Authors:
Yasmine Amor
1
;
2
;
Lilia Rejeb
1
;
Nabil Sahli
3
;
Wassim Trojet
4
;
Lamjed Ben Said
1
and
Ghaleb Hoblos
2
Affiliations:
1
Université de Tunis, Institut Supérieur de Gestion de Tunis, SMART Lab, Tunis, Tunisia
;
2
IRSEEM, Technopole du Madrillet, Av. Galilee, Saint-Etienne du Rouvray, Normandy, France
;
3
German University of Technology in Oman, Oman
;
4
Higher Colleges of Technology, U.A.E.
Keyword(s):
Online Learning Methods, Real-Time Data, Traffic Prediction, Stochastic Gradient Descent.
Abstract:
The escalating challenges of urban traffic congestion pose a critical issue that calls for efficient traffic management system solutions. Traffic forecasting stands out as a paramount area of exploration in the field of Intelligent Transportation Systems. Various traditional machine learning techniques have been employed for predicting traffic congestion, often requiring a significant amount of data to train the model. For that reason, historical data are usually used. In this paper, our first concern is to use real-time traffic data. We adopted Stochastic Gradient Descent, an online learning method characterized by its ability to continually adapt to incoming data, facilitating real-time updates and rapid predictions. We studied a network of streets in the city of Muscat, Oman. Our model showed its accuracy through comparisons with actual traffic data.