Revealing an Inherently Limiting Factor in Human Mobility Prediction

Predicting how humans move within space and time is a central topic in many scientific domains such as epidemic propagation, urban planning, and ride-sharing. However, current studies neglect individuals' preferences to explore and discover new areas. Yet, neglecting novelty-seeking activities...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing 2023-07, Vol.11 (3), p.635-649
Main Authors: Amichi, Licia, Carneiro, Aline Viana, Crovella, Mark, Loureiro, Antonio
Format: Article
Language:English
Subjects:
Computer Science
Global Positioning System
Individual mobility analytic
Mobile Computing
Mobility
mobility understanding and profiling
Numerical Analysis
predictability
prediction
Predictions
Predictive models
Soft sensors
Spatial resolution
Task analysis
Trajectory
Upper bound
Urban planning
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Summary:Predicting how humans move within space and time is a central topic in many scientific domains such as epidemic propagation, urban planning, and ride-sharing. However, current studies neglect individuals' preferences to explore and discover new areas. Yet, neglecting novelty-seeking activities at first glance appears to be inconsequential on the ability to understand and predict individuals' trajectories. We claim and show the opposite in this work: exploration-like visits strongly impact mobility understanding and anticipation. We start by proposing a new approach to identifying exploration visits. Based on that, we construct individuals' mobility profiles using their exploration inclinations - Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., with no extreme behavior). Finally, we evaluate the impacts of novelty-seeking, quality of the data, and the prediction task formulation on the theoretical and practical predictability extents. The results show the validity of our profiling and highlight the obstructive impacts of novelty-seeking activities on the predictability of human trajectories. In particular, in the next-place prediction task, from 40% to 90% of predicted locations are wrong, notably with Scouters .
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2022.3229088