{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T14:14:11Z","timestamp":1783692851385,"version":"3.55.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001779","name":"Monash University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001779","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Public Transp"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s12469-025-00419-5","type":"journal-article","created":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T16:46:09Z","timestamp":1775407569000},"page":"659-689","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time-weighted ensemble long\u2013short-term memory for delay prediction in railway systems"],"prefix":"10.1007","volume":"18","author":[{"given":"Zhi Yang","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1326-8634","authenticated-orcid":false,"given":"Joanne Mun-Yee","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles Raymond","family":"Sarimuthu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ratan","family":"Mukhopadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,5]]},"reference":[{"key":"419_CR1","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1080\/01441647.2020.1728419","volume":"40","author":"N Be\u0161inovi\u0107","year":"2020","unstructured":"Be\u0161inovi\u0107 N (2020) Resilience in railway transport systems: a literature review and research agenda. Transp Rev 40:457\u2013478. https:\/\/doi.org\/10.1080\/01441647.2020.1728419","journal-title":"Transp Rev"},{"key":"419_CR2","doi-asserted-by":"publisher","first-page":"4195","DOI":"10.1109\/TIE.2021.3076713","volume":"69","author":"C Bian","year":"2022","unstructured":"Bian C, Yang S, Xu Q, Meng J (2022) Speed adaptability assessment of railway balise transmission module using a deep-adaptive-attention-based encoder\u2013decoder network. IEEE Trans Ind Electron 69:4195\u20134204. https:\/\/doi.org\/10.1109\/TIE.2021.3076713","journal-title":"IEEE Trans Ind Electron"},{"key":"419_CR3","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/s10479-021-04178-x","volume":"320","author":"AMC Bretas","year":"2023","unstructured":"Bretas AMC, Mendes A, Jackson M, Clement R, Sanhueza C, Chalup S (2023) A decentralised multi-agent system for rail freight traffic management. Ann Oper Res 320:631\u2013661. https:\/\/doi.org\/10.1007\/s10479-021-04178-x","journal-title":"Ann Oper Res"},{"key":"419_CR4","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0191-2615(94)90001-9","volume":"28","author":"M Carey","year":"1994","unstructured":"Carey M, Kwieci\u0144ski A (1994) Stochastic approximation to the effects of headways on knock-on delays of trains. Transp Res Part B Methodol 28:251\u2013267. https:\/\/doi.org\/10.1016\/0191-2615(94)90001-9","journal-title":"Transp Res Part B Methodol"},{"key":"419_CR6","doi-asserted-by":"publisher","unstructured":"Chen Y, Zhang C, Zhang N, Chen Y, Wang H (2019) Multi-task learning and attention mechanism based long short-term memory for temperature prediction of EMU bearing. In: 2019 prognostics and system health management conference (PHM-Qingdao), pp 1\u20137. https:\/\/doi.org\/10.1109\/PHM-Qingdao46334.2019.8942914","DOI":"10.1109\/PHM-Qingdao46334.2019.8942914"},{"key":"419_CR7","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/2470171","volume":"2018","author":"JY Choi","year":"2018","unstructured":"Choi JY, Lee B (2018) Combining LSTM network ensemble via adaptive weighting for improved time series forecasting. Math Probl Eng 2018:e2470171. https:\/\/doi.org\/10.1155\/2018\/2470171","journal-title":"Math Probl Eng"},{"key":"419_CR8","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.1201\/b15938","volume-title":"Safety, reliability and risk analysis: beyond the horizon","author":"O Fink","year":"2014","unstructured":"Fink O, Weidmann U, Zio E (2014) Extreme learning machines for predicting operation disruption events in railway systems. In: Steenbergen R, VanGelder P, Miraglia S, Vrouwenvelder A (eds) Safety, reliability and risk analysis: beyond the horizon. CRC Press-Taylor & Francis Group, Boca Raton, pp 1781\u20131787. https:\/\/doi.org\/10.1201\/b15938"},{"key":"419_CR9","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s12469-022-00301-8","volume":"14","author":"L Ge","year":"2022","unstructured":"Ge L, Vo\u00df S, Xie L (2022) Robustness and disturbances in public transport. Public Transp 14:191\u2013261. https:\/\/doi.org\/10.1007\/s12469-022-00301-8","journal-title":"Public Transp"},{"key":"419_CR10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jrtpm.2018.02.002","volume":"8","author":"N