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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

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

Colleges, School and Institutes

External organisations

  • University of Warwick
  • Trl

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.

Bibliographic note

Funding Information: The lead author gratefully acknowledges funding by the UK Engineering and Physical Sciences Research Council (grant no. EP/L016400/1), the EPSRC Centre for Doctoral Training in Urban Science. The lead author also gratefully acknowledges funding by TRL. Publisher Copyright: Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

Details

Original languageEnglish
Title of host publicationGISTAM 2019 - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management
EditorsCedric Grueau, Robert Laurini, Lemonia Ragia
Publication statusPublished - 2019
Event5th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2019 - Heraklion, Crete, Greece
Duration: 3 May 20195 May 2019

Publication series

NameGISTAM 2019 - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management

Conference

Conference5th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2019
Country/TerritoryGreece
CityHeraklion, Crete
Period3/05/195/05/19