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


  • Yanying Li

    (Transportation Research Group, School of Civil Engineering and the Environment, University of Southampton, UK)


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.

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  • Yanying Li, 2008. "Short-term prediction of motorway travel time using ANPR and loop data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 507-517.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:6:p:507-517
    DOI: 10.1002/for.1070

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    References listed on IDEAS

    1. Carey, Malachy & McCartney, Mark, 2002. "Behaviour of a whole-link travel time model used in dynamic traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 83-95, January.
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