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Spatially-Intensive Decision Tree Prediction of Traffic Flow across the Entire UK Road Network by Henry Crosby; Paul Davis; Stephen A Jarvis is a scholarly article available to read on EtoBox.

What is Spatially-Intensive Decision Tree Prediction of Traffic Flow across the Entire UK Road Network about?

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; ( ) 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.

Author

Henry Crosby; Paul Davis; Stephen A Jarvis

Published

2016

Language

EN