Road pricing is increasingly proposed as a policy tool to address urban challenges such as congestion, pollution, and infrastructure funding. However, public acceptance of such policies often hinges on perceptions of fairness, particularly regarding cost distribution and revenue allocation. This study investigates how Dutch citizens perceive the fairness and acceptability of road pricing schemes, with a focus on revenue redistribution strategies. The knowledge gap lies in the limited understanding of how the Dutch public perceives the fairness and acceptability of such policies, particularly concerning fairness dimensions, revenue allocation strategies, and socio-demographic influences. Using a three-stage methodology—literature review, expert interviews, and a public survey—this research explores key fairness dimensions, including procedural, distributive, horizontal, vertical, and geographical equity. Findings reveal that fairness perceptions are shaped by factors such as car ownership, residential context, and transparency in communication. Distance-based and emission-based pricing models are generally perceived as fairest, particularly by non-car owners. Preferences for revenue use, such as reinvestment in public transport or tax reductions, significantly influence acceptance. Car owners tend to perceive road pricing policies, including distance-based, emission-based, and congestion pricing, as less fair compared to non-car owners, who generally view these schemes more positively. Moreover, non-car owners show stronger support for allocating revenues to public transport and tax reductions, while car owners prefer investments in road infrastructure. The study highlights the importance of transparent communication, inclusive policy design, and visible societal benefits in fostering public support. By developing a conceptual model, this research provides theoretical insights and practical recommendations for equitable and publicly acceptable road pricing policies in the Netherlands and beyond.

Cities increasingly face challenges from urbanization and climate change, with transportation playing a central role. In car-oriented cities, it consumes space and contributes to emissions, making sustainable solutions more urgent. However, the effectiveness of such solutions depends strongly on urban context. To avoid one-size-fits-all strategies, cities can benefit from learning from similar cases, but identifying comparable cities remains a challenge.
Previous studies have classified cities based on individual features like road layout or mobility patterns. While informative, these studies often overlook interdependencies between domains. This research addresses that limitation by integrating road network structure, activity distribution, and mobility behavior into a single analysis using clustering methods.
The main question guiding this research is:
How can the application of multiple clustering methods reveal distinct groups of European cities based on road network, activity distribution and mobility characteristics?
Data was obtained for 32 European cities, capturing indicators from five domains: road topology, population, economic activity, mobility, and congestion. Indicators were standardized, and redundancy was reduced through correlation analysis and Principal Component Analysis (PCA). Clustering was then performed using K-Means, K-Medoids, and Ward’s Method, and evaluated using silhouette scores, Adjusted Rand Index (ARI), and Jaccard Similarity.
The results showed that cities could be grouped meaningfully and consistently using combined structural and behavioral indicators. Two- and seven-cluster results emerged as the most stable. The two-cluster split revealed a broad regional divide, while the seven-cluster result uncovered distinct urban typologies with full agreement across methods.
This study contributes to urban transport research by offering a replicable and holistic clustering approach that uses open data and multiple analytical techniques. It shows that meaningful typologies can be created by combining structural, functional, and behavioral dimensions, helping cities identify similar urban contexts and learn from specific strategies.
Overall, this research demonstrates that multi-domain clustering offers a valuable tool for comparing cities and supporting more targeted, evidence-based planning for sustainable urban transport.

With the increasing integration of Automated Vehicles (AVs) into our daily traffic, validating their performance poses a significant challenge. Virtual testing, where simulated AVs operate in a simulated environment, has become a widely adopted approach for efficient and cost-effective validation. However, the lack of realistic human behaviour models hinders the realism of these simulations, particularly in simulating reciprocal driver interactions. In this research, I introduce a discretionary lane change model based on the Communication-Enabled Interaction (CEI) framework, which simulates reciprocal driver interactions through implicit communication and belief modelling. These reciprocal interactions involve mutual behaviours, where individual drivers contribute through high-level decisions and low-level control actions. By employing the CEI framework, decision-making and control actions are integrated into a unified model. The proposed model is validated against naturalistic driving data in discretionary lane change scenarios to assess its validity. Results demonstrate that the model successfully reproduces qualitative and quantitative characteristics of human driving behaviour, reflecting both individual behaviours and the collective contributions of multiple drivers. Moreover, it reflects how varied tactical decisions yield distinct, human-like operational execution characteristics. Thereby improving the realism of interactive traffic simulations and posing a step towards improving virtual testing environments for AVs.

