A comprehensive initiative called the Smart Traffic Flow Management System (STFMS) aims to address the growing problems caused by urban traffic congestion. Surging urbanization and rising car density necessitate creative ways to improve traffic flow, cut down on travel time, and lessen environmental effect. This proposal presents an intelligent and adaptable system that integrates state-of-the-art technology to optimize traffic flow. A Smart Traffic Flow Management System (STFMS) is a digital system used to regulate the sequence of traffic lights at a junction. It was developed using image processing and verilog. Usually, a finite state machine (FSM) method or any combinational digital circuit can be used, in which different states stand for distinct light sequences, such as red, green, and yellow. To decide when to change states, the controller gets information from sensors or timers. The logic, states, and transitions for the output signals that operate the real traffic lights are defined in the Verilog code or in the external processor which is used. By optimizing the timing and switching of lights based on real-time traffic conditions and algorithms, it assures a safe and efficient flow of traffic. Environmental sustainability is a key focus of this project. By minimizing traffic congestion and optimizing traffic signal timings, the STFMS aims to reduce overall carbon emissions and fuel consumption. The project can also extend to the consideration of integration of electric and autonomous vehicles, promoting eco-friendly modes of transportation. STFMS offers a holistic approach to urban mobility challenges. By leveraging the power of data-driven insights, machine learning, and real-time communication, the system strives to create a more efficient, sustainable, and user-friendly urban transportation network. This project contributes to the development of smart cities by enhancing the quality of life for residents and optimizing the use of transportation resources
Smart Traffic Flow Management System using FPGA
15.11.2024
799544 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Smart Traffic Management System
IEEE | 2024
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