Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit

Short-term passenger flow forecasting is a crucial task for urban rail transit operations. Emerging deep-learning technologies have become effective methods used to overcome this problem. In this study, the authors propose a deep-learning architecture called Conv-GCN that combines a graph convolutional network (GCN) and a three-dimensional (3D) convolutional neural network (3D CNN). First, they introduce a multi-graph GCN to deal with three inflow and outflow patterns (recent, daily, and weekly) separately. Multi-graph GCN networks can capture spatiotemporal correlations and topological information within the entire network. A 3D CNN is then applied to deeply integrate the inflow and outflow information. High-level spatiotemporal features between different inflow and outflow patterns and between stations that are nearby and far away can be extracted by 3D CNN. Finally, a fully connected layer is used to output results. The Conv-GCN model is evaluated on smart card data of the Beijing subway under the time interval of 10, 15, and 30 min. Results show that this model yields the best performance compared with seven other models. In terms of the root-mean-square errors, the performances under three time intervals have been improved by 9.402, 7.756, and 9.256%, respectively. This study can provide critical insights for subway operators to optimise urban rail transit operations.

[1]  Jaegul Choo,et al.  STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting , 2019, ArXiv.

[2]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[3]  Yong Wang,et al.  Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.

[4]  Yang Zhang,et al.  Deep spatio-temporal residual neural networks for road-network-based data modeling , 2019, Int. J. Geogr. Inf. Sci..

[5]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[6]  Shu Yu,et al.  Hybrid GA Based Online Support Vector Machine Model for Short-Term Traffic Flow Forecasting , 2007, APPT.

[7]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[9]  Jing Li,et al.  Graph CNNs for Urban Traffic Passenger Flows Prediction , 2018, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[10]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[11]  Jieping Ye,et al.  Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting , 2019, AAAI.

[12]  Zijian Liu,et al.  A novel generative adversarial network for estimation of trip travel time distribution with trajectory data , 2019, Transportation Research Part C: Emerging Technologies.

[13]  Yinhai Wang,et al.  Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting , 2018, IEEE Transactions on Intelligent Transportation Systems.

[14]  Zhiyong Cui,et al.  Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit , 2019, IEEE Transactions on Intelligent Transportation Systems.

[15]  Yunpeng Wang,et al.  Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks , 2017, Sensors.

[16]  Bernard Ghanem,et al.  Can GCNs Go as Deep as CNNs? , 2019, ArXiv.

[17]  Enrique Onieva,et al.  A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data , 2020 .

[18]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[19]  Yang Zhang,et al.  Traffic forecasting using least squares support vector machines , 2009 .

[20]  Bernard Ghanem,et al.  DeepGCNs: Can GCNs Go As Deep As CNNs? , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[21]  Yu Liu,et al.  T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction , 2018, IEEE Transactions on Intelligent Transportation Systems.

[22]  Hao Yu,et al.  Short-term FFBS demand prediction with multi-source data in a hybrid deep learning framework , 2019 .

[23]  Der-Horng Lee,et al.  Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system , 2019, Transportation Research Part C: Emerging Technologies.

[24]  Yinhai Wang,et al.  Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies , 2019, Transportation Research Part C: Emerging Technologies.

[25]  Qiang Yang,et al.  Bike flow prediction with multi-graph convolutional networks , 2018, SIGSPATIAL/GIS.

[26]  Xianfeng Tang,et al.  Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction , 2018, AAAI.

[27]  Lei Lin,et al.  Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach , 2017, Transportation Research Part C: Emerging Technologies.

[28]  Mengning Yang,et al.  Urban rail transit passenger flow forecast based on LSTM with enhanced long‐term features , 2019, IET Intelligent Transport Systems.

[29]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[30]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[31]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Billy M. Williams,et al.  Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow , 2007 .

[33]  Joan Bruna,et al.  Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.

[34]  Byeonghyeop Yu,et al.  Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN) , 2020 .

[35]  Xi Jiang,et al.  Deep learning‐based hybrid model for short‐term subway passenger flow prediction using automatic fare collection data , 2019, IET Intelligent Transport Systems.

[36]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[37]  Zhanxing Zhu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.

[38]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[39]  Peng Gao,et al.  Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks , 2019, ISPRS Int. J. Geo Inf..

[40]  Wei Wu,et al.  Short‐term passenger flow forecast of urban rail transit based on GPR and KRR , 2019, IET Intelligent Transport Systems.

[41]  Feng Chen,et al.  Cluster-Based LSTM Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit , 2019, IEEE Access.

[42]  Manoranjan Parida,et al.  Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network , 2013 .

[43]  Ning Feng,et al.  Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting , 2019, AAAI.