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Visual Analytics for Supporting Conflict Resolution in Large Railway Networks

Visual Analytics for Supporting Conflict Resolution in Large Railway Networks

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SCHLEGEL, Udo, Wolfgang JENTNER, Juri BUCHMÜLLER, Eren CAKMAK, Giuliano CASTIGLIA, Renzo CANEPA, Simone PETRALLI, Luca ONETO, Daniel A. KEIM, Davide ANGUITA, 2020. Visual Analytics for Supporting Conflict Resolution in Large Railway Networks. INNS Big Data and Deep Learning conference : INNSBDDL 2019. Sestri Levante, Italy, Apr 18, 2019 - Apr 19, 2019. In: ONETO, Luca, ed. and others. Recent Advances in Big Data and Deep Learning : Proceedings of the INNS Big Data and Deep Learning Conference, INNSBDDL2019. Cham:Springer, pp. 206-215. ISSN 2661-8141. eISSN 2661-815X. ISBN 978-3-030-16840-7. Available under: doi: 10.1007/978-3-030-16841-4_22

@inproceedings{Schlegel2020Visua-45762, title={Visual Analytics for Supporting Conflict Resolution in Large Railway Networks}, year={2020}, doi={10.1007/978-3-030-16841-4_22}, number={1}, isbn={978-3-030-16840-7}, issn={2661-8141}, address={Cham}, publisher={Springer}, series={Proceedings of the International Neural Networks Society}, booktitle={Recent Advances in Big Data and Deep Learning : Proceedings of the INNS Big Data and Deep Learning Conference, INNSBDDL2019}, pages={206--215}, editor={Oneto, Luca}, author={Schlegel, Udo and Jentner, Wolfgang and Buchmüller, Juri and Cakmak, Eren and Castiglia, Giuliano and Canepa, Renzo and Petralli, Simone and Oneto, Luca and Keim, Daniel A. and Anguita, Davide} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/45762"> <dc:contributor>Oneto, Luca</dc:contributor> <dc:contributor>Castiglia, Giuliano</dc:contributor> <dc:creator>Castiglia, Giuliano</dc:creator> <dcterms:issued>2020</dcterms:issued> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:creator>Anguita, Davide</dc:creator> <dc:creator>Jentner, Wolfgang</dc:creator> <dc:creator>Oneto, Luca</dc:creator> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:rights>terms-of-use</dc:rights> <dc:contributor>Schlegel, Udo</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-05-03T10:52:32Z</dcterms:available> <dc:language>eng</dc:language> <dc:contributor>Buchmüller, Juri</dc:contributor> <dc:creator>Petralli, Simone</dc:creator> <dc:creator>Schlegel, Udo</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-05-03T10:52:32Z</dc:date> <dc:creator>Cakmak, Eren</dc:creator> <dc:contributor>Cakmak, Eren</dc:contributor> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:contributor>Canepa, Renzo</dc:contributor> <dc:creator>Canepa, Renzo</dc:creator> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:title>Visual Analytics for Supporting Conflict Resolution in Large Railway Networks</dcterms:title> <dc:creator>Buchmüller, Juri</dc:creator> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/45762"/> <dc:contributor>Anguita, Davide</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Petralli, Simone</dc:contributor> <dc:contributor>Jentner, Wolfgang</dc:contributor> <dcterms:abstract xml:lang="eng">Train operators are responsible for maintaining and following the schedule of large-scale railway transport systems. Disruptions to this schedule imply conflicts that occur when two trains are bound to use the same railway segment. It is upon the train operator to decide which train must go first to resolve the conflict. As the railway transport system is a large and complex network, the decision may have a high impact on the future schedule, further train delay, costs, and other performance indicators. Due to this complexity and the enormous amount of underlying data, machine learning models have proven to be useful. However, the automated models are not accessible to the train operators which results in a low trust in following their predictions. We propose a Visual Analytics solution for a decision support system to support the train operators in making an informed decision while providing access to the complex machine learning models. Different integrated, interactive views allow the train operator to explore the various impacts that a decision may have. Additionally, the user can compare various data-driven models which are structured by an experience-based model. We demonstrate a decision-making process in a use case highlighting how the different views are made use of by the train operator.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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