A Sequential Decision-Making Model for Perimeter Identification
This work addresses perimeter identification for applications like traffic monitoring, but it appears incremental as it builds on existing methodologies without specifying major breakthroughs.
The paper tackles perimeter identification by proposing a sequential decision-making framework that operates in real-time using publicly accessible information, and demonstrates its efficacy in a real-world scenario.
Perimeter identification involves ascertaining the boundaries of a designated area or zone, requiring traffic flow monitoring, control, or optimization. Various methodologies and technologies exist for accurately defining these perimeters; however, they often necessitate specialized equipment, precise mapping, or comprehensive data for effective problem delineation. In this study, we propose a sequential decision-making framework for perimeter search, designed to operate efficiently in real-time and require only publicly accessible information. We conceptualize the perimeter search as a game between a playing agent and an artificial environment, where the agent's objective is to identify the optimal perimeter by sequentially improving the current perimeter. We detail the model for the game and discuss its adaptability in determining the definition of an optimal perimeter. Ultimately, we showcase the model's efficacy through a real-world scenario, highlighting the identification of corresponding optimal perimeters.