An Adaptive Machine Learning Approach to Sustainable Traffic Planning: High-Fidelity Pattern Recognition in Smart Transportation Systems
Future Transportation, Vol. 5, No. 4, Article 152
Editorial summary
HDBSCAN and k-means group traffic patterns, with weighted voting combining their results. A simulation of the Khmelnytskyi road network tests how clearly and consistently the method identifies traffic modes. The output provides a basis for analysing transport demand and planning control strategies.
Bibliographic reference
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Vitaliy Pavlyshyn, Eduard Manziuk, Oleksander Barmak, Pavlo Radiuk, Iurii Krak. "An Adaptive Machine Learning Approach to Sustainable Traffic Planning: High-Fidelity Pattern Recognition in Smart Transportation Systems". Future Transportation, Vol. 5, No. 4, Article 152, 2025. https://doi.org/10.3390/futuretransp5040152
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