This thesis investigates how green mobility hubs—integrating bicycles, shared micro-mobility, shared cars, and zero-emission buses—can reshape commuter behaviour, cut congestion, and improve air quality in high-demand transport regions. The central objective is to understand how mobility hubs—offering green alternatives—affect commuters’ firstand last-mile decisions when private vehicle access is restricted at destinations like airports, industrial zones and city centres. The study further explores how these behavioural changes translate into network-wide traffic and air quality impacts, using a combination of discrete choice modelling and advanced Digital Twin simulation. The research builds on an extensive body of literature that positions mobility hubs as enablers of multimodal and sustainable transport systems. Yet, three key gaps remain. First, most studies focus on public transport users. Secondly, studies look into hubs that offer one single type of services (just e-hubs or shared mobility hubs) or use assumed splits rather than analysing the integrated realistic effects of multiple green modes in a hub setting. Thirdly, very few attempt to link individual-level behaviour with system-level performance indicators such as vehicle kilometres travelled or pollutant concentrations. This study addresses both gaps by combining behavioural insights from a stated preference survey with network-level simulations for Schiphol Airport, one of Europe’s busiest transport hubs and an area heavily affected by congestion and air quality concerns. The methodology followed two main phases. In the first phase, a stated preference survey is conducted to capture how individuals would respond to different combinations of travel time, cost, bus waiting time, and weather conditions. Respondents also provided socio-demographic information including age, gender, education, employment, and digital comfort. A total of 131 valid responses are collected. These data are analysed using discrete choice models: a base Multinomial Logit (MNL), a final MNL and a Panel Mixed Logit (PML). Although the PML model provided a better statistical fit, it was less stable and showed lower prediction ability in simulation, so the MNL model was ultimately chosen for further analysis. Its specification, which included both mode attributes and sociodemographic interactions, achieved good explanatory power (Rho-square bar of 0.309) and produced interpretable parameters for use in policy-oriented applications. The modelling results revealed clear behavioural patterns. Travel time and cost were the most influential determinants, with students particularly sensitive to cost and younger or digitally skilled individuals highly sensitive to time. Employment status mattered as well: students and full-time workers demonstrated the strongest aversion to time loss, reflecting their more rigid schedules. Weather significantly altered preferences: under rainy conditions, travellers placed less weight on cost and more on comfort, favouring protected modes such as buses or shared cars, while active and exposed modes lost appeal. Sensitivity analyses showed that demand for active modes declined steeply beyond two to three kilometres, while bus usage dropped sharply when waiting times exceeded fourteen minutes. Importantly, even respondents with limited digital comfort expressed willingness to use shared modes in the hub setting, highlighting the potential for inclusive design to broaden adoption. In the second phase, the estimated choice model was integrated into TNO’s Digital Twin simulation platform to assess system-level impacts for the Schiphol region. Two scenarios were tested: a baseline scenario without hubs and an intervention scenario in which two strategically located hubs restricted car access and offered green alternatives. The simulations showed that hubs produced a measurable reduction in vehicle kilometres travelled and concentrations of NO2 and particulate matter on peripheral municipalities such as Haarlemmermeer and Haarlem, which experienced declines in through-traffic. At the same time, some central zones like Amsterdam and Amstelveen recorded modest increases in traffic and emission due to redistribution effects and rerouting around car-free zones. Despite these localised shifts, the overall balance showed net reductions in traffic volumes. On the environmental side, the simulations confirmed decreases in regional concentrations of NO2 and particulate matter, particularly along major corridors such as the A4 and A10. Localised increases occurred around hub access points, but the net result was a substantial improvement in air quality. These findings contribute both empirically and methodologically. Empirically, the study provides new evidence on how multiple green modes interact within a hub setting and how sensitivities vary across socio-demographic groups and weather conditions. Methodologically, unlike most hub studies, this thesis explicitly links individual-level behavioural sensitivities with system-wide traffic and emissions via a Digital Twin, providing a novel, scalable evaluation framework. The case study at Schiphol illustrates that mobility hubs can simultaneously reduce congestion, and improve environmental outcomes, when they are strategically located and integrated into existing transport networks. From a policy perspective, the results suggest that the effectiveness of hubs depends on several conditions. High-frequency, weather-resilient services such as zero-emission buses are essential to maintain reliability. Shared micro-mobility must be protected from weather through covered docking and supported by pricing incentives that make these modes financially attractive. Inclusivity requires providing access both digitally and through alternative channels for those less comfortable with apps. Strategic placement of hubs along ring roads or park-and-ride facilities is critical to intercept car traffic before it enters congested centres. Partnerships with major employers can further support uptake by subsidising passes or memberships for staff, and broader regulatory frameworks such as low car zones or zero-emission zones can be reinforced by hub provision, ensuring that restrictions are paired with attractive alternatives. This thesis is the first to combine empirically estimated mode choice behaviour of private vehicle users in a mandatory hub setting with a digital twin traffic-environment model, providing both behavioural realism and system-level policy insights. Overall, this study demonstrates that mobility hubs are more than physical infrastructures: they are systemic interventions capable of reshaping both individual behaviour and regional mobility patterns. The integration of behavioural modelling with simulation provides a replicable framework for evaluating such interventions. The findings show that when designed to balance cost, time, weather resilience, and digital inclusivity, hubs can deliver both behavioural and environmental benefits. By confirming their potential in a complex and high-demand setting like Schiphol, this thesis demonstrates not only feasibility but also transferability of hub-based interventions. These insights provide a concrete evidence base for future policy pilots, particularly zero-emission zones at European airports and other high-demand transport hubs.

