In this study, a context-dependent discrete choice model to investigate cyclist’s preferences on link-level cycling infrastructure design features is to investigate the extent the preferences on cycling infrastructure design is affected by the characteristics of a road segment. Stated preference experiment utilizing visual representation of the alternatives were conducted. The model estimation indicates that cyclists attach different values on certain design features of a cycling infrastructure under different urban contexts. Nevertheless, there are significant heterogeneity on the preferences across respondents which still cannot be explained by the variation of contexts. Segmenting the data based on personal characteristic attributes, we found that differences in traveler’s personal characteristics are associated with certain differences in valuation of design features and also in sensitivity toward context effects.

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.

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.

Agent-based modelling (ABM) is a widespread and commonly used technique for the understanding of social system behaviour. Despite ABM being a commonly used method for the analysis of complex social systems, simulated agents still can best be described as autistic before a specific set of social rules is ascribed to them. This discrepancy leads to model outcomes that can differ from reality. For improving agent-based models on this aspect, a social practice theory-based approach can be used as a theoretical framework. The main question to be researched is: How does enabling agents to reason about others' beliefs on social practices affect dynamics of belief and behaviour formation as opposed to action adaption models, with respect to transport mode choices? Two agent-based models have been created within this study to provide an answer to this research question. Through including a case study within the transport domain, model results can be hold against macro-behaviour within the state of the art of models on transport mode choices. The case study depicts a group of students making a decision for a transport mode to move from their university faculty to a compulsory seminar. The students influence each other, leading to belief adaption. Within the transport domain, usually this social influence is translated to compared and adapted actions instead. This study differs itself by letting beliefs be the reference point for social comparison. Therefore, belief dynamics and agent behaviour for the models with belief adaption are compared to the state of the art action adaption models. Lastly, the effect of limitations towards having insight to other agents' personal characteristics is analysed. The analysis provides the following conclusions. Within the field of transport mode choices, enabling agents to reason about others' beliefs on social practices results in the same opinion dynamics and behaviour as is presented by action adaption models. However, an agent-based model that allows agents to reason about others' beliefs on social practices with limited insight in others' private characteristics through a theory of mind capacity, does provide a different reaction to an increase in the social capital of agents. More specifically, enhancing social network connectivity through increasing the group size or similarity threshold does not lead to more consensus within the theory of mind model, which is the case in the other models presented within this study. This stresses the importance of considering the extent to which are capable of forming a just conjection on others' private characteristics when designing an agent-based model with social interaction.

