Public transport disruptions can significantly affect passengers' travel behavior, yet little is known about how route choice preferences evolve after a disruption ends. This study investigates how passengers' route choices change in response to a planned disruption, using Automated Fare Collection (AFC) data from the Washington DC metro system. Unlike previous research that often relies on stated preference surveys or focuses only on the disruption period, this study analyzes several months of pre- and post-disruption behavior to assess whether changes in preferences persist over time.

Descriptive analysis reveals that although travel times and wait times remained relatively stable, passengers shifted from preferring direct routes to favoring routes with more transfers but shorter in-vehicle times after the disruption. Discrete choice models, including Multinomial Logit and Mixed Logit models, were estimated to explore these shifts, but the results showed unexpected coefficient signs, likely due to strong multicollinearity between travel time and transfers, and the presence of dominated alternatives. While the Mixed Logit model improved the model fit slightly, practical interpretability remained limited.

The findings suggest that disruptions can lead to lasting behavioral changes, with passengers reassessing their travel options rather than returning to previous habits. Although AFC data is valuable for detecting such shifts, it alone is insufficient to fully capture the factors driving route choice behavior, highlighting the need for complementary perception-based data in future research. From a policy perspective, understanding these behavioral adaptations can help transit agencies design better service recovery strategies that sustain ridership after disruptions, supporting broader goals of promoting sustainable urban mobility.

This thesis explores the relationship between urban fragmentation and the spatial segregation of non-EU immigrant communities in Europe. While previous research has linked infrastructural barriers to ethnic group boundaries in American cities, this study argues that such findings cannot be universally applied due to unique historical contexts in different regions. The study aims to not only identify connections between urban fragmentation and segregation but also to contextualize these connections within the local dynamics of various European cities. We employ a mixed-method approach. The quantitative component involves constructing spatial patterns of urban fragmentation using OpenStreetMap data and segregation patterns using data from the European Commission's Data for Integration (D4I) initiative. These patterns are compared using mutual information to assess their similarity. Synthetic urban fragmentation patterns are also generated to ensure observed similarities are not the result of random chance. The qualitative component involves examining the local contexts of selected cities to understand the specific factors influencing segregation. From the 106 cities analyzed, only 33 showed statistically significant relationships between urban fragmentation and segregation patterns, with 26 showing positive and 7 showing negative correlations. These findings indicate that the relationship between urban fragmentation and immigrant segregation is not a generalizable phenomenon in Europe. The city-level analysis of nine cities across seven countries reveals that factors such as urban decay and housing quality, rather than infrastructure, are more closely associated with immigrant concentration. Future research should investigate segregation patterns among other social groups and explore broader impacts of infrastructure on vulnerable populations beyond urban fragmentation.

Recent studies have highlighted the significant impact of built environments (BE) in residential neighbourhoods on well-being, focusing on correlations between micro-scale BE features—such as trees, grass, fences, and bikes—and specific well-being aspects like physical health, social interaction, or perceptions of safety. However, these studies often fail to comprehensively explore how these features influence residential preferences or provide clear design guidance. To address this gap, examining residential location choice (RLC) datasets can offer valuable insights into how various micro-scale BE features affect the attractiveness of residential neighbourhoods and residents' well-being. This thesis proposes a semantic Computer Vision-enriched Discrete Choice Model (CV-DCM), which uses a panoptic segmentation model to quantify micro-scale BE features and integrates with traditional discrete choice models.

A manual evaluation of 400 masks generated by the semantic computer vision model ensured accurate quantification of BE features, enhancing choice modelling interpretation. The choice modelling results highlight the specific impacts of various micro-scale BE features on RLC, providing valuable insights for urban planners. For instance, restricting unattractive features like motorcycles in residential neighbourhoods and planting or maintaining more trees are the most attractive among vegetation. By pinpointing the most influential elements, the study facilitates the cost-effective restructuring of residential neighbourhoods to boost their attractiveness and enhance residents' well-being.

