In recent years, the importance of integrating sustainability into infrastructure and mobility projects has been increasingly acknowledged. While such ambitions are often clearly formulated during early planning phases, such as during policy alignment, scope definition, or ambition-setting, they frequently lose traction as projects move closer to execution. This thesis investigates this phenomenon, known as sustainability ambition erosion, by examining how and why initial sustainability goals weaken throughout the decision-making process and what success factors may help to counter this.

The study begins with a systematic literature review, which categorises the main barriers to sustained sustainability ambition into six thematic groups: conceptual and motivational, economic and financial, organisational and cultural, knowledge-related, governance and policy, and stakeholder and participation. For each category, corresponding success factors are identified to explore potential counterforces to ambition erosion. These theoretical insights are then empirically tested through a qualitative multiple-case study of three Dutch infrastructure projects, combining ten in-depth interviews with public clients and engineering consultancies. The projects span different scales, phases, and governance arrangements, and are analysed along three dimensions: the project life cycle, the stakeholder structure, and the decision-making levels: strategic, tactical, and operational.

The empirical findings confirm many of the literature-based barriers and success factors, but also reveal that several of the predefined categories were either too narrow or not sufficiently reflective of practical realities. In particular, certain codes, such as time pressure, or project-specific constraints, proved difficult to categorise within the existing thematic structure, while others overlapped across conceptual, organisational, and behavioural domains. As a result, the categorisation of both barriers and success factors was revised to better account for how these dynamics manifest in real-world projects. This led to a more practice-informed classification, which more accurately captures the interdependencies and contextual nuances of ambition erosion and reinforcement mechanisms.
Notably, intrinsic motivation, informal leadership, and team culture emerge as more influential than formal role or mandate. Furthermore, stakeholder influence is found to depend more on behavioural agency than on their specified roles. Intermediate users, such as project managers, technical managers, and sustainability advisors, played a decisive role in maintaining or abandoning sustainability goals, especially during transitions between project phases.

Ambitions are most vulnerable to erosion during the elaboration phase, where design choices and technical detailing occur, and during phase transitions, where lack of continuity, timing misalignments, and weak anchoring often lead to fragmentation. Financial constraints, conceptual vagueness, and temporal pressure and misalignment were particularly salient across cases. At the same time, success factors such as intrinsic motivation and leadership, engagement and steering power, and contractual and procedural governance mechanisms were found to support the retention of sustainability ambitions. Additionally, several context-specific barriers and success factors emerged that could not be generalised, underlining the importance of project-specific reflection and flexibility.

The study concludes that maintaining sustainability ambition throughout the project lifecycle requires both systemic and human-centred interventions. Rather than relying solely on frameworks or instruments, projects need an integrated strategy that combines structural anchoring (e.g. KPIs, contracts), behavioural ownership (e.g. motivation, leadership), and procedural attentiveness (e.g. continuity, timing, phase-specific tools). These three conditions, structure, behaviour, and procedure, must be addressed in parallel if sustainability ambitions are to withstand the complex realities of infrastructure development and result in lasting impact.

Workday transport models are extensively used in practice to guide policy development while the more leisure-oriented weekend days are left out of the analysis. This is peculiar, as weekend travel can be as busy as workday travel, or even worse. Consequently, weekend travel behaviour is fundamentally different, showcasing more variability in the activities that people perform. Given this, can such a model be relevant for policy makers and does this outweigh the effort that is required to make such a model? These questions are investigated by thinking whether and how an operational workday transport model can be adapted to represent Saturday travel behaviour in an aggregate approach. Due to the lack of a clear starting point for model development, literature and travel data were used to propose a set of adaptations which can be used to transform a workday model. From this, two Saturday model versions were created which contain different trip purposes and Saturday travel data, whereby one of the models is improved in the trip generation component. The results show that adapting a model is feasible but more work is required for a proper implementation. Nevertheless, a Saturday model can be used in specific use cases but a clear purpose to regard Saturdays in policy development is still lacking. This research further recommends to explore the applicability of Saturday transport models and to conceptually develop a model which can portray complex aspects of travel behaviour in an Activity-Based approach.

This research explores various mobility measures in the battle with excessive nitrogen deposition. It comprises a case study research of two study areas in the Netherlands with Natura2000. By employing the MOVE Meter as a quickscan tool with traffic modelling, effective local mobility measures have been identified. Establishing a no-entry zone emerged as the most effective measure for the reduction of nitrogen deposition due to its proximity to natural areas, highlighting the significance of spatial factors. This research also revealed modelling constraints related to the implementation of emission factors in traffic modelling.

