{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T20:29:55Z","timestamp":1764880195075,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030645793"},{"type":"electronic","value":"9783030645809"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-64580-9_15","type":"book-chapter","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T18:17:02Z","timestamp":1609957022000},"page":"181-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Coking Coal Railway Transportation Forecasting Using Ensembles of ElasticNet, LightGBM, and Facebook Prophet"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0338-1227","authenticated-orcid":false,"given":"Vladimir","family":"Soloviev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3559-9984","authenticated-orcid":false,"given":"Nikita","family":"Titov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6493-0149","authenticated-orcid":false,"given":"Elena","family":"Smirnova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"issue":"1","key":"15_CR1","first-page":"7","volume":"12","author":"PS Rao","year":"1978","unstructured":"Rao, P.S.: Forecasting the demand for railway freight services. J. Transp. Econ. Policy 12(1), 7\u201326 (1978)","journal-title":"J. Transp. Econ. Policy"},{"issue":"3","key":"15_CR2","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/BF00148619","volume":"13","author":"M Doi","year":"1986","unstructured":"Doi, M., Allen, W.B.: A time series analysis of monthly ridership for an urban rail rapid transit line. Transportation 13(3), 257\u2013269 (1986). https:\/\/doi.org\/10.1007\/BF00148619","journal-title":"Transportation"},{"issue":"1","key":"15_CR3","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/09720510.2006.10701192","volume":"9","author":"VA Profillidis","year":"2006","unstructured":"Profillidis, V.A., Botzoris, G.N.: Econometric models for the forecast of passenger demand in Greece. J. Stat. Manag. Syst. 9(1), 37\u201354 (2006). https:\/\/doi.org\/10.1080\/09720510.2006.10701192","journal-title":"J. Stat. Manag. Syst."},{"key":"15_CR4","unstructured":"Shen, S., Fowkes, T., Whiteing, T., Johnson, D.: Econometric modelling and forecasting of freight transport demand in Great Britain. In: Proceedings of the European Transport Conference, Noordwijkerhout, The Netherlands, 5 October 2009, pp. 1\u201321. Association for European Transport, Henley-in-Arden (2009). https:\/\/aetransport.org\/public\/downloads\/GNK3F\/3978-514ec5c9deed2.pdf. Accessed 09 May 2020"},{"key":"15_CR5","unstructured":"Odgers, J.F., Schijndel, A.V.: Forecasting annual train boardings in Melbourne using time series data. In: Proceedings of the 34th Australian Transport Research Forum (ATRF), Adelaide, Australia, 28\u201330 September 2011, pp. 1\u201320. Australian Transport Research Forum, Canberra (2011). https:\/\/www.researchgate.net\/publication\/241809181_Forecasting_annual_train_boardings_in_Melbourne_using_time_series_data. Accessed 09 May 2020"},{"issue":"1","key":"15_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/S1366-5545(98)00024-6","volume":"35","author":"M Babcock","year":"1999","unstructured":"Babcock, M., Lu, X., Norton, J.: Time series forecasting of quarterly railroad grain carloadings. Transp. Res. Part E: Logist. Transp. Rev. 35(1), 43\u201357 (1999). https:\/\/doi.org\/10.1016\/S1366-5545(98)00024-6","journal-title":"Transp. Res. Part E: Logist. Transp. Rev."},{"key":"15_CR7","unstructured":"Kulshreshtha, M., Nag, B., Kulshreshtha, M.: A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian Railways. Transp. Res. Part A: Policy Pract. 35, 29\u201345 (2001). https:\/\/ideas.repec.org\/a\/eee\/transa\/v35y2001i1p29-45.html. Accessed 09 May 2020"},{"issue":"4","key":"15_CR8","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/S0968-090X(02)00009-8","volume":"10","author":"BL Smith","year":"2002","unstructured":"Smith, B.L., Williams, B.M., Oswald, R.K.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C: Emerg. Technol. 10(4), 303\u2013321 (2002). https:\/\/doi.org\/10.1016\/S0968-090X(02)00009-8","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"issue":"2","key":"15_CR9","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1061\/JHTRCQ.0000255","volume":"3","author":"M Tong","year":"2008","unstructured":"Tong, M., Xue, H.: Highway traffic volume forecasting based on seasonal ARIMA model. J. Highway Transp. Res. Dev. 3(2), 109\u2013112 (2008). https:\/\/doi.org\/10.1061\/JHTRCQ.0000255","journal-title":"J. Highway Transp. Res. Dev."