Spatially-Intensive Decision Tree Prediction of Traffic Flow across the Entire UK Road Network

Henry Crosby, Paul Davis, Stephen A. Jarvis

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

7 Citations (Scopus)

Abstract

This paper introduces a novel approach to predicting UK-wide daily traffic counts on all roads in England and Wales, irrespective of sensor data availability. A key finding of this research is that many roads in a network may have no local connection, but may still share some common law, and this fact can be exploited to improve simulation. In this paper we show that: (1) Traffic counts are a function of dependant spatial, temporal and neighbourhood variables, (2) Large open-source data, such as school location and public transport hubs can, with appropriate GIS and machine learning, assist the prediction of traffic counts, (3) Real-time simulation can be scaled-up to large networks with the aid of machine learning and, (4) Such techniques can be employed in real-world tools. Validation of the proposed approach demonstrates an 88.2% prediction accuracy on traffic counts across the UK.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2016
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages116-119
Number of pages4
ISBN (Electronic)9781509035045
DOIs
Publication statusPublished - 16 Dec 2016
Event20th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2016 - London, United Kingdom
Duration: 21 Sep 201623 Sep 2016

Publication series

NameProceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications, DS-RT
ISSN (Print)1550-6525

Conference

Conference20th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2016
Country/TerritoryUnited Kingdom
CityLondon
Period21/09/1623/09/16

Bibliographical note

Funding Information:
We thank the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Urban Science (EP/L016400/1) for their support.

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Machine Learning
  • Real Estate
  • REPTree
  • Traffic Flow

ASJC Scopus subject areas

  • Engineering(all)

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