Caspar G. Chorus

Use and Effects of Advanced Traveller Information Services (ATIS): A Review of the Literature

Abstract Rapid technological developments in the field of personal communication services probe visions of a next generation in Advanced Traveller Information Services (ATIS). These technological developments provoke a renewed interest in the use and effect of such next‐generation ATIS among academia as well as practitioners. To understand better the potential use and effects of such next‐generation ATIS, a thorough review is warranted of contemporary conceptual ideas and empirical findings on the use of travel information (services) and their effects on travellers’ choices. This paper presents such a review and integrates behavioural determinants such as the role of decision strategies with manifest determinants such as trip contexts and socio‐economic variables into a coherent framework of information acquisition and its effect on travellers’ perceptions.

Random Regret Minimization: An Overview of Model Properties and Empirical Evidence

This paper presents an overview of model properties and empirical evidence related to the recently introduced discrete choice paradigm of random regret minimization (RRM). The RRM approach to discrete choice modelling provides an alternative to the conventional, linear-additive random utility maximization (RUM)-based approach which has dominated the field since its inception. Section of Transport and Logistics RRM models postulate that when choosing, decision-makers are concerned with avoiding the situation where one or more non-chosen alternatives perform better than a chosen one in terms of one or more attributes. From this central behavioural premise, semi-compensatory decision-making and choice set composition effects like the compromise effect emerge as RRM model features. Being as parsimonious as RUMs linear-additive multinomial logit model, RRM features logit choice probabilities and is easily estimable using conventional discrete choice software packages. This paper ties together the main insights and results from a number of recent studies that have explored RRMs model properties and empirically tested RRM-based models Delft University of Technology, based on a range of revealed and stated choice data sets. As such, the paper allows for an early assessment of RRMs potential and its limitations as a model of discrete (travel) choice behaviour.

Responses to Transit Information among Car-drivers: Regret-based Models and Simulations

Abstract This article investigates the use and effects of transit information among car drivers that consider transit as a mode-option in their choice set. It does so by first presenting a theoretical model of travel information use and effect, based on the integration of notions of Bayesian updating into a regret-based framework of travel choice. Subsequently, numerical simulation of the model provides insights into the mechanisms behind information use and effect in a mode-choice context where a traveler has both car– as well as transit-options in their choice set, and prefers traveling by car over riding by transit. These simulations show that the perceived value of acquiring transit information is limited by a number of factors. Furthermore they demonstrate that, even in the case where transit information is acquired, and the message is favorable to transit, its impact on mode choices will also be limited. Given these results for non-habitual car-drivers, it is suggested that for car-drivers in general (thus including the large share of habitual drivers), conservative estimates regarding the impact of transit information provision on modal shift would be realistic.

Regret theory-based route choices and traffic equilibria

This article studies route choices and traffic equilibria when travel times are risky, and travellers are risk averse and regret averse. It is shown how regret theory, being one of the most popular contenders of expected utility theory throughout the social sciences, can be applied to model risky route choices by means of an expected modified utility function. Subsequently this function is used to study numerically how risk aversion and regret aversion jointly determine equilibrium outcomes in a simple binary route choice situation. It is found that increasing levels of regret aversion lead to equilibrium shifts towards routes whose mean travel time is low, routes that are less risky and especially routes whose worst-case travel time is low compared to that of the competing route. Furthermore, risk aversion and regret aversion are found to reinforce each others impact on equilibrium towards a situation where safer routes are preferred over riskier (but faster) ones.

Regret Minimization or Utility Maximization: It Depends on the Attribute

In this study we show how the coexistence of different decision rules can be accommodated in discrete choice models. Specifically, in this paper we present a generic hybrid model specification that allows for some attributes being processed using conventional linear-additive utility-maximization-based rules, while others are being processed using regret-minimization-based rules. We show that on two revealed and stated choice datasets particular specifications of hybrid models, containing both regret-based and utility-based attribute decision rules, outperform—in terms of model fit and out-of-sample predictive ability—choice models where all attributes are assumed to be processed by means of one and the same decision rule. However, in our data differences between models are very small. Implications, in terms of marginal willingness-to-pay measures (WtP), are derived for the different hybrid model specifications and applied in the context of the two datasets. It is found that in the context of our data hybrid WtP measures differ substantially from conventional utility-based WtP measures, and that the hybrid WtP specifications allow for a richer (choice-set-specific) interpretation of the trade-offs that people make.

