Highway Tollgates Travel Time & Volume Predictions using Support Vector Regression with Scaling Methods

Visa enkel post
dc.contributor.authorLin, Amanda Yan
dc.contributor.authorZhang, Mengcheng
dc.contributor.departmentChalmers tekniska högskola / Institutionen för tillämpad mekaniksv
dc.contributor.departmentChalmers University of Technology / Department of Applied Mechanicsen
dc.date.accessioned2019-07-03T14:38:07Z
dc.date.available2019-07-03T14:38:07Z
dc.date.issued2017
dc.description.abstractToll roads or controlled-access roads are widely used around the world, for instance in Asian countries. It is often expected that drivers can drive smoother and faster on the toll roads or controlled-access roads compared to on regular roads. However, long queues happen frequently on toll roads and cause lots of problems, especially at the tollgates. Accurate predictions of travel time and volume at the tollgates are necessary for traffic management authorities in order to take appropriate measures to control future traffic flow and to improve traffic safety. This thesis describes a novel investigation on the combination of Support Vector Regression (SVR) and scaling methods for highway tollgates travel time and volume predictions. The major contribution of this thesis includes 1) an approach to handling the missing data; 2) selection of important features; 3) investigation of three scaling methods and discussion of their suitability. Experiments were done as part of the Knowledge Discovery and Data Mining (KDD) Cup 2017.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/252103
dc.language.isoeng
dc.relation.ispartofseriesDiploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2017:79
dc.setspec.uppsokTechnology
dc.subjectTransport
dc.subjectHållbar utveckling
dc.subjectStatistik
dc.subjectMatematisk statistik
dc.subjectDatavetenskap (datalogi)
dc.subjectDatabehandling
dc.subjectDatalogi
dc.subjectTransport
dc.subjectSustainable Development
dc.subjectStatistics
dc.subjectMathematical statistics
dc.subjectComputer Science
dc.subjectData processing
dc.subjectComputer science
dc.titleHighway Tollgates Travel Time & Volume Predictions using Support Vector Regression with Scaling Methods
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
252103.pdf
Storlek:
11.46 MB
Format:
Adobe Portable Document Format
Beskrivning:
Fulltext
Ladda ner
Samling
Examensarbeten för masterexamen