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The PRISM model is a highly complex strategic transport model for the West Midlands region, developed by RAND Europe and Mott MacDonald for the Highways Agency of the UK and seven local authorities of the West Midlands. In a normal application of PRISM the demand response models and assignment models are iterated with a feedback loop amongst them several times to achieve overall convergence. The run time of the PRISM model is a critical issue, especially since it has been extended to include an income segmentation module in order to allow charging policies to be assessed in more detail. The addition of the income segmentation module has increased run times by about a factor of two to three. Destination sampling is a method by which the run time of a disaggregate demand model can be significantly reduced with minimal loss of accuracy. This is achieved by only performing utility calculations for a sample of destinations, for each origin. Destinationsampling in forecasting should be distinguished from alternative samplingin estimation using the method of McFadden. For a given origin zone (in atour based model), the majority of demand is concentrated over a small number of nearby, large and highly accessible/attractive destinations. Destination sampling involves selecting a limited sample of destinations for each origin, taking advantage of this concentration of demand, and carrying out detailed demand calculations for only those destinations. Demand for the remaining destinations is forecast approximately, as a function of demand for the sampled destinations. Using destination sampling, a large proportion of the total demand can be forecast accurately using a fraction of the total number of destinations. In this paper, the implementation of destination sampling in the PRISM demand model is described. The run time savings achieved by destination sampling are described. The amount of error inthe model forecasts when compared to a model run with no destination sampling, including a comparison of assigned flows at the corridor level is explained. The assumptions implicit in the destination sampling calculationsare presented, and how a theoretical measure of the amount of error in the forecasts might be obtained is outlined. For the covering abstract see ITRD E137145.
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