This thesis investigates how demographic and behavioural factors shape public support for sustainable mobility policies in the Netherlands. Motivated by climate ambitions such as the European Green Deal and the Dutch Climate Agreement, it recognises that technical solutions alone are insufficient. Public acceptance and behavioural change are essential to achieving a successful mobility transition. By focusing on which social groups support sustainable policies, and why, the study offers valuable insights for inclusive, context-sensitive policymaking.

A mixed-methods approach was adopted. The research draws on data from a large-scale Participatory Value Evaluation (PVE) involving 5,643 Dutch participants. The study first analyses quantitative data, applying correlation analysis, policy preference ranking, and K-means clustering to identify patterns in support. This is followed by qualitative thematic analysis of participants’ open-ended responses to capture underlying motivations.

The literature review provided the theoretical grounding, highlighting two key behavioural models: the Theory of Planned Behaviour (TPB) and the Norm Activation Model (NAM). These frameworks explore how attitudes, social norms, personal responsibility, and behavioural control shape mobility behaviour. The literature also classifies policy strategies as hedonic, gain-driven, or normative, and categorises tools as “carrots” (incentives), “sticks” (regulations), and “sermons” (information).

Key findings show that support for sustainable mobility varies significantly across demographic groups and mobility behaviours. Younger, higher-educated, and urban residents are more supportive of sustainability goals, while older, lower-educated, and rural populations tend to be less supportive. Behaviourally, frequent car use correlates negatively with policy support, while regular users of public transport and bicycles are more supportive. These relationships underscore how lived mobility experiences shape public attitudes.

Three distinct respondent clusters were identified:
• Suburban & Traditional: Older, moderately educated, frequent car users with moderate support for sustainability.
• Urban & Green: Young, highly educated urban residents using public transport and bicycles, showing strong support.
• Lower Educated & Motorist: Older, rural, lower-educated respondents with low public transport or cycling use and minimal support.

Policy preference analysis revealed broad support for measures that improve and subsidise public transport. These policies were favoured across all groups, especially by urban and cost-conscious respondents. Conversely, policies that raise fuel prices were unpopular across all groups. Young and urban participants supported policies related to cycling infrastructure and transit-oriented housing. Lower-educated groups preferred car-supportive policies like electric vehicle subsidies.

The qualitative analysis reinforced these findings. Supportive arguments commonly cited climate and environmental protection, the need to encourage sustainable travel, and the role of government leadership. Opposition was often rooted in scepticism, perceived overemphasis on sustainability, cost concerns, and mistrust in government.

The thesis concludes that effective sustainable mobility policies must be tailored to diverse societal groups. Policymakers should frame policies in ways that emphasise affordability, safety, and convenience. Information campaigns grounded in TPB and NAM principles can increase receptivity, especially among less supportive populations. Overall, the integration of demographic, behavioural, and motivational insights can enhance both the fairness and effectiveness of the mobility transition.

Past bicyclist subjective safety studies have evaluated different intersection types and bicycle facilities at roundabouts. None have focused on the different design elements of a dedicated bicycle facility and analyzed both perceptions of safety and comfort of bicyclists in a single model. To address this, this study focuses on the subjective safety and comfort perceptions of bicyclists at Dutch urban roundabouts, focusing on certain bicycle design elements. Infrastructure evaluated included: bi-directional bicycle paths; yielding priority; bicycle volumes; the presence of art or advertisements in the center island; buffer width between the bicycle path and the vehicle lane;
and the number of vehicular entrance/exit points. Using a multivariate random effects ordered probit model, the results revealed that roundabouts where cars have to yield to bicycles and fewer vehicular legs are perceived as safer and more comfortable by cyclists. Conversely, high bicycle volumes and increasing age negatively impacted perceptions of safety. The study found that bicyclists’ place of residence, their likelihood to commit traffic violations, their recent crash history, and the type of bicycle used (e.g., cargo bicycle, e-bicycle, etc.) significantly affect their perceptions of safety. The results suggest that consistent adherence to Dutch design guidelines can enhance bicyclists’ sense of comfort and safety at roundabouts. The findings highlight the need for designers to consider bicyclists’ perspectives in roundabout design. To improve perceived safety and comfort for bicyclists recommendations include: ensuring bicycle yielding priority; designing roundabouts to reduce bicycle congestion to the extent possible; and maintaining uniformity in bicycle infrastructure.

