Airport Operational Database (AODB)

Airport Operational Database (AODB)

An Airport Operational Database (AODB) is a central repository of real-time and historical flight-related data from daily and seasonal flight schedules that supports the operations of an airport. It serves as a hub for integrating information from various systems and stakeholders, providing a comprehensive view of airport activities as the central basis for airport management. Each employee or instance receives the information they need to fulfil their tasks.

Key Features and Benefits of an AODB:

• Data Integration: An AODB consolidates data from diverse sources, including flight plans, gate assignments, baggage tracking systems, and weather information.
• Real-time Updates: The database provides up-to-date information on airport operations, enabling stakeholders to make informed decisions based on the latest data.
• Operational Efficiency: By streamlining data access and reducing manual processes, an AODB enhances operational efficiency and productivity.
• Decision Support: The database supports decision-making by providing insights into resource allocation, capacity planning, and performance analysis.
• Collaboration: An AODB facilitates collaboration among different departments and stakeholders within the airport, improving communication and coordination.

Key Data Elements Typically Stored in an AODB:

• Flight Information: Flight plans, arrival and departure times, gate assignments, and aircraft types.
• Resource Allocation: Stand assignments, gate usage, and equipment availability.
• Passenger Data: Passenger check-in and boarding information, security screening data, and baggage handling details.
• Operational Events: Delays, cancellations, and irregularities.

ARC’s CAST Real-time solutions leverage AODB

• In real time as a essential input for predictive simulations, including scheduled, estimated, and actual flight times, and extensive passenger information
• Historically for deep learning calibration of the predictive simulation models, providing the baseline the highly accurate and reliable predictions of the CAST model.
   

Results are:

• Highly accurate predict passenger flows and resource requirements in real-time.
• Optimized ressource untilsaiton for maximum efficiency and cost-effectiveness.
• Enhanced resource allocation: Optimize resource utilization and avoid bottlenecks.
• Improved passenger experience and LOS by reducing delays and congestion.
• Informed decision making and proactive management.