Imperial College London
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Dr Fangce Guo

Faculty of Engineering, Department of Civil and Environmental Engineering

Advanced Research Fellow
 
 
 
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Contact

 

fangce.guo

 
 
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Location

 

308ASkempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Guo:2017:10.1080/15472450.2017.1283989,
author = {Guo, F and Krishnan, R and Polak, JW},
doi = {10.1080/15472450.2017.1283989},
journal = {Journal of Intelligent Transportation Systems: Technology, Planning, and Operations},
pages = {214--226},
title = {The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework},
url = {http://dx.doi.org/10.1080/15472450.2017.1283989},
volume = {21},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Guo,F
AU  - Krishnan,R
AU  - Polak,JW
DO  - 10.1080/15472450.2017.1283989
EP  - 226
PY  - 2017///
SN  - 1547-2450
SP  - 214
TI  - The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework
T2  - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
UR  - http://dx.doi.org/10.1080/15472450.2017.1283989
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000401775400005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/65187
VL  - 21
ER  -
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