Trajectory User Linking With Self Organizing Trees
Eric Christensen, Kade Shoemaker, Sonja Hardy · 2024
The explosion of mobile devices, GPS technology, and IoT sensors has generated an unprecedented volume of spatiotemporal data, offering immense potential for insights into human behavior and spatial patterns. In this paper, we introduce a novel self-organizing tree algorithm designed to aggregate and organize human mobility data for downstream tasks. Our approach addresses the scalability issues faced by traditional Trajectory User Linking (TUL) techniques, enabling efficient handling of large numbers of users and dynamic addition of new users. This advancement paves the way for more personalized and innovative applications across various industries and. Our findings highlight the transformative potential of self-organizing trees in spatiotemporal data analysis, setting a new benchmark for future research.