The growing population in the Netherlands is straining the accessibility and liveability of cities. To address these issues, new mobility policies prioritize quiet, low-emission, and space-efficient modes of transport, creating a conflict with the characteristics of private fuel-engine cars, which are noisy, polluting, and space-inefficient. For example, a private car trip in Amsterdam requires about 95m² of public space, including 15m² for parking. Consequently, Dutch cities are considering measures to reduce car mobility, with parking regulations viewed as a potential tool to decrease car ownership and enhance urban liveability. However, there is a lack of evidence supporting the effectiveness of these policies, as car ownership research typically focuses on sociodemographic and household factors.

This study aims to fill this knowledge gap and assist municipalities in balancing car accessibility with urban liveability. It begins by defining urban parking availability, influenced not only by the number of parking spaces but also by various regulations. Two main types of parking are identified: private parking on premises and public on-street parking. Additionally, parking permits, which subsidize parking costs for residents, are considered.

To understand the influence of parking availability on household car ownership, a multinomial logistic regression (MNL) model was used. Data from 80,527 urban households over three years from the Dutch National Traffic Survey (ODiN) was analyzed. The study operationalized the number of on-street parking bays per household, parking places on premises per household, and maximum parking costs in the municipality.

The results showed that parking availability in a neighborhood does affect car ownership. Both types of parking were positively related to car ownership, while higher permit costs in a municipality decreased the likelihood of car ownership. However, household composition and income were the most influential factors. The number of driving licenses in a household, closely linked to the number of adults, and disposable income were the strongest predictors of car ownership.

The study also examined the combined influence of multiple parking availability dimensions. It compared car ownership probabilities in three neighborhoods for average households and predicted car ownership levels for a potential new neighborhood under different parking availability scenarios. The findings indicated significant increases in car ownership with higher parking availability, emphasizing the need for balanced parking strategies.

The relationship between parking availability and car ownership is crucial for policymakers, as it supports the formulation of parking regulations aimed at reducing car ownership. Municipalities are advised to develop parking policies that consider the identified dimensions of parking availability to optimize public space use.

Future research should focus on proving causality, improving spatial data, and exploring additional factors of car ownership and parking availability. The study's limitations include the inability to establish causality and the need for more reliable parking data. Further research should also consider attitudinal factors towards car ownership and investigate the relationship between parking availability and car ownership in rural areas.

The study emphasises the importance of understanding individuals' mode choice between train and car to reduce CO2 emissions. It addresses two significant gaps in the existing literature, focusing on the impact of detailed attitudes towards mode use over time and the impact of train travel time components on actual mode use. The study reveals that environmental awareness and cost-consciousness drive increased train usage and reduced car usage, while status-sensitivity has no significant influence on mode use over time. Additionally, travelers place varying importance on different train travel components, with transfer time significantly impacting the modal share of the train. Furthermore, the perceived valuation of train travel time components emerges as a more reliable indicator of individuals' mode choice between train and car than the actual door-to-door train travel time. To encourage a shift from car to train use, the study emphasizes the need to encourage travelers to consider cost and environmental factors related to mode use, as well as the need to minimize transfer time and enhance the appreciation of transfer time

On January 1st 2024, the Environment and Planning Act (EPA) took effect in the Netherlands. This legislation aims to simplify existing environmental legislation for spatial projects by combining them into one. An important pillar of the EPA, specifically of the environmental permit, is public participation. However, the safeguarding of this in the law is limited since its organization is not regulated. In general, the methods for involving the public in environmental decision-making remain contentious, which has previously led to research into principles that define good public participation processes. The majority of the literature assumes that all parties involved in a process share the same principles, while it may be the case that stakeholders hold differing views on what constitutes a good process. In the EPA, a conscious choice was made to prohibit municipalities from imposing requirements on initiators on how they implement participation, the reason for this being enabling local customization. Yet, this uncertainty regarding the best practices for public participation can make it difficult for an initiator to set up participation for their project. This research aimed to gain insight into what constitutes a good public participation process by determining if and how stakeholders characterize this differently, specifically for real estate construction in residential areas. Therefore, the research question was as follows: What perspectives exist on what constitutes a good public participation process for real estate construction projects in residential areas and what characterizes the stakeholders within these perspectives?

