Antifragile perimeter control

  • The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that often struggle to cope with real-world uncertainties, leading to a significant decline in their effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to enhancing the robustness or resilience of transportation systems against disruptions, in this paper, we use the concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such disruptions. Hence, modern transport systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) algorithm to regulate perimeter control in a two-region urban traffic network to exploit and strengthenThe optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that often struggle to cope with real-world uncertainties, leading to a significant decline in their effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to enhancing the robustness or resilience of transportation systems against disruptions, in this paper, we use the concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such disruptions. Hence, modern transport systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) algorithm to regulate perimeter control in a two-region urban traffic network to exploit and strengthen the learning capability of RL under disruptions and achieve antifragility. By incorporating antifragility terms based on the change rate and curvature of the traffic state into the RL framework, the proposed algorithm further gains knowledge of the traffic state, which helps in anticipating imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to enhance the performance under disruption scenarios. When compared to a state-of-the-art model predictive control approach and a state-of-the-art RL algorithm, our proposed method demonstrates two antifragility-related properties: (a) gradual performance improvement under disruptions of similar magnitude; and (b) increasingly superior performance under growing disruptions.show moreshow less

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Metadaten
Author:Linghang Sun, Michail Makridis, Alexander Genser, Cristian AxenieORCiD, Margherita Grossi, Anastasios Kouvelas
DOI:https://doi.org/10.48550/arXiv.2402.12665
ArXiv Id:http://arxiv.org/abs/2402.12665
Subtitle (English):Anticipating and gaining from disruptions with reinforcement learning
Document Type:conference proceeding (article)
Language:English
Date of first Publication:2024/01/08
Reviewed:Begutachtet/Reviewed
Release Date:2025/02/17
Pagenumber:34
Konferenzangabe:103rd Annual Meeting of the Transportation Research Board (TRB 2024), Washington, DC, USA, January 7-11, 2024
institutes:Fakultät Informatik
Research Themes:Mobilität, Verkehr, Logistik
Digitalisierung & Künstliche Intelligenz
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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