This paper presents a comprehensive and quantitative framework for enhancing the safety of autonomous vehicles by integrating sensor detection performance with braking dynamics under extreme weather conditions and variable road slopes. Using high-fidelity simulations in CARLA and PyChrono, extreme weather datasets are generated, sensor detection distances are evaluated and braking distances are simulated under varying friction coefficients and slopes. This study quantifies the relationship between detection distance and safe braking performance, establishing objective metrics for the dynamic Operational Design Domain of AVs. Furthermore, an adaptive speed control strategy based on reinforcement learning — implemented via the Soft Actor-Critic algorithm — is proposed, which continuously adjusts vehicle speed to ensure that braking distances remain within sensor detection limits. Experimental results demonstrate that models trained with extreme weather data significantly outperform general-purpose detectors and that integrating real-time braking distance predictions into the control strategy improves safety margins and operational efficiency across diverse driving scenarios.

The field of automated vehicles gains more and more attention each year as the effective implementation of such vehicles in real life is imminent. The increasing amount of scientific studies addressing the impact of automated vehicles on the traffic network shows the same trend. While much research focuses on regular intersections, only a few investigate the impact of turbo-roundabouts. In particular, studies researching the obtainable capacity increase on turbo-roundabouts under fully automated conditions are scarce. A turbo-roundabout is a multi-lane roundabout with separated lanes that require vehicles to choose their desired destination before entering the turbo-roundabout, effectively eliminating most weaving conflicts on the roundabout itself. Only a single paper researches how much additional merging capacity can be obtained by automated vehicles on a turbo-roundabout compared to human-driven vehicles (Fortuijn and Salomons, 2020). This study takes a theoretical approach and uses existing theory about turbo-roundabout capacity to generate five graphs depicting the merging capacity of the left lane of a minor access branch for varying circulating traffic volumes on the turbo-roundabout: four models for differing automated vehicle settings and one human-driven vehicle model. These four automated vehicle models differ in complexity, where the most complex model features gap synchronisation, headway optimisation, and path guidance. Each model, therefore, has different following distances, vehicle speeds, critical gaps, etc. The results of the theoretical models are capacity curves depicting the merging traffic capacity over the circulating traffic volumes.
This thesis investigates whether the capacity increase obtained from the most simplified automated vehicle model of the theoretical study can be obtained through microscopic traffic simulation in VISSIM. What must be noted is that the most simplified model of this theoretical study features communication between vehicles, which is not the case in the simulation models of this thesis. Therefore, higher follow-up times must be maintained in the simulation models, whereas the theoretical model uses minimal follow-up times to obtain a capacity increase of 58%. Initially, this thesis aimed to determine the achievable capacity increase for Connected and Automated Vehicles. However, due to limitations in VISSIM, the capacity increase could only be obtained for Automated Vehicles as the software cannot model communication between vehicles. Therefore, this report does not obtain the highest potential capacity increase for automated vehicles but serves as a stepping stone for more complex AV models. It determines the additional merging capacity of automated vehicles on turbo-roundabouts, only considering key parameters such as the car-following behaviour, critical gap and follow-up times. Gap synchronisation, an important aspect of the study of Fortuijn and Salomons, is not included in the simulation models due to shortcomings in VISSIM. Before making the AV models, this thesis aims to calibrate the VISSIM model for human drivers with real traffic data at a turbo-roundabout in Nieuwerkerk aan den IJssel, the Netherlands. Only when the VISSIM model is capable of accurately modelling human behaviour can it be used to determine the capacity increase through AVs. The thesis starts with developing a human-driven vehicle model to determine the merging capacity of the turbo-roundabout in current traffic conditions. The calibration is conducted on the traffic flow level through an iterative process where the critical gap and follow-up times are adjusted. The root-mean-normalised-squared error (RMNSE) determines the difference between regression lines of the merging capacities of the simulation model and the real traffic measurements. Based on the calculated differences between both lines, the critical gap and follow-up time parameters are adjusted.
For the left lane, the iteration process reduced the RMNSE from 0.427 to 0.072, equaling an improvement of 72.5%. For the right lane, the iteration procedure gave an unsatisfactory result. The RMNSE increased from 0.408 to 0.427, which signifies an increase of 4.7%. The final capacity curve after seven iterations was unrealistic. It was concluded that the VISSIM model performs adequately at simulating realistic behaviour on the left lane but that the same accuracy could not be achieved for the right lane. The calibration procedure is only applied to the minor branch of the turbo-roundabout as the data for the major branch could not be used for calibration. Subsequently, the calibrated model serves as the basis for developing three automated vehicle models: cautious, normal, and aggressive, each representing a different aggression level of automated vehicles. Each of these models features a different car-following model to simulate the various levels of aggression. Furthermore, the critical gap times of the human model are changed to model Avs properly, and for each of the three models, a penetration rate of 100% of AVs is applied. For each of the three automated vehicle models, this thesis measures the increase in capacity. It compares it to the theoretically obtained capacity increase of 58% for the left lane of the minor access branch of the turbo-roundabout. This thesis finds capacity increases of 7.47%, 38.62%, and 24.32% for the cautious, normal, and aggressive automated vehicle models, respectively. For the right lane, the obtained capacity increases were not calculated as the final HDV model is inaccurate for this lane. An unexpected result is that the normal automated vehicle model outperforms the aggressive model for the left lane, even though the parameter settings should result in a higher capacity for the aggressive model. Comparing the resulting capacity increases to the 58% increase of Fortuijn and Salomons, one can conclude that the simulation model does not reach the same increase in capacity as the theoretical model. This is not an unexpected result, as a theoretical simulation model has fewer interactions, and the theoretical model assumes communication between vehicles, whereas the simulation models do not. Therefore, concluding that the microscopic traffic simulation models in VISSIM cannot accurately model the behaviour of automated vehicles is premature. The models leave room for improvement by fine-tuning various parameters that were ignored in this thesis. This thesis concludes that, while VISSIM is adequate at simulating human-driven vehicles, it has various limitations for modelling automated vehicles. First of all, gap synchronisation and headway optimisation cannot be modelled, while these are the most critical elements to unlock the full potential of automated vehicles on turbo-roundabouts. If one wants to incorporate these into VISSIM, an external mathematical software package is necessary. Considering other software packages for studying (C)AVs on turbo-roundabouts is advised. This thesis is a foundation for future research on turbo-roundabouts, both for the calibration process and for simulating automated vehicles.

