Automated vehicles (AVs) may transform not only our travel experience but our complete daily schedules. This study analyses the data from an interactive stated activity-travel survey using latent class cluster analysis to uncover the types and prevalence of schedule changes with AVs. The analysis reveals that the majority of respondents expected little to no changes in their schedules. Importantly however, these responses are correlated with low commitment to the survey, evident in unrealistically short response times to non-central survey parts and simpler representations of their current schedules. The remaining responses reveal significant and varied changes in activities on board and outside travel, and in commute departure times. We conclude that the prevalence of schedule changes may be underestimated in our and possibly other AV studies due to low survey commitment. Our findings also highlight diverse potential motivations behind schedule changes with AVs: while some travellers may desire to free up time for other activities during the day (time saving), others may satisfy an unmet activity need by engaging in on-board activities (time spending). Considering this heterogeneity is crucial in endeavours to quantify the total benefits and costs that automated vehicles will bring to their users.@en

Fully Automated Vehicles (AVs) have been widely expected to revolutionise the future travel experience. Recent studies have shown that their impact may also reach beyond the travel episode, and lead their users to alter other activities performed during the day – their daily lifestyles. This study is among the first to empirically investigate the changes that travellers expect in their daily activities with AVs. To this aim, we created an interactive stated activity-travel survey, in which respondents designed their current daily schedule and, following that, redesigned it while imagining that their most frequently used travel mode is replaced with an AV. We administered the survey to 509 commuters in the Netherlands and analysed (changes in) on-board and stationary activity patterns using the multiple discrete-continuous extreme value (MDCEV) model. Results show a clear increase in the prevalence of various on-board activities in the AV compared to current modes, and even stronger increase for the high income and higher educated groups. Changes in stationary activities are less pronounced: no changes in the aggregate, but some changes within particular socio-demographic groups. Specific changes in stationary activities were associated with specific changes in on-board activities for the higher educated respondents: switching to AVs, they were more likely than others to add on-board work, meals, and leisure to their trips and more likely to add a getting ready activity to their stationary schedules. This study contributes to the growing body of literature that recognises and models on-board activities as an integral part of daily schedules.@en

Academics are often environmentally-minded, and they often travel internationally to share their research. Are they prone to attitude-behaviour gap? We collected data from 104 academics in Europe regarding attitudes towards online conferences, flight shame and carbon offsetting, and analysed their trip-making and mode choices in hypothetical conference travel situations. We find that while respondents and their social environments are conscious of the environmental impacts of flying, travel time considerations dominate their travel choices. Respondents are willing to buy a € 5 more expensive ticket or extend their travel by train by 30 seconds to reduce 1 kg of CO2.@en

It is widely expected that automated vehicles (AVs) will revolutionise travel experience by better facilitating various on-board activities. While these activities could make travel more pleasant, as is often supposed, they could also affect daily schedules, the related travel choices, and finally, the aggregate travel patterns – possible influences that are still insufficiently studied. For example, a morning commuter deciding to perform some home or work activities during travel, instead of at home or work, could also reconsider his departure time to work. More such travellers together could reshape traffic congestion. This paper models exactly this scenario. It formulates new scheduling preferences, which account for home and/or work activities during morning commute, and uses these (1) to analyse the optimal departure times when there is no congestion, and (2) to obtain the equilibrium congestion patterns in a bottleneck setting. If there is no congestion, it is predicted that AV users would depart earlier (later), if the on-board environment supports their home (work) activities. If there is congestion, AV users that perform home (work) activities during travel skew the congestion to earlier (later) times, and AV users that perform both activities increase both early and late congestion. Engaging in any activity during travel worsens congestion, at least when assuming that AVs do not increase bottleneck capacity. If future AVs would be specialised to support only home, only work, or both home and work activities, and would do so to a similar extent, then ‘Work AVs’ would increase the congestion the least.@en

This note revises the theoretical insights concerning the Value of Travel Time for automated vehicles as derived in a recent paper in this journal (Correia et al., 2019). That paper concluded that Value of Travel Time in an automated vehicle should be lower than in a conventional vehicle by salary rate, if the traveller works during the trip, and unchanged compared to conventional vehicles, if the traveller engages in leisure activities while travelling. However, these conclusions have limited validity, because the models, upon which they are based, contain a term whose interpretation differs across the models. This note clarifies this interpretation and offers an alternative extended model, which allows comparison across models. The alternative model provides an intuitive result: the facilitation-level of on-board activities determines the reduction of the Value of Travel Time in the automated vehicle. If automated vehicles provide identical work or leisure experience to out-of-vehicle locations, then the opportunity costs of travel time are erased and the Value of Travel Time equals the intrinsic costs of travel, which is strictly smaller than the Value of Travel Time in a conventional vehicle.@en

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.@en

This extensively updated textbook introduces the transport system and its societal impacts in a holistic and multidisciplinary way. A timely second edition, it includes new analyses of travel behaviour and the transport system’s impacts on health and well-being.@en

