Improving the Treatment of Cost in Large Scale Models

Abstract

The paper presents the results of analyses using mixed linear and logarithmic functions in two major cities in England: Greater Manchester and West Midlands (the latter centered on Birmingham). It shows how the use of these functions improves the fit of the models to the data and produces elasticities that are considerably more plausible. In addition, the values of time that are implied by the models are also more reasonable, an unexpected bonus of the approach. The use of data from two major cities allows the models to be compared and contrasted, giving a broader basis for the claims that are made. The paper also discusses some model specification issues where lessons have been learned from Manchester and the West Midlands. The first is the treatment of intra-zonal movements, where using a ‘nearest neighbor’ assumption can have an impact the model elasticity and values-of-time, as well as improving the model fit. The issue of how best to treat car costs is discussed, with discussion of findings of how best to allocate total car costs between drivers and passengers, and the impact of different assumptions regarding the perception of non-fuel costs. Finally, a technical issue about the treatment of small or zero costs with a logarithmic form is discussed.

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