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.

This study investigates the impact of lowered lock capacity on lock demand shifts within the Weurt- Grave system, a critical link in the Southeast inland waterway corridor of the Netherlands. It answers the following research question: What impact does a reduction in capacity, caused by low water levels, have on alternative locks within the inland waterway system, with a focus on the Weurt- Grave system? Understanding the interactions is important for improving waterway infrastructure planning. Lock ca-pacity constraints caused by extreme water levels are becoming more likely to occur due to climate change. This increases the importance of being able to predict the effects these disruptions will have on the larger waterway system. This research shows from historical data how lock capacity decreases can affect other locks. This helps give insight in the scope that might need to be assessed when researching a single lock that is part of a larger system. During dry periods, low water levels in the Waal limit lock Weurt’s capacity, redirecting larger vessels to lock Grave. This redirection alters waiting times, fleet composition, and traffic patterns within the system. It shows the system’s sensitivity to environmental conditions. Using lock usage and water level data from Rijkswaterstaat, the study applies statistical methods, including logit regression, time series analysis, and moving averages, to quantify water level impacts on lock utilization and traffic patterns. The findings reveal redistributions in vessel traffic and load, primarily driven by vessel draught, origin- destination patterns. While the system shows adaptability, capacity constraints at Grave raise concerns about infrastructure durability under increased demand. Furthermore, the research identifies limitations in existing traffic models, such as SIVAK and BIVAS, which fail to capture dynamic interdependencies between locks under changing environmental effects. This study shows that lock capacity constraints should not be viewed as an isolated problem. These constraints could be causing network wide effects, changing traffic flow and fleet compositions at other points in the system. By revealing how vessel behaviour responds to changing capacities, the research emphasizes the necessity of network wide approaches to lock modelling. Knowing of these possible system-wide effects is essential for modelling waterway infrastructure that need to handle the results of climate change and changing transport demands. This will improve the resilience and efficiency of the inland waterway network.

Governments worldwide are planning car-free policies to increase the livability of cities. However, car-free policies often face opposition from local stakeholders, and policy-makers often lack relevant model indicators to represent all stakeholder interests in their ex-ante car-free policy evaluations. Well-being (Dutch: Brede Welvaart) indicators can be more relevant for the various stakeholder interests in car-free policies by covering aspects in the domains of living environment, accessibility, safety and health. This thesis investigates how relevant well-being indicators can be selected and operationalised for the ex-ante evaluation of car-free policies. A well-being indicator selection process was developed and applied to a case study in the Oude Westen neighbourhood in Rotterdam. This process involved conducting thirteen semi-structured stakeholder interviews. The resulting stakeholder interests were analysed to determine the relevant aspects of well-being and the relevant indicators for these aspects. From the shortlist of relevant indicators, three indicators were selected and operationalised in this thesis. At last, two validation interviews were conducted to validate the results of this case study and the developed indicator selection process.

The most relevant stakeholders - municipality, local residents, and local business owners - were selected to be involved in the well-being indicator selection process in the case study. Based on their interests, the most relevant aspects of well-being were found to be the use of space, accessibility of mobility options, accessibility of activities, traffic safety, and noise pollution. This resulted in the operationalisation of three indicators: the use of space for car parking, the number of mobility options, and the traffic mix safety warning.

While the small sample size does not allow for generalisation of the case study results, the results of this thesis do show that the well-being indicator selection process can yield relevant indicators of well-being and that this process can be applied in practice. The operationalised indicators can be relevant to represent stakeholder interests in car-free policy evaluation and can be applied to other cases as well. However, the relevance of the indicators does depend on the type of policy and the stage of the planning process. Using well-being indicators in policy evaluation can enhance the relatability of the evaluation results for stakeholders and potentially increase stakeholder support for policies.

