Výsledky vyhledávání - "Tobias Skovgaard Jepsen"

UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation

Autor: Christian Søndergaard Jensen, Tobias Skovgaard Jepsen, Thomas Dyhre Nielsen

Publikováno v: Jepsen, T S, Jensen, C S & Nielsen, T D 2022, ' UniTE-The Best of Both Worlds-Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation. ', Transactions on Spatial Algorithms and Systems, vol. 8, no. 4, 30, pp. 1 . https://doi.org/10.1145/3517335

Travel time and speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different tradeoffs between generalizability and accuracy. Fun

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Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining

Autor: Stefan Funke, Claudius Proissl, Florian Barth, Tobias Skovgaard Jepsen

Publikováno v: BigSpatial@SIGSPATIAL
Barth, F, Funke, S, Skovgaard Jepsen, T & Proissl, C 2020, Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining . in V Chandola, R R Vatsavai & A Shashidharan (eds), BIGSPATIAL '20 : Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data ., 6, Association for Computing Machinery, pp. 1-10, The 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, 03/11/2020 . https://doi.org/10.1145/3423336.3429348

We present analysis techniques for large trajectory data sets that aim to provide a semantic understanding of trajectories reaching beyond them being point sequences in time and space. The presented techniques use a driving preference model w.r.t. ro

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On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network

Autor: Kristian Torp, Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas D. Nielsen

Publikováno v: Jepsen, T S, Jensen, C S, Nielsen, T D & Torp, K 2018, On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network . in Proceedings of the 2018 IEEE International Conference on Big Data . IEEE, pp. 3421-3430, 2018 IEEE International Conference on Big Data, Seattle, Washington, United States, 10/12/2018 . https://doi.org/10.1109/BigData.2018.8622416
IEEE BigData

Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13%

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http://arxiv.org/abs/1911.06217

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Graph Convolutional Networks for Road Networks

Autor: Tobias Skovgaard Jepsen, Thomas D. Nielsen, Christian S. Jensen

Publikováno v: Skovgaard Jepsen, T, Jensen, C S & Nielsen, T D 2019, Graph Convolutional Networks for Road Networks . in F Banaei-Kashani, G Trajcevski, R H Guting, L Kulik & S Newsam (eds), Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . Association for Computing Machinery, pp. 460-463, 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, Illinois, United States, 05/11/2019 . https://doi.org/10.1145/3347146.3359094
SIGSPATIAL/GIS

Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a road network by utili

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Extended Dependency Graphs and Efficient Distributed Fixed-Point Computation

Autor: Samuel Pastva, Søren Enevoldsen, Tobias Skovgaard Jepsen, Andreas Engelbredt Dalsgaard, Peter Fogh, Mads Chr. Olesen, Søren M. Nielsen, Jiri Srba, Lasse S. Jensen, Isabella Kaufmann, Kim Guldstrand Larsen

Publikováno v: Lecture Notes in Computer Science
Dalsgaard, A E, Enevoldsen, S, Fogh, P, Jensen, L S, Jepsen, T S, Kaufmann, I, Larsen, K G, Nielsen, S M, Olesen, M C, Pastva, S & Srba, J 2017, Extended dependency graphs and efficient distributed fixed-point computation . in Application and Theory of Petri Nets and Concurrency-38th International Conference, PETRI NETS 2017, Proceedings . vol. 10258 LNCS, Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10258 LNCS, pp. 139-158, 38th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2017, Zaragoza, Spain, 25/06/2017 . https://doi.org/10.1007/978-3-319-57861-3_10
Lecture Notes in Computer Science-Application and Theory of Petri Nets and Concurrency
Application and Theory of Petri Nets and Concurrency ISBN: 9783319578606
Petri Nets

Equivalence and model checking problems can be encoded into computing fixed points on dependency graphs. Dependency graphs represent causal dependencies among the nodes of the graph by means of hyper-edges. We suggest to extend the model of dependenc

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