},{"key":"15_CR10","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3141\/2136-07","volume":"2136","author":"M Cools","year":"2009","unstructured":"Cools, M., Moons, E., Wets, G.: Investigating the variability in daily traffic counts through use of ARIMAX and SARIMAX models: assessing the effect of holidays on two site locations. Transp. Res. Rec.: J. Transp. Res. Board 2136, 57\u201366 (2009). https:\/\/doi.org\/10.3141\/2136-07","journal-title":"Transp. Res. Rec.: J. Transp. Res. Board"},{"key":"15_CR11","unstructured":"Guo, Y.N., Shi, X.P., Zhang, X.D.: A study of short term forecasting of the railway freight volume in China using ARIMA and Holt-Winters models. In: Proceedings of the 8th International Conference on Supply Chain Management and Information, SCMIS \u2013 2010, Hong Kong, 6\u20139 October 2010, pp. 304\u2013309. IEEE, Piscataway (2010). https:\/\/ieeexplore.ieee.org\/document\/5681738. Accessed 09 May 2020"},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Jiuran, H., Bingfeng, S.: The application of ARIMA-RBF model in urban rail traffic volume forecast. In: Proceedings of the 2nd International Conference on Computer Science and Electronic Engineering, ICCSEE \u2013 2013, Dubai, UAE, 16\u201317 November 2013, pp. 1662\u20131665. Atlantis Press, Paris (2013). https:\/\/doi.org\/10.2991\/iccsee.2013.416","DOI":"10.2991\/iccsee.2013.416"},{"issue":"3","key":"15_CR13","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.eap.2014.08.003","volume":"44","author":"A Wijeweera","year":"2014","unstructured":"Wijeweera, A., To, H., Charles, M.B., Sloan, K.: A time series analysis of passenger rail demand in major Australian cities. Econ. Anal. Policy 44(3), 301\u2013309 (2014). https:\/\/doi.org\/10.1016\/j.eap.2014.08.003","journal-title":"Econ. Anal. Policy"},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.4028\/www.scientific.net\/amm.587-589.1954","volume":"587\u2013589","author":"Y Liu","year":"2014","unstructured":"Liu, Y., Lang, M.X.: Railway freight volume prediction based on support vector regression (SVR). Appl. Mech. Mater. 587\u2013589, 1954\u20131957 (2014). https:\/\/doi.org\/10.4028\/www.scientific.net\/amm.587-589.1954","journal-title":"Appl. Mech. Mater."},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Plakandaras, V., Papadimitriou, Th., Gogas, P.: Forecasting transportation demand for the U.S. market. Transp. Res. Part A: Policy Pract. 126, 195\u2013214 (2019). https:\/\/doi.org\/10.1016\/j.tra.2019.06.008","DOI":"10.1016\/j.tra.2019.06.008"},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"19717","DOI":"10.1109\/ACCESS.2020.2967867","volume":"8","author":"C Li","year":"2020","unstructured":"Li, C., Wang, X., Cheng, Z., Bai, Y.: Forecasting bus passenger flows by using a clustering-based support vector regression approach. IEEE Access 8, 19717\u201319725 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2967867","journal-title":"IEEE Access"},{"issue":"2","key":"15_CR17","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1080\/23249935.2019.1692956","volume":"156","author":"Z Shi","year":"2020","unstructured":"Shi, Z., Zhang, N., Schonfeld, P.M., Zhang, J.: Short-term metro passenger flow forecasting using ensemble-chaos support vector regression. Transportmetrica A: Transp. Sci. 156(2), 194\u2013212 (2020). https:\/\/doi.org\/10.1080\/23249935.2019.1692956","journal-title":"Transportmetrica A: Transp. Sci."},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.4028\/www.scientific.net\/amm.543-547.2093","volume":"543\u2013547","author":"Y Sun","year":"2014","unstructured":"Sun, Y., Lang, M.X., Wang, D.Z., Liu, L.Y.: Prediction models for railway freight volume based on artificial neural networks. Appl. Mech. Mater. 543\u2013547, 2093\u20132098 (2014). https:\/\/doi.org\/10.4028\/www.scientific.net\/amm.543-547.2093","journal-title":"Appl. Mech. Mater."},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":"Yongbin, X.Y., Xie, H., Wu, L.: Analysis and forecast of railway coal transportation volume based on BP neural network combined forecasting model. AIP Conf. Proc. 1967 (2018). Article 040052. https:\/\/doi.org\/10.1063\/1.5039126","DOI":"10.1063\/1.5039126"},{"key":"15_CR20","doi-asserted-by":"publisher","unstructured":"Tsai, Ts.-Hs., Lee, Ch.-K., Wei, Ch.-H.: Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Syst. Appl. 36(2), 3728\u20133736 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2008.02.071","DOI":"10.1016\/j.eswa.2008.02.071"},{"issue":"6","key":"15_CR21","doi-asserted-by":"publisher","first-page":"1786","DOI":"10.1109\/TITS.2015.2511156","volume":"17","author":"Z Hou","year":"2016","unstructured":"Hou, Z., Li, X.: Repeatability and similarity of freeway traffic flow and long-term prediction under big data. IEEE Trans. Intell. Transp. Syst. 17(6), 1786\u20131796 (2016). https:\/\/doi.org\/10.1109\/TITS.2015.2511156","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"15_CR22","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1515\/jisys-2016-0172","volume":"27","author":"Z Guo","year":"2018","unstructured":"Guo, Z., Fu, J.-Y.: Prediction method of railway freight volume based on genetic algorithm improved general regression neural network. J. Intell. Syst. 27(2), 291\u2013302 (2018). https:\/\/doi.org\/10.1515\/jisys-2016-0172","journal-title":"J. Intell. Syst."