{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T15:32:50Z","timestamp":1768923170001,"version":"3.49.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62162012"],"award-info":[{"award-number":["62162012"]}],"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":["62173278"],"award-info":[{"award-number":["62173278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07482-0","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T05:03:11Z","timestamp":1656478991000},"page":"19035-19050","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A distributed EMDN-GRU model on Spark for passenger waiting time forecasting"],"prefix":"10.1007","volume":"34","author":[{"given":"Dawen","family":"Xia","sequence":"first","affiliation":[]},{"given":"Yu","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Geng","sequence":"additional","affiliation":[]},{"given":"Wenyong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yantao","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6310-8965","authenticated-orcid":false,"given":"Huaqing","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"7482_CR1","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1109\/TKDE.2016.2531684","volume":"29","author":"C Yang","year":"2016","unstructured":"Yang C, Chen J (2016) A scalable data chunk similarity based compression approach for efficient big sensing data processing on cloud. IEEE Transactions on Knowledge and Data Engineering 29:1144\u20131157","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"7482_CR2","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TBDATA.2016.2622719","volume":"2","author":"P Basanta-Val","year":"2016","unstructured":"Basanta-Val P, Audsley NC, Wellings AJ, Gray I, Fern\u00e1ndez-Garc\u00eda N (2016) Architecting time-critical big-data systems. IEEE Transactions on Big Data 2:310\u2013324","journal-title":"IEEE Transactions on Big Data"},{"key":"7482_CR3","doi-asserted-by":"publisher","first-page":"24645","DOI":"10.1007\/s11042-020-09099-8","volume":"79","author":"S Asadianfam","year":"2020","unstructured":"Asadianfam S, Shamsi M, Kenari AR (2020) Big data platform of traffic violation detection system: identifying the risky behaviors of vehicle drivers. Multimedia Tools and Applications 79:24645\u201324684","journal-title":"Multimedia Tools and Applications"},{"key":"7482_CR4","doi-asserted-by":"publisher","first-page":"2393","DOI":"10.1007\/s00521-020-05076-2","volume":"33","author":"D Xia","year":"2021","unstructured":"Xia D, Zhang M, Yan X, Bai Y, Zheng Y, Li Y, Li H (2021) A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction. Neural Computing and Applications 33:2393\u20132410","journal-title":"Neural Computing and Applications"},{"key":"7482_CR5","doi-asserted-by":"publisher","first-page":"126056","DOI":"10.1016\/j.physa.2021.126056","volume":"578","author":"D Xia","year":"2021","unstructured":"Xia D, Jiang S, Yang N, Hu Y, Li Y, Li H, Wang L (2021) Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data. Physica A: Statistical Mechanics and its Applications 578:126056","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"7482_CR6","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1109\/TKDE.2018.2866863","volume":"31","author":"R Lu","year":"2018","unstructured":"Lu R, Jin X, Zhang S, Qiu M, Wu X (2018) A study on big knowledge and its engineering issues. IEEE Transactions on Knowledge and Data Engineering 31:1630\u20131644","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"7482_CR7","unstructured":"Guo S, Yu L, Chen X, Zhang Y (2010) The modeling of waiting time for passengers to transfer from rail to buses based-on passenger classification, Technical Report"},{"key":"7482_CR8","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.trpro.2020.03.079","volume":"47","author":"JJ V\u00e1zquez","year":"2020","unstructured":"V\u00e1zquez JJ, Arjona J, Linares M, Casanovas-Garcia J (2020) A comparison of deep learning methods for urban traffic forecasting using floating car data. Transportation Research Procedia 47:195\u2013202","journal-title":"Transportation Research Procedia"},{"key":"7482_CR9","doi-asserted-by":"publisher","first-page":"2050039","DOI":"10.1142\/S0129065720500392","volume":"30","author":"F Sa\u00e2daoui","year":"2020","unstructured":"Sa\u00e2daoui F, Messaoud OB (2020) Multiscaled neural autoregressive distributed lag: A new empirical mode decomposition model for nonlinear time series forecasting. International Journal of Neural Systems 30:2050039","journal-title":"International Journal of Neural Systems"},{"key":"7482_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2020.106105","volume":"203","author":"Z Liu","year":"2020","unstructured":"Liu Z, Liu J (2020) A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps. Knowledge-Based Systems 203:1\u201333","journal-title":"Knowledge-Based