{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T16:10:07Z","timestamp":1778861407953,"version":"3.51.4"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["SJCX23_1964"],"award-info":[{"award-number":["SJCX23_1964"]}]},{"name":"Youth Program of the National Natural Science Foundation of China","award":["52302395"],"award-info":[{"award-number":["52302395"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Faced with the complex and diversified disturbances of abnormal events in high-speed railway, studying the characteristics of delay scenario parameters and related propagation influence laws plays a fundamental role in analyzing the applicability of operation plans, formulating train operation adjustments, and evaluating the operation decision plans, as well as providing practical data support for the establishment of theoretical models. Based on the mechanical analysis of the primary and knock-on delays, this paper combines the daily safety information data and timetable data, and takes into account the modified primary delays upon speed loss, by taking the high-speed railway network in the Yangtze River Delta region as an example. On this basis, this paper further studies the statistical distribution of primary delay duration, event location distribution, emergent measures distribution, occurrence time and event cause distribution, and other characteristics. Based on this research, the distribution characteristics of the influenced train number and the cumulative delay under kinds of disturbances have been discussed, where the effects of running redundancy, propagation rate, and related parameters on delay propagation have got quantitative analysis. Research shows that the number of abnormal event samples distributed in the sections is 1.53 times that of stations. Based on the Fuzzy C-Means clustering results, abnormal events in the section are more likely to propagate than abnormal events at stations. For trains experiencing primary delay, the relationship between the maximum cumulative train delay and primary delay mostly obey a power-law distribution and the values of R<jats:sup>2<\/jats:sup> are all greater than 0.65, indicating a high correlation at the theoretical level.<\/jats:p>","DOI":"10.1371\/journal.pone.0314293","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T18:35:31Z","timestamp":1739298931000},"page":"e0314293","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["A study of high-speed train delays and relevant propagation influence characteristics"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5743-7398","authenticated-orcid":true,"given":"Jingyi","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2285-3093","authenticated-orcid":true,"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shejun","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"pone.0314293.ref001","first-page":"8","article-title":"Running conflict mechanism of high-speed railway train","volume":"20","author":"C. 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