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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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 247; s. 31 - 38 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
19.07.2017
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Han, Guoqiang Xu, Xuemiao Han, Chu Lin, Zhizhe Zhou, Teng Huang, Yuchang Qin, Jing |
| Author_xml | – sequence: 1 givenname: Teng surname: Zhou fullname: Zhou, Teng organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, China – sequence: 2 givenname: Guoqiang surname: Han fullname: Han, Guoqiang organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, China – sequence: 3 givenname: Xuemiao surname: Xu fullname: Xu, Xuemiao email: xuemx@scut.edu.cn organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, China – sequence: 4 givenname: Zhizhe surname: Lin fullname: Lin, Zhizhe organization: Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, Guangdong, 515000, China – sequence: 5 givenname: Chu surname: Han fullname: Han, Chu organization: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China – sequence: 6 givenname: Yuchang surname: Huang fullname: Huang, Yuchang organization: College of Mathematics and Information, South China Agricultural University, Guangzhou, Guangdong, 510642, China – sequence: 7 givenname: Jing surname: Qin fullname: Qin, Jing organization: Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, 999077, China |
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| Keywords | Stacked autoencoder AdaBoost Dynamic system Time-series model Short-term traffic flow forecasting |
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