Estimation of the relative importance of components of travel disutility (or generalised cost) is of fundamental importance in transportation planning, whether modelling the choices of individual travellers or assessing the value they attach to travel time or other components for evaluation purposes. The most common way in which the values of these journey attributes is estimated is through the use of choice models, in which the choice is interpreted as an observation that the traveller has preferred one combination of journey attributes to the other available combinations, i.e. that the utility of the alternative is greater than the utility of the available non-chosen alternatives. An alternative data form that has looked attractive in principle for many years is 'transfer price' data, in which respondents are asked how much better their choice is than a specified alternative. Such data has also been called Contingent Valuation and a substantial literature exists documenting its advantages and disadvantages. The key aspect of this data which makes it attractive is that the amount of utility difference is collected, rather than, as with choice data, simply asking which alternative has the greater utility. The increased information content given by transfer price data can greatly increase the estimation accuracy and potentially help to reduce biases arising from the use of SP data. The theoretical framework of utility maximisation used in choice modelling is also applicable to transfer price data, which raises the possibility of analysing both types of data together. In separate analysis of transfer price data, which has been used hitherto, the magnitude of the utility difference expressed by the transfer price is regressed, usually in a simple linear regression, on the explanatory time and cost variables. However, this analysis ignores the fact that it is known absolutely, from the choice that is also observed, what the sign of that utility difference is. It is in principle possible to use the choice and transfer price data together, using the information on both the size and sign of the utility difference. The simplest formulation of this simultaneous estimation would imply models of the 'Tobit' type, or close relatives to that form. The key characteristic of these models is that they recognise that the traveller's unmeasured preferences, i.e. what is represented by the error term, are effectively the same, or at least highly correlated, when he or she makes a choice as when he or she responds to the transfer price question. Further sophistication of such approaches involves extending the analysis to considering Stated Preference as well as Revealed Preference data and incorporating the correlations of the error terms in these data types with that of Transfer Price. Response biases of various types in the data, including the rounding which is characteristic of Transfer Price data, can also be included in the most sophisticated analyses. The assumptions and analysis methods which allow simultaneous use of these data types is discussed and conclusions drawn for their appropriate use in a range of circumstances. For the covering abstract see ITRD E124693.
Abstract