Real-Time Traffic Prediction Through Stochastic Gradient Descent

Yasmine Amor, Yasmine Amor, Lilia Rejeb, Nabil Sahli, Wassim Trojet, Lamjed Ben Said, Ghaleb Hoblos

2024

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.

Paper Citation

in Harvard Style

Amor Y., Rejeb L., Sahli N., Trojet W., Ben Said L. and Hoblos G. (2024). Real-Time Traffic Prediction Through Stochastic Gradient Descent. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 361-369. DOI: 10.5220/0012687400003702

in Bibtex Style

@conference{vehits24,
author={Yasmine Amor and Lilia Rejeb and Nabil Sahli and Wassim Trojet and Lamjed Ben Said and Ghaleb Hoblos},
title={Real-Time Traffic Prediction Through Stochastic Gradient Descent},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={361-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012687400003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Real-Time Traffic Prediction Through Stochastic Gradient Descent
SN - 978-989-758-703-0
AU - Amor Y.
AU - Rejeb L.
AU - Sahli N.
AU - Trojet W.
AU - Ben Said L.
AU - Hoblos G.
PY - 2024
SP - 361
EP - 369
DO - 10.5220/0012687400003702
PB - SciTePress