In order to increase traffic safety in the Netherlands, the government implemented the GOW30-policy. However, evaluation of this policy shows that drivers do not comply with this newly imposed speed limit sufficiently. For this reason, roads need to be designed in such a way that speed limits are perceived as credible. At the same time, the need for sustainable road designs arises, due to factors as increased urban heat stress and population growth. Thus, road designers need to make trade-offs between spatial quality and mobility. Incorporating greenery is a promising way to reduce urban heat stress, but the presence of greenery, particularly thick trees, can also increase the severity of traffic accidents. This study evaluated the impact of roadside greenery on driving behaviour, utilising a driving simulator experiment complemented by a post-experiment survey. Linear Mixed Model analyses (LLM’s) together with survey insights revealed that perceived driving comfort leads to increased driving speeds. Comfort increases on wider roads and on roads with more overview. For this reason, it is not recommended to incorporate low greenery on GOW30 roads. Moreover, high and dense greenery is shown to reduce driving speeds while promoting better positioning on the road. It is furthermore recommended that research is performed in real-life, whilst evaluating roadside greenery in combination with cycling lanes under varying traffic and weather conditions.

Traffic congestion and undeveloped public transport define the modus operandi in Kampala, the capital city of Uganda. Most people see motorcycles as a solution to escape the growing traffic congestion and poor public transport. This study thus aimed to determine the traffic efficiency and safety effectiveness of introducing motorcycle dedicated lanes in non–lane based and mixed traffic. These effects were studied by microsimulation of unsignalized intersections without priority markings in urban areas of developing countries. The study centered on an intersection commonly known as “Spear Motors” in Kampala. In addition, off-peak traffic was simulated since traffic police controls the intersection during peak time and microsimulation model cannot simulate traffic while a policeman (or two) is on duty.
Video images of the intersection were officially obtained from Uganda Police and processed to traffic data such as traffic volume, trajectories, composition, and conflicts. In addition, geometric data (lane width, slope, intersection dimensions, coordinates, and aerial pictures) about the intersection was physically collected during a daytime site visit.
A representation of the current traffic conditions at the intersection, referred to as the base model, was developed in VISSIM 21. The base model was calibrated using Particle Swarm Optimization (PSO). Thereafter, the model was microscopically and macroscopically validated.
Three scenarios were considered in this study, which are: scenario 1 with straight crossings of motorcycles in the intersection, scenario 2 with deflected crossings and scenario 3 with a roundabout separated for the main road traffic and motorcycles.
Simulations with driver behavior parameters set to optimum values were performed to replicate the actual behavior. Consequently, traffic conflicts were generated from vehicle trajectories per scenario. Post Encroachment Time (PET) and Time to Collision (TTC) were calculated for the conflicts. The use of conflicts is proactive compared to reactive approaches that use crashes.
The use of deflected crossings for motorcycles and then a roundabout separated for main traffic and motorcycles increased the flow of the intersection by 31% and 20%, respectively. Additionally, the implementation of deflected motorcycle crossings and roundabout reduced critical density by 38.6% and 63.7%, respectively.
Regarding safety, more benefits are expected by using either deflected crossings or roundabout than straight crossings. The number of severe critical conflicts reduced by 87.9% with the application of a roundabout separated for main road traffic and motorcycles. This is higher than the decrease of 48.5% after the application of dedicated motorcycle lanes with deflected crossings. To determine the conflict rates per scenario, the number of severe critical conflicts was divided by total traffic simulated. Deflected crossings for motorcycles reduced severe critical conflict rate by 40% whilst it decreased by 75% with intersection improvement to a roundabout separated for main traffic and motorcycles.
With the high motorcycle demand of over 50% of traffic, implementation of dedicated motorcycle lanes at unsignalized intersections as well as urban roads in general will result into better flow of traffic. In addition, the motorcycle lanes will contribute to the general road safety in Kampala.

Disruptions occur frequently in railway networks, requiring adjustments to the timetable, rolling stock planning and crew planning while causing delays and cancellations. Although the evolu­tion of system performance during a disruption can be visualized in the resilience curve, not much is known about performance during disruptions or the extent to which the curve applies in practice. The limited quantitative knowledge about the resilience of railway networks makes it hard to design appropriate recovery measures. In this thesis, a data-driven evaluation approach is presented to make an ex post assessment of the resilience of railway networks. Several resilience metrics are extracted from literature and two new resilience metrics are introduced. Using historical traffic realization data, resilience curves are reconstructed for a large and heterogeneous set of single disruptions and are quantified in terms of the resilience metrics. Among others, the values of the resilience metrics are compared across disruptions of different causes using Welch’s ANOVA and the Games-Howell test. The approach is applied to a case study of the Dutch railway network, with a focus on the five most common disruption causes. The results of the case study show that there is significant heterogeneity in the shape of the resilience curve, even within disruptions of the same cause. Train defects are found to be the least impactful disruptions on multiple resilience metrics, while collisions are found to be the most impactful disruptions on multiple resilience metrics. The successful application of the approach shows that it can be used by practitioners to assess which types and which parts of disruptions deserve attention to improve disruption management practices, and thus, improve resilience.