Prediction intervals to account for uncertainties in travel time prediction

Khosravi, Abbas, Mazloumi, Ehsan, Nahavandi, Saeid, Creighton, Doug and van Lint, J.W.C. (Hans) 2011, Prediction intervals to account for uncertainties in travel time prediction, IEEE transactions on intelligent transportation systems, vol. 12, no. 2, pp. 537-547.

Attached Files
Name Description MIMEType Size Downloads

Title Prediction intervals to account for uncertainties in travel time prediction
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Mazloumi, Ehsan
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DougORCID iD for Creighton, Doug orcid.org/0000-0002-9217-1231
van Lint, J.W.C. (Hans)
Journal name IEEE transactions on intelligent transportation systems
Volume number 12
Issue number 2
Start page 537
End page 547
Total pages 11
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2011-06
ISSN 1524-9050
1558-0016
Keyword(s) Bayesian inference
delta method
neural networks (NNs)
prediction intervals (PIs)
Summary The accurate prediction of travel times is desirable but frequently prone to error. This is mainly attributable to both the underlying traffic processes and the data that are used to infer travel time. A more meaningful and pragmatic approach is to view travel time prediction as a probabilistic inference and to construct prediction intervals (PIs), which cover the range of probable travel times travelers may encounter. This paper introduces the delta and Bayesian techniques for the construction of PIs. Quantitative measures are developed and applied for a comprehensive assessment of the constructed PIs. These measures simultaneously address two important aspects of PIs: 1) coverage probability and 2) length. The Bayesian and delta methods are used to construct PIs for the neural network (NN) point forecasts of bus and freeway travel time data sets. The obtained results indicate that the delta technique outperforms the Bayesian technique in terms of narrowness of PIs with satisfactory coverage probability. In contrast, PIs constructed using the Bayesian technique are more robust against the NN structure and exhibit excellent coverage probability.
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 850603 Energy Systems Analysis
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044119

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 91 times in TR Web of Science
Scopus Citation Count Cited 104 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 687 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Thu, 05 Apr 2012, 15:50:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.