{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T17:01:36Z","timestamp":1774976496624,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T00:00:00Z","timestamp":1592870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51878166"],"award-info":[{"award-number":["51878166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71701047"],"award-info":[{"award-number":["71701047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.<\/jats:p>","DOI":"10.3390\/s20123555","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T09:05:33Z","timestamp":1592903133000},"page":"3555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3608-1047","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"},{"name":"Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China"},{"name":"Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"},{"name":"Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China"},{"name":"Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China"}]},{"given":"De","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"},{"name":"Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China"},{"name":"Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gallo, M., De Luca, G., D\u2019Acierno, L., and Botte, M. (2019). Artificial neural networks for forecasting passenger flows on metro lines. Sensors, 19.","DOI":"10.3390\/s19153424"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wen, D., Li, X., Chen, D., Lv, H., Zhang, J., and Gao, P. (2019). Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0222365"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lin, P., Weng, J., Fu, Y., Alivanistos, D., and Yin, B. (2020). Study on the topology and dynamics of the rail transit network based on automatic fare collection data. Phys. A Stat. Mech. Appl., 545.","DOI":"10.1016\/j.physa.2019.123538"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.physa.2015.12.060","article-title":"Characteristics on hub networks of urban rail transit networks","volume":"447","author":"Zhang","year":"2016","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_5","first-page":"194","article-title":"Reliability analysis of Guangzhou rail transit with complex network theory","volume":"10","author":"Liu","year":"2010","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Du, Z., Tang, J., Qi, Y., Wang, Y., Han, C., and Yang, Y. (2020). Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city\u2014China. Phys. A Stat. Mech. Appl., 539.","DOI":"10.1016\/j.physa.2019.122926"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1109\/TITS.2018.2879497","article-title":"Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro","volume":"20","author":"Tang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"57415","DOI":"10.1109\/ACCESS.2019.2914239","article-title":"Subway Passenger Flow Forecasting with Multi-Station and External Factors","volume":"7","author":"Danfeng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jvcir.2018.11.033","article-title":"The passenger flow status identification based on image and WiFi detection for urban rail transit stations","volume":"58","author":"Ding","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, S., and Yao, E. (2017). Holiday passenger flow forecasting based on the modified least-square support vector machine for the metro system. J. Transp. Eng., 143.","DOI":"10.1061\/JTEPBS.0000010"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/9717582","article-title":"Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction","volume":"2016","author":"Jiao","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.trc.2019.01.027","article-title":"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_13","doi-asserted-by":"crossref","unstructured":"Fu, X., and Gu, Y. (2018). Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data. J. Adv. Transp., 2018.","DOI":"10.1155\/2018\/9247102"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.trf.2018.06.037","article-title":"Modelling passenger waiting time using large-scale automatic fare collection data: An Australian case study","volume":"58","author":"Tavassoli","year":"2018","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.eswa.2017.11.043","article-title":"Learning the route choice behavior of subway passengers from AFC data","volume":"95","author":"Xu","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.trc.2019.08.005","article-title":"Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system","volume":"107","author":"Hao","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, S., and Fambro, D.B. (1999). Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp. Res. Rec., 179\u2013188.","DOI":"10.3141\/1678-22"},{"key":"ref_18","first-page":"1113","article-title":"SARIMA modelling approach for railway passenger flow forecasting","volume":"33","author":"Melichar","year":"2018","journal-title":"Transport"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.4028\/www.scientific.net\/AMM.409-410.1315","article-title":"Forecasting the section passenger flow of the subway based on exponential smoothing","volume":"409\u2013410","author":"Wang","year":"2013","journal-title":"Appl. Mech. Mat."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yu, B., Song, X., Guan, F., Yang, Z., and Yao, B. (2016). K-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition. J. Transp. Eng., 142.","DOI":"10.1061\/(ASCE)TE.1943-5436.0000816"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.trc.2015.11.002","article-title":"A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting","volume":"62","author":"Cai","year":"2016","journal-title":"Transp. Res. Part C. Emerg. Technol."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.trc.2013.11.011","article-title":"A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model","volume":"43","author":"Zhang","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_24","unstructured":"Zeng, D., Xu, J., Gu, J., Liu, L., and Xu, G. (2008, January 2\u20133). Short term traffic flow prediction using hybrid ARIMA and ANN models. Proceedings of the 2008 Workshop on Power Electronics and Intelligent Transportation System (PEITS 2008), Guangzhou, China."