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Dynamic Purpose Decomposition of Mobility Flows Based on Geographical Data

Authors Etienne Thuillier, Laurent Moalic, Alexandre Caminada

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  • Human mobility
  • Purpose decomposition
  • Information extraction
  • Linear model

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Abstract

Spatial and temporal decomposition of aggregated mobility flows is nowadays a commonly addressed issue, but a trip-purpose decomposition of mobility flows is a more challenging topic, which requires more sensitive analysis such as heterogeneous data fusion. In this paper, we study the relation between land use and mobility purposes. We propose a model that dynamically decomposes mobility flows into six mobility purposes. To this end, we use a national transportation database that surveyed more than 35,000 individuals and a national ground description database that identifies six distinct ground types. Based on these two types of data, we dynamically solve several overdetermined systems of linear equations from a training set and we infer the travel purposes. Our experimental results demonstrate that our model effectively predicts the purposes of mobility from the land use. Furthermore, our model shows great results compared with a reference supervised learning decomposition.

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Etienne Thuillier, Laurent Moalic, and Alexandre Caminada. Dynamic Purpose Decomposition of Mobility Flows Based on Geographical Data. In 24th International Symposium on Temporal Representation and Reasoning (TIME 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 90, pp. 20:1-20:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017) https://doi.org/10.4230/LIPIcs.TIME.2017.20

Author Details

Etienne Thuillier
Laurent Moalic
Alexandre Caminada

References

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