Intelligent Traffic Control System Using YOLO And Reinforcement Learning for Real-Time Adaptation

DOI

10.2991/978-94-6463-858-5_15How to use a DOI?

Keywords

Traffic monitoring; AI-driven control; YOLO; reinforcement learning; traffic optimization; computer vision

Abstract

Efficient traffic management in urban areas is a growing concern due to increasing vehicle density, traffic congestion, and accident risks. Traditional traffic control methods often lack the ability to adapt dynamically to real-time traffic conditions. This paper proposes an AI-driven traffic monitoring and control system integrating computer vision techniques like YOLO (You Only Look Once) for object detection and reinforcement learning for decision-making. The proposed system captures real-time traffic video feeds, detects vehicles and pedestrians, analyzes traffic patterns, and optimizes traffic signal timings to minimize congestion and enhance road safety. Reinforcement learning is employed to adaptively adjust traffic signals based on real-time data, maximizing traffic flow efficiency. The YOLO algorithm’s fast and accurate object detection capabilities enable timely responses to dynamic traffic scenarios. The proposed approach aims to reduce congestion, minimize travel time, and lower accident risks, contributing to more sustainable urban transportation systems. Despite challenges like handling large-scale data and maintaining low latency, this AI-driven system has the potential to revolutionize traffic management in smart cities. Future work will focus on refining algorithms to handle complex traffic scenarios and enhancing scalability for broader deployment.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - A. Parimala
AU  - V. Praveen
AU  - S. Pradeep Kumar
AU  - S. Vishwa
AU  - B. Prakash
PY  - 2025
DA  - 2025/11/04
TI  - Intelligent Traffic Control System Using YOLO And Reinforcement Learning for Real-Time Adaptation
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 157
EP  - 174
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_15
DO  - 10.2991/978-94-6463-858-5_15
ID  - Parimala2025
ER  -