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Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple...
Summary. Linear multiregression dynamic models, 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 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...
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