Automated driving systems (ADS) can improve traffic safety, improve accessibility and reduce environmental impact (Shladover, 2016). On the contrary, on May 7th 2016, a fatal accident with a Tesla on autopilot in U.S. Florida was a harsh reminder that the technology is still in its testing phase (Greenemeier, 2016). In complex technical systems like ADS technical failure may occur, which forms a serious threat to human well-being. Moreover, studies have shown that citizens are very concerned about deliberate misuse of ADS (Kyriakidis, Happee, & De Winter, 2015), e.g. people purposely abusing ADS to cause damage or even hurt someone. Therefore, according to many experts, the implementation of ADS does not only entail technical issues but also normative issues. To bridge the gap between technical- and normative issues, responsible innovation can be applied (Santoni de Sio, 2016). Friedman et. al. (2006) propose value-sensitive design to achieve responsible innovation via technical-, empirical- and conceptual research. As following from responsible innovation and value-sensitive design and in particular its focus on empirical research as one of its necessary elements, in this research an effort is made to use empirical research methods to provide more insights in normative issues of automated driving systems (ADS). The focus lies on social acceptance, particularly with respect to traffic safety. Also accessibility, environmental impact and heterogeneity among citizens will be analysed. Social acceptance is defined as “a person's assent to the reality of a situation, recognizing a process or condition (often a negative or uncomfortable situation) without attempting to change it, protest, or exit” (Fish, 2014, page 1). The following questions will be answered in this research: What is the social acceptance of automated driving systems from the perspective of safety, accessibility and environmental impact and what is the corresponding heterogeneity? 1. What percentage of citizens thinks automated driving systems are socially accepted? 2. How is the social acceptance influenced by safety, accessibility and environmental impact? 3. Are traffic fatalities caused by automated vehicles valued differently than current traffic fatalities? 4. Is there heterogeneity in the social acceptance among citizens? A survey is chosen as research method since it is a relatively inexpensive, flexible method to achieve extensive information about characteristics of a population. After an extensive theoretical analysis, seven attributes were identified that possibly influence the social acceptance: level of automation, road exemption, travel time, emissions, human error fatalities, technical failure fatalities and deliberate misuse fatalities. After the experiment was fine-tuned by a pilot study, it was held among a representative sample of 510 Dutch adults during the spring of 2017. The respondents had to state if they were in favour or against ADS for each of the twelve hypothetical futures that were presented to them. In these hypothetical futures, the attributes were systematically varied and described as a change to the current situation. Using a MNL RUM model, the results show that 63% of all citizens prefer ADS over the current system. It is therefore concluded that citizens have a high social acceptance and thus are rather positive towards ADS. Also, citizens prefer a system where human drivers are still in control and can intervene in case necessary. Next, it is concluded that the social acceptance is mostly influenced by fatalities caused by automated vehicles (AVs), while travel time is the least important attribute. However, the differences in influence of the attributes were not substantial. Safety, accessibility and environmental impact are all important for the social acceptance. Nevertheless, technical failure fatalities weigh as much as 4 human error fatalities. For deliberate misuse fatalities this is a factor 5.5. Although these relations coincide with literature, the magnitude is larger than expected. It implies that ADS have to be very safe in order to reach social acceptance. Since AVs are still ‘learning’ how to drive, this might cause problems for current and future experiments. A latent class choice model is estimated to answer the final research question. Results show that large heterogeneity exists among citizens in the social acceptance. Citizens can be segmented into three classes (% of citizens): automated driving enthusiasts (32%), central mass (52%) and risk-averse class (16%). Contradictory to average citizens, automated driving enthusiasts prefer high automation levels. Even so, they still weigh fatalities caused by AVs as much as 3 human error fatalities on average. The central mass shows similar results to the results of the MNL model estimated on the full sample. The risk-averse class has a strong dislike for fatalities caused by AVs. This class weighs technical failure fatalities (deliberate misuse fatalities) as much as 5.5 (10) human error fatalities. In conclusion, primarily two discrepancies are identified that are critical for the implementation of ADS: 1) High social acceptance versus strong dislike for fatalities caused by AVs; 2) Citizens who are enthusiastic about ADS versus citizens who are risk-averse. They lead to the following recommendations: The social acceptance for ADS is high, so it is recommended for policy makers to have a positive and active approach towards ADS. By conducting experiments for professional users, safety risks can be minimalized while a learning curve is ensured. Also technology producers and policy makers should intensify research into cooperate driving. According to experts, ADS and cooperate driving are inseparable (Shladover, 2016), but globally the research into cooperate driving is lacking (Roland Berger, 2017). Since cooperate driving can lead to an increased risk of deliberate misuse, it is deemed critical for the implementation of ADS. Next, policy makers and especially the RDW should review the licensing of AVs. Currently, hardly any restrictions are in place for the licensing of AVs, which can cause dangerous situations on public roads. Finally, information campaigns can help to make citizens aware of the risks and benefits of ADS.

This Master Thesis document presents a research on trade-off analysis between security and efficiency factors during airport operations. Over the past few decades there has been an increase in security in the air transportation industry, with even more measures taken since 9/11. Besides affecting the operational costs of airports, the stricter controls also have been having an impact on the efficiency of the operations. Although that security and efficiency factors influence each other and on a frequent basis are being compared and considered in combination to take airport security operational decisions, the trade-offs made in order to take such decisions are not fully explicit. In order to make these security and efficiency trade-offs (which these decisions are based on) more explicit; a trade-off analysis formalized by Discrete Choice Modelling was conducted. Based on this analysis; implications were provided to evaluate, by the means of Agent-based Modelling, the effects of security and efficiency trade-offs made by airport security screeners on the operations of airports.

