Derailment Prediction Models for Canadas Rail Network

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dc.contributor.advisorPark, Peter Y.
dc.contributor.authorWing Tung Chow, Tavia
dc.date.accessioned2020-08-11T12:39:34Z
dc.date.available2020-08-11T12:39:34Z
dc.date.copyright2020-03
dc.date.issued2020-08-11
dc.date.updated2020-08-11T12:39:34Z
dc.degree.disciplineCivil Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractTrain related accidents, particularly derailments, can lead to severe consequences especially when they involve injuries or fatalities or involve dangerous goods that might result in environmental impacts. A literature review found that rail safety assessment and derailment prediction models have often been constrained by aggregated data which may yield inaccurate assessments of the safety performance of a rail network by, for example, failing to consider segment-level characteristics. This study focused on the development of segment-level derailment prediction models for Canadas rail network using negative binomial and logistic regression modelling methods. The study used a network screening process to identify key segments of derailment concerns. A thorough quantitative review of the models results and performance was conducted to understand the predictive capabilities and applications of the models in derailment prediction. The analytical approach and findings in this study have strong implications for advancing research on rail safety in North America.
dc.identifier.urihttp://hdl.handle.net/10315/37698
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectCivil engineering
dc.subject.keywordsrail safety
dc.subject.keywordstrack segment
dc.subject.keywordsnetwork screening
dc.subject.keywordssafety performance function
dc.subject.keywordsderailments
dc.subject.keywordsrisk prediction
dc.subject.keywordsnegative binomial regression
dc.subject.keywordslogistic regression
dc.titleDerailment Prediction Models for Canadas Rail Network
dc.typeElectronic Thesis or Dissertation

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