Train Traffic Control 🚆

A comprehensive, enterprise-grade Train Movement Optimization and Traffic Control System designed for nationwide railway networks. This project features a full-stack, event-driven architecture that provides real-time train tracking, automated emergency handling, and intelligent route optimization using a modern React dashboard and a robust Spring Boot backend powered by Apache Kafka.

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🌟 Features

• Event-Driven Live Tracking (Kafka): High-volume, synthetic IoT telemetry is pumped into an Apache Kafka topic in real-time, instantly broadcasting train positions to the frontend via WebSockets.
• Nationwide Route Optimization: Visualizes the shortest optimal paths across complex geographic networks (e.g., New Delhi to Mumbai), highlighting the AI's routing choices over alternative tracks.
• Emergency Handling & Manual Overrides: Instantly halt all traffic or reroute specific trains during emergencies. Triggers WebSocket alerts seamlessly to all connected clients.
• Premium Dashboard UI: Government-ready, high-contrast, dark-mode glassmorphism interface built with React 19.
• Geospatial Precision: Leaflet-powered maps rendering highly-detailed geographic routes and train interpolation across thousands of waypoints.

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🏗️ Architecture & Technologies

Frontend

The frontend is a modern single-page application focused on high performance and real-time telemetry rendering.

• Framework: React 19 + Vite
• Mapping: Leaflet & React-Leaflet for interactive geographic track visualization.
• Real-time Communication: @stomp/stompjs & sockjs-client for connecting to backend WebSockets.
• Styling: Custom Vanilla CSS with a premium dark-mode glassmorphism design.

Backend

The backend is a robust Java service responsible for heavy computations, state management, and real-time event streaming.

• Framework: Java 17 + Spring Boot 3.5
• Event Streaming: Apache Kafka & Zookeeper (via Docker)
• Database: MongoDB (Spring Data MongoDB)
• Real-time Communication: Spring WebSocket & STOMP for broadcasting live telemetry and emergencies.

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🚀 Getting Started

Prerequisites

• Node.js (v18+)
• Java 17
• Maven
• Docker & Docker Compose (Required for Kafka)
• MongoDB (Local or Atlas)

1. Backend Setup

1. Navigate to the backend directory:

   cd train-scheduler-backend

2. Start the Kafka & Zookeeper containers:

   docker-compose up -d

3. Configure your MongoDB connection in src/main/resources/application.properties.
4. Build and run the Spring Boot application (ensure the simulation profile is active for live telemetry):

   mvn clean install
   .\mvnw.cmd spring-boot:run

   The backend will start on http://localhost:8080.

2. Frontend Setup

1. Navigate to the frontend directory:

   cd train-scheduler-frontend

2. Install dependencies:

   npm install

3. Start the Vite development server:

   npm run dev

   The frontend will be accessible at http://localhost:5173.

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🛠️ Usage

1. Dashboard: Upon launching the frontend, you will see the main Traffic Control Dashboard.
2. Route Optimization: Select a source and destination (e.g. New Delhi to Mumbai) and click "Find Trains" to watch the system plot the optimal route across the geographic network.
3. Live Map: The map will automatically update, snapping train markers to real GeoJSON tracks based on live telemetry streamed from Kafka.
4. Emergencies:
   • Click Halt All Traffic to simulate a system-wide critical halt via API override.

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📄 License

This project is for educational and simulation purposes.