{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T22:04:40Z","timestamp":1779141880649,"version":"3.51.4"},"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T00:00:00Z","timestamp":1712707200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Philosophy and Social Science Research Project of Hubei Provincial Department of Education","award":["23Q171"],"award-info":[{"award-number":["23Q171"]}]},{"name":"Hubei University of Arts and Science Cultivation Project","award":["2023pygpzk04"],"award-info":[{"award-number":["2023pygpzk04"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>This paper focuses on optimizing the management of delayed trains in operational scenarios by scientifically categorizing train delay levels. It employs static and dynamic models grounded in real-world train delay data from high-speed railways. This classification aids dispatchers in swiftly identifying and predicting delay extents, thus enhancing mitigation strategies\u2019 efficiency. Key indicators, encompassing initial delay duration, station impacts, average station delay, delayed trains\u2019 cascading effects, and average delay per affected train, inform the classification. Applying the K-means clustering algorithm to standardized delay indicators yields an optimized categorization of delayed trains into four levels, reflecting varying risk levels. This static classification offers a comprehensive overview of delay dynamics. Furthermore, utilizing Markov chains, the study delves into sequential dynamic analyses, accounting for China\u2019s railway context and specifically addressing fluctuations during the Spring Festival travel rush. This research, combining static and dynamic approaches, provides valuable insights for bolstering railway operational efficiency and resilience amidst diverse delay scenarios.<\/jats:p>","DOI":"10.1371\/journal.pone.0301762","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T17:42:00Z","timestamp":1712770920000},"page":"e0301762","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Measuring high-speed train delay severity: Static and dynamic analysis"],"prefix":"10.1371","volume":"19","author":[{"given":"Bing","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenglan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5705-9650","authenticated-orcid":true,"given":"Wenxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"issue":"1","key":"pone.0301762.ref001","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/23248378.2022.2143918","article-title":"Metro train delay-recovery strategy considering passenger waiting time and energy consumption: a real-world case study","volume":"12","author":"W. Li","year":"2024","journal-title":"International Journal of Rail Transportation"},{"issue":"11","key":"pone.0301762.ref002","doi-asserted-by":"crossref","first-page":"04023114","DOI":"10.1061\/JTEPBS.TEENG-8017","article-title":"Passenger-oriented resilience assessment of an urban rail transit network under partial disturbances","volume":"149","author":"J. Chen","year":"2023","journal-title":"Journal of Transportation Engineering, Part A: Systems"},{"key":"pone.0301762.ref003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/23248378.2023.2256731","article-title":"Joint optimization of energy conservation and transfer passenger service quality in rail transit system","author":"C. Wen","year":"2023","journal-title":"International Journal of Rail Transportation"},{"issue":"1","key":"pone.0301762.ref004","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/19439962.2019.1605640","article-title":"Risk factors affecting the severity of disruptions in metro operation in shanghai, 2013\u20132016","volume":"13","author":"J. Lu","year":"2021","journal-title":"Journal of Transportation Safety & Security"},{"issue":"4","key":"pone.0301762.ref005","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.jrtpm.2015.11.004","article-title":"Benchmarking and evaluation of railway operations performance","volume":"5","author":"G. L. Nicholson","year":"2015","journal-title":"Journal of Rail Transport Planning & Management"},{"issue":"8","key":"pone.0301762.ref006","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1111\/risa.12836","article-title":"A big data analysis approach for rail failure risk assessment","volume":"37","author":"A. Jamshidi","year":"2017","journal-title":"Risk analysis"},{"issue":"8","key":"pone.0301762.ref007","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1002\/for.2685","article-title":"Predictive models for influence of primary delays using high\u2010speed train operation records","volume":"39","author":"Z. Li","year":"2020","journal-title":"Journal of Forecasting"},{"key":"pone.0301762.ref008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tranpol.2020.11.008","article-title":"Predicting weather-induced delays of high-speed rail and aviation in China","volume":"101","author":"Z. Chen","year":"2021","journal-title":"Transport policy"},{"key":"pone.0301762.ref009","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jtrangeo.2017.01.008","article-title":"Weather and rail delays: Analysis of metropolitan rail in Dublin","volume":"59","author":"W. Brazil","year":"2017","journal-title":"Journal of Transport Geography"},{"issue":"1","key":"pone.0301762.ref010","doi-asserted-by":"crossref","first-page":"124","DOI":"10.3141\/2261-14","article-title":"Train delay and economic impact of in-service failures of railroad rolling stock","volume":"2261","author":"B. W. Schlake","year":"2011","journal-title":"Transport Res Rec"},{"key":"pone.0301762.ref011","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s12469-017-0157-z","article-title":"Railway disruption management challenges and possible solution