Systems"},{"key":"7482_CR11","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1016\/S0262-8856(03)00094-5","volume":"21","author":"JC Nunes","year":"2003","unstructured":"Nunes JC, Bouaoune Y, Delechelle E, Niang O, Bunel P (2003) Image analysis by bidimensional empirical mode decomposition. Image and Vision Computing 21:1019\u20131026","journal-title":"Image and Vision Computing"},{"key":"7482_CR12","unstructured":"Wang Z, Tianrui LI, Cheng Y, Wang Y, Xiuwen YI Prediction of probability of hitting vacant taxi and waiting time based on empirical distribution, In: 2015 Computer Engineering and Applications, IEEE, pp. 254\u2013259"},{"key":"7482_CR13","doi-asserted-by":"crossref","unstructured":"Qi G, Pan G, Li S, Wu Z, Zhang D, Sun L, Yang LT How long a passenger waits for a vacant taxi\u2013large-scale taxi trace mining for smart cities, In: 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, IEEE, pp. 1029\u20131036","DOI":"10.1109\/GreenCom-iThings-CPSCom.2013.175"},{"key":"7482_CR14","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1109\/TITS.2014.2371815","volume":"16","author":"X Xu","year":"2014","unstructured":"Xu X, Zhou J, Liu Y, Xu Z, Zhao X (2014) Taxi-RS: Taxi-hunting recommendation system based on taxi GPS data. IEEE Transactions on Intelligent Transportation Systems 16:1716\u20131727","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"7482_CR15","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ins.2015.03.068","volume":"314","author":"R-H Hwang","year":"2015","unstructured":"Hwang R-H, Hsueh Y-L, Chen Y-T (2015) An effective taxi recommender system based on a spatio-temporal factor analysis model. Information Sciences 314:28\u201340","journal-title":"Information Sciences"},{"key":"7482_CR16","doi-asserted-by":"crossref","unstructured":"Qiu Z, Li H, Hong S, Lin Y, Fan N, Ou G, Wang T, Fan L Finding vacant taxis using large scale GPS traces, In: 2014 International Conference on Web-Age Information Management, Springer, pp. 793\u2013804","DOI":"10.1007\/978-3-319-08010-9_85"},{"key":"7482_CR17","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s00779-016-0925-9","volume":"20","author":"W Jing","year":"2016","unstructured":"Jing W, Hu L, Shu L, Mukherjee M, Hara T (2016) RPR: recommendation for passengers by roads based on cloud computing and taxis traces data. Personal and Ubiquitous Computing 20:337\u2013347","journal-title":"Personal and Ubiquitous Computing"},{"key":"7482_CR18","doi-asserted-by":"publisher","first-page":"2659","DOI":"10.1109\/TII.2019.2943906","volume":"16","author":"J Qiu","year":"2019","unstructured":"Qiu J, Du L, Zhang D, Su S, Tian Z (2019) Nei-TTE: intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city. IEEE Transactions on Industrial Informatics 16:2659\u20132666","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7482_CR19","unstructured":"Wang D, Zhang J, Cao W, Li J, Zheng Y When will you arrive? estimating travel time based on deep neural networks, In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2500\u20132507"},{"key":"7482_CR20","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.procs.2020.06.062","volume":"174","author":"J Fei","year":"2020","unstructured":"Fei J, Lu Y, Guo Y, Zhang H (2020) Predicting bus arrival time using BP neural network and dynamic transfer. Procedia Computer Science 174:95\u2013100","journal-title":"Procedia Computer Science"},{"key":"7482_CR21","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1109\/TITS.2018.2873747","volume":"20","author":"J Pang","year":"2018","unstructured":"Pang J, Huang J, Du Y, Yu H, Huang Q, Yin B (2018) Learning to predict bus arrival time from heterogeneous measurements via recurrent neural network. IEEE Transactions on Intelligent Transportation Systems 20:3283\u20133293","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"7482_CR22","doi-asserted-by":"publisher","first-page":"10435","DOI":"10.1007\/s00521-019-04579-x","volume":"32","author":"C Chen","year":"2020","unstructured":"Chen C, Wang H, Yuan F, Jia H, Yao B (2020) Bus travel time prediction based on Deep Belief Network with back-propagation. Neural Computing and Applications 32:10435\u201310449","journal-title":"Neural Computing and Applications"},{"key":"7482_CR23","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.trc.2019.06.008","volume":"105","author":"J Ma","year":"2019","unstructured":"Ma J, Chan J, Ristanoski G, Rajasegarar S, Leckie C (2019) Bus travel time prediction with real-time traffic information. Transportation Research Part C: Emerging Technologies 105:536\u2013549","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"7482_CR24","doi-asserted-by":"publisher","first-page":"4192","DOI":"10.1109\/TITS.2018.2883342","volume":"20","author":"P He","year":"2018","unstructured":"He P, Jiang G, Lam S-K, Tang D (2018) Travel-time prediction of bus