Mobility modes awareness from trajectories based on clustering and a convolutional neural network

Abstract

Massive trajectory data generated by ubiquitous position acquisition technology are valuable for knowledge discovery. The study of trajectory mining that converts knowledge into decision support becomes appealing. Mobility modes awareness is one of the most important aspects of trajectory mining. It contributes to land use planning, intelligent transportation, anomaly events prevention, etc. To achieve better comprehension of mobility modes, we propose a method to integrate the issues of mobility modes discovery and mobility modes identification together. Firstly, route patterns of trajectories were mined based on unsupervised origin and destination (OD) points clustering. After the combination of route patterns and travel activity information, different mobility modes existing in history trajectories were discovered. Then a convolutional neural network (CNN)-based method was proposed to identify the mobility modes of newly emerging trajectories. The labeled history trajectory data were utilized to train the identification model. Moreover, in this approach, we introduced a mobility-based trajectory structure as the input of the identification model. This method was evaluated with a real-world maritime trajectory dataset. The experiment results indicated the excellence of this method. The mobility modes discovered by our method were clearly distinguishable from each other and the identification accuracy was higher compared with other techniques.