Highway tollgates traffic prediction using a stacked autoencoder neural network

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/255407
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Type: Examensarbete för masterexamen
Master Thesis
Title: Highway tollgates traffic prediction using a stacked autoencoder neural network
Authors: Kärrman, Oskar
Otterlind, Linnea
Abstract: Traffic 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.
Keywords: Data- och informationsvetenskap;Hållbar utveckling;Datavetenskap (datalogi);Databehandling;Datalogi;Annan teknik;Övrig annan teknik;Transport;Computer and Information Science;Sustainable Development;Computer Science;Data processing;Computer science;Other Engineering and Technologies;Other Engineering and Technologies not elsewhere specified;Transport
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Chalmers University of Technology / Department of Mechanics and Maritime Sciences
Series/Report no.: Master's thesis - Department of Mechanics and Maritime Sciences : 2018:52
URI: https://hdl.handle.net/20.500.12380/255407
Collection:Examensarbeten för masterexamen // Master Theses



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