Ghaemi","year":"2018","unstructured":"Ghaemi N, Zilko AA, Yan F, Cats O, Kurowicka D, Goverde RMP (2018) Impact of railway disruption predictions and rescheduling on passenger delays. J Rail Transp Plan Manag 8:103\u2013122. https:\/\/doi.org\/10.1016\/j.jrtpm.2018.02.002","journal-title":"J Rail Transp Plan Manag"},{"key":"419_CR11","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.trc.2010.01.002","volume":"18","author":"RMP Goverde","year":"2010","unstructured":"Goverde RMP (2010) A delay propagation algorithm for large-scale railway traffic networks. Transp Res Part C Emerg Technol 18:269\u2013287. https:\/\/doi.org\/10.1016\/j.trc.2010.01.002","journal-title":"Transp Res Part C Emerg Technol"},{"key":"419_CR12","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1049\/itr2.12095","volume":"15","author":"BS Grandhi","year":"2021","unstructured":"Grandhi BS, Chaniotakis E, Thomann S, Laube F, Antoniou C (2021) An estimation framework to quantify railway disruption parameters. IET Intell Transp Syst 15:1256\u20131268. https:\/\/doi.org\/10.1049\/itr2.12095","journal-title":"IET Intell Transp Syst"},{"key":"419_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2023.116740","volume":"441","author":"F Hateffard","year":"2024","unstructured":"Hateffard F, Steinbuch L, Heuvelink GBM (2024) Evaluating the extrapolation potential of random forest digital soil mapping. Geoderma 441:116740. https:\/\/doi.org\/10.1016\/j.geoderma.2023.116740","journal-title":"Geoderma"},{"key":"419_CR14","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. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"419_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2020.102022","volume":"141","author":"P Huang","year":"2020","unstructured":"Huang P, Wen C, Fu L, Lessan J, Jiang C, Peng Q, Xu X (2020) Modeling train operation as sequences: a study of delay prediction with operation and weather data. Transp Res Part E Logist Transp Rev 141:102022. https:\/\/doi.org\/10.1016\/j.tre.2020.102022","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"419_CR16","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.ins.2019.12.053","volume":"516","author":"P Huang","year":"2020","unstructured":"Huang P, Wen C, Fu L, Peng Q, Tang Y (2020) A deep learning approach for multi-attribute data: a study of train delay prediction in railway systems. Inf Sci 516:234\u2013253. https:\/\/doi.org\/10.1016\/j.ins.2019.12.053","journal-title":"Inf Sci"},{"key":"419_CR17","doi-asserted-by":"publisher","unstructured":"Huang P, Wen C, Li J, Peng Q, Li Z, Fu Z (2019) Statistical analysis of train delay and delay propagation patterns in a high-speed railway system. In: 2019 5th international conference on transportation information and safety (ICTIS), pp 664\u2013669. https:\/\/doi.org\/10.1109\/ICTIS.2019.8883805","DOI":"10.1109\/ICTIS.2019.8883805"},{"key":"419_CR18","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/S0191-2615(99)00051-X","volume":"35","author":"T Huisman","year":"2001","unstructured":"Huisman T, Boucherie RJ (2001) Running times on railway sections with heterogeneous train traffic. Transp Res Part B Methodol 35:271\u2013292. https:\/\/doi.org\/10.1016\/S0191-2615(99)00051-X","journal-title":"Transp Res Part B Methodol"},{"key":"419_CR19","doi-asserted-by":"publisher","first-page":"567","DOI":"10.17531\/ein.2019.4.5","volume":"21","author":"R Kang","year":"2019","unstructured":"Kang R, Wang J, Cheng J, Chen J, Pang Y (2019) Intelligent forecasting of automatic train protection system failure rate in China high-speed railway. Eksploatacja i Niezawodno\u015b\u0107 Maintenance Reliab 21:567\u2013576. https:\/\/doi.org\/10.17531\/ein.2019.4.5","journal-title":"Eksploatacja i Niezawodno\u015b\u0107 Maintenance Reliab"},{"key":"419_CR20","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1109\/TITS.2018.2829165","volume":"20","author":"H Khadilkar","year":"2019","unstructured":"Khadilkar H (2019) A scalable reinforcement learning algorithm for scheduling railway lines. IEEE Trans Intell Transp Syst 20:727\u2013736. https:\/\/doi.org\/10.1109\/TITS.2018.2829165","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"419_CR21","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s12469-020-00233-1","volume":"12","author":"E K\u00f6nig","year":"2020","unstructured":"K\u00f6nig E (2020) A review on railway delay management. Public Transp 12:335\u2013361. https:\/\/doi.org\/10.1007\/s12469-020-00233-1","journal-title":"Public