Contrary to the usual assumption of fixed, well-defined preferences, it is increasingly evident that individuals are likely to approach a choice task using rules and decision heuristics that are dependent on the choice environment. More specifically, heuristics that are defined by the local choice context, such as the gains or losses of an attribute value relative to the other attributes, seem to be consistently employed. Recent empirical findings also demonstrate that previous choices and previously encountered choice tasks shown to respondents can affect the current choice outcome, indicating a form of inter-dependence across choice sets. This paper is primarily focused on reviewing how heuristics have been modelled in stated choice data. The paper begins with a review of the heuristics that may be relevant for coping with choice task complexity and then proceeds to discuss some modelling approaches. Next, relational heuristics, such as prospect theory, random regret minimisation and extremeness aversion (compromise effect) are discussed.These are heuristics which operate within the local choice set. Another major class of heuristics reviewed in this paper pertains to ordering effects and more generally, on past outcomes and past attribute levels of the alternatives.

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In February 2014, Singapore embarked on a 2-year trial of a Bus Service Reliability Framework (BSRF) to improve en-route bus regularity and reduce instances of bus bunching and prolonged waiting times. Based on London's Quality Incentive Contract, the Singapore model also imposes penalties or provides incentives to operators for increases/reductions of Excess Wait Time (EWT) beyond a certain route-specific baseline.Drawing on insights derived from research on performance-based contracts, this paper describes some key considerations surrounding this particular innovation in Singapore's overall bus regulatory framework. We also discuss an important advancement in our understanding of how bus users value reliability improvements through estimates obtained from stated preference data. At the same time, early indications from the trial have been encouraging.

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Pseudopanel data have been increasingly applied in travel demand analysis to investigate the long-run travel demand when genuine panel data are unavailable. However, conventional estimation techniques have typically been used without a careful consideration of some unique properties of pseudopanel data. This paper shows that ignoring these properties potentially leads to estimation bias or inefficiency not observed in genuine panel data. The method used is a Monte Carlo experiment with scenarios designed to generate various data possessing pseudopanel data characteristics under conditions of limited observations; the performance of various estimator is evaluated with the use of the simulation results. This research found that the large between-group variation of the exogenous variable and the variance of unobserved group effects in pseudopanel data are the primary causes of estimation bias and inefficiency. Other factors such as cohort sizes and nonspherical errors have a smaller effect on the estimators’ performance. An empirical application using Sydney Household Travel Survey data is also presented to illustrate the simulation findings.