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

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

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

1 Citation (Scopus)

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.

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
PublisherSciTePress
Pages41-52
Number of pages12
ISBN (Electronic)9789897583711
DOIs
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

Keywords

  • Data Partitioning
  • Data Reduction
  • Spatio-temporal Data

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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