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dc.contributor.authorTesar, Markus
dc.date.accessioned2023-04-24T11:20:18Z
dc.date.available2023-04-24T11:20:18Z
dc.date.issued2023
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/62535
dc.description.abstractThis work investigates how the energy efficiency and punctuality of streetcars can be increased by using AI. The AI is trained on two scenarios at three traffic times each. The determined driving profiles are compared with those of drivers from regular passenger operation as well as with a theoretical optimum determined by Dynamic Programming. In addition, transfer learning capabilities of the AI will be investigated.en_US
dc.languageGermanen_US
dc.relation.ispartofseriesKarlsruher Schriftenreihe Fahrzeugsystemtechniken_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materialsen_US
dc.subject.otherStraßenbahn; KI; Energie; effizienz; Pünktlichkeit; Modellierung; Light Rail; AI; Energy Efficiency; Punctuality; Modellingen_US
dc.titleDeep Reinforcement Learning zur Steigerung von Energieeffizienz und Pünktlichkeit von Straßenbahnenen_US
dc.typebook
oapen.identifier.doi10.5445/KSP/1000155565en_US
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9en_US
oapen.collectionAG Universitätsverlage
oapen.series.number20en_US
oapen.pages280en_US
peerreview.anonymityAll identities known
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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