Modeling travel behavior similarity with trajectory embedding

The prevalence of GPS-enabled devices and wireless communication technologies has led to myriads of spatial trajectories describing the movement history of moving objects. While a substantial research effort has been undertaken on the spatio-temporal features of trajectory data, recent years have witnessed the flourish of location-based web applications (i.e., Foursquare, Facebook), enriching the traditional trajectory data by associating locations with activity information, called activity trajectory. These trajectory data contain a wealth of activity information and offer unprecedented opportunities for heightening our understanding about human behaviors. In this paper, we propose a novel framework, called TEH (Trajectory Embedding and Hashing), to mine the similarity among users based on their activity trajectories. Such user similarity is of great importance for individuals to effectively retrieve the information with high relevance. With the time being separated into several slots according to the activity-based temporal distribution, we utilize trajectory embedding technique to mine the sequence property of the activity trajectories by treating them as paragraphs. Then a hash-based method is presented to reduce the dimensions for improving the efficiency of users’ similarity calculation. Finally, extensive experiments on a real activity trajectory dataset demonstrate the effectiveness and efficiency of the proposed methods.