Road Traffic Prediction System Using Deep Neural Multilayer Perceptron (MLP)

Abstract The development of a road traffic model has numerous benefits, including the reduction of congestion in cities and precise estimated travel times. This study proposes a travel time prediction model based on a deep neural multilayer perceptron trained on GPS data collected in the city of Bechar, Algeria. GPS data was collected using an Android application and a learning model was built using Artificial Intelligence and deep learning techniques. The model was integrated into a web application that allows users to choose a route and make traffic and time predictions. Tests conducted in comparison with Google Maps and real travel experiences in Bechar showed promising results with high accuracy values for most tested routes. However, the accuracy was low in some tests due to the short duration of the data collection process. To improve the project, a data collection phase should be renewed to obtain high accuracy for all destinations and time periods.