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Back propagation bidirectional extreme learning machine for traffic flow time series prediction

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Abstract

On account of transportation management, a predictive model of the traffic flow is built up that would precisely predict the traffic flow, reduce longer travel delays. In prediction model of traffic flow based on traditional neural network, the parameters of prediction model need to be tuned through iterative processing, and these methods easily get stuck in local minimum. The paper presents a novel prediction model based on back propagation bidirectional extreme learning machine (BP-BELM). Parameters of BP-BELM are not tuned by experience. Compared with back propagation neural network, radial basis function, support vector machine and other improved incremental ELM, the combined simulations and comparisons demonstrate that BP-BELM is used in predicting the traffic flow for its suitability and effectivity.

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Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Weidong Zou & Yuanqing Xia

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  1. Weidong Zou
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  2. Yuanqing Xia
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Correspondence to Weidong Zou.

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Conflict of interest

The authors (Weidong Zou, Yuanqing Xia) of paper (Title: Back propagation bidirectional extreme learning machine for traffic flow time series prediction, NCAA-D-17-01893) declare that there is no conflict of interests.

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Zou, W., Xia, Y. Back propagation bidirectional extreme learning machine for traffic flow time series prediction. Neural Comput & Applic 31, 7401–7414 (2019). https://doi.org/10.1007/s00521-018-3578-y

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  • DOI: https://doi.org/10.1007/s00521-018-3578-y

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