Cities today are home to more than half of the world’s population, where growing wealth in Western economies is pushing demand for better quality of housing. Many residents prioritise living in tranquil, green environments that offer peace and natural beauty, yet they also place significant importance on maintaining convenient and easy access to their cars for everyday mobility and flexibility. The competition for land to accommodate these landscaping amenities intensifies pressure on limited urban space, often conflicting with consumers’ desires for green, accessible environments and government goals for compact, mixed-use development. As a result, urban planning increasingly grapples with balancing car parking accessibility and exposure to green spaces, an issue that is especially evident in new car-free districts and cities worldwide adopting car-restricted zones for sustainability. Against this backdrop, this study investigates the trade-off between car parking accessibility and exposure to green environments in residential decision-making. Using street-level images generated by artificial intelligence to represent variations in residential environment, the study applies a state-of-the-art computer vision-enriched discrete choice model (CV-DCM) to analyse how both visual and numerical information influence residential choices. Results reveal a preference for a greener environment among all age groups, but a higher sensitivity for accessible parking. These insights highlight the complex trade-offs residents make and equip policymakers with valuable tools for more nuanced residential planning.

Shared mobility has emerged as a promising strategy for promoting sustainable and efficient urban transport, yet its broader impacts—particularly when multiple shared modes are combined—remain insufficiently understood. This study investigates the relationship between shared mobility usage and private vehicle ownership and use in the Netherlands, drawing on a comprehensive national survey of shared mobility users. Employing binary logistic regression and latent class cluster analysis (LCCA), the research identifies six distinct user groups based on their shared mobility patterns, socio-demographic profiles, and spatial characteristics. The findings reveal that frequent use of station-based and community car sharing, as well as OV-fiets, is significantly associated with reductions in private car ownership and use. Other shared modes, such as free-floating car sharing, moped sharing, and shared e-bikes, also contribute to these reductions, though their effects are more modest and context-dependent. The study highlights that the most substantial decreases in car dependency occur among user groups with high engagement in integrated shared mobility services, particularly in urban areas with supportive infrastructure and policies. The study highlights the need for targeted policy interventions to improve accessibility, multimodal integration, and incentives for shared mobility, while also monitoring potential shifts away from public transport and active travel. This research advances understanding of the diverse effects of shared mobility in the Dutch context and offers evidence-based recommendations for maximising its sustainability benefits in urban transport systems.

Private rail sidings form the physical access gates for companies to the rail network. Without these rail freight, transport is not possible and these private sidings can be seen as an indicator of the competitiveness of rail transport. However, the number of private sidings has dropped significantly over the last decades, which limits the rail freight sector and the resilience of the rail network. A resilient rail network is important to reach societal goals such as emission reduction and less road nuisance. This research focuses on this problem by proposing concrete strategies to help the companies using rail transport and allow them to use their private sidings. Through the help of literature, data and stakeholder interviews, an Osterwalder business model canvas, SWOT analysis and TOWS matrix are constructed to identify the problems and propose strategies. Causal diagrams show the coherence of the sector and how problems, strategies and private sidings influence each other. The first result is a new way of categorising companies and their respective private sidings. This reveals the underlying problems and strengths per type of private siding. The most important problems are rising costs, unreliability of the rail network and limited usability of the private siding due to regulations and non-electrified infrastructure. To combat this, six strategies are proposed with the introduction of a second infrastructure manager to combat costs and regulations and specific infrastructure investments as the most promising strategies. Effective policies require tailored solutions based on the type of company and private siding. This can help the Dutch rail sector and increase the resilience of the transport network.

