Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, such as weather, events, accidents, and road construc...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	International Conference on Big Data and Smart Computing S. 1 - 8
Hauptverfasser:	Essien, Aniekan, Petrounias, Ilias, Sampaio, Pedro, Sampaio, Sandra
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.02.2019
Schlagworte:	Computational modeling Data models data-fusion deep learning intelligent transportation systems (ITS) Long short-term neural networks Mathematical model Meteorology Predictive models Roads Time series analysis traffic data science
ISSN:	2375-9356
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!