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Neural Computing and Applications

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11-06-2018 | Original Article

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

Authors: Weidong Zou, Yuanqing Xia

Published in: Neural Computing and Applications | Issue 11/2019

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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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Title: Back propagation bidirectional extreme learning machine for traffic flow time series prediction
Authors: Weidong Zou
Yuanqing Xia
Publication date: 11-06-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 11/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3578-y

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