A new Spring, a new sound

Special editorial from the outgoing and incoming Editor in Chief

The Need for Advanced Public Transport Information Services When Making Transfers

This paper reports on a stated choice experiment examining the determinants of travelers' need and willingness to pay for advanced public transport information services. Specific attention is given to the role of making transfers in the decision to acquire specific types of information. Intercity train travelers are asked to choose among information services that varied in type of information provided by the services, precision of provided dynamic travel time estimates, whether or not the system can provide information unasked for and price. Respondents made these choices conditional on a specified transfer context, denoting whether or not transfers had to be made during the trip, and whether these were transfers to high or low frequency train services. Modeling results indicate that as hypothesized, transferring during the trip, especially towards low frequency connections, induces a larger need for travel information, a higher preference for services that can provide advice relative to other information and a higher willingness to pay for information in general. However, the choice for information services is highly price sensitive, which suggest a low overall willingness to pay

Taboo trade-off aversion: a discrete choice model and empirical analysis

An influential body of literature in moral psychology suggests that decision makers consider trade-offs morally problematic, or taboo, when the attributes traded off against each other belong to different ‘spheres’, such as friendship versus market transactions. This study is the first to model and empirically explore taboo trade-off aversion in a discrete choice context. To capture possible taboo trade-off aversion, we propose to extend the conventional linear in parameters logit model by including penalties for taboo trade-offs. Using this model, we then explore the presence (and size) of taboo trade-off aversion in a data set specifically collected for this purpose. Results, based on estimation of a variety of (Mixed) Logit models with and without taboo trade-off penalties, suggest that there is indeed a significant and sizeable taboo trade-off aversion underlying choice behaviour of respondents

Explaining Cost Overruns of Large-Scale Transportation Infrastructure Projects using a Signalling Game

Strategic behaviour is one of the main explanations for cost overruns. It can theoretically be supported by agency theory, in which strategic behaviour is the result of asymmetric information between the principal and agent. This paper gives a formal account of this relation by a signalling game. This is a game with incomplete information which considers the way in which parties anticipate upon other parties' behaviour in choosing a course of action. The game shows how cost overruns are the result of an inappropriate signal. This makes it impossible for the principal to distinguish between the types of agents, and hence, allows for strategic behaviour. It is illustrated how cost overruns can be avoided by means of two policy measures, e.g. an accountability structure and benchmarking

Travel information as an instrument to change cardrivers’ travel choices: a literature review

This paper aims to provide insights that help transport academics and policy makers appreciate the potentials and limitations of information provision as a means to changing car-drivers’ travel choices. The focus is on a modal shift from private car to public transport and changes in car-drivers’ choices for departure times and routes towards a more even distribution of traffic within the available road network. These insights are gained through a review of more than 15 years of literature concerning the use and effects of travel information among car-drivers. Based on the performed review, a number of generic, integrative insights are derived, including the following: it appears that our expectations with respect to the effects of information provision on travel choices in general may be mildly optimistic, particularly for behavioural changes not involving changes in mode-choice. In the longer term, the effects of information provision, when presented to travelers in suitable formats, are likely to be somewhat stronger than the short term effects, due to learning dynamics

Random regret minimization or random utility maximization: An exploratory analysis in the context of automobile fuel choice

Interest in alternative behavioural paradigms to random utility maximisation (RUM) has existed ever since the dominance of the RUM formulation. One alternative is known as random regret minimisation (RRM), which suggests that when choosing between alternatives, decision-makers aim to minimise anticipated regret. Although the idea of regret is not new, its incorporation into the same discrete choice framework of RUM is very recent. This paper is the first to apply the RRM-model framework to model choice among durable goods. Specifically, we estimate and compare RRM- and RUM-models in a stated choice context of choosing amongst petrol, diesel and hybrid fuelled vehicles (associated with specific levels of fuel efficiency and engine capacity). The RRM-model is found to achieve a marginally better fit (using a non-nested test of differences) than its equally parsimonious RUM-counterpart. As a second contribution, we derive a formulation for regret-based elasticities, and compare utility- and regret-based elasticities in the context of stated vehicle-type choices. We find that in the context of our choice-data, mean estimates of elasticities are different for many of the attributes and alternatives

Contrasts between utility maximisation and regret minimisation in the presence of opt out alternatives

An increasing number of studies of choice behaviour are looking at Random Regret Minimisation (RRM) as an alternative to the well established Random Utility Maximisation (RUM) framework. Empirical evidence tends to show small differences in performance between the two approaches, with the implied preference between the models being dataset specific. In the present paper, we discuss how in the context of choice tasks involving an opt out alternative, the differences are potentially more clear cut. Specifically, we hypothesise that when opt out alternatives are framed as a rejection of all the available alternatives, this is likely to have a detrimental impact on the performance of RRM, while the performance of RUM suffers more than RRM when the opt out is framed as a respondent being indifferent between the alternatives on offer. We provide empirical support for these hypotheses through two case studies, using the two different types of opt out alternatives. Our findings suggest that analysts need to carefully evaluate their choice of model structure in the presence of opt out alternatives, while any a priori preference for a given model structure should be taken into account in survey framing

BAIT:A New Medical Decision Support Technology Based on Discrete Choice Theory

We present a novel way to codify medical expertise and to make it available to support medical decision making. Our approach is based on econometric techniques (known as conjoint analysis or discrete choice theory) developed to analyze and forecast consumer or patient behavior; we reconceptualize these techniques and put them to use to generate an explainable, tractable decision support system for medical experts. The approach works as follows: using choice experiments containing systematically composed hypothetical choice scenarios, we collect a set of expert decisions. Then we use those decisions to estimate the weights that experts implicitly assign to various decision factors. The resulting choice model is able to generate a probabilistic assessment for real-life decision situations, in combination with an explanation of which factors led to the assessment. The approach has several advantages, but also potential limitations, compared to rule-based methods and machine learning techniques. We illustrate the choice model approach to support medical decision making by applying it in the context of the difficult choice to proceed to surgery v. comfort care for a critically ill neonate