All Outputs (4)

Modeling Long-Range Travelling Times with Big Railway Data (2022)
Presentation / Conference Contribution
Sun, W., Grubenmann, T., Cheng, R., Kao, B., & Ching, W. (2022). Modeling Long-Range Travelling Times with Big Railway Data. In Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022 (443-454). https://doi.org/10.1007/978

Big Railway Data, such as train movement logs and timetables, have become increasingly available. By analyzing these data, insights about train movement and delay can be extracted, allowing train operators to make smarter train management decisions.... Read More about Modeling Long-Range Travelling Times with Big Railway Data.

DeepTEA: effective and efficient online time-dependent trajectory outlier detection (2022)
Journal Article
Han, X., Cheng, R., Ma, C., & Grubenmann, T. (2022). DeepTEA: effective and efficient online time-dependent trajectory outlier detection. Proceedings of the VLDB Endowment, 15(7), 1493-1505. https://doi.org/10.14778/3523210.3523225

In this paper, we study anomalous trajectory detection, which aims to extract abnormal movements of vehicles on the roads. This important problem, which facilitates understanding of traffic behavior and detection of taxi fraud, is challenging due to... Read More about DeepTEA: effective and efficient online time-dependent trajectory outlier detection.

Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks (2022)
Presentation / Conference Contribution
Han, X., Cheng, R., Grubenmann, T., Maniu, S., Ma, C., & Li, X. (2022). Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks. In A. Banerjee, Z. Zhou, E. E. Papalexakis, & M. Riondato (Eds.), Proceedings of the 2022 SIAM I

Road network applications, such as navigation, incident detection, and Point-of-Interest (POI) recommendation, make extensive use of network edge weights (e.g., traveling times). Some of these weights can be missing, especially in a road network wher... Read More about Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks.

Spatial concept learning and inference on geospatial polygon data (2022)
Journal Article
Westphal, P., Grubenmann, T., Collarana, D., Bin, S., Bühmann, L., & Lehmann, J. (2022). Spatial concept learning and inference on geospatial polygon data. Knowledge-Based Systems, 241, Article 108233. https://doi.org/10.1016/j.knosys.2022.108233

Geospatial knowledge has always been an essential driver for many societal aspects. This concerns in particular urban planning and urban growth management. To gain insights from geospatial data and guide decisions usually authoritative and open data... Read More about Spatial concept learning and inference on geospatial polygon data.