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Statistisk analys avtågförseningar i Sverige
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2017 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Statistical analysis of train delays in Sweden (English)
Abstract [sv]

I detta arbete analyserades data för tågresor för att undersöka om faktorer kring resan kunde påverka förseningstiden, och förseningsrisken. Data var erhållen av Trafikverket för samtliga tågresor i Sverige (n = 827087) under året 2013 exklusive tunnelbane- och spårvagnsresor. Modeller med en multipel linjär regression och två logistiska regressioner analyserades, och resultatet från logistiska regressioner betraktades närmare.

Beroende variabler i modeller som betraktades var vilka tåg som kom sent till slutstationen, och vilka tåg som hade någon försening alls. Variablerna som visade korrelation (p<0.01) med sena tåg var icke-helgdagar, planerad körtid för sträckan, och tågsorten. Antal dagar till sommarsolstånd, variabeln som skulle approximera vädret, visade en svag koppling med tågförseningar. Orsaker till merförseningar under färden var också positivt korrelerade med förseningar till slutstation, och bland dessa var orsakskoder för ”olyckor/tillbud och yttre faktorer” och ”infrastrukturorsaker” mest korrelerade med förseningar till slutstation.

Det föreslås utifrån resultatet att faktorer som starkt påverkar förseningar är sträckor som tågen kör, och sträckornas trafikkapacitet.

Abstract [en]

In this work, data for train travel was analyzed to determine if any factors could affect the delay time, and risk of delay. Data was given by Trafikverket for all railway travels in Sweden (n = 827087) for year 2013, excluding metro and tram travels. Models used were a multiple linear regression, and two logistic regressions, the result of the latter was examined.

The dependent variables in the models that were examined were which trains had delays to the final destination, and which trains had delays at all. Variables that showed correlation (p<0.01) with delayed trains were non-holidays, planned travel time, and type of train. Number of days to summer solstice, a variable for approximating the weather, showed a weak correlation with train delays. Reasons for delay during the travel were also positively correlated with delays to final destination, and among these the greatest correlation belonged to “Accidents/danger and external factors” and “Infrastructure reasons”.

It is suggested from the results that factors that strongly affect delays are train routes, and the route traffic capacity.

Place, publisher, year, edition, pages
2017.
Series
TRITA-MAT-K ; 2017:22
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-215418OAI: oai:DiVA.org:kth-215418DiVA, id: diva2:1147875
Subject / course
Applied Mathematics and Industrial Economics
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2017-10-09Bibliographically approved

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