The value of time and forecasting of flowsin freight transportation
This paper gives an overview of the current state of research and present own results in some areas regarding cost benefit analysis (CBA) for road infrastructure investments. It deals with the robustness of the estimated average value of time (VOT) currently used in Swedish infrastructure appraisals and also analyses the estimator in the case of different heteroskedastic errors and functional forms. It finallly consider forecasts of road freight flows in cases where information are scarce and may lack precision. Critical questions are how heterogeneity and non-linearities influences forecasts in the freight market. In detail it consdier the following: The impact of VOT for road freight when the origins of transports are taken into account. VOT is found to be dependent on combinations of region, transported distance, industry bransch and if transports are owned or hired. However, available data does not allow for significant values in each category. Altogether the study indicates a VOT spanning from 0 to 732 SEK, which should be compared with the average value of 80 SEK used today. The results also indicate pair-wise differences in VOT between short and long as well as hired and internal transports. The ownership condition is found to have a significant impact on VOT, although current data not gives significant differences between each of the four ownership/distance categories. It is found that further studies should focus on the hypothesis that VOT for short internal freight flows are significantly higher than the average VOT. The traditional logit model is compared with the semi-parametric weighted average density (WAD) estimator. It is found that the performance for the WAD estimator in terms of bias and mean square error is similar to the logit ML estimator for spherical errors in a latent variable specification. Methods for prediction of road freight flows are also investigated. Three traditional gravity model specifications (OLS, NLS, and Poisson regression) are compared with a neural network specification. On a data set of Norwegian inter-regional freight flows it is found that the Poisson model performs best in terms of root mean square error (RMSE) but also that the size of predicted flows is dependent on the method chosen to evaluate available estimation methods. Finally, we integrate freight flow prediction and estimation of VOT in one analysis. Logit models and neural networks with linear and non-linear profit functions are compared. The study indicates that the average VOT may decrease when prediction improves as models are given more non-linear specifications.
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