After years of steady passenger growth before 2020, and a natural strong focus on optimizing passenger movement, baggage handling systems have and will continue to be a major efficiency focus for airports. With peak demand reaching, and in some cases exceeding, 2019 levels, airports will be seeking ways to mitigate the challenges in baggage handling processes.

Baggage handling systems vary in design and throughput capacity. With increased flights and passenger volume over the years, airports are finding that their existing baggage handling systems reach capacity limits quickly and cannot keep up with current throughput demands.  There is a need to evaluate long-term development scenarios for baggage handling in the future.

Expanding a baggage handling system or building anew are both costly options. Hence, it is important to understand when capacity is likely to be reached, what effects changes in the process can have on throughput, and what can be done to leverage the existing infrastructure effectively for as long as possible.

Staff shortages are presenting additional challenges on baggage handling, such as screening stations and loading activities where properly trained staff are needed to effectively operate. Even though a good forecast and plan does not solve the staff shortage, it is crucial to be able to deploy the staff that is available more efficiently. An important prerequisite for this is to have an accurate baggage demand forecast that is updated regularly and accessible by stakeholders.

BEONTRA Solutions 

We have recently released a new feature as part of our Operational Forecasting solution. It is machine learning based prediction of expected baggage numbers.

• View automatic prediction of future baggage numbers down to the single flight level
• Analyze local, transfer and transit bag data aligned with passenger prediction

• Gain access to key patterns, such as seasonality and special events

In addition, we have extended the flow modelling capabilities of our Terminal Capacity Management solution. All key facilities of the baggage handling system are included.

• Access all passenger and checked baggage processes covered in one integrated model
• Enjoy modelling capabilities for baggage processes in local arrival (e.g. reclaim hall processes) and departure flows (e.g. checked baggage screening, early bag storage, make-up areas), as well as transfer flows to depict the complexity of the terminal setup
• Get an understanding of baggage distributions and peaks, as well as information and suggestions for optimal setup of staffed processes (e.g. number of 3^rd level screening stations required to prevent bottlenecks)
• View modelling options and capacity analysis for checked baggage screening, early baggage storage, transfer injection points, and baggage make up areas

If your focus is more short-term driven, all baggage forecasting, and flow modelling capabilities can be used as part of the Operational Terminal Awareness solution.

• Include the latest available passenger and baggage information in the predictions
• Show the effects of delays and changes in passenger and baggage numbers on your baggage flows to identify peaks and potential bottlenecks
• Providing all baggage handling stakeholders with a single common source of information and support information sharing

Contact us or request a free demo to find out more about how BEONTRA can support your airport.

Flight cancellations are the dominating headlines in aviation news. Cancellation levels are nowhere close to the highs during the heights of COVID in 2020. Analysis shared in OAG’s recent webinar states that the percentage of cancelled flights is multiple percentage points above pre-COVID levels. As there is no quick solution in sight to resolve the omnipresent staff shortages, one of the main drivers of cancellations, these levels are here to stay – at least for a while.   

Cancellations are rarely known prior to creating security checkpoint staff plans or when preparing and planning the operations of other key areas in and around the terminal. Understanding which flights are more or less likely to be cancelled is critical, as is knowing how to best allocate available staff.  Gaining insight into flight cancellation patterns and trends will help to create more timely, accurate planning, and prevent, or at least minimize, disruptions for passengers. 

BEONTRA Solutions 

To help airports figure out patterns and identify flights that are most likely to be cancelled, we offer machine learning based cancellation probability and status predictions. This feature is available in our Operational Forecasting solution. The algorithms identify and learn from patterns in the past and use that data to predict cancellation probabilities for flights in the future, helping save time and create more accurate operational forecasts.  

Analysts have the flexibility set thresholds based on which flight should be considered as cancelled or scheduled. For example, flights with a predicted cancellation probability of more than 50% can be set as cancelled. Then both the probability as well as the status classification of the respective flight event is used to analyze and visualize the predicted impact of each flight cancellation, and the corresponding onboard passengers. These insights can then be easily shared with stakeholders. 

We also have extended the flow modelling capabilities of our Terminal Capacity Management solution to make use of the status information.  Based on that data, it is easy to create what-if scenarios and predict and simulate passenger flows, both with and without the passengers of the flights expected to be cancelled.

Contact us or request a free demo to find out more about how BEONTRA can support your airport.