},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1049\/iet-its.2018.5511","article-title":"Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features","volume":"13","author":"Yang","year":"2019","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.asoc.2017.05.011","article-title":"A multi-pattern deep fusion model for short-term bus passenger flow forecasting","volume":"58","author":"Bai","year":"2017","journal-title":"Appl. Soft Comput. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.trc.2017.08.001","article-title":"A novel passenger flow prediction model using deep learning methods","volume":"84","author":"Liu","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Yang, C., Guo, Z., and Xian, L. (2019). Time series data prediction based on sequence to sequence model. IOP Conf. Ser. Mat. Sci. Eng., 692.","DOI":"10.1088\/1757-899X\/692\/1\/012047"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.trc.2019.12.007","article-title":"Short-term traffic state prediction from latent structures: Accuracy vs. efficiency","volume":"111","author":"Li","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"158025","DOI":"10.1109\/ACCESS.2019.2950327","article-title":"Short-Term Passenger Flow Prediction Based on Wavelet Transform and Kernel Extreme Learning Machine","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.jtrangeo.2010.04.003","article-title":"Exploring time variants for short-term passenger flow","volume":"19","author":"Chen","year":"2011","journal-title":"J. Trans. Geogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.neucom.2019.04.061","article-title":"Effective passenger flow forecasting using STL and ESN based on two improvement strategies","volume":"356","author":"Qin","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"91181","DOI":"10.1109\/ACCESS.2020.2995044","article-title":"Forecasting the Short-Term Metro Ridership with Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physa.2019.121063","article-title":"Multivariate singular spectrum analysis for traffic time series","volume":"526","author":"Mao","year":"2019","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shang, Q., Lin, C., Yang, Z., Bing, Q., and Zhou, X. (2016). A hybrid short-term traffic flow prediction model based on singular spectrum analysis and kernel extreme learning machine. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0161259"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1080\/03081060.2012.745721","article-title":"A computationally efficient two-stage method for short-term traffic prediction on urban roads","volume":"36","author":"Guo","year":"2013","journal-title":"Transp. Plan. Technol."},{"key":"ref_40","first-page":"86","article-title":"Research of Architecture on Rail Transit\u2019s AFC System","volume":"27","author":"Qiu","year":"2014","journal-title":"Urb. Rapid Rail Transit"},{"key":"ref_41","unstructured":"Taieb, S.B. (2012). and Hyndman, R.J. Recursive and Direct Multi-Step Forecasting: The Best of Both Worlds, Monash University. Monash Econometrics and Business Statistics Working Papers."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/978-3-642-36318-4_3","article-title":"Machine learning strategies for time series forecasting","volume":"Volume 138","author":"Bontempi","year":"2013","journal-title":"Lecture Notes in Business Information Processing, LNBIP"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Golyandina, N., Nekrutkin, V.V., and Zhigljavsky, A.A. (2001). Analysis of Time Series Structure: SSA and Related Techniques, Chapman & Hall\/CRC. Monographs on Statistics and Applied Probability.","DOI":"10.1201\/9781420035841"},{"key":"ref_44","unstructured":"Freund, Y., and Schapire, R.E. (1996, January 3\u20136). Experiments with a New Boosting Algorithm. Proceedings of the 13th International Conference on Machine Learning, Bari, Italy."},{"key":"ref_45","unstructured":"Drucker, H. (1997, January 8\u201312). Improving regressors using boosting techniques. Proceedings of the 14th International Conference on Machine Learning, San Francisco, CA, USA."},{"key":"ref_46","unstructured":"Solomatine, D.P., and Shrestha, D.L. (2004, January 25\u201329). AdaBoost.RT: A boosting algorithm for regression problems. Proceedings of the 2004 IEEE International Conference on Neural Networks, Budapest, Hungary."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1162\/neco.2006.18.7.1678","article-title":"Experiments with AdaBoost.RT, an improved boosting scheme for regression","volume":"18","author":"Shrestha","year":"2006","journal-title":"Neural Comput."},{"key":"ref_48","unstructured":"Huang, G.B., Zhu, Q.Y., and Siew, C.K. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Conference on Neural Networks, Budapest, Hungary."},{"key":"ref_49","unstructured":"Tianchi, A. (2020, February 20). The AI Challenge of Urban Computing. Available online: https:\/\/tianchi.aliyun.com\/competition\/entrance\/231712\/information."},{"key":"ref_50","first-page":"1","article-title":"Passenger flow prediction of subway transfer stations based on nonparametric regression model","volume":"2014","author":"Sun","year":"2014","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Harvey, A.C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press.","DOI":"10.1017\/CBO9781107049994"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Diebold, F.X. (2013). Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests. SSRN Electr. J.","DOI":"10.3386\/w18391"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/j.eneco.2019.05.026","article-title":"A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM","volume":"81","author":"Zhang","year":"2019","journal-title":"Energy Econ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3555\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:41:48Z","timestamp":1760175708000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,23]]},"references-count":53,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123555"],"URL":"https:\/\/doi.org\/10.3390\/s20123555","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,23]]}}}