Big Data Mining for Smart Cities: Predicting Traffic Congestion using Classification

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 2 nd 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.