Cities are increasingly pressured by rapid urbanisation, climate change, and congested transport networks, intensifying the need for sustainable mobility solutions that prioritise walking and cycling. Designing effective pedestrian-friendly interventions requires reliable, city-scale models of pedestrian activity. However, most existing transport models remain car-oriented or focus on microscopic pedestrian dynamics. Macroscopic pedestrian models are often highly data-intensive, rely on household surveys or extensive pedestrian counts, and are poorly suited to the Dutch context, where such data are limited. This creates a clear gap: the need for a practical, open-data-based, building-level pedestrian trip-generation model for Dutch cities.

To address this gap, this study develops a building-level pedestrian trip-generation model with fine spatial and temporal resolution, tailored to Dutch conditions and relying exclusively on widely available open data. The central research question is: To what extent can an existing pedestrian trip-generation model be adapted to reflect pedestrian trip-generation dynamics in the Dutch urban context?

The study adapts Sevtsuk’s Urban Network Analysis (UNA) framework into a Dutch-specific, building-level model referred to as BPT-Gen (Building-level Pedestrian Trip Generation). The UNA framework was selected because it offers a practical balance between spatial detail and data requirements while remaining transparent and reproducible. Several key adaptations are introduced to ensure applicability to Dutch cities.

First, buildings are identified and classified using the Dutch BAG dataset, supplemented with OpenStreetMap (OSM) data to capture land use, amenities, and public transport facilities. This enables a detailed and consistent representation of pedestrian trip origins and destinations at the building level.

Second, the derivation of building activity weights is modified. These weights represent the estimated number of unique daily users per building. Where detailed local data are available, weights are calculated directly; otherwise, Dutch building and occupancy standards or carefully selected proxy indicators are applied. This approach allows the model to remain operational across data-limited contexts while explicitly acknowledging uncertainty.

Building on the UNA framework, the model incorporates accessibility-adjusted activity weights. Each building is connected to the pedestrian network, and destination-specific Reach indices are calculated using a walking radius relevant to Dutch conditions. These indices adjust baseline activity weights to account for behavioural tendencies and destination attractiveness. Sensitivity analyses examine how assumptions regarding walking radius and normalisation methods influence model outcomes.

A major extension beyond the original UNA framework is the integration of temporal dynamics through hourly walking trip rates. Trip purposes derived from the Dutch ODiN travel survey are mapped to building types, and corresponding hourly origin–destination trip rates are applied. Combining these rates with accessibility-adjusted weights yields hourly, building-level pedestrian trip-generation estimates for the study area.

Model performance is assessed through face validation using observed pedestrian counts. Results show that the adapted model reproduces realistic spatial and temporal patterns for building types supported by reliable activity data, particularly housing and major train stations. Underprediction is observed for offices and leisure facilities, where activity weights rely on proxy indicators and where walking trips are likely underreported in ODiN. Sensitivity analyses confirm that trip-generation outcomes depend strongly on proxy selection and accessibility assumptions, revealing structural uncertainties.

Despite these limitations, the BPT-Gen model provides clear practical value. It identifies key generators of walking trips, peak periods, and accessibility-driven hotspots using only open data. By linking trip purposes to building types at hourly resolution, the framework fills an important gap in Dutch pedestrian modelling and offers a transparent foundation for future extensions. The findings demonstrate that open-data pedestrian models can provide meaningful planning insights while highlighting the need for improved activity indicators and further validation using street-level pedestrian flow data.
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One of the challenges municipalities face when trying to prevent parking problems is the difficulty of predicting the effect possible policy implementations will have on the parking occupancies. Therefore, a conceptual model was developed that uses the interactions between parking supply, demand, and choice behaviour to forecast the parking occupancy. In this research, this model is operationalised using existing data and techniques to form an operational model framework. The framework is tested and validated using a case study on the Dutch municipality of The Hague. While there are many ways the model can still be improved and further developed, the framework developed in this thesis shows that the underlying concept is able to accurately describe the balance between parking supply and demand and, from that, predict the resulting parking occupancies. It combines several existing techniques and data sources with some new techniques, providing an initial step to a full parking occupancy model. ... Read more

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. ... Read more

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. ... Read more

Bicycle travel demand analysis remains relatively underexplored, yet it is becoming increasingly important for urban and campus planning. In the Netherlands—particularly within the TU Delft community—cycling is deeply embedded in daily life, creating a strong demand for supportive infrastructure. While congestion may not pose the same threat to bicycle transport as it does to motorized traffic, maintaining the performance and safety of the bicycle network is essential. A busy cycling network may not always be visible at a glance, yet it can present safety risks.

