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Title:

Traffic State Estimation with Loss Constraint

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
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...     »
Keywords:
traffic state estimation; fundamental diagram; multimodal urban traffic; physics-informed; machine-learning
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Book / Congress title:
IEEE International Conference on Intelligent Transportation Systems
Organization:
IEEE
Year:
2022
Pages:
6
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1109/ITSC55140.2022.9921815
TUM Institution:
Lehrstuhl für Verkehrstechnik
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