CityAI Lab
A place where data, AI and behavioural theory come together
Cities are hotspots of human activity that have increased our prosperity, happiness, and health. Yet the future liveability of cities around the world is under pressure as they face major social-technical challenges. Those challenges include crumbling social cohesion, income inequality, overcrowding of public spaces, and unhealthy local environments caused by factors such as heavy traffic and noise pollution. CityAI Lab examines the pivotal role that the urban environment plays in tackling such challenges.
Our research focusses on unravelling how the urban environment and human behaviour dance a tango. It capitalises on advances in machine learning and on the wealth of data available now at a city level. We combine these with established theories on planning and behaviour, hoping to contribute to the development of more attractive and liveable cities.
The CityAI Lab is part of the TU Delft AI Labs programme.
Drone operation. Ready for take-off.
Discussing the hardware design of a noise sensor. The noise sensor uses machine learning to classify sources of noise pollution.
Discussing research results that are produced by machine learning clustering algorithms.
The team
Directors
Sander van Cranenburgh
Faculty of TPM
Lab director
Simeon Calvert
Faculty of CEG
Lab director
Oded Cats
Faculty of CEG
Lab director
PhD candidates
Lucas Spierenburg
PhD candidate
Yiru Jiao
PhD candidate
Francisco Garrido Valenzuela
PhD candidate
Lion Cassens
PhD candidate
Gabriel Nova
PhD candidate
Nicholas Smeele
PhD candidate
Associated faculty
Maarten Kroesen
Associated faculty
Hans van Lint
Associated faculty
Education
Courses
2023/2024
- TIL Research Project | TIL4020-20
- Fundamentals of Artificial Intelligence Programme | IFEEMCS520100
2022/2023
- Machine learning for socio-technical systems | TPM034A
- Statistical analysis of choice behaviour | SEN1221
- Simulation and Uncertainty in Transport Engineering | CIEM6110
- Seminars in Mobiliteit & Transport | CT3520
2021/2022
- Applied Machine Learning | CS4305TU
- Applied AI Project | CS4320TU
- Capstone Applied AI project | TI3150TU
- Travel Behaviour Research | SEN1721
- Emerging Topics Transport & Planning | CIE4845
- Onderzoek en data-analyse | TB232B
- Multivariate data-analyse | TB234B
Master Projects
Ongoing
- Impacts of built environment features on residential location choice incorporating computer vision, Sander van Cranenburgh, LanLan Yan (2023/2024)
- Exploring the potential enhancement of predictive accuracy for minority classes in travel mode choice models, Sander van Cranenburgh, Aspasia Panagiotidou (2023/2024)
- Measuring the Evolution of Social Segregation using Public Transport Smart Card Data, Oded Cats, Lukas Kolkowski (2022/2023)
Finished
- TULIPS (European Green Deals): Sustainable Inter-modal transport connections, using data driven approaches, Simeon Calvert, Asli Bali (2022/2023)
- Tranquilitree: the Potential of Trees to Mitigate Aircraft Noise Pollution from Schiphol Airport, Sander van Cranenburgh, Lanie Preston (2022/2023)
- Searching for the built environment: Clustering built environment typologies to find spatial patterns and areas of deprivation using remote sensing techniques, Sander van Cranenburgh, Stephan Olde (2022/2023)
- Do signals interact? A machine learning approach to study the multi-signal environment of Initial Coin Offerings, Sander van Cranenburgh, Job Wever (2022/2023)
- Quantifying and visualising anthropogenic emissions of CO2 in the urban environment, Sander van Cranenburgh, Koen Vierling (2022/2023)
- An Explainable Network-wide Metro Passenger Delay Prediction Model, Oded Cats, Yuxing Cheng (2022/2023)
- Cycling in a green city, Oded Cats, Steven Barendregt (2022/2023)
- Accesible and Viable Seaside City : Creating an accessible and attractive seaside city by improving bicycle parking conditions, Oded Cats, Gaby van der Star (2022/2023)
- Waalhaven Metro Station, Oded Cats, Jasper van den Broek (2022/2023)
- Redesign of the traffic axis in Rotterdam South, Oded Cats, Maxime Van Elsué (2022/2023)
- Explainable AI: A Proof of Concept Demonstration in Financial Transaction Fraud Detection using TreeSHAP & Diverse Counterfactuals, Sandert van Cranenburgh, Pratheep Balakrishnan (2021/2022)
- Bus Management using Multi-agent Reinforcement Learning, Oded Cats, George Weijs (2021/2022)
- Modelling air quality around highways in the Netherlands, Sander van Cranenburgh, Tamar Vooijs (2021/2022)
- Automated Disruption Detections in Metro Networks using Smart Card Data, Oded Cats, Faye Jasperse (2019/2020)