COVID-19 had (and still has) a huge impact on travel behaviour. Most research on the effects of COVID-19 on travel behaviour is based on daily travel behaviour. Long-distance travel is most of the time not included. Therefore, this paper studies the impact of perceived COVID-19 risk on mode-choice for long-distance travel in Europe. This paper uses a Stated-Preference approach which consists of two parts, the perceived risk rating experiment and the main mode choice experiment. In total
1147 responses were collected. In the rating experiment, respondents rated their perceived risk based on eight attributes. In the main choice experiment, respondents chose between train, plane and care based on travel time, travel cost, travel comfort and perceived risk. With this,
the Value of Risk (VoR) for a decrease in perceived risk is derived, both expressed in travel cost and travel comfort. Moreover, the Willingness to Pay (WtP) for comfort and Value of Time (VoT) are derived. With the combination of both models, the WtP for risk attributes given a perceived risk level is derived. The combination of both models shows the Willingness to Pay values for the different risk attributes, given a certain perceived risk level. To test the influence of perceived risk on modal split, three routes with real-world values are used. The results implicate a maximum of 5% market share difference possible. This shows that perceived COVID-19 has a moderate effect on modal split.

Making decisions regarding delayed line haul transport is a very demanding and complex process in E-commerce distribution centres. Automating this process can decrease decision-maker discussion and assessment time and, as a result, allow decision-makers to spend more time on other demanding tasks like sorting and distributing. Automation in this domain increases on-time deliveries and strengthens the E-commerce firms’ competitive position. However, such decisions involve experts’ knowledge, discussions among planners, and complex thought processes. Therefore, it is necessary to involve planners’ inclinations and preferences to automate their decisions. This paper proposes the Behavioural Artificial Intelligence Technology (BAIT) to automate expert decisions in delayed line haul deliveries. BAIT uses fundamental Discrete Choice theory under the hood to capture expert preferences effectively, and incorporates these into a decision-making tool. We use this method in a case study to replicate expert decisions in delayed line haul deliveries at DHL Express. The case study results show that BAIT can accurately replicate expert decisions.

The accelerating development of algorithms causes a disruptive effect in many domains, including the complex decision-making of knowledge workers. Experts can manage difficult but repetitive decisions with software technologies like a Decision Support System (DSS). A DSS is used to monitor decisions, get additional insights and improve decisions over time. Their supportive performance characterises these systems to assist human decision-makers. To answer to the pressing demand for transparency in DSSs, Councyl developed Behavioural Artificial Intelligence Tenchnology (BAIT). BAIT is a DSS that adequately supports experts with making decisions. However, algorithms like BAIT may affect the autonomy of experts and their decisions in numerous ways. This thesis studies human moral autonomy (HMA) of end-users in the context of BAIT. We do this by measuring perceptions of end-users. The product arising from this study is the HMA Survey.

