Next.jsReact.jsNode.jsExpress.jsOpenCVTensorFlow

Project Traffix

Project Traffix is an innovative traffic management system designed to address urban congestion and road safety challenges. The project comprises two key components:

Project Traffix

Problem Statement

Traffic congestion and inefficient road management lead to increased travel time, fuel wastage, and accidents. Project Traffix introduces two key solutions: M-Park – A smart parking system that helps users find and reserve parking spots in real-time, reducing unnecessary road congestion. M-Traffic – An AI-powered traffic control system that monitors vehicle movement, detects violations, and dynamically adjusts signals to optimize road flow.

Key Features

Real-Time Traffic Analysis

Live monitoring and analysis of traffic density using computer vision to optimize signal timing.

Adaptive Signal Control

Dynamic adjustment of traffic signals based on real-time traffic conditions and patterns.

Computer Vision Integration

Advanced object detection and tracking using YOLOv5 and OpenCV for accurate vehicle counting and classification.

Data Analytics Dashboard

Comprehensive dashboard showing real-time traffic data, patterns, and analytics for better decision making.

Smart Parking Management

Real-time availability updates and automated payment system for seamless parking experiences.

Automated Violation Detection

AI-powered cameras detect traffic violations like signal-jumping, speeding, and lane-cutting.

Tech Stack

Frontend

Next.js
React.js

Backend

Node.js
Express.js

Machine Learning

TensorFlow
OpenCV
YOLOv5

Database

MongoDB for real-time traffic and parking data management

Future Scope

Flask
Django

Benefits

Real-Time Traffic Optimization
Reduced Congestion
Lower Emissions
Improved Road Safety
Enhanced Parking Efficiency
Automated Traffic Violation Monitoring
Data-Driven Urban Planning