Detecting the transportation mode for context-aware systems using smartphones

This paper presents a classification method for smartphone users mobility data in urban environments according to the used transportation mode. This classification is possible among several different transportation modes and using only the location data from user's mobility. Among the methods applied, includes data mining with machine learning techniques for the inference. This paper also presents: the performance analysis for several machine-learning algorithms for the proposed task; the process used to collect mobility data for nine users along six months; the process used for data pre-processing and the computational architecture used to collect participatory sensing data.