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

Amanda Yan Lin ; Mengcheng Zhang
Göteborg : Chalmers tekniska högskola, 2017. Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, ISSN 1652-8557; 2017:79, 2017.
[Examensarbete på avancerad nivå]

Toll 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.

Nyckelord: Traffic flow prediction, traffic volume prediction, highway tollgates, time series analysis, SVR with scaling, robust scaling, SVR.

Publikationen registrerades 2017-09-28. Den ändrades senast 2017-09-28

CPL ID: 252103

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