M2M Interface for IoT Traffic Light with Computer Vision and AnyLogic PLE

This paper proposes an advanced traffic light control system using IoT devices and computer vision, integrated through M2M interactions and modeled with AnyLogic PLE. The key contribution is the combination of IoT and computer vision for real-time, adaptive traffic light control. The study highlights the practical value of M2M technology, facilitating seamless interaction between web camera-equipped traffic lights and personal computers, overcoming the complexity of traditional wired methods like Siemens microcontrollers. Using a socket library for communication between Windows and Linux-based Raspberry Pi, the system implements interactive Wi-Fi information exchange for video monitoring and real-time road situation recognition. These data inputs control traffic lights via computer vision, enabling automated, adaptive traffic management. The prototype demonstrates real-time animated simulation managed by a dispatcher, enhancing the efficiency of traffic systems. The integration of M2M, IoT, and computer vision marks a significant advancement in intelligent transportation systems.