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One of the main endeavors of smart cities is the organization and subsidization of public transportation. To achieve this, it is important to obtain information about the way in which people move. This once-difficult problem can now be addressed by using smartphones. This paper introduces a machine learning-based framework that is able to ascertain the usage of a public or a private transportation mode by analyzing a little amount of data sampled by a user's smartphone. The presented method exhibits a good accuracy and a limited battery consumption. A public anonymized dataset based on real measurements is also provided along with this study. To the best of our knowledge, this is the first dataset of this kind that is offered to the public.

Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes