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