Widely used for analysing travel behaviour, SP (Stated Preference) data analysis examines respondent preferences under hypothetical conditions. However, no consensus has been achieved concerning the appropriate approachesfor SP data analysis and researchers continue to use the process of trialand error. Two of the issues examined in this study are closely related to each other: the response formats of the SP experiment design and the related modelling framework. Regarding the first issue, although the choice format is currently the most frequently used for transport research, attempts have been made to apply the iterative choice format that is commonly used in the CVM (Contingent Valuation Method). One of the iterative choice formats most frequently used, although not for transport analysis, applies a family of double-bounded (DB) formats. Regarding the second issue, the modelling framework must suit the design of the experiment (including the response format), since unsuitable mathematical models produce incorrect estimations. Suitable models of a family of DB formats for transport behaviour analysis are rarely explored. In this preliminary study, a family of DBformats, particularly the one-and-one-half bound (1.5B) format, is applied to an analysis of commuter travel mode switching behaviour in the Kyoto-Osaka-Kobe metropolitan area in Japan. For mode switching behaviour between mass transit and car, the 1.5B format is applied in the following way: In the first bound, mass transit users are asked, would he or she change transport mode if the level of service for mass transit became worse or if that of the car became better? Only if the respondent does not change modesin the first bound is the second bound applied: Would the respondent change transport mode if the level of service for mass transit became much worse or if that of the car became much better? Not only does the cost level change in the survey, but other level-of-service variables also change, which is very different from CVM. Of course, car users also can be respondents. Initially, it is found that models with inertia give a better model fit and the following results are obtained by analysing them: the 2nd bound follow-up questions yield information that is lacking in the 1st bound questions. The bivariate probit model with correlation estimate and inertia is the best from the viewpoints of standard errors, parameter equalities between RP (revealed preference) and SP models, and model fit. Using the interval data model without considering the difference term is dangerous. Thediscussion of inertia and the difference term suggests that RP information is important in the SP model. That is, although it is difficult to use RP information in the CVM model, in transport modelling RP information is very important. For the covering abstract see ITRD E145999
Abstract