Q-methodology was used to extract stakeholders' views on successful participation from the results of a survey. This resulted in the identification of four perspectives, representing four different collective views on good participation shared by several stakeholders. Perspective one, a trusted process leading to influence by the right people, considers participation to be good if trust is created and the input provided actually influences the initiative. The second perspective, a clearly defined process resulting in greater support, has a positive opinion about participation as long as the organized process is very clear, professional, and properly followed, and results in greater support for the initiative. The third perspective, achieving satisfaction with a tailor-made process, believes that participation is good if the process is situation-specific, followed well and there is satisfaction with how it went down, regardless of whether it resulted in adjustments to the design of the initiative. Lastly, perspective four, as long as everyone is heard, feels that participation is good when the design of the initiative is changed according to the input provided by participants who were involved from the start and whose expertise is valued. After the identification of these perspectives, it was investigated whether a relationship could be observed between a stakeholder's characteristic and their perspective. Here, it was observed that civil servants relatively adhere to perspective three more than the other groups.

This research has provided new scientific insights by showing that there are different views on what constitutes 'good' public participation. These perspectives have unique aspects but they also have some similarities. It is recommended that project initiators incorporate important aspects of all four perspectives when designing a participation process for an initiative, to ensure that it is as 'good' as possible. To aid initiators in doing so, a one-pager has been drafted which contains an overview of the important aspects of each of the perspectives. With this, the practical aim of the research - which was to provide support to project initiators for the setup of participation processes - was achieved.

Political and societal attention on transport inequality, transport vulnerabilities and transport poverty risk has been increasing the last five years. The Dutch government has committed multiple large sums of resources to abate the issue, 120 million euros to cancel the increase in price of train tickets, 300 million to cancel the increase in price and preserve the provision of the regional public transit and 6 million to start a pilot of ‘innovative public transport’ in the Zeeland region. A review of the literature on transport poverty risk reveals that past studies analyze the concept using a limited number of pre-selected variables. For example, car ownership and residence area. This carries the risk for misinterpretation as an accurate representation can only be achieved by considering all relevant variables. Such a comprehensive analysis could allow policymakers to formulate tailor-made policies according to the specific problems and needs of certain groups in society. This study aims to include the entire complexity of the concept of transport poverty risk in the analysis. Relevant factors include affordability, accessibility and mobility. Then there are individual differences, and for every individual changing over time, between needs of participating in society and social life. This study aims to incorporate this comprehensive definition in a Latent Class Analysis approach to construct groups of individuals based on the MPN and ODiN national datasets. The different groups of individuals can be then used to indicate the effect of policies on transport inequality. Results show three groups that are most transport ‘fortunate’, with high income, high car ownership, high accessibility and the potential to use different travel modes. Results also show one group with substantial transport vulnerabilities due to higher car dependency, lower car ownership and lower household income. A policy thought experiment is established through the application of mobility policy packages of three political parties. Car policies that reduce costs per driven kilometer such as reduction in excise tax will increase transport inequality due to the three groups that are the most transport ‘fortunate’, which also have the highest car use. This study provides the scientific community with a comprehensive definition and conceptual model of transport poverty risk and for policymakers contributes a comprehensive understanding of transport inequality in the Netherlands. Future research needs to focus on identifying specific minority groups that are not or less captured in the national datasets. Transport problems are experiences by an individual, while transport inequality is represented by the differences in the extent of transport problems between certain individuals or groups. In this case, certain groups in the Netherlands. This calls for the need to differentiate the government levels at which transport inequality and transport problems are addressed. While this study presents the transport inequality at the national level, municipalities will be able to identify specific individuals and neighborhoods who are more prone to transport problems.

