Computer Science ›› 2014, Vol. 41 ›› Issue (2): 276-279.

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Design and Simulation of Passenger Flow Forecast Algorithm for Urban Rail Transit

LI Shao-wei and CHEN Yong-sheng   

  • Online:2018-11-14 Published:2018-11-14

Abstract: To forecast exactly the passenger flow of the urban rail transit,a hierarchical framework based on neural network and Kalman-filter model was presented.First,ELAN neural network model is employed to implement the prediction of the passenger flow.Then the Kalman-filter was used to refine the forecast data of the passenger flow so as to advance the accuracy of the predicted results.Finally,in order to validate the proposed model,the passenger flow of Shanghai subway transport hub was observed and simulated.Experimental results show that the proposed hierarchical model reduces error about 0.8% and has better effects in contrast with any single algorithm.

Key words: Rail transit,Passenger flow forecast,ELAN neural network,Kalman filter,System simulation

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