journey with multiple bus trips. IEEE Transactions on Intelligent Transportation Systems 20:4192\u20134205","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"7482_CR25","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.eswa.2018.11.028","volume":"120","author":"NC Petersen","year":"2019","unstructured":"Petersen NC, Rodrigues F, Pereira FC (2019) Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Systems with Applications 120:426\u2013435","journal-title":"Expert Systems with Applications"},{"key":"7482_CR26","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1016\/j.ins.2019.10.073","volume":"512","author":"P He","year":"2020","unstructured":"He P, Jiang G, Lam S-K, Sun Y (2020) Learning heterogeneous traffic patterns for travel time prediction of bus journeys. Information Sciences 512:1394\u20131406","journal-title":"Information Sciences"},{"key":"7482_CR27","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1109\/TITS.2019.2909314","volume":"21","author":"A Achar","year":"2019","unstructured":"Achar A, Bharathi D, Kumar BA, Vanajakshi L (2019) Bus arrival time prediction: A spatial kalman filter approach. IEEE Transactions on Intelligent Transportation Systems 21:1298\u20131307","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"7482_CR28","doi-asserted-by":"crossref","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N.-C, Tung C.C, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454 ,903\u2013995","DOI":"10.1098\/rspa.1998.0193"},{"key":"7482_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11207-020-01653-9","volume":"295","author":"T Lee","year":"2020","unstructured":"Lee T (2020) EMD and LSTM hybrid deep learning model for predicting sunspot number time series with a cyclic pattern. Solar Physics 295:1\u201323","journal-title":"Solar Physics"},{"key":"7482_CR30","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1631\/jzus.A1500040","volume":"16","author":"WY Duan","year":"2015","unstructured":"Duan WY, Huang LM, Han Y, Zhang YH, Huang S (2015) A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion. Journal of Zhejiang University-SCIENCE A 16:562\u2013576","journal-title":"Journal of Zhejiang University-SCIENCE A"},{"key":"7482_CR31","doi-asserted-by":"publisher","first-page":"e0222365","DOI":"10.1371\/journal.pone.0222365","volume":"14","author":"Q Chen","year":"2019","unstructured":"Chen Q, Wen D, Li X, Chen D, Lv H, Zhang J, 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:e0222365","journal-title":"PloS one"},{"key":"7482_CR32","doi-asserted-by":"publisher","first-page":"114332","DOI":"10.1016\/j.eswa.2020.114332","volume":"169","author":"H Rezaei","year":"2021","unstructured":"Rezaei H, Faaljou H, Mansourfar G (2021) Stock price prediction using deep learning and frequency decomposition. Expert Systems with Applications 169:114332","journal-title":"Expert Systems with Applications"},{"key":"7482_CR33","doi-asserted-by":"crossref","unstructured":"Zhao W, Yang H, Li J, Shang L, Hu L, Fu Q Network traffic prediction in network security based on EMD and LSTM, In: 2021 Proceedings of the 9th International Conference on Computer Engineering and Networks, Springer, pp. 509\u2013518","DOI":"10.1007\/978-981-15-3753-0_50"},{"key":"7482_CR34","doi-asserted-by":"crossref","unstructured":"Jiang T, Zhou C, Zhang H Time series forecasting with an EMD-LSSVM-PSO ensemble adaptive learning paradigm, In: Proceedings of the 2018 International Conference on Computational Intelligence and Intelligent Systems, pp. 44\u201350","DOI":"10.1145\/3293475.3293477"},{"key":"7482_CR35","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780","journal-title":"Neural Computation"},{"key":"7482_CR36","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y Learning phrase representations using RNN encoder-decoder for statistical machine translation, in: Empirical Methods in Natural Language Processing, pp. 1724\u20131734"},{"key":"7482_CR37","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima K (1980) A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36:193\u2013202","journal-title":"Biological Cybernetics"},{"key":"7482_CR38","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536","journal-title":"Nature"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07482-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07482-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07482-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T21:32:58Z","timestamp":1666301578000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07482-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":38,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7482"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07482-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]},"assertion":[{"value":"1 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}