The escalating trends in e-commerce have led to a surge in parcel delivery demands, necessitating innovative solutions for efficient and environment friendly last-mile deliveries. Parcel Lockers (PLs) have emerged as a potential answer to address the environmental impact of traditional delivery methods. The core of PLs lies in a benefit trade-off between the customer and the Logistic Service Providers (LSPs), where PLs lead to reduced vehicle kilometers (VKT) by LSPs but result in inconvenience to customers by enforcing them to travel to PL locations. However, a research gap exists concerning the assessment of the environmental implications of PL
systems holistically which comprise the customers, the LSPs and the PL distributors. This master thesis aims to bridge this gap by conducting a comprehensive life cycle assessment (LCA) of a PL system via the Environment Footprint (EF) methodology. Extensive literature research is undertaken to understand the various aspects and factors that affect the usage of the PLs, leading to the development of a conceptual model encompassing its key components. The EF method is applied to analyze the climate change impacts specifically in areas of De Pijp and Ten Boer respectively. The method considers relevant characteristics, such as customer
travel behavior and mode choice. The total environmental impact in the rural setup, where customers utilized cars to access the PL, is found to be the highest leading to 1028 g CO2 eq emissions per parcel produced. The urban setting has the least overall environmental impact, due to customers walking to the PLs which amounts to 42.46 g CO2 eq emissions per parcel. The PL system is also compared to conventional home deliveries (HDs) in this thesis which indicates that the PL system performs better environmentally, unless the mode choice by customers is cars. When considering only the transport emissions by LSPs in PLs and HDs, the PLs outperform HDs in both the scenarios. It is found that the PL systems reduce the emissions by upto 75% in urban setup and about 50% in rural setups which amount to about 36.3 g CO2 eq emissions per parcel. Furthermore, a sensitivity analysis is conducted to evaluate the potential impacts of future policy implications and developments regarding the environmental performance of the PL system. The results furnish valuable insights into the different scenario factors and system design factors, facilitating informed decision-making abilities for policymakers to promote eco-friendly last-mile delivery practices.

Comparing the performance of distribution functions and a discrete choice model, on a mode & destination choice in a tour-based model. An discrete choice model is composed for a tour-based model. Both choice models are implemented in a large-scale tour-based model and the performances are compared.

European cities are implementing diverse strategies to curtail car usage. Understanding the impact of these policies necessitates insights into mode choice behaviour. However, for conventional discrete choice models, utility specifications must be defined upfront, potentially leading to misleading policy recommendations. This problem is solved by Supervised machine learning (ML) models. However, they are challenging to interpret, which is crucial for evaluating transportation policies. We employ Shapley additive explanations (SHAP), a model- agnostic explainable artificial intelligence (XAI) tool to address this gap. The main advantages of SHAP are its foundation in game theory, the ability to highlight individual taste heterogeneity and non-linear effects. This paper aims to shed light on the potential of SHAP to improve current transportation mode choice models. Using a random forest (RF) model with the TreeSHAP estimation method, we compare SHAP insights with those derived from a traditional multinomial logit (MNL) model. The results indicate that SHAP can detect the absolute importance of features. Substantial preference heterogeneity for car choice is perceived for features reducing car usage, as opposed to features increasing car usage. Non-linear effects, such as reciprocal functions and clustered patterns, are observed for certain features. MNL and RF models disagree on the importance and heterogeneity of features, and the MNL model fails to model highly nonlinear effects. For policymakers, insights suggest that increasing parking fees and promoting car sharing may be feasible options. However, the efficiency of these measures may vary due to preference hetero- geneity. The results underscore the need for further investigation into the reasons behind the different model results and different behaviour notions of SHAP, MNL and RF.

With the number of passenger cars increasing worldwide, shared modes are being introduced in addition to public transport and active modes to reduce private car usage. The concept of mobility hubs was introduced to improve the accessibility of the different available transport modes. At a mobility hub, all sorts of mobility come together, and additional facilities such as pick-up points and restaurants are added to increase the attractiveness of the hub. Based on stated-preference surveys and expert interview reports, it was identified that mobility hubs could change people's travel behaviour in the region where it is located. However, these studies only provided insights and were insufficient to assess the mobility hub’s impact on the transport network. The parking demand in residential areas is also increasing pressure on residents to switch to different modes, and these hubs could play a key role in providing alternate transport options. As a result, this research aims to study the impact of mobility hubs in a residential area on the transport network in which it is placed. This study identifies the typology of mobility hub suitable for a residential area and the components required at the hub. This study also proposes a generalized methodology for introducing mobility hubs with shared modes in aggregate transport models. A case study is performed on the Delft transport network in OmniTRANS based on the proposed methodology to understand the impacts the mobility hubs can cause. In this case study, only the shared modes are assumed to be available at the hubs, and the additional facilities are not considered. Based on the simulation results, it was found that the travel behaviour changes majorly in the residential regions where the mobility hubs have been modelled. The private car trips were found to decrease marginally, but the total number of car trips, including both private and shared cars, increased. This research recommends further research on people's behaviour in the region in the form of pilot studies before modelling for simulation as people’s behaviour varies from region to region.  