While urbanisation has increased and more efficient means of mobility are required, consumers are decreasingly interested in owning cars. One solution to meet people's mobility needs is to provide mobility as a service (MaaS). This study looks into consumers’ preferences on monthly MaaS packages and tries to identify the most important factors that affect their choices on the packages as well as consumers’ willingness to pay for those factors. Data was collected in Finland with a discrete choice experiment where the following factors were included: price, public transport, bike sharing, car sharing, taxi, sharing the package within a household, and promise of the service speed. MNL model was estimated for the purposes of data analysis. The results show that current public transport users are most interested in the use of MaaS packages. Additionally, the most important factors were found to be price and public transport. The results also suggest that MaaS packages should be built around public transport.

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 value of travel time saving(VOTTS) is very important in cost benefit analysis(CBA) as it is used to calculate the monetary gain in case of an improvement in transport that leads to reduction in travel time. Although it is easy to directly calculate monetary gains due to a speed improvement, monetary gains due to transport service improvement like comfort or ability to perform useful activities during a trip are far more difficult to include in CBA. This is because the total travel time stays the same before and after such service improvement since there is no visible reduction in total travel time. This is of course not true because travelers who travel comfortably and are able to engage in productive and pleasant activities during their trip can derive some benefits from the trip. These benefits can have an impact on the traveler’s VOTTS e.g. a reduction in the VOTTS of the traveler. In order to measure this impact and calculate the monetary value of activities, a new methodology is proposed. The intuition behind the method is that the same traveler might have two different VOTTS. One VOTTS when he/she is able to make productive or pleasant use of the travel time by engaging in a preferred activity and another VOTTS when he/she cannot engage in the preferred activity during the journey. If the VOTTS without the activity is higher than the VOTTS with the activity, then the difference between these two VOTTS can be conceptualized as the monetary value of the activity. The proposed methodology was translated into a hypothetical stated choice experiment with in-vehicle time and cost attributes. Respondents were recruited from train travellers on Netherlands Railways(NS) panel. The VOTTS of respondents with and without the ability to perform their preferred activity was then calculated. The results show that the ability to perform certain preferred activities like reading, working and listening to music during train trips, reduces the VOTTS especially for commuters by about 5euros/hour. For leisure travellers, reading was found to reduce the VOTTS by about 3euros/hr. The research concludes with a discussion on the general implication of results for transport project evaluations and investments. Based on this, some recommendations are made for NS and also for the Netherlands institute for transport policy analysis(KiM) regarding future VOTTS calculations that will be used in transport project evaluations.

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.

In 2050, it is expected that 70% of the world’s population will live in cities (Jin et al., 2014), leading to increasing congestion in and surrounding cities. This will raise new challenges, requiring more efficient and interactive cities. A novel paradigm contributing to these so-called smart cities is participatory sensing. Also known as mobile crowdsourcing, this solution enables both public and professional users to actively gather, analyse, and share local data about the urban environment using built-in sensors in smart devices (Truong et al., 2019). Considering that over 94% of the population has access to a mobile network, obtaining real-time data from these already existing sensors can be a low-cost solution for acquiring a huge amount of information (International Telecommunication Union, 2016). These real-time data can be used to analyse and predict mobility flows, and make public and private transport more efficient, safe, and sustainable. However, a clear benefit is required to motivate smart device users to share data about their activities and their environment. Sharing data comes with the risk of disclosing private information, as location data can lead to the identification of living and work locations, as well as individual habits. Research on motivations of smart device users to engage in participatory sensing tasks is required in order to be able to design valuesensitive participatory sensing applications. This study aims to identify factors related to incentives and privacy that explain choice behaviour of users in participatory sensing applications. The main research question being addressed is as follows: “How do factors relating to incentives and privacy affect the willingness among smart device users to contribute to participatory sensing systems for smart mobility?” A choice modelling approach was taken in order to identify the trade-offs made by users between potential benefits and costs of sharing data. This is an approach not often used before in the field of participatory sensing and provides novel insights in user behaviour in these systems. First, a literature review was conducted identifying possible factors relating to incentives and privacy, influencing the willingness of people to share data. Five factors were selected: monetary reward, effort, risk of re-identification, types of data, and data use. These factors were incorporated in a stated choice experiment distributed among smart device users through an online survey. In total, 125 valid responses were collected. The required effort of participating was regarded the most important factor influencing the willingness to share data in sensing applications for smart mobility. This provides new insights, as previous studies do not include effort in choice experiments regarding data sharing. As expected, the perceived ease of use declines if more inputs by the user are required. Moreover, respondents are reluctant to the collection of contextual and multimedia in addition to location and motion data, a finding which is confirmed by recent studies. Almost half of the respondents indicated to be highly concerned about their privacy. Therefore, a surprising finding is that the risk of re-identification was regarded the least important factor influencing the willingness to share data. However, when taking a deeper look at the data, it appears there is a group of people having extreme preferences regarding privacy and trust, who assign a higher importance to privacy related factors (risk of re-identification, types of data, data use). The identified trade-offs were used to evaluate the implications for different use cases in the field of smart mobility. Three interviews were conducted, which each led to the definition of a use case, being crowd management in a city, safety research using car accident information, and real-time travel information in public transport. By aggregating the quantitative and qualitative parts of the research, it can be concluded that the accuracy of collected information can be improved by collecting more types of data in addition to location data. However, this will lead to a decline in the acceptance rate. A proposed solution is to provide tailor-made sensing applications, giving the user control to indicate which data they agree to share. Furthermore, the communication of the purpose of the data collection is important to users. Moreover, being transparent about the risks related to the data collection can help users to make a well-informed decision and will ensure an ethical design of sensing applications. Finally, increasing the attractiveness of the application is recommended to reduce the perceived effort, which could be done by gamification of sensing tasks. Besides societal implications, this study provides several recommendations for future research. First, it is recommended to repeat the experiment without the provision of a financial compensation, in order to see if this leads to different choice behaviour and preferences of users. Furthermore, research on the understanding of privacy risks among users is recommended. Finally, this study can be used outside the smart mobility field, in order to analyse the willingness to share data in a broader sense.

