mcp-traffic 1.0.0

Tokyo traffic data collection and analysis system using ODPT API

🚇 MCP Traffic - Tokyo Traffic Data Collection System

Real-time Tokyo transportation data collection and visualization system using ODPT API

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🎯 Live Dashboard & Demo

📊 🔴 LIVE Tokyo Traffic Dashboard

Interactive real-time visualization of Tokyo's transportation network

✨ Dashboard Features

• 🗺️ Interactive Tokyo Station Map with real-time markers
• 📈 Live Charts & Metrics showing passenger flow and performance
• 🚂 Real-time Train Tracking with delay status
• 🚌 Bus Route Monitoring with live positions
• 📱 Mobile Responsive design for all devices
• ⚡ Auto-refresh with 30-second data updates

📊 Current System Status

🔴 LIVE: Tokyo Transportation Monitor
├── Daily Passengers: 2.15M across 5 major stations
├── System Performance: 94.2% punctuality rate
├── Average Delay: 1.0 minutes
└── Active Alerts: 0 service disruptions

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🚀 Quick Start

Prerequisites

• Python 3.8+
• Git
• Ruby 3.1+ (for Jekyll documentation)

Installation

# Clone the repository
git clone https://github.com/Tatsuru-Kikuchi/MCP-traffic.git
cd MCP-traffic

# Set up Python environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install Python dependencies
pip install -r requirements.txt

# Set up Jekyll for documentation
bundle install

# Configure the system
cp config/api_config.example.json config/api_config.json
# Edit config/api_config.json with your ODPT API settings

Running the System

# Test the installation
python src/collectors/traffic_collector.py --catalog-only

# Run a single data collection
python scripts/collect_all_data.py

# Start continuous scheduled collection
python scripts/schedule_collection.py

# Serve documentation locally
bundle exec jekyll serve

📊 What is MCP Traffic?

A comprehensive Tokyo traffic data collection and analysis system that leverages the ODPT (Open Data Platform for Transportation) API to gather, process, and monitor real-time transportation data across Tokyo's extensive public transit network.

MCP Traffic (Model Control Protocol for Traffic) is an automated system designed for:

• 🔄 Real-time Data Collection: Continuous gathering of Tokyo transportation data
• ⚡ Data Processing: Automated cleaning, validation, and transformation
• 📈 Analysis & Monitoring: Traffic pattern analysis and system health monitoring
• 🗄️ Data Management: Organized storage with automatic archiving
• 📚 Documentation: Comprehensive guides and API documentation

🏗️ System Architecture

Data Flow

ODPT API → Data Collectors → Data Processors → Storage → Analysis
    ↓              ↓              ↓            ↓         ↓
Raw Data → Validation → Transformation → Archive → Insights

Directory Structure

MCP-traffic/
├── src/                    # Source code
│   ├── collectors/         # Data collection modules
│   └── processors/         # Data processing utilities
├── data/                   # Data storage
│   ├── raw/               # Original API responses
│   ├── processed/         # Cleaned data
│   └── archives/          # Historical data
├── scripts/               # Automation scripts
├── config/                # Configuration files
├── monitoring/            # Health checks and alerts
├── logs/                  # System logs
├── docs/                  # Documentation
└── .github/workflows/     # CI/CD workflows

🔧 Configuration

API Configuration

Edit config/api_config.json:

{
  "odpt_api": {
    "base_url": "https://ckan.odpt.org/api/3/action/",
    "api_key": "your-api-key",
    "rate_limit": 100,
    "timeout": 30
  },
  "collection": {
    "schedule_interval": 300,
    "data_types": ["train", "bus", "station"],
    "regions": ["tokyo"]
  },
  "storage": {
    "format": "json",
    "compression": true,
    "retention_days": 365
  }
}

Jekyll Configuration

The documentation site is configured in _config.yml with:

• GitHub Pages deployment
• SEO optimization
• Responsive design
• Multi-language support

📈 Data Collection Features

Supported Data Types

• 🚆 Train Data: Real-time positions, delays, service status
• 🚌 Bus Data: Vehicle locations, route information, schedules
• 🏢 Station Data: Facility information, accessibility features
• 🗺️ Route Data: Network topology, connections, fare information
• 📊 Operational Data: Service disruptions, maintenance schedules