The absence of medicines to cure COVID-19 calls for preventive strategies, including mask-wearing. Despite its protection against exposure to coronavirus, not everyone chooses to wear a mask. Some studies addressed mask-wearing behaviour from the standpoint of behavioural economics, one being the effect of herding behaviour. This occurs when people base their decision on the decisions of others. A literature review shows that herding can play a role, yet it has only been empirically studied up to a correlational relationship and not a causal relationship. This study focuses on quantifying the effect of factors that influence mask-wearing, emphasising herding. This study can be used in a more realistic epidemic transmission study that involves preventive health behaviours and can also help policymakers simulate the impacts of their policy designs more accurately. The study comprises two phases: choice and agent-based modelling. A literature review is performed to identify possible factors. Four possible factors are identified: Health Belief Model (HBM), herding-related, situational cues, and demographics. After collecting 151 questionnaire respondents within the Netherlands, a Latent Class Cluster Analysis identifies two clusters representing health beliefs. Class 1 are people who are more risk-averse towards COVID-19 and believe more in mask-wearing efficacy. Class 2 is the opposite. The choice modelling confirms a statistically significant effect of herding, only within friends and/or family and the random people. To explore the macro-level population dynamics, the interdependent behaviour between people is considered. An agent-based model (ABM) has been formalised, implemented, verified, and validated. The main insight is that the herding effect is stronger when the majority is not wearing masks than when the majority is wearing masks. In other words, there is a tendency towards no-mask-wearing, instead of the opposite. These discoveries unveils a new insight on how herding affects mask-wearing using an under-explored way of combining static and dynamic research methods. This study has several limitations. First, the risk of low-quality responses from online survey is uncertain. Second, care should be taken in generalising the result to the Netherlands population. Third, the literature review may miss important factors that were undiscovered in the previous literature up to April 2021. Fourth, this study generalises the factors in a broad categorisation. Finally, the ABM employs simplified input values. Future studies may also incorporate interviews, a more representative sampling method, and a more fine-grained specification of factors. Finally, five recommendations are derived for the policy-making process. First, this study recommends policy-makers maintain clarity in communicating mask-wearing policy. Second, enforcement of mandatory policy is recommended, especially in outdoor spaces. Third, mask-wearing can be encouraged through social campaigns, if necessary. Such campaigns may contain figures that people can closely relate to. Another alternative is to increase the importance of policy by putting signs in more prominent places and informing people about the enforcement. Fourth, the modellers in the policy-making domain could look at incorporating herding to such research and enrich its realism. Lastly, this study can also be utilised for other policy-making processes outside the mask-wearing context and/or outside the COVID-19 context.

Resulting from the Dutch commitment to the 2015 Paris Agreement, the Dutch government has set the goal of making all 7.9 million Dutch houses natural gas-free. The present study has focussed on the challenge of removing the currently predominantly installed natural gas fired central heating boiler since such residential heating systems (RHS) are responsible for 78% of the natural gas demand in houses. Municipalities are responsible for this so-called heat transition and use public participation to gain insights into the needs and wishes of citizens, which will help to speed up the transition towards natural gas-free RHS. The present study focusses on homeowners in the municipality of Delft and argues that a variety of insights cannot be obtained by the current participation process in Delft. The main research question of this study is: How do different classes of Delft homeowners make trade-offs between attributes of substitute residential heating systems and how do these trade-offs change due to relocation? This question has been answered with a context dependent latent class discrete choice model for which a stated preference survey was conducted. In this survey, respondents were asked eight times to choose between three natural gas-free RHS which each varied on six characteristics. The varied characteristics are: the type of RHS (all-electric heat pump, district heating, and hybrid heat pump with green gas), investment costs, operational costs, popularity in the neighbourhood (as a percentage of people with a particular RHS), required inside space (compared to the space required for a conventional central heating boiler), and construction time (needed for the installation of the RHS and insulation work). The results of the present study show that the RHS choice of Delft homeowners is positively influenced by higher values for the popularity in the neighbourhood. The higher the value for investment costs, operational costs, required inside space, and construction time the lower the chance that a homeowner chooses that RHS. Although all homeowners show similar reactions with respect to these five characteristics, the severity of these reactions does differ between homeowners. The model results show that Delft homeowners can be separated into two groups which each make different trade-offs. The first and largest group is characterised by homeowners with a relatively high income living in poorly insulated houses. These homeowners base their natural gas-free RHS choice primarily on investment costs, operational costs, and required inside space. Moreover, this group prefers district heating. The second group is likely to be consisting of low income homeowners living in relatively well insulated houses. This group is, similar to the first group, heavily influenced by investment costs and operational cost, but also by the type of RHS (preference for all-electric heat pumps and hybrid heat pumps with green gas) and popularity in the neighbourhood. When homeowners are moving to a new house, they pay less attention to investment costs, while paying more attention to construction time. The effect of relocation is especially strong in the second group of homeowners. Given the results for investment costs and operational costs and that the national government is responsible for the affordability of the heat transition, the municipality of Delft should try to influence the national government. It should do this via four ways: the Association of Netherlands Municipalities, the G40 city network, the Metropolitan Region Rotterdam The Hague, and via the inter-government layer lobby connections between municipal aldermen and national ministers of the same political party. The inside space requirement concerns of the first group of homeowners should be mitigated by Energieloket Delft and 015 Duurzaam. The municipality should also have citizen organisations (Platform Energietransitie Delft and neighbourhood associations) to gather and spread information to the second group of homeowners about the popularity in the neighbourhood. The municipality of Delft should take advantage of the window of opportunity that relocation provides by starting partnerships with real estate offices. In this way, new homeowners can be made aware of the heat transition in an early stage and can account for the extra required construction time and investment costs for a natural gas-free RHS in their bid. The municipality and real estate agent should have an advisor from Energieloket Delft or 015 Duurzaam to formulate a plan in which is stated what the best natural gas-free RHS is for a to-be-sold house, including an overview of the required investment costs and construction time. The advisor should consider increasing the investment costs in order to decrease the required construction time.