The aim of this research is to develop a transport model that is able to identify potential conflicts and bottlenecks for nautical recreational activities in the IJburgbaai, an open-space water area in the eastern part of Amsterdam.

Due to the continuous expansion of the urban scale, the urban metro transportation system also needs to be constantly updated, especially those cities that need to carry mega-events. Larger carrying capacity, better collaboration mode and more optimized operation mode are required to handle the sudden increase in travel demand. The ever-growing micromobility system and the multimodal transport system of micromobility and other public transportation methods can well meet this trend.

This thesis aims to develop a multimodal transport optimization model that takes full advantage of the micromobility mode. This thesis, from the perspective of the traffic system administrator, explores the influence of micromobility on the cost of the entire traffic system under the premise of maintaining the normal operation of the metro system after micromobility joins the traffic system at the same level as the metro.

Firstly, through literature review, the research object is determined to be a representative shared bike in micromobility mode. The way to deal with the excessive travel demand brought by mega-events is to add shared bikes to the transportation system with the same status as the metro on the basis of maintaining the normal operation of the metro system, and cooperate with the metro system to meet the demand.

Secondly, the research methods are qualitative research and quantitative research. In the qualitative research, model design, adjustment and optimization are carried out in combination with actual experience to realize the design of the multimodal transport system of shared bike and urban metro system. In quantitative research, this thesis describes a case study area based on real condition and proposes six scenarios. And through demand forecasting and parameter changes, this thesis explores the situation that only metro participates in the transportation system, and micromobility mode participates in the transportation system with metro with different parameters, etc., under the premise of meeting the excessive travel demand brought by mega-events, impact on total travel costs.

Through model optimization and scenario verification, it is concluded that when the micromobility mode participates in the transportation system in a reasonable way, the multimodal transport system will be able to reduce the total cost, and the degree of reduction is related to the characteristics and parameters of the mode.

The COVID-19 pandemic has changed travel behaviour and mobility in the Netherlands. It is questioned if the mobility system is subject to long-lasting change in the near and distant future. This study aims to observe the development of mobility impacts over time with a quantitative approach.

Current traditional transport models have difficulties implementing uncertainties such as the mobility disruptions of COVID-19. These models lack the exploratory nature to cope with quickly arising events and changes. Using system dynamics, a model is developed to observe the mobility impacts of disruption and trends in transportation. The developed model and alternative transport modelling approach are tested and validated by applying the case of the COVID-19 mobility disruptions in the Netherlands. By including a tele-activity alternative in the mode choice component of the model, an option not to travel is modelled as a contender to the traditional modes of transport using choice models.

Subsequently, applying the model to the COVID-19 situation provided insight into new relations and understandings of the development of the mobility impacts over time in the near and distant future. It was found that the future attractiveness of modes of transport changes as a result. Travellers' preference for private modes of transport will remain high in the forthcoming years, causing public transportation to recover slowly. It can be concluded that the system dynamics modelling approach is beneficial and has accomplished the design of a model widely applicable to COVID-19 and other mobility disruptions.

