Bridging Semantics: Mobility Analytics Framework for Knowledge Transfer

This paper introduces MoveInsight, a novel framework, leveraging a Mobility Knowledge Graph and deep learning architecture to analyze individuals' GPS traces from sensor-equipped smartphones for extracting trip purposes and understanding spatio-temporal mobility patterns. Unlike traditional information retrieval methods, MoveInsight deciphers the motivations behind travels by examining relations among individuals' movement behaviors, locations, and semantic contexts. The framework employs a multi-task learning approach for annotating trajectories and a transfer learning method for extending analysis to different regions, utilizing insights from comparable areas. Through real-world dataset testing, MoveInsight outperformed baseline methods in trip-purpose extraction and Point-of-Interest annotations by around 18% to 30%, showcasing its promise in enhancing location-centric services by providing deeper insights into human mobility dynamics.