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Classification of Travel Modes from Cellular Network Data Using Machine Learning Algorithms

Data availability in recent years has grown exponentially, allowing researchers in the transport sector to harness valuable information regarding traffic flows. In that sense, cellular network data represents valuable traffic information when dealing with spatially large areas due to its property of collecting route data using distant mobile base stations. This property enables the automatic collection of origin-destination data, which is traditionally collected using field or online questionnaires. This paper aims to present the possibility of using origin-destination data extracted from cellular network dataset to classify travel modes. A case study was performed on the dataset collected in the City of Rijeka, Croatia. Dataset is evaluated on five machine learning algorithms, which resulted in Random forest as the highest performing algorithm with an accuracy score of 99.93%.