Unveiling the Power of Deep Learning in Traffic Control: A Comprehensive Review
Author(s):
Subathra K V, Raghul Ram R A, Konar Shivaraj Suresh, Bhavish, Ashwin A J
Keywords:
Autonomous Vehicles, Deep Learning, Predictive Analytics, Real-Time Traffic Monitoring, Smart Traffic Control, Traffic Flow Optimization
Abstract
Traffic congestion is a prevalent issue in urban areas, leading to significant economic losses, environmental pollution, and decreased quality of life. Traditional traffic management systems often fall short in effectively alleviating congestion due to their static and rule-based nature. In recent years, deep learning techniques have emerged as promising tools for addressing traffic management challenges by enabling intelligent prediction and control systems. This research article explores the application of deep learning methods in smart traffic control and the prediction of traffic flow. We review recent advancements, discuss challenges, and propose potential directions for future research in this domain. This paper presents an innovative approach to address urban traffic congestion through the application of deep learning techniques for traffic prediction and control. Traditional traffic management systems often struggle to adapt to dynamic traffic conditions, leading to inefficiencies and increased congestion. Leveraging deep learning models, particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs), we propose a framework for smart traffic control that integrates real-time traffic data to predict traffic flow and optimize signal timings. This paper discusses the implementation of deep learning-based traffic prediction models, the development of adaptive traffic control algorithms, and the potential benefits of this approach in improving traffic efficiency and reducing congestion in urban environments.
Article Details
Unique Paper ID: 163220

Publication Volume & Issue: Volume 0, Issue no

Page(s): 91 - 95
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