Reinforcement Learning-Based Control of Signalized Intersections Having Platoons
<p dir="ltr">Smart transportation cities are based on intelligent systems and data sharing, whereas human drivers generally have limited capabilities and imperfect traffic observations. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513561749880832 |
|---|---|
| author | Anas Berbar (16905102) |
| author2 | Adel Gastli (14151273) Nader Meskin (14147796) Mohammed A. Al-Hitmi (14070780) Jawhar Ghommam (16891377) Mostefa Mesbah (16891380) Faical Mnif (16891383) |
| author2_role | author author author author author author |
| author_facet | Anas Berbar (16905102) Adel Gastli (14151273) Nader Meskin (14147796) Mohammed A. Al-Hitmi (14070780) Jawhar Ghommam (16891377) Mostefa Mesbah (16891380) Faical Mnif (16891383) |
| author_role | author |
| dc.creator.none.fl_str_mv | Anas Berbar (16905102) Adel Gastli (14151273) Nader Meskin (14147796) Mohammed A. Al-Hitmi (14070780) Jawhar Ghommam (16891377) Mostefa Mesbah (16891380) Faical Mnif (16891383) |
| dc.date.none.fl_str_mv | 2022-02-04T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3149161 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Reinforcement_Learning-Based_Control_of_Signalized_Intersections_Having_Platoons/24056550 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Transportation, logistics and supply chains Engineering Automotive engineering Information and computing sciences Artificial intelligence Machine learning Delays Reinforcement learning Fuels Sequential analysis Smart cities Safety Oscillators Traffic intersection Traffic signal control Platoon control Artificial intelligence |
| dc.title.none.fl_str_mv | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Smart transportation cities are based on intelligent systems and data sharing, whereas human drivers generally have limited capabilities and imperfect traffic observations. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to improve driving behaviors and reduce traffic delays and fuel consumption. This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method, where the first agent, the Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent, the Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. Several simulation studies have been conducted for a signalized intersection with different traffic flows and the performance of the single-agent with only VA, DA with both VA and SA, and Intelligent Driver Model (IDM) are compared. It is shown that the proposed DA solution improves the average delay by 47.3% and the fuel efficiency by 13.6% compared to the Intelligent Driver Model (IDM).</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3149161" target="_blank">https://dx.doi.org/10.1109/access.2022.3149161</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8351fa178e025bea173c8387c0b36e61 |
| identifier_str_mv | 10.1109/access.2022.3149161 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056550 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Reinforcement Learning-Based Control of Signalized Intersections Having PlatoonsAnas Berbar (16905102)Adel Gastli (14151273)Nader Meskin (14147796)Mohammed A. Al-Hitmi (14070780)Jawhar Ghommam (16891377)Mostefa Mesbah (16891380)Faical Mnif (16891383)Transportation, logistics and supply chainsEngineeringAutomotive engineeringInformation and computing sciencesArtificial intelligenceMachine learningDelaysReinforcement learningFuelsSequential analysisSmart citiesSafetyOscillatorsTraffic intersectionTraffic signal controlPlatoon controlArtificial intelligence<p dir="ltr">Smart transportation cities are based on intelligent systems and data sharing, whereas human drivers generally have limited capabilities and imperfect traffic observations. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to improve driving behaviors and reduce traffic delays and fuel consumption. This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method, where the first agent, the Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent, the Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. Several simulation studies have been conducted for a signalized intersection with different traffic flows and the performance of the single-agent with only VA, DA with both VA and SA, and Intelligent Driver Model (IDM) are compared. It is shown that the proposed DA solution improves the average delay by 47.3% and the fuel efficiency by 13.6% compared to the Intelligent Driver Model (IDM).</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3149161" target="_blank">https://dx.doi.org/10.1109/access.2022.3149161</a></p>2022-02-04T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3149161https://figshare.com/articles/journal_contribution/Reinforcement_Learning-Based_Control_of_Signalized_Intersections_Having_Platoons/24056550CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240565502022-02-04T00:00:00Z |
| spellingShingle | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons Anas Berbar (16905102) Transportation, logistics and supply chains Engineering Automotive engineering Information and computing sciences Artificial intelligence Machine learning Delays Reinforcement learning Fuels Sequential analysis Smart cities Safety Oscillators Traffic intersection Traffic signal control Platoon control Artificial intelligence |
| status_str | publishedVersion |
| title | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons |
| title_full | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons |
| title_fullStr | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons |
| title_full_unstemmed | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons |
| title_short | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons |
| title_sort | Reinforcement Learning-Based Control of Signalized Intersections Having Platoons |
| topic | Transportation, logistics and supply chains Engineering Automotive engineering Information and computing sciences Artificial intelligence Machine learning Delays Reinforcement learning Fuels Sequential analysis Smart cities Safety Oscillators Traffic intersection Traffic signal control Platoon control Artificial intelligence |