TY - JOUR A1 - Chevance, Guillaume A1 - Nieuwenhuijsen, Mark A1 - Braga, Kaue A1 - Clifton, Kelly A1 - Hoadley, Suzanne A1 - Kaack, Lynn H. A1 - Kaiser, Silke K. A1 - Lampkowski, Marcelo A1 - Lupu, Iuliana A1 - Radics, Miklós A1 - Velázquez-Cortés, Daniel A1 - Williams, Sarah A1 - Woodcock, James A1 - Tonne, Cathryn T1 - Data gaps in transport behavior are bottleneck for tracking progress towards healthy sustainable transport in European cities JF - Environmental Research Letters Y1 - 2024 U6 - https://doi.org/10.1088/1748-9326/ad42b3 VL - 19 IS - 5 ER - TY - JOUR A1 - Kaiser, Silke K. A1 - Klein, Nadja A1 - Kaack, Lynn H. T1 - From counting stations to city-wide estimates: data-driven bicycle volume extrapolation JF - Environmental Data Science N2 - Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Access to street-level data on bicycle traffic would assist cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists’ needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate street-level bicycle counts for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, holiday data, and centrality measures. Our analysis reveals that crowdsourced cycling flow data from Strava in the area around the point of interest are most important for the prediction. To provide guidance for future data collection, we analyze how including short-term counts at predicted locations enhances model performance. By incorporating just 10 days of sample counts for each predicted location, we are able to almost halve the error and greatly reduce the variability in performance among predicted locations. Y1 - 2025 U6 - https://doi.org/10.1017/eds.2025.5 SN - 2634-4602 N1 - Open Access publication is funded by the Hertie School Library VL - 4 PB - Cambridge University Press (CUP) ER - TY - JOUR A1 - Kaiser, Silke K. A1 - Rodrigues, Filipe A1 - Azevedo, Carlos Lima A1 - Kaack, Lynn H. T1 - Spatio-temporal graph neural network for urban spaces: Interpolating citywide traffic volume JF - Expert Systems with Applications N2 - Graph Neural Networks have shown strong performance in traffic volume forecasting, particularly on highways and major arterial networks. Applying these models to urban street networks, however, presents unique challenges: urban networks are structurally more diverse, traffic volumes are highly overdispersed with many zeros, spatial dependencies are complex, and sensor coverage is often very sparse. To address these challenges, we introduce the Graph Neural Network for Urban Interpolation (GNNUI), a model designed specifically for citywide traffic volume interpolation. GNNUI employs a designated masking strategy to learn interpolation, integrates node features to capture the different functional roles across the street network, and uses a loss function tailored to zero-inflated traffic distributions. We evaluate GNNUI on two newly constructed, large-scale urban traffic volume benchmarks, covering different transportation modes: Strava cycling data from Berlin and New York City taxi data. Across multiple evaluation metrics, GNNUI outperforms both the state-of-the-art graph-based interpolation model IGNNK and the widely used machine-learning baseline XGBoost, reducing MAE by at least 13% on Strava data and 7% on Taxi data while better capturing the empirical traffic distribution, and improving the identification of zero-traffic streets. Additionally, the model remains robust under the realistic case of extremely scarce ground truth sensor data. When sensor coverage is reduced from 90% to 1%, the MAE increases by approximately 48% on Strava and 76% on the taxi data, despite the near-complete removal of sensor information. We also examine how graph connectivity choices influence model performance, and find that a simple and computationally efficient binary adjacency matrix outperforms distance or similarity based ones. Y1 - 2026 U6 - https://doi.org/10.1016/j.eswa.2026.131823 N1 - Open Access publication is funded by the Hertie School Library. VL - 316 PB - Elsevier BV ER -