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Authors: Liam Steadman 1 ; Nathan Griffiths 1 ; Stephen Jarvis 1 ; Stuart McRobbie 2 and Caroline Wallbank 2

Affiliations: 1 Department of Computer Science, University of Warwick, Coventry, CV4 7AL and U.K. ; 2 TRL, Wokingham, RG40 3GA and U.K.

Keyword(s): Spatio-temporal Data, Data Reduction, Data Partitioning.

Abstract: Spatio-temporal data generated by sensors in the environment, such as traffic data, is widely used in the transportation domain. However, learning from and analysing such data is increasingly problematic as the volume of data grows. Therefore, methods are required to reduce the quantity of data needed for multiple types of subsequent analysis without losing significant information. In this paper, we present the 2-Dimensional Spatio-Temporal Reduction method (2D-STR), which partitions the spatio-temporal matrix of a dataset into regions of similar instances, and reduces each region to a model of its instances. The method is shown to be effective at reducing the volume of a traffic dataset to <5% of its original volume whilst achieving a normalise root mean squared error of <5% when reproducing the original features of the dataset.

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Paper citation in several formats:
Steadman, L. ; Griffiths, N. ; Jarvis, S. ; McRobbie, S. and Wallbank, C. (2019). 2D-STR: Reducing Spatio-temporal Traffic Datasets by Partitioning and Modelling. In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-371-1; ISSN 2184-500X, SciTePress, pages 41-52. DOI: 10.5220/0007679100410052

@conference{gistam19,
author={Liam Steadman and Nathan Griffiths and Stephen Jarvis and Stuart McRobbie and Caroline Wallbank},
title={2D-STR: Reducing Spatio-temporal Traffic Datasets by Partitioning and Modelling},
booktitle={Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM},
year={2019},
pages={41-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007679100410052},
isbn={978-989-758-371-1},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM
TI - 2D-STR: Reducing Spatio-temporal Traffic Datasets by Partitioning and Modelling
SN - 978-989-758-371-1
IS - 2184-500X
AU - Steadman, L.
AU - Griffiths, N.
AU - Jarvis, S.
AU - McRobbie, S.
AU - Wallbank, C.
PY - 2019
SP - 41
EP - 52
DO - 10.5220/0007679100410052
PB - SciTePress

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