Revealed Preference (RP) studies based on actual behaviour suffer from a number of statistical problems. Furthermore, RP methods are of little use when the effects of a new or radically altered service need to be considered. As a result, for a case study of demand forecasting for new passenger rail services, a new approach has been developed. It starts by seeking what are called Stated Intentions (SI)responses as to the likely usage of a new rail service. However, due to a combination of systematic biases, these responses may be taken to be gross overestimates. A check on the biases of this SI data may be supplied by a Stated Preference (SP) survey. Respondents are asked to make hypothetical choices which are sufficiently complex for there to be little chance of policy bias. Choices have to contain useful 'boundary values' - the values for which respondents would express indifference between two alternatives. It is, however, crucial to ensure that the SP survey is simple enough for respondents to manage, since excessive error variability in the responses will cause the calibrated coefficients to be re-scaled, presenting problems for forecasting. From the SP surveys, it is estimated that SI data overstates usage of new rail services by around 50%, even if it is assumed that non-respondents to the SI survey are non-users. It is concluded th-SP approach can potentially provide accurate forecasts, although there are a number of practical constraints that may prevent this.
Abstract