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Datadriven Prognostisering: En Regressionsmodell för Bättre Beslutsfattande inom Kollektivtrafiken
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Data-Driven Forecasting : A Regression Model for Better Decision Making in Public Transportation (English)
Abstract [sv]

Hur kommer det sig att antalet resenärer inom Stockholms kollektivtrafik skiljer sig kraftigt från dag till dag? Är skillnaden rent slumpmässig eller spelar faktorer som befolkningsmängd, lufttemperatur, nederbörd, månad eller veckodag en signifikant roll för att förklara variationen?

Denna uppsats ämnar att utforska dessa externa variablers påverkan på kollektivtrafiken och hur denna typ av datadriven information kan leda till bättre understödda beslut. Den applicerade metoden var multipel linjär regression och data som användes mottogs från Trafikförvaltningen, SMHI och SCB. Slutsatsen från studien visar att variationerna i antal resenärer i Stockholms Lokaltrafik kan förklaras med cirka 84\% från befolkningsmängden, månad och veckodag.

Abstract [en]

Why is it that the number of travellers in Stockholm's public transportation differs from day to day? Is the difference arbitrary or do factors such as population, temperature, weather conditions, months, or even weekdays have a significant role in this variation?

This thesis aims to explore these external variables and their effect on public transportation, as well as how this type of data driven information can result in well supported decisions. The method applied to the study was multiple linear regression and the data used was retrieved from Trafikförvaltningen, SMHI, and SCB. The study concluded that the variations in the number of travellers in Stockholm's public transportation is up to 84\% explained by population, as well as month and weekday.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:147
Keywords [en]
Statistics, applied mathematics, data analysis, public transportation
Keywords [sv]
Statistik, tillämpad matematik, dataanalys, kollektivtrafik
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252744OAI: oai:DiVA.org:kth-252744DiVA, id: diva2:1333887
External cooperation
Trafikförvaltningen
Subject / course
Applied Mathematics and Industrial Economics
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-07-02Bibliographically approved

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