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The presence of respondents with apparently extreme sensitivities in choice data may have an important influence on model results, yet their role is rarely assessed or even explored. Irrespective of whether such outliers are due to genuine preference expressions, their presence suggests that specifications relying on preference heterogeneity may be more appropriate. In this paper, we compare the potential of discrete and continuous mixture distributions in identifying and accommodating extreme coefficient values. To test our methodology, we use five stated preference datasets (four simulated and one real). The real data were collected to estimate the existence value of rare and endangered fish species in Ireland.

It has long been recognised that humans draw from a large pool of processing aids to help manage the everyday challenges of life. It is not uncommon to observe individuals adopting simplifying strategies when faced with ever increasing amounts of information to process, and especially for decisions where the chosen outcome will have a very marginal impact on their well-being. The transactions costs associated with processing all new information often exceed the benefits from such a comprehensive review. The accumulating life experiences of individuals are also often brought to bear as reference points to assist in selectively evaluating information placed in front of them. These features of human processing and cognition are not new to the broad literature on judgment and decision-making, where heuristics are offered up as deliberative analytic procedures intentionally designed to simplify choice. What is surprising is the limited recognition of heuristics that individuals use to process the attributes in stated choice experiments. In this paper we present a case for a utility-based framework within which some appealing processing strategies are embedded (without the aid of supplementary self-stated intentions), as well as models conditioned on self-stated intentions represented as single items of process advice, and illustrate the implications on willingness to pay for travel time savings of embedding each heuristic in the choice process. Given the controversy surrounding the reliability of self-stated intentions, we introduce a framework in which mixtures of process advice embedded within a belief function might be used in future empirical studies to condition choice, as a way of increasingly judging the strength of the evidence.

This chapter proposes a new mixture model which allows for heterogeneity in sensitivities and decision rules across decision makers and attributes.

A new mixture model is put forward in which the different latent classes make use of different decision rules, where the use of generalised random regret minimisation kernel allows for within class heterogeneity in the decision rules applied across attributes.

Our theoretical developments are supported by the findings of an empirical application using data from a typical stated choice survey.

Existing work has looked at heterogeneity in decision rules and sensitivities across respondents. Other work has focused on the possibility that different decision rules apply to different attributes. This chapter puts forward a model that combines these two directions of research and does so in a way that lets the optimal specification be driven by the data rather than being imposed by the analyst.

There have always been concerns about task complexity and respondent burden in the context of stated choice (SC) studies, with calls to limit the number of alternatives, attributes and choice sets. At the same time, some researchers have also made the case that too simplistic a design might be counterproductive given that such designs may result in issues of omitting important decision variables. This paper aims to take another look at the effects of design complexity on model results. Specifically, we make use of an approach devised by Hensher (2004)¹ in which different respondents in the study are presented with designs of different complexity, and look specifically at effects on model scale in a UK context, adding to previous Chilean evidence by Caussade et al. (2005). The results of our study indicate that the impact of design complexity may be somewhat lower than anticipated, and that more complex designs may not necessarily lead to poorer results. In fact, some of the more complex designs lead to higher scale in the models. Overall, our findings suggest that respondents can cope adequately with large number of attributes, alternatives and choice sets. The implications for practical research are potentially significant, given the widespread use, especially in Europe, of stated choice designs with a limited number of alternatives and attributes.

Mixed logit models can represent heterogeneity across individuals, in both observed and unobserved preferences, but require computationally expensive calculations to compute probabilities. A few methods for including error covariance heterogeneity in a closed form models have been proposed, and this paper adds to that collection, introducing a new form of a Network GEV model that sub-parameterizes the allocation values for the assignment of alternatives (and sub-nests) to nests. This change allows the incorporation of systematic (nonrandom) error covariance heterogeneity across individuals, while maintaining a closed form for the calculation of choice probabilities. Also explored is a latent class model of nested models, which can similarly express heterogeneity. The heterogeneous models are compared to a similar model with homogeneous covariance in a realistic scenario, and are shown to significantly outperform the homogeneous model, and the level of improvement is especially large in certain market segments. The results also suggest that the two heterogeneous models introduced herein may be functionally equivalent.

