On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method
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 fl...
| Main Authors: | , |
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| Format: | Journal Article |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/16440 |
| _version_ | 1848749177438208000 |
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| author | Chan, Kit Yan Dillon, Tharam |
| author_facet | Chan, Kit Yan Dillon, Tharam |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:16:47Z |
| format | Journal Article |
| id | curtin-20.500.11937-16440 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:16:47Z |
| publishDate | 2013 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-164402017-09-13T15:52:27Z On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method Chan, Kit Yan Dillon, Tharam orthogonal array traffic flow control on-road sensor Taguchi method sensor configuration fuzzy neural networks traffic flow prediction fuzzy systems 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. 2013 Journal Article http://hdl.handle.net/20.500.11937/16440 10.1109/TIM.2012.2212506 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC fulltext |
| spellingShingle | orthogonal array traffic flow control on-road sensor Taguchi method sensor configuration fuzzy neural networks traffic flow prediction fuzzy systems Chan, Kit Yan Dillon, Tharam On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method |
| title | On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method |
| title_full | On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method |
| title_fullStr | On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method |
| title_full_unstemmed | On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method |
| title_short | On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method |
| title_sort | on-road sensor configuration design for traffic flow prediction using fuzzy neural networks and taguchi method |
| topic | orthogonal array traffic flow control on-road sensor Taguchi method sensor configuration fuzzy neural networks traffic flow prediction fuzzy systems |
| url | http://hdl.handle.net/20.500.11937/16440 |