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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.

There is extensive evidence that decision-makers, faced with increasing information load, may simplify their choice by reducing the amount of information to process. One simplification, commonly referred to as attribute non-attendance (ANA), is a reduction of the number of attributes of the choice alternatives. Several previous studies have identified relationships between varying information load and ANA using self-reported measures of ANA. This chapter revisits this link, motivated by recognition in the literature that such self-reported measures are vulnerable to reporting error.

This chapter employs a recently developed modelling approach that has been shown to effectively infer ANA, the random parameters attribute non-attendance (RPANA) model. The empirical setting systematically varies the information load across respondents, on a number of dimensions.

Confirming earlier findings, ANA is accentuated by an increase in the number of attribute levels, and a decrease in the number of alternatives. Additionally, specific attributes are more likely to not be attended to as the total number of attributes increases. Willingness to pay (WTP) under inferred ANA differs notably from when ANA is self-reported. Additionally accounting for varying information load, when inferring ANA, has little impact on the WTP distribution of those that do attend. However, due to varying rates of non-attendance, the overall WTP distribution varies to a large extent.

This is the first examination of the impact of varying information load on inferred ANA that is identified with the RPANA model. The value lies in the confirmation of earlier findings despite the evolution of methodologies in the interim.

COVID-19 has changed the landscape within which we travel. Working from Home (WFH) in many countries has increased significantly, and while it was often forced on a society it has delivered some unintended positive consequences associated in particular with the levels of congestion on the roads and crowding on public transport. With a likelihood of some amount of WFH continuing as we move out of the active COVID-19 period, the question being asked is whether the post-COVID-19 period will return the pre-COVID-19 levels of traffic congestion and crowding. In many jurisdictions, there is a desire to avoid this circumstance and to use WFH as a policy lever that has appeal to employees, employers and government planning agencies in order to find ways of better managing future levels of congestion and crowding. This chapter uses the ongoing research and surveys we have been undertaking in Australia since March 2020 to track behavioural responses that impact on commuting and non-commuting travel, and to examine what the evidence tells us about opportunities into the future in many geographical settings to better manage congestion and crowding. This is linked to a desire by employers to maintain WFH where it makes sense as a way of not only supporting sustainability charters but also the growing interest in a commitment to a broader social licence. We discuss ways in which WFH can contribute to flattening peaks in travel; but also the plans that some public transport authorities are putting in place to ensure that crowding on public transport is mitigated as people increasingly return to using public transport. Whereas we might have thought that we now have plenty of public transport capacity, this may not be the case if we want to control crowding, and more capacity may be needed which could be a challenge for trains more than buses given track capacity limits. We conclude the chapter by summarising some of the positive benefits associated with WFH, and the implications not just for transport but for society more widely.