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Smart city article details

Title	Proposal Of A Machine Learning Approach For Traffic Flow Prediction
ID_Doc	43485
Authors	Berlotti M.; Di Grande S.; Cavalieri S.
Year	2024
Published	Sensors, 24, 7
DOI	http://dx.doi.org/10.3390/s24072348
Abstract	Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered. © 2024 by the authors.
Author Keywords	artificial intelligence; forecasting; smart city; traffic congestion; urban scenario

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