The primary aim of this research is to identify a modeling process and specifications that are compatible with the available data, while laying the groundwork for future improvements to the bicycle network, especially TU Delft Campus. This will help ensure the system remains adaptable and relevant for long-term planning.

To identify an appropriate modeling approach, an exploratory analysis of the data was conducted. A clear pattern emerged in bicycle traffic, characterized by short-interval fluctuations corresponding closely with lecture schedules. An additional notable observation is the occurrence of an average peak in bicycle traffic during midday. These findings support a dynamic analysis approach with a 5-minute interval.

Moreover, the model incorporates specialized variables defined by the study’s scope, focusing on trip generation and trip distribution within the established four-step modeling framework, specifically tailored for Origin-Destination (OD) matrix estimation in transportation engineering.

For trip generation, linear regression coupled with backward stepwise elimination via the Ordinary Least Squares (OLS) method was employed to identify significant predictors. For trip distribution, the Iterative Proportional Fitting (IPF) method was utilized. This approach was chosen based on the assumption that impedance is minimal for short-distance travel, a scenario particularly relevant within the TU Delft campus context.

Ultimately, this methodology provides a flexible and responsive framework tailored to the specific transportation dynamics at TU Delft, producing valuable insights for optimizing bicycle network planning.

The developed model is relatively simple but exhibits several shortcomings. One significant limitation is related to data collection, as the available data lack the temporal resolution necessary to fully capture the dynamic travel patterns targeted by the model. Additionally, the linear regression approach used for modeling trip production and attraction yielded unsatisfactory results, with $R^2$ values below 0.5. Another issue is potential underfitting, as indicated by the improved explanatory power of the model when trained on smaller datasets. Validation using RMSE and comparative plots of modeled versus actual flows further confirms that substantial improvement is needed in the model’s reliability and predictive capability.

The trip distribution process, conducted using the Iterative Proportional Fitting (IPF) method, reveals additional areas for improvement. The OD matrix underestimated total production by two bicycles in a 5-minute interval. Although seemingly small, this discrepancy underscores the necessity for more robust input data and methodological refinements. Additionally, direct validation of the OD matrix is crucial to enhance accuracy and reliability in representing actual travel flows.

