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dc.contributor.authorTesar, Markus
dc.date.accessioned2025-03-08T11:08:18Z
dc.date.available2025-03-08T11:08:18Z
dc.date.issued2023
dc.date.submitted2023-04-24T11:20:18Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/62535
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/196330
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.
dc.languageGerman
dc.relation.ispartofseriesKarlsruher Schriftenreihe Fahrzeugsystemtechnik
dc.rightsopen access
dc.subject.otherStraßenbahn; KI; Energie; effizienz; Pünktlichkeit; Modellierung; Light Rail; AI; Energy Efficiency; Punctuality; Modelling
dc.subject.otherthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials
dc.titleDeep Reinforcement Learning zur Steigerung von Energieeffizienz und Pünktlichkeit von Straßenbahnen
dc.typebook
oapen.identifier.doi10.5445/KSP/1000155565
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages280
dc.seriesnumber20


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