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

Martin, Henry; Bucher, Dominik; Suel, Esra; Zhao, Pengxiang; Perez-Cruz, Fernando; Raubal, Martin; (2018) Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data. In: Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018). (pp. p. 48). Neural Information Processing Systems (NeurIPS): Montréal, Canada. Green open access

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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 spatiotemporal 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.

Type: Proceedings paper
Title: Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data
Event: NeurIPS 2018 Workshop on Modelling and Decision-Making in the Spatiotemporal Domain
Open access status: An open access version is available from UCL Discovery
Publisher version: https://openreview.net/forum?id=H1xYUOmy1V
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: human mobility, graph convolutional neural networks, trip purpose prediction
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10183383
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