In de bereikbaarheidsliteratuur is nauwelijks aandacht voor het ter beschikking hebben van alternatieven, en de waarde ervan voor bereikbaarheid. We stellen het begrip ‘substitueerbaarheid’ voor en definiëren het als ‘de mate waarin één of meerdere karakteristieken van de voorkeursoptie om te verplaatsen vervangen kan worden door alternatieven’. Karakteristieken betreffen de activiteitenkeuze, vervoerwijzekeuze, tijdstipkeuze en route keuze. We stellen dat substitueerbaarheid een veelbelovend concept is voor zowel het begrijpen van verplaatsingsgedrag als bereikbaarheid, en vanuit het ethische perspectief van de ‘vrijheid van keuze’. Dit paper conceptualiseert substitueerbaarheid, stelt een mathematische benadering voor, en bediscussieert de relatie met vrijheid van keuze, bereikbaarheid en robuustheid/betrouwbaarheid. We stellen dat het een gradueel concept is, dat voor en tijdens een verplaatsing gemeten kan worden, en ook tijdens het uitvoeren van een geheel activiteitenprogramma. Verder bediscussiëren we het modelleren van substitueerbaarheid en het gebruik van het concept voor evaluatiedoeleinden, en we presenteren een onderzoekagenda. @en

Introduction Stated Choice (SC) experiments are widely used to acquire understanding on choice behaviour. Nowadays, SC experiments are increasingly being based on so-called “efficient designs”. Efficient designs aim to generate stated choice tasks that maximize the collected information in the data, yielding more reliable parameter estimates with an equal or lower number of observations than traditional orthogonal designs. While earlier research efforts on efficient experimental design mainly focussed on extending the design theory to encompass more advanced models of choice, such as Nested Logit and (Panel) Mixed Logit models, recent efforts are shifting towards understanding the robustness of the modelling results towards the efficient experimental design. These studies focus on exploring to what extent a particular experimental design loses efficiency when the data generating process does not match the model on which the design is based. This literature has predominantly focussed on misspecification in terms of parameter priors, interaction effects and the way in which (correlations between) error terms are modelled (Ferrini and Scarpa 2007; Yu et al. 2008; Bliemer and Rose 2011; Ojeda-Cabral et al. 2016) However, despite compelling evidence that decision-makers use a wide range of decision rules when making choices (Hess et al. 2012), and despite the rapidly growing interest in the choice modelling community into alternative decision rules (Leong and Hensher 2012; Guevara and Fukushi 2016), robustness issues concerning potential misspecification of the presumed decision rule has attracted only very limited attention within the literature. In fact, to the authors’ knowledge, research on experimental designs has exclusively been based on the (often implicit) assumption that decision-makers make choices based on a (linear-additive) Random Utility Maximization (RUM) rule. As a consequence, it is currently unclear what is the influence of different assumptions regarding the decision rules on the statistical efficiency of the design, and on the performance of different choice models under different design assumptions. For instance, do non-RUM designs differ much from RUM-designs?; does a misspecification of the decision rule result in perhaps highly suboptimal designs?; do RUM designs favour RUM models in terms of model fit? This paper aims to fill these knowledge gaps. To do so, we construct efficient designs based on a non-RUM model – in casu: a Random Regret Minimization (RRM) model – and assess the effects of the design decision rule[1] analytically as well as empirically. We use an RRM model for our analyses because RRM models are among the more popular non-RUM models. Specifically, we use the P-RRM model (Van Cranenburgh et al. 2015) as this model has very convenient mathematical properties for constructing efficient designs. First, we investigate the effect of decision rule misspecification on the statistical efficiency of the SC design. Specifically, we consider two cases: (1) the case in which the experimental designs are optimized for linear-additive RUM while the true Data Generating Process (DGP) is P-RRM, and (2) the case in which the experimental efficient designs are optimized for P-RRM while the true DGP is linear-additive RUM. After that, we use empirical data (collected specifically for this study) to investigate the influence of the design decision rule on the modelling results. Contributions The methodological contributions of this paper to the experimental design literature are twofold. Firstly, we show that for the P-RRM model (Van Cranenburgh et al. 2015) efficient designs can relatively easily be constructed. Because the P-RRM model has a piecewise linear form, the Asymptotic Variance Covariance matrix – which is needed to construct efficient designs – can be determined analytically. Thereby, we complement the choice modeller’s toolbox for designing efficient experimental designs. Secondly, we extend the experimental design literature by developing new insights on the effects of misspecification of the assumed design decision rule on the statistical efficiency. Finally, the substantive contribution of this paper is that we develop new empirical insights on the robustness of modelling results with respect to the assumed design decision rule. Key findings Conventional RUM efficient designs can be statistically highly inefficient in case RRM is the better representation of the actual choice behaviour Inferences based on empirical SC data on what decision rule (in casu: RUM or RRM) best explains the observed choices are found to be highly sensitive to the particular design that is being used. Model fit differences can be substantial and highly significant. To the extent that a design is more efficient for one particular decision rule (RUM or RRM), the choice modeller is more likely to conclude – based on comparison of the final Log-Likelihoods – that that particular decision rule is the better representation of the actual observed choice behaviour.@en

