Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data

Henry Martin, Dominik Bucher, Esra Suel, Pengxiang Zhao, Fernando Perez-Cruz, Martin Raubal

Nov 30, 2018 NIPS 2018 Workshop Spatiotemporal Blind Submission readers: everyone
  • 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.
  • TL;DR: We use graphs to model human mobility (based on smart phone recorded GPS tracks) and apply graph convolutional neural networks to predict the purpose of their trips
  • Keywords: human mobility, graph convolutional neural networks, trip purpose prediction
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