2024

Conférence

10th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2024)

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.

Amor, Y., Rejeb, L., Sahli, N., Trojet, W., Said, L. B., & Hoblos, G. (2024). Real-Time Traffic Prediction Through Stochastic Gradient Descent. In VEHITS (pp. 361-369).