@article{ChevanceNieuwenhuijsenBragaetal., author = {Chevance, Guillaume and Nieuwenhuijsen, Mark and Braga, Kaue and Clifton, Kelly and Hoadley, Suzanne and Kaack, Lynn H. and Kaiser, Silke K. and Lampkowski, Marcelo and Lupu, Iuliana and Radics, Mikl{\´o}s and Vel{\´a}zquez-Cort{\´e}s, Daniel and Williams, Sarah and Woodcock, James and Tonne, Cathryn}, title = {Data gaps in transport behavior are bottleneck for tracking progress towards healthy sustainable transport in European cities}, series = {Environmental Research Letters}, volume = {19}, journal = {Environmental Research Letters}, number = {5}, doi = {10.1088/1748-9326/ad42b3}, language = {en} } @techreport{KaiserRodriguesLimaAzevedoetal., type = {Working Paper}, author = {Kaiser, Silke K. and Rodrigues, Filipe and Lima Azevedo, Carlos and Kaack, Lynn H.}, title = {Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume}, publisher = {arXiv}, doi = {10.48550/arXiv.2505.06292}, pages = {36}, abstract = {Reliable street-level traffic volume data, covering multiple modes of transportation, helps urban planning by informing decisions on infrastructure improvements, traffic management, and public transportation. Yet, traffic sensors measuring traffic volume are typically scarcely located, due to their high deployment and maintenance costs. To address this, interpolation methods can estimate traffic volumes at unobserved locations using available data. Graph Neural Networks have shown strong performance in traffic volume forecasting, particularly on highways and major arterial networks. Applying them to urban settings, however, presents unique challenges: urban networks exhibit greater structural diversity, traffic volumes are highly overdispersed with many zeros, the best way to account for spatial dependencies remains unclear, and sensor coverage is often very sparse. We introduce the Graph Neural Network for Urban Interpolation (GNNUI), a novel urban traffic volume estimation approach. GNNUI employs a masking algorithm to learn interpolation, integrates node features to capture functional roles, and uses a loss function tailored to zero-inflated traffic distributions. In addition to the model, we introduce two new open, large-scale urban traffic volume benchmarks, covering different transportation modes: Strava cycling data from Berlin and New York City taxi data. GNNUI outperforms recent, some graph-based, interpolation methods across metrics (MAE, RMSE, true-zero rate, Kullback-Leibler divergence) and remains robust from 90\% to 1\% sensor coverage. On Strava, for instance, MAE rises only from 7.1 to 10.5, on Taxi from 23.0 to 40.4, demonstrating strong performance under extreme data scarcity, common in real-world urban settings. We also examine how graph connectivity choices influence model accuracy.}, language = {en} } @techreport{KaiserKleinKaack, type = {Working Paper}, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn}, title = {Predicting cycling traffic in cities: Is bikesharing data representative of the cycling volume?}, doi = {10.48462/opus4-4942}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:b1570-opus4-49429}, pages = {5}, abstract = {A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, we predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems in addition to weather, vacation, infrastructure, and socioeconomic indicators. To reach a high prediction accuracy given the diverse data, we make use of machine learning techniques. Our goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Results indicate that bike-sharing data are valuable to improve the predictive performance, especially in cases with high outliers, and help generalize the models to new locations.}, language = {de} } @article{KaiserKleinKaack, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn H.}, title = {From counting stations to city-wide estimates: data-driven bicycle volume extrapolation}, series = {Environmental Data Science}, volume = {4}, journal = {Environmental Data Science}, publisher = {Cambridge University Press (CUP)}, issn = {2634-4602}, doi = {10.1017/eds.2025.5}, abstract = {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.}, language = {en} } @techreport{KaiserKleinKaack, type = {Working Paper}, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn}, title = {From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation}, doi = {10.48550/arXiv.2406.18454}, pages = {30}, abstract = {Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid 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 bicycle volume for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various public data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, and holiday data. Our analysis reveals that the best-performing model is XGBoost, and crowdsourced cycling and infrastructure data are most important for the prediction. We further simulate how collecting short-term counts at predicted locations improves performance. By providing ten days of such sample counts for each predicted location to the model, we are able to halve the error and greatly reduce the variability in performance among predicted locations.}, language = {en} } @article{KaiserRodriguesAzevedoetal., author = {Kaiser, Silke K. and Rodrigues, Filipe and Azevedo, Carlos Lima and Kaack, Lynn H.}, title = {Spatio-temporal graph neural network for urban spaces: Interpolating citywide traffic volume}, series = {Expert Systems with Applications}, volume = {316}, journal = {Expert Systems with Applications}, publisher = {Elsevier BV}, doi = {10.1016/j.eswa.2026.131823}, abstract = {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.}, language = {en} }