Despite the shortcomings, the framework provides a balance between interpretability and flexibility, enabling both accurate representation of observed travel behavior and ease of scenario testing—making it a practical tool for supporting data-driven mobility planning and policy evaluation on campus.
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As cities increasingly prioritize sustainable and low-car urban environments, road capacity reductions have become a widely applied policy measure. While traditional transport models predict severe congestion resulting from such interventions, empirical studies suggest that traffic volumes tend to decrease, a phenomenon known as ‘disappearing traffic.’ This study develops a model-based approach to estimate the behavioural adaptations underlying disappearing traffic and their implications for travel demand and network performance.
Using a three-step iterative framework, the model adjusts the traffic demand based on observed travel times in response to capacity reductions. The process accounts for modal shifts, destination changes and trip frequency reduction. The methodology is applied to a real-world case study where significant road space reallocation is implemented, allowing for an evaluation of the extent and mechanisms of disappearing traffic.
Findings indicate that a considerable share of the traffic does not reroute but instead disappears due to behavioural changes. The extent of disappearing traffic depends on the severity of capacity reductions, the existing congestion levels on both intervening and alternative routes, and the availability of viable transport alternatives. The findings highlight the importance of incorporating adaptive travel behaviour in transport models to better support decision-making in sustainable urban mobility planning.
This research contributes to the growing body of knowledge on the effects of road capacity reductions and provides a modelling framework for policymakers and urban planners to anticipate and manage disappearing traffic effects effectively.
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Against the backdrop of the increasing maturity of connected automatic driving technologies and the gradually expanding market share of CAVs, this thesis explores the optimal traffic management strategies to cope with road closures in the context of Connected and Automated Vehicles (CAVs) and Intelligent Transportation Systems (ITS).
A rerouting strategy is designed based on the rerouting behaviour of vehicles when road closure occurs in life, the control parameters include the control of the CAV's automatic rerouting period, rerouting probability, HDV Knowledge of the time of lane closure, as well as their rerouting probability. The aim of this study is to find the optimal combination of these five parameters. Four levels of CAV penetration (20\%, 40\%, 60\%, and 80\%) are considered with the objective of minimizing the total travel time on a mixed CAV and human-driven vehicle (HDV) traffic flow network. The main question is \textbf{What is the optimal rerouting strategy for CAV and HDV mixed traffic when road closure happens?} and in the process of answering this question, the effects of CAV penetration, individual rerouting parameters and different road closure locations are considered and analyzed.
In this thesis, a simulation-based approach is used to model the traffic flow applying both micro and meso scale models. Then, the simulation is conducted for the predefined scenarios, then the sensitivity analysis of each relevant parameter is performed using a one-factor-at-a-time approach to understand the impact of each parameter on the network traffic condition. Finally, Bayesian optimisation is used to find the optimal rerouting strategy within a certain search range and number of times, where the results obtained from the sensitivity analysis are used to determine the parameter search space.
The grid network and the Sioux Falls network are simulated respectively and the relatively optimal rerouting strategies are found for them. The grid network can be regarded as a local area on the network, while the results of Sioux Falls, as a larger network, can provide some basis for city-level traffic management.
The key findings of this thesis include (1) CAV penetration increases bring reductions in TTT and TWT and increase in TTD to the network, overall, the traffic flow movement improves and severe congestion decreases, and network conditions improve significantly during the growth phases of 20\%-40\% and 60\%-80\%; (2) the importance of each rerouting parameter varies for different networks and at different penetration rates, and the results fluctuate significantly between different test values, with no single increasing and decreasing trend; (3) road closures at entrances and exits located at intersections are more critical and require targeted rerouting strategies; the traffic demand distribution has a significant impact on it; (4) Bayesian optimization can find the optimal rerouting strategy in a finite amount of time, where the specific strategy and the improvement effect varies for different networks and levels of demand. ... Read more

Amsterdam's extensive network of bridges and quay walls is facing a critical maintenance backlog due to structural underfunded infrastructure maintenance. The many upcoming bridge maintenance projects to ensure safety are expected to cause significant more hindrance to road users. However, complex road networks with high traffic density in urban areas like Amsterdam make it difficult to foresee how the effects of different maintenance projects interact and how the projects can be planned efficiently to reduce hindrance.
This study aims to optimise the planning of a given set of bridge maintenance projects to reduce hindrance to road users, combining a genetic algorithm with traffic simulations using a new estimation method to improve the assessment of simultaneous closures. The genetic algorithm selects the optimal starting time and execution duration for each bridge while minimising maintenance cost and additional travel times for the modalities car, freight and bicycle. Using the fast static traffic simulation of the Urban Strategy Tool of TNO, a new estimation method for evaluating simultaneous closures is proposed, accounting for interdependencies between bridges. Results of a case study on four urban bridges in Amsterdam show that the new method provides more accurate estimates of additional travel time compared to previous methods and generates better solutions. Moreover, the fast proposed optimisation framework makes it possible to evaluate multiple scenarios in reasonable time to assist in decision making of maintenance planning.
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The outbreak of the COVID-19 pandemic and the resulting corona crisis has had a profound impact on mobility in the Netherlands and in the entire world. This study aims at predicting, modeling, and analyzing changes in the activity-travel behavior of individuals in the Rotterdam-The Hague Metropolitan Area, the Netherlands, during different stages of the COVID-19 pandemic and assess their effects on mobility. This is done by using the activity-based travel modeling approach. A modeling framework is developed and used for this study. The modeling framework provides an innovative approach to study the impacts of changes in activity-travel behavior caused by emergency situations such as the COVID-19 pandemic in a disaggregated manner. It combines the outputs of ABMs and a mix of aggregated and disaggregated data of changes in in-home and out-home activity frequencies and re-estimates the daily schedule of individuals considering factors and attributes that were not considered before, such as the weekly frequency at which agents do different activities and the sector where agents work. With the outcomes of the case study simulations, insights about the effects on mobility were identified and compared to real data counts and results of other studies. The conclusion is that the vast majority of the model outcomes proved to be in line with the results of other sources, and the modeling framework can be a useful tool to study the changes in activity-travel behavior of individuals during emergency situations and their effects on mobility. ... Read more

