Short-time Inflow and Outflow Prediction of Metro Stations Based on Hybrid Deep Learning

被引：0

作者：

Zhao J.-L. ^{[1
]}

Shi J.-S. ^{[1
]}

Sun Q.-X. ^{[2
]}

Ren L. ^{[3
]}

Liu C.-H. ^{[3
]}

机构：

[1] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao

[2] College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao

[3] Qingdao Metro Group Co., Ltd, Qingdao

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2020年 / 20卷 / 05期

关键词：

CNN; Deep learning; Metro card-swiping data; ResNet; Short-time passenger volume prediction; Urban traffic;

D O I：

10.16097/j.cnki.1009-6744.2020.05.019

中图分类号：

学科分类号：

摘要：

This paper proposes a prediction model (ResNet-CNN1D) combining convolutional neural network (CNN) and residual network (ResNet) for multi-station short-term passenger volume prediction of urban rail transit. The original passenger volume data is used as input of the model. The deep network composed of two-dimensional CNN and ResNet is used to mine the spatial features between the stations. The one-dimensional CNN is used to mine the temporal features of the passenger flow. Based on the parametric matrix, the temporal and spatial features are weighted to obtain the multi-station inflow and outflow during the research period. The model is verified by the card-swiping data of the Qingdao No.3 metro line. Compared with existing traditional prediction models (ARIMA, SVR, LSTM, CLTFP, ConvLSTM), the proposed ResNet-CNN1D model in this paper has the best prediction accuracy. Copyright © 2020 by Science Press.

引用

页码：128 / 134

页数：6

共 10 条

[1]

BAI L., Urban rail transit normal and abnormal short-term passenger flow forecasting method, Journal of Transportation Systems Engineering and Information Technology, 17, 1, pp. 127-135, (2017)

[2]

HUAN N, XIE Q, YE H X, Et al., Real-time forecasting of urban rail transit ridership at the station level based on improved KNN algorithm, Journal of Transportation Systems Engineering and Information Technology, 18, 5, pp. 121-128, (2018)

[3]

TANG Q C, YANG M N, YANG Y., ST-LSTM: A deep learning approach combined spatio-temporal features for short-term forecast in rail transit, Journal of Advanced Transportation, 2019, 6, pp. 1-8, (2019)

[4]

MA X L, ZHANG J Y, DU B W, Et al., Parallel architecture of convolutional bi-directional LSTM neural networks for network-wide metro ridership prediction, IEEE Transactions on Intelligent Transportation Systems, 20, 6, pp. 2278-2288, (2019)

[5]

LIU Y, LIU Z Y, JIA R., DeepPF: A deep learning based architecture for metro passenger flow prediction, Transportation Research Part C: Emerging Technologies, 101, pp. 18-34, (2019)

[6]

ZHANG J L, CHEN F, CUI Z Y, Et al., Deep learning architecture for short-term passenger flow forecasting in urban rail transit, IEEE Transactions on Intelligent Transportation Systems, (2020)

[7]

ZHANG J B, ZHENG Y, QI D K., Deep spatio-temporal residual networks for citywide crowd flows prediction, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 1655-1661, (2017)

[8]

HE K M, ZHANG X Y, REN S Q, Et al., Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, (2016)

[9]

YU B, YIN H T, ZHU Z X., Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 3634-3640, (2018)

[10]

WU Y K, TAN H C., Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework

← 1 →