth of transportation systems, the number of vehicles continues to increase which causes a significant traffic jam problem especially in big cities. Consequently, the prediction of traffic flows is a key component to an optimal traffic management. As a solution to this issue, the present paper aims at applying the artificial intelligence of neural networks, which offers an interesting approach to modelling in complex, and nonlinear situations. Our resolution method is based on the design of a neural network to predict daily traffic flow. Then, the forecasted traffic flow is compared with a real dataset recorded on a road section and provided by a recognized infrastructure manager in Morocco. Indeed, neural networks have the ability to learn from the past and predict the future. In this study, various neural networks structures are exanimated and simulation results show that the best forecasts are obtained with the use of Multi-Layer Perceptron architecture that has a good generalization capacity with a total Mean Square Error of 0.00927 in the train set and 0.01321 in the test set.