- Titel
- AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-859698
- Konferenz
- 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS). Dresden, 30. November - 2. Dezember
- Quellenangabe
- Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022 - 9
Herausgeber: Wang, Meng
Herausgeber: Jaekel, Birgit
Herausgeber: Lehnert, Martin
Herausgeber: Zhou, Runhao
Herausgeber: Li, Zirui
Erscheinungsort: Dresden
Verlag: TUDpress
Erscheinungsjahr: 2023
Titel Schriftenreihe: Verkehrstelematik
Bandnummer Schriftenreihe: 9
Seiten: 113-118
ISBN: 978-3-95908-296-9 - DOI
- https://doi.org/10.25368/2023.105
- Abstract (EN)
- 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.
- Freie Schlagwörter (DE)
- KI-basiertes Verkehrsmodell, steuerungsorientiertes Verkehrsmodell, Mehrklassen-Verkehrsmodell, Autobahn-Verkehrsnetze
- Freie Schlagwörter (EN)
- AI-based traffic model, control-oriented traffic model, multi-class traffic model, freeway traffic networks
- Klassifikation (DDC)
- 360
- Klassifikation (RVK)
- ZO 4620
- ZO 3100
- Herausgeber (Institution)
- Technische Universität Dresden
- Verlag
- TUDpress, Dresden
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-859698
- Veröffentlichungsdatum Qucosa
- 23.06.2023
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
