Open Access Symposium: Opening & An editor-in-chief’s perspective

Engineering, Systems and ServicesTechnology, Policy and Managemen

The practice of strategic journal self-citation: It exists, and should stop (a note from the editor-in-chief)

This note highlights how journal self-citation practices substantially influence impact factor-based journal rankings in the field of Transportation. Furthermore, by means of analyzing Thomson Reuters’ most recent Journal Citation Report (JCR), I show that a substantial share of these self-citations is likely to be the result of strategic behavior by editors of journals. I conclude with a call to editors to stop requesting or nudging authors to add journal self-citations to their papers; and a call to authors to stop giving in to editors when being asked to provide such citations.Engineering, Systems and ServicesTechnology, Policy and Managemen

Turning the light on in Virginia: New perspectives on choice behavior modeling

Engineering, Systems and ServicesTechnology, Policy and Managemen

Paving the way towards superstar destinations: Models of convex demand for quality

This article highlights the importance for urban planning, of the under-researched notion of superstar destinations. Furthermore, it presents and compares destination choice models that generate a convex demand for destination quality, and thereby explain and predict the existence of so-called superstar destinations. When compared to their competition, superstar destinations are much more popular than differences in quality between the superstar and other destinations would suggest at first sight. Although convexity of demand for quality is a known precondition for the existence of superstars, it remains unclear what mechanism might cause this imperfect substitution between different quality levels. The article proposes several choice models that generate a convexity of demand for quality, thereby paving the way for (modelling) the existence of superstar destinations. These models are compared using numerical simulations, which show that each of the proposed models has the potential to generate superstar effects, although for most models the effect decreases for larger choice sets. Results suggest that including reference-dependency into choice models helps overcome this potential limitation, as it leads to superstar effects for larger choice sets typically encountered in real life destination choice situations.Transport and Logistic

A new model of Random Regret Minimization

A new choice model is derived, rooted in the framework of Random Regret Minimization (RRM). The proposed model postulates that when choosing, people anticipate and aim to minimize regret. Whereas previous regret-based discrete choice-models assume that regret is experienced with respect to only the best of foregone alternatives, the proposed model assumes that regret is potentially experienced with respect to each foregone alternative that performs well. In contrast with earlier regret-based discrete-choice approaches, this model can be estimated using readily available discrete-choice software packages. Using formal derivations and numerical examples, the proposed model is contrasted with Random Utility Maximization’s linear-additive MNL-model. It is illustrated how the new RRM-model provides an intuitive and parsimonious means to capture so-called compromise effects, which have received much attention lately in fields outside transportation. Empirical comparisons between the two models on four revealed and stated travel choice datasets show a promising performance of the RRM-model.Infrastructures, Systems and ServicesTechnology, Policy and Managemen

Traveler response to information

The past few years have witnessed an impressive progress in the capabilities of travel information services. It is expected that in a few years, travelers will be constantly informed, pre-trip as well as en-route, about their optimal departure time, route and transport mode. The information is based on a careful monitoring of the transport network as well as travelers' personal preferences and schedules. Traffic jams, train delays and the like will no longer be unpleasant surprises. Unfortunately, our knowledge concerning how travelers will respond to this stream of increasingly advanced information seriously lags behind these technological advances themselves. A number of important questions have not been addressed adequately yet: To what extent do travelers actually use information available to them? Are they able to deal in an intelligent way with the immense complexity that is associated with travel in nowadays dense and multimodal transport networks? This dissertation answers these and other questions by integrating theories from the fields of microeconomics, psychology, marketing and transportation into mathematical models of traveler behavior. Subsequently, a computer-based travel environment is developed that simulates actual travel situations (involving for example time pressure, traffic jams and train delays). By observing the behavior of hundreds of participants to an experiment using the artificial travel environment, a unique dataset is obtained. Advanced econometrical analyses of the data show that the developed theoretical models form an adequate description of actual traveler behavior. And more importantly, they suggest that travelers are pretty good at dealing intelligently with complex travel situations and sophisticated information services.Technology, Policy and Managemen

A Generalized Random Regret Minimization Model

This paper presents, discusses and tests a generalized Random Regret Minimization (G-RRM) model. The G-RRM model is created by replacing a fixed constant in the attribute-specific regret functions of the RRM model, by a regret-weight variable. Depending on the value of the regret-weights, the G-RRM model generates predictions that equal those of, respectively, the canonical linear-in-parameters Random Utility Maximization (RUM) model, the conventional Random Regret Minimization (RRM) model, and hybrid RUM-RRM specifications. When the regret-weight variable is written as a binary logit function, the G-RRM model can be estimated on choice data using conventional software packages. As an empirical proof of concept, the G-RRM model is estimated on a stated route choice dataset, and its outcomes are compared with RUM and RRM counterparts.Infrastructures, Systems and ServicesTechnology, Policy and Managemen

Random Regret Minimization for consumer choice modelling: Assessment of empirical evidence

This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization model (RRM), which has two years ago been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization paradigm (RUM). Since its very recent introduction, a relatively small but growing body of literature has emerged focusing on RRM’s theoretical properties and on empirical comparisons with RUM. This paper assesses theoretical and empirical results that have been obtained so far, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeller’s toolkit. The paper shows that RRM or hybrid RRM-RUM models outperform RUM counterparts (which are equally parsimonious) in a majority of studies in terms of model fit and predictive ability. Although these differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to often considerable differences in elasticities and market share forecasts. We elaborate on how RRM and RUM can be used jointly to arrive at ‘behaviourally robust’ marketing strategies.Engineering, Systems and ServicesTechnology, Policy and Managemen

A new perspective on the role of attitudes in explaining travel behavior: A psychological network model

Psychological factors are generally thought to play an important role in the prediction of individual variations in travel behavior and travel related choices. To assess their effects in statistical models, three assumptions are typically made, namely: (1) the psychological factors influence behavior/choices and not vice versa, (2) psychological factors can be conceptualized as latent variables measured by observed indicators and (3) estimated between-person relationships are indicative of within-person relationships. Recent research has shown that each of these assumptions is conceptually and empirically problematic. This paper introduces to the field of travel behavior research an alternative modeling approach which has its roots in the emerging field of Network Psychometrics. This so-called psychological network model avoids the above mentioned problematic assumptions, by modeling the relationships between attitudinal and behavioral indicators as dynamic causal systems which can be operationalized as a network. We illustrate the new insights that may be gained from this approach in a travel behavior context. In particular, we estimate between-person and within-person network models using data from a (two-wave) panel survey containing indicators regarding travel modality use and related attitudes. Our results indicate that the extent to which the use of a mode is considered convenient is most strongly connected to the actual use of the corresponding mode, and that the convenience of using the car takes a central position in the attitude-behavior network. At the within-person level, no strong connections between attitudes and behaviors seem to exist. This latter finding serves as a warning against the practice, embodied in many popular travel behavior models, of interpreting associations between attitudes and (travel) behaviors as causal within-person relations.Transport and Logistic