Transp"},{"key":"419_CR22","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.ejor.2020.05.055","volume":"288","author":"E K\u00f6nig","year":"2021","unstructured":"K\u00f6nig E, Sch\u00f6n C (2021) Railway delay management with passenger rerouting considering train capacity constraints. Eur J Oper Res 288:450\u2013465. https:\/\/doi.org\/10.1016\/j.ejor.2020.05.055","journal-title":"Eur J Oper Res"},{"key":"419_CR23","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/j.procs.2021.08.101","volume":"192","author":"H Laifa","year":"2021","unstructured":"Laifa H, Khcherif R, Ben Ghezalaa HH (2021) Train delay prediction in Tunisian railway through LightGBM model. Procedia Comput Sci 192:981\u2013990. https:\/\/doi.org\/10.1016\/j.procs.2021.08.101","journal-title":"Procedia Comput Sci"},{"key":"419_CR24","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.trc.2016.10.009","volume":"73","author":"W-H Lee","year":"2016","unstructured":"Lee W-H, Yen L-H, Chou C-M (2016) A delay root cause discovery and timetable adjustment model for enhancing the punctuality of railway services. Transp Res Part C Emerg Technol 73:49\u201364. https:\/\/doi.org\/10.1016\/j.trc.2016.10.009","journal-title":"Transp Res Part C Emerg Technol"},{"key":"419_CR25","doi-asserted-by":"publisher","first-page":"8193","DOI":"10.1109\/TITS.2024.3409754","volume":"25","author":"J Li","year":"2024","unstructured":"Li J, Xu X, Ding X, Liu J, Ran B (2024) Bayesian spatio-temporal graph convolutional network for railway train delay prediction. IEEE Trans Intell Transp Syst 25:8193\u20138208. https:\/\/doi.org\/10.1109\/TITS.2024.3409754","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"419_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111640","volume":"159","author":"Z Li","year":"2024","unstructured":"Li Z, Huang P, Wen C, Dong W, Ji Y, Rodrigues F (2024) Railway network delay evolution: a heterogeneous graph neural network approach. Appl Soft Comput 159:111640. https:\/\/doi.org\/10.1016\/j.asoc.2024.111640","journal-title":"Appl Soft Comput"},{"key":"419_CR27","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1080\/23248378.2020.1843194","volume":"9","author":"Z Li","year":"2021","unstructured":"Li Z, Wen C, Hu R, Xu C, Huang P, Jiang X (2021) Near-term train delay prediction in the Dutch railways network. Int J Rail Transp 9:520\u2013539. https:\/\/doi.org\/10.1080\/23248378.2020.1843194","journal-title":"Int J Rail Transp"},{"key":"419_CR28","doi-asserted-by":"publisher","unstructured":"Liu C, Chen J, Liu H, Jiang H, He S (2021) A self-attention based method for wind speed forecasting in high-speed railway system. In: IEEE 1st international conference on digital twins and parallel intelligence (DTPI), pp 106\u2013109. https:\/\/doi.org\/10.1109\/DTPI52967.2021.9540068","DOI":"10.1109\/DTPI52967.2021.9540068"},{"key":"419_CR29","unstructured":"Makov\u0161ek D, Benezech V, Perkins S, ITF (2019) Efficiency in railway operations and infrastructure management. ITF roundtable reports, no. 177"},{"key":"419_CR30","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1049\/iet-its.2018.0064","volume":"12","author":"H Nguyen","year":"2018","unstructured":"Nguyen H, Kieu L-M, Wen T, Cai C (2018) Deep learning methods in transportation domain: a review. IET Intell Transp Syst 12:998\u20131004. https:\/\/doi.org\/10.1049\/iet-its.2018.0064","journal-title":"IET Intell Transp Syst"},{"key":"419_CR31","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.bdr.2017.05.002","volume":"11","author":"L Oneto","year":"2018","unstructured":"Oneto L, Fumeo E, Clerico G, Canepa R, Papa F, Dambra C, Mazzino N, Anguita D (2018) Train delay prediction systems: a big data analytics perspective. Big Data Res 11:54\u201364. https:\/\/doi.org\/10.1016\/j.bdr.2017.05.002","journal-title":"Big Data Res"},{"key":"419_CR32","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1016\/j.ejor.2023.03.040","volume":"310","author":"L Sobrie","year":"2023","unstructured":"Sobrie L, Verschelde M, Hennebel V, Roets B (2023) Capturing complexity over space and time via deep learning: an application to real-time delay prediction in railways. Eur J Oper Res 310:1201\u20131217. https:\/\/doi.org\/10.1016\/j.ejor.2023.03.040","journal-title":"Eur J Oper Res"},{"key":"419_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrtpm.2022.100312","volume":"22","author":"T Spanninger","year":"2022","unstructured":"Spanninger T, Trivella A, B\u00fcchel B, Corman F (2022) A review of train delay prediction approaches. J Rail Transp