A recent paper published in this journal compares two regret based choice models, and concludes that one of them is theoretically inferior and has a worse empirical performance in the context of a particular data set [Rasouli and Timmermans, Transportation 6:1–22, 2016]. Unfortunately, those conclusions are ill-founded as they are based on a misinterpretation and misrepresentation of one of the two considered models. Furthermore, the paper overlooks highly relevant recent work on the topic, and contains insufficient empirical analyses. Together, these issues make that the paper provides a confusing addition to the literature. With the aim of lifting some of this confusion, this commentary sets out to highlight, and correct where possible, the paper’s shortcomings.@en

Recent empirical evidence suggests that travellers are becoming increasingly multimodal. Coinciding with this trend, a growing interest can be observed in the transport literature to study the concept of multimodality. Most studies, in this regard, have focused on assessing the determinants of multimodal travel behaviour. While it is interesting to know which factors, at a certain moment in time, affect the membership of mono/multimodal travel patterns, one general omission in the current literature relates to the questions how and why travellers switch between the mono/multimodal travel patterns over time. This study aims to fill this knowledge gap. To this end, a mixture latent Markov model is specified and estimated using data from the German mobility panel. Our mixture latent Markov models consist of latent travel patterns as well as latent mobility styles. To acquire insights on changes in travel behaviour various model specifications are tested. The travel data is best explained by a model consisting of five latent travel patterns and three mobility styles. The five travel patterns are can be conceived as (1) strict car users, (2) public transport and occasional car users, (3) car passengers, (4) car and bicycle users and (5) bicycle and occasional public transport users, and the three underlying mobility styles are identified as (1) habitual travellers, who stay in their respective pattern for three consecutive years, (2) car (in)dependent choice travellers, who switch within car and non-car patterns, and (3) car users with an alternative mode preference, who switch between car and non-car patterns. Overall, it is concluded that mixture latent Markov models are effective to reveal (heterogeneity in) transition patterns.

@en

As discrete choice models may be misspecified, it is crucial for choice modellers to have knowledge on the robustness of their modelling outcomes towards misspecification. This study investigates the robustness of Random Regret Minimization (RRM) modelling outcomes towards one sort of model misspecification: the omission of relevant attributes. We explore the effect of omitted attributes (orthogonal and correlated) in the context of labelled and unlabeled data. In the context of labelled data, we show that - just as in RUM models - in RRM models Alternative Specific Constants (ASCs) can be used to capture the average effect of omitted attributes. However, in contrast to RUM models, ASCs in RRM models are choice set composition specific. This implies that in order to achieve consistent parameter estimates when the choice set composition varies across choice observations, different sets of ASCs need to be estimated for each unique choice set composition. In the context of unlabeled data, we show - using Monte Carlo simulations - that RRM models are fairly robust towards the presence of an orthogonal omitted attribute, though not as robust as the linear-additive RUM model. Specifically, we find that: (1) Aggregate Demand Elasticities (ADEs) implied by RRM models are less robust towards the presence of an orthogonal omitted attributes than those implied by linear-additive RUM models, and (2) Average Sample Effects (ASEs) implied by RRM models are - in the presence of an omitted orthogonal attribute - more sensitive towards misspecification in terms of the underlying decision rule than those implied by its linear-additive RUM counterpart.