A multilateral organ, a national public and a national government are three entities that are inseparably intertwined. Together they form the dynamic policy triangle: a vibrant political system, constantly changing due to insights in the scholarly and public debate. This vibrancy leads to the fact that, despite an abundance of performed academic research, the relationship between the three entities remains a relevant field of investigation. A relatively unexplored aspect of the dynamic policy triangle is the working of a multilateral policy package within the relationship between national public and national government. An example of such a multilateral policy package is the United Nations’s collection of Sustainable Development Goals (SDGs). The general assumption is that all the goals are of equal importance, and that no country should be left behind when attempting to achieve them. National governments seem to follow this rationale, and claim not to make any prioritisation of one SDG over another. However, literature has shown that prioritisations of certain SDGs are likely to be made by national governments. Additionally, it is conceivable that citizens also prioritise certain goals over others. Both seem previously unexplored in academic literature and government publications. This research partially fills these gaps in knowledge by performing a Discrete Choice Modelling experiment within the domain of the Netherlands. This is a method in which respondents of a survey are asked to review several hypothetical scenarios and to choose their preferred option out of a finite set of choices. In total, 36 Discrete Choice Models were estimated, either for the total sample of respondents, or for the samples of Dutch citizens and policy makers separately. The results provide indicative evidence that Dutch citizens and Dutch policy makers are to a great extent aligned in terms of their SDG preferences. For the main part, both groups do not prioritise certain goals over others. Also, when comparing individual SDGs between the two groups, no significant differences are to be found. The results, however, do suggest several differences between the two groups. Firstly, Dutch citizens relatively reject SDG 17 - to revitalise global partnership for sustainable development - to several others. In this finding, a preference for planet- and people related SDGs becomes apparent. Policy makers do not show this behaviour. Secondly, Dutch policy makers prefer SDG 15 over SDGs 7, 8, and 9 in the Netherlands. This shows that biodiversity in their own country is prioritised, and that certain economical and innovative SDGs are deemed of lesser importance. Citizens do not show this behaviour. The results suggest a preference for SDG action in the Global South; 13% more weight is attributed to SDG action in the Global South than in the Netherlands. This preference does not significantly differ between the two study groups. Also, the results indicate that both groups are loss-averse in the SDG context, meaning that SDG action is considered more important in a scenario when a certain SDG decreases than when it increases. It was found that citizens are more loss-averse than policy makers.