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.

This research examines the incorporation of morality in discrete choice modelling, applied to the specific case of the choice for ADAS in lease cars. Implicitly, the decision to include or exclude ADAS in a car affects the safety of the driver himself and other road users. Therefore this decision has a moral dimension; a moral dilemma arises during the trade-off between safety, comfort, and investments in ADAS. This research describes a stated preference experiment in which respondents choose between different option packages for lease cars. The target respondents need to make trade-offs between costs, comfort, and safety provided by ADAS. The results show that an increased perceived safety and comfort for the driver/passengers are both more preferred than an increased perceived safety for other road users. Also, the results show that innate morality plays an important role in these preferences, which is tested regarding five moral foundations. Furthermore, a booster that aims at increasing moral choice behaviour is tested, which results to have the opposite effect.

In this report an analysis is presented on the impacts of weather codes on travel behavior. Loop detector data of seven segments in different provinces in the Netherlands are analyzed with regression models with autoregressive errors. Weather codes were found to be significantly influencing travel demand. Particularly codes orange for slipperiness and snow, and codes red for slipperiness, snow and wind yield significant results for most road segments. Furthermore, some trip rescheduling behavior was observed. The unreliability of the previous weather code was found to reduce the impacts for the next weather codes. The analysis of Twitter data was not useful to confirm hypotheses on the incentives for changing travel behavior. As this study is the first revealed preference study into the effects of weather codes, lots of research gaps remain. Secondary roads were not included in the study, which might be affected in other ways than highways. Furthermore, interaction effects between time and weather effects might explain some of the unexplained variability of the model results.

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 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.

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.

Capturing the individual’s behavioral responses to the actions driven by market and government will always be an interesting activity towards a broad spectrum of society. It could not be neglected that an individual may have different views in responding towards a new policy intervention or a new product market. However, references to use of the consumer–citizen concept in transport research studies are still scarce. Knowing this condition, this research is trying to close the gap by conducting an extensive analysis of the consumer–citizen duality concept. This study intends to test to what extent the consumer and citizen preferences may differ when the users make tradeoff between safety and travel time, in a case study of on-demand motorbike taxi services in Jakarta using stated-choice experiment. By elaborating on the role of an individual as a citizen, the research proposed in this study expect could contribute to an empirical evaluation of the operation of on-demand motorbike taxis, not only from a consumer perspective but also from the perspective of citizens. Subsequently, it also expects to gives nuances to the research field with citizen-stated choice experiments.

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.

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.