Monte Carlo Simulation of Household Travel Survey Data for Sydney, Australia: Bayesian Updating Using Different Local Sample Sizes

Abstract

Interest is increasing in the potential to simulate household travel survey data as an alternative to collecting large sample household travel surveys or as a means to augment sample sizes. In earlier research on simulating such data, it was shown that it was possible to reproduce, within reasonable bounds of accuracy, an actual household travel survey. It has also been found that the procedure for updating the distributions of the simulated variables, by using Bayesian updating with subjective priors, can provide significant improvement in the accuracy with which an actual household travel survey can be simulated. In work performed to date, the optimal size for the update sample to be used in the Bayesian updating has not been determined. Rather, earlier work used a sample of approximately 500 households, primarily because of convenience and cost. In further research that compared different sample sizes for the local update data, it was found that a reasonable updating could be obtained from a sample as small as 300 households, chosen through a stratified sampling procedure, and that results improved substantially when the update sample was increased to 500. However, an increase in the sample to 750 did not produce much improvement, which suggested that samples of this size and larger may not be economically justified. The research suggests that there may be room for a more targeted sampling procedure, which might allow smaller samples to be more cost-effective.

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