Application of LVQ neural network in real–time adaptive traffic signal control
Real-time road traffic data analysis is the cornerstone for the modern transport system. The real-time adaptive traffic signal control system is an essential part for the system. This analysis is to describe a traffic scene in a way similar to that of a human reporting the traffic status and the ext...
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Universiti Teknologi Malaysia
2005
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| Online Access: | http://irep.iium.edu.my/37327/ http://irep.iium.edu.my/37327/ http://irep.iium.edu.my/37327/ http://irep.iium.edu.my/37327/1/azmi.pdf |
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iium-373272014-08-07T00:42:24Z http://irep.iium.edu.my/37327/ Application of LVQ neural network in real–time adaptive traffic signal control Priyono, Agus Ridwan, Muhammad Alias, Ahmad Jais Rahmat, Riza Atiq Hassan, Azmi Mohd Ali, Mohd Alaudin QA76 Computer software T Technology (General) Real-time road traffic data analysis is the cornerstone for the modern transport system. The real-time adaptive traffic signal control system is an essential part for the system. This analysis is to describe a traffic scene in a way similar to that of a human reporting the traffic status and the extraction of traffic parameters such as vehicle queue length, traffic volume, lane occupancy and speed measurement. This paper proposed the application of two stage neural network in real-time adaptive tratfic signal control system capable of analysing the traffic scene detected by video camera processing the data, determining the traffic parameters and using the parameters to decide the control strategies. The two-stage neural network is used to process the traffic scene and decide the traffic control methods: optimum priority or optimum locality. Based on simulation in the traffic laboratory and field testing, the proposed control system is able to recognise the traffic pattern and enhance the traffic parameters, thus easing traffic congestion more effectively than existing control systems. Universiti Teknologi Malaysia 2005-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37327/1/azmi.pdf Priyono, Agus and Ridwan, Muhammad and Alias, Ahmad Jais and Rahmat, Riza Atiq and Hassan, Azmi and Mohd Ali, Mohd Alaudin (2005) Application of LVQ neural network in real–time adaptive traffic signal control. Jurnal Teknologi, 42. pp. 57-73. ISSN 2180–3722 (O), 0127–9696 (P) http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/739 10.11113/jt.v42.739 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA76 Computer software T Technology (General) |
| spellingShingle |
QA76 Computer software T Technology (General) Priyono, Agus Ridwan, Muhammad Alias, Ahmad Jais Rahmat, Riza Atiq Hassan, Azmi Mohd Ali, Mohd Alaudin Application of LVQ neural network in real–time adaptive traffic signal control |
| description |
Real-time road traffic data analysis is the cornerstone for the modern transport system. The real-time adaptive traffic signal control system is an essential part for the system. This analysis is to describe a traffic scene in a way similar to that of a human reporting the traffic status and the extraction of traffic parameters such as vehicle queue length, traffic volume, lane occupancy and speed measurement. This paper proposed the application of two stage neural network in real-time adaptive tratfic signal control system capable of analysing the traffic scene detected by video camera processing the data, determining the traffic parameters and using the parameters to decide the control strategies. The two-stage neural network is used to process the traffic scene and decide the traffic control methods: optimum priority or optimum locality. Based on simulation in the traffic laboratory and field testing, the proposed control system is able to recognise the traffic pattern and enhance the traffic parameters, thus easing traffic congestion more effectively than existing control systems. |
| format |
Article |
| author |
Priyono, Agus Ridwan, Muhammad Alias, Ahmad Jais Rahmat, Riza Atiq Hassan, Azmi Mohd Ali, Mohd Alaudin |
| author_facet |
Priyono, Agus Ridwan, Muhammad Alias, Ahmad Jais Rahmat, Riza Atiq Hassan, Azmi Mohd Ali, Mohd Alaudin |
| author_sort |
Priyono, Agus |
| title |
Application of LVQ neural network in real–time adaptive traffic signal control |
| title_short |
Application of LVQ neural network in real–time adaptive traffic signal control |
| title_full |
Application of LVQ neural network in real–time adaptive traffic signal control |
| title_fullStr |
Application of LVQ neural network in real–time adaptive traffic signal control |
| title_full_unstemmed |
Application of LVQ neural network in real–time adaptive traffic signal control |
| title_sort |
application of lvq neural network in real–time adaptive traffic signal control |
| publisher |
Universiti Teknologi Malaysia |
| publishDate |
2005 |
| url |
http://irep.iium.edu.my/37327/ http://irep.iium.edu.my/37327/ http://irep.iium.edu.my/37327/ http://irep.iium.edu.my/37327/1/azmi.pdf |
| first_indexed |
2023-09-18T20:53:33Z |
| last_indexed |
2023-09-18T20:53:33Z |
| _version_ |
1777410154325606400 |