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6.1. Introduction

The issue of heterogeneity in urban transport demand analysis is of considerable importance. Conventional transport models assume all travellers of the same socioeconomic characteristics demonstrate the same type of behaviour. Models are based on averages and thus do not capture any behavioural differences in the structure of the model. In discrete choice modelling, heterogeneity is generally conceptualised as unobserved taste heterogeneity. Scholars are dealing with this issue in different ways (for a review, see Greene, Hensher, & Rose, 2006). One stream of research, focusing on mixed logit models, is estimating the parameters with some distribution for each parameter in order to reflect heterogeneity with regard to the effect of explanatory variables on the dependent variable of interest. Hence, such models still assume that the nature of the relationships between the explanatory variables and the dependent variable is the same for all respondents. A second approach identifies latent classes, each class having a different utility function; for example, depending on socio-demographic or context variables. Although both these approaches break down the choice problem into subproblems by segment respectively content, within each breakdown the assumption of homogeneous responses/behaviour is still needed. Psychological factors to explain taste heterogeneity have received attention in recent extensions of the standard discrete choice model (Ben-Akiva et ah, 2002). This has led to so-called hybrid choice models which include attitudes and perceptions of individuals as latent factors causally related to preferences. Also in these hybrid frameworks, however, the assumption of stable, time-invariant preferences needs to be made.

Thus, regardless of their sophistication and relative success, all these approaches are fundamentally limited in the sense that some degree of aggregation is still used. From a truly behavioural perspective, however, individuals and households face a different space – time environment in which they need to cope with a different set of constraints in satisfying their needs and organising their activities. They have different experiences and hence will have different mental or cognitive maps of their built environment, the transportation system and the institutional context. They will vary in terms of their perception of the environment, which will be incomplete and partially incorrect. They hold different beliefs with regard to the most effective strategy of coping with constraints. Moreover, individual differences are not the only source of heterogeneity. Mental representations may also differ for one and the same individual depending on the specific situation that he or she faces. Different situational settings may activate different needs and hence different benefits and attributes in mental representations of choice alternatives. Arguably, modelling such individual variability and situational dependence could provide further insight in individuals' decision-making and, therefore, is an important research goal to pursue.

Especially in the domain of daily activity-travel choice behaviour the role of mental representations may be important given the complexity of such decisions. Travel is a derived demand from the activities individuals need or wish to perform in time and space and, consequently, travel decisions are an integral part of a broader activity scheduling problem. The decision process inherent to the realized activity-travel behaviour is thus a complex choice process with several interdependent decisions. Due to the complexity of such scheduling decisions, decision-makers' mental representations of the decision are necessarily a simplification of reality (Beach & Mitchell, 1987; Johnson-Laird, 2001; Weber & Johnson, 2006). This allows them to evaluate their possible courses of action and to oversee the choice consequences. These mental representations are hence increasingly in the focus of transport research and policymaking as they are the key to understand and predict people's travel and activity behaviour (Dellaert, Arentze, Chorus, Oppewal, & Wets, 2013; Dellaert, Arentze, & Horeni, 2014; Hannes et al., 2012). By knowing which attributes of choice alternatives people consider and which benefits they try to gain and how situational circumstances impact them in their decisions, better predictions about the effectiveness and consequences of policies on choice behaviour might be achieved.

In this chapter, we first review recent progress in the modelling and measurement of mental representations involved in activity-travel choice of people (Section 6.2). Then we describe an application to illustrate the new approach (Section 6.3). In this application, the Causal Network Elicitation Technique (CNET) is used to collect data about mental representations in the context of an activity-travel scheduling task. To investigate how new alternatives may affect the activation of new benefits and thus lead to a shift in the consideration of spatial choice alternatives, the availability of online alternatives is varied in the experiment. The research method, sample and analytical results are presented next. The chapter closes with a section on conclusions and discussion (Section 6.4).

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