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

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/252103
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Type: Examensarbete för masterexamen
Master Thesis
Title: Highway Tollgates Travel Time & Volume Predictions using Support Vector Regression with Scaling Methods
Authors: Lin, Amanda Yan
Zhang, Mengcheng
Abstract: 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.
Keywords: Transport;Hållbar utveckling;Statistik;Matematisk statistik;Datavetenskap (datalogi);Databehandling;Datalogi;Transport;Sustainable Development;Statistics;Mathematical statistics;Computer Science;Data processing;Computer science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för tillämpad mekanik
Chalmers University of Technology / Department of Applied Mechanics
Series/Report no.: Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2017:79
URI: https://hdl.handle.net/20.500.12380/252103
Collection:Examensarbeten för masterexamen // Master Theses



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