Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties
Abstract The perimeter control method is an effective way to alleviate traffic congestion that is based on Macroscopic Fundamental Diagrams (MFDs). However, the strategy may lead to congestion when it ignores the uncertainty of MFDs. To address this problem, this paper presents a risk‐averse perimet...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12434 |
| _version_ | 1797354989935394816 |
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| author | Yuntao Shi Ying Zhang Xiang Yin Weichuan Liu Tingshen Cheng |
| author_facet | Yuntao Shi Ying Zhang Xiang Yin Weichuan Liu Tingshen Cheng |
| author_sort | Yuntao Shi |
| collection | DOAJ |
| description | Abstract The perimeter control method is an effective way to alleviate traffic congestion that is based on Macroscopic Fundamental Diagrams (MFDs). However, the strategy may lead to congestion when it ignores the uncertainty of MFDs. To address this problem, this paper presents a risk‐averse perimeter control method. First, the urban traffic network system with uncertainty is modeled using a neural network and scenario tree. Then, this research quantifies the congestion risk caused by uncertainty using an average value‐at‐risk. The next step sees the design of a risk‐averse model predictive control (MPC) controller that takes the multi‐stage risk as the optimization objective and improves robustness by interpolating between the conventional stochastic and worst‐case MPC formulations. Finally, this paper analyzes the risk‐sensitive stability of an urban traffic network system and gives a solvable form of risk‐averse optimal control for this system. Finally, two simulations are conducted to verify the presented method's validity and superiority for an urban traffic network system with uncertainties. The simulation results show that the risk‐averse perimeter control method presented by this paper is superior because it reduces the total travel time by 12.98% compared to Stochastic MPC, by 15.96% compared to bang‐bang control, and by 14.54% compared to proportional‐integral. |
| first_indexed | 2024-03-08T13:57:46Z |
| format | Article |
| id | doaj.art-2075efc3b1af4fe39e107f5be8be339c |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-03-08T13:57:46Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-2075efc3b1af4fe39e107f5be8be339c2024-01-15T09:12:38ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-01-01181728710.1049/itr2.12434Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertaintiesYuntao Shi0Ying Zhang1Xiang Yin2Weichuan Liu3Tingshen Cheng4School of Electrical and Control Engineering North China University of Technology Beijing ChinaSchool of Electrical and Control Engineering North China University of Technology Beijing ChinaSchool of Electrical and Control Engineering North China University of Technology Beijing ChinaSchool of Electrical and Control Engineering North China University of Technology Beijing ChinaSchool of Electrical and Control Engineering North China University of Technology Beijing ChinaAbstract The perimeter control method is an effective way to alleviate traffic congestion that is based on Macroscopic Fundamental Diagrams (MFDs). However, the strategy may lead to congestion when it ignores the uncertainty of MFDs. To address this problem, this paper presents a risk‐averse perimeter control method. First, the urban traffic network system with uncertainty is modeled using a neural network and scenario tree. Then, this research quantifies the congestion risk caused by uncertainty using an average value‐at‐risk. The next step sees the design of a risk‐averse model predictive control (MPC) controller that takes the multi‐stage risk as the optimization objective and improves robustness by interpolating between the conventional stochastic and worst‐case MPC formulations. Finally, this paper analyzes the risk‐sensitive stability of an urban traffic network system and gives a solvable form of risk‐averse optimal control for this system. Finally, two simulations are conducted to verify the presented method's validity and superiority for an urban traffic network system with uncertainties. The simulation results show that the risk‐averse perimeter control method presented by this paper is superior because it reduces the total travel time by 12.98% compared to Stochastic MPC, by 15.96% compared to bang‐bang control, and by 14.54% compared to proportional‐integral.https://doi.org/10.1049/itr2.12434intelligent controlintelligent transportation systemsnonlinear control systemspredictive control |
| spellingShingle | Yuntao Shi Ying Zhang Xiang Yin Weichuan Liu Tingshen Cheng Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties IET Intelligent Transport Systems intelligent control intelligent transportation systems nonlinear control systems predictive control |
| title | Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties |
| title_full | Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties |
| title_fullStr | Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties |
| title_full_unstemmed | Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties |
| title_short | Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties |
| title_sort | risk averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties |
| topic | intelligent control intelligent transportation systems nonlinear control systems predictive control |
| url | https://doi.org/10.1049/itr2.12434 |
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