# 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">

Smart Traffic Light Management System employs SUMO and reinforcement learning to dynamically optimize signal timings, reducing congestion and enhancing traffic flow at San Jose downtown. Adapting to real-time patterns, the system efficiently minimizes delays, elevating the overall urban mobility experience

FiveM resource for smarter traffic lights

The ZigBee receiver waits for a signal from a ZigBee transmitter which is implemented in each ambulance. When the ZigBee receives this signal, the traffic light will turn to green. The traffic light turns back to red as soon as the ambulance passed.

Problem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:  On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.  On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.

Smart Traffic Light System (Internet of Things Project)

Computing the time to clear the intersection using Machine Learning , Image Processing and OpernCv in python

No description available

University project (Hardware/Software plateforms)

we will be going to accomplish this project by using Python Networking, BLOB, Haar-Cascade, OpenCV, Raspberry Pi

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

This project is a smart traffic management system designed to optimize traffic light cycles based on real-time data, with special prioritization for emergency vehicles. By dynamically adjusting green-light timings, it aims to reduce congestion and enable emergency vehicles to navigate intersections more efficiently.

An automatic traffic red-light violation detection system was implemented, which may play a big role in transportation management in smart cities. The system mainly relies on modern computer vision techniques, which was implemented in OpenCV under Python environment. Mainly, the system consists of object detector and object tracker which work in an integrated manner in order to precisely keep position of the existing cars. The primary task of the system is to eventually indicate locations of violating vehicles. The output showed accurate results, as all violating vehicles were detected and distinguished precisely.

滴滴Di-Tech算法大赛第3届智能信号灯https://ditech.didichuxing.com/

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 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.

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.

Smarter, cheaper traffic lights utilizing existing traffic cameras equipped with Tensorflow to control traffic patterns on a Neural Network

Smart Traffic Light Control System

A smart Al based solution for traffic management on routes with heavy traffic from different directions, with real-time monitoring and adaptation of traffic light timings.

A wireless traffic light control system using LoRa technology for long-range communication. It enables remote control and coordination of traffic signals, prioritizes emergency vehicles, and displays real-time status with low power consumption, improving traffic flow and safety. This system is scalable for multiple intersections and helps reduce co

App for traffic control, security and smart city integration research. Detect and count vehicles from streams on traffic + Calculate and create efficient traffic light control + Secure pedestrians and cars from crashю

在智能车上实现自动行驶，红绿灯识别和避障Realize automatic driving, traffic light recognition and obstacle avoidance in smart cars

No description available

A smart traffic light controller using fuzzy logic to adjust signal timings based on traffic density, time of day, and weather conditions.

OpenCV video processing software for Smart Traffic Light capstone project

The Project for Coding Thailand 2025 - Regional Competitive

An Omnet++ Simulation for a Smart Traffic Light Network uses edge computing to choose which traffic light should be green.