Skip to main content

ORIGINAL RESEARCH article

Front. Sustain. Cities
Sec. Smart Technologies and Cities
Volume 6 - 2024 | doi: 10.3389/frsc.2024.1443165
This article is part of the Research Topic Sustainable Development in Artificial Intelligence, Blockchain and Internet of Things View all 4 articles

Robust Traffic System for Congestion Control using Gradient Boosting in Smart City

Provisionally accepted
Samiya Sharma Samiya Sharma 1Ridhima Sharma Ridhima Sharma 1Atif Chowhan Atif Chowhan 2*Mudita Uppal Mudita Uppal 1Deepali Gupta Deepali Gupta 1Shilpa Saini Shilpa Saini 1Yasir Hamid Yasir Hamid 2
  • 1 Chitkara Institute of Engineering & Technology, Chitkara University, Punjab, Punjab, India
  • 2 Abu Dhabi Polytechnic, Abu Dhabi, United Arab Emirates

The final, formatted version of the article will be published soon.

    With the ever-increasing and ever-changing population of the world, the need for efficient transportation and a well-organized traffic system has become increasingly critical. The convergence of Machine Learning (ML) and Internet of Things (IoT) technologies is propelling the global emergence of a new era of intelligent transportation. This paper meticulously investigates different models for traffic prediction by utilizing four strategies, i.e., Random Forest, Gradient Boosting, Decision Tree and Support Vector Machine. The proposed models' accuracy and efficacy are highlighted by rigorous evaluation with metrics namely Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R-squared (R2) score. The study addresses key challenges such as ensuring computational efficiency, maintaining model interpretability, and avoiding overfitting. Among the evaluated models, Gradient Boosting proved to be the most effective ML method, achieving an accuracy of 85.6%, with the lowest MAE and MSE, and the highest RMSE and R2 score. Conversely, the Support Vector Machine model showed the weakest performance, with the highest errors and lowest R2 score among the models evaluated. This paper's key findings demonstrate that Gradient Boosting is the most effective model for traffic prediction, significantly improving the accuracy and reliability of smart transportation systems. These results show that advanced traffic management systems have the ability to significantly boost the effectiveness and dependability of smart transportation infrastructure, paving the way for future innovation and progress in this pivotal field. Future directions involve integration of real-time data, scalability, support for autonomous vehicles, multi-modal analysis, adaptive ML models and enhanced security.

    Keywords: traffic, Congestion control, Internet of Things, Smart city, machine learning, gradient boosting

    Received: 03 Jun 2024; Accepted: 19 Aug 2024.

    Copyright: © 2024 Sharma, Sharma, Chowhan, Uppal, Gupta, Saini and Hamid. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Atif Chowhan, Abu Dhabi Polytechnic, Abu Dhabi, United Arab Emirates

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.