},{"issue":"2","key":"15_CR23","doi-asserted-by":"publisher","first-page":"4239","DOI":"10.1007\/s10586-018-1794-y","volume":"22","author":"P Wang","year":"2018","unstructured":"Wang, P., Zhang, X., Han, B., Lang, M.: Prediction model for railway freight volume with GCA-genetic algorithm-generalized neural network: empirical analysis of China. Cluster Comput. 22(2), 4239\u20134248 (2018). https:\/\/doi.org\/10.1007\/s10586-018-1794-y","journal-title":"Cluster Comput."},{"issue":"2","key":"15_CR24","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","volume":"11","author":"Z Zhao","year":"2017","unstructured":"Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68\u201375 (2017). https:\/\/doi.org\/10.1049\/iet-its.2016.0208","journal-title":"IET Intell. Transp. Syst."},{"key":"15_CR25","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.trc.2019.09.008","volume":"108","author":"LNN Do","year":"2019","unstructured":"Do, L.N.N., Vu, H.L., Vo, B.Q., Liu, Z., Phung, D.: An effective spatial-temporal attention based neural network for traffic flow prediction. Transp. Res. Part C: Emerg. Technol. 108, 12\u201328 (2019). https:\/\/doi.org\/10.1016\/j.trc.2019.09.008","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"15_CR26","doi-asserted-by":"publisher","unstructured":"Zhai, H., Tian, R. Cui, L., Xu, X., Zhang, W.: A novel hierarchical hybrid model for short-term bus passenger flow forecasting. J. Adv. Transp. 2020 (2020). https:\/\/doi.org\/10.1155\/2020\/7917353. Article 7917353","DOI":"10.1155\/2020\/7917353"},{"key":"15_CR27","doi-asserted-by":"publisher","unstructured":"Xie, M.-Q., Li, X.-M., Zhou, W.-L., Fu, Y.-B.: Forecasting the short-term passenger flow on high-speed railway with neural networks. Comput. Intell. Neurosci. 2014 (2014). Article 375487. https:\/\/doi.org\/10.1155\/2014\/375487","DOI":"10.1155\/2014\/375487"},{"key":"15_CR28","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.eswa.2018.12.031","volume":"121","author":"Q Licheng","year":"2019","unstructured":"Licheng, Q., Wei, L., Wenjing, L., Dongfang, M., Yinhai, W.: Daily long-term traffic flow forecasting based on a deep neural network. Expert Syst. Appl. 121, 304\u2013312 (2019). https:\/\/doi.org\/10.1016\/j.eswa.2018.12.031","journal-title":"Expert Syst. Appl."},{"issue":"15","key":"15_CR29","doi-asserted-by":"publisher","first-page":"3424","DOI":"10.3390\/s19153424","volume":"19","author":"M Gallo","year":"2019","unstructured":"Gallo, M., De Luca, G., D\u2019Acierno, L., Botte, M.: Artificial neural networks for forecasting passenger flows on metro lines. Sensors 19(15), 3424\u20133436 (2019). https:\/\/doi.org\/10.3390\/s19153424","journal-title":"Sensors"},{"key":"15_CR30","doi-asserted-by":"publisher","unstructured":"Yang, C., Li, X.: Research on railway freight volume prediction based on neural network. E3S Web Conf. 143 (2020). Article 01050. https:\/\/doi.org\/10.1051\/e3sconf\/202014301050","DOI":"10.1051\/e3sconf\/202014301050"},{"issue":"2","key":"15_CR31","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the ElasticNet. J. Roy. Stat. Soc. B 67(2), 301\u2013320 (2005). https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00503.x","journal-title":"J. Roy. Stat. Soc. B"},{"key":"15_CR32","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st Conference on Neural Information Processing Systems, NIPS \u2013 2017, Long Beach, CA, USA, 4\u201312 December 2017, pp. 1\u20139. Neural Information Processing Systems, San Diego (2017). https:\/\/papers.nips.cc\/paper\/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf. Accessed 09 May 2020"},{"issue":"1","key":"15_CR33","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","volume":"72","author":"SJ Taylor","year":"2017","unstructured":"Taylor, S.J., Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37\u201345 (2017). https:\/\/doi.org\/10.1080\/00031305.2017.1380080","journal-title":"Am. Stat."},{"key":"15_CR34","unstructured":"Russian Railways coking coal freight transportation dataset. https:\/\/is.gd\/cox_freight. Accessed 09 May 2020"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64580-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T04:39:16Z","timestamp":1609994356000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-64580-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030645793","9783030645809"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64580-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Siena","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2020.icas.xyz\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"in-house system and easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"209","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"116","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5-6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1-2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}