# Intelligence traffic monitoring system ### About Due to a huge number of vehicles ,very busy road and parking which may not be possible manually as a human being, tends to get fatigued due to monotonous nature of the job and they cannot keep track of the vehicles when there are multiple vehicles are passing in a very short time. So modern cities need to establish effective automatic systems for traffic management and scheduling. The objective of this project is to design and develop an accurate and automatic number plate recognition system, Automatic traffic light control using google Api live traffic density data, smart fine system and also We can track the lost vehicle using vehicle number plate detection and find its location by google Map API. Intelligent Traffic Monitoring System (ITMS) is an image processing and machine learning technology to identify vehicles by their license plates and we uses the microService of google API for live traffic density. ### Features 1. License plate number recognition. 2. Matching the plate number with Database. 3. Intelligence traffic light control using live traffic density data. 4. Show traffic density of particular area for some duration of month in form of graph. 5. Online Vehicle license registration. 6. Smart fine system. ###Applications 1. Automated track the location of stolen vehicle 2. Anti-Theft/ Vehicle detection. 3. Traffic light automation ,no requirement of Traffic police. 4. Smart fine /E Challan Systems. 5. Car Parking / Automatic Toll Deduction. 6. Law Enforcement 7. VIP/Ambulance path Clearance 8. Help the government to take ● Increase the efficiency of existing transport infrastructure ● Develop a license plate recognition system, ● Build a smart fine system and in future enhancement automated fine systems for vehicles. ● Live Traffic detection system and automated traffic light control system. ● Predict the traffic density using machine learning for specific areas by its previous data. ● Automated lost vehicle detection system and information to administration. ● Handle traffic congestion using automated light control system. ### Installation * Clone the project. * Run `yarn install` to install the dependencies. * Run `yarn start` to view the project in action. ### OpenCV Demo to Count Vehicles * In "countingCars" directory, run 'python count.py' . ### License plate detection go to vehicle_number_by_its_pate folder and type python3 licenseplateDetection.py 1.jpg #secreenshot <img src="./screenshot/IMG_20200901_103735.jpg"> <img src="./screenshot/IMG_20200901_103751.jpg"> <img src="./screenshot/IMG_20200901_103811.jpg"> <img src="./screenshot/IMG_20200901_103826.jpg"> <img src="./screenshot/IMG_20200901_103844.jpg"> <img src="./screenshot/IMG_20200901_103906.jpg"> <img src="./screenshot/IMG_20200901_103943.jpg"> <img src="./screenshot/IMG_20200901_104003.jpg"> <img src="./screenshot/IMG_20200901_104044.jpg"> <img src="./screenshot/IMG_20200902_032314.jpg">

This GitHub repo hosts an RL-based Traffic Light Control System (TLCS) for optimizing intersections. Employing Reinforcement Learning, it adapts signal timings in real-time, reducing congestion and enhancing traffic efficiency. Explore and contribute to smarter traffic solutions here.

Smart Traffic Control System to analyze traffic patterns and determine best traffic light outputs

A smart traffic light control system using DQN reinforcement learning model, trained on simulated traffic data generated by SUMO and deployed in real-world environments with cameras, OpenCV, and YOLOv5 for object detection.

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.

Smart Traffic Light Control System

Use computer vision to detect vehicle density and optimize signal timing using reinforcement learning.

Control System project on Smart Traffic. The pupose is to automate the traffic light which weighs in not only time but the number of cars on a lane.

A modular AI-based traffic monitoring and control system for smart intersections. Uses YOLOv8 and OpenCV to detect vehicles, track movement, calculate turning ratios, and optimize traffic light durations in real time based on actual traffic patterns.

IoT-based Smart Traffic Light System integrating hardware (Arduino, sensors, XBee, boom barriers) and software logic (density detection, emergency siren handling, wireless control). Fully functional prototype with IEEE-published research.

Models the multi-agent system required to simulate an intersection controlled by smart traffic light signals:

Smart traffic light control system developed during a hackathon, using computer vision for real-time traffic monitoring and adaptive signal optimization.

The Smart Traffic Light System is an intelligent traffic management solution designed to optimize traffic flow and prioritize emergency vehicles. By leveraging computer vision, simulated DSRC (Dedicated Short-Range Communication) technology, and advanced traffic control algorithms, the system creates a more efficient .

This project aims to design a Smart Traffic Management System (STMS) that utilizes artificial intelligence (AI) to address traffic congestion issues. The system will be capable of monitoring, analyzing, and controlling traffic flow in real-time, enabling faster and more efficient decision-making in traffic light management.

Smart Traffic Light Control System

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Developed a real-time traffic monitoring solution using YOLOv3-tiny for fast vehicle detection and Firebase for cloud-based data storage. Leveraged distributed computing for parallel processing at the edge. System classifies traffic as smooth or jam, supports cloud syncing, and is scalable for smart city deployment.

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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.

🚦 Smart Emergency-Responsive Traffic Light System using ESP32 + Arduino Mega. Detects ambulance sirens via I²S mic and IR sensors, communicates through I²C, and integrates with Blynk Cloud for remote manual control and real-time status display.

A private research initiative to build a "smart" traffic light control system

Smart traffic light system using AI for vehicle detection (toy car-trained model) with Arduino-controlled signals for dynamic traffic management.

Smart traffic light system using Arduino Uno with ultrasonic sensor for vehicle detection, automatic light cycling, and IR remote control for emergency vehicle priority override.

This repo is containing the necessary files for an advanced traffic light control system. A smart embedded traffic light control system developed on the STM32F405 microcontroller, simulating real-world intersection management using sensors, timing logic, and emergency power failure handling.

Our project introduces an Adaptive Traffic Light Control System powered by computer vision. By strategically deploying smart cameras at intersections, we capture real-time data on vehicle density, enabling dynamic traffic analysis.

The "SmartTrafficLight.ino" code is an Arduino sketch that implements a smart traffic light system. It intelligently controls the traffic light signals, including pedestrian signals, based on predefined timings and sensor inputs. It ensures efficient traffic flow, prioritizes pedestrian safety, and can be customized for specific requirements.

My BSAI 2nd-semester OOP project to conceptualize optimized traffic control. This C++/Qt app uses YOLOv8 vision to dynamically manage signals based on traffic density and controls red-light violations. It also features Arduino support, representing my best effort to design a complete Smart Traffic Control System.

AI Traffic Manager An AI-powered traffic management system that uses YOLOv8 for real-time vehicle detection and machine learning models to predict traffic density and suggest optimized green light durations. The system is designed for smart city traffic control to reduce congestion, improve flow, and enhance road safety.

Implementation Plan Goal Description Build a "Smart AI Traffic Light Control" system using the MindSpore framework. The system will simulate traffic flow, detect vehicle density (Cars/Trucks) using a YOLOv5-based architecture (simulated or placeholder for integration), and dynamically adjust traffic lights to prioritize lanes with higher density.