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This is a Experimental Study study.

Approach of Agglomerative Clustering Algorithm for Deep Learning-Based Spatial and Temporal Road Traffic Data Analysis

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This framework integrates agglomerative clustering and deep learning to improve traffic analysis, suggesting better congestion management.

The hybrid approach enhances traffic pattern recognition and forecasting, mitigating congestion.
Real-world traffic datasets show significant improvements in anomaly detection with this method.
Agglomerative clustering groups traffic data, making it easier to identify congestion-prone areas.
Deep learning models capture complex dependencies in traffic flow, increasing estimation accuracy.

Gudadhe et al. (2025) studied this question.

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This framework integrates agglomerative clustering and deep learning to improve traffic analysis, suggesting better congestion management.