{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:11:14Z","timestamp":1784733074833,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T00:00:00Z","timestamp":1559001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of Qingdao","award":["16-6-2-61-NSH"],"award-info":[{"award-number":["16-6-2-61-NSH"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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\u2014inflow and outflow\u2014in 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.<\/jats:p>","DOI":"10.3390\/ijgi8060243","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T11:18:09Z","timestamp":1559042289000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks"],"prefix":"10.3390","volume":"8","author":[{"given":"Yong","family":"Han","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 238, Songling Road, Qingdao 266100, China"},{"name":"Pilot National Laboratory for Marine Science and Technology (Qingdao), No. 1, Wenhai Road, Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shukang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 238, Songling Road, Qingdao 266100, China"},{"name":"Pilot National Laboratory for Marine Science and Technology (Qingdao), No. 1, Wenhai Road, Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibin","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 238, Songling Road, Qingdao 266100, China"},{"name":"Pilot National Laboratory for Marine Science and Technology (Qingdao), No. 1, Wenhai Road, Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 238, Songling Road, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"Qingdao Transportation Public Service Center, No. 163, Shenzhen Road, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Ocean University of China, No. 238, Songling Road, Qingdao 266100, China"},{"name":"Pilot National Laboratory for Marine Science and Technology (Qingdao), No. 1, Wenhai Road, Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.trc.2011.06.009","article-title":"Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks","volume":"21","author":"Wei","year":"2012","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neucom.2015.03.085","article-title":"A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system","volume":"166","author":"Sun","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3846\/16484142.2011.555472","article-title":"The use of LS-SVM for short-term passenger flow prediction","volume":"26","author":"Chen","year":"2011","journal-title":"Transport"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, J., Cheng, T., and Li, X. (2007, January 24\u201327). Nonlinear integration of spatial and temporal forecasting by support vector machines. Proceedings of the Fuzzy Systems and Knowledge Discovery (FSKD 2007), Haikou, China.","DOI":"10.1109\/FSKD.2007.424"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lin, E., Park, J.D., and Z\u00fcfle, A. (2017, January 7\u201310). Real-Time Bayesian Micro-Analysis for Metro Traffic Prediction. Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, Redondo Beach, CA, USA.","DOI":"10.1145\/3152178.3152190"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Truong, R., Gkountouna, O., Pfoser, D., and Z\u00fcfle, A. (2018). Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro. Urban. Sci., 2.","DOI":"10.3390\/urbansci2030065"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dai, X., Sun, L., and Xu, Y. (2018). Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach. J. Adv. Transp.","DOI":"10.1155\/2018\/5942763"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gong, Y., Li, Z., Zhang, J., Liu, W., Zheng, Y., and Kirsch, C. (2018, January 22\u201326). Network-wide Crowd Flow Prediction of Sydney Trains via customized Online Non-negative Matrix Factorization. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy.","DOI":"10.1145\/3269206.3271757"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3728","DOI":"10.1016\/j.eswa.2008.02.071","article-title":"Neural network based temporal feature models for short-term railway passenger demand forecasting","volume":"36","author":"Tsai","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1111\/j.1467-9671.2008.01117.x","article-title":"Integrated spatio-temporal data mining for forest fire prediction","volume":"12","author":"Cheng","year":"2008","journal-title":"Trans. Gis"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.engappai.2016.02.012","article-title":"A space\u2013time delay neural network model for travel time prediction","volume":"52","author":"Wang","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","unstructured":"Biship, C.M. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1080\/13658816.2018.1460753","article-title":"Fine-grained prediction of urban population using mobile phone location data","volume":"32","author":"Chen","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.artint.2018.03.002","article-title":"Predicting citywide crowd flows using deep spatio-temporal residual networks","volume":"259","author":"Zhang","year":"2018","journal-title":"Artif. Intell."},{"key":"ref_16","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT press Cambridge."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2015, January 7\u201312). Show and tell: A neural image caption generator. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yu, H., Wang, J., Huang, Z., Yang, Y., and Xu, W. (2016, January 27\u201330). Video paragraph captioning using hierarchical recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.496"},{"key":"ref_19","unstructured":"Srivastava, N., Mansimov, E., and Salakhudinov, R. (2015, January 6\u201311). Unsupervised learning of video representations using lstms. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep learning: Methods and applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends\u00ae Signal. Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lee, D., and Kim, K. (2019). Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information. Energies, 12.","DOI":"10.3390\/en12020215"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, T., Song, S., Li, S., Ma, L., Pan, S., and Han, L. (2019). Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series. Energies, 12.","DOI":"10.3390\/en12010161"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.trc.2019.01.027","article-title":"DeepPF: A deep learning based architecture for metro passenger flow prediction","volume":"101","author":"Liu","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data","volume":"54","author":"Ma","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_26","unstructured":"Wang, Y., Currim, F., and Ram, S. (2017). Deep Learning for Bus Passenger Demand Prediction Using Big Data, Social Science Electronic Publishing."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ren, Y., Cheng, T., and Zhang, Y. (2019). Deep spatio-temporal residual neural networks for road-network-based data modeling. Int. J. Geogr. Inf. Sci., 1\u201319.","DOI":"10.1080\/13658816.2019.1599895"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., and Qi, D. (2017, January 4\u20139). Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. Proceedings of the Thirty-First AAAI Conference on Artificial IntelligenceI, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., and Wang, Y. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17.","DOI":"10.3390\/s17040818"},{"key":"ref_30","unstructured":"Defferrard, M., Bresson, X., and Vandergheynst, P. (2016, January 5\u201310). In Convolutional neural networks on graphs with fast localized spectral filtering. Proceedings of the Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_31","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_32","unstructured":"Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2013). Spectral networks and locally connected networks on graphs. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.trc.2018.10.011","article-title":"Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach","volume":"97","author":"Lin","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_34","unstructured":"Bing, Y., Yin, H., and Zhu, Z. (2017). Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.trc.2013.12.008","article-title":"Predicting short-term bus passenger demand using a pattern hybrid approach","volume":"39","author":"Ma","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_36","unstructured":"Mathieu, M., Couprie, C., and LeCun, Y. (2015). Deep multi-scale video prediction beyond mean square error. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jain, V., Murray, J.F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K.L., Helmstaedter, M.N., Denk, W., and Seung, H.S. (2007, January 14\u201321). Supervised learning of image restoration with convolutional networks. Proceedings of the IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4408909"},{"key":"ref_38","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_39","unstructured":"(2019, May 27). Shanghai Metro Roadmap. Available online: http:\/\/sh.bendibao.com\/ditie\/linemap.shtml."},{"key":"ref_40","unstructured":"Chollet, F. (2019, January 16). Keras: Deep Learning Library for Theano and Tensorflow. Available online: https:\/\/keras.io\/."},{"key":"ref_41","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Komer, B., Bergstra, J., and Eliasmith, C. (2014, January 6\u201312). Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn. Proceedings of the 13th Python in Science Conference (SciPy 2014), Austin, TX, USA.","DOI":"10.25080\/Majora-14bd3278-006"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.trc.2017.10.016","article-title":"Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach","volume":"85","author":"Ke","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_44","unstructured":"(2019, January 16). These stations of the Shanghai Metro, I Was Crying and Being Squeezed Down. Available online: http:\/\/news.fdc.com.cn\/mrrd\/977358.shtml."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/6\/243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:00Z","timestamp":1760187240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/6\/243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,28]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["ijgi8060243"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8060243","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,28]]}}}