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 language | English |
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Title of host publication | Proceedings - 2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2016 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 116-119 |
Number of pages | 4 |
ISBN (Electronic) | 9781509035045 |
DOIs | |
Publication status | Published - 16 Dec 2016 |
Event | 20th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2016 - London, United Kingdom Duration: 21 Sept 2016 → 23 Sept 2016 |
Publication series
Name | Proceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications, DS-RT |
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ISSN (Print) | 1550-6525 |
Conference
Conference | 20th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2016 |
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Country/Territory | United Kingdom |
City | London |
Period | 21/09/16 → 23/09/16 |
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)