Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method
Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the o...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/21506 |
| _version_ | 1848750609471111168 |
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| author | Chan, Kit Khadem, Saghar Dillon, Tharam Palade, Vasile Singh, Jaipal Chang, Elizabeth |
| author_facet | Chan, Kit Khadem, Saghar Dillon, Tharam Palade, Vasile Singh, Jaipal Chang, Elizabeth |
| author_sort | Chan, Kit |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase. |
| first_indexed | 2025-11-14T07:39:33Z |
| format | Journal Article |
| id | curtin-20.500.11937-21506 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:39:33Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-215062017-09-13T13:55:04Z Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method Chan, Kit Khadem, Saghar Dillon, Tharam Palade, Vasile Singh, Jaipal Chang, Elizabeth neural network configuration Taguchi method traffic flow forecasting Input patterns neural networks sensor data Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase. 2012 Journal Article http://hdl.handle.net/20.500.11937/21506 10.1109/TII.2011.2179052 IEEE fulltext |
| spellingShingle | neural network configuration Taguchi method traffic flow forecasting Input patterns neural networks sensor data Chan, Kit Khadem, Saghar Dillon, Tharam Palade, Vasile Singh, Jaipal Chang, Elizabeth Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method |
| title | Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method |
| title_full | Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method |
| title_fullStr | Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method |
| title_full_unstemmed | Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method |
| title_short | Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method |
| title_sort | selection of significant on-road sensor data for short-term traffic flow forecasting using the taguchi method |
| topic | neural network configuration Taguchi method traffic flow forecasting Input patterns neural networks sensor data |
| url | http://hdl.handle.net/20.500.11937/21506 |