Safelane: Real-Time Traffic Management And Accident Detection Using YOLO And LSTM

DOI

10.2991/978-94-6463-858-5_26How to use a DOI?

Keywords

Traffic management; real-time vehicle recognition; YOLO; LSTM network; traffic signal control; deep learning; congestion reduction; accident detection; emergency notification; dynamic signal adjustment

Abstract

There is a significant challenge in densely populated areas which is traffic management in traditional ways leading to increased travel time, fuel consumption, and environmental pollution. Traffic signal controls operate on pre-programmed timings, failing to adapt dynamically to real-time traffic conditions. This paper describes an intelligent traffic control system with real-time vehicle recognition using You Only Look Once and predictive traffic signal control based on a Long Short-Term Memory network. The system uses video feed data for vehicle detection, traffic density estimation, and dynamic signal time adjustments according to levels of congestion. The system uses deep-learning based approaches to reduce traffic and manage it efficiently which reduces congestions and the need for traditional traffic management methods. This also leads to reduces emission of CO from the vehicles during congestion due to lesser waiting time in the traffic signals. The system also detects accidents and informs the nearby authorities or ambulance through notification which leads to faster assistance to the people in need. The need for expensive hardware is also eliminated through this system’s use.

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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TY  - CONF
AU  - K. Shreya
AU  - R. Arun
AU  - A. Gowtham
AU  - R. Arunkumar
PY  - 2025
DA  - 2025/11/04
TI  - Safelane: Real-Time Traffic Management And Accident Detection Using YOLO And LSTM
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 283
EP  - 301
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_26
DO  - 10.2991/978-94-6463-858-5_26
ID  - Shreya2025
ER  -