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A Clustering-Based Framework for Understanding Individuals' Travel Mode Choice Behavior., , , and . AGILE Conference, page 77-94. Springer, (2019)A Clustering-Based Framework for Individual Travel Behaviour Change Detection., , , , and . GIScience (II), volume 208 of LIPIcs, page 4:1-4:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2021)Graph-ResNets for short-term traffic forecasts in almost unknown cities., , , , , and . NeurIPS (Competition and Demos), volume 123 of Proceedings of Machine Learning Research, page 153-163. PMLR, (2019)Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities., , , , , , , , , and . CoRR, (2023)Computational Methods for Sustainable Mobility - Interpretation and Prediction of Tracking Data using Graphs and Machine Learning.. ETH Zurich, Zürich, Switzerland, (2023)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/646673).How do you go where?: improving next location prediction by learning travel mode information using transformers., , and . SIGSPATIAL/GIS, page 61:1-61:10. ACM, (2022)Identifying and Interpreting Clusters of Persons with Similar Mobility Behaviour Change Processes., , , and . AGILE Conf., page 291-307. Springer, (2018)Visual Analysis of Spatio-temporal Phenomena with 1D Projections., , , and . Comput. Graph. Forum, 40 (3): 335-347 (2021)Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors., , , , , , , , , and 20 other author(s). NeurIPS (Competition and Demos), volume 220 of Proceedings of Machine Learning Research, page 251-278. PMLR, (2021)The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task - Insights from the IARAI Traffic4cast Competition at NeurIPS 2019., , , , , , , , and . NeurIPS (Competition and Demos), volume 123 of Proceedings of Machine Learning Research, page 232-241. PMLR, (2019)