Graph Convolutional Neural Networks for Human Activity Purpose Imputation
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Date
2018-12-08
Publication Type
Conference Paper
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yes
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Abstract
Automatic location tracking of people has recently become a viable source for mobility and movement data. Such data are used in a wide range of applications, from city and transport planning to individual recommendations and schedule optimization. For many of these uses, it is of high interest to know why a person visited at a given location at a certain point in time. We use multiple personalized graphs to model human mobility behavior and to embed a large variety of spatio-temporal information and structure in the graphs' weights and connections. Taking these graphs as input for graph convolutional neural networks (GCNs) allows us to build models that can exploit the structural information inherent in human mobility. We use GPS travel survey data to build person specific mobility graphs and use GCNs to predict the purpose of a user's visit at a certain location. Our results show that GCNs are suitable to exploit the structure embedded in the mobility graphs.
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published
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Book title
NIPS 2018 Spatiotemporal Workshop
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Pages / Article No.
Publisher
OpenReview
Event
NIPS Spatiotemporal Workshop at the 32nd Annual Conference on Neural Information Processing Systems (NIPS 2018)
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Subject
Human mobility; Graph convolutional neural networks; Trip purpose imputation
Organisational unit
03901 - Raubal, Martin / Raubal, Martin