Intelligent Traffic Flow Prediction and Congestion Control Using Deep Learning Techniques
Abstract
Urban transportation systems worldwide are experiencing unprecedented congestion due to rapid urbanization, population growth, and increased vehicle ownership. Traditional traffic management systems rely heavily on static models and rule-based control mechanisms, which are often insufficient to handle the dynamic and nonlinear nature of modern traffic environments. This research explores an intelligent traffic flow prediction and congestion control framework leveraging deep learning techniques to improve real-time decision-making in urban traffic systems.
The study focuses on applying advanced neural network architectures such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Graph Neural Networks (GNN) to capture temporal and spatial dependencies in traffic data. By integrating data from sensors, GPS devices, and historical traffic records, the proposed system aims to accurately predict traffic congestion patterns and proactively optimize traffic signals and routing decisions.
Furthermore, reinforcement learning-based control mechanisms are explored to dynamically adjust traffic signal timing and reroute vehicles to minimize congestion. The combination of predictive analytics and adaptive control provides a scalable solution for smart city infrastructure. The findings suggest that deep learning-based traffic management systems significantly outperform traditional approaches in terms of accuracy, efficiency, and adaptability, paving the way for intelligent and autonomous urban mobility systems
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 4 No. 4 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
5806-5813 |
Published |
July 7, 2019 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Abdul Mulani (%2019). Intelligent Traffic Flow Prediction and Congestion Control Using Deep Learning Techniques. International Journal of Emerging Trends in Engineering and Management Research , Vol. 4 No. 4 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 5806-5813. https://doi.org/10.15662/ijetemr.2019.0404001 |
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