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Harvard
Lin, A. och Zhang, M. (2017) Highway Tollgates Travel Time & Volume Predictions using Support Vector Regression with Scaling Methods. Göteborg : Chalmers University of Technology (Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, nr: 2017:79).
BibTeX
@mastersthesis{
Lin2017,
author={Lin, Amanda Yan and Zhang, Mengcheng},
title={Highway Tollgates Travel Time & Volume Predictions using Support Vector Regression with Scaling Methods},
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.},
publisher={Institutionen för tillämpad mekanik, Fordonssäkerhet, Chalmers tekniska högskola},
place={Göteborg},
year={2017},
series={Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2017:79},
keywords={Traffic flow prediction, traffic volume prediction, highway tollgates, time series analysis, SVR with scaling, robust scaling, SVR.},
}
RefWorks
RT Generic
SR Electronic
ID 252103
A1 Lin, Amanda Yan
A1 Zhang, Mengcheng
T1 Highway Tollgates Travel Time & Volume Predictions using Support Vector Regression with Scaling Methods
YR 2017
AB 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.
PB Institutionen för tillämpad mekanik, Fordonssäkerhet, Chalmers tekniska högskola,PB Institutionen för tillämpad mekanik, Fordonssäkerhet, Chalmers tekniska högskola,
T3 Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2017:79
LA eng
LK http://publications.lib.chalmers.se/records/fulltext/252103/252103.pdf
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