Paper Info
Title
An Improved Feedback Wavelet Neural Network For Short-Term Passenger Entrance Flow Prediction In Shanghai Subway System
Abstract
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.
Year
DOI
Venue
2017
10.1007/978-3-319-70139-4_4
NEURAL INFORMATION PROCESSING, ICONIP 2017, PT V
Keywords
Field
DocType
Wavelet neural network, Subway flow prediction, Travel pattern, Data mining
Wavelet neural network,Nonlinear system,Traffic flow,Scheduling (computing),Computer science,Simulation,Flow (psychology),Robustness (computer science),Real-time computing,Artificial intelligence,Traffic prediction,Machine learning
Conference
Volume
ISSN
Citations 
10638
0302-9743
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Bo Zhang100.34
Shuqiu Li200.34
Liping Huang343.87
Yongjian Yang43914.05