Plann Manage 22:100312. https:\/\/doi.org\/10.1016\/j.jrtpm.2022.100312","journal-title":"J Rail Transp Plann Manage"},{"key":"419_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2022.116192","volume":"428","author":"B Takoutsing","year":"2022","unstructured":"Takoutsing B, Heuvelink GBM (2022) Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors. Geoderma 428:116192. https:\/\/doi.org\/10.1016\/j.geoderma.2022.116192","journal-title":"Geoderma"},{"key":"419_CR35","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1080\/15472450.2020.1858822","volume":"26","author":"P Taleongpong","year":"2022","unstructured":"Taleongpong P, Hu S, Jiang Z, Wu C, Popo-Ola S, Han K (2022) Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network. J Intell Transp Syst 26:311\u2013329. https:\/\/doi.org\/10.1080\/15472450.2020.1858822","journal-title":"J Intell Transp Syst"},{"key":"419_CR36","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1080\/23248378.2017.1307144","volume":"5","author":"C Wen","year":"2017","unstructured":"Wen C, Li Z, Lessan J, Fu L, Huang P, Jiang C (2017) Statistical investigation on train primary delay based on real records: evidence from Wuhan\u2013Guangzhou HSR. Int J Rail Transp 5:170\u2013189. https:\/\/doi.org\/10.1080\/23248378.2017.1307144","journal-title":"Int J Rail Transp"},{"key":"419_CR5","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1002\/for.2639","volume":"39","author":"C Wen","year":"2020","unstructured":"Wen C, Mou W, Huang P, Li Z (2020) A predictive model of train delays on a railway line. J Forecast 39:470\u2013488. https:\/\/doi.org\/10.1002\/for.2639","journal-title":"J Forecast"},{"key":"419_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2023.109302","volume":"181","author":"J Xu","year":"2023","unstructured":"Xu J, Wang W, Gao Z, Luo H, Wu Q (2023) A novel Markov model for near-term railway delay prediction. Comput Ind Eng 181:109302. https:\/\/doi.org\/10.1016\/j.cie.2023.109302","journal-title":"Comput Ind Eng"},{"key":"419_CR38","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/s40534-019-0188-z","volume":"27","author":"Y Yang","year":"2019","unstructured":"Yang Y, Huang P, Peng Q, Li J, Wen C (2019) Statistical delay distribution analysis on high-speed railway trains. J Mod Transp 27:188\u2013197. https:\/\/doi.org\/10.1007\/s40534-019-0188-z","journal-title":"J Mod Transp"},{"key":"419_CR39","doi-asserted-by":"publisher","DOI":"10.3390\/fi11040094","volume":"11","author":"F Zantalis","year":"2019","unstructured":"Zantalis F, Koulouras G, Karabetsos S, Kandris D (2019) A review of machine learning and IoT in smart transportation. Future Internet 11:94. https:\/\/doi.org\/10.3390\/fi11040094","journal-title":"Future Internet"},{"key":"419_CR40","doi-asserted-by":"publisher","first-page":"2434","DOI":"10.1109\/TITS.2021.3097064","volume":"23","author":"D Zhang","year":"2022","unstructured":"Zhang D, Peng Y, Zhang Y, Wu D, Wang H, Zhang H (2022) Train time delay prediction for high-speed train dispatching based on spatio-temporal graph convolutional network. IEEE Trans Intell Transp Syst 23:2434\u20132444. https:\/\/doi.org\/10.1109\/TITS.2021.3097064","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"419_CR41","doi-asserted-by":"publisher","unstructured":"Zhao Z, Rao R, Tu S, Shi J (2017) Time-weighted LSTM model with redefined labeling for stock trend prediction. In: IEEE 29th international conference on tools with artificial intelligence (ICTAI), pp 1210\u20131217. https:\/\/doi.org\/10.1109\/ICTAI.2017.00184","DOI":"10.1109\/ICTAI.2017.00184"}],"container-title":["Public Transport"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12469-025-00419-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12469-025-00419-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12469-025-00419-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:21:37Z","timestamp":1783689697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12469-025-00419-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,5]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["419"],"URL":"https:\/\/doi.org\/10.1007\/s12469-025-00419-5","relation":{},"ISSN":["1866-749X","1613-7159"],"issn-type":[{"value":"1866-749X","type":"print"},{"value":"1613-7159","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,5]]},"assertion":[{"value":"28 December 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2026","order":2,"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 they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}