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Stated choice (SC) experiments are the most popular method to estimate the value of travel time changes (VTTC) of a population. In the simplest VTTC experiment, the SC design variables are time changes and cost changes. The levels of these variables create a particular setting from which preferences are inferred. This paper tries to answer the question "do preferences vary with SC settings?". For this, we investigate the role of the variables used in the SC experiment on the estimation of the set of VTTC (i.e. mean and covariates). Ideally, one would like to observe the same individuals completing different SC experiments. Since that option is not available, an alternative approach is to use a large dataset of responses, and split it according to different levels of the variable of interest. We refer to this as partial data analysis. The estimation of the same model on each sub-sample provides insights into potential effects of the variable of interest. This approach is applied in relation to three design variables on the data for the last national VTTC study in the UK, using state-of-the-art model specifications. The results show several ways in which the estimated set of VTTC can be affected by the levels of SC design variables. We conclude that model estimates (including the VTTC and covariates) are different in different settings. Hence by focussing the survey on specific settings, sample level results will be affected accordingly. Our findings have implications for appraisal and can inform the construction of future SC experiments.

Understanding valuation of travel time changes: are preferences different under different stated choice design settings?