Transportation Mode Estimation from Sparse GPS Trajectories: A Sensitivity Analysis of Features, Preprocessings, and Algorithms

Trajectories recorded via the Global Positioning System (GPS) can be used to estimate the transport mode, such as train, bus, or car. This study estimated the modes from sparsely sampled data designed to resemble real-world trajectories. Different sampling frequencies, preprocessing methods, and other classification techniques were compared as sensitivity analyses within the sparse dataset. The proposed model achieved an accuracy of 74.8% and an F1-score of 75.2% at a 5-minute sampling frequency. Speed and geographic information contributed the most to the classification task. The collection of additional trajectory data may further improve accuracy and influence the outcomes of comparative analyses.