A joint model for stated choice and best-worst scaling data using latent attribute importance: application to rail-air intermodality

This paper looks at modelling choices in the presence of a new mode of transport, where there is a need to understand the sensitivities to a number of new attributes. Stated choice (SC) data and two types of Best-worst scaling (BWS) data (i.e. case 1 and case 2) are collected from the same respondents. We mix survey methods rather than using a longer SC survey to better understand choice behaviour whilst reducing the boredom caused by one very long set of SC choices. Although BWS data has been increasingly collected alongside stated choice (SC) data, little is known about the relationships between BWS responses and SC responses at the level of individual respondents. Also, little effort has been made to jointly exploit the behavioural information from BWS data and SC data to improve the understanding of choices. This paper proposes a joint model which links the BWS and SC data through the notion of latent attribute importance. The modelling results show that people perceive attribute importance in a relatively consistent way across different survey methods, i.e. a person who perceives higher importance from an attribute is likely to show stronger sensitivity to that attribute in SC tasks, give more weight to the same attribute in BWS1 tasks and exhibit a wider gaps in terms of attractiveness between levels for the same attribute – in comparison with other individuals. This consistency shows that the additional behavioural information can be gained by using a joint model estimated on BWS1 and BWS2 data alongside more traditional SC data, helping us to improve the explanation of the choices and the role of the attributes. Our results however do not find a one-to-one relationship between different survey methods and analysts thus need to be mindful that there remain some differences in how attributes are evaluated between SC, BWS1 and BWS2 surveys.