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Titel:

Spatio-Temporal Prediction of Freeway Congestion Patterns Using Neural Networks -A Conceptual Approach

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Poster
Autor(en):
Metzger, Barbara; Kessler, Lisa; Bogenberger, Klaus
Abstract:
An accurate prediction of actual traffic conditions on freeways is essential for efficient traffic management, safety, and planning. To this end, the knowledge on which traffic state or more exactly which congestion pattern is prevailing, is the crucial basis for any analysis. In this paper, we propose two models, a standard neural network (NN) and a Long Short-Term Memory (LSTM) neural network, for predicting traffic congestion patterns. We provide a concept containing an overview of the proble...     »
Stichworte:
traffic state prediction, neural network (NN), recurrent neural network (RNN), long short-term memory (LSTM), congestion patterns, freeway
Kongress- / Buchtitel:
ISFO - 4th International Symposium on Freeway and Tollway Operations
Datum der Konferenz:
26.06.2023
Verlag / Institution:
Unpublished
Publikationsdatum:
26.06.2023
Jahr:
2023
Volltext / DOI:
doi:10.13140/RG.2.2.21500.51840
TUM Einrichtung:
Lehrstuhl für Verkehrstechnik
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