Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Smart Traffic Signal with Emergency Response Optimization

Authors
Khan Mohammad Moin1, *, Antuley Aman Siraj1, Khalife Abdul Sami1, Khan Mohd Irfan1, Tabassum Maktum2
1Department of Computer Engineering, School of Engineering and Technology, Anjuman-I-Islam’s Kalsekar Technical Campus, New Panvel, Maharashtra, India
2Department of Computer Science & Engineering (Data Science), Anjuman-I-Islam’s Kalsekar Technical Campus, New Panvel, Maharashtra, India
*Corresponding author. Email: khanmoin3757@gmail.com
Corresponding Author
Khan Mohammad Moin
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_27How to use a DOI?
Keywords
Smart Traffic System; Emergency Response; Accident Detection
Abstract

This integrated smart traffic signal and emergency response optimization system increases the efficiency of urban traffic and also reduces emergency response time. Every signal has two Artificial Intelligence models powered by YOLO for detecting accidents and counting vehicles during the red phases of the traffic signal. Based on vehicle counts, adaptive traffic control is done by making dynamic green-signal timing through Webster’s formula. The computed timings are stored and managed in the server, which enables the real-time update and persistence of the data. Two applications are designed that support the entire workflow of the system. The first allows users to call for ambulances, whereas the second one leads drivers into a route to patients and hospitals using custom routing through Railway and the Google maps API. Green hits were triggered for the approaching vehicles to the intersections using GPStracked ambulances. However, a hospital dashboard automates driver registration, ambulance tracking, and notifications. Firebase unifies the authentication provided across all these components to give greater security. The optimization of daily traffic system will optimize emergency response operations through advanced computer vision, dynamic traffic algorithms, and real-time communication between traffic control systems and emergency response agencies.

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.

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Volume Title
Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_27How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Khan Mohammad Moin
AU  - Antuley Aman Siraj
AU  - Khalife Abdul Sami
AU  - Khan Mohd Irfan
AU  - Tabassum Maktum
PY  - 2025
DA  - 2025/10/07
TI  - Smart Traffic Signal with Emergency Response Optimization
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 427
EP  - 446
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_27
DO  - 10.2991/978-94-6463-852-3_27
ID  - Moin2025
ER  -