TRAFFIQ — Intelligent Traffic Management Ecosystem

Modernizing urban mobility across India through AI, real-time data, and inclusive design.

Features · Tech Stack · ML Models · Getting Started · Project Structure

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🚦 Overview

TRAFFIQ is India's premier intelligent traffic management ecosystem, developed under the Digital India Mission by the Ministry of Urban Development. It bridges the gap between legacy traffic infrastructure and modern AI-driven urban mobility by unifying signal control, emergency response, real-time analytics, and citizen communication into a single, scalable platform.

Urban India loses billions of hours annually to traffic congestion. Emergency vehicles face critical delays. Road accidents claim thousands of lives. TRAFFIQ addresses all of these with a data-first, AI-powered approach — built for Indian roads, Indian cities, and Indian languages.

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✨ Key Features

🚥 Adaptive Traffic Control

AI-driven signal synchronization that dynamically responds to real-time traffic density, reducing average intersection wait times and minimizing urban gridlock.

🚨 Emergency Green Corridor

Immediate signal preemption for ambulances, fire engines, and other emergency vehicles — automatically clearing a path through intersections to cut response times and save lives.

📊 Precision Analytics

Advanced data insights for urban planners and traffic authorities to identify bottlenecks, predict congestion patterns, and make evidence-based decisions for city infrastructure planning.

🔔 Public Safety Alerts

Real-time incident detection and reporting with instant broadcasting to commuters via the citizen dashboard — keeping the public informed about accidents, road closures, and hazards.

👥 Multi-Role Dashboards

Dedicated, purpose-built interfaces tailored for three distinct user groups:

• Admins — full system control, analytics, and configuration
• Citizens — live traffic updates, route advisories, and safety alerts
• Drivers — navigation assistance and ETA predictions

🌐 Multi-Lingual Support

Accessible in English, Hindi, Marathi, Tamil, and Telugu — ensuring the platform serves all of urban India, not just English speakers.

♿ Accessibility First

Built with inclusivity at its core — featuring High Contrast mode, adjustable font sizes, and screen-reader compatibility for equitable access across all user groups.

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🛠 Technology Stack

Frontend

Technology	Purpose
React 18 + Vite	Core UI framework with lightning-fast HMR
TypeScript	Type-safe development at scale
Tailwind CSS	Utility-first responsive styling
Shadcn UI (Radix UI)	Accessible, composable component library
Framer Motion	Smooth, performant animations
Recharts	Interactive data visualization and dashboards
Google Maps API	Live map rendering and geospatial features

Backend & Data

Technology	Purpose
Supabase	Authentication, PostgreSQL database, Realtime subscriptions
React Query	Efficient data fetching, caching, and synchronization

Machine Learning

Model	Purpose
Route Risk Predictor	Assesses road segment danger based on historical accident data
Traffic Congestion Predictor	Forecasts congestion windows across city zones
Traffic & ETA Predictor	Estimates accurate travel times using live and historical patterns

Models are trained on traffic.csv and road_accidents.csv datasets using Jupyter notebooks in the ML models/ directory.

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🤖 Machine Learning Models

The ML models/ directory contains Jupyter notebooks for training and evaluating all three predictive models. Datasets used include real-world historical traffic and road accident records.

To explore or retrain the models:

# Open with Jupyter Lab
jupyter lab

# Or use Google Colab by uploading the notebooks directly

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📁 Project Structure

TRAFFIQ/
├── src/                    # React frontend source code
│   ├── components/         # Reusable UI components
│   ├── pages/              # Route-level page components
│   ├── hooks/              # Custom React hooks
│   └── lib/                # Utility functions and helpers
├── public/                 # Static assets
├── ML models/              # Jupyter notebooks for ML training
├── supabase/               # Supabase config and migrations
├── scripts/                # Utility and build scripts
├── .env                    # Environment variables (not committed)
├── tailwind.config.ts      # Tailwind configuration
├── vite.config.ts          # Vite build configuration
└── README.md

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🚀 Getting Started

Prerequisites

• Node.js v18 or higher
• npm or yarn
• A Supabase account
• A Google Maps API Key

Installation

1. Clone the repository:

git clone https://github.com/neeldas0032/TRAFFIQ.git
cd TRAFFIQ

2. Install dependencies:

npm install

3. Set up environment variables:

Create a .env file in the root directory:

VITE_SUPABASE_URL=your_supabase_url
VITE_SUPABASE_ANON_KEY=your_supabase_anon_key
VITE_GOOGLE_MAPS_API_KEY=your_google_maps_api_key

⚠️ Never commit your .env file. It is listed in .gitignore by default.

4. Start the development server:

npm run dev

The application will be available at http://localhost:5173.

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📜 License & Copyright

© 2026 Ministry of Urban Development · Government of India.
Designed and developed under the Digital India Initiative.

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_{Built with ❤️ for smarter, safer Indian cities.}