δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting

Accurate and timely traffic flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Recently, the stack...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 247; S. 31 - 38
Hauptverfasser:	Zhou, Teng, Han, Guoqiang, Xu, Xuemiao, Lin, Zhizhe, Han, Chu, Huang, Yuchang, Qin, Jing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 19.07.2017
Schlagworte:	AdaBoost Dynamic system Short-term traffic flow forecasting Stacked autoencoder Time-series model Stacked autoencoder AdaBoost Dynamic system Time-series model Short-term traffic flow forecasting
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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