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Title	Big Data Mining For Smart Cities: Predicting Traffic Congestion Using Classification
ID_Doc	12109
Authors	Mystakidis A.; Tjortjis C.
Year	2020
Published	11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
DOI	http://dx.doi.org/10.1109/IISA50023.2020.9284399
Abstract	This paper provides an analysis and proposes a methodology for predicting traffic congestion. Several machine learning algorithms and approaches are compared to select the most appropriate one. The methodology was implemented using Data Mining and Big Data techniques along with Python, SQL, and GIS technologies and was tested on data originating from one of the most problematic, regarding traffic congestion, streets in Thessaloniki, the 2nd most populated city in Greece. Evaluation and results have shown that data quality and size were the most critical factors towards algorithmic accuracy. Result comparison showed that Decision Trees were more accurate than Logistic Regression. © 2020 IEEE.
Author Keywords	Big Data; Classification; Data Mining; Machine learning; Prediction; Smart Cities; Traffic Congestion

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