Highway tollgates traffic prediction using a stacked autoencoder neural network

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dc.contributor.authorKärrman, Oskar
dc.contributor.authorOtterlind, Linnea
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.date.accessioned2019-07-03T14:48:11Z
dc.date.available2019-07-03T14:48:11Z
dc.date.issued2018
dc.description.abstractTraffic flow prediction is an important area of research with a great number of applications such as route planning and congestion avoidance. This thesis explored artificial neural network performance as travel time and traffic volume predictors. Stacked autoencoder artificial neural networks were studied in particular due to recent promising performance in traffic flow prediction, and the result was compared to multilayer perceptron networks, a type of shallow artificial neural networks. The Taguchi design of experiments method was used to decide network parameters. Stacked autoencoder networks generally did not perform better than shallow networks, but the results indicated that a bigger dataset could favor stacked autoencoder networks. Using the Taguchi method did help cut down on number of experiments to test, but choosing network settings based on the Taguchi test results did not yield lower error than what was found during the Taguchi tests.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/255407
dc.language.isoeng
dc.relation.ispartofseriesMaster's thesis - Department of Mechanics and Maritime Sciences : 2018:52
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectHållbar utveckling
dc.subjectDatavetenskap (datalogi)
dc.subjectDatabehandling
dc.subjectDatalogi
dc.subjectAnnan teknik
dc.subjectÖvrig annan teknik
dc.subjectTransport
dc.subjectComputer and Information Science
dc.subjectSustainable Development
dc.subjectComputer Science
dc.subjectData processing
dc.subjectComputer science
dc.subjectOther Engineering and Technologies
dc.subjectOther Engineering and Technologies not elsewhere specified
dc.subjectTransport
dc.titleHighway tollgates traffic prediction using a stacked autoencoder neural network
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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