➡️

Breaking the Red Tape of Congestion and Inefficiency

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Click the button above to visit the live version of the Signal-X project hosted on Vercel.

🌟 Overview

Signal-X is an AI-driven smart traffic management system that uses advanced computer vision, Arduino-controlled signals, and MapmyIndia’s geospatial analytics to optimize traffic flow in real-time. It prioritizes emergency vehicles, prevents congestion, and enhances road safety through dynamic signal adjustments using live video feeds. Scalable and future-ready, Signal-X is adaptable for cities of all sizes, making urban commuting smarter and more efficient.

🚧 Problem Statement

❗ The Urban Gridlock Crisis

With rapid urbanization and an ever-increasing number of vehicles, cities are choking under intense traffic congestion. What should be smooth commutes turn into daily nightmares, leading to:

⏱️ Wasted Time stuck in endless queues
⛽ Higher Fuel Consumption and rising transportation costs
🌫️ Increased Pollution and carbon emissions
🚨 Delayed Emergency Responses for ambulances, fire trucks, and police
😠 Driver Frustration and rising road rage incidents

🚦 The Flaws of Conventional Traffic Lights

Traditional traffic systems follow fixed-timer logic, blindly rotating signals without considering real-time traffic flow. This outdated mechanism leads to:

🟢 Green lights on empty roads, wasting valuable intersection time
🔴 Vehicle pile-ups in busy lanes, creating bottlenecks
🚑 Emergency vehicles stuck, unable to move swiftly through intersections
🧍‍♂️ Manual control dependency, requiring more manpower
🧠 No AI, No Adaptability, making systems unresponsive and inefficient

It’s time to move beyond timers and take the smart route to traffic management.

💡 Our Solution – Signal-X

Signal-X is an AI-driven Smart Traffic Management System that transforms urban mobility using the power of computer vision, IoT, and geospatial intelligence. Designed to be scalable, real-time, and intelligent, Signal-X optimizes traffic flow, reduces congestion, and ensures faster emergency response through dynamic signal control.

🚀 Key Features

🧠 AI-Powered Traffic Analysis
YOLOv8-based object detection continuously monitors vehicle flow across all junction lanes.
⏱️ Dynamic Signal Adjustment
Adaptive signal control based on real-time congestion and traffic density patterns.
🌐 IoT Integration
Raspberry Pi/Arduino-based controllers ensure seamless traffic light control and emergency detection.
🗺️ Geospatial Intelligence
Integration with MapMyIndia's API offers live congestion updates and smart rerouting.
🚑 Emergency Vehicle Prioritization
Automatically detects emergency vehicles (ambulance, fire brigade, police) and gives them green-light priority.
🔮 Congestion Prediction
AI models analyze historical + real-time data to predict and prevent traffic bottlenecks.

🧩 How Signal-X Works – Workflow

Signal-X operates through a seamless, AI-driven pipeline that integrates video processing, intelligent decision-making, and hardware-level execution.

AI Model Processing the Traffic

➡️

Result of the Processing Model

⚙️ Step-by-Step Workflow

🎥 Live Data Collection
- RTSP streams from CCTV cameras and drones provide real-time traffic video feeds.
🗺️ Traffic API Integration
- Feeds are passed to MapMyIndia Traffic API for real-time congestion data enrichment.
🧠 Vehicle Detection using YOLOv8
- Vehicles are identified and classified into 5 categories:
  🚑 Ambulance, 🧬 Organ Transport, 🚒 Fire Brigade, 🚓 Police, 🆘 Disaster Units
🔄 Real-Time Multiprocessing
- Uses Python’s multiprocessing to analyze all four lanes at a junction simultaneously for accurate density analysis.
🧮 Smart Signal Optimization
- A custom AI model determines the optimal green-light timing using:
  - 📊 Vehicle Density
  - 🕓 Historical Traffic Patterns (via Reinforcement Learning)
  - 📡 Live API Congestion Data
🔌 IoT-Based Hardware Integration
- Arduino module integrated into the junction system.
- If an emergency vehicle is detected, immediate green signal is activated and maintained until it passes.
🖥️ Real-Time Dashboard (React)
- Officers monitor and visualize traffic flow and system decisions via a React-based dashboard in real-time.

🎯 Why Signal-X?

Unlike traditional fixed-timer systems, Signal-X uses real-time intelligence and automation to enhance traffic efficiency, reduce delays, and save lives.