This research uses high resolution satellite images in combination with an unsupervised Convolutional Neural Network Autoencoder to identify features that can be used to cluster different built environment typologies. Previous remote sensing research uses ground truth data which for some areas is not available or needs manually labeled training data. This research attempts to circumvent the issue of information scarcity in order to create a methodology that can be applied on any city as long as satellite images are available. From the resulting clusters, clusters can be selected which represent areas with high levels of deprivation which in turn can help identifying the deprived areas.

Public transport (PT) plays a vital role in commuting billions of travellers in cities all over the world, providing a mode that is both sustainable and accessible. Metro networks are especially apt at this considering their high-capacity and high-speed operation in urban environments. Comparing different metro networks to one another is a suitable manner for transport planners to gain insights into the characteristics of their networks and which areas of improvement exist. In the field of network science, metro networks have been studied extensively in recent decades. While this provided many new insights in the field of network science, the practical relevance for the field of transport science often remained limited. This limited relevance is primarily caused by the lack of realism of the network representations used, not incorporating the actual operation and service that the network provides. As such, this study proposes a comprehensive comparison of metro networks worldwide including service information. This comparison study includes service characteristics in the form of the total travel time indicator for shortest path calculations, which is a combination of the in-vehicle time, waiting time and number of transfers. The median of this total travel time is taken for each network and compared to that of other networks. This metric in turn is contrasted with network- and city-related characteristics in order to explore relations between these factors and to explain the patterns discovered. From this analysis, it is revealed that the travel time increases with network size. The indicator that is especially apt at explaining the differences in total travel time between networks is the number of stations combined with the average direct station distance. The total travel time methodology applied in this study shows significantly different results to other commonly used methods that rely only on in-vehicle time or hops to calculate shortest path travel times. The waiting time turns out to be the main contributor to these significant differences. Future studies can expand on this by considering other network science indicators and looking further into local indicators. In addition, the methodology could be expanded with more detailed transfer information and other PT modes.

In this thesis an investigation is performed into the effect of different speed limits on freeway capacity. From literature, much is known about the variety of factors that affect capacity, but the exact effect of the speed limit on capacity is not yet clear. In recent years, several speed limits changes have taken place at multiple two-lane freeway bottlenecks throughout The Netherlands, which makes it possible to compare effects of different speed limits at the same location. To evaluate the effect of the speed limit on capacity, the Product Limit Method has been applied to identify breakdown flows and generate capacity distributions, which could subsequently be compared for different limits. In the comparison of capacity distributions under different speed limits, it was found that significant changes in capacity had occurred, but that no uniform direction of the effect could be found. Subsequently, to control for location specific factors and other variables, Fixed Effects regression has been used to determine the effect of the speed limit on the breakdown flow. It was found that the breakdown flow under the 120 km/h limit was significantly higher than under the 130 km/h limit (in the range of 60 to 190 vehicles per hour) and that the breakdown flow under the 100 km/h limit was, in some cases, also higher than under the 130 km/h limit. In addition to this, it was found that a significant positive relation exists between the height of the speed limit and the fraction of flow in the passing lane. Moreover, it was shown that the relation between the fraction of flow in the passing lane and the level of breakdown flow was best represented by a quadratic relation, which could indicate that an “optimal” distribution of flows may exist. Given the results of this thesis, it is posed that a change in the speed limit is likely to affect capacity primarily through altering the lane flow distribution and that it will depend on the layout of a freeway location what the optimal lane flow distribution is and which speed limit leads to this optimal lane flow distribution.

The total investments in the global energy system amounted to 1.85 trillion in 2018 [1]. Globally over 400 exajoule [2] was provided to energy consumers. The energy consumption is closely related to the emission of green house gasses [3] [4] [5]. In the Kyoto and Paris agreements the international community formalized the intention to reduce the emission of green house gasses in an effort to mitigate global warming [6] [7]. Economic progress has long been linked to increased energy consumption [8] [9]. Mitigation of green house gasses whilst facilitating economic growth poses one of the major challenge of the twenty first century [10] [11]. Development of climate policy and business strategy that facilitates both the mitigation of global warming and economic growth requires understanding of the energy market. In an effort to expand the understanding of the energy market we examine the historic effects of a country’s wealth on the country’s energy mix by applying a multinomial logit choice model for energy carriers to various sectors on a global scale. We find evidence for ordered wealth effects in sector categories Heavy Industry, Agriculture & Other Industry, Passenger Transport Rail, Freight Transport Rail, Residential Heating & Cooking, and Services.

