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Queen, Catriona M.; Wright, Ben J. and Albers, Casper J.
(2007).
DOI: https://doi.org/10.1111/j.1467-842X.2007.00477.x
Abstract
The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models.
The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process.
A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.
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About
- Item ORO ID
- 17547
- Item Type
- Journal Item
- ISSN
- 1467-842X
- Extra Information
-
Author Posting. Copyright Statement on behalf of the Publisher:
© Blackwell Publishing (2007) This is the author's version of the work. It is posted here by permission of Blackwell Publishing for personal use, not for redistribution. The definitive version was published in Australian & New Zealand Journal of Statistics, Volume 49 Issue 3 pp221-239 http://dx.doi.org/10.1111/j.1467-842X.2007.00477.x
The definitive version is available at www.blackwell-synergy.com. - Keywords
- conditional independence; dynamic linear model; linear multiregression dynamic model; model elicitation; traffic modelling
- Academic Unit or School
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Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2007 Australian Statistical Publishing Association Inc.
- Depositing User
- Catriona Queen