To ensure transport models are effective tools for planning, they must not only adequately explain current travel choices but also maintain predictive accuracy over forecast horizons while being computationally feasible for practical use. One simple approach to achieving this is by including behavioral theories in transport models to improve the behavioral representation of models.
One such theory is Spatial Competition and Agglomeration Effects (SC&AE). This theory examines how opportunities present nearby influence the attractiveness of a destination. This influence can be either positive (Agglomeration) or negative (Spatial Competition). Although SC&AE is widely recognized in the literature for enhancing the explanatory power of computationally simple Multinomial Logit (MNL) destination choice models, its impact on the temporal transferability of these models remains unexplored.
This study assesses the impact of SC&AE on MNL destination choice models for home-based maintenance, work, and education trips in the Metropolitan Region of Amsterdam on a 5-year short-term forecast horizon (2018-2022) using Dutch National Travel Survey Data (ODiN). The findings indicate that SC&AE has a positive but limited effect on the temporal transferability of these models. This impact decreases with decreasing traveler autonomy and ease of switching to alternate destinations. In terms of percentage improvement, Transfer index metric shows an exaggerated impact (35.2% - 286.36%) while the other performance indicators, such as Fitting Factor (1.1% 3.6%) and % Correct predictions (0.14%-2.05%) confirm that the impact is limited.

The Circular City Index (CCI) project for Barcelona, conducted in partnership with the Barcelona Supercomputing Center, aimed to assess urban circularity across multiple districts. Utilizing the City Circularity Index model as introduced by Muscillo et al. (2021), this research adapted the framework to Barcelona’s unique urban context, focusing on the four main components: Digitalization (D), Energy, Climate and Resources (ECR), Mobility (M), and Waste (W). The project benchmarks city performance against Sustainable Development Goals (SDGs) to guide strategic planning for a sustainable and resilient urban environment. Each district’s performance was measured through tailored Key Performance Indicators (KPIs) reflecting accessibility, infrastructure, and environmental data. Spatial and statistical analyses were applied to assess parameters such as waste collection accessibility, air quality, and renewable energy coverage. Data was gathered from open-source and municipal datasets, complemented by geographic analysis for a precise district-level understanding...

When a crime is committed, the task of the regional control rooms is to use the available situational information to identify the possible movements of a fugitive suspect to use in positioning police units. Currently, the methods to do this rely heavily on the intuition and experience of the control room employees and the speed of technology and communication. To reduce this reliance, there is an increasing demand for methods to objectively determine the tasks to undertake in a fugitive escape situation. Two methods to do this are under development which help in determining the location of a fugitive suspect and the optimal positioning of police units to these locations. However both of these methods
still require a demarcation of the possible routes that a criminal fugitive will take to be used effectively. Therefore, this study explored the possibility of making likelihood estimations of possible escape routes.

Because of a lack of reliable data, alternative methods to determine likelihood of escape routes are needed. A method that could be used is simulation. Simulation of human behaviour is however complex and careful consideration of the
assumptions in such a model is needed to be able to have a high level of confidence in the resulting outcome. To do this, it is important that the theoretical background on which behavioural factors influence the criminal fugitive route-choice behaviour is complete and it is known how these factors affect the resulting routes. This is the knowledge gap addressed in this study.

To address this knowledge gap, the question of what effect behavioural factors from criminal route choice behaviour have on escape routes will be answered. This is done by determining which main factors influence criminal fugitive route-choice behaviour and how these factors influence the resulting escape routes. The method used to answer these questions is a combination the development of
a theoretical background based on a literature review of existing research and expert opinion and a quantitative sensitivity analysis on a simulation model.

Because of a lack of research on criminal fugitive route-choice behaviour, it was necessary to use literature from the following research fields to find relevant topics: criminal decision-making, rationality in decision making and route-choice decision-making. From the literature in these fields, it was found that many different personal and crime characteristics exist, but it is unknown how these affect route choice behaviour. Next to this, it was found that rational decision-making cannot be assumed for the criminal situation and that bounded rationality needs to be considered. Lastly, from the route-choice decision-making literature, it was found that many different route-choice factors are relevant. The following list of route-choice behavioural factors was found: obstacle avoidance, risky behaviour, traffic avoidance, route distance and maximum speed, and preference for main or residential roads. For the route choice decision-making modelling methods, the following relevant topics were found: cost benefit
calculations, short or long-term goals, emotional state, choice prioritisation and timing. These two lists of factors should be considered when conceptualising criminal fugitive route-choice behaviour.

