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Mobilytics: Mobility Analytics Framework for Transferring Semantic Knowledge
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PRE
AI
DOI：10.1109/TMC.2024.3413589.png)
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摘要
En 中文
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. This 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 (approximate to 18% to approximate to 30%). Moreover, the analysis on huge volume of simulated traces (10,000 users) illustrates the scalability and robustness of the framework.
Keyword:
Knowledge transfer
Mobility knowledge graph
POI (point-of-interest)
semantics
spatio-temporal trajectory
transfer learning
Knowledge transfer
Mobility knowledge graph
POI (point-of-interest)
semantics
spatio-temporal trajectory
transfer learning
期刊
IF:
9.2
论文数:
6.0K
被引数:
2.0W
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