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

Political and societal attention on transport inequality, transport vulnerabilities and transport poverty risk has been increasing the last five years. The Dutch government has committed multiple large sums of resources to abate the issue, 120 million euros to cancel the increase in price of train tickets, 300 million to cancel the increase in price and preserve the provision of the regional public transit and 6 million to start a pilot of ‘innovative public transport’ in the Zeeland region. A review of the literature on transport poverty risk reveals that past studies analyze the concept using a limited number of pre-selected variables. For example, car ownership and residence area. This carries the risk for misinterpretation as an accurate representation can only be achieved by considering all relevant variables. Such a comprehensive analysis could allow policymakers to formulate tailor-made policies according to the specific problems and needs of certain groups in society. This study aims to include the entire complexity of the concept of transport poverty risk in the analysis. Relevant factors include affordability, accessibility and mobility. Then there are individual differences, and for every individual changing over time, between needs of participating in society and social life. This study aims to incorporate this comprehensive definition in a Latent Class Analysis approach to construct groups of individuals based on the MPN and ODiN national datasets. The different groups of individuals can be then used to indicate the effect of policies on transport inequality. Results show three groups that are most transport ‘fortunate’, with high income, high car ownership, high accessibility and the potential to use different travel modes. Results also show one group with substantial transport vulnerabilities due to higher car dependency, lower car ownership and lower household income. A policy thought experiment is established through the application of mobility policy packages of three political parties. Car policies that reduce costs per driven kilometer such as reduction in excise tax will increase transport inequality due to the three groups that are the most transport ‘fortunate’, which also have the highest car use. This study provides the scientific community with a comprehensive definition and conceptual model of transport poverty risk and for policymakers contributes a comprehensive understanding of transport inequality in the Netherlands. Future research needs to focus on identifying specific minority groups that are not or less captured in the national datasets. Transport problems are experiences by an individual, while transport inequality is represented by the differences in the extent of transport problems between certain individuals or groups. In this case, certain groups in the Netherlands. This calls for the need to differentiate the government levels at which transport inequality and transport problems are addressed. While this study presents the transport inequality at the national level, municipalities will be able to identify specific individuals and neighborhoods who are more prone to transport problems.

In today’s world, the transport system is essential in helping people reach the activities they want or need to attend. However, like in many other fields, inequality can exist in access to various opportunities based on personal characteristics. Little is known about how gender and intersectional factors impact people's perceived accessibility. This thesis aims to fill this knowledge gap using a mixed methods approach. First, desk research shows that perceived accessibility can be influenced by a large variety of factors, including, but not limited to, the primary mode used, perceived safety in transport and socio-demographic factors. Second, Structural Equation Modelling is used to investigate the strength of relationships between the various found factors and gender intersectional factors. This analysis shows that being a woman has a negative effect on perceived accessibility, which can for a part be explained by women feeling less safe in transport at night. Additionally, the analysis shows that for women having young children negatively impacts perceived accessibility, but not for men, while for men income positively relates to access to a car and in turn to perceived accessibility, an effect which is not found for women. Finally, the subsequent qualitative analysis, using workshops, shows that little attention has been paid in the past to (social) safety issues in transport and to differences in needs between different kinds of travellers. In the future, more cooperation between transport institutions and integrated intersectional policymaking in transport would be of significant value.

The residential energy consumption and energy affordability are crucial in policy design, with regard to energy efficiency and energy poverty. This has become even more relevant now, as the energy transition is taking off and the energy prices are soaring. Given the inequality in the energy consumption and energy affordability, it is therefore critical to know which factors are impacting these phenomena, and how they are impacting these phenomena. The housing-related factors are the least understood factors impacting the energy consumption and energy affordability, even though these factors are key pillars of energy policy. Thus, this research assesses how housing-related factors impact the residential energy consumption and energy affordability in the Netherlands. For this assessment, multiple data sources are combined over the years 2016 to 2018, covering a total of more than 8 million Dutch households. A regression analysis is performed on a postal code 6 level using the housing-related factors as predictors, while controlling for socio-demographic and weather factors. This revealed that the housing-related factors impacting energy consumption and energy affordability are relatively similar. This study finds that the WOZ-value is a strong predictor for both phenomena as houses with a higher WOZ-value have a lower energy efficiency. Furthermore, it shows that rental households consume less energy but spend a similar percentage of their income on energy, and that the impact of the year of construction on both phenomena has increased significantly, when compared to earlier studies. Based on this, it is concluded that rental houses and houses with an older year of construction deserve more attention in Dutch energy policy. Furthermore, low-income households should be targeted in short-term energy policy, as this study shows that the current soaring energy prices have a severe impact on the energy affordability in the Netherlands. Insights are however broader, and can be pivotal in energy demand and energy affordability projections, both in the Netherlands and the rest of Western Europe.

The Covid-19 pandemic has outlined the creativeness of each country in the definition of the most appropriate public health management strategies to curb the spread of the SARS-CoV-2 virus, ranging from total lockdowns to trusting in people’s common sense to act properly. Several non-pharmaceutical health interventions have nationally been issued either on a voluntary bases or required by law. Maintaining 1.5 m distance and wearing protective face masks provide two respective examples. Proximity contact-tracing apps have furthermore been launched at a national scale to alleviate the pressure on the overburdened testing capabilities by means of automatic contact-tracing. Also the Dutch Ministry of Health launched its CoronaMelder on October 10, 2020. However, its efficacy is being questioned with the consideration of its instigated user behaviour. More specifically, it is being hypothesized that the CoronaMelder app instigates users to comply less with the issued non-pharmaceutical health interventions, thereby countering their expected beneficial effects in curbing the spread of the virus. An explorative sequential design provided the answer, where literature review findings were combined to conceptualize the effect of the app on attitude, perception and eventually behaviour. Structural equation modelling performed on data collected through an online survey (N=776) outlined the lack of causality from the CoronaMelder to behaviour and as such, compensatory behaviour was excluded. Moreover, it was found that older and higher educated people were more prone to install the app, similarly to people at risk and those who had previously contracted the virus. Eventually these results were placed in the context of behaviourally-informed politics to assess the management of nationally launched digital contact-tracing apps.

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.