APPROACH OF AGGLOMERATIVE CLUSTERING ALGORITHM FOR DEEP LEARNING-BASED SPATIAL AND TEMPORAL ROAD TRAFFIC DATA ANALYSIS
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| Název: | APPROACH OF AGGLOMERATIVE CLUSTERING ALGORITHM FOR DEEP LEARNING-BASED SPATIAL AND TEMPORAL ROAD TRAFFIC DATA ANALYSIS |
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| Autoři: | Kavita Gudadhe, null Divya Rohatgi, null Sonali. A. Pakhmode-Lohbare, null Baddepaka Prasad, null Dipti Patil, null Dr. Purnima Niranjane |
| Zdroj: | Lex localis - Journal of Local Self-Government. 23:46-62 |
| Informace o vydavateli: | UK Zhende Publishing Limited Company, 2025. |
| Rok vydání: | 2025 |
| Popis: | The rapid growth of urbanization and the increasing number of vehicles have made road traffic management a critical research challenge. Effective analysis of spatial and temporal traffic data is essential for reducing congestion, enhancing safety, and improving transportation efficiency. In this study, an agglomerative clustering algorithm is integrated with deep learning techniques to model and analyze complex traffic patterns. Agglomerative clustering is employed to group similar spatial–temporal traffic data segments, enabling the identification of hidden structures and congestion-prone zones. The clustered data is then processed using deep learning models to capture nonlinear dependencies and dynamic fluctuations in traffic flow. This hybrid approach leverages the unsupervised learning capability of hierarchical clustering to provide meaningful data partitioning while utilizing the predictive power of deep learning for accurate traffic state estimation. Experimental evaluations on real-world traffic datasets demonstrate that the proposed framework achieves significant improvements in pattern recognition, anomaly detection, and congestion forecasting compared to conventional methods. The results highlight the potential of combining agglomerative clustering with deep learning for intelligent transportation systems and smart city applications. |
| Druh dokumentu: | Article |
| ISSN: | 1855-363X 1581-5374 |
| DOI: | 10.52152/800458 |
| Rights: | CC BY NC ND |
| Přístupové číslo: | edsair.doi...........7d2437db7ff2c81bf41ca6ee7bece743 |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | The rapid growth of urbanization and the increasing number of vehicles have made road traffic management a critical research challenge. Effective analysis of spatial and temporal traffic data is essential for reducing congestion, enhancing safety, and improving transportation efficiency. In this study, an agglomerative clustering algorithm is integrated with deep learning techniques to model and analyze complex traffic patterns. Agglomerative clustering is employed to group similar spatial–temporal traffic data segments, enabling the identification of hidden structures and congestion-prone zones. The clustered data is then processed using deep learning models to capture nonlinear dependencies and dynamic fluctuations in traffic flow. This hybrid approach leverages the unsupervised learning capability of hierarchical clustering to provide meaningful data partitioning while utilizing the predictive power of deep learning for accurate traffic state estimation. Experimental evaluations on real-world traffic datasets demonstrate that the proposed framework achieves significant improvements in pattern recognition, anomaly detection, and congestion forecasting compared to conventional methods. The results highlight the potential of combining agglomerative clustering with deep learning for intelligent transportation systems and smart city applications. |
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| ISSN: | 1855363X 15815374 |
| DOI: | 10.52152/800458 |