✅ Smart
✅ Scalable
✅ Emergency-Ready
✅ Real-Time
✅ AI-Optimized

🔧 Technical Architecture

Components Overview

Data Collection Layer
- CCTV cameras & drones capture live video feeds
- Microphone sensors detect emergency vehicle sirens
- IoT devices (Raspberry Pi & Arduino) process sensor inputs
AI Processing Layer
- YOLOv8 for vehicle detection and classification
- DeepSORT for vehicle tracking
- OpenCV for image processing
- Flask for API management
Backend Processing
- MongoDB for data storage
- WebSocket for real-time communication
- Traffic analysis algorithms
Frontend Dashboard
- React-based responsive interface
- Real-time traffic analytics visualization
- MapMyIndia integration for geospatial display
Signal Control System
- Priority-based signal switching for emergency vehicles
- Adaptive traffic light control based on congestion analysis

💻 Technology Stack

🧰 Tech Stack – What Powers Signal-X 🚦

Signal-X is built using a powerful blend of AI, hardware, geospatial analytics, real-time systems, and scalable web technologies. Here's everything under the hood:

🌐 Frontend – React Dashboard

Tech	Purpose
⚛️ React.js	Dynamic, component-based UI
🗺️ MapMyIndia SDK	Interactive maps & live traffic visualization
🌈 Tailwind CSS	Modern utility-first styling
📡 WebSocket	Real-time UI updates & traffic stats
🔗 Axios	API requests and data fetching
📱 React Native (Expo)	Mobile traffic monitor app (optional extension)

🧠 AI & Computer Vision – Smart Traffic Brain

Tech	Purpose
🧠 YOLOv8	High-speed vehicle detection & classification
🎥 OpenCV (cv2)	Image & video stream processing
📊 NumPy, SciPy	Traffic data analytics
📈 Matplotlib	Data visualization and graphs
🔬 Ultralytics	YOLOv8 training and inference
🧠 Reinforcement Learning	Intelligent signal timing optimization

🧪 Backend – Core Logic & APIs

Tech	Purpose
🐍 Flask	RESTful API backend (Python)
🌐 WebSocket (Flask-SocketIO)	Real-time data sync with frontend
🍃 MongoDB	NoSQL database for storing traffic history & logs
⚙️ Express.js + Node.js	API support for mobile & hardware integrations

🔌 Hardware – IoT-Powered Signal Controller

Component	Role
💡 Arduino UNO	Signal & sensor control unit
🔊 CZN15E Sound Sensor	Detects approaching emergency sirens
🎥 OV7670 Camera Module	Captures live video for CV analysis
📏 HC-SR04 Ultrasonic Sensor	Measures distance & lane occupancy
💡 LEDs, 🧲 Buzzer, 🔌 Jumper Wires, 🔳 Breadboard	Circuit components for signal light system
🧠 Arduino IDE + Embedded C/C++	Logic programming and testing
🖥️ Serial Monitor	Debugging and communication with PC

🎮 Bonus Tools & Utilities

Tool	Use
🧩 Pygame	Simulating traffic flow for testing
📈 Scikit-learn	Optional ML utilities
🧪 Matplotlib	Data analysis visualization
🧠 Gemini API	Optional AI assistance (integration idea)

🚀 Signal-X combines real-world sensors, cutting-edge AI, and cloud-based data pipelines to create a future-ready traffic management solution.

🚀 Getting Started

Prerequisites

Python 3.8+
Node.js and npm
MongoDB
MapmyIndia API key
Gemini API key
Arduino IDE

Installation

Clone the repository

git clone https://github.com/ArshTiwari2004/Signal-X.git
cd Signal-X

Setup Frontend

cd traffic-monitoring/frontend/web/signalx-web
npm install

Create a .env file in the root of the frontend directory with:
```
VITE_MAPPLS_SDK_KEY=your_mapmy_india_sdk_key
```
Start the development server:
```
npm run dev
```

Setup Backend - Create and activate a virtual environment:

python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # Linux/Mac

Install the required packages:
```
pip install -r requirements.txt
```
Start the Flask server:
```
python backend/flask_api.py
```
For running the Expo project on mobile
```
cd mobile-app/signalx-mobile
npm i
```

Set up your GEMINI_API_KEY in .env

VITE_GEMINI_API_KEY=your_gemini_api_key

Run the app

npx expo start

Setting up the expo backend

cd mobile-app/mobile-backend
npm i

Set up env variables for expo backend

 TWILIO_ACCOUNT_SID=your_twilio_account_sid_here
 TWILIO_AUTH_TOKEN=your_twilio_auth_token_here 
 TWILIO_PHONE_NUMBER=your_twilio_phone_number_here 
 MONGO_URI=your_mongodb_connection_string_here

Run the Mobile Backend Server
```
npm run dev 
```
Setup the Arduino IDE

# ⚙️ How to Upload 
1. Open the `.ino` files using the Arduino IDE. 
2. Select the correct board and port: - Tools > Board > (e.g., Arduino UNO) - Tools > Port > (Select the connected COM port) 
3. Connect your Arduino (e.g., UNO, Nano, etc.) via USB. 
4. Click the "Upload" button in the Arduino IDE (➡️ icon). 
5. Ensure all required components (Servo motors, LEDs, Buzzer, etc.) are connected as per the logic in the code.