Amsterdam Airport Schiphol (AAS) is the third largest airport in Europe in terms of passenger volumes and has been growing rapidly over the past years. This growth is expected to continue in the future (KiM, 2018). Due to the rapid growth in travel demand, AAS reached the maximum allowed number of flight movements of 500.000 per year in 2018 (Schiphol Group, 2018a). According to an Environmental Impact Assessment (EIA) carried out by Schiphol Group (2018), growth to 540.000 flight movements per year in the period of 2020-2023 is possible without violating current restrictions on noise nuisance and emissions. This number of 540.000 flight movements per year also represents the maximum number of flights that can be safely handled at AAS without further expansion and will soon be reached as well1 (Schiphol Group, 2018a). In the case of WLO scenario high, more than a quarter of the passenger demand in 2050 cannot be handled at AAS without violating set restrictions (CPB & PBL, 2015). AAS wants to maintain its leading position in the European transport market and wants to deal with the growing passenger demand, while staying within the set restrictions in terms of the number of flight movements per year. In order to deal with the expected increase in travel demand for long-distance travel in the future, AAS is considering alternative ways of transport and aspires to become the multimodal hub of Europe. To realize this, the focus of AAS is on short-haul flight substitution within Europe by HSR or by innovative modes. The hyperloop, initially introduced by Elon Musk (2013), is one of the innovative modes being considered by AAS and the Dutch Government (Ministery of Infrastructure and Water Management, 2020). Hyperloop consists of pods travelling through a tube, propelled by magnetic levitation, while maintaining a partial vacuum in this tube (Musk, 2013). The literature on hyperloop has focused on the different technical aspects and the technical feasibility of hyperloop (Gkoumas & Christou, 2020a, 2020b), others highlight the energy consumption, and some criticize hyperloop for not being feasible, but nobody has examined to what extent HPT can function as substitute for short-haul flights and which role HPT could play in the future of multi-modal transport. The aim of this study is to design the future transport system of hyperloop passenger transport (HPT) in the transport market with air passenger transport (APT), highspeed rail (HSR) and HPT for longdistance travel within Europe and to assess whether or not the passenger demand in WLO scenario high can be met, including the share of passengers that cannot be dealt with by means of APT alone. The methodology this thesis has used is discrete choice modelling (DCM). Trade-offs and mode choice of passengers when choosing between APT, HSR and HPT are analysed in order to assess the impact of different system designs of HPT on the potential of HSR and HPT in the substitution of short-haul flights at AAS. A stated preference (SP) experiment has been carried out in order to collect choice data. The focus of this study is solely on substitution of flights at AAS with both origin and destination in Europe, i.e. OD-substitution, and examines destinations that are located approximately 500 km from AAS. Substitution of transfer passengers, both within Europe and for intercontinental destinations, is disregarded in this study. The second research objective this study seeks to address is a methodological research objective. The aim is to examine the impact of using images in the presentation of unfamiliar alternatives in the introduction of the SP experiment on preferences, attitude and drop-out of respondents. Based on these two research objectives, the following research questions have been defined 1. How could different design scenarios for hyperloop passenger transport influence traveller’s mode choice between, and the transport demand for, air passenger transport, highspeed rail, and hyperloop passenger transport for the future long-distance transport market within Europe at AAS? 2. What is the impact of the way in which HPT is introduced in the stated preference experiment on preferences, attitude and drop-out of respondents?

Every day physicians make choices on clinical treatment that directly influence patients’ well-being. To deal with these critical decisions and avoid treatment errors and costs, physicians show a growing interest in Clinical Decision Support Systems (IDSS). Current CDSSs, however, suffer from limited transparency and flexibility, which gives rise to ethical concerns when applied in healthcare environments. A novel CDSS approach overcomes these limitations: Behavioral Artificial Intelligence (BAIT). However, a BAIT-based CDSS is not yet capable of incorporating new clinical developments to keep its recommendations accurate over time. Because healthcare decision-making is highly dynamic, the static BAIT-based CDSS needs a transformation into a dynamic BAIT-based CDSS that retains its accuracy and, therefore, clinical relevance over time. However, the preferences regarding a dynamic BAIT-based CDSS vary among healthcare decision-making contexts. Therefore, CDSS developers need an architecture that illustrates how to create and customize a dynamic BAIT-based CDSS that matches physicians' preferences in a particular healthcare context. Because designing these architectures is challenging, this research aims to formulate design principles that guide the design of dynamic BAIT-based CDSS architectures. By following the Action Design Research (ADR) framework, this research identified and tested a set of architecture requirements by building an architecture in a situated problem context. The generalization of the requirements that worked in a situated context resulted in ten design principles guiding the design of a dynamic BAIT-based CDSS architecture. The greater part of the design principles is specific to the design of a dynamic BAIT-based CDSS architecture. By providing these novel insights, the design principles contribute to the CDSS architecture design knowledge base. The research also contributes to CDSS architecture design knowledge because it tackles the challenges of designing a dynamic BAIT-based CDSS architecture. By doing so, the research outcomes eliminate the barriers to design such an architecture and lay a foundation to continue the work on transparent and dynamic CDSSs. The findings highlight new design challenges for further research, like designing a dynamic BAIT-based CDSS architecture with the ten design principles in another sector to investigate how to modify the design principles, so the principles are useful outside the healthcare sector.

