10 papers analyzed
These studies suggest machine learning techniques have been increasingly employed for traffic control, improving traffic signal timing, predicting traffic flow, and reducing congestion and delays.
10 papers analyzed
The application of machine learning (ML) techniques in traffic control has gained significant attention in recent years. This interest stems from the need to optimize traffic flow, reduce congestion, and improve overall transportation efficiency. Various ML methods, including supervised, unsupervised, and reinforcement learning, have been explored to address these challenges.
Superiority of ML Methods Over Traditional Methods:
Unsupervised Learning for Traffic Management:
Deep Learning for Traffic Flow Prediction:
Reinforcement Learning for Traffic Signal Control:
IoT and ML Integration for Traffic Management:
Traffic Prediction and Management Using ML:
Machine learning techniques have been increasingly employed for traffic control, demonstrating superior performance over traditional methods. The use of supervised, unsupervised, and reinforcement learning methods has shown significant improvements in traffic signal timing, flow prediction, and overall traffic management. The integration of IoT and ML further enhances the efficiency of traffic systems, making them more adaptive and responsive to real-time conditions. As research continues to evolve, these intelligent systems are expected to play a crucial role in optimizing urban traffic and reducing congestion.
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