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Modelling a traffic network with missing data
Author(s) -
Whitlock Mark E.,
Queen Catriona M.
Publication year - 2000
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/1099-131x(200012)19:7<561::aid-for785>3.0.co;2-4
Subject(s) - missing data , computer science , markov chain , series (stratigraphy) , markov chain monte carlo , multivariate statistics , data collection , time series , data series , monte carlo method , data mining , econometrics , operations research , artificial intelligence , statistics , machine learning , economics , engineering , mathematics , paleontology , bayesian probability , biology
Whitlock and Queen (1998) developed a dynamic graphical model for forecasting traffic flows at a number of sites at a busy traffic junction in Kent, UK. Some of the data collection sites at this junction have been faulty over the data collection period and so there are missing series in the multivariate problem. Here we adapt the model developed in Whitlock and Queen (1998) to accommodate these missing data. Markov chain Monte Carlo methods are used to provide forecasts of the missing series, which in turn are used to produce forecasts for some of the other series. The methods are used on part of the network and shown to be very promising. Copyright © 2000 John Wiley & Sons, Ltd.