Approach to Optimize the Airplane Boarding Process by Using Image Recognition and Stochastic Simulation to Predict Overhead Bin Fill Levels

<div class="section abstract"><div class="htmlview paragraph">In single-aisle aircraft, the available storage space for carry-on baggage is inherently limited. When the aircraft is fully booked, it often results in insufficient overhead bin space, necessitating last-minute gate-checking of carry-on items. Such disruptions contribute to delays in the boarding process and reduce operational efficiency. A promising approach to mitigate this issue involves the integration of computer vision technologies with an appropriate data storage system and stochastic simulation to enable accurate and supportive predictions that enhance planning, reduce uncertainty, and improve the overall boarding process.</div><div class="htmlview paragraph">In this work, the YOLOv8 image recognition algorithm is used to identify and classify each passenger’s carry-on baggage into predefined categories, such as handbags, backpacks, and suitcases. This classified data is then linked to passenger information stored in a NoSQL database MongoDB, which includes seat assignments and the number of carry-on items associated with each passenger. Stochastic analysis is applied to predict the occupancy levels of overhead storage bins across different seat rows during the boarding of the passengers. This allows for real-time assessment of whether the remaining storage capacity is sufficient to accommodate additional baggage items.</div><div class="htmlview paragraph">The results of the stochastic analysis reveal potential bottlenecks in baggage storage even before the boarding process is completed. By identifying these critical points in real time, the system can alert gate agents to proactively manage baggage distribution and mitigate overcrowding in aircraft overhead bins. This approach has the potential to streamline the boarding process, thereby reducing aircraft turnaround times and improving overall efficiency within commercial aviation.</div></div>