18 citations · 21 across the 8 of their papers we have counts for
4 papers · 1 filter
GeOT: A spatially explicit framework for evaluating spatio-temporal predictions
Nina Wiedemann, Théo Uscidda, Martin Raubal
When predicting observations across space and time, the spatial layout of errors impacts a model's real-world utility. For instance, in bike sharing demand prediction, error patter…
Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
Yatao Zhang, Yi Wang, Song Gao +1
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffi…
Bike network planning in limited urban space
Nina Wiedemann, Christian Nöbel, Lukas Ballo +2
The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, e…
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
Rushan Wang, Yanan Xin, Yatao Zhang +2
Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box nature of those models makes the results…