Models including error components are today at the frontier of application and development in transport modelling. The name Error Component Models is often used indiscriminately with Random Parameters Logit, Models with Stochastic (or Distributed) Preferences (or Coefficients), Logit Kernel Models or Mixed Logit Models. In general the method of Maximum Simulated Likelihood (MSL) is applied, although this only optimises within a given a priori distribution of the error components (EC). Only few of the analyses so far have dealt with the interesting question of correlation between these error components. An alternative method to determine the distributions is to uncover the empirical distribution of the data by repeated estimations. The purpose of the method is to determine the type of distribution, though (for some distributions) it can determine the parameters of the distribution. It was found that it is likely that the error components (random coefficients) are log-normally distributed - and perhaps more interesting, that correlation between the error components is outspoken. The paper compares logit models built, assuming a traditional utility function (linear, without EC), random coefficients (linear, with ECs added to the coefficients); ECs on orthogonal elements (principal components), without linear utility (primarily for reference); and linear utility with ECs on orthogonal elements (whereby independent distributions of EC can be expected). All utility functions with ECs are set up two times, specified as independent distributions and simultaneous distributions. This paper makes a full comparison of models based on the above 7 different utility functions (everything else being equal) with a focus on model fit as well as (potentially different) distribution of error components dependent on the functional form of the utility function. The paper concludes with guidelines on how to include error components in demand estimation. For the covering abstract see ITRD E124693.
Samenvatting