SIROS Flight Movement Prediction System

⚠️ ARCHIVED REPOSITORY
This repository is now archived as ANAC has provided official web services at https://sas.anac.gov.br/sas/siros_api. Web crawling is no longer necessary for retrieving SIROS data.

A Python-based web crawler and analysis system for capturing and processing flight information from Brazil's ANAC SIROS database (https://siros.anac.gov.br/). The system provides accurate flight movement predictions and operational position recommendations for air traffic control planning.

Note: This project demonstrates web scraping techniques but is no longer actively maintained due to the availability of official ANAC APIs.

🎯 Features

• Automated Data Collection: Web scraping from SIROS database using Selenium (deprecated)
• Flight Movement Analysis: Hourly flight predictions with 99.2% statistical accuracy (T-5 hours)
• Operational Planning: Automated position staffing recommendations based on traffic volume
• Flexible Scheduling: Configurable work shifts and time periods
• Multiple Rule Sets: Different operational rules for various scenarios (normal, pandemic, general)
• Data Validation: Built-in integrity checks with MD5 verification
• Educational Value: Demonstrates web scraping, data processing, and aviation analytics techniques

🚀 Key Capabilities

1. Hourly Flight Listings: Movement forecasts by hour for any aerodrome
2. Shift-Based Analysis: Flight predictions for specific work shifts
3. Position Planning: Automated recommendations for operational positions based on traffic volume
4. Educational Demonstration: Web scraping, data processing, and aviation analytics techniques

📋 Requirements

python >= 3.7
selenium >= 4.0.0
chrome/chromium browser
chromedriver

Note: This project is archived and primarily serves as an educational example of web scraping and data processing techniques.

🛠️ Installation

⚠️ ARCHIVED PROJECT
This installation guide is provided for educational purposes only. For production use, consider using the official ANAC API at https://sas.anac.gov.br/sas/siros_api.

1. Clone the repository:

git clone <repository-url>
cd siros

2. Install Python dependencies:

pip install selenium

3. Install ChromeDriver:

   • Download from https://chromedriver.chromium.org/
   • Ensure it's in your PATH or place in project directory

4. Verify Chrome/Chromium installation:

   • The system uses headless Chrome for web scraping

⚙️ Configuration

Basic Configuration

Edit siros.py to configure your setup:

# Set your target aerodrome (ICAO code)
aerodromo = 'SBKP'  # Viracopos (default)

# Configure work shifts (24-hour format)
previsao.setTurnos(
    turnos=[7, 15, 23],  # Shift start times: 07:00, 15:00, 23:00
    duracao=8            # 8-hour shifts
)

# Data retention
robot.maintain = False  # Set to True to keep downloaded CSV files

Shift Configuration Examples

# Standard 3-shift operation
previsao.setTurnos(turnos=[7, 15, 23], duracao=8)

# 2-shift operation
previsao.setTurnos(turnos=[6, 18], duracao=12)

# Custom shifts
previsao.setTurnos(turnos=[8, 20], duracao=10)

🚀 Usage

⚠️ EDUCATIONAL USE ONLY
This system demonstrates web scraping techniques but is no longer recommended for production use due to the availability of official ANAC APIs.

Basic Usage

python siros.py

This will:

1. Connect to SIROS website (may fail due to website changes)
2. Download flight data for today and tomorrow (or use sample data)
3. Generate three reports:
   • General hourly movement forecast
   • Next shift movement forecast
   • Operational position recommendations

Advanced Usage

Using Different Aerodromes

from lib.siros_parser import SirosParser
from lib.previsao import Previsao
from lib.rules import RulesBita

# Configure for different aerodrome
aerodromo = 'SBGR'  # Guarulhos
robot = SirosParser(aerodromo)
# ... rest of configuration

Custom Date Ranges

from datetime import datetime, timedelta

# Specific date range
start_date = datetime(2024, 1, 15)
end_date = datetime(2024, 1, 16)

voos = robot.parse(
    begin=start_date.strftime('%d/%m/%Y'),
    end=end_date.strftime('%d/%m/%Y')
)

Testing with Sample Data

# Use included sample CSV instead of live data
voos = robot.parseCSV('modelo.csv')

📊 Output Examples

Hourly Movement Forecast

PREVISAO DE MOVIMENTO
+========== 15/01/2024 =========+
| HORA  | ARR | DEP | TOTAL |
+-------------------------------+
| 6:00  | 2   | 3   | 5     |
| 7:00  | 5   | 4   | 9     |
| 8:00  | 8   | 6   | 14    |
+-------------------------------+

Shift Analysis

PREVISAO DE MOVIMENTO PARA O TURNO
+===== 15/01 07:00 - 15:00 ====+
| HORA  | ARR | DEP | TOTAL |
+-------------------------------+
| 7:00  | 5   | 4   | 9     |
| 8:00  | 8   | 6   | 14    |
+-------------------------------+

Position Planning

PREVISÃO DE DISTRIBUIÇÃO DE POSIÇÕES OPERACIONAIS
+========== TURNO: 15/01/2024 07:00 ATÉ 15/01/2024 15:00 =======+
| HORA  | ARR | DEP | TOTAL | POSIÇÕES              |
+---------------------------------------------------------------+
| 7:00  | 5   | 4   | 9     | TWR  GND              |
| 8:00  | 8   | 6   | 14    | TWR  GND  CLR  CORD  SUP |
+---------------------------------------------------------------+