In the conceptualisation phase of this study, it was found that while many different suspect and crime characteristics might affect suspect behaviour, no specific behavioural profiles could be used to conceptualise route-choice behaviour. Therefore it was chosen to conceptualise the behaviour by creating
dynamic strategy profiles based on behavioural route-choice factors. From the list of behavioural route choice factors to include in these strategy profiles, it was found that they can be described as either a preference or avoidance of road characteristics. The road characteristics seen to be avoided are cameras, obstacles, one-way roads and high traffic. The preferred road characteristics are a high number of lanes, residential roads, a high maximum speed and short roads. Next, it was found that there is a distinction in decisions based on long or short-term goals, which require either low or full network familiarity. For general route-choice behaviour, the conceptualisation of a route choice as a whole route between an origin and destination location was found to be most appropriate. When considering the rationality of the decisions made for the route choices, it was found that there is too much uncertainty and ambiguity in the considered bounded rationality conceptualisation to use them for aconcrete route-choice conceptualisation. Therefore, alterations to the assumptions of rationality are
used to concentualise this. Finally, the emotional state of a fugitive is included in the conceptualisation through the possibility of changing route-choice strategies. This conceptualisation is further used to describe the general criminal fugitive route-choice behaviour in this study.

To measure the influence of the behavioural route-choice factors in the conceptualisation, a route cost model was developed. In this model, the cost of a route is calculated using the characteristics of the edges in a road network. Based on this model, an experimental design is defined including a case study and sensitivity analysis to find the quantitative influence of route-choice behaviour on route metrics describing differences in escape routes through route length and overlap.

When evaluating the results of the case studies and sensitivity analysis, it was found that the influence of behavioural route-choice factors on routes depends on the origin and destination locations and the distribution of edge characteristics over a road network. Next to this, it was found that there were no
behavioural profiles leading to routes with specific characteristics and that in practical application, a broad set of strategies should be included when finding important locations is a road network to use for positioning police units. To do this, a method of using heat maps to find these locations was proposed.
This method combined with the route cost model described in this study was found to have high applicability but more research needs to be done on the usability of this method.

From the findings of this study, it can be concluded that criminal fugitive route-choice behaviour is complex and that different possible conceptualisations exist to be used for different purposes of studying general route-choice behaviour or specific behavioural factors. This affects the ability to measure the influence of behavioural factors on the resulting routes. Limitations were found on the
measurement techniques used in the quantitative method to measure differences in routes which reduced the ability to interpret the resulting influence of behavioural factors on the routes. This showed that to find the influence of behavioural factors on the routes, the results of this study can show that the route-choice factors defined in the conceptualisation affect the routes but that more qualitative research is needed to find how these factors influence the resulting routes.

To conclude, the findings of this study add to current research by showcasing the complexity of modelling route-choice decision-making and human behaviour in general and the many considerations that need to be taken when doing so. Next, it shows the difficulty of using quantitative and qualitative methods on the data type of routes to determine relations between factors influencing route-choice behaviour and resulting routes. And lastly, it adds to the current literature by developing an overview of the factors influencing criminal fugitive route-choice behaviour that need to be considered in the simulation of fugitive escape routes.

Passenger traffic by car is regarded as one of the main contributors to energy consumption and emission in the transport sector. Car dependency and limited shifts to more carbon-friendly alternative travel modes in industrialised countries play a major role in maintaining unsustainable mobility systems, despite governments' increased attention and effort in enhancing multimodal travel behaviour. In other words, having a diverse mode usage as a travel user. Compared to travellers who only use the car, multimodal travellers are likely easier to shift to more sustainable and health-enhancing modes, such as the bicycle and public transport, when applying policies. However, how (multi)modal travel patterns developed over time and the determinants of being multimodal are not often researched in combination with measuring multimodality and showing corresponding travel patterns. A Latent Class Cluster Analysis is performed once for 2010-2017 using cross-sectional data from the Dutch National Travel Survey (OViN) to measure multimodality whilst capturing distinct travel user groups per year, based on the frequency of travel mode use. Socio-demographic, mobility resource and built-environment variables are included as potential determinants of belonging to a specific group. The main results are that overall mobility patterns of the captured travel user classes were hardly subject to change. Moreover, only the smallest two out of five identified classes have travel behaviour with a higher degree of multimodality. Besides, the likely strong effect of owning mobility resources or not (e.g., a licensure, household car, company car, or household bicycle) on being likely in a car-dependent or a multimodal travel user group is shown. Most remarkably, our findings add to the existing knowledge by revealing that company car ownership plays a significant role in being a car-dependent travel user. Based on our results, identified policy directions include, but are not limited to, affecting mode choices of employees (owning a company car) via employer-based programs to incentivise them to use the bicycle or public transportation. Nevertheless, the knowledge about several travel user classes comprising multimodal travel patterns can be extended in several areas, most notably, by including attitudinal factors which could be tracked longitudinally on the individual level, such as perceptions about willingness to use travel modes, to acquire a more profound view about what strives people to behave in a certain way over a more extended period.

