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This paper aims at working the problem of business traffic in metropolitan metropolises by coordinating signals, addressing traffic and reducing the average staying time at signals using Artificial Intelligence and machine literacy ways and algorithms. Then, the author makes use of being architectures similar as CCTV cameras which are formerly installed in metropolitan metropolises for the purpose of surveillance, and Google maps API in case of unreliable cameras of absence of cameras. The author will be using surveying algorithms like Linear Quadratic Regulator (LQR), Quantized Linear Quadratic Regulator (QLQR),You only Look Once (YOLO), multi-agent underpinning literacy(Shingle), Graph Convolutional Networks (GCN) and Attention Medium, Clip-Proximal Policy Optimization (ClipPPO), Deep underpinning literacy (DRL), Max Pressure(MP) Algorithm, Dynamic Traffic Light Controller (DTLC), Self-Organizing Traffic Lights(SOTL), Business Prophetic underpinning literacy (PRLight), FRAP (underpinning literacy-grounded algorithm), MaCAR (Multi-Agent Cooperative Adaptive Routing) and other underpinning literacy algorithms.

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AI-Driven Smart Traffic Management System: Dynamic Timing of Traffic Lights

  • 会议论文

  • 发表日期：

    2026年

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  • Mayank Kishor Bobade,
  • Suvansh Shankar Tembe,
  • Rahul Vijay Patil,
  • Aditya Ankush Darade,
  • Kavita Moholkar,
  • Renuka Gound

摘要

This paper aims at working the problem of business traffic in metropolitan metropolises by coordinating signals, addressing traffic and reducing the average staying time at signals using Artificial Intelligence and machine literacy ways and algorithms. Then, the author makes use of being architectures similar as CCTV cameras which are formerly installed in metropolitan metropolises for the purpose of surveillance, and Google maps API in case of unreliable cameras of absence of cameras. The author will be using surveying algorithms like Linear Quadratic Regulator (LQR), Quantized Linear Quadratic Regulator (QLQR),You only Look Once (YOLO), multi-agent underpinning literacy(Shingle), Graph Convolutional Networks (GCN) and Attention Medium, Clip-Proximal Policy Optimization (ClipPPO), Deep underpinning literacy (DRL), Max Pressure(MP) Algorithm, Dynamic Traffic Light Controller (DTLC), Self-Organizing Traffic Lights(SOTL), Business Prophetic underpinning literacy (PRLight), FRAP (underpinning literacy-grounded algorithm), MaCAR (Multi-Agent Cooperative Adaptive Routing) and other underpinning literacy algorithms.