A YOLOv8-based project for real-time traffic density estimation. It employs fine-tuned vehicle detection models to analyze and count vehicles per frame, aiding urban traffic management and planning. The repository includes model training, traffic intensity analysis, and deployment strategies.

Smart Traffic Management System which changes window time of green light based on density of vehicles.

Smart Traffic Congestion Management System (TCMS) is an AI-powered system that dynamically adjusts traffic light timings based on real-time vehicle density using YOLOv5 and adaptive algorithms. It reduces congestion, optimizes traffic flow, and improves urban mobility.

There has been a population increase which has consequently led to traffic congestion in the city of Karachi. Making a smart traffic management system that makes use of video and picture data of the traffic on the roads of Karachi, Pakistan. This works by performing machine learning using an algorithm over the recent frame obtained from the video to estimate the number of vehicles present in a scene. Cameras will be installed on the opposite of the lane, beside the traffic light and will take its real-time video. At the back-end, Raspberry Pi would be connected to handle video processing. Raspberry pi would receive video as input from the camera of each road. Image framing would capture frames from the video at several fixed intervals. By taking our city, Karachi, into consideration we are creating our data set based on images captured from within the city. The proposed project aims to make decisions for the traffic signal timings based on vehicle densities. The project will be deployed on a four-way traffic signal. It will make use of image processing to separate image frames while machine learning algorithms will perform the task of signal controlling and vehicle detection. The reason for using Image Processing and machine learning is because it keeps production costs are low while achieving high speed and accuracy.

A cloud-based, AI-powered solution for real-time traffic monitoring and management. This system analyzes CCTV and GPS data to predict traffic density, prioritize emergency vehicles, and optimize traffic signal timing. Features include dynamic route suggestions, accident detection alerts, and a centralized dashboard for city-wide traffic control.

This computer vision based intelligent crowd traffic management system is a robust frameworks that managers and analyses road traffic flow in real time by estimating the traffic density near traffic signals and roads via CCTV.

LaneSense: YOLO-Based Traffic Signal Optimization** LaneSense uses YOLO object detection to monitor vehicle density in each lane, optimizing traffic signals for smoother flow. Developed for the 6th semester, it aims to reduce congestion and enhance safety. Join us in revolutionizing traffic management.

The Density-Based Traffic Control System is designed to regulate the flow of vehicles at an intersection based on the density of traffic detected by infrared sensors. The system uses Arduino Uno as the microcontroller to control the traffic signal lights.

🛰️ ATLAS – Adaptive Traffic Light Allocation System ATLAS is an intelligent traffic management system that uses real-time vehicle detection and classification to optimize signal timings. Powered by YOLOv8 and a Density-Based Weighted Algorithm, it dynamically adjusts green, yellow, and red signals based on actual road usage,

Introducing Dhaka City's Smart Traffic Management: uses data to optimize flow via operations like insertion, deletion, search, and density-based sorting. Manages parking in real-time to ease congestion, suggests alternate routes for efficiency and city resilience. A leap forward in urban infrastructure for smarter, livable cities.

The object of our project is to develop an intelligent traffic management system based on image processing and machine learning techniques to collect the traffic data(basically a video stream) and monitor accordingly to avoid big jams and accidents. It will provide a distributed way of traffic maintenance and detects the traffic density and chaos level too.

The Density-Based Traffic Light Controller is an intelligent traffic management system designed to reduce unnecessary congestion at four-way intersections. Unlike traditional traffic lights that operate on fixed timers, this system uses real-time data to decide which lane should be green.

SmartLane AI is an advanced AI-powered traffic management system that analyzes 4-way intersections using YOLOv8 vehicle detection and a CNN-based emergency vehicle classifier. The platform identifies traffic density, detects ambulances through multi-layer emergency recognition (YOLO + Color Pattern + Text + CNN), and automatically adjusts signals.

This project consists of smart traffic management system in which the duration of the green signal depend on incoming traffic . It is implemented in python and uses object detection.

Density Based Traffic Management System using AVR Microcontroller(ATmega32)

An intelligent traffic management system that uses real-time data from sensors and cameras, analyzed with machine learning, to optimize traffic flow and reduce congestion. It prioritizes emergency vehicles and provides real-time traffic updates and alternative routes to drivers via mobile apps and public display systems.

IoT enabled density based traffic management system

A Density-Based Traffic Signal Control System (DBTSCS) is an intelligent traffic management solution that dynamically adjusts signal timings based on the real-time traffic density observed at intersections.

This project proposes an AI-based Intelligent Traffic Management System that dynamically analyzes traffic density and adjusts traffic signal timings accordingly. The system estimates vehicle count, classifies traffic density as low, medium, or high, and determines an optimal green signal duration based on real-time conditions.

Vehicle Density Prediction is a computer vision-based system that detects and counts vehicles in real-time video feeds to estimate traffic density. It helps monitor congestion levels using deep learning techniques for smart traffic management.

An intelligent traffic management system leveraging Google Maps, IoT sensors, and AI. The system dynamically adjusts traffic signals and guides autonomous vehicles based on real-time traffic density and emergency vehicle detection.

Real-time AI traffic signal management — YOLOv8 vision + Multi-Agent PPO RL + SUMO simulation. Area-based density, emergency vehicle preemption, live dashboard.

This project proposes a vision-based intelligent traffic monitoring system that uses deep learning and computer vision to analyze real-time traffic video streams. The system detects vehicles, estimates traffic density, and predicts congestion levels, supporting data-driven decision-making for improved traffic flow management .

An AI-based smart traffic signal management system that dynamically adjusts traffic light timing based on real-time vehicle density and prioritizes ambulances to ensure minimal delay in emergency response. Built using Python, Computer Vision, and a custom simulation framework.

The Crowd Traffic Management System is a web-based application designed to monitor and manage crowd movement and traffic density in real time. The system helps in identifying congested areas and supports better decision-making to improve public safety and traffic flow.

This project is a high-tech Smart Traffic Management System (STMS) designed to optimize urban traffic flow using Computer Vision and AI. It functions as a real-time Human-Machine Interface (HMI) for monitoring and controlling traffic signals based on vehicle density.

AI-based Traffic Management System using Computer Vision to dynamically control signal timing based on real-time vehicle density. Built with OpenCV, YOLO (Ultralytics), Flask, and Streamlit, the system detects and counts vehicles from live video and adjusts green signal duration to reduce congestion and improve traffic flow efficiency.

This research proposes a Smart Traffic Light Control System Using Ultrasonic Sensors, which enhances conventional traffic management by adapting signal durations based on vehicle density. The system also includes pedestrian safety mechanisms, such as controlled barricades, to regulate vehicle movement before zebra crossings.

Developed a system that relies on image analysis using cameras installed at traffic signals. The system counts the number of vehicles in each lane and determines the most congested lane to activate the green light based on traffic density. This project contributes to improving traffic management and reducing vehicle wait times.

An AI-driven traffic management system using real-time camera feeds and deep learning to optimize signal control. It adjusts timings based on traffic density, reducing congestion. Future IoT hardware integration will enable vehicle prioritization for emergency services and public transport, ensuring smarter urban mobility