Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning

Abstract

The optimal operation of transportation systems is often susceptible to unexpected disruptions, such as traffic accidents and social events. Many established control strategies relying on mathematical models can struggle with real-world disruptions, leading to a significant deviation from their anticipated efficiency. This work applies the cutting-edge concept of antifragility to design a traffic control strategy for urban road networks against disruptions. Antifragility sets itself apart from robustness, resilience, and reliability as it represents a system's ability to not only withstand stressors, shocks, and volatility but also to thrive and enhance performance in the presence of such adversarial events. Incorporating antifragile terms composed of traffic state derivatives and redundancy, a model-free deep reinforcement learning algorithm is developed and subsequently evaluated in a two-region cordon-shaped urban traffic perimeter network. Promising results highlight (a) the superior performance of the proposed algorithm compared to the state-of-the-art methods under incremental magnitude of disruptions, (b) distribution skewness as the antifragility indicator demonstrating its relative antifragility, and (c) its effectiveness under limited observability due to real-world data availability constraints. The proposed antifragile methodology is generalizable and holds potential for application beyond perimeter control, offering integration into systems exposed to disruptions across various disciplines.

Tasks

Reproductions