Collection Strategies

• Scheduled Collection: Automated data gathering at configurable intervals
• On-demand Collection: Manual collection for specific data types
• Error Recovery: Robust retry mechanisms with exponential backoff
• Rate Limiting: Compliance with API usage policies

🖥️ Deployment Options

Local Development

# Standard Python development setup
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python scripts/collect_all_data.py

Docker Deployment

# Build and run with Docker Compose
docker-compose up -d

# Monitor logs
docker-compose logs -f mcp-traffic

Server Deployment

# Install as systemd service
sudo cp deployment/mcp-traffic.service /etc/systemd/system/
sudo systemctl enable mcp-traffic
sudo systemctl start mcp-traffic

Cloud Deployment

• AWS: EC2 + S3 + CloudWatch
• Google Cloud: Compute Engine + Cloud Storage
• Azure: Virtual Machines + Blob Storage

See the Deployment Guide for detailed instructions.

📊 Monitoring & Health Checks

System Monitoring

• API Connectivity: Continuous monitoring of ODPT API availability
• Data Quality: Validation and completeness checks
• Storage Usage: Disk space monitoring with alerts
• Performance Metrics: Response times and throughput tracking

Health Check Commands

# System health overview
python monitoring/health_check.py

# Detailed performance metrics
python monitoring/performance_monitor.py

# Data quality report
python monitoring/data_quality_check.py

Alerting

• Email notifications for system failures
• Slack integration for real-time alerts
• Custom webhook support for external monitoring

🔍 Data Analysis Tools

Built-in Analytics

• Traffic pattern analysis
• Peak hour identification
• Service reliability metrics
• Historical trend analysis

Integration Options

• Jupyter Notebooks: Interactive data exploration
• Pandas/NumPy: Statistical analysis
• Matplotlib/Plotly: Data visualization
• Apache Spark: Large-scale data processing

📚 Documentation & Links

🎯 Main Dashboard Links

• 📊 Live Dashboard - Interactive Tokyo traffic visualization
• 📖 Dashboard User Guide - Complete tutorial and help
• 🌐 Project Website - Main documentation site

📋 Technical Documentation

• Deployment Guide: Complete deployment instructions
• API Documentation: API reference and examples
• Configuration Guide: System configuration options
• Troubleshooting: Common issues and solutions

🔐 Security & Privacy

Data Privacy

• No Personal Data: Only collects aggregate transportation data
• Anonymized Data: All data is inherently anonymous
• Compliance: Adheres to data protection regulations

Security Features

• API key encryption
• Secure configuration management
• Network security best practices
• Regular security updates

🤝 Contributing

We welcome contributions! Here's how to get started:

Development Setup

# Fork and clone the repository
git clone https://github.com/YOUR-USERNAME/MCP-traffic.git
cd MCP-traffic

# Create development branch
git checkout -b feature/your-feature-name

# Set up development environment
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Run tests
python -m pytest tests/

Contribution Guidelines

1. Code Style: Follow PEP 8 for Python, use meaningful variable names
2. Testing: Add tests for new functionality
3. Documentation: Update documentation for changes
4. Commits: Use clear, descriptive commit messages

Types of Contributions

• 🐛 Bug Fixes: Report and fix issues
• ✨ Features: New functionality and improvements
• 📚 Documentation: Improve guides and examples
• 🧪 Testing: Enhance test coverage
• 🔧 DevOps: Improve deployment and CI/CD

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Getting Help

1. Documentation: Check the docs/ directory
2. Issues: Search existing issues
3. Discussions: Join GitHub Discussions
4. Dashboard Help: Read the User Guide

Reporting Issues

When reporting issues, please include:

• Operating system and Python version
• Error messages and stack traces
• Steps to reproduce the issue
• Configuration (without sensitive data)

🏆 Acknowledgments

Data Sources

• ODPT (Open Data Platform for Transportation): Primary data source
• Tokyo Metropolitan Government: Open data initiatives
• Transportation Operators: Real-time operational data

Technology Stack

• Python: Core programming language
• Jekyll: Documentation site generator
• GitHub Actions: CI/CD and deployment
• Docker: Containerization
• Chart.js: Interactive data visualization
• Leaflet: Interactive mapping

Community

Special thanks to all contributors who have helped improve this project through code, documentation, testing, and feedback.

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🎯 Ready to Explore Tokyo's Transportation System?

🔴 LIVE Demo Available Now!

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For more information, visit the project website or check out the live dashboard!