Due to the challenges of traffic congestion, pollution, and low transportation efficiency in urban areas, Shared Automated Vehicles (SAVs) are considered a solution that can integrate into existing transportation frameworks to improve traffic performance and environmental sustainability. Currently, there is a research gap in managing the fleet size of SAVs under different matching strategies. Although the literature on the operational and environmental benefits of SAVs is growing, few studies systematically analyze how different fleet management strategies affect service levels and road congestion. This thesis fills this gap by exploring the impacts of Immediate Decision and Batch Offer strategies on fleet size and their corresponding effects on urban traffic performance.

The thesis focuses on the integration of SAV with mixed mobility systems, using SAVs to entirely replace current Mobility on Demand (MoD) services, employing an agent-based simulation method to study the impacts of fleet size and matching strategies on urban traffic performance. This research employs a case study approach, selecting the Haidian District in Beijing as the research area. It uses an innovative demand generation model based on travel distances as simulation request inputs. The model utilizes travel distance distribution functions based on historical data, road network information, and taxi GPS data to generate traffic demand based on network nodes through traffic zone division, probability density calculation, and travel distribution. The model employs the K-means clustering algorithm to divide traffic areas based on the density of traffic nodes rather than specific road network conditions, a macroscopic, low-resolution method with lower computational complexity suitable for large-scale road networks.

The mode choice model in this study analyzes how passengers make decisions between different modes of travel. The model, using a discrete choice framework, studies how passengers choose between SAVs(Shared Automated Vehicles) and POVs(privately operated vehicles). Employing the Logit model, the study considers several key factors influencing travel choice, including travel time, comfort, reliability, and environmental impact, to provide mode selection in simulations. This part emphasizes the importance of travelers' subjective perceptions.

The application of simulation and the Macroscopic Fundamental Diagram (MFD) are central methodologies of this study. In the simulation analysis part, this research uses the agent-based simulation tool FleetPy, focusing on evaluating two key matching strategies: Immediate Decision Simulation (IDS) and Batch Offer Simulation (BOS). By simulating different fleet sizes and matching strategies, it explores the dispatching and operational performance of Shared Autonomous Vehicles in urban road networks and compares them with traditional taxi systems.

The findings indicate that replacing taxi systems with an SAV system can significantly improve fleet efficiency, achieving the same service level with a smaller fleet size. In both small and large fleet scenarios, the SAV system demonstrates higher operational efficiency. In scenarios with smaller fleet sizes, the Batch Offer (BOS) strategy provides higher service levels. By batch processing and optimizing multiple requests, it can effectively improve the success rate of matching and reduce waiting times, while Immediate Decision (IDS) performs better in terms of service timeliness. Simulation results also show that as the fleet size increases, the advantages of the IDS strategy become apparent but may lead to wastage of vehicle resources.