In recent years, the amount of traffic on the highways has increased continuously and in particular freight traffic. Despite solutions to maximize roadway capacity, the drop of capacity after congestions sets in, remains an active field of study. The influence of freight traffic on traffic flow has received little attention, especially regarding the capacity drop. The Kaplan-Meier Product Limit Method was used to estimate capacity and recovery distributions for selected sites to overcome stochastic characteristics of traffic flow and investigate the relation between heavy vehicle share and capacity drop at Dutch highways. Furthermore, a simulation study was executed to investigate increased heavy vehicle share scenarios and changing physical and operational characteristics of heavy vehicles. The empirical results show a connection between heavy vehicle share and the capacity drop, although not statistically significant. Currently, chaotic properties of breakdown flow seem to superimpose the impact. However, it is possible that the effect becomes influential as the current maximum observed share of heavy vehicles during breakdown grows from 9% up to 15%, which appeared to be the worst case scenario in simulation. Besides, concern is raised as a decreasing breakdown capacity was observed on several Dutch highways, even after correction with the PCE values of the increasing heavy vehicle share. ... Read more

This research focuses on the analysis of Floating Car Data (FCD) data to understand sustainable transportation behavior in the area of Amsterdam. Using data collected by mobile devices, trips are analyzed by their distance and transportation method. Sustainability is a relevant factor to the social and scientific community. With increasing population and growing cities, the impact of travel on the environment is also increasing. New policies are required to stimulate a more sustainable approach to transportation. Research on sustainable travel behavior provides input for the policy makers. The main research question is defined by what extent FCD can be used to provide insights in the sustainable mobility behavior in Amsterdam. In the existing literature, there are four objectives for sustainable mobility: hazards reduction, travel reduction, modal shift and accessibility. The modal shift (e.g. replacing care usage by public transport) is one of the key drivers for this research. To quantify the behavior, different sustainable mobility indicators are identified to determine the sustainable direction related to the mode of transportation. In the literature there are many methods to quantify sustainable transportation behavior ranging from the traditional methods like counting and surveys to modern approaches including smartphone and sensor data. The methodology used starts with the problem statement and literature review. The data sets are selected and analyzed, providing the results and conclusions. A decision tree is used for categorizing different trips people make, where a difference between short and longer distance trips is made. This research makes use of a wide range of tools ranging from PostgreSQL databases to advanced features of ESRI to visualize data. Five different available data sets are analyzed for their suitability for this research. Based on several requirements like availability, having Origin and Destination (OD) information, usability and documentation, the data sets are assessed and two data sets (Google OD and LMS) are chosen to be analyzed in more detail. The data is filtered and cleaned to make sure it fits the scope (Amsterdam) and the two sets are compared to each other. The trips are split into short and long distance trips, where for both a detailed analysis is performed on the trips which can be easily replaced by more sustainable trips. Regarding the short distance trips, analysis shows that walking and biking are the most common option in busy areas as the city center and the business district. The less sustainable car trips for short distances show several patterns in the city. Using interactive maps, these patterns are identified and both data sets are compared. For longer distance car trips, train transportation is the more sustainable option. There is however a tradeoff between the most sustainable and least time-consuming transportation option. This makes that train trips are not always the most logical or even sustainable choice for transportation. A method is developed and applied to the data set to test if a train trip is a realistic alternative. The results show for both data sets that there is a significant amount of car trips which could easily be replaced by the train. During the analysis of the data for long distances, a problem has been found with the Google data set. It seems that this data set is showing most of the time the same noise. This phenomenon was found by comparing the different modes of transportation available in the Google data set. The error is not in all the data, but it seems to have an impact on the data set. Conclusion is that the data provides many insights on travel behavior and different available data sets can be linked together to provide deeper insights. The Google data set shows interesting results for shorter distances but gives less reliable results for longer distances. The LMS data set is used to compare the results for both short and long distances. The use of FCD data to study and stimulate sustainable mobility behavior seems very promising, but the quality of the available data sets has a large influence on the usability. ... Read more