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Titel:

Network-wide Traffic State Forecast Using Discrete Wavelet Transform and Deep Learning

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Hassan Zada, Mohammad Javad; Yamnenko, Iuliia; Antoniou, Constantinos
Abstract:
Traffic state prediction models are a crucial element with many applications in intelligent transportation systems. Short-term network-wide modeling of traffic states is a challenging task due to the existence of inherent characteristics such as nonlinearity, periodicity and stochasticity in the traffic state time series. This issue was responded by the evolution of advanced machine learning algorithms, e.g. deep learning. Deep neural networks can cope with high dimensionality, and also, are cap...     »
Stichworte:
traffic state prediction; preprocessing; discrete Haar wavelet transform; MLP; deep learning; decomposition level
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
IEEE Transactions on Intelligent Transportation Systems, 2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Ausrichter der Konferenz:
Communication Systems Department of EURECOM
Datum der Konferenz:
14-16 June 2023
Verlag / Institution:
IEEE
Publikationsdatum:
11.09.2023
Jahr:
2023
Quartal:
1. Quartal
Jahr / Monat:
2023-09
Monat:
Sep
Seiten:
6
Impact Factor:
8.5
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1109/MT-ITS56129.2023.10241683
WWW:
https://ieeexplore.ieee.org/abstract/document/10241683
Semester:
SS 23
Format:
Text
Letzte Änderung:
20.09.2023
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