Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks
This paper presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and im...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/21786 |
| _version_ | 1848750687817564160 |
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| author | Chan, Kit Dillon, Tharam S. Chang, Elizabeth Singh, Jaipal |
| author_facet | Chan, Kit Dillon, Tharam S. Chang, Elizabeth Singh, Jaipal |
| author_sort | Chan, Kit |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and improve mobility of transportation. The proposed algorithm aims to address the issues of development of short-term traffic flow predictors which have not been addressed fully in the current literature namely that: a) strongly non-linear characteristics are unavoidable in traffic flow data; b) memory space for implementation of short-term traffic flow predictors is limited; c) specification of model structures for short-term traffic flow predictors which do not involve trial and error methods based on human expertise; d) adaptation to newly-captured, traffic flow data is required. The proposed algorithm was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information is newly-captured. These results clearly demonstrate the effectiveness of using the proposed algorithm for real-time traffic flow forecasting. |
| first_indexed | 2025-11-14T07:40:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-21786 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:40:48Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-217862017-09-13T16:00:44Z Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks Chan, Kit Dillon, Tharam S. Chang, Elizabeth Singh, Jaipal particle swarm optimization traffic management time-varying modeling evolutionary algorithm traffic flow forecasting Adaptive neural network This paper presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and improve mobility of transportation. The proposed algorithm aims to address the issues of development of short-term traffic flow predictors which have not been addressed fully in the current literature namely that: a) strongly non-linear characteristics are unavoidable in traffic flow data; b) memory space for implementation of short-term traffic flow predictors is limited; c) specification of model structures for short-term traffic flow predictors which do not involve trial and error methods based on human expertise; d) adaptation to newly-captured, traffic flow data is required. The proposed algorithm was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information is newly-captured. These results clearly demonstrate the effectiveness of using the proposed algorithm for real-time traffic flow forecasting. 2012 Journal Article http://hdl.handle.net/20.500.11937/21786 10.1109/TCST.2011.2180386 IEEE fulltext |
| spellingShingle | particle swarm optimization traffic management time-varying modeling evolutionary algorithm traffic flow forecasting Adaptive neural network Chan, Kit Dillon, Tharam S. Chang, Elizabeth Singh, Jaipal Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks |
| title | Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks |
| title_full | Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks |
| title_fullStr | Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks |
| title_full_unstemmed | Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks |
| title_short | Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks |
| title_sort | prediction of short-term traffic variables using intelligent swarm-based neural networks |
| topic | particle swarm optimization traffic management time-varying modeling evolutionary algorithm traffic flow forecasting Adaptive neural network |
| url | http://hdl.handle.net/20.500.11937/21786 |