This study investigates paternalistic and non-paternalistic altruistic preferences in the Willingness to Allocate Public Budget approach (WTAPB) in a transport context. To the best of my knowledge the extent to which citizens are willing to sacrifice effects for themselves to realize effects for other people, has not been studied in a WTAPB setting. This is the most important research gap that this study aims to alleviate. Furthermore, it is investigated to what extent citizens express paternalistic and non-paternalistic altruistic preferences. Two stated choice experiments were conducted by asking citizens to advice the government with regard to an investment in a bicycle infrastructure project which differ in travel time savings and safety. The first experiment focused on altruistic preferences for 10.000 citizens including themselves and 10.000 other citizens respectively. The second experiment focused on paternalistic and non-paternalistic preferences for 10.000 other citizens. This research has shown that respondents are willing to sacrifice more than 6 minutes travel time savings for 10.000 citizens including themselves, for a 10% risk reduction for 10.000 other citizens. Furthermore, individuals are willing to sacrifice 7% risk reduction for 10.000 citizens including themselves for 10% risk reduction for 10.000 other citizens.

All over the world, governments are facing sustainability challenges, which are among others related to car use and ownership. Car ownership research is traditionally mainly focussed on the effect of socio-demographic characteristics, like age, income and having a driving license. More recent research showed that life events related to work, family and residential location, and built environment factors are other promising explanatory factors of changing car ownership. However, the Dutch car ownership models do not incorporate these factors adequately, due to a lack of data. Next to that, it was found that it is theoretically better to model car ownership in a dynamic way (taking into account car ownership of last year). Since such dynamic models are still a rarity in the literature, this thesis examines to what extent the forecasts of household car ownership in the Netherlands are affected by the inclusion of life events and built environment factors in a dynamic model. To this end, the dynamic model is enriched in two steps: first in terms of structure (static versus dynamic), and secondly regarding substance (without or with the effect of life events and the built environment). The step from a static to a dynamic model (by using a transaction choice model) resulted in a significantly higher model fit (p<0.001), which was true as well for including life events and built environment factors. A variety of life events were found to affect car transaction behaviour, especially those with a spatial component (relocation and job transition). Furthermore, the absence or free nearby parking is found to reduce car ownership.The forecasts of the dynamic model were especially on a short term better in line with the historic car ownership trend than that of the static model. Including the effects of life events and built environment factors in the dynamic model resulted in a lower car ownership trend (up to 1.4% lower), while a similar effect was found of increased paid parking on a national level as well as on the level of Rotterdam. Therefore, reduced free parking at the street – especially near new business and residential areas – and campaigns aimed at people experiencing the habit-breaking effect of life events, are policy measures with the potential to reduce car ownership and to work towards a more sustainable and accessible future.

For decades researchers deliberated and continue to debate on how to support and assist humans in decision-making. This resulted in the development of Intelligent Decisions Support Systems (IDSSs). An IDSS is an application of artificial intelligence (AI) that desires to enhance and support decision making by enabling tasks to be performed by a computer while mimicking human capabilities. The two most generally classified types of IDSSs are knowledge-based and non-knowledge based systems. A knowledge-based system, also called an Expert system, directly translates domain knowledge into a set of rules or cases to support human decisions. In contrast, non-knowledge based IDSSs apply the rapidly growing branch of AI known as machine learning (ML), that grounds its decision-support on feature extraction of labelled training data. Recently, a company called Councyl, in collaboration with the TU Delft, developed a new approach to AI that has the potential to constitute a novel type of IDSS for judgement purposes. The new approach to AI is called BAIT (Behavioural artificial intelligence technology). BAIT utilises discrete choice modelling (DCM) to codify the domain expertise of experts’ in order to provide introspection on their decisions and support future judgments.
As BAIT is a new IDDS approach it requires testing in different settings to gain insight into the usefulness and effectiveness of this new method.

