In this extended abstract, we propose an Artificial Intelligence-based model dedicated to the representation of a multi-class traffic flow, i.e. a traffic flow in which different vehicle
classes (at least cars and trucks) are explicitly represented, with the aim of using it for the development of freeway traffic control schemes based on ramp management. Specifically, the goal of this work is to develop a hybrid modelling technique in which a Machine Learning component and the multi-class version of METANET model are adopted to determine a better estimation and forecasting tool for freeway traffic. The resulting model is specifically devised in order to be included in a Model Predictive Control (MPC) scheme for the required traffic state prediction.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:85969 |
| Date | 23 June 2023 |
| Creators | Binjaku, Kleona, Pasquale, Cecilia, Sacone, Simona, Meçe, Elinda Kajo |
| Contributors | Technische Universität Dresden |
| Publisher | TUDpress |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-95908-296-9, urn:nbn:de:bsz:14-qucosa2-858198, qucosa:85819 |
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