The traditional transport hubs are shaping into the idea of the mobility hubs with the advent of multiple vehicle-sharing forms, such as bike-sharing and car-sharing. Also, a gradual shift in the culture of consumption towards more usage and less ownership, as well as the shared economy supported by internet platforms and mobile apps, allows easy access to multiple daily mobility choices, especially in urban environments. The Mobility hubs seek to merge conventional public transport with these new shared services which have the potential to serve as a solution to the first/last mile problem within the public transport and will allow operators increase their ridership. Transport demand models are used to forecast future travel demand but consider the same travel behaviour as that of today. Mobility Hubs have the potential to change the travel behaviour of travellers and travel demand models should not only be able to forecast the future travel demand but also take into consideration the potential changes in travel behaviour due to the mobility hubs. This study deals with a microsimulation travel demand model, OCTAVIUS and to identify the extent to which such a microsimulation demand model can capture the travel behaviour associated with the future mobility hubs.

The built environment needs to become completely sustainable before 2050. Hydrogen is seen as a suitable resource to help in achieving this goal. In a hydrogen system, the predefined high level of reliability, as stands today, affects the affordability of the system. The households are the users and payers of the system. The method used is Discrete Choce Modeling. This research identifies a gap between the Willingness To Pay (WTP) for reliability and the current cost of this level of reliability. Reliability should not be a goal on its own. The maintained level of reliability should be optimized together with the affordability of the system.

The acute obstetrics (AO) sector in the Netherlands faces two challenges. The increasing difficulty to find personnel and the closing down of several locations have put a major pressure on the existing AO locations. This has led to questions about the quality of the spatial distribution. The measures currently in place are based the travel time to the closest hospital. However, the recent developments have forced AO locations to say ‘no’ when asked to take care of a woman in labour. In other words, the unavailability of beds in the nearest hospital. The current accessibility measures cannot capture this and therefore the concept of substitutability is introduced. The mathematical model for substitutability offers a possibility to assess the effect of elimination of the preferred option. The input parameters for the substitutability calculations have been derived from a stated choice experiment among 151 female residents of the Netherlands. The cost and time parameters were estimated using a multi nominal logit model. A comparison between minimum travel time, LogSum accessibility values and normalised substitutability values guided the assessment of the spatial distribution. This comparison provided insights in new critical areas of the network. The study serves as an exploration of substitutability and it has proved it to be a promising concept for future research.

This research investigates the travel behavior of users of an integrated mobility platform. Passive revealed preference data is collected from a smartphone application for integrated mobility. This data, consisting of the choices in a multimodal journey planner, allows evaluating travel behavior in integrated environments with high ecological validity. As such data has not been used yet for conventional travel behavior researches, this research also experiments with the application of conventional methods to this type of data. It is found that for the specific integrated platform, mostly public transport users engage with the platform. Public transport is dominant within the planned trips of users, and extreme situations are required for users to deviate in the mode choice between PT and non-PT alternatives. Furthermore, it is shown that discrete choice modeling is a suitable method for analyzing for this type of data.

Studies have shown that car-sharing has a lot of potential. Because of this, municipalities require a tool to evaluate the effects of car-sharing. This research has developed a mode choice model that includes a car-sharing alternative considering an activity-based framework, for the city of Rotterdam.

This research combines two relatively new additions to the field of discrete choice modelling: sequential best worst discrete choice experiments (SBWDCE) and random regret minimisation (RRM) modelling, with the hope of developing a more behaviourally realistic choice model. SBWDCEs are able to gather a larger number of stated choice observations from fewer respondents, while RRM models challenge the notion of fully compensatory behaviour implied by the traditional RUM model and suggest that consumers choose to minimise regret. According to image theory, best and worst choices are not made with the same kind of decision rule, so accounting for that variability using a RRM model would produce a more realistic model with better model fit. Estimating the combined model proves that people do in fact use a compensatory decision rule when selecting the best alternatives and a semi- to non-compensatory decision rule when selecting the worst. The results also show that the way choice set size variation is accounted for can greatly impact the scale parameters, as these and the choice set size constants are inversely related. Although a better model fit was achieved, using best-worst tasks is contested and researchers also warn against the lower reliability of additional choices in the same choice set. Nevertheless, SBWDCEs provide great benefits in fields with small population sizes and can potentially help in obtaining higher quality prior parameter values for use in efficient experimental design generation.

