Sampling, Specification and Estimation As Sources of Inaccuracy in Complex Transport Models - Some Examples Analysed by Monte Carlosimulation and BOOTSTRAP



Sampling, Specification and Estimation As Sources of Inaccuracy in Complex Transport Models - Some Examples Analysed by Monte Carlosimulation and BOOTSTRAP

Authors

BRUNDELL-FREIJ, Lund University, Sweden

Description

Models are simplified representations of reality, and simplification is a winning that comes to a price. The price is, generally, that model predictions and estimation results are erroneous. We obviously do not know how large the error is when we apply a

Abstract

Models are simplified representations of reality, and simplification is a winning that comes to a price. The price is, generally, that model predictions and estimation results are erroneous. We obviously do not know how large the error is when we apply a model result - had we done so, we would have compensated for it. Never the less, professionalism requires that we couple the model results we use, with some kind of measure of their estimated quality. Accuracy thus is a key issue of modelling - not only to obtain, but to properly describe.

The standard tools we would use for description of model accuracy is standard errors of obtained parameter estimates. However, standard errors are only designed to illustrate a smaller part of those model errors that may arise from the complex process of developing a transport model. This paper discusses and investigates some of those limitations. We will with some initial analyses show that there may be considerable inaccuracy and bias caused by systematic factors that are outside the scope of standard error.

Despite the fact that the concrete examples, and implicitly much of the discussion, relates to models for discrete choice applied to transport demand, the general conclusions would apply also to a vast range of other types of modelling.

Publisher

Association for European Transport