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

  • Weidong Zou
  • Yuanqing Xia
Original Article


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.


Traffic flow Transportation management Hidden nodes parameters Back propagation bidirectional extreme learning machine 


Compliance with ethical standards

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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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of AutomationBeijing Institute of TechnologyBeijingPeople’s Republic of China

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