S Varunika.

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R. Kingsy Grace

PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH

Jayasakthi Balaji. G

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PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH

Sri Ramakrishna Engineering College

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Enhancing Traffic Management Using Deep Learning for Realtime Classification

2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (2024)

Jayasakthi Balaji. G S Varunika.R. Kingsy Grace

In this modern era, the use of vehicles is increasing tremendously due to the increase in the population. The escalating issue of traffic congestion has emerged as a critical challenge in the real world. We currently face a lack of viable solutions to efficiently manage and alleviate traffic-related issues. In the event of an accident, our ability to swiftly locate the incident and initiate requisite actions is hindered by existing constraints, contributing to delays in addressing the situation effectively. The delay in finding what the incident is and locating the problem results in the increased severity of the situation. In response to these challenges, we have devised a solution through deep learning models. Upon the occurrence of an incident, our system promptly dispatches immediate alerts to authorized personnel, and checks for the manipulation of the traffic signals, facilitating a faster and more efficient response. Therefore, action could be taken at the same time as the incident, which will reduce the impact of the situation.

Incident management

10.1109/icrito61523.2024.10522426

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