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Transportation modes prediction is a fundamental task for decision making insmart cities and traffic management systems. Traffic policies designed based ontrajectory mining can save money and time for authorities and the public. Itmay reduce the fuel consumption and commute time and moreover, may provide morepleasant moments for residents and tourists. Since the number of features thatmay be used to predict a user transportation mode can be substantial, finding asubset of features that maximizes a performance measure is worth investigating.In this work, we explore wrapper and information retrieval methods to find thebest subset of trajectory features. After finding the best classifier and thebest feature subset, our results were compared with two related papers thatapplied deep learning methods and the results showed that our frameworkachieved better performance. Furthermore, two types of cross-validationapproaches were investigated, and the performance results show that the randomcross-validation method provides optimistic results.

On feature selection and evaluation of transportation mode prediction strategies