Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation te...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	PloS one Jg. 10; H. 3; S. e0119044
Hauptverfasser:	Ma, Xiaolei, Yu, Haiyang, Wang, Yunpeng, Wang, Yinhai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 17.03.2015 Public Library of Science (PLoS)
Schlagworte:	Architectural engineering Artificial neural networks Calibration China Collaboration Computer simulation Data mining Deep learning Engineering Evolution Geographic Information Systems Global positioning systems GPS Graphics processing units Intelligent transportation systems Internet of Things Laboratories Learning Learning theory Mathematical models Mathematical problems Mitigation Models, Theoretical Natural language processing Network analysis Neural networks Neural Networks (Computer) Predictions Process parameters Recurrent neural networks Satellite navigation systems Studies Theory Traffic congestion Traffic flow Traffic models Transportation Transportation - methods Transportation industry Transportation networks Urban areas China Beijing China United States > US
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!