As cities are densifying, low-car neighbourhoods are increasingly seen as a solution to reduce car dependency, improve public space, and encourage alternative mobility options. This study examines the impact of such a development by evaluating the mobility behaviour and perceptions of residents in Cartesius, a newly developed low-car neighbourhood in Utrecht. Using a revealed preference approach, this research analyses survey data (N=70) and qualitative insights from conversations with residents to explore the influence of residential self-selection on mobility adaptation. Findings indicate that even non-self-selected residents—those who did not actively choose a low-car environment—reduce car ownership and car use while increasing reliance on public transport and shared mobility. This suggests that well-designed low-car policies can influence travel behaviour beyond pre-existing preferences. Meaning that large-scale low-car policies could be viable for a broader population beyond those who actively choose this lifestyle.

With advancements in the photovoltaics (PV) market, involving increased PV module efficiency and reduced costs, the logical progression is the integration of PV into various surfaces, including vehicles (VIPV). For a driving VIPV, the constant change in irradiance presents a significant challenge. The location, weather, and time are key factors that affect the irradiance prediction. An accurate prediction of solar energy generation is crucial for realizing the full potential of VIPV. In this study, the incident irradiance loss is referred to as shading loss.

This thesis is conducted in collaboration with Delft University of Technology (TU Delft) and the Netherlands Organisation for Applied Scientific Research (TNO). TNO has an existing shading loss model that accounts for the surrounding obstructions and weather effects using a fixed value, where the only variable input is the day of the year. On average, it assumes 27% shading losses yearly. This study has improved TNO’s VIPV shading assessment by incorporating more variables into a new model, thereby contributing to a better estimation of the VIPV energy yield. The inputs of the new model are based on key factors causing irradiance loss, where location data is obtained using a Digital Surface Model (DSM). The new contribution to the DSM-based approach is the Web Coverage Service (WCS), which retrieves height data directly through a URL, eliminating the need for the previous manual operations and building upon the model’s scalability. The improved model determines shading losses along a route using two varying shading indicators: Sky View Factor (SVF) and Shading Factor (SF). It can input various irradiance datasets, including or excluding weather effects, and precisely determines the Sun’s position based on the time and day of the year. The DSM-based approach is validated using a dynamic and static validation dataset, with a SVF≤1. The accurate representation of surrounding obstructions during validation contributes to a good-quality model. Both validation studies differ in inputs based on location, time, and weather conditions. The differences in measured and simulated irradiance values are larger in the dynamic dataset than in the static dataset. Deviations are caused by uncertainties and errors, such as local weather effects, sensor inaccuracies, traffic shading, and LiDAR discrepancies, which is the inability to simulate open or changing structures. Weather variability is the primary factor affecting model quality in this validation study, resulting in no fixed inaccuracy factor for model validity. The validated model can accurately represent the environment, but weather variability should be minimized when comparing measured and simulated irradiance. Therefore, averaging is chosen to compare results in this work as it removes unknown or untraceable data points.

Beyond previous research studies, this work focuses on two public transport case studies, given the systematic routing with a fixed timetable. The first case study includes a car and a bus, and the second, more extensive case study focuses solely on buses. The choice of inputs in both case studies centers on key factors influencing VIPV irradiance loss. In the Bus 40 study, simulations under clear sky conditions and a fixed time around solar noon resulted in shading losses of 22.7% for the bus and 27.7% for the car. Moreover, the simulated shading loss shows a 2.6% relative difference compared to the yearly average TNO value. For the case study considering 27 bus routes in Amsterdam, simulations are performed using averaged KNMI irradiance and all hours a day, where the annual average shading loss resulted in 24.8%. The SF and clear sky irradiance correlate in the first case study and the SVF and average KNMI irradiance correlate in the second case study. However, using clear sky conditions and uniform overcast conditions does not represent realistic weather conditions and leads to a bias in the correlation. Moreover, classified terrain types do not correlate with SVF, in contrast to the reversed approach by Araki et al.

Finally, the model can effectively characterize the shading losses on the route level. However, this methodology falls short when reaching finer resolution, such as when examining smaller route segments or partial shading. The determination of shading losses at the route level can be of further benefit to various stakeholders, such as bus operators who want to determine the optimal bus routes for maximum solar energy generation. In addition, this model can be helpful for policymakers, to predict the required charging infrastructure in a scenario when vehicles are equipped with solar panels. Moreover, this model improves shading loss estimation by accepting a range of datasets instead of TNO’s current approach. By inputting irradiance data for clear skies to complete overcast skies, the shading losses can be predicted in a safer range instead of over- or underestimation based on one shading value.

