Stated preference (SP) data and revealed preference (RP) data have been in- tensively and widely used in the field of discrete choice modelling, and in the transport realm in particular. RP data is collected from actual choice observa- tions in a real-world environment so that RP data is not applicable in situations where an alternative of interest has not yet entered the market. In contrast, SP surveys can be used to retrieve people’s preferences in hypothetical situations (Ben-Akiva et al., 2019; Train, 2009).
A SP survey can come in different formats, e.g. stated choice survey, best- worst scaling survey, rating survey, ranking survey, etc. This section briefly describes the basic principles of the preference elicitation methods used in this thesis. This section can be jointly read with section 1.4 which summarises the empirical information of the surveys and data used in the subsequent three chap- ters of this thesis.
1.2.3.1 Rating data
Rating method directly asks respondents to state their preferences. A rating task normally requires an individual to give a point for each item based on a given Likert scale (e.g. a psychometric scale), with each point corresponding to a specific level of the preference.
Despite the easiness in conducting a rating survey, rating method has its own limitations. Firstly, rating method could easily lead to bias, as respondents may respond to the scale in different ways, such that some people prefer to avoid extreme options while others are opposite; or some people tend to use more scale points whereas others prefer fewer (Software,2013). Secondly, respondents do not need to make serious trade-offs between items and thus some of them may rate everything being the same important. Therefore, data gained from rating tasks cannot provide adequate valuable information about discrimination of preferences, making it difficult for researchers to interpret the real priorities of each item (Finn and Louviere,1992).
1.2.3.2 Stated choice data
Stated choice (SC) data plays an important role in applying discrete choice mod- elling techniques for preference elicitation and choice behaviour analyses.5 A SC survey could consist of a one-off choice task, forming cross-sectional data, or include multiple choice tasks, forming panel data, i.e. more than one choice ob- servation is recorded from each respondent. A typical SC survey with a panel of repeated choice tasks is usually obtained from a specific choice experiment de- sign, e.g. orthogonal design, fractional factorial design, D-efficient design. Each SC task consists of a finite set of alternatives, where each alternative is depicted by a combination of attributes, each attribute taking a certain level value.
The choice experiment usually imitates the real-world choice situations to a certain degree and requires respondents to state their preferences amongst differ- ent alternatives in hypothetical choice situations. Therefore, SC data could be used to understand preferences towards new alternatives which are not observed in real-world situations. This also allows greater variations in attribute levels of SC data, whereas RP data collected from a real-world market usually have lim- ited variations in attribute values. SC data can thus enable researchers to better analyse trade-offs among different attributes, especially in situations where an existing alternative exhibits new attributes which are not yet observed in the real-world market or where attribute levels take values much different from what have been observed in the real-world market (Hensher, 1994).
1.2.3.3 Best-worst scaling data
A best-worst scaling (BWS) survey (Finn and Louviere, 1992) usually presents respondents with a series of choice sets and requires them to make discriminating choices for both the best and the worst items from each choice set which consists of at least three items. The notions of “best” and “worst” could stand for dif- ferent concepts as required by the research objectives, with a common idea that they represent the two extremes of a “continuum”.6 Since a BWS task requires respondents to consider two extremes on the underlying scale from a relatively small choice set, it is considered easier to respond than in rating or raking tasks. As such, BWS approach outweighs rating or ranking method as it can take ad- vantage of respondents’ tendency of responding more consistently and accurately to extreme options (Marley and Louviere, 2005). Though BWS tasks may be more tedious than rating and ranking methods from the perspective of survey participants, BWS data can provide much more “readily understandable” and
5SeeLouviere et al. (2000) for detailed introductions to SC methods. 6
“managerially meaningful” results to analysts (Finn and Louviere,1992).
There are three types of BWS surveys which differ mainly in respect of the complexity of items in the choice set. BWS case 1 (BWS1) measures a list of objects (e.g. attributes) themselves on an underlying scale, without the consid- eration of their values (e.g. attribute levels). The results can assist policymakers to understand the relative importance of attributes per se and where the im- provement of service should be carried out in a relatively straightforward way (Auger et al.,2007;Louviere et al.,2013;Marti,2012). The balanced incomplete block design (BIBD, Hanani 1975) is the most frequently used experimental de- sign method to organise various to-be-assessed items into a number of choice sets. With BIBD, each item occurs the same often and co-occurs with any other item the same often across all the choice sets which are of the same size.
BWS case 2 (BWS2) compares among different attribute levels within a pro- file of an alternative at a common underlying utility scale (Flynn et al., 2007,
2008). A profile means a combination of attribute levels that describes an alter- native, and each attribute can take the values of two or more attribute levels. Those attribute levels within a profile constitute a choice set, from which re- spondents need to pick the best and worst attribute levels. Through a BWS2 survey, both the relative importance of attributes and the relative gaps between different attribute levels in terms of “utility” can be inferred.
BWS case 3 (BWS3), compares among different alternatives, each depicted by a profile comprised of various attribute levels. Respondents need to pick the best and worst alternatives (i.e. profiles) from each choice set. A BWS3 survey is similar to a conventional SC survey except for that both best and worst alternatives need to be selected (Adamsen et al.,2013;Marley and Pihlens,
2012). BWS3 is more complex than BWS1 or BWS2, as the latter are both direct preference elicitation approaches, which present respondents with choice tasks out of multi-alternative settings and do not require trade-offs among alternatives.