An Improved Feedback Wavelet Neural Network for Short-Term Passenger Entrance Flow Prediction in Shanghai Subway System
Subway traffic prediction is of great significance for scheduling and anomalies detection. A novel model of multi-scale mixture feedback wavelet neural network(MMFWNN) is proposed to predict the short-term entrance flow of Shanghai subway stations. Firstly, passengers are classified into two categories of commuter and non-commuter by mining the travel pattern and identifying the travel pattern stability, which finds that the non-commuters travel is more susceptible to the meteorology status. The proposed prediction model adds a transitional layer to adapt the feedback mechanism, thus to improve the robustness with associative memorizing and optimization calculation. Thus MMFWNN is advantageous to the nonlinear time-varying short-term traffic flow prediction. We evaluate our model in the Shanghai subway system. The experimental results show that the MMFWNN model is more accurate in predicting the short-term passenger entrance flow in subway stations.
KeywordsWavelet neural network Subway flow prediction Travel pattern Data mining
This research is partly supported by the National Nature Science Foundation of China under Grand no. 61272412 and Jilin Province Science and Technology Development Program under Grant no. 20160204021GX.
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