@en

Bij beleidskeuzes over transportbeleid moet regelmatig een afweging worden gemaakt tussen reistijd en veiligheid. Een goed voorbeeld is het verhogen van de maximumsnelheid naar 130 kilometer per uur. Transporteconomen stellen het welvaartseffect van dergelijke maatregelen vast in een maatschappelijke kosten-batenanalyse (MKBA) door de verwachte reistijdverandering te vermenigvuldigen met de waarde van een uur reistijdwinst (Value of Time) en door de verwachte verandering in het aantal verkeersdoden te vermenigvuldigen met de waarde van een statistisch mensenleven (Value of a Statistical Life). De dominante methode om de ‘Value of Time’ en de ‘Value of a Statistical Life’ vast te stellen zijn keuze-experimenten waarin aan respondenten wordt gevraagd om routekeuzes te maken. De routes waaruit respondenten kunnen kiezen verschillen bijvoorbeeld in reistijd, reiskosten en verkeersveiligheid. Aan de hand van de routekeuzes wordt vastgesteld hoeveel euro Nederlanders overhebben voor reistijdwinst en verbetering van verkeersveiligheid. Verschillende wetenschappers bekritiseren deze methode, omdat de afwegingen tussen reistijd en veiligheid van individuen als automobilist (consument van mobiliteit) volgens hen niet overeenkomen met de manier waarop dezelfde individuen als burger vinden dat de overheid afwegingen moet maken tussen reistijd en veiligheid. In dit onderzoek tonen wij middels keuze-experimenten aan dat individuen inderdaad verschillende preferenties hebben als consument en burger, wanneer zij een afweging maken tussen reistijd en veiligheid. Als burger kennen zij meer waarde toe aan verkeersveiligheid dan aan reistijd vergeleken met de keuzes die zij maken als consument. We hebben ook onderzocht hoe dit verschil kan worden verklaard door individuen zowel consumenten- als burgerkeuzes voor te leggen en hen te vragen waarom ze verschillende keuzes maakten. De volgende drie verklaringen werden het vaakst genoemd: 1) De overheid heeft een grotere verantwoordelijkheid voor de veiligheid van burgers dan voor de reistijd van burgers en automobilisten hebben een relatief grote verantwoordelijkheid voor hun reistijd; 2) De overheid kan verkeersveiligheid gemakkelijker meewegen dan het individu. De overheid heeft meer kennis over verschil in veiligheid tussen routes, terwijl het individu in de werkelijkheid niet of nauwelijks kennis heeft over verschil in veiligheid tussen routes; 3) Consumenten hebben het gevoel dat hun routekeuze hun reistijd beïnvloedt, maar zij hebben niet het gevoel dat hun routekeuze hun veiligheid beïnvloedt, ondanks het feit dat de veiligheid van de routes verschilt. Consumenten geven bijvoorbeeld aan dat beide routes voor hen even veilig zijn vanwege hun goede rijstijl. Daarom kennen zij als consument minder waarde toe aan veiligheid dan als burger.@en

This paper investigates vacationers’ short-term responses to a sharp increase in transport costs. It aims to (1) acquire an understanding of the relative popularity of the different types of responses among vacationers and (2) explore whether there are distinct market segments of vacationers that respond differently to a sharp increase in transport costs. Data are obtained from a novel, tailor-made questionnaire which was done in the Netherlands in 2012. Results show that no single response is either very popular or unpopular. Furthermore, using latent class cluster analysis, four market segments (classes) of vacationers are identified that have markedly distinct response patterns. It was found that Age and Income explain class membership.

@en

Om de doorstroming in een netwerk te optimaliseren, moeten we verkeersmanagementmaatregelen in samenhang inzetten. Maar hoe bepaal je wat de optimale – meest samenhangende – inzet is? Lange tijd leek het ondoenlijk om zo’n regelprobleem met behulp van (microscopische) modelvoorspellingen op te lossen: het probleem was daarvoor te complex. Een slimme wiskundige techniek die gebruik maakt van verschillende modellen lijkt echter voor een doorbraak te zorgen. Volgens Osorio et al is de nieuwe modelgebaseerde optimaliserende aanpak zelfs geschikt voor het doorrekenen van complete netwerken. @en

This paper derives a trick to account for variation in choice set size in Random Regret Minimization (RRM) models. In many choice situations the choice set size varies across choice observations. As in RRM models regret level differences increase with increasing choice set size, not accounting for variation in choice set size results in RRM models to predict relatively deterministic choice behaviour in observations where the choice set is large and relatively random choice behaviour in observations where the choice set is small. Such variation in choice consistency across observations is behaviourally unrealistic and leads to inferior performance of RRM models in the context of data sets with varying choice set sizes. The proposed trick resolves this in an econometrically pragmatic and behaviourally meaningful way by rescaling the regret levels as a function of the choice set size. The trick can be applied in the estimation phase when the choice set size varies across choice observations as well as in the forecasting phase when forecasts are made over choice sets of varying sizes.@en

Aviation carbon taxes have occasionally been debated as a measure to curb aviation carbon dioxide (CO₂) emissions. This paper presents a simulation study on the effects of high aviation carbon taxes on tourism and its related CO₂ emissions. The paper investigates the scenario in which, as a result of high aviation carbon taxes, air fares increase by 50%. To assess such impacts, choice probabilities of vacation alternatives, which were conceptualized as portfolios consisting of a destination, mode of transport, accommodation type, and length of stay, were simulated by using a portfolio vacation choice model. Lower and upper bounds of the impacts were derived. Simulation results suggested that a substantial reduction of the contribution of tourism travel to anthropogenic climate change could be achieved. Tourism trani-related CO₂-equivalent emissions were estimated to drop between 9% and 32%. Furthermore, it was found that the choice probability of nearby destinations (<200 km) increased considerably: between 4% and 18%. It was also found that the choice probability of short vacations (<1 week) also increased considerably: between 3% and 11%.

@en