  Predicting transportation modes from GPS (Global Positioning System) records
is a hot topic in the trajectory mining domain. Each GPS record is called a
trajectory point and a trajectory is a sequence of these points. Trajectory
mining has applications including but not limited to transportation mode
detection, tourism, traffic congestion, smart cities management, animal
behaviour analysis, environmental preservation, and traffic dynamics are some
of the trajectory mining applications. Transportation modes prediction as one
of the tasks in human mobility and vehicle mobility applications plays an
important role in resource allocation, traffic management systems, tourism
planning and accident detection. In this work, the proposed framework in Etemad
et al. is extended to consider other aspects in the task of transportation
modes prediction. Wrapper search and information retrieval methods were
investigated to find the best subset of trajectory features. Finding the best
classifier and the best feature subset, the framework is compared against two
related papers that applied deep learning methods. The results show that our
framework achieved better performance. Moreover, the ground truth noise removal
improved accuracy of transportation modes prediction task; however, the
assumption of having access to test set labels in pre-processing task is
invalid. Furthermore, the cross validation approaches were investigated and the
performance results show that the random cross validation method provides
optimistic results.
