Trust in Smart City Mobility Applications: A Multi-Agent System Perspective
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Abstract
This chapter presents a recommendation system framework for smart mobility applications, emphasizing traffic monitoring and parking management in smart cities. Using Reinforcement Learning (RL) and Social Network (SN) concepts, the methodology classifies agents as trustworthy or untrustworthy, tackling multi-agent system challenges in uncertain environments. The research aims to create algorithms and models for safe, efficient, sustainable mobility solutions, addressing data exchange and decision-making issues. Agents gather and process information, make decisions with incomplete data, and interact to achieve goals. Real-world data will validate the approach, enhancing decision-making and improving urban mobility.
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