Short-term prediction of motorway travel time using ANPR and loop data

Yanying Li
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Yanying Li: Transportation Research Group, School of Civil Engineering and the Environment, University of Southampton, UK, Postal: Transportation Research Group, School of Civil Engineering and the Environment, University of Southampton, UK

Journal of Forecasting, 2008, vol. 27, issue 6, 507-517

Abstract: Travel time is a good operational measure of the effectiveness of transportation systems. The ability to accurately predict motorway and arterial travel times is a critical component for many intelligent transportation systems (ITS) applications. Advanced traffic data collection systems using inductive loop detectors and video cameras have been installed, particularly for motorway networks. An inductive loop can provide traffic flow at its location. Video cameras with image-processing software, e.g. Automatic Number Plate Recognition (ANPR) software, are able to provide travel time of a road section. This research developed a dynamic linear model (DLM) model to forecast short-term travel time using both loop and ANPR data. The DLM approach was tested on three motorway sections in southern England. Overall, the model produced good prediction results, albeit large prediction errors occurred at congested traffic conditions due to the dynamic nature of traffic. This result indicated advantages of use of the both data sources. Copyright © 2008 John Wiley & Sons, Ltd.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:27:y:2008:i:6:p:507-517

DOI: 10.1002/for.1070

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