This master thesis provides an approach to develop Adaptation Pathways to prevent local flooding of the Dutch national highway network, using the A2 between the Vught and Sint-Michielsgestel junctions as a case study. Local flooding as result of extreme precipitation has the highest urgency on the Dutch national highway network, due to its increasing chance of occurrence as a result of climate change and increasing consequences on the traffic flow. Hydrological modelling through a bucket approach, complemented by the groundwater dynamics of time series analysis, was done to quantify a range of tipping points between 6 and 60 mm/hr for the Adaptation Pathways, i.e. the hourly precipitation intensity at which water exceeds the edge line of the road for a precipitation event with a maximum return period of 10 years. This was done for three scenarios that embed uncertainties in interception and infiltration parameters and variations in the boundary conditions of groundwater levels. The bucket approach revealed that processes of interception by porous asphalt and sewer discharge are most influential to delay the tipping point, whereas infiltration is the least influential to delay the tipping point. The modelling results showed that adaptation measures that result in (1) an increase in the storage capacity of the catchment of the road (either on the road itself or in the verge) or (2) an increase in the discharge capacity of the catchment of the road are possible to control flooding of the case highway. Possible adaptation measures to increase the storage capacity on the road that were identified are cleaning porous asphalt, increasing the thickness of the porous asphalt and installing a Plastic Road. Possible adaptation measures to increase the storage capacity of the verge that were gathered are removing the top layer of the verge, installing an infiltration trench, raising the road and creating a descending verge. An increase in the discharge capacity of the catchment of the road can be established through a new sewer layout, increasing the sewer capacity and installing manholes with holes. A focus group revealed the interests of stakeholders with respect to flooding. (1) Traffic flow, (2) water buffering capacity, (3) water quality, (4) ecological value and (5) costs and maintenance reflect the criteria relevant to the stakeholders of the focus group and therefore provide information on the societal robustness of an adaptation measure. A scorecard ranked the potential adaptation measures according to the defined criteria which indicate the societal robustness of adaptation measures. Overall, the design of Adaptation Pathways for measures to prevent local flooding on the Dutch national highway network revealed a wide range of possible futures for the defined measures, in which the fulfillment of the T=10 year design storm that is associated with 2050 requirement is either already met or possible through the implementation of adaptation measures. The combination of the Adaptation Pathways map together with the scorecard provided an assessment of the climate resilience of the A2 as an integral part of the surrounding environment. It was found that the designed Adaptation Pathways promote incremental changes, such as cleaning porous asphalt and enlarging the sewer capacity, which enhance path-dependency. Moreover, it was discovered that using a combination of Adaptation Pathways with a scorecard provides insight on transformational adaptation measures that focus on achieving hydrologic neutrality. Hydrologic neutrality can be either be achieved by storing precipitation in the catchment of the road or by discharging precipitation to a nearby location out-side the catchment of the road, where it is used. The findings of the research can be generalized to others places of the Dutch national highway network, to the design of Adaptation Pathways elsewhere and to a variety of climate effects. Finally, as the transformational change towards hydrologically neutral highways complies with the principles of systematic water management of the 21st century and low impact development, one could argue for generalization towards hydrologically neutral highways.

Bus bunching is a problem that occurs in many high frequent bus systems. This can be averted by several countermeasures of which holding control is the most popular one in practice. Holding control strategies are often implemented using predefined rules. In this study, multi-agent reinforcement learning is selected to develop an effective holding strategy since it is more flexible and can adjust its strategy to the conditions of the system. This approach is tested on five bus systems with different characteristics. The first system is idealised with deterministic driving times, deterministic dwell times, where all route sections have equal length and all stops have the same demand. In the other systems several complexities are added: stochastic driving times, stochastic dwell times, route sections of varying length, varying demand at the stops and traffic lights on the route. Additionally, in two systems the traffic lights are modelled as reinforcement learning agents. The results show that this approach can outperform the rule-based benchmark in four out of five systems. Furthermore, the MARL approach can handle the stochastic processes better than the rule-based benchmark. This difference is not observed for the varying route characteristics. Lastly, the MARL approach is able to learn an effective cooperation between the bus agents and traffic light agents. This indicates the potential of system wide cooperation and optimisation.

This study addresses the truck rescheduling problem as the consequence of uncertain arrival time. It proposes an integrated system of predictive model powered by machine learning algorithm and exact optimization model such that it is distinct from most existing literatures in this domain. The uncertainty of truck arrival time is captured as presence probability by the developed predictive model. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) is built to solve the Probabilistic Slot Rescheduling Problem (P-SRP) in which the rescheduling is subject to expected value constraint that incorporates the presence probability of incoming trucks. The objective is to minimize the expected cost of rescheduling that would lead to more efficient and robust operation. In regard to the predictive model, evaluation according to the standardized KPI shows the ANN is the best algorithm to fit the input historical data with overall F1 score of 73%. Moreover, adding real-time elements enhances the prediction result by 20%. In regard to optimization model, the P-SRP model results on expected cost that is 42% lower than the P-SRP model. Numerical experiment based on multiple scenarios indicates that the proposed solution yields optimal added values in situation characterized by high number of reschedule being required, large scale of operation in terms of quantity of loading bay and utilization rate, the priority is to maintain initial schedule, and there is no stand-in loading bay available. Lastly, it is found that increasing operational efficiency comes at the expense of the schedule robustness, although the value is negligible. This study is limited to the conceptual setting and the use of synthetic data; therefore it should be extended to include empirical result.