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dc.contributor.authorBugeja, Mark-
dc.contributor.authorDingli, Alexiei-
dc.contributor.authorAttard, Maria-
dc.contributor.authorSeychell, Dylan-
dc.identifier.citationBugeja, M., Dingli, A., Attard, M., & Seychell, D. (2019). An adaptive transport management approach using imitation learning. 3rd ACM Computer Science in Cars Symposium (CSCS 2019), Kaiserslautern.en_GB
dc.description.abstractThe area of Intelligent Transport Systems has been critical in traffic management and intelligent systems for the past decades. In this paper, we introduce a novel approach to traffic management. We develop a process that that starts with the development of a "game" based upon different road networks that are used to gather data based upon user actions. The user’s decision directly affect how traffic light states change. This data is then passed to an Imitation Learning model that can observe actions and imitate the same decisions on a similar road network.en_GB
dc.subjectData setsen_GB
dc.subjectNeural networks (Computer science)en_GB
dc.titleAn adaptive transport management approach using imitation learningen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holderen_GB
dc.bibliographicCitation.conferencename3rd ACM Computer Science in Cars Symposium (CSCS 2019)en_GB
dc.bibliographicCitation.conferenceplaceKaiserslautern, Germany, 18/10/2019en_GB
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