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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Neurocomputing (Amsterdam) Ročník 247; s. 31 - 38
Hlavní autoři:	Zhou, Teng, Han, Guoqiang, Xu, Xuemiao, Lin, Zhizhe, Han, Chu, Huang, Yuchang, Qin, Jing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 19.07.2017
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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 stacked autoencoder has been proven promising for traffic flow forecasting but still exists some drawbacks in certain conditions. In this paper, a training samples replication strategy is introduced to train a series of stacked autoencoders and an adaptive boosting scheme is proposed to ensemble the trained stacked autoencoders to improve the accuracy of traffic flow forecasting. Furthermore, sufficient experiments have been conducted to demonstrate the superior performance of the proposal.
ISSN:	0925-2312 1872-8286
DOI:	10.1016/j.neucom.2017.03.049