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Understanding human mobility has become an important aspect of location-based services in tasks such as personalized recommendation and individual moving pattern recognition, enabled by the large volumes of data from geo-tagged social media (GTSM). Prior studies mainly focus on analyzing human historical footprints collected by GTSM and assuming the veracity of the data, which need not hold when some users are not willing to share their real footprints due to privacy concerns—thereby affecting reliability/authenticity. In this study, we address the problem of Inferring Real Mobility (IRMo) of users, from their unreliable historical traces. Tackling IRMo is a non-trivial task due to the: (1) sparsity of check-in data; (2) suspicious counterfeit check-in behaviors; and (3) unobserved dependencies in human trajectories. To address these issues, we develop a novel Graph-enhanced Attention model calledIRMoGA, which attempts to capture underlying mobility patterns and check-in correlations by exploiting the unreliable spatio-temporal data. Specifically, we incorporate the attention mechanism (rather than solely relying on traditional recursive models) to understand the regularity of human mobility, while employing a graph neural network to understand the mutual interactions from human historical check-ins and leveraging prior knowledge to alleviate the inferring bias. Our experiments conducted on four real-world datasets demonstrate the superior performance of IRMoGA over several state-of-the-art baselines, e.g., up to 39.16% improvement regarding the Recall score on Foursquare.
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In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)
Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.
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- Award ID(s):
- 2109647
- PAR ID:
- 10582628
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Spatial Algorithms and Systems
- Volume:
- 10
- Issue:
- 2
- ISSN:
- 2374-0353
- Page Range / eLocation ID:
- 1 to 27
- Subject(s) / Keyword(s):
- Spatial Simulation, Mobility Data Science, Trajectory Data, Location Based Social Network Data, In Silico
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3672557
