An object-oriented neural network approach to short-term traffic forecasting

Dia, Hussein F. (2001) An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research, 131 2: 253-261. doi:10.1016/S0377-2217(00)00125-9

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Author Dia, Hussein F.
Title An object-oriented neural network approach to short-term traffic forecasting
Journal name European Journal of Operational Research   Check publisher's open access policy
ISSN 0377-2217
Publication date 2001-01-01
Sub-type Article (original research)
DOI 10.1016/S0377-2217(00)00125-9
Open Access Status File (Author Post-print)
Volume 131
Issue 2
Start page 253
End page 261
Total pages 9
Language eng
Abstract This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword Operations Research & Management Science
Neural Networks
Traffic Forecasting
Intelligent Transportation Systems
Advanced Traffic Management Systems
Advanced Traffic Information Systems
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Civil Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 149 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 190 times in Scopus Article | Citations
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Created: Mon, 13 Aug 2007, 22:16:42 EST