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Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors

Anacleto Junior, Osvaldo; Queen, Catriona and Albers, Casper (2013). Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors. Journal of the Royal Statistical Society: Series C (Applied Statistics), 62(2) pp. 251–270.

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DOI (Digital Object Identifier) Link: http://doi.org/10.1111/j.1467-9876.2012.01059.x
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Abstract

Linear multiregression dynamic models (LMDMs), which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting of traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending on the time of day and accommodating measurement errors occurring due to data collection errors. This paper extends LMDMs to address these issues. Additionally, the paper investigates how close the approximate forecast limits usually used with the LMDM are to the true, but not so readily available, forecast limits.

Item Type: Journal Article
Copyright Holders: 2012 Royal Statistical Society
ISSN: 1467-9876
Keywords: data collection error; dynamic linear model; linear multiregression dynamic model; traffic modelling; variance law
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
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Item ID: 33949
Depositing User: Catriona Queen
Date Deposited: 05 Jul 2012 08:00
Last Modified: 24 Feb 2016 12:02
URI: http://oro.open.ac.uk/id/eprint/33949
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