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Title	Traffic Congestion Prediction And Missing Data: A Classification Approach Using Weather Information
ID_Doc	58543
Authors	Mystakidis A.; Tjortjis C.
Year	2024
Published	International Journal of Data Science and Analytics
DOI	http://dx.doi.org/10.1007/s41060-024-00604-y
Abstract	Traffic congestion in major cities is becoming increasingly severe. Numerous academic and commercial initiatives were conducted over the past decades to address this challenge, often delivering accurate and timely traffic condition predictions. Furthermore, traffic congestion forecasting has recently become, more than ever, an expanding study field, particularly due to growth of machine learning and artificial intelligence. This paper proposes a low-cost methodology to predict and fill current traffic congestion values for road parts having insufficient or missing historical data for timestamps with missing information, while reviewing several machine learning algorithms to select the most suitable ones. The methodology was evaluated on several open source data originated from one of the most challenging, regarding traffic, streets in Thessaloniki, the second largest city in Greece and was further validated over a second time period. Through experimentation with various cases, result comparison indicated that utilizing road segments proximate to those with missing data, in conjunction with a Multi-layer Perceptron, facilitates the accurate filling of missing values. Result evaluation revealed that dealing with data imbalance issues and importing weather features increased algorithmic accuracy for almost all classifiers, with Multi-layer Perceptron being the most accurate one. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Author Keywords	Classification; Deep learning; Machine learning; Missing data; Smart cities; Traffic congestion prediction

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