# 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">

A system and method for the prediction of vehicle traffic congestion on a given roadway within a region. In particular, the computer implemented method of the present disclosure utilize real time traffic images from traffic cameras for the input of data and utilizes computer processing and machine learning to model a predictive level of congestion within a category of low congestion, medium congestion, or high congestion. By implementing machine learning in the comparison of exemplary images and administrator review, the computer processing system and method steps can predict a more efficient real-time congestion prediction over time.

Develop a deep learning model capable of predicting traffic flow in urban environments. The model will utilize historical traffic data, weather conditions, and road configurations to forecast traffic patterns. This information can be invaluable for traffic management systems, helping to optimize traffic signals and reduce congestion, ultimately.

We have used Support Vector Regression and Random Forest Regression to predict traffic or congestion.

This project utilizes machine learning to predict extreme weather events and analyze traffic congestion in Southeast Asia. The repository includes data analysis, model training, and visualization scripts to forecast weather conditions for the next 30 days and enhance transportation efficiency.

Using the Backpropagation Neural Network to predict traffic congestion data collected on the US-101 highway by the Next Generation Simulation in the USA.

In recent years, Road Accidents (RAs) have emerged as an important public health issue which needs to be tackled by a multi-disciplinary approach. The trend in RA injuries and death is becoming alarming. A road traffic accident can be defined as, an event that occurs on a way or street open to public traffic resulting in one or more persons being injured or killed, where at least one moving vehicle is involved. The important factors are human errors, driver fatigue, poor traffic sense, mechanical fault of vehicle, speeding and overtaking violation of traffic rules, poor road conditions, traffic congestion, road encroachment etc. This analytical project will analyze traffic accidents more deeply to determine the intensity of accidents by using machine learning approaches. It also figures out those significant factors that have a clear effect on road accidents and provide some beneficent suggestions regarding this issue. In this project, we will be using classification methods to predict the severity of the road accidents.

Repository for Predicting Traffic Congestion

LogiTraffic is an IoT based Deep Learning Powered Traffic Management and Theft Detection Solution. It’s an online website platform using which user can keep check on certain real-time parameters associated with the vehicle which includes fuel-level, GPS location, Brake System Temperature, Speed, Traffic Forecasting using Vehicle Detection and obtaining vehicle count through different road nodes and predicting Traffic Congestion/Jams. In case the user suspects his/her car has been stolen by logging in using the credentials one can lock the vehicles and see driver’s real time video stream and a picture of the driver is downloaded on the system so that it can be used for further investigation and police cases. (YouTube Video Presentation by Team Aztecs: https://youtu.be/rP2OGjZJ5NY) – Presented in E-Ujjwala Hackathon 2020 by Birsa Institute of Technology, Jharkhand (Team Aztecs - Finalists)

The Connected Vehicle Big Data Analytics Tool code is designed to predict traffic congestion in an 100 ft x 100 ft area every minute an hour ahead of time using Basic Safety Messages (BSM) or average speed data from INRIX.

Chennai Traffic Predictor is a machine learning–based system to predict traffic congestion on Chennai roads. It uses historical traffic data along with time and location features. The model estimates traffic density for different routes and peak hours. This helps commuters choose better travel times and routes.

In this project, I am trying to predict traffic congestion, based on an aggregate measure of stopping distance and waiting times, at intersections in 4 major US cities: Atlanta, Boston, Chicago & Philadelphia.

UrbanFlow applies machine learning to analyze urban traffic data, using clustering, regression, and classification to predict congestion levels, identify high-risk areas, and uncover key traffic patterns. The system combines K-Means, Random Forest, and XGBoost with feature engineering and PCA for accurate, data-driven urban mobility insights.

Development of Traffic Signal Duration Control using Deep Learning is the title of our project. Our system will monitor a traffic junction. It will collect the video data and send it to the cloud. It will then detect the number of vehicles, the direction of the vehicle and the type of vehicle. By applying an algorithm on the data received it will suggest the most ideal timing to switch between Green to Red or Vice-Versa in real time. This system will thereby reduce the traffic flow in real time. Our system will also make prediction based on previous data of congestion. It will take data from the previous week to make sure frequently happening congestions are avoided. Our system will also find patterns and will predict if a congestion can occur by using previously available data of traffic hence suggesting the most ideal timing to switch between Green to Red or Vice Versa in real time. It will also predict the traffic signal duration based on the information gained from the peer signals hence creating a massive grid which is controlled by central unit. Each signal can inform the adjacent signal about the outgoing traffic from its area. This can help the adjacent signals to take appropriate measures to avoid congestion hence reducing traffic flow and creating an optimal go green time for a user.

