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dc.contributor.authorChan, Kit Yan
dc.contributor.authorDillon, Tharam
dc.identifier.citationChan, Kit Yan and Dillon, Tharam S. 2013. On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method. IEEE Transactions on Instrumentation and Measurement. 62 (1): pp. 50-59.

On-road sensors provide proactive traffic control centers with current traffic flow conditions in order to forecast the future conditions. However, the number of on-road sensors is usually huge, and not all traffic flow conditions captured by these sensors are useful for predicting future traffic flow conditions. The inclusion of all captured traffic flow conditions is an ineffective means of predicting future traffic flow. Therefore, the selection of appropriate on-road sensors, which are significantly correlated to future traffic flow, is essential, although the trial and error method is generally used for the selection. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for determinations of appropriate on-road sensors, in order to capture useful traffic flow conditions for forecasting. The effectiveness of the Taguchi method is demonstrated by developing a traffic flow predictor based on the architecture of fuzzy neural networks which can perform well on traffic flow forecasting. The case study was conducted based on traffic flow data captured by on-road sensors located on a Western Australia freeway. The advantages of using the Taguchi method can be indicated: (a) traffic flow predictors with high accuracy can be designed; and (b) development time of traffic flow predictors is reasonable.

dc.subjectorthogonal array
dc.subjecttraffic flow control
dc.subjecton-road sensor
dc.subjectTaguchi method
dc.subjectsensor configuration
dc.subjectfuzzy neural networks
dc.subjecttraffic flow prediction
dc.subjectfuzzy systems
dc.titleOn-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on instrumentation and Measurement

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curtin.accessStatusOpen access

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