Social Interaction using Human Mobility Prediction

Leveraging the regularities of people’s trajectories, mobility prediction can help forecast social interaction opportunities. In this project, in order to facilitate real-world social interaction, we aim to predict “serendipitous” social interactions, which are defined as unplanned encounters and interaction opportunities and regarded as emerging social interactions. We collected GPS trajectory data from people’ daily life on campus and use it as empirical mobility traces to generate decision trees and model trees to predict next venues, arrival times, and user encounter. Mobility regularities are mainly considered in these prediction models, and mobility contexts (e.g., time, location, and speed) act as decision nodes in the classification trees. Two prototype applications were developed to support serendipitous social interaction on campus, and the feedback from a user study with 25 users demonstrated the usability of these two applications.

This work appears in IEEE Transactions on Human-Machine Systems.

Tao Gu
Tao Gu
Email: firstname dot lastname AT
Phone Number: +61-2-9850-4357
Address: Room 267, 4 Research Park Drive, North Ryde, NSW, 2109, Australia

My research interests include Internet of Things, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics.