STATED CHOICE - A STUDY IN PREDICTIVE-VALIDITY USING AN AGGREGATE TRUTH SET

Stated Choice models expand the ability of transportation planners to forecast future trends. The Stated Choice approach can forecast demand for new services or policies. However, Stated Choice models are subje ct to a range of experimental error not found within Revealed Preferen ce (RP) designs. Primary among the concerns facing researchers is the ability of respondents to understand and operate upon hypothetical cho ice scenarios in a manner that will reproduce choices made under actua l situations. These concerns are specified in the magnitude of a scali ng factor. Efforts to estimate the scaling factor has proceeded by lin king real decisions taken from a revealed preference survey with compa rable decisions made under hypothetical conditions. However, where the alternative is new, actual decision data is not available. This study examines the level of error incorporated in a study where no RP data is available. The test of predictive validity focuses on the switching behavior of commuters at a single employment site. The actual data us ed to test the forecast is limited to company wide or aggregate riders hip levels on the public transit mode taken two years after estimation of the SC model. The Fowkes and Preston hypothesis is examined and sh own to bound the future actual value between forecasts derived from pr obabilistic and deterministic methods. The results show that with the passage of time, the probabilistic method approaches the reported ride rship levels within 15 percent error.