Spatial choice: a matter of utility or regret?

This paper provides an empirical comparison between utility-maximization and regret-minimization perspectives of spatial-choice behaviour. The key difference between these two perspectives is that the regret-minimization perspective implies that the anticipated satisfaction associated with a chosen spatial alternative depends on the anticipated performance of nonchosen alternatives. In order to provide a meaningful statistical comparison, we formulate a model of regret minimization such that it reduces to utility maximization for a given parameter restriction. Estimation results, based on a binary stated travel-mode-choice experiment, show how the regret-based model outperforms its utilitarian counterpart. Furthermore it is shown how participants in the experiment attached relatively much weight to the situation where the nonchosen alternative is slightly better than the chosen one, and they tend to discount larger differences. We show how this concavity of the regret function is in line with the prospect theoretical notion of risk-seeking behaviour in the domain of losses.

Stochastic User Equilibrium for Route Choice Model Based on Random Regret Minimization

A static stochastic user equilibrium (SUE) problem was formulated: the mode of random regret minimization (RRM) was used for route choices. The RRM approach assumes that individuals minimize anticipated regret, rather than maximize expected utility, when choosing from alternative routes. The cost function for the RRM model is not separable, and so a variational inequality approach was adopted to formulate the problem. A path-based algorithm was applied to solve the RRM-SUE problem with the method of successive averages. Implementation of the algorithm in a real-world network is illustrated, and the trade-offs and differences between the proposed model and the SUE based on random utility models is discussed.

Determinants of Stated and Revealed Mental Map Quality: An Empirical Study

This paper presents the results of an empirical study into the role of travel behaviour and socio-demographic factors as determinants of mental map quality. Several interesting findings are reported. In line with previous research, conducted in quite different urban contexts, the study finds that travelling by means of active modes, requiring active navigation of the traveller, leads to higher quality mental maps. Strong effects are found for both the car and bicycle modes (relative to the more passive bus-mode). Furthermore, there appears to be a rather strong correspondence between the stated (or: perceived) and revealed (or: actual) quality of peoples mental maps. This correspondence is particularly strong among women and non-residents of the study area.

Substantial Changes and Their Impact on Mobility: A Typology and an Overview of the Literature

During the past 40 years, mobility patterns have enduringly changed several times as a result of the occurrence of a number “substantial changes”. Examples of such substantial changes are the rapid emergence of affordable air travel, the oil crises, and profound ICT developments. To most researchers and policy-makers in transportation, it seems more than likely that the next 40 years will also witness a number of substantial changes, some of which might even have larger impacts on mobility than the ones described above. However, literature on substantial changes and their impact on mobility are difficult to access as it is spread across different research fields and suffers from ambiguous use of terminology. As a result, overview of the literature on substantial changes and their findings is missing and discussions on the impacts on future mobility of potential substantial changes are hampered. To overcome these problems, this paper (1) proposes a typology of substantial changes and (2) ties together and reviews the scholarly literature that has focused on identifying the impacts of past substantial changes on mobility patterns. In this paper, we show how the proposed typology on substantial changes can be applied to contemplate on substantial changes and on their impacts.

Sampling of Alternatives in Random Regret Minimization Models

We propose a methodology to achieve consistency, asymptotic normality and efficiency, while sampling alternatives in Random Regret Minimization models. Our method is an extension of previous results for Logit and MEV models. We illustrate the methodology using Monte Carlo experimentation. Experiments show that the proposed methodology is practical, that it outperforms the uncorrected model, and that it yields acceptable results.