Rising food commodity prices pose a severe threat to vulnerable populations globally. While market fundamentals contribute to this increase, excessive speculation by financial investors is believed to play a significant role. This study aims to examine the relationship between financial speculation and agricultural commodity prices between 2000 and 2023, focusing on eight commodities: cacao, coffee, corn, rice, wheat, soybean meal, and soybeans. Additionally, the research reviews regulatory measures to mitigate the impact of financial speculators in food markets through expert interviews.

The first part of this research examines the relationship between spot and futures prices and between speculation and prices quantitatively by means of econometric statistical analyses. The econometric analyses consist of descriptive statistics, correlations, and linear Granger causality tests. Monthly data are gathered from the Food and Agricultural Organization (FOA), the International Monetary Fund (IMF), and the Commodity Futures Trading Commission (CFTC). The Granger causality tests are performed both over the whole period of research (2000-2023) and over a period with a certain price peak (2020-2023), as speculation comes often in peaks.

The findings reveal a strong relationship between spot and futures prices, except for rice, specifically futures markets seem to dominate spot markets. Granger causality tests demonstrate that futures prices can be utilized to forecast spot prices the commodities.

Regarding the relationship between prices and speculation, the descriptive statistics and correlations show weak evidence. The analysis reveals that prices and price volatility Granger cause speculation rather than the opposite, except for coffee, which exhibits bi-directional linkages. The research does not provide definitive evidence that speculation causes higher prices. However, there are issues with the "measurement" and classification (non-commercial vs. commercial traders) of the speculation variables. That ’flawed’ data could be a reason why this research cannot support the hypothesis.

Despite the inconclusive findings, the study supports the notion that market fundamentals alone do not determine prices. Excessive liquidity and the presence of more speculators than hedgers contribute to rising prices in commodity food markets, disrupting their functioning. Expert interviews highlight the need for effective regulation to address speculation. Policy recommendations include enhancing market transparency, stronger regulation of futures markets, limiting market access to actors with genuine commodity interests, and implementing global windfall taxation and a "food speculation tax" to combat financialization.

The detrimental effects of speculation in food-importing developing countries are not temporary and exacerbate existing challenges, including increased import bills, trade balance deterioration, currency depreciation, and inflation. Banning open capital accounts is recommended to mitigate the destabilizing impact of global capital flows on these economies.

The study acknowledges limitations with the use of a statistical approach. Especially since time series models, like Granger causality, cannot claim causality. This research is based on monthly data, so a lot of information within weeks (or even days) is lost. In the case of Granger causality testing, the external validity is limited because the findings only pertain to the data obtained from a particular exchange, for a particular period, and also of a particular form. Moreover, this research does not capture non-linear effects (since it uses linear Granger causality tests). Future research therefore should adopt a multi-method approach, preferably using daily data, to capture non-linear effects and enhance external validity.

The aim of this study was to identify relevant relations between the adoption of electric vehicles at public charging stations and the characteristics of the neighbourhood through a case study in the Netherlands. The study started with creating such measurements of EV adoption in the transaction data of public chargers in the MRA-Elektrisch region. Subsequently, the relevant variables found in the literature study on the drivers of electric vehicle adoption have been translated into a set of neighbourhood characteristics using open data sources. Based on the identified relationships, a further analysis was conducted in order to develop policy recommendation for the municipality. It is suggested that these municipalities increase the capacity of public charging points in high-populated regions, as both the relative and absolute numbers of residents were positively correlated with the occupancy rate. Furthermore, it is recommended to prioritise the neighbourhoods with high potential and a high potential EV user per charger ratio that is high under current conditions for the placement of additional chargers.

