Deep Feature Extraction from Trajectories for Transportation Mode Estimation

This paper addresses the problem of feature extraction for estimating users' transportation modes from their movement trajectories. Previous studies have adopted supervised learning approaches and used engineers' skills to find effective features for accurate estimation. However, such hand-crafted features cannot always work well because human behaviors are diverse and trajectories include noise due to measurement error. To compensate for the shortcomings of hand-crafted features, we propose a method that automatically extracts additional features using a deep neural network (DNN). In order that a DNN can easily handle input trajectories, our method converts a raw trajectory data structure into an image data structure while maintaining effective spatio-temporal information. A classification model is constructed in a supervised manner using both of the deep features and hand-crafted features. We demonstrate the effectiveness of the proposed method through several experiments using two real datasets, such as accuracy comparisons with previous methods and feature visualization.