This article addresses simultaneously two important features in random utility maximisation (RUM) choice modelling: choice set generation and unobserved taste heterogeneity. It is proposed to develop and to compare definitions and properties of econometric specifications that are based on mixed logit (MXL) and latent class logit (LCL) RUM models in the additional presence of prior compensatory screening decision rules. The latter allow for continuous latent bounds that determine choice alternatives to be or not to be considered for decision making. It is also proposed to evaluate and to test each against the other ones in an application to home-to-work mode choice in the Paris region of France using 2002 data.

We investigate discrepancies between willingness to pay (WTP) and willingness to accept (WTA) in the context of a stated choice experiment. Using data on customer preferences for water services where respondents were able to both ‘sell’ and ‘buy’ the choice experiment attributes, we find evidence of non-linearity in the underlying utility function even though the range of attribute levels is relatively small. Our results reveal the presence of significant loss aversion in all the attributes, including price. We find the WTP–WTA schedule to be asymmetric around the current provision level and that the WTP–WTA ratio varies according to the particular provision change under consideration. Such reference point findings are of direct importance for practitioners and decision-makers using choice experiments for economic appraisal such as cost–benefit analysis, where failure to account for non-linearity in welfare estimates may significantly over- or under-state individual's preferences for gains and avoiding losses respectively.

This paper applies the mixed logit and the latent class models to analyse the heterogeneity in foreign investment location choices in Central and Eastern Europe. The empirical results show that the responsiveness of the probabilities of choices to invest in a particular location to country-level variables differs both across sectors and across firms of different characteristics. The paper highlights the superiority of the latent class model with regards to the model fit and the interpretation of results.

Interest in car-sharing initiatives, as a tool for improving transport network efficiency in urban areas and on interurban links, has grown in recent years. They have often been proposed as a more cost effective alternative to other modal shift and congestion relief initiatives, such as public transport or highway improvement schemes; however, with little implementation in practice, practitioners have only limited evidence for assessing their likely impacts.

This study reports the findings of a Stated Preference (SP) study aimed at understanding the value that car drivers put on car sharing as opposed to single occupancy trips. Following an initial pilot period, 673 responses were received from a web-based survey conducted in June 2008 amongst a representative sample of car driving commuters in Scotland.

An important methodological aspect of this study was the need to account for differences in behaviour to identify those market segments with the greatest propensity to car share. To this end, we estimated a range of choice model forms and compared the ability of each to consistently identify individual behaviours. More specifically, this included a comparison of:

Standard market segmentation approaches based on multinomial logit with attribute coefficients estimated by reported characteristics (e.g. age, income, etc.);

A two-stage mixed logit approach involving the estimation of random parameters logit models followed by an examination of individual respondent's choices to arrive at estimates of their parameters, conditional on know distributions across the population (following Revelt & Train, 1999); and

A latent-class model involving the specification of C classes of respondent, each with their own coefficients, and assigning each individual a probability that they belongs to a given class based upon their observed choices, socioeconomic characteristics and their reported attitudes.

As hypothesised, there are significant variations in tastes and preferences across market segments, particularly for household car ownership, gender, age group, interest in car pooling, current journey time and sharing with a stranger (as opposed to family member/friend). Comparing the sensitivity of demand to a change from a single occupancy to a car-sharing trip, it can be seen that the latter imposes a ‘penalty’ equivalent to 29.85 IVT minutes using the mixed logit structure and 26.68 IVT minutes for the multinomial specification. Segmenting this latter value according to the number of cars owner per household results in ‘penalties’ equivalent to 46.51 and 26.42 IVT minutes for one and two plus car owning households respectively.