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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.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

The chapter outlines the principles underlying relative utility models, discusses the results of empirical applications and critically assesses the usefulness of this specification against commonly used random utility models and other context dependence models. It also discusses how relative utility can be viewed as a generalisation of context dependency.

In contrast to the conventional concept of random utility, relative utility assumes that decision-makers derive utility from their choices relative to some threshold(s) or reference points. Relative utility models thus systematically specify the utility against such thresholds or reference points.

Examples in the chapter show that relative utility model perform well in comparison to conventional utility-maximising models in some circumstances.

Examples of relative utility models are rare in transportation research. The chapter shows that several recent models can be viewed as special cases of relative utility models.

Increasing evidence suggests that choice behaviour in real world may be guided by principles of bounded rationality as opposed to typically assumed fully rational behaviour, based on the principle of utility-maximization. Under such circumstances, conventional rational choice models cannot capture the decision processes. The purpose of the chapter is to propose a modeling framework that can capture both decision outcome and decision process.

The modeling framework incorporates a discrete cognitive representation structure and implies several decision heuristics, such as conjunctive, disjunctive and lexicographic rules. This allows modeling unobserved decision heterogeneity involved in a single decision, for example, in the form of a latent-class specification, taking into account mental effort, risk perception and expected outcome as explanatory factors.

Two models based on this framework are applied to decision problems underlying pedestrian shopping behaviour and compared with conventional multinomial logit models. The results show that the proposed models may not be superior to logit models in terms of model selection criteria due to the extra complexity in selecting heuristics, but suggest more interesting insights to the underlying decision mechanisms.

Understanding decision processes additional to outcomes is a promising research direction. A more developed model should take into account more contextual and socio-demographic factors in the heuristic selection part. The assumptions of information processing must be subject to empirical tests to validate the model.

The proposed modeling framework bridges the long-existing contradicting approaches in the field of decision modeling, namely the rational approach and the bounded rational approach, by proving that non-compensatory decision heuristics can be inferred from compensatory model formulations with discretized information representations and decision criteria assumed. It also incorporates a heuristic choice part into the decision processes in the form of latent-class specifications and shows the viability of the new modeling framework.