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' 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 Mobilytics designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) and a deep learning architecture that automatically annotates the GPS log. Additionally, we propose a novel “transfer learning” 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 ($\approx$ 18% to $\approx$ 30%). Moreover, the analysis on huge volume of simulated traces (10,000 users) illustrates the scalability and robustness of the framework.

Department(s)

Computer Science

Keywords and Phrases

Data mining; Global Positioning System; Knowledge graphs; Knowledge transfer; Mobility knowledge graph; POI (point-of-interest); Security; Semantics; Semantics; Spatio-temporal trajectory; Trajectory; Transfer learning

International Standard Serial Number (ISSN)

1558-0660; 1536-1233

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2024

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