In English

Highway tollgates traffic prediction using a stacked autoencoder neural network

Oskar Kärrman ; Linnea Otterlind
Göteborg : Chalmers tekniska högskola, 2018. Master's thesis - Department of Mechanics and Maritime Sciences; 2018:52, 2018.
[Examensarbete på avancerad nivå]

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.

Nyckelord: stacked autoencoder, multilayer perceptron, neural network, traffic prediction, traffic flow, taguchi

Publikationen registrerades 2018-07-02. Den ändrades senast 2019-06-05

CPL ID: 255407

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