Development of neural network based traffic flow predictors using pre-processed data
Neural networks have commonly been applied for traffic flow predictions. Generally, the past traffic flow data captured by on-road detector stations, is used to train the neural networks. However, recently research mostly focuses on development of innovative neural networks, while it lacks developme...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Book Chapter |
| Published: |
Springer
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/18134 |
| _version_ | 1848749657909362688 |
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| author | Chan, Kit Yan Yiu, Ka Fai |
| author2 | Honglei Xu |
| author_facet | Honglei Xu Chan, Kit Yan Yiu, Ka Fai |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Neural networks have commonly been applied for traffic flow predictions. Generally, the past traffic flow data captured by on-road detector stations, is used to train the neural networks. However, recently research mostly focuses on development of innovative neural networks, while it lacks development of mechanisms on pre-processing traffic flow data priors on training in order to obtain more accurate neural networks. In this chapter, a simple but effective training method is proposed by incorporating the mechanisms of back-propagation algorithm and the exponential smoothing method, which is proposed to pre-process traffic flow data before training purposes. The pre-processing approach intends to aid the back-propagation algorithm to develop more accurate neural networks, as the pre-processed traffic flow data is more smooth and continuous than the original unprocessed traffic flow data. This approach was evaluated based on some sets of traffic flow data captured on a section of the freeway in Western Australia. Experimental results indicate that the neural networks developed based on this pre-processed data outperform those that are developed based on either original data or data which is preprocessed by the other pre-processing approaches. |
| first_indexed | 2025-11-14T07:24:26Z |
| format | Book Chapter |
| id | curtin-20.500.11937-18134 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:24:26Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-181342023-02-13T08:01:38Z Development of neural network based traffic flow predictors using pre-processed data Chan, Kit Yan Yiu, Ka Fai Honglei Xu Xiangyu Wang Data cleansing Data pre-processing Time-series forecasting Traffic flow predictions Neural network Intelligent traffic management Neural networks have commonly been applied for traffic flow predictions. Generally, the past traffic flow data captured by on-road detector stations, is used to train the neural networks. However, recently research mostly focuses on development of innovative neural networks, while it lacks development of mechanisms on pre-processing traffic flow data priors on training in order to obtain more accurate neural networks. In this chapter, a simple but effective training method is proposed by incorporating the mechanisms of back-propagation algorithm and the exponential smoothing method, which is proposed to pre-process traffic flow data before training purposes. The pre-processing approach intends to aid the back-propagation algorithm to develop more accurate neural networks, as the pre-processed traffic flow data is more smooth and continuous than the original unprocessed traffic flow data. This approach was evaluated based on some sets of traffic flow data captured on a section of the freeway in Western Australia. Experimental results indicate that the neural networks developed based on this pre-processed data outperform those that are developed based on either original data or data which is preprocessed by the other pre-processing approaches. 2014 Book Chapter http://hdl.handle.net/20.500.11937/18134 10.1007/978-94-017-8044-5_8 Springer restricted |
| spellingShingle | Data cleansing Data pre-processing Time-series forecasting Traffic flow predictions Neural network Intelligent traffic management Chan, Kit Yan Yiu, Ka Fai Development of neural network based traffic flow predictors using pre-processed data |
| title | Development of neural network based traffic flow predictors using pre-processed data |
| title_full | Development of neural network based traffic flow predictors using pre-processed data |
| title_fullStr | Development of neural network based traffic flow predictors using pre-processed data |
| title_full_unstemmed | Development of neural network based traffic flow predictors using pre-processed data |
| title_short | Development of neural network based traffic flow predictors using pre-processed data |
| title_sort | development of neural network based traffic flow predictors using pre-processed data |
| topic | Data cleansing Data pre-processing Time-series forecasting Traffic flow predictions Neural network Intelligent traffic management |
| url | http://hdl.handle.net/20.500.11937/18134 |