In light of growing environmental challenges, the need to reconsider how we approach personal transportation is becoming increasingly evident. A shift from a private car-focused mobility system towards a more sustainable and equitable transportation system is desired. Car sharing is considered a means to achieve this. However, car sharing use and its related impact are not entirely understood, as many studies do not consider the car sharing motives of individuals and treat the car sharing population as a homogeneous group. This study aims to reveal distinct car sharing usage profiles to gain a thorough understanding of car sharing utilisation and its related impact. Six distinct user profiles are uncovered using a Latent Class Cluster Analysis (LCCA) based on station-based car sharer data gathered through an online survey (N=1281). This segmentation underscores the diversity in car sharing motives and reveals that the impacts associated with car sharing use differ across the identified user groups. The cluster profiles show that environmentally motivated car sharers use the shared car as a complete replacement for their private car, causing a substantial decrease in car ownership and use. More utility-focused car sharers, and especially initially carless individuals, show a less substantial decrease in car ownership and sometimes even an increase in car use. This study concludes with recommendations based on these findings and discusses its limitations.

The Dutch Ministry of Infrastructure and Water Management is committed to enhancing the accessibility and sustainability of the Dutch mobility system. Working from home (WFH) has been discussed as a policy lever to decrease commute travel and, thereby, congestion for decades. During the COVID-19 pandemic, this discussion regained momentum. WFH was a key factor for the decrease in commute travel by public transport and car in the Netherlands, which decreased congestion. Against this background, the Ministry wants to stimulate WFH post-pandemic to retain some of the sustainability and accessibility benefits identified during the pandemic. WFH can enhance the accessibility of the mobility system thanks to reduced commute trips during peak hours.
Although the stimulation of WFH may be a policy lever to increase the accessibility of the mobility system, the full effect of WFH on overall personal mobility is unknown. Studying pre-, during and post-COVID-19 measures data, this study assesses the impact of a change in WFH on activity-travel patterns. Commute travel, non-work travel, and mode use together define activity-travel patterns in this research. The main research question is as follows: How did changes in WFH influence activity-travel patterns during the pandemic in the Netherlands? To answer this question, data from the Netherlands Mobility Panel (MPN), collected between 2019-2022 is used. Latent class analysis is used to identify seven distinct activity-travel patterns. Next, two latent transition models are estimated. The first model, for 2019-2021, studies the effect of an increase in WFH on transitions in activity-travel patterns. The second model, for 2021-2022, studies the effect of a decrease in WFH on transitions in activity-travel patterns.

The application of LTA, a longitudinal clustering method, sheds light on complex transition behaviour in the population during- and post-COVID-19 measures. The results show that activity-travel patterns before a change in WFH and the level of an increase in WFH matter for the impact of WFH on transitions. Depending on the initial class membership, differences in transitions emerged. To a certain extent, all effects of WFH on travel, substitution, complementarity, modification, and neutrality exist in the population, but these depend on initial class membership. The results for an increase in WFH are significant, while results for a decrease are not. For the time being, these findings do not exclude the existence of structural changes in working and travel behaviour. Significant effects on transitions appear for a small and a large increase in WFH. After an increase in WFH, most classes are more prone to take up another activity-travel pattern. In both cases, most classes have an increased probability of transitioning towards the low mobility class, which underpins more sustainable patterns. Nonetheless, more diverse results appear for a small increase in WFH, and significant transitions towards other car profiles appear. Analysing the impact of a small increase on class sizes confirms this. Overall, the car class sizes remain relatively high after a small increase in WFH. Thus, an increase by 1-3 days working from home may not lead to more sustainable activity-travel patterns in terms of trip rates per purpose and mode. Nevertheless, it needs to be kept in mind that this study only analysed trip rates per purpose and mode, a small increase in WFH may still lead to a peak-travel reduction, which benefits accessibility. Further analyses should investigate peak-travel to conclude on the accessibility benefits of small increases in WFH. At the same time, governments and employers should take measures to counter-act low mobility associated with WFH which is detrimental for physical health.