Project Structure

signal-x/
├── Arduino_code/
│   ├── sketch_Camera/
│   │   └── sketch_Camera.ino           # Controls CCTV pan/tilt
│   └── sketch_LED_Buzzer/
│       └── sketch_LED_Buzzer.ino       # Controls signal lights & alarms
│
├── mobile-app/
│   ├── mobile-backend/
│   │   ├── config/
│   │   │   ├── db.js                   # MongoDB connection
│   │   │   └── dotenv.js               # API keys config
│   │   ├── models/
│   │   │   ├── Incident.js             # Report schema
│   │   │   └── User.js                 # Auth schema
│   │   └── routes/
│   │       ├── authRoutes.js           # Login/register endpoints
│   │       └── incidentRoutes.js       # Report submission
│   │
│   └── signalx-mobile/
│       ├── components/
│       │   ├── EmergencyAlert.js       # Siren detection UI
│       │   └── LiveTrafficLight.js     # Real-time signal status
│       └── screens/
│           ├── HomeScreen.js           # Main dashboard
│           └── ReportScreen.js         # Incident form
│
├── traffic-monitoring/
│   ├── backend/
│   │   ├── templates/                  # Admin panel HTML
│   │   ├── ambulance_detection.py      # Priority vehicle logic
│   │   └── traffic_control.py          # Signal timing algorithms
│   │
│   ├── frontend/
│   │   └── web/
│   │       └── signalx-web/
│   │           └── src/
│   │               ├── components/     # Reusable UI elements
│   │               ├── pages/          # Route-specific pages
│   │               └── routes/         # Navigation config
│   │
│   ├── models/                         # AI models
│   │   ├── yolov8n.pt                 # Vehicle detection weights
│   │   └── siren_detection.h5         # Audio classification
│   │
│   ├── simulations/                    # Traffic simulations
│   │   ├── rippling_effect.py         # Congestion spread model
│   │   └── traffic_simulator.py       # Junction flow simulator
│   │
│   ├── utils/                          # Utility scripts
│   │  
│   │                  
│   │
│   ├── alert.wav                      # Emergency sound
│   ├── dashboard.png                  # Admin UI mockup
│   └── README.md                      # Setup instructions

🖼️ Snippets

Landing page of Signal-X

Live CCTV Feeds in Signal-X

RTSP streams processed in real-time

Auth workflow

Snapshots of the Citizen App

🔮 Future Enhancements

1. AI-Powered Red-Light Violation Detection :

Use AI-based license plate recognition to detect vehicles running red lights. Integrated with government databases, the system can auto-generate challans, improving law enforcement efficiency.

2. Smart Signal Optimization with Reinforcement Learning:

Leverage reinforcement learning to optimize signal timings based on real-time and historical traffic data, ensuring faster clearance for emergency vehicles and smoother traffic flow.

3. AI-Based Incident Detection & Rerouting:

Utilize AI to detect accidents or breakdowns via CCTV analysis. The system can instantly alert authorities and reroute traffic to prevent congestion.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

❤️ Made with love by Team Synapse — Arsh Tiwari, Priyanshi Bothra, and Nibedan Pati

📩 Have a suggestion or want to connect? Drop a mail at: [email protected]

Name		Name	Last commit message	Last commit date
Latest commit History 154 Commits
Arduino code		Arduino code
mobile-app		mobile-app
traffic-monitoring		traffic-monitoring
Home.png		Home.png
Landingpage.png		Landingpage.png
Mobile.png		Mobile.png
Mobile2.png		Mobile2.png
README.md		README.md
Snapshots .png		Snapshots .png
aivideo.gif		aivideo.gif
dashboard.png		dashboard.png
live-cctv-feed.png		live-cctv-feed.png
live-cctv-feeds.png		live-cctv-feeds.png
modal-result.png		modal-result.png
modal-video.mov		modal-video.mov
results-rtsp-stream.png		results-rtsp-stream.png
results-rtspstream.png		results-rtspstream.png
userflow.png		userflow.png
video-modal.gif		video-modal.gif
workflow.png		workflow.png

ArshTiwari2004/Signal-X

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🌟 Overview

🚧 Problem Statement

❗ The Urban Gridlock Crisis

🚦 The Flaws of Conventional Traffic Lights

💡 Our Solution – Signal-X

🚀 Key Features

🧩 How Signal-X Works – Workflow

AI Model Processing the Traffic

➡️

Result of the Processing Model

⚙️ Step-by-Step Workflow

🎯 Why Signal-X?

🔧 Technical Architecture

Components Overview

💻 Technology Stack

🧰 Tech Stack – What Powers Signal-X 🚦

🌐 Frontend – React Dashboard

🧠 AI & Computer Vision – Smart Traffic Brain

🧪 Backend – Core Logic & APIs

🔌 Hardware – IoT-Powered Signal Controller

🎮 Bonus Tools & Utilities

🚀 Getting Started

Prerequisites

Installation

Project Structure

🖼️ Snippets

🔮 Future Enhancements

1. AI-Powered Red-Light Violation Detection :

2. Smart Signal Optimization with Reinforcement Learning:

3. AI-Based Incident Detection & Rerouting:

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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