The project in five facts.
Challenge, approach and results.
Modern airports face challenges in managing passenger flows and ensuring efficient boarding processes, which affects passenger satisfaction, staff workload and costs. Delays are often the result of poor information and problems with hand luggage – larger airports are introducing advanced technologies such as artificial intelligence, computer vision and smart navigation systems to optimise these processes. The implementation of artificial intelligence for predicting queues and distances at smaller airports reduces the burden on staff by enabling passengers to make better judgements about the need for assistance. Ultimately, the use of artificial intelligence contributes to increasing the airport's capacity to process passengers, relieving staff and more accurate boarding, which consequently increases passenger satisfaction. The methodology included quantitative and experimental-prototype analysis using simulated data from AODB and DCS systems.
Problem
Modern airports face challenges in managing passenger flows and ensuring efficient boarding processes, which affects passenger satisfaction, staff workload and costs. Delays are often the result of poor information and problems with hand luggage – larger airports are introducing advanced technologies such as artificial intelligence, computer vision and smart navigation systems to optimise these processes. The purpose of this diploma thesis was to investigate how artificial intelligence improves passenger flows and accelerates boarding, with an emphasis on the needs of smaller airports.
The methodological objective was also to examine the role of artificial intelligence in supporting the research project. The research tested four key hypotheses, all of which were confirmed. The combination of artificial intelligence and beacons with navigation and smart notifications improves the timeliness of passengers at boarding.
Artificial intelligence and computer vision before security control effectively prevent problems due to oversized hand luggage, reducing delays and conflicts.
Solution and Method
The implementation of artificial intelligence for predicting queues and distances at smaller airports reduces the burden on staff by enabling passengers to make better judgements about the need for assistance.
Results
Ultimately, the use of artificial intelligence contributes to increasing the airport's capacity to process passengers, relieving staff and more accurate boarding, which consequently increases passenger satisfaction. The methodology included quantitative and experimental-prototype analysis using simulated data from AODB and DCS systems. A simulator prototype was developed that visualises passenger flows and checks luggage.
The qualitative part was based on observation with participation and dialogue with artificial intelligence tools. Artificial intelligence at the airport transforms reactive problem-solving into proactive, as it enables predictive insights and automated decisions. Although appropriate system integration at smaller airports remains a challenge, the simulation prototype demonstrates that measurable improvements can be achieved with limited integrations.
The research contributes to the understanding of digital transformation in aviation and provides guidance for implementing artificial intelligence solutions for greater efficiency and passenger satisfaction.
Module, programme and stories.
Your diploma project for a real company.
A diploma thesis at Academia solves a challenge from a company – often the one where you do your 800 hours of placement. Read about the Programsko inženirstvo module or book a consultation.