A Scalable IoT-Driven Framework for Real-Time Traffic Management and Accident Prevention Using Edge Intelligence and Adaptive Safety Analytics

R. Ashok Kumar, Indrani Hazarika, S. Thomas Praveen Joseph, K. Arulini, R. Prabhu, M. Srinivasulu

2025

Abstract

The urban traffic is becoming more and more complex, and requires intelligent and adaptive transportation systems for safety, efficiency and sustainability. This study presents a novel scale able IoT-based framework using edge intelligence and real-time analytics for controlling traffic flow and for preventing accidents in a proactive manner. Contrary to classical approaches, we resort to low-latency edge processing, federated learning and predictive modeling to dynamically respond to variations occurring on the road. Real traffic and sensor datasets are used to train and validate the model at different intersections. The system also includes built-in support for pedestrian safety, emergency vehicle response, as well as cloud-edge setup for easy deployment. The experimental results show that the response time and traffic congestion are significantly reduced in the presence of an accident, indicating that the proposed approach can effectively improve urban mobility.

Paper Citation

in Harvard Style

Kumar R., Hazarika I., Joseph S., Arulini K., Prabhu R. and Srinivasulu M. (2025). A Scalable IoT-Driven Framework for Real-Time Traffic Management and Accident Prevention Using Edge Intelligence and Adaptive Safety Analytics. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 63-70. DOI: 10.5220/0013857600004919

in Bibtex Style

@conference{icrdicct`2525,
author={R. Kumar and Indrani Hazarika and S. Joseph and K. Arulini and R. Prabhu and M. Srinivasulu},
title={A Scalable IoT-Driven Framework for Real-Time Traffic Management and Accident Prevention Using Edge Intelligence and Adaptive Safety Analytics},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={63-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013857600004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - A Scalable IoT-Driven Framework for Real-Time Traffic Management and Accident Prevention Using Edge Intelligence and Adaptive Safety Analytics
SN - 978-989-758-777-1
AU - Kumar R.
AU - Hazarika I.
AU - Joseph S.
AU - Arulini K.
AU - Prabhu R.
AU - Srinivasulu M.
PY - 2025
SP - 63
EP - 70
DO - 10.5220/0013857600004919
PB - SciTePress