In this report an analysis is presented on the impacts of weather codes on travel behavior. Loop detector data of seven segments in different provinces in the Netherlands are analyzed with regression models with autoregressive errors. Weather codes were found to be significantly influencing travel demand. Particularly codes orange for slipperiness and snow, and codes red for slipperiness, snow and wind yield significant results for most road segments. Furthermore, some trip rescheduling behavior was observed. The unreliability of the previous weather code was found to reduce the impacts for the next weather codes. The analysis of Twitter data was not useful to confirm hypotheses on the incentives for changing travel behavior. As this study is the first revealed preference study into the effects of weather codes, lots of research gaps remain. Secondary roads were not included in the study, which might be affected in other ways than highways. Furthermore, interaction effects between time and weather effects might explain some of the unexplained variability of the model results.

Crowdshipping has emerged as an avenue to provide sustainable option for the
future of urban package delivery. The main challenge in crowdshipping realm is to maintain sustainable network of customers (demand) and couriers (supply). To help resolving the challenge, this research offers a comprehensive approach to examine market potential of bicycle crowdshipping through concurrent evaluation of its supply and demand side, which thus far has been absent from the literature. By deploying discrete choice models, the study generated supply, demand, and market equilibrium model to aid crowdshipping platform in understanding influential service attributes and its effect to the market penetration level. The research concluded adjustable delivery time window as a key differentiating attribute to entice customer preference towards crowdshipping. According to the model. demand side of bicycle crowdshipping was found to be highly elastic towards price, while oppositely supply side of crowdshipping is rather inelastic towards monetary compensation.

Congestion and emissions have lately been termed as one of the major challenges for the urban traffic network through various governmental bodies. In this work, a potential solution to this, namely a functional combination between platooning and traffic- adaptive intersection control is first developed and then evaluated. The evaluation is concerned with the expected congestion and emission savings on a 6-km- long provincial road over a period of 60 minutes. For the purpose of evaluation a VISSIM simulation is em- ployed, calibrated and executed. The simulation results show that the proposed combination of platooning and JUNO, a traffic-adaptive intersection controller, allows for emission savings of 7.85 g/km and an increase in average velocity of 1.04 km/h when compared to conventional cars and model-predictive control. The work is embedded in a high-level context and concluded with a policy-development framework that can help to promote the proposed technical design.

In the last years, Floating Car Data (FCD) has risen as a technology that has the potential to replace legacy sensors, reduce associated installation and maintenance costs and offer new possibilities in the field of traffic management. FCD refers to the use of sources such as in-vehicle systems with network connectivity or road users’ mobile phones to collect relevant traffic information (e.g. vehicle speed or intensity). There is extensive literature on the feasibility of using FCD for traffic management purposes and on the added value of fusing legacy sensors data with FCD. These studies have been mostly experimental and a literature review shows that there is no academic work on how to deploy and integrate legacy sensors data and FCD in an actual live setting. This research aims to fill that gap by designing a flexible ICT Architecture that integrates FCD into Rijkswaterstaat’s Traffic Management and Information Systems.

Taking an architecture point of view to address this challenge is not only novel but also appropriate because it provides a comprehensive view of what changes need to take place at different levels of the organization (business, information systems and technology) for this to happen. This research follows a design science research approach, where four main phases can be distinguished: (1) Problem formulation, (2) Building, (3) Reflection and learning and (4) Formalization of learning. The Building phase is further subdivided into the specification of design principles and requirements and the development of the solution ICT Architecture.

The outcomes of this research are relevant not only to Rijkswaterstaat but also to other organizations looking to integrate FCD in their traffic systems, to design science researchers and to ICT architects.