MSc thesis project @ CityAI Lab

We are always looking for highly motivated TUD students who want to work with us on state-of-the-art research. Note that slots for supervision usually fill up quickly, especially in Q3.
The ideal student for an MSc project at the CityAI Lab has the following profile:

Interested? Please send us an email with an introduction and brief motivation, clarifying:

Current opportunities

The table below provides research directions that would fit within CityAI Lab and the person to get in contact with.

Research direction Contact person
  • Extending discrete choice models and theory with images, see e.g. this paper
  • Mapping liveability in the city (opportunities to work with/at the municipality of Rotterdam)
    		•  Sander van Cranenburgh
  • Modelling multi-modal passenger transport networks by combining advancements from behavioural sciences, operations research and complex network theory
    		•  Oded Cats
  • Modelling traffic behaviour forecast the impacts of automated driving, connected traffic and external disturbances on traffic flows
    		•  Simeon Calvert
  • Dynamics of travel behaviour using panel data analysis techniques
  • Using panel models to shed light on the factors driving changes in (sustainable) travel behaviour patterns
    		•  Maarten Kroesen
  • Utilising image embeddings to study human perception of and behaviour in the urban environment

    		•  Francisco Garrido-Valenzuela
  • Machine learning approaches to study noise pollution and soundscapes
  • Perception-focused soundscape mapping with noise sensors
    		•  Lion Cassens
  • Data-driven analysis of segregation patterns in cities

    		•  Lucas Spierenburg
  • Data analysis on the interactions between Automated Vehicles and human road users in urban traffic. Data has been well processed and handy to use.
  • Safety evaluation of multi-vehicle interactions on highways. The evaluation will be based on a framework we are publishing. This project requires experience with pytorch.

    		• Yiru Jiao
  • Understanding the choice modeller's decisions and their impacts on modelling outcomes

    		•  Gabriel Nova

    Ongoing projects

    Unveiling preference-based liveability in Rotterdam, exploring XAI techniques for computer vision-enriched discrete choice models

    Keeping cities liveable is increasingly a challenge with increasing urbanisation. Most literature on liveability concerns perceived liveability: they study what factors, e.g. physical, social, or economic, impact peoples' liveability perceptions. Perceptions are subjective interpretations of sensory stimuli, which may influence but do not necessarily determine individuals' choice behaviour. In contrast, I study preference-based liveability. Preferences govern what people choose and do. More specifically, I aim to provide explanations for predictions made by a recently developed computer vision-enriched discrete choice model. This model produces preference-based liveability scores, taking street-view images as inputs. To achieve this, I will assess the efficacy of eXplainable Artificial Intelligence (XAI) techniques, such as LIME and Shapley. Also, I will investigate how the combination of computer vision-enriched discrete choice models and XAI can effectively be used by municipalities to support policy-making.

    Bastiaan Bakker

    Master student

    Investigating the impacts of built-environment features on residential location choice behaviour using computer vision techniques

    Visual stimuli (i.e. images) play a crucial role in many multi-attribute decision situations, such as residential location choice behaviour. On housing platforms like Funda, street-view images showing the surrounding Built-Environment (BE) near residences under consideration offer the most direct means for individuals to understand their potential living environments. However, BE features are hard to quantify and incorporate in traditional discrete choice models as most are amorphous (i.e. without a clearly defined shape or form). This thesis proposes quantifying BE features in images into pixels or instances using panoptic image segmentation models. These pixel and instance counts, in turn, are used in a traditional discrete choice model to explain choices collected in a recently conducted residential location stated-choice experiment. Studying the extent to which visual features of the BE affect residential location choices and how people make trade-offs between BE features and numeric attributes, like cost and travel time, will offer valuable insights to urban planners.

    Lanlan Yan

    Master student

    Operationalising liveability using urban embeddings from a transport policy perspective

    Transport policy is increasingly studied through the lens of broad prosperity and liveability. The Leefbaarometer offers an operationalisation of liveability, combining indicators that describe geographic areas and peoples' subjective valuations (perceived liveability). However, creating, updating, and collecting the data behind these indicators and valuations is labour-intensive. Machine learning could streamline this process and reduce the effort involved. Urban representation learning transforms complex data into simpler numerical forms for analysis. My thesis explores the creation of urban representations using diverse data sources, including street-view images and road network characteristics. It also considers how the connectivity of multi-modal transport networks (walking, cycling, driving, and public transport) can guide the learning process. These representations will be examined to determine their ability to predict Leefbaarometer scores and their components accurately.

    Bert Berkers

    Master student

    Urban fragmentation and spatial segregation patterns in Western-Europe - A similarity analysis and identification of general trends

    Transportation infrastructures play a paradoxical role in the urban space. While they facilitate the movement between certain points, they also generate fragmentation and separation. Highways, railways and congested roads, to name a few, produce barrier effects that limit the movement and interaction of people at the local scale. A certain degree of urban fragmentation is inevitable, but there is a question to be asked about its severity and the groups impacted by the resulting severance. My thesis project aims to explore the relationship between urban fragmentation patterns and residential segregation patterns of people with non-EU backgrounds across major cities in Western Europe. Spatial and statistical analysis are at the core of this research, with the purpose of identifying general trends and expanding our knowledge on the role of transportation infrastructures in segregation.

    Esteban Ralon

    Master student

    Unmasking cycling infrastructure safety in Rotterdam through street-view images and discrete choice models

    It is well-known that cycling infrastructure's safety plays an important role in peoples' decisions to make a trip by bike or another mode (e.g. car). As such, safe cycling infrastructure is crucial for promoting sustainable transport. But what does safe cycling infrastructure look like, and to whom? What is considered a safe cycling infrastructure by, say, young people may feel unsafe to older people, or vice versa. To shed light on people's preferences for cycling infrastructure and the heterogeneity thereof across segments of the population, I will conduct a stated choice experiment in which people face trade-offs between travel time and cycling infrastructure (as presented using street-view images). Then, I'll analyse these data using traditional discrete choice models and the recently proposed computer vision-enriched discrete choice models. Finally, I'll use the estimated discrete choice models to produce maps showing utility-based safety scores for cycling routes in Rotterdam. I hope my study delivers insights that the municipality of Rotterdam can use to devise policies aiming to promote cycling.

    Roos Terra

    Master student

    Finished projects