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<p>This paper presents a novel approach to sustainable traffic management in autonomous smart cities, utilizing a multi-agent system (MAS) integrated with fuzzy logic system. Fuzzy inference system (FIS) can use their adaptive characteristics to cope with uncertainty and complexity in real-time traffic situations. Generally, Multi Agent System (MAS) which comprises of autonomous and independent agents cooperating with each other to take decentralized decisions would act with fuzzy logic controllers (FLC) to learn, adapt and dynamically respond to varying flow and congestion levels, maximizing flow and minimizing delays and traffic hazards. They communicate with one another and the environment, processing traffic information gathered from different sources like sensors, cameras, and vehicles, to make certain localized choices about route optimization, signal control, and incident management. Through simulation, the results show that the proposed fuzzy-integrated MAS significantly surpass the recent traffic management systems in response, efficiency, and adaptability. The proposed system achieves the highest average flow rate (850 vehicles per hour) and the lowest average congestion (15%) thereby reducing the impact of erratic traffic behaviors and enhancing the reliability of the system.</p>

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Fuzzy-integrated multi-agent system for autonomous smart city traffic management

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Goverdhan Reddy Jidiga,
  • Chaitali Bhattacharya,
  • Cindhe Ramesh,
  • Avneesh Vashistha,
  • Kriti Srivastava,
  • Sajjan Singh

摘要

This paper presents a novel approach to sustainable traffic management in autonomous smart cities, utilizing a multi-agent system (MAS) integrated with fuzzy logic system. Fuzzy inference system (FIS) can use their adaptive characteristics to cope with uncertainty and complexity in real-time traffic situations. Generally, Multi Agent System (MAS) which comprises of autonomous and independent agents cooperating with each other to take decentralized decisions would act with fuzzy logic controllers (FLC) to learn, adapt and dynamically respond to varying flow and congestion levels, maximizing flow and minimizing delays and traffic hazards. They communicate with one another and the environment, processing traffic information gathered from different sources like sensors, cameras, and vehicles, to make certain localized choices about route optimization, signal control, and incident management. Through simulation, the results show that the proposed fuzzy-integrated MAS significantly surpass the recent traffic management systems in response, efficiency, and adaptability. The proposed system achieves the highest average flow rate (850 vehicles per hour) and the lowest average congestion (15%) thereby reducing the impact of erratic traffic behaviors and enhancing the reliability of the system.