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In this chapter, we address the question if and how modern technology can be used to design questionnaires, diaries, web sites, and experiments to improve the validity of reliability of active data collection instruments. In particular, it discusses the history of computer-assisted activity diary data, reenactment sessions, stated preference methods, and interactive computer experiments with a special focus on the design of these instruments in terms of respondent support and user interfaces. Empirical evidence and experience suggests that although fascinating instruments may increase respondent motivation and involvement and therefore improve the reliability of the measurements, there is also the danger that respondents' answers are influenced by features of the electronic instrument that are not essential, reducing validity and reliability.

This chapter reviews models of decision-making and choice under conditions of certainty. It allows readers to position the contribution of the other chapters in this book in the historical development of the topic area.

Bounded rationality is defined in terms of a strategy to simplify the decision-making process. Based on this definition, different models are reviewed. These models have assumed that individuals simplify the decision-making process by considering a subset of attributes, and/or a subset of choice alternatives and/or by disregarding small differences between attribute differences.

A body of empirical evidence has accumulated showing that under some circumstances the principle of bounded rationality better explains observed choices than the principle of utility maximization. Differences in predictive performance with utility-maximizing models are however small.

The chapter provides a detailed account of the different models, based on the principle of bounded rationality, that have been suggested over the years in travel behaviour analysis. The potential relevance of these models is articulated, model specifications are discussed and a selection of empirical evidence is presented. Aspects of an agenda of future research are identified.

This chapter discusses the formulation of an agent-based model to simulate day-to-day dynamics in activity-travel patterns, based on short and long-term adaptations to exogenous and exogenous changes.

The model is based on theoretical considerations of bounded rationality. Agents are able to explore the area, adapt their aspirations and develop habitual behaviour. If they experience dissatisfaction, stress emerges and this may lead to short or long-term adaptations of an agent’s activity-travel patterns. Both cognitive and affective responses are taken into account, when agents evaluate available options. Moreover, memory-activation and forgetting processes play a significant role in the development of habitual behaviour.

Results of numerical simulations show the effect of memory-activation and emotion-related parameters on habit formation, on the decision-making process and on overall model behaviour. Effects of specific aspects of bounded rationality on the evolution of dynamics in the activity-travel patterns of an individual are illustrated. Effects seem realistic, behaviourally rich and, therefore, more sensitive to a larger spectrum of policies.

The model is unique in its kind. It is one of the first attempts to formulate a dynamic model of activity-travel behaviour, based on principle of bounded rationality, which includes both cognitive and affective mechanism of adaptation.

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application, we will use decision rules to support the decision-making of the model instead of principles of utility maximization, which means our work can be interpreted as an application of the concept of bounded rationality in the transportation domain. In this chapter we explored a novel idea of combining decision trees and Bayesian networks to improve decision-making in order to maintain the potential advantages of both techniques. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of a travel demand model with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

This chapter explores Prospect Theory — a descriptive model of modelling individual choice making under risk and uncertainty, and its applications to a range of travel behaviour contexts.

The chapter provides background on Prospect Theory, its basic assumptions and formulations, and summarises some of its theoretical developments, applications and evidence in the field of transport research.

A body of empirical evidence has accumulated showing that the principle of maximisation of expected utility provides limited explanation of travel choices under risk and uncertainty. Prospect Theory can be seen as an alternative and promising framework for travel choice modelling (although not without theoretical and practical controversy). These findings are supported by empirical observations reported in the literature reviewed in this chapter.

The chapter provides a detailed account of the design and results of accumulated research in travel behaviour research that is based on Prospect Theory’s observations, insights and formulations. The potential of Prospect Theory for particular decision-making in travel behaviour research is articulated, main findings are presented and discussed, and limitations are identified, leading to further research needs.