The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130961
PDF

Deep Q-learning Approach based on CNN and XGBoost for Traffic Signal Control

Author 1: Nada Faqir
Author 2: Chakir Loqman
Author 3: Jaouad Boumhidi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Traffic signal control is a way for reducing traffic jams in urban areas, and to optimize the flow of vehicles by minimizing the total waiting times. Several intelligent methods have been proposed to control the traffic signal. However, these methods use a less efficient road features vector, which can lead to suboptimal controls. The objective of this paper is to propose a deep reinforcement learning approach as the hybrid model that combines the convolutional neural network with eXtreme Gradient Boosting to traffic light optimization. We first introduce the deep convolutional neural network architecture for the best features extraction from all available traffic data and then integrated the extracted features into the eXtreme Gradient Boosting model to improve the prediction accuracy. In our approach; cross-validation grid search was used for the hyper-parameters tuning process during the training of the eXtreme Gradient Boosting model, which will attempt to optimize the traffic signal control. Our system is coupled to a microscopic agent-based simulator (Simulation of Urban MObility). Simulation results show that the proposed approach improves significantly the average waiting time when compared to other well-known traffic signal control algorithms.

Keywords: Convolutional neural network; extreme gradient; traffic control; traffic optimization; urban mobility

Nada Faqir, Chakir Loqman and Jaouad Boumhidi, “Deep Q-learning Approach based on CNN and XGBoost for Traffic Signal Control” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130961

@article{Faqir2022,
title = {Deep Q-learning Approach based on CNN and XGBoost for Traffic Signal Control},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130961},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130961},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Nada Faqir and Chakir Loqman and Jaouad Boumhidi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org