Reward Design for Intelligent Intersection Control to Reduce Emission
The transportation industry is one of the main contributors to global warming since it is responsible for a quarter of greenhouse gas emissions. Due to society’s crucial dependence on fossil fuels and the rapid increase in mobility demands, the reduction of global vehicle emissions evolve...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9754511/ |
| _version_ | 1811204351300993024 |
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| author | Balint Kovari Balint Pelenczei Szilard Aradi Tamas Becsi |
| author_facet | Balint Kovari Balint Pelenczei Szilard Aradi Tamas Becsi |
| author_sort | Balint Kovari |
| collection | DOAJ |
| description | The transportation industry is one of the main contributors to global warming since it is responsible for a quarter of greenhouse gas emissions. Due to society’s crucial dependence on fossil fuels and the rapid increase in mobility demands, the reduction of global vehicle emissions evolved into a significant challenge. In the urban transportation areas, signalized intersections can be considered the main bottlenecks in mitigating congestion and, therefore, vehicle emission. Our research focuses on the Traffic Signal Control problem since the efficient control of these intersections can significantly impact the productive hours and, through emission, the health of the citizens along with the depressing challenge of climate change. The Traffic Signal Control problem is well-studied and solved via several different techniques. However, most recently, Single and Multi-Agent Reinforcement Learning methods have arisen thanks to their performance and real-time applicability. Although rewarding schemes, which are the most crucial aspects of this method, do not seem to evolve at the same pace as the utilized techniques. In this paper, we propose a novel rewarding concept to compare its performance with the most common rewarding strategies in the literature. The results indicate that our approach outperforms its contenders from the literature in both classic and sustainability measures. |
| first_indexed | 2024-04-12T03:11:39Z |
| format | Article |
| id | doaj.art-05519280b37140a08bf02b13e6a9501c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T03:11:39Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-05519280b37140a08bf02b13e6a9501c2022-12-22T03:50:18ZengIEEEIEEE Access2169-35362022-01-0110396913969910.1109/ACCESS.2022.31662369754511Reward Design for Intelligent Intersection Control to Reduce EmissionBalint Kovari0https://orcid.org/0000-0003-2178-2921Balint Pelenczei1https://orcid.org/0000-0001-9194-8574Szilard Aradi2https://orcid.org/0000-0001-6811-2584Tamas Becsi3https://orcid.org/0000-0002-1487-9672Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryThe transportation industry is one of the main contributors to global warming since it is responsible for a quarter of greenhouse gas emissions. Due to society’s crucial dependence on fossil fuels and the rapid increase in mobility demands, the reduction of global vehicle emissions evolved into a significant challenge. In the urban transportation areas, signalized intersections can be considered the main bottlenecks in mitigating congestion and, therefore, vehicle emission. Our research focuses on the Traffic Signal Control problem since the efficient control of these intersections can significantly impact the productive hours and, through emission, the health of the citizens along with the depressing challenge of climate change. The Traffic Signal Control problem is well-studied and solved via several different techniques. However, most recently, Single and Multi-Agent Reinforcement Learning methods have arisen thanks to their performance and real-time applicability. Although rewarding schemes, which are the most crucial aspects of this method, do not seem to evolve at the same pace as the utilized techniques. In this paper, we propose a novel rewarding concept to compare its performance with the most common rewarding strategies in the literature. The results indicate that our approach outperforms its contenders from the literature in both classic and sustainability measures.https://ieeexplore.ieee.org/document/9754511/Reinforcement learningtraffic signal controlair pollution |
| spellingShingle | Balint Kovari Balint Pelenczei Szilard Aradi Tamas Becsi Reward Design for Intelligent Intersection Control to Reduce Emission IEEE Access Reinforcement learning traffic signal control air pollution |
| title | Reward Design for Intelligent Intersection Control to Reduce Emission |
| title_full | Reward Design for Intelligent Intersection Control to Reduce Emission |
| title_fullStr | Reward Design for Intelligent Intersection Control to Reduce Emission |
| title_full_unstemmed | Reward Design for Intelligent Intersection Control to Reduce Emission |
| title_short | Reward Design for Intelligent Intersection Control to Reduce Emission |
| title_sort | reward design for intelligent intersection control to reduce emission |
| topic | Reinforcement learning traffic signal control air pollution |
| url | https://ieeexplore.ieee.org/document/9754511/ |
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