This study aims to provide insight into how factors relating to privacy and incentives influence people's willingness to participate in sharing their vehicle based sensing data with governmental parties for the purposes of improved dynamic traffic management in the Netherlands. Through the use of a stated preference experiment data is gathered in order to estimate a discrete choice model using binary logistic regression. Respondents are most likely willing to share their data when trip registration is not personally identifiable and this data is not shared with third parties. Sharing of data with emergency services and for research purposes actually increases the odds of participation. Furthermore, potential users who have not been exposed alternatives which offer monetary reward are more likely to participate for free. Clear communication of the purpose and the social benefits of participation is important for obtaining sufficient levels of participation without offering monetary reward. Being parsimonious in data collection will result in the least amount of privacy harm and avoid the perception of a system as unfair and inefficient.

This research examined the effect of an improved road network on the factors economic growth, road safety, emissions, education, health, employment and tourism in developing countries. A case study is conducted on the country Namibia in Southern Africa. A System Dynamics (SD) model is developed to assess the effects of future scenarios in terms of road infrastructure on the mentioned factors. The SD model is based on a literature study on developing countries in general and a data analysis on Namibia in specific. The identified scenarios are (i) Namibia becoming the logistics hub of Southern Africa by 2022 and (ii) enhance tourism by improving road infrastructure between the major tourism attractions. Both scenarios lead to improvements in health, tourism and employment. The primary school enrolment rates remained the same over time for both scenarios. Road safety increases for both scenarios due to upgrades to bitumen, but the extent to which it increases depends on the extra measures that are taken with regards to road safety. It can be increased more by campaigns or more controls. Emissions go up in both scenarios, because emissions are a direct result of constructing and upgrading roads. The tourism scenario attracts more tourists because the roads between the major attractions are upgraded to bitumen. Both scenarios are realistic and might happen in the future. The scenarios can and might be combined.

Hely has developed Mobility as a Service (MaaS) through multimodal Hubs,
which are offered within closed-user residential areas and made accessible
through an application. This exploratory study aims to assess people’s mobility
behaviour by the usage of Hely in a closed user group. It focused on two
aspects: the indication of (1) the community and (2) the use on the hubs by this
community. The community is characterized by mainly innovative bike- and
public transport-minded young professionals that seek a flexible and
convenient service. The motivation to use Hubs for its multimodality is
currently not widely supported by the community. Moreover, the Hubs reveal
travel behaviour with the shared (e-)car in its centre, independent of all trip
characteristics. Therefore, it can be concluded that currently a multimodal
MaaS-Hub mainly eases accessibility of cars for individuals that do not use
one often, which emphasizes the car as a lasting dominant factor within the
current mobility market.

This research investigates the travel behavior of users of an integrated mobility platform. Passive revealed preference data is collected from a smartphone application for integrated mobility. This data, consisting of the choices in a multimodal journey planner, allows evaluating travel behavior in integrated environments with high ecological validity. As such data has not been used yet for conventional travel behavior researches, this research also experiments with the application of conventional methods to this type of data. It is found that for the specific integrated platform, mostly public transport users engage with the platform. Public transport is dominant within the planned trips of users, and extreme situations are required for users to deviate in the mode choice between PT and non-PT alternatives. Furthermore, it is shown that discrete choice modeling is a suitable method for analyzing for this type of data.

The increased usage of Electric Vehicles and Renewable Energy Sources causes issues regarding the balancing of the electricity grid. To avoid investment costs, Distribution System Operators desire flexibility solutions. One of these flexibility solutions is the usage of the battery of the Electric Vehicle as an electricity source. This concept is known as Vehicle to Grid (V2G). However, providing V2G services might cause discomfort for the Electric Vehicle user. A contract can be used to compensate the user for the experienced discomfort. Literature on these contracts is lacking and the behaviour of EV user to these contracts is unknown. This study aims to close this gap. Data is collected by means of a web survey and evaluated with a multinomial logit model. It is shown that the difference in expected demand for price- and volume-based contracts is minimal. In addition, three contract elements can solely increase demand for V2G but require high levels. More value is created when a combination of these three contract elements is used. It would be valuable to understand how and where V2G can provide value. To do so, the results of this study can be used as input for a dynamic model that evaluates day-to-day electricity supply and demand.

