Route guidance system using multi-agent reinforcement learning
Nowadays, the problems of urban traffic in most big cities are more complex. Increasing population and road requirements has caused the complexity in traffic management systems. The main challenge for network traffic is to direct vehicles to their destination with the aim of reducing travel times an...
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2011
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| Online Access: | http://eprints.utm.my/id/eprint/46233/ http://dx.doi.org/10.1109/CITA.2011.5999388 |
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my.utm.462332017-08-29T01:14:26Z http://eprints.utm.my/id/eprint/46233/ Route guidance system using multi-agent reinforcement learning Selamat, Ali Mohd. Hashim, Siti Zaiton Selamat, Md. Hafiz Arokhlo, Mortaza Zolfpou Nowadays, the problems of urban traffic in most big cities are more complex. Increasing population and road requirements has caused the complexity in traffic management systems. The main challenge for network traffic is to direct vehicles to their destination with the aim of reducing travel times and efficient use of available network capacity. This paper proposes a new agent model and algorithm based on multi-agent reinforcement learning to find a best and shortest path between the origin and destination nodes. Furthermore, the proposed algorithm is compared with Dijkstra algorithm to find optimal solution using some simple real sample of Kuala Lumpur (KL) road network map. Experimental results affirmed the same results to find the optimal solutions. 2011 Conference or Workshop Item PeerReviewed Selamat, Ali and Mohd. Hashim, Siti Zaiton and Selamat, Md. Hafiz and Arokhlo, Mortaza Zolfpou (2011) Route guidance system using multi-agent reinforcement learning. In: 7th International Convergences And Singularity Of Forms. http://dx.doi.org/10.1109/CITA.2011.5999388 |
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Nowadays, the problems of urban traffic in most big cities are more complex. Increasing population and road requirements has caused the complexity in traffic management systems. The main challenge for network traffic is to direct vehicles to their destination with the aim of reducing travel times and efficient use of available network capacity. This paper proposes a new agent model and algorithm based on multi-agent reinforcement learning to find a best and shortest path between the origin and destination nodes. Furthermore, the proposed algorithm is compared with Dijkstra algorithm to find optimal solution using some simple real sample of Kuala Lumpur (KL) road network map. Experimental results affirmed the same results to find the optimal solutions. |
| format |
Conference or Workshop Item |
| author |
Selamat, Ali Mohd. Hashim, Siti Zaiton Selamat, Md. Hafiz Arokhlo, Mortaza Zolfpou |
| spellingShingle |
Selamat, Ali Mohd. Hashim, Siti Zaiton Selamat, Md. Hafiz Arokhlo, Mortaza Zolfpou Route guidance system using multi-agent reinforcement learning |
| author_facet |
Selamat, Ali Mohd. Hashim, Siti Zaiton Selamat, Md. Hafiz Arokhlo, Mortaza Zolfpou |
| author_sort |
Selamat, Ali |
| title |
Route guidance system using multi-agent reinforcement learning |
| title_short |
Route guidance system using multi-agent reinforcement learning |
| title_full |
Route guidance system using multi-agent reinforcement learning |
| title_fullStr |
Route guidance system using multi-agent reinforcement learning |
| title_full_unstemmed |
Route guidance system using multi-agent reinforcement learning |
| title_sort |
route guidance system using multi-agent reinforcement learning |
| publishDate |
2011 |
| url |
http://eprints.utm.my/id/eprint/46233/ http://dx.doi.org/10.1109/CITA.2011.5999388 |
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1643651975032602624 |
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13.211869 |