directions","volume":"9","author":"N. Ghaemi","year":"2017","journal-title":"Public Transport"},{"key":"pone.0301762.ref012","first-page":"1","article-title":"A multi-output deep learning model based on Bayesian optimization for sequential train delays prediction","author":"J. Luo","year":"2022","journal-title":"International Journal of Rail Transportation"},{"key":"pone.0301762.ref013","doi-asserted-by":"crossref","first-page":"120500","DOI":"10.1016\/j.eswa.2023.120500","article-title":"Prediction of departure delays at original stations using deep learning approaches: A combination of route conflicts and rolling stock connections","volume":"229","author":"Z. Li","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"pone.0301762.ref014","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MIM.2022.9847199","article-title":"Network delay measurement with machine learning: From lab to real-world deployment","volume":"25","author":"S. A. Mohammed","year":"2022","journal-title":"IEEE Instrumentation & Measurement Magazine"},{"issue":"7","key":"pone.0301762.ref015","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1080\/19427867.2019.1634902","article-title":"Cause-specific investigation of primary delays of Wuhan\u2013Guangzhou HSR","volume":"12","author":"C. Wen","year":"2020","journal-title":"Transportation Letters"},{"key":"pone.0301762.ref016","unstructured":"C.-W. Palmqvist and M. Ochsner, \"Train Delays due to Extreme Weather Events in Sweden 2001\u20132020,\" in World Conference on Transport Research, 2023."},{"key":"pone.0301762.ref017","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.trc.2019.02.022","article-title":"A closed form railway line delay propagation model","volume":"102","author":"S. Harrod","year":"2019","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"5","key":"pone.0301762.ref018","doi-asserted-by":"crossref","first-page":"269","DOI":"10.3390\/info14050269","article-title":"Delay Indices for Train Punctuality","volume":"14","author":"E. Denti","year":"2023","journal-title":"Information"},{"key":"pone.0301762.ref019","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.cor.2013.08.011","article-title":"Determining operations affected by delay in predictive train timetables","volume":"41","author":"R. Burdett","year":"2014","journal-title":"Computers & Operations Research"},{"issue":"2","key":"pone.0301762.ref020","first-page":"73","article-title":"Analysis of delay causes in railway passenger transportation","volume":"43","author":"E. Nagy","year":"2015","journal-title":"Periodica Polytechnica Transportation Engineering"},{"issue":"1","key":"pone.0301762.ref021","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1287\/trsc.2022.1158","article-title":"A Combined Simulation-Optimization Approach for Robust Timetabling on Main Railway Lines","volume":"57","author":"J. H\u00f6gdahl","year":"2023","journal-title":"Transportation Science"},{"issue":"1","key":"pone.0301762.ref022","first-page":"572","article-title":"A stochastic model for reliability analysis of periodic train timetables","volume":"11","author":"M. \u015e. Artan","year":"2023","journal-title":"Transportmetrica B: Transport Dynamics"},{"issue":"5","key":"pone.0301762.ref023","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1177\/0361198119840339","article-title":"Predicting near-term train schedule performance and delay using bi-level random forests","volume":"2673","author":"M. A. Nabian","year":"2019","journal-title":"Transport Res Rec"},{"key":"pone.0301762.ref024","doi-asserted-by":"crossref","first-page":"100298","DOI":"10.1016\/j.jrtpm.2022.100298","article-title":"Excess probability of dwell time delays from train meets and passes","volume":"21","author":"C.-W. Palmqvist","year":"2022","journal-title":"Journal of Rail Transport Planning & Management"},{"issue":"1","key":"pone.0301762.ref025","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s12469-020-00236-y","article-title":"Punctuality development and delay explanation factors on Norwegian railways in the period 2005\u20132014","volume":"13","author":"A. \u00d8kland","year":"2021","journal-title":"Public Transport"},{"key":"pone.0301762.ref026","article-title":"A Multistage Decision Optimization Approach for Train Timetable Rescheduling Under Uncertain Disruptions in a High-Speed Railway Network","author":"P. Zhang","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"1","key":"pone.0301762.ref027","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1080\/15472450.2013.801714","article-title":"Evaluating disturbance robustness of railway schedules","volume":"18","author":"F. Corman","year":"2014","journal-title":"Journal of Intelligent Transportation Systems"},{"issue":"4","key":"pone.0301762.ref028","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.jrtpm.2014.02.002","article-title":"Improving railway performance in Norway","volume":"3","author":"N. G. Harris","year":"2013","journal-title":"Journal of Rail Transport Planning & Management"},{"key":"pone.0301762.ref029","doi-asserted-by":"crossref","first-page":"108183","DOI":"10.1016\/j.ress.2021.108183","article-title":"Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach","volume":"219","author":"J. Yin","year":"2022","journal-title":"Reliability Engineering & System Safety"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0301762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T17:42:32Z","timestamp":1712770952000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0301762"}},"subtitle":[],"editor":[{"given":"Walid","family":"Al-Shaar","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,4,10]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4,10]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0301762","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,10]]}}}