Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Data-Driven Analysis of the Fuel Saving Potentialof Road Vehicle Platooning: A data-driven approach for quantifying the fuel saving effects of platooning based ondata collected in real traffic conditions.
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Platooning with trucks is showing promising theoretical results in its

ability to lower fuel consumption due to reduction in air drag. Currently,

Scania is test driving their trucks in convoys in order to evaluate

the concept, in terms of driving behavior and actual fuel saving, under

realistic conditions. However, due to traffic conditions it is hard to keep

the convoys intact for longer routes and therefore the actual fuel saving

effect is hard to evaluate. In this thesis, data collected from a fleet

of long haulage trucks is analyzed with four different machine learning

predictors in order quantify the fuel saving potential of platooning. The

analyzed machine learning methods are Support Vector regression, Multilayer

Perceptrons, Random Forests and Decision Trees. The models

obtained from the methods coherently suggest that platooning reduces

the average fuel consumption by several percent.

Abstract [sv]

Fordonståg med lastbilar visar goda teoretiska resultat för att minska

bränsleförbrukningen, då fordonståg minskar det totala luftmotståndet.

För att utvärdera konceptet med fordonståg har Scania ombett sina

förare att köra i formation. Vad man är intresserad av är att utvärdera

hur förarbeteenden samt bränslebesparingar under naturliga trafikförhållanden

påverkas. På grund av trafikförhållanden är det mycket svårt

att hålla fordonståg intakta längre sträckor. Det har därför varit svårt

att dra konkreta slutsatser för hur mycket bränsle som faktiskt sparats

på grund av reducerat luftmotstånd. Data, som samlats av en flotta lastbilar,

har under detta examensarbete analyserats med hjälp av fyra olika

maskininlärningsmetoder i ett försök att kvantifiera hur mycket bränsle

som kan sparas genom att åka i fordonståg. De maskininlärningsmetoder

som använts i detta arbete är Support Vector regression, Multilayer

Perceptron, Random Forest och Decision Trees. De resulterande modellerna

visar entydigt att den genomsnittliga bränsleförbrukningen kan

minskas med flera procentenheter genom att köra i fordonståg.

Place, publisher, year, edition, pages
2013.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-142403OAI: oai:DiVA.org:kth-142403DiVA: diva2:700143
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2014-03-11 Created: 2014-03-03 Last updated: 2014-03-11Bibliographically approved

Open Access in DiVA

fulltext(1119 kB)630 downloads
File information
File name FULLTEXT01.pdfFile size 1119 kBChecksum SHA-512
15d5d7dfb84bc1367936ed7ed7bd34d295f0cf5a03230fc4733e224183e7fd0468df95c8ad7d3380ea367eb01efaaadb2eb3b9259005d02be90de1c1f23ba1e1
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 630 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 296 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf