A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing
This paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2282 |
| _version_ | 1811186051903913984 |
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| author | Hossam M. Abdelghaffar Hesham A. Rakha |
| author_facet | Hossam M. Abdelghaffar Hesham A. Rakha |
| author_sort | Hossam M. Abdelghaffar |
| collection | DOAJ |
| description | This paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player in a game, where players cooperate to reach a mutually agreeable outcome. The controller is implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software, comparing its performance to that of a traditional decentralized adaptive cycle length and phase split traffic signal controller and a centralized fully-coordinated adaptive phase split, cycle length, and offset optimization controller. The comparisons are conducted in the town of Blacksburg, Virginia (38 traffic signalized intersections) and in downtown Los Angeles, California (457 signalized intersections). The results for the downtown Blacksburg evaluation show significant network-wide efficiency improvements. Specifically, there is a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>23.6</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in travel time, a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>37.6</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in queue lengths, and a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>10.4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in <inline-formula> <math display="inline"> <semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics> </math> </inline-formula> emissions relative to traditional adaptive traffic signal controllers. In addition, the testing on the downtown Los Angeles network produces a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>35.1</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in travel time on the intersection approaches, a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>54.7</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in queue lengths, and a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in <inline-formula> <math display="inline"> <semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics> </math> </inline-formula> emissions compared to traditional adaptive traffic signal controllers. The results demonstrate significant potential benefits of using the proposed controller over other state-of-the-art centralized and de-centralized adaptive traffic signal controllers on large-scale networks both during uncongested and congested conditions. |
| first_indexed | 2024-04-11T13:40:34Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:40:34Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5d95bfc513d3477092c2bce32256680c2022-12-22T04:21:16ZengMDPI AGSensors1424-82202019-05-011910228210.3390/s19102282s19102282A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale TestingHossam M. Abdelghaffar0Hesham A. Rakha1Department of Computers & Control Systems, Engineering Faculty, Mansoura University, Mansoura, Dakahlia 35516, EgyptCharles E. Via, Jr. Dept. of Civil and Environmental Engineering, Director of the Center of Sustainable Mobility, Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA 24061, USAThis paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player in a game, where players cooperate to reach a mutually agreeable outcome. The controller is implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software, comparing its performance to that of a traditional decentralized adaptive cycle length and phase split traffic signal controller and a centralized fully-coordinated adaptive phase split, cycle length, and offset optimization controller. The comparisons are conducted in the town of Blacksburg, Virginia (38 traffic signalized intersections) and in downtown Los Angeles, California (457 signalized intersections). The results for the downtown Blacksburg evaluation show significant network-wide efficiency improvements. Specifically, there is a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>23.6</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in travel time, a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>37.6</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in queue lengths, and a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>10.4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in <inline-formula> <math display="inline"> <semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics> </math> </inline-formula> emissions relative to traditional adaptive traffic signal controllers. In addition, the testing on the downtown Los Angeles network produces a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>35.1</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in travel time on the intersection approaches, a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>54.7</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in queue lengths, and a <inline-formula> <math display="inline"> <semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> reduction in <inline-formula> <math display="inline"> <semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics> </math> </inline-formula> emissions compared to traditional adaptive traffic signal controllers. The results demonstrate significant potential benefits of using the proposed controller over other state-of-the-art centralized and de-centralized adaptive traffic signal controllers on large-scale networks both during uncongested and congested conditions.https://www.mdpi.com/1424-8220/19/10/2282traffic signal controlgame theorydecentralized controllarge-scale network control |
| spellingShingle | Hossam M. Abdelghaffar Hesham A. Rakha A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing Sensors traffic signal control game theory decentralized control large-scale network control |
| title | A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing |
| title_full | A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing |
| title_fullStr | A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing |
| title_full_unstemmed | A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing |
| title_short | A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing |
| title_sort | novel decentralized game theoretic adaptive traffic signal controller large scale testing |
| topic | traffic signal control game theory decentralized control large-scale network control |
| url | https://www.mdpi.com/1424-8220/19/10/2282 |
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