Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models
Takagi-Sugeno neural fuzzy models (TS-models) have commonly been applied in the development of traffic flow predictors based on traffic flow data captured by the on-road sensors installed along a freeway. However, using all captured traffic flow data is ineffective for the TS-models for traffic flow...
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/34191 |
| _version_ | 1848754155629314048 |
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| author | Chan, Kit Yan Dillon, T. |
| author2 | IEEE |
| author_facet | IEEE Chan, Kit Yan Dillon, T. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Takagi-Sugeno neural fuzzy models (TS-models) have commonly been applied in the development of traffic flow predictors based on traffic flow data captured by the on-road sensors installed along a freeway. However, using all captured traffic flow data is ineffective for the TS-models for traffic flow predictions. Therefore, an appropriate on-road sensor configuration consisting of significant sensors is essential to develop an accurate TS-model for traffic flow forecasting. Although the trial and error method is usually used to determine the appropriate on-road sensor configuration, it is time-consuming and ineffective in trialing all individual configurations. In this paper, a systematic and effective experimental design method involving orthogonal arrays is used to determine appropriate on-road sensor configurations for TS-models. A case study was conducted based on the development of TS-models using traffic flow data captured by on-road sensors installed on a Western Australia freeway. Results show that an appropriate on-road sensor configuration for the TS-model can be developed in a reasonable amount of time when an orthogonal array is used. Also, the developed TS-model can generate accurate traffic flow forecasting. |
| first_indexed | 2025-11-14T08:35:55Z |
| format | Conference Paper |
| id | curtin-20.500.11937-34191 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:35:55Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-341912017-09-13T15:09:36Z Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models Chan, Kit Yan Dillon, T. IEEE orthogonal array Sensor configuration traffic flow forecasting experimental design methods Takagi-Sugeno neural fuzzy models Takagi-Sugeno neural fuzzy models (TS-models) have commonly been applied in the development of traffic flow predictors based on traffic flow data captured by the on-road sensors installed along a freeway. However, using all captured traffic flow data is ineffective for the TS-models for traffic flow predictions. Therefore, an appropriate on-road sensor configuration consisting of significant sensors is essential to develop an accurate TS-model for traffic flow forecasting. Although the trial and error method is usually used to determine the appropriate on-road sensor configuration, it is time-consuming and ineffective in trialing all individual configurations. In this paper, a systematic and effective experimental design method involving orthogonal arrays is used to determine appropriate on-road sensor configurations for TS-models. A case study was conducted based on the development of TS-models using traffic flow data captured by on-road sensors installed on a Western Australia freeway. Results show that an appropriate on-road sensor configuration for the TS-model can be developed in a reasonable amount of time when an orthogonal array is used. Also, the developed TS-model can generate accurate traffic flow forecasting. 2014 Conference Paper http://hdl.handle.net/20.500.11937/34191 10.1109/IJCNN.2014.6889374 IEEE restricted |
| spellingShingle | orthogonal array Sensor configuration traffic flow forecasting experimental design methods Takagi-Sugeno neural fuzzy models Chan, Kit Yan Dillon, T. Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models |
| title | Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models |
| title_full | Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models |
| title_fullStr | Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models |
| title_full_unstemmed | Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models |
| title_short | Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models |
| title_sort | traffic flow prediction using orthogonal arrays and takagi-sugeno neural fuzzy models |
| topic | orthogonal array Sensor configuration traffic flow forecasting experimental design methods Takagi-Sugeno neural fuzzy models |
| url | http://hdl.handle.net/20.500.11937/34191 |