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Titel:

Traffic State Estimation with Loss Constraint

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Dahmen, Victoria; Loder, Allister; Tilg, Gabriel; Kutsch, Alexander; Bogenberger, Klaus
Abstract:
Traffic state estimation is relevant for real-time traffic control, providing travel information as well as for expost analysis of traffic patterns. While the output is usually the average speed and vehicle flow along street segments, the type of input data and the existing methods to obtain the output are diverse. Recently, physics-informed data-driven approaches started to emerge that enrich the estimation process with information taken from physical models. In traffic, so far, these have bee...     »
Stichworte:
traffic state estimation; fundamental diagram; multimodal urban traffic; physics-informed; machine-learning
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
IEEE International Conference on Intelligent Transportation Systems
Ausrichter der Konferenz:
IEEE
Jahr:
2022
Seiten:
6
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1109/ITSC55140.2022.9921815
TUM Einrichtung:
Lehrstuhl für Verkehrstechnik
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