Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes—inflow and outflow—in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors.

[1]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[2]  Dieter Pfoser,et al.  Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro , 2018, Urban Science.

[3]  Wei Xu,et al.  Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Chi-Kang Lee,et al.  Neural network based temporal feature models for short-term railway passenger demand forecasting , 2009, Expert Syst. Appl..

[5]  Xiqun Chen,et al.  Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach , 2017, ArXiv.

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

[7]  Andreas Züfle,et al.  Real-Time Bayesian Micro-Analysis for Metro Traffic Prediction , 2017, UrbanGIS@SIGSPATIAL.

[8]  Bin Yu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017 .

[9]  Sudha Ram,et al.  Deep Learning for Bus Passenger Demand Prediction Using Big Data , 2016 .

[10]  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.

[11]  Xia Li,et al.  Nonlinear Integration of Spatial and Temporal Forecasting by Support Vector Machines , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[12]  Wei Liu,et al.  Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization , 2018, CIKM.

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

[14]  Tao Pei,et al.  Fine-grained prediction of urban population using mobile phone location data , 2018, Int. J. Geogr. Inf. Sci..

[15]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[16]  Jiaqiu Wang,et al.  Integrated Spatio‐temporal Data Mining for Forest Fire Prediction , 2008, Trans. GIS.

[17]  Jiaqiu Wang,et al.  A space-time delay neural network model for travel time prediction , 2016, Eng. Appl. Artif. Intell..

[18]  Joseph F. Murray,et al.  Supervised Learning of Image Restoration with Convolutional Networks , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[19]  Yanyan Xu,et al.  Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach , 2018, Journal of Advanced Transportation.

[20]  Wenquan Li,et al.  The use of LS-SVM for short-term passenger flow prediction , 2011 .

[21]  Tianrui Li,et al.  Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks , 2017, Artif. Intell..

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

[23]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[24]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[25]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Nitish Srivastava,et al.  Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.

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

[28]  Yang Liu,et al.  DeepPF: A deep learning based architecture for metro passenger flow prediction , 2019, Transportation Research Part C: Emerging Technologies.

[29]  Biao Leng,et al.  A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system , 2015, Neurocomputing.

[30]  Kwanho Kim,et al.  Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information , 2019, Energies.

[31]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[32]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  Zhenliang Ma,et al.  Predicting short-term bus passenger demand using a pattern hybrid approach , 2014 .

[35]  Yann LeCun,et al.  Deep multi-scale video prediction beyond mean square error , 2015, ICLR.

[36]  Shugang Li,et al.  Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series , 2019, Energies.

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

[38]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[39]  Mu-Chen Chen,et al.  Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks , 2012 .

[40]  Chris Eliasmith,et al.  Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn , 2014, SciPy.