Traffic congestion prediction and missing data: a classification approach using weather information

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.