Volume 39 Issue 3
Jun.  2021
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ZHANG Yiming, CHEN Mingming, SHI Lei, KANG Ronggui. A Forecast of Short-term Passenger Flow of Rail Transit Based on IGWO-BP Algorithm[J]. Journal of Transport Information and Safety, 2021, 39(3): 85-92. doi: 10.3963/j.jssn.1674-4861.2021.03.011
Citation: ZHANG Yiming, CHEN Mingming, SHI Lei, KANG Ronggui. A Forecast of Short-term Passenger Flow of Rail Transit Based on IGWO-BP Algorithm[J]. Journal of Transport Information and Safety, 2021, 39(3): 85-92. doi: 10.3963/j.jssn.1674-4861.2021.03.011

A Forecast of Short-term Passenger Flow of Rail Transit Based on IGWO-BP Algorithm

doi: 10.3963/j.jssn.1674-4861.2021.03.011
  • Received Date: 2020-11-18
  • Short-term passenger flow of rail transit has the characteristics of randomness and nonlinearity. An IGWO-BP algorithm is developed to forecast short-term passenger flow based on improved grey wolf optimization (IGWO) and BP neural network to improve the accuracy of predicting the short-term passenger flow of rail transit. The correlation coefficients of different time series of the rail-transit passenger flow are calculated to determine the input and output modes of the BP neural network. The cosine thought and dynamic weighting strategy are used to improve the orginal grey wolf optimization algorithm, thus enhancing the algorithm's global search and optimization. The IGWO algorithm is used to optimize the initial weights and thresholds of the BP neural network, which can improve the accuracy of predicting the short-term passenger flow. The work predicts the short-term passenger flow at the 15-min time granularity of the LONGSHOUYUAN Station of Xi'an Rail Transit Line 2 on Wednesday morning peak. The predicting results of the IGWO-BP algorithm are compared with those of the other five models (KF, GM, SVM, BPNN, and GWO-BP). For the IGWO-BP algorithm, the RMSE is 89.65, and the MAPE is 1.16%. The results show that the IGWO-BP algorithm has optimal accuracy and stability.

     

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