People with a Disability (PwD) often rely on public transport, towards their need for accessibility. Knowledge about the user's perspective on accessibility policies is crucial to decide what is most urgent, yet remains limited. Hence, this study researches what accessibility policies people with a disability and citizens in general prefer. The preferences of citizens are relevant, for they illustrate public support for different accessibility measures.
To fill this knowledge gap, Participatory Value Evaluation (PVE) is performed in the Netherlands. 1254 respondents, of whom 328 had a disability, advised policymakers on which accessibility policies are most urgent. In the PVE, participants could indicate for nine policy measures whether they should get more priority, subject to an effort constraint.
The results show that both PwD and citizens prioritised the repair of broken elevators most, followed by wheelchair accessible vehicles. These policies were prioritised despite the high effort required. The results show great homogeneity of results amongst different disabilities. Participants prioritised policies because of their perceived necessity and effectiveness in helping people with a disability use public transport. The research suggests that people prioritise more expensive, long-term policies that facilitate independent travel for most PwD, over less expensive measures that do not improve independence in the long term.
Policymakers and scholars are recommended to focus on the necessity and effectiveness of accessibility policies, prioritising long-term improvements.

In this thesis, an exploration into the potential use of path-planning algorithms in modeling cyclist behavior is made, and a novel model utilizing such algorithms, incorporating the commonly found overtaking behavior on bike paths, is developed and assessed.

The investigation started with a literature review on the existing behavioral interpretations and findings of bicycle riding, where the cyclists' interaction with the environment and other cyclists is found to be at different task levels of the riding process. Based on the findings, further analysis into how existing techniques replicate the different layers of behavior is made and assessed. During the assessment, this research determined that path-planning algorithms could best be used to replicate the physical steering behavior of cyclists. An investigation into the inner workings of path-planning algorithms is also done, resulting in the final four-layer conceptual framework based on the two-layer operational framework of bicycle riding, adapting the process into mental (perception, goal orientation) and physical (path planning, movement) layers.

With the adapted modeling framework, a model is developed, verified, and assessed with face-validation against real-world trajectory data. The development and verification step provides insights into the inner workings of the model, showcasing how the four layers of the framework are realized, and how the changing of used parameters would affect the intermediate output between the model layers. The face validation consists of two scenarios: a physical steering and pedaling-focused scenario of chicanes, which is a series of bottlenecks, and an overtaking scenario that focuses on the mental process of overtaking decisions. The results showcased that the developed model can create plausible steering and pedaling behaviors in the chicane scenario. However, the model showed lower accuracy and consistency in predicting the mental overtaking maneuvers.

The assessment result of the developed model showcased the strength of path-planning algorithms in augmenting the existing model with the physical steering capability of the cyclists. The limited accuracy in the overtaking scenario highlights the importance of capturing the mental process of bicycle riding. Future work could further refine the mental layers of the framework, specifically the goal orientation process. The adapted modeling framework also provides a new direction and foundation for further work on the path-planning additions and improvement of bicycle behavioral modeling.

This studydives into factors influencing the shared e-moped usage in first- and last mile. Other studies show that the service area size, vehicle availability the shape of city and the temperature affect the shared e-moped usage. This study confirms the service catchment, temperature and vehicle availability factors. It adds to this that also factors such as the share of young people, bus frequency, shared bike availability, walking distance, visibility and points of interest have an influence. This study also provides a quantification to show which factor has the biggest influence. According to the model results, the vehicle availability at a station is the most important factor. Next to the factors, this study also shows what the usage patterns of shared e-mopeds in first- and last mile transportation are. This study, therefore, helps in understanding the usage of shared e-mopeds as a first- and last mile mode.