The Impact of Artificial Intelligence on System Response to Changing Traffic Signals
Collage Of Nursing, University of Babylon, Babylon, Hillah,51001, Iraq.
Research Article
Open Access Research Journal of Science and Technology, 2025, 15(02), 041-043.
Article DOI: 10.53022/oarjst.2025.15.2.0134
Publication history:
Received on 18 October 2025; revised on 23 November 2025; accepted on 26 November 2025
Abstract:
Urban traffic signal systems are facing increasing strain due to rising vehicle volumes, dynamic travel patterns, and unexpected disruptions. This paper investigates how artificial intelligence (AI) techniques especially deep reinforcement learning (DRL) and multi‐agent/cooperative frameworks affect system responsiveness to changing traffic signals. We compare traditional signal control approaches (fixed‐time, actuated, adaptive like SCOOT/SCATS) with AI‐enhanced controllers, and propose an experimental framework using a microscopic traffic simulator (for example, SUMO) to assess performance under variable traffic demands. Metrics include average delay, queue length, throughput, and responsiveness to sudden changes (e.g., surge in traffic, incident). Results are expected to demonstrate significant improvements in responsiveness and efficiency while highlighting remaining challenges such as transferability, real‐world deployment, and data requirements.
Keywords:
Artificial Intelligence; Traffic system; DRL; Simulator
Full text article in PDF:
Copyright information:
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0