Traffic information interpolation method based on traffic flow emergence using swarm intelligence
Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as...
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| Autor*in: |
Suga, Satoshi [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Artificial life and robotics - Springer Japan, 1997, 28(2023), 2 vom: 03. Jan., Seite 367-380 |
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| Übergeordnetes Werk: |
volume:28 ; year:2023 ; number:2 ; day:03 ; month:01 ; pages:367-380 |
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| DOI / URN: |
10.1007/s10015-022-00847-7 |
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OLC213469954X |
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| 520 | |a Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. | ||
| 650 | 4 | |a Swarm intelligence | |
| 650 | 4 | |a Ant colony optimization (ACO) | |
| 650 | 4 | |a Intelligent transport systems (ITS) | |
| 650 | 4 | |a Multi-agent systems | |
| 700 | 1 | |a Fujimori, Ryu |4 aut | |
| 700 | 1 | |a Yamada, Yuji |4 aut | |
| 700 | 1 | |a Ihara, Fumito |4 aut | |
| 700 | 1 | |a Takamura, Daiki |4 aut | |
| 700 | 1 | |a Hayashi, Ken |4 aut | |
| 700 | 1 | |a Kurihara, Satoshi |4 aut | |
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10.1007/s10015-022-00847-7 doi (DE-627)OLC213469954X (DE-He213)s10015-022-00847-7-p DE-627 ger DE-627 rakwb eng 004 VZ Suga, Satoshi verfasserin aut Traffic information interpolation method based on traffic flow emergence using swarm intelligence 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. Swarm intelligence Ant colony optimization (ACO) Intelligent transport systems (ITS) Multi-agent systems Fujimori, Ryu aut Yamada, Yuji aut Ihara, Fumito aut Takamura, Daiki aut Hayashi, Ken aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Springer Japan, 1997 28(2023), 2 vom: 03. Jan., Seite 367-380 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:28 year:2023 number:2 day:03 month:01 pages:367-380 https://doi.org/10.1007/s10015-022-00847-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 28 2023 2 03 01 367-380 |
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10.1007/s10015-022-00847-7 doi (DE-627)OLC213469954X (DE-He213)s10015-022-00847-7-p DE-627 ger DE-627 rakwb eng 004 VZ Suga, Satoshi verfasserin aut Traffic information interpolation method based on traffic flow emergence using swarm intelligence 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. Swarm intelligence Ant colony optimization (ACO) Intelligent transport systems (ITS) Multi-agent systems Fujimori, Ryu aut Yamada, Yuji aut Ihara, Fumito aut Takamura, Daiki aut Hayashi, Ken aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Springer Japan, 1997 28(2023), 2 vom: 03. Jan., Seite 367-380 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:28 year:2023 number:2 day:03 month:01 pages:367-380 https://doi.org/10.1007/s10015-022-00847-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 28 2023 2 03 01 367-380 |
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10.1007/s10015-022-00847-7 doi (DE-627)OLC213469954X (DE-He213)s10015-022-00847-7-p DE-627 ger DE-627 rakwb eng 004 VZ Suga, Satoshi verfasserin aut Traffic information interpolation method based on traffic flow emergence using swarm intelligence 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. Swarm intelligence Ant colony optimization (ACO) Intelligent transport systems (ITS) Multi-agent systems Fujimori, Ryu aut Yamada, Yuji aut Ihara, Fumito aut Takamura, Daiki aut Hayashi, Ken aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Springer Japan, 1997 28(2023), 2 vom: 03. Jan., Seite 367-380 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:28 year:2023 number:2 day:03 month:01 pages:367-380 https://doi.org/10.1007/s10015-022-00847-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 28 2023 2 03 01 367-380 |
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10.1007/s10015-022-00847-7 doi (DE-627)OLC213469954X (DE-He213)s10015-022-00847-7-p DE-627 ger DE-627 rakwb eng 004 VZ Suga, Satoshi verfasserin aut Traffic information interpolation method based on traffic flow emergence using swarm intelligence 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. Swarm intelligence Ant colony optimization (ACO) Intelligent transport systems (ITS) Multi-agent systems Fujimori, Ryu aut Yamada, Yuji aut Ihara, Fumito aut Takamura, Daiki aut Hayashi, Ken aut Kurihara, Satoshi aut Enthalten in Artificial life and robotics Springer Japan, 1997 28(2023), 2 vom: 03. Jan., Seite 367-380 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:28 year:2023 number:2 day:03 month:01 pages:367-380 https://doi.org/10.1007/s10015-022-00847-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 28 2023 2 03 01 367-380 |
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Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. © The Author(s) 2023 |
| abstractGer |
Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. © The Author(s) 2023 |
| abstract_unstemmed |
Abstract Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. © The Author(s) 2023 |
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| title_short |
Traffic information interpolation method based on traffic flow emergence using swarm intelligence |
| url |
https://doi.org/10.1007/s10015-022-00847-7 |
| remote_bool |
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| author2 |
Fujimori, Ryu Yamada, Yuji Ihara, Fumito Takamura, Daiki Hayashi, Ken Kurihara, Satoshi |
| author2Str |
Fujimori, Ryu Yamada, Yuji Ihara, Fumito Takamura, Daiki Hayashi, Ken Kurihara, Satoshi |
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| doi_str |
10.1007/s10015-022-00847-7 |
| up_date |
2024-07-04T02:12:43.275Z |
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