Research

Research Interest

My research focuses on machine learning applications in sustainable transport, alongside interests in applied econometrics and spatio-temporal statistics.

Published Papers

From counting stations to city-wide estimates: data-driven bicycle volume extrapolation (2025) Kaiser, Silke K., Nadja Klein, and Lynn H. Kaack. Environmental Data Science 4 (2025): e13. Link

Data gaps in transport behavior are bottleneck for tracking progress towards healthy sustainable transport in European cities. (2024) Guillaume Chevance, Mark Nieuwenhuijsen, Kaue Braga, Kelly Clifton, Suzanne Hoadley, Lynn Kaack, Silke K Kaiser, Marcelo Lampkowski, Iuliana Lupu, Miklós Radics, Daniel Velázquez-Cortés, Sarah Williams, James Woodcock, Cathryn Tonne Environmental Research Letters 19.5 : 051002. Link

Under Review Papers

Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume (2025) Kaiser, S. K., Rodrigues, F., Azevedo, C. L., & Kaack, L. H. arXiv preprint arXiv:2505.06292. Under Review with Expert Systems with Applications. Link

Working Papers

Traffic Sensor Location Problem: A Reinforcement Learning and Explainability Approach to Urban Sensor Placement (2025)

Kaiser, S.K.

Miscellaneous

Podcast: Environment Variables - Academic Forefronts, from Green Software Foundation 05/09/24, Link.

Opinion piece: “Pedalling Towards a Greener Future”, in CATALYSE 08/09/23, Link.