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Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning
While previous research works have dedicated efforts to improving the robustness or resilience of transportation systems against disruptions, this paper applies the cutting-edge concept of antifragility to better design a traffic control strategy for urban road networks.
Time-to-Green predictions for fully-actuated signal control systems with supervised learning
Recently, efforts have been made to standardize signal phase and timing (SPaT) messages.
An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation
However, the problem of traffic state estimation from various sensors within an urban environment is very complex for several different reasons, such as availability of sensors, different noise levels, different output quantities, sensor accuracy, heterogeneous data fusion, and many more.
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