2D-STR: Reducing Spatio-temporal Traffic Datasets by Partitioning and Modelling

Liam Steadman, Nathan Griffiths, Stephen Jarvis, Stuart McRobbie, Caroline Wallbank

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 Harvard Style

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 - Volume 1: GISTAM, ISBN 978-989-758-371-1, pages 41-52. DOI: 10.5220/0007679100410052


in Bibtex Style

@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 - Volume 1: GISTAM,},
year={2019},
pages={41-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007679100410052},
isbn={978-989-758-371-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - 2D-STR: Reducing Spatio-temporal Traffic Datasets by Partitioning and Modelling
SN - 978-989-758-371-1
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