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Förutsäga data för lastbilstrafik med maskininlärning
KTH, School of Computer Science and Communication (CSC).
2017 (Swedish)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Predicting data for truck traffic with machine learning (English)
Abstract [sv]

Artificiella neuronnätverk används idag frekvent för att försöka se mönster i stora mängder data. Ser man mönster kan man till viss del se framtiden, och hur väl det fungerar på lastbilstrafik undersöks i den här rapporten. Historisk data om lastbilstrafik används med ett framåtkopplat artificiellt neuronnätverk för att skapa prognoser för lastbilars ankomster till en logistisk plats. Med ett program som skapats för att testa vilka paramterar som ger bäst resultat för det artificiella neuronnätverket så undersöks vilken datastruktur och vilken typ av prognos som ger det bästa resultatet. De två typer av prognoser som testas är tiden till nästa lastbils ankomst samt intensiteten av lastbilarnas an- komster nästa timme. De bästa prognoserna skapades när intensiteten av lastbilar för nästa timme förutspåddes, och prognoserna visade sig då vara bättre än de prognoser nuvarande statistiska metoder kan ge. 

Abstract [en]

Artificial neural networks are used frequently today in order to find patterns in large amounts of data. If one can see the patterns one can to some extent see the future, and how well this works for truck tra c is researched in this report. Historical data about truck tra c is used with a feed-forward artificial neural network to create forecasts for arrivals of trucks to a logistic location. With a program that was created to test what data structure and what parameters give the best results for the artificial neural network it is researched what type of forecast gives the best result. The two forecasts that are tested are the time to the next trucks arrival and the intensity of truck arrivals the next hour. The best forecasts were created when the intensity of trucks for the next hour were predicted, and the forecasts were shown to be better than the forecasts present statistical methods can give. 

Place, publisher, year, edition, pages
2017.
Keyword [sv]
maskininlärning, artificiella neuronnätverk, artificiella, neuronnätverk, ann, lastbil, lastbilar, lastbilstrafik, trafik, förutse, förutspå, förutsäga, data
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-205190OAI: oai:DiVA.org:kth-205190DiVA: diva2:1087529
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Presentation
2017-02-09, Rum 1537, Lindstedtsvägen 3, Stockholm, 15:15 (Swedish)
Supervisors
Examiners
Available from: 2017-05-05 Created: 2017-04-07 Last updated: 2017-05-05Bibliographically approved

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fulltext(3043 kB)21 downloads
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Type fulltextMimetype application/pdf

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