The valuation of travel time savings and other willingness to pay indicators are crucial inputs into cost benefit analyses carried out in the planning for new transport infrastructure developments or in the appraisal of new policy schemes such as congestion charging. Typically these measures are produced with the help of econometric choice structures belonging to the family of random utility models. Overwhelmingly, in this context, these models are estimated on stated choice (SC) data, collected through surveys that present each respondent with a set of hypothetical choice situations, where the respondent is asked to indicate his or her preferred alternative in each of these choice situations. In the design of these surveys, analysts face a number of important decisions in terms of the alternatives and attributes presented to respondents, the number of choice situations faced, and the attribute levels used to describe the choices. The influence that each of these factors has upon the outcomes of SC studies have been extensively researched in recent years, with many different conclusions being drawn. In the United Kingdom, the designs used in value of time studies have typically been very simplistic, presenting respondents with a choice between a limited number of alternatives (often only two) described by a limited number of attributes (often only time and money). While there are many compelling reasons for not wanting to burden respondents with too much information, there are also grounds to suggest that simplistic designs may not replicate real world behaviour. Furthermore, information relevant to respondent?s choices that is not captured in SC surveys is forced into the unobserved effects of estimated models, and as such, simple designs that omit relevant information may place more emphasis on the unobserved effects of discrete choice models. Conversely, the opposite may also be true with too much information also impacting on the unobserved effects of these models. Thus, a balance between providing too little relevant information or too much information to respondents is important when conducting SC surveys. Unfortunately, most studies do not vary the information provided or vary only one dimension of information at a time and hence, tests of the impacts of such information are not possible to conduct. In this paper, we present evidence from a study making use of state of the art survey techniques in a route choice context in the United Kingdom. This represents a departure from the traditional studies undertaken which typically employ ad hoc attribute level combinations or orthogonal designs, and employ designs whereby only a small number of attributes and alternatives are presented. In this study we move away from such simplistic designs and employ state of the art experimental design methods in constructing the SC survey. In a further departure from currently employed approaches, the study, which is being carried out in Bristol, presents respondents with varying levels of design complexity, changing the number of choice sets, alternatives and attributes as well as the number of attribute levels. This allows us to investigate the differences in behaviour depending on choice task complexity, with results on preliminary data suggesting that respondents are able to cope with far more complex choice situations than those traditionally used in UK studies. Additionally, our study aims to place the results from this study within the broader context of value of time work in the UK.
