Author:
Ma Donglin,Guo Yating,Ma Sizhou
Abstract
Abstract
Aiming at the problem that the accuracy of passenger flow prediction is not high, this paper presents a short-term passenger flow forecasting model based on Graph Convolutional Neural Network (GCN) and Bidirectional Long-term Memory Network (BiLSTM). Firstly, the historical traffic time series is divided into three time modes: recent period, daily period and weekly period; Secondly, we construct three models based on GCN and BiLSTM to capture the spatial and temporal dependence of the three patterns; Finally, the parameter matrix is used to fuse the output of the three time modes to obtain the final prediction result. By testing the data set of subway passenger flow in a city in January 2019, the experimental results show that the root mean square error of the model is reduced by 8.515% and the average absolute error is reduced by 4.239% compared with the single BiLSTM model, it has a high fitting degree with the real passenger flow value and has certain application value for the reasonable allocation of subway capacity.
Reference11 articles.
1. Metro transfer passenger forecasting based on Kalman filter[J];Xiong;Journal of Beijing Jiaotong University,2013
2. Prediction of urban railway station’s entrance and exit passenger flow based on multiply ARIMA model[J];Cai;Journal of Beijing Jiaotong University,2014
3. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification[J];Guo;Transportation Research Part C Emerging Technologies,2014
4. A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system[J];Sun;Neurocomputing,2015
5. A Short-term Forecasting of Traffic Flow Parameters Based on Decision Tree Theory[J];Xue;Traffic information and safety,2016
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