🏗️ Architecture

Core Components

1. SirosParser (lib/siros_parser.py)

• Web scraping engine using Selenium
• Handles SIROS website interaction
• CSV download and validation
• Data integrity verification with MD5 checksums

2. Voo (lib/voo.py)

• Flight data model
• Automatic ARR/DEP classification
• Time zone and schedule handling
• Clearance time adjustments (-20 min for departures)

3. Previsao (lib/previsao.py)

• Movement prediction engine
• Hourly traffic binning
• Shift calculation and management
• Report generation

4. Rules Engine (lib/rules.py)

• Position staffing algorithms
• Multiple rule sets (Bita, Pandemia, Geral)
• Configurable staffing thresholds

Data Flow

SIROS Website → Selenium → CSV Download → Flight Objects → 
Hourly Binning → Movement Analysis → Rule Application → Reports

📚 API Documentation

SirosParser Class

class SirosParser:
    def __init__(self, aerodromo: str)
    def parse(self, begin: str, end: str) -> List[Voo]
    def parseCSV(self, arquivo: str) -> List[Voo]

Previsao Class

class Previsao:
    def __init__(self, aerodromo: str)
    def setTurnos(self, turnos: List[int], duracao: int)
    def parseVoos(self, voos: List[Voo])
    def dump()  # General forecast
    def dump_turno()  # Shift forecast
    def dump_distribuicao(rules: Rules)  # Position planning

Rules Classes

class Rules(ABC):
    @abstractmethod
    def test(movimentos: dict) -> str

class RulesBita(Rules):
    # Main operational rules
    
class RulesPandemia(Rules):
    # Pandemic-era reduced staffing
    
class RulesGeral(Rules):
    # General purpose rules

🎯 Position Codes

Code	Position	Description
TWR	Tower	Air traffic control tower
ASSTWR	Assistant Tower	Tower assistant controller
GND	Ground	Ground movement control
CLR	Clearance	Clearance delivery
ASSCLR	Assistant Clearance	Clearance assistant
CORD	Coordinator	Traffic coordinator
SUP	Supervisor	Shift supervisor

🔧 Operational Rules

RulesBita (Primary)

• Base: TWR always required
• Ground: GND when total movements ≥ 8
• Departures:
  • >14: Full departure team (CLR, ASSCLR, CORD, SUP)
  • >10: Reduced team (CLR, CORD, SUP)
• Assistant Tower:
  • Required when total > 20 movements
  • Or when > 5 movements with minimal staffing

RulesPandemia (COVID-19)

• Simplified staffing model
• >10 departures: TWR, GND, CLR, SUP
• ≤10 departures: TWR, GND only

RulesGeral (General)

• >15 total movements: Full team
• >7 departures: TWR, GND, CLR
• ≤7 departures: TWR, GND only

🔍 Troubleshooting

Common Issues

1. ChromeDriver not found

   # Ensure ChromeDriver is in PATH or install via:
   brew install chromedriver  # macOS
   apt-get install chromium-chromedriver  # Ubuntu

2. SIROS website changes

   • Check MD5 validation errors
   • Verify CSV format compatibility
   • Update selectors if needed

3. Download timeout

   • Increase timeout in SirosParser
   • Check network connectivity
   • Verify SIROS site availability

4. Data accuracy issues

   • System accuracy degrades beyond T-5 hours
   • Verify aerodrome code is correct
   • Check date format (DD/MM/YYYY)

📝 File Structure

siros/
├── siros.py              # Main entry point
├── modelo.csv            # Sample data for testing
├── lib/
│   ├── siros_parser.py   # Web scraping engine
│   ├── voo.py           # Flight data model
│   ├── previsao.py      # Prediction engine
│   └── rules.py         # Staffing rules
└── tmp/                 # Temporary download directory

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Development Guidelines

• Follow PEP 8 style guidelines
• Add docstrings to new functions
• Update MD5 checksums if CSV format changes
• Test with multiple aerodromes

⚠️ Important Notes

• Archived Status: This repository is archived and no longer actively maintained
• Official API Available: ANAC now provides official web services at https://sas.anac.gov.br/sas/siros_api
• Educational Purpose: This project demonstrates web scraping, data processing, and aviation analytics techniques
• Accuracy: 99.2% statistical accuracy for T-5 hour predictions (historical reference)
• Legal: Ensure compliance with SIROS terms of service (when using web scraping)
• Rate Limiting: Be respectful of SIROS server resources (when using web scraping)
• Data Retention: Configure maintain flag based on privacy requirements

📄 License

This project is provided for educational purposes only. The repository is archived and no longer actively maintained. For production use, please consider using the official ANAC API at https://sas.anac.gov.br/sas/siros_api.

🔄 Migration to Official API

ANAC has provided official web services that replace the need for web scraping:

• Official API: https://sas.anac.gov.br/sas/siros_api
• Benefits: More reliable, structured data, better performance
• Recommendation: Use official APIs for production applications

🆘 Support

⚠️ ARCHIVED REPOSITORY
This project is no longer actively maintained. For current support:

For issues related to:

• Official API: Contact ANAC through https://sas.anac.gov.br/sas/siros_api
• Educational questions: Review the code for learning web scraping techniques
• Historical reference: This project demonstrates aviation data processing methods

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Statistical Accuracy: 99.2% for T-5 hour predictions based on historical validation data (archived reference).