Transportation's effects on health and well-being are widely recognized. In the near future, autonomous vehicles (AVs) are expected to revolutionize transportation options and ways of travel. Consequently, the effect of AVs on population health and well-being is a crucial topic of interest for transportation policymaking, one that has received comparatively little attention. This chapter discusses (and anticipates) potential AV impacts on health and well-being. First, we summarize knowledge surrounding effects of transportation on physical health (traffic safety, air and noise pollution, and physical activity) and well-being (travel satisfaction, access to activities, etc.). We then discuss how AVs may affect traveler behaviors, focusing on mode shifts toward private, shared, and/or pooled AVs, and how these shifts may lead to an overall increase in automobile travel, even if not necessarily in person-travel. Finally, we interpret the previous two sections to deduce potential positive, negative, and uncertain health/well-being effects of AVs. We expect benefits from improved safety, well-being, and access to opportunities; disadvantages from reduced physical activity; and uncertain impacts around land use changes and emissions. We conclude by discussing policy implications and research paths forward.@en

non-driving activities while travelling, such as working, sleeping, playing games. The impact of this possibility on the satisfaction with travel and on travel demand has been extensively discussed in the literature. However, it has been hardly recognised that the availability of on-board activities influences the (time-geographic) constraints of daily activities and may alter the selection, location, and sequencing of other activities in the day. This hampers correct representation of travel behaviour in activity-based models aiming to predict the effects of AVs on mobility and environment (e.g., greenhouse gas emissions). To help fill this gap, we gathered and analysed qualitative data from focus groups, in which 27 commuters discussed their expectations concerning on-board activities and daily schedules in the AV-era. Among the core insights are the following three. First, it is useful to separate in modelling the satisfaction with travel and the potential for on-board activities during travel: they have different determinants and different consequences for activity schedules and individual travel demand. Second, on-board activities may be classified in 4 quadrants according to their novelty and priority level: this classification is helpful in understanding the potential re-arrangements of daily activities. Third, performing new activities during travel may lead to complex re-arrangements of daily activity patterns; the re-arrangements may ease or also increase time pressure. These, and other reported insights may facilitate more realistic representation of activity-travel behaviour in future travel behaviour models.@en

Automated vehicles (AVs) have been a dream for a long time. From science fiction in the 1930s to countless prototypes, extensive road testing, and first use cases at present, the technology has clearly come a long way. So too has the vision of practitioners and academics matured to recognise the various potential benefits (e.g., accessibility, traffic safety, productivity, well-being) as well as threats (e.g., safety and security risks, induced travel demand, urban sprawl) of automation. The task at hand is to comprehensively assess these impacts in preparation for the AV future. In order to perform such assessment, the analyst needs to anticipate the travel behaviour and aggregate travel patterns of the future AV users. This is not a trivial task: letting go of the steering wheel may mean more than making travel more pleasant for some travellers (or perhaps less so for others who prefer to stay in control). For current car drivers, this may mean gained time and energy in a day that could let them re-optimise their activity schedule. For instance, they may choose to perform work tasks during commute, and spend less time at work as a result. That would let them increase the time spent – and potentially, trips made – for leisure. The schedule changes may be even larger for those who may become new car users with the introduction of AVs. In the aggregate, such individual-level transitions will likely form complex and significant trends in the transport system, in terms of, for example, changing person- and vehicle-kilometres, modal split, spatial and temporal distribution of travel demand and land-use patterns. How can the policy makers anticipate such complex developments? The answer to such queries has, for a long time, been provided by the coupling of travel behaviour and (large-scale) transport models. However, these models have so far been developed, successfully applied and fine-tuned for predicting travel patterns with the current, non-AV travel modes. The question that needs to be answered before using them to predict transport system developments with AVs is evident: can they reliably describe the travel behaviour of future AV users? This PhD is, for the largest part, inspired by my conviction that the answer to this question is ‘no’. In particular, I argue that the time-use dimension of travel demand models – that is, the effects of time-use in AVs on daily time-use – has not been sufficiently developed. Even state-of-the-art models commonly assume that on-board activities in AVs will lower the so-called travel time penalty or the value of travel time. In the prediction context, this inevitably leads to a prediction of more person- (and vehicle-) travel. In the evaluation context, this approach gives an illusion that the benefits from travel time savings will accrue gradually and not step-wise, due to, for example, discrete schedule re-arrangements. A simplified modelling approach such as this can bias the predictions of aggregate travel patterns, which can lead to misguided policy decisions. This thesis aims to narrow the gap between the expected travel and time-use behaviour of AV users on the one hand and the models that describe it on the other. Throughout the chapters, it, first, provides intuition that such gap indeed exists. Second, it analyses empirical evidence that partially supports this intuition. Third, it develops three time-use and travel behaviour models that incorporate some of the missing behavioural elements. Lastly, this thesis provides first insights into how these model updates make a difference for the predictions of aggregate travel patterns – a crucial input for transport policy making for the AV era. @en

