Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design
Neural networks have been applied for short-term traffic flow forecasting with reasonable accuracy. Past traffic flow data, which has been captured by on-road sensors, is used as the inputs of neural networks. The size of this data significantly affects the performance of short-term traffic flow for...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/3270 |
| _version_ | 1848744185888243712 |
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| author | Chan, Kit Yan Khadem, Saghar Dillon, Tharam |
| author2 | IEEE |
| author_facet | IEEE Chan, Kit Yan Khadem, Saghar Dillon, Tharam |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Neural networks have been applied for short-term traffic flow forecasting with reasonable accuracy. Past traffic flow data, which has been captured by on-road sensors, is used as the inputs of neural networks. The size of this data significantly affects the performance of short-term traffic flow forecasting, as too many inputs result in over-specification of neural networks and too few inputs result in under-learning of neural networks. However, the amount of past traffic flow data input, is usually determined by the trial and error method. In this paper, an experimental design method, namely orthogonal design, is usedto determine appropriate amount of past traffic flow data for neural networks for short-term traffic flow forecasting. The effectiveness of the orthogonal design is demonstrated by developing neural networks for short-term traffic flow forecasting based on past traffic flow data captured by on-road sensors located on a freeway in Western Australia. |
| first_indexed | 2025-11-14T05:57:27Z |
| format | Conference Paper |
| id | curtin-20.500.11937-3270 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T05:57:27Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-32702017-09-13T16:08:45Z Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design Chan, Kit Yan Khadem, Saghar Dillon, Tharam IEEE short-term traffic flow forecasting orthogonal design neural networks sensor data Neural networks have been applied for short-term traffic flow forecasting with reasonable accuracy. Past traffic flow data, which has been captured by on-road sensors, is used as the inputs of neural networks. The size of this data significantly affects the performance of short-term traffic flow forecasting, as too many inputs result in over-specification of neural networks and too few inputs result in under-learning of neural networks. However, the amount of past traffic flow data input, is usually determined by the trial and error method. In this paper, an experimental design method, namely orthogonal design, is usedto determine appropriate amount of past traffic flow data for neural networks for short-term traffic flow forecasting. The effectiveness of the orthogonal design is demonstrated by developing neural networks for short-term traffic flow forecasting based on past traffic flow data captured by on-road sensors located on a freeway in Western Australia. 2012 Conference Paper http://hdl.handle.net/20.500.11937/3270 10.1109/CEC.2012.6252933 IEEE restricted |
| spellingShingle | short-term traffic flow forecasting orthogonal design neural networks sensor data Chan, Kit Yan Khadem, Saghar Dillon, Tharam Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design |
| title | Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design |
| title_full | Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design |
| title_fullStr | Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design |
| title_full_unstemmed | Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design |
| title_short | Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design |
| title_sort | optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design |
| topic | short-term traffic flow forecasting orthogonal design neural networks sensor data |
| url | http://hdl.handle.net/20.500.11937/3270 |