The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework
File(s)
Accepted version
Author(s)
Guo, F
Krishnan, R
Polak, JW
Type
Journal Article
Abstract
This article investigates the impact of alternative data smoothing and traffic prediction methods on the accuracy of the performance of a two-stage short-term urban travel time prediction framework. Using this framework, we test the influence of the combination of two different data smoothing and four different prediction methods using travel time data from two substantially different urban traffic environments and under both normal and abnormal conditions. This constitutes the most comprehensive empirical evaluation of the joint influence of smoothing and predictor choice to date. The results indicate that the use of data smoothing improves prediction accuracy regardless of the prediction method used and that this is true in different traffic environments and during both normal and abnormal (incident) conditions. Moreover, the use of data smoothing in general has a much greater influence on prediction performance than the choice of specific prediction method, and this is independent of the specific smoothing method used. In normal traffic conditions, the different prediction methods produce broadly similar results but under abnormal conditions, lazy learning methods emerge as superior.
Date Issued
2017-02-22
Date Acceptance
2016-11-21
Citation
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 2017, 21 (3), pp.214-226
ISSN
1547-2450
Publisher
Taylor & Francis
Start Page
214
End Page
226
Journal / Book Title
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
Volume
21
Issue
3
Copyright Statement
© 2017 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Intelligent Transportation Systems on 22 Feb 2017, available online: https://www.tandfonline.com/doi/full/10.1080/15472450.2017.1283989
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000401775400005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/F005156/1
Subjects
Science & Technology
Technology
Transportation Science & Technology
Transportation
data smoothing
intelligent transportation systems (ITS)
machine learning method
short-term traffic prediction
NONPARAMETRIC REGRESSION
TRAFFIC VOLUME
RANDOM FORESTS
WAVELET
MODEL
TRANSFORM
Publication Status
Published
Date Publish Online
2017-01-20