Automated Vehicles (AVs) offer their users a possibility to perform new non-driving activities while being on the way. The effects of this opportunity on travel choices and travel demand have mostly been conceptualised and modelled via a reduced penalty associated with (in-vehicle) travel time. This approach invariably leads to a prediction of more car-travel. However, we argue that reductions in the size of the travel time penalty are only a crude proxy for the variety of changes in time-use and travel patterns that are likely to occur at the advent of AVs. For example, performing activities in an AV can save time and in this way enable the execution of other activities within a day. Activities in an AV may also eliminate or generate a need for some other activities and travel. This may lead to an increase, or decrease in travel time, depending on the traveller’s preferences, schedule, and local accessibility. Neglecting these dynamics is likely to bias forecasts of travel demand and travel behaviour in the AV-era. In this paper, we present an optimisation model which rigorously captures the time-use effects of travellers’ ability to perform on-board activities. Using a series of worked out examples, we test the face validity of the model and demonstrate how it can be used to predict travel choices in the AV-era. @en

The idea that decision makers have to, and are willing to, make tradeoffs between the attributes of choice alternatives lies at the heart of microeconomic consumer theory (e.g., Lancaster, 1966; Keeney & Raiffa, 1976). This notion is embedded in the overwhelming majority of choice models, including but not limited to those which contain a conventional linear additive utility function (e.g., Ben-Akiva & Lerman, 1985; Train, 2009; Hensher et al., 2015). Even the majority of discrete choice models based on alternative, semi-compensatory decision rules such as loss aversion or regret minimization implicitly presume that decision makers are still willing to trade off one attribute (such as price) against another one (such as quality) to arrive at a choice that is optimal for them (e.g., Leong & Hensher, 2012; Chorus, 2014). However, a large and growing body of literature in psychology, based on the seminal work of Philip Tetlock and co-workers (e.g., Fiske & Tetlock, 1997; Tetlock et al., 2000), disputes this viewpoint; it puts forward the idea that decision makers consider some types of trade-offs morally problematic, or taboo. A body of theoretical considerations and empirical findings suggest that there are indeed situations where decision makers refuse to make a tradeoff between different attributes and even become upset (express moral outrage) when being asked to consider such tradeoffs. These studies find that tradeoffs are considered taboo when the two attributes belong to different ‘spheres’; usually one attribute belongs to the sphere of market transactions (e.g. a price attribute), while another attribute belongs to, for example, the sphere of social relations (e.g. friendship) or another sphere in which market transactions are frowned upon (e.g. healthcare, or matters of war and peace). To consider just one example: while a decision maker, e.g. a traveler, may have no trouble trading off money and travel time, she is likely to consider a tradeoff between money and traffic safety (of herself and others) more morally problematic. While the notion of taboo tradeoffs is by now firmly established in the behavioral sciences, its incorporation in an operational econometric model for discrete choice analysis is lacking. This is problematic, in light of the fact that many domains of application of discrete choice theory are likely to contain taboo tradeoffs. Think of health decision making, studies into the value of nature, the value of statistical life, or political decision making. The application of misspecified compensatory choice models in situations featuring taboo tradeoffs is likely to lead to biased estimates and misleading behavioral forecasts. This study derives an operational, discrete choice theory based model which accommodates taboo tradeoffs. In a crucial departure from existing choice models including lexicographic choice models (Saelensminde, 2006), the taboo trade off model features an estimable penalty which is associated with the mere act of trading off two attributes; that is, our model separates the taste of an individual for a particular attribute, from the penalty she may assign to trading off the attribute with another one. Our model specification is econometrically tractable (and estimable using conventional software). In a crucial departure from transitivity axioms, our model allows for the potential situation where an individual is willing to trade off attribute A (e.g., travel time) with attribute B (e.g. non-fatal traffic accidents), and attribute B with attribute C (e.g. fatal traffic accidents), but hesitates to trade off attribute A with attribute C. The model allows for two different behavioral responses to taboo tradeoffs: a strong preference for the status quo alternative and a strong inclination to opt out of the choice situation altogether. After introducing the model, we derive and interpret its econometric and behavioral properties. Then, we proceed with an empirical application in the context of traffic safety, based on a data set specifically collected for the purpose of testing our model. We compare our taboo trade off model with conventional linear additive models as well as with more recently proposed, semi- and non-compensatory choice models. To conclude, we provide an extensive discussion of the potential and limitations of our taboo trade off model for discrete choice analysis; and of the implications of (modeling) taboo tradeoffs for policy design in a variety of application domains. Note: at the moment of submitting this abstract, the model has been derived, and a selection of its properties have been studied. The model has been successfully tested using synthetic data. Stated Choice data collection is underway. Our empirical analyses should be ready, well before the start of the conference, where we would like to present results ‘hot from the press’.@en