Urban densification is one of the most relevant strategies to reduce the housing shortage in the Netherlands, as it also aims to prevent additional pressure on transportation networks and contributes to more sustainable travel. The effects of the built environment on travel behaviour have been studied extensively. To date, most studies focused on homogeneous effects of the built environment on travel behaviour indicators. Less attention is paid to possible heterogeneity in the effects dependent on other variables. This study applies moderated multiple regression on Netherlands Mobility Panel data to analyse possible heterogeneity of the effects of determinants of travel behaviour on daily travel distance, dependent on the level of urbanisation. The results show that various determinants indeed have effects with varying strengths, dependent on the level of urbanisation. The findings advance the understanding of the relationships between built environment and travel behaviour, which could be used to design tailor-suited policies that efficiently target specific groups. In relation with the built environment, the results show that densification strategies have the desired effects. However, accompanying mobility strategies are necessary to achieve the full potential of urban densification.

The objective of this study is to strengthen literature about cyclist safety and help prevent bicycle crashes in the future. It looks into the gap about the relation between the obstacle space of cycling infrastructure (that involves the space that is available for cyclists to avoid crashes with the obstacles therein) and the visibility thereof, and bicycle crashes. Next to a brief literature research, the main method used to answer this question is modelling of generalised linear models with a negative binomial distribution, that link independent variables concerning the obstacle space to the dependent count variable crashes. Results show that the relation is apparent in data concerning 50 km/h streets in the municipality of Amsterdam: infrastructure on a street level can indeed predict bicycle crashes on the same level. Fewer obstacles next to the road and higher widths were found to lead to fewer crashes. For lanes and one-way tracks holds that an increase in width of smaller sections has a bigger impact. The effect of obstacles in the shoulder also reduces at higher widths. A significant relation for light conditions was not found. Further research should look into different types and clustering of obstacles and improve the light analysis. Recommendations include providing dedicated cycling infrastructure with sufficient widths and few obstacles, and better registration of bicycle crashes. The study demonstrates that infrastructure is an important factor for the occurrence of bicycle crashes, that should certainly not be disregarded alongside the road user and vehicle. It hopes to push infrastructure policies for cycling more on the agenda.

The combination of cycling and public transport (PT) use has been found to be very successful in the Netherlands. On average, 47% of daily train users cycle to a railway station and this number is expected to grow to until at least 2030. As a result, railway stations suffer from bicycle parking capacity shortages. Large investments are needed to meet the growing demand for bicycle parking capacity, while the expansions take up the limited space around railway stations. To address the ever-growing demand for bicycle parking capacity and to continue the success of the combined use of bicycle and PT, alternative solutions beyond building new bicycle parking facilities are needed.
Bicycle sharing is a promising strategy to tackle bicycle parking capacity shortages at railway stations as it could significantly reduce the number of parked bicycles. A fundamental prerequisite is that a bicycle sharing system (BSS) makes use of the already parked bicycles, which is not the case with the available PT-bicycle system in the Netherlands. Existing literature shows that bicycle sharing between current cyclists has the potential, in theory, to reduce the number of parked bicycles by 22-25% at the central railway stations of the four largest cities in the Netherlands. When bicycles are also shared with travellers currently using other modes 37-50% capacity savings could be achieved. Currently there is a lack of knowledge about bicycle sharing from a user’s perspective to make a more realistic estimation of the expected potential. The important factors in bicycle sharing among potential users need to be clarified in order to estimate the potential demand and to progress towards the design of the organisation of an efficient system. Therefore the objective of this study is to investigate the preferences of current bicycle-train users and the demand for bicycle sharing in order to design an efficient bicycle sharing system (BSS).