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Scientific Article details

Title Proposal of a Machine Learning Approach for Traffic Flow Prediction
ID_Doc 43947
Authors Berlotti, M; Di Grande, S; Cavalieri, S
Title Proposal of a Machine Learning Approach for Traffic Flow Prediction
Year 2024
Published Sensors, 24, 7
DOI 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.
Author Keywords forecasting; artificial intelligence; traffic congestion; urban scenario; smart city
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED)
EID WOS:001200909100001
WoS Category Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation
Research Area Chemistry; Engineering; Instruments & Instrumentation
PDF https://www.mdpi.com/1424-8220/24/7/2348/pdf?version=1712483114
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