Mobilytics: Mobility Analytics Framework for Transferring Semantic Knowledge

Abstract

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. his paper focuses on how individuals&#x0027; GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named <italic>Mobilytics</italic> designed to identify trip purposes from individual GPS traces by leveraging a &#x201C;mobility knowledge graph&#x201D; (MKG) and a deep learning architecture that automatically annotates the GPS log. Additionally, we propose a novel &#x201C;transfer learning&#x201D; approach to explore movement dynamics in a geographically distant area by leveraging knowledge obtained from a comparable region, such as an academic campus. In terms of major contributions and novelty, this is the first work to present end-to-end daily mobility trip purpose extraction and mobility knowledge transfer for trip annotation and POI-tagging where the labeled data are insufficient. Experimental results on real-life datasets of five different regions demonstrate the efficacy of our proposed Mobilytics framework which outperforms the baselines for trip-purpose extraction and POI annotations by a significant margin (<inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 18&#x0025; to <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 30&#x0025;). Moreover, the analysis on huge volume of simulated traces (10,000 users) illustrates the scalability and robustness of the framework. IEEE