Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2009.01.005
DC FieldValue
dc.titleComputational intelligence-based congestion prediction for a dynamic urban street network
dc.contributor.authorSrinivasan, D.
dc.contributor.authorWai Chan, C.
dc.contributor.authorBalaji, P.G.
dc.date.accessioned2014-06-17T02:42:26Z
dc.date.available2014-06-17T02:42:26Z
dc.date.issued2009-06
dc.identifier.citationSrinivasan, D., Wai Chan, C., Balaji, P.G. (2009-06). Computational intelligence-based congestion prediction for a dynamic urban street network. Neurocomputing 72 (10-12) : 2710-2716. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2009.01.005
dc.identifier.issn09252312
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55381
dc.description.abstractThis paper develops a hybrid model for single point short term traffic flow forecasting in an urban traffic network. The hybrid model consists of two main modules: a fuzzy input fuzzy output filter (FIFO-filter) and a multi-layer feed-forward artificial neural network architecture optimized using evolution strategies (MLFN-ES). The FIFO-filter performs the data clustering operation and provides a rough forecasted prediction value based on the input data to the MLFN-ES associated with each cluster for modeling the input-output relation to provide accurate short term forecast value. The performance of the proposed model is demonstrated by predicting the traffic flow for an intersection in the central business district (CBD) area of Singapore. The hybrid model proposed in this paper gave a mean absolute percentage error (MAPE) of 8.35% on weekdays and 9.73% on weekends for the test data. A comparison analysis shows improved performance of the proposed hybrid method in short term traffic prediction over popular approaches like ARIMA and artificial neural network based systems. © 2009 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.neucom.2009.01.005
dc.sourceScopus
dc.subjectEvolutionary computation
dc.subjectFuzzy logic
dc.subjectNeural networks
dc.subjectTraffic flow prediction
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.neucom.2009.01.005
dc.description.sourcetitleNeurocomputing
dc.description.volume72
dc.description.issue10-12
dc.description.page2710-2716
dc.description.codenNRCGE
dc.identifier.isiut000266702300069
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

35
checked on Dec 1, 2022

WEB OF SCIENCETM
Citations

26
checked on Nov 23, 2022

Page view(s)

168
checked on Nov 24, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.