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.
|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)|
|Number of pages||4|
|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
|Name||Proceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications, DS-RT|
|Conference||20th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2016|
|Period||21/09/16 → 23/09/16|
Bibliographical noteFunding Information:
We thank the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Urban Science (EP/L016400/1) for their support.
© 2016 IEEE.
- Machine Learning
- Real Estate
- Traffic Flow
ASJC Scopus subject areas