Airports have undergone a significant digital evolution over the past decades, enhancing efficiency, effectiveness, and user-friendliness through various technological advancements. Initially, airports deployed basic IT solutions as support tools, but with the increasing integration of digital systems, understanding the detailed digital ecosystem behind airports has become crucial. This research aims to classify technological maturity in airports, using the access control process as an example to demonstrate the benefits of the proposed taxonomy. The study highlights the current digital ecosystem and its future trends and challenges, emphasizing the importance of distinguishing between different levels of technological maturity. The role of biometric technology in security access control is examined, highlighting the importance of proper identification and classification. Future research could explore data collection, privacy, and cybersecurity impacts, particularly regarding biometric technologies in Smart Access Level 4.0. The transition from Smart Access Level 3.0 to 4.0 involves process automation and the introduction of AI, offering opportunities to increase efficiency and improve detection capabilities through advanced data analytics. The study underscores the need for global legislative frameworks to regulate and support these technological advancements.
City authorities want to know how to match the charging infrastructures for electric vehicles with the demand. Using camera recognition algorithms from artificial intelligence we investigated the behavior of taxis at a charging stations and a taxi stand.
MULTIFILE
This study introduces a novel methodology for the post-analysis of operational predictability by leveraging timestamps collected through the Airport Collaborative Decision Making (A-CDM) framework. Focusing on the start-up and departure phases, the analysis highlights the importance of accurately planning and managing key timestamps, such as the Target Off-Block Time (TOBT) and Target Start-Up Approval Time (TSAT), which are critical for operational efficiency. Using one week of sample data from Schiphol Airport, this research demonstrates the potential benefits of the proposed framework in improving predictability during the start-up phase, particularly by identifying and analyzing outliers and anomalies. The start-up phase, a critical component of the outbound process, was broken down into subphases to allow for a more detailed assessment. The findings suggest that while 96% of flights maintain TOBT accuracy within ±20 minutes, 68% of flights miss their TOBT by 2 to 17.5 minutes, with 364 notable outliers. These deviations highlight areas for further investigation, with future work aiming to explore the impact of influencing factors such as weather, resource availability, and support tools. The proposed framework serves as a foundation for improving operational predictability and efficiency at airports.