Yulu is India’s leading micro-mobility service provider, which offers unique vehicles for the daily commute. Starting off as a mission to eliminate traffic congestion in India, Yulu provides the safest commute solution through a user-friendly mobile app to enable shared, solo and sustainable commuting. Yulu zones are located at all the appropriate locations (including metro stations, bus stands, office spaces, residential areas, corporate offices, etc) to make those first and last miles smooth, affordable, and convenient! Yulu has recently suffered considerable dips in its revenues. They have contracted a consulting company to understand the factors on which the demand for these shared electric cycles depends. Specifically, they want to understand the factors affecting the demand for these shared electric cycles in the Indian market. The company wants to know: Which variables are significant in predicting the demand for shared electric cycles in the Indian market? How well those variables describe the electric cycle demands?

The Project solution utilizes spatial and temporal correlations, incorporating neighboring road traffic data through the KNN algorithm to predict traffic congestion, resulting in a two-fold improvement in accuracy compared to existing methods.

Evaluation CityMov, is a well-known firm in the Automotive industry with a continuous drive to addressing traffic congestion. This drive led them to the manufacturing of a mini scooter and then wonder if their customers are likely to buy this new solution despite having other means of transportation. The first release of the Scooter called (NeverLate) recorded much success and since then look forward to releasing the improved version. You have been appointed as the Lead Data Scientist to build a predictive model to determine if a customer will buy this product or not. The model will be based on the customer characteristics. The target variable, BuyScooter, is a: 1 if the customer buys the scooter. 0 if the customer didn’t.

Software-based information system that reduce traffic congestion, provide predictive traffic analytics, and improve the safety of surface transportation systems

Predicts live traffic congestion using ML , weather, time, temp and other parameters

This project implements an end-to-end machine learning pipeline for predicting network traffic congestion.

AI-Based Traffic Flow Prediction – This project applies Time-Series Forecasting (LSTM) to predict future traffic conditions. Using historical traffic data, the model forecasts congestion patterns, helping cities and transportation systems optimize traffic flow and reduce delays.

📌 Project Description: "A Flask-based Traffic Flow Prediction system using YOLO for vehicle detection and SORT tracking. It analyzes video feeds to predict congestion levels, vehicle speeds, and overall traffic trends. Features real-time video processing, graph generation, and congestion analysis."

Instituted an algorithm to predict & classify traffic conditions (weather, directions, traffic speed & congestions) via existing traffic data collected throughout the years in Chicago. Exploited KNN and RFR algorithms to fulfil the task. Accepted for publication by 4th SMART CITIES SYMPOSIUM, University of Bahrain

This project presents a scalable Smart City solution designed to predict traffic congestion and energy consumption using big data processing with Hadoop and advanced Machine Learning models. It aims to assist urban planners, government bodies, and energy agencies in optimizing resource allocation and improving city infrastructure planning.

Implementation of paper "Predicting Traffic Congestion Evolution: A Deep Meta Learning Approach"

Used python to build a model using Random Forest Regressor to predict traffic patterns. This allowed us to apply ML concepts we have learned from class to help predict traffic flow. We can interpret these results to offer insights about the data and validate if RF would be a viable forecasting method for this data.

CS224W Project - Predicting Traffic Congestion on City Road Networks

⚙️ Predicting traffic congestion and optimizing flow using machine learning models on traffic data within the Federal University Of Technology Campus.This project aimed to optimize traffic flow and predict congestion within the campus

AI-powered system for managing traffic and crowd near religious places using real-time and predictive analytics. It provides heatmaps, smart routing, and gate-level insights to reduce congestion and improve visitor experience.

Road transport analysis & statistical modeling ML project optimizes traffic flow & predicts congestion using data-driven models.