| Issue |
ITM Web Conf.
Volume 81, 2026
International Conference on Emerging Technologies for Multidisciplinary Innovation and Sustainability (ETMIS 2025)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/itmconf/20268101004 | |
| Published online | 23 January 2026 | |
Intelligent Traffic Signal Optimization for Automated Emergency Vehicles Using Machine Learning
1 Research Scholar, SEDA E(CSE), GNA University, Phagwara, India
2 Dean & Professor, SEDA E(CSE), GNA University, Phagwara, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Effective management of traffic for emergency vehicles continues to pose a significant challenge in contemporary urban environments. Delays in emergency responses can result in dire outcomes, such as loss of life and damage to property. Conventional traffic light systems function on predetermined cycles and do not adjust dynamically to the requirements of emergencies. This paper introduces an intelligent model for traffic signal optimization that utilizes machine learning techniques to give precedence to automated emergency vehicles. The system evaluates real-time traffic data, forecasts vehicle movement, and creates adaptive green corridors specifically for emergency vehicles. Simulation outcomes indicate a reduction in response times and minimal interference with overall traffic flow.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.