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In our research, we have introduced an Adaptive and Intelligent Traffic Management System (AITMS) for smart cities. This system aims to address the inefficiencies of traditional traffic control systems by using real-time data to enhance road efficiency and manage traffic flow more effectively. AITMS adjusts traffic light timings based on current traffic conditions and operates in three modes: Fair Mode, Priority Mode, and Emergency Mode, which prioritize vehicles as needed. We have integrated deep reinforcement learning with a fuzzy inference system to optimize traffic signal phases. We have validated the effectiveness of our model through simulations conducted in the city of Bhubaneswar using the SUMO platform. The results demonstrate significant improvements in reducing vehicle waiting times at intersections and better coordination of traffic signals between roads, surpassing existing techniques.

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Hybrid CNN-Fuzzy Logic Model for Adaptive Traffic Signal Control in Vehicular Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Sudhansu Ranjan Lenka,
  • Suresh Kumar Grandhi,
  • Santosh Kumar Sahoo,
  • Sukant Kishoro Bisoy

摘要

In our research, we have introduced an Adaptive and Intelligent Traffic Management System (AITMS) for smart cities. This system aims to address the inefficiencies of traditional traffic control systems by using real-time data to enhance road efficiency and manage traffic flow more effectively. AITMS adjusts traffic light timings based on current traffic conditions and operates in three modes: Fair Mode, Priority Mode, and Emergency Mode, which prioritize vehicles as needed. We have integrated deep reinforcement learning with a fuzzy inference system to optimize traffic signal phases. We have validated the effectiveness of our model through simulations conducted in the city of Bhubaneswar using the SUMO platform. The results demonstrate significant improvements in reducing vehicle waiting times at intersections and better coordination of traffic signals between roads, surpassing existing techniques.