Although fully-automated vehicles (FAVs) are not yet allowed on public roads and are barely present in people’s daily lives, according to experts their wide implementation will happen within the next few decades. The technology is expected to bring major benefits in terms of safety, traffic flow efficiency and environmental impact. Furthermore, by eliminating the necessity of actively steering the vehicle, automated vehicles (AVs) are expected to bring extra free time during the day to perform activities, as well as increase mobility of some excluded societal groups (e.g. elderly). However, taking away the burden of driving might also bring negative results. The convenience of the technology might encourage people to travel further and more often. Moreover, AVs users might transfer some of the activities from the traditional environments into the vehicle (e.g. work). The associated rearrangement of daily activity plans might have an impact on travel and residential location choices, possibly leading to further increase in overall travel distance. In such way, the expected positive impact of the technology could be compromised.
For that reason, it is necessary to examine behavioural response to AVs, namely the way travel time will be used in the vehicle and its consequences for daily activity plans, travel patterns and residential location choices. In order to fill in this scientific gap five focus groups were conducted. The participants were confronted with a simplified scenario of the future after broad AVs implementation and were asked a set of questions regarding the aforementioned topics. It was found that, in contrary to the current modes, time inside AVs will be perceived not in terms of burden or opportunity, but rather in terms of pure opportunity. The participants perceive AV time as an extension of either work or private time. In context of daily activity plans it brings about a number of possible behavioral adaptations. In case of regular commuters, a willingness to use the in-vehicle time to release pressure from daily life was often identified, whereas people with less stationary jobs claim that the AV would allow them to bring more flexibility into their daily plans, or move their actual office or even the entire business into the vehicle.
However, the findings regarding daily activity plans do not provide a clear answer on their impact on travel and location decisions. At the same time, a number of advantages of AVs for irregular, long travels was identified, and many participants explicitly expressed their willingness to increase the amount of such travels. The impact of AVs on residential location choices appears to be limited: although people are not willing to move because of the AV, they claim it would still play a significant role in their relocation decisions if it was necessary for other reasons. Moreover, some AV-specific phenomena were discovered, which might have an important significance on the actual AV use: namely increased pressure to perform onboard activities and feeling of “being trapped” in the vehicle. Furthermore, the participants pointed at the striking easiness of making travel decisions in case of AVs, compared to other modes. Although the results bring a valuable indication of possible behavioral adaptation to the technology, one must also take into account limitations of the study. The most important of them is the speculative, imaginary character of the focus groups discussions, simplified future scenario and subjective character of qualitative analysis. Therefore, in the future studies on behavioural adaptation to AVs it is suggested to take a more experimental path, aiming at emulating the actual AV experience as far as possible. In that way the credibility of the findings from the present study could be further validated and extended.

This research combines two relatively new additions to the field of discrete choice modelling: sequential best worst discrete choice experiments (SBWDCE) and random regret minimisation (RRM) modelling, with the hope of developing a more behaviourally realistic choice model. SBWDCEs are able to gather a larger number of stated choice observations from fewer respondents, while RRM models challenge the notion of fully compensatory behaviour implied by the traditional RUM model and suggest that consumers choose to minimise regret. According to image theory, best and worst choices are not made with the same kind of decision rule, so accounting for that variability using a RRM model would produce a more realistic model with better model fit. Estimating the combined model proves that people do in fact use a compensatory decision rule when selecting the best alternatives and a semi- to non-compensatory decision rule when selecting the worst. The results also show that the way choice set size variation is accounted for can greatly impact the scale parameters, as these and the choice set size constants are inversely related. Although a better model fit was achieved, using best-worst tasks is contested and researchers also warn against the lower reliability of additional choices in the same choice set. Nevertheless, SBWDCEs provide great benefits in fields with small population sizes and can potentially help in obtaining higher quality prior parameter values for use in efficient experimental design generation.