Increasing the Efficiency and Capacity of Airport Security Checking by Artificial Intelligence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00564654" target="_blank" >RIV/60162694:G43__/26:00564654 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/02819180:_____/25:#0000149

Výsledek na webu

<a href="http://transportproblems.polsl.pl/pl/Archiwum/2025/zeszyt2/2025t20z2_14.pdf" target="_blank" >http://transportproblems.polsl.pl/pl/Archiwum/2025/zeszyt2/2025t20z2_14.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.20858/tp.2025.20.2.14" target="_blank" >10.20858/tp.2025.20.2.14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Increasing the Efficiency and Capacity of Airport Security Checking by Artificial Intelligence

Popis výsledku v původním jazyce

Airports face significant pressure to meet various interconnected performance objectives, including ensuring security and capacity. This aspect of airport operations is crucial regardless of the airport’s size. The solution to the security dilemma is the implementation of new technologies combined with the use of artificial intelligence (AI). Artificial narrow intelligence (ANI), mostly based on machine learning, is being introduced in most areas of security inspections. This usually starts with personal identification with biometric verification, followed by passenger profiling, where ANI is also already being applied. The effective security screening of people, luggage, and shipments relies on imaging and volume detection technologies using electromagnetic radiation spectra. ANI with machine learning is beginning to be used in imaging technologies for image analysis, in metal detectors for signal analysis of wanted contraband, in classical spectrometric trace particle detectors, and in optical spectrometric methods for the spectral detection of wanted substances. At the same time, computerized scanner cooperation with occupant profiling is beginning to be promoted as part of the risk-based approach. This paper analyses the potential for introducing advanced AI into airport security processes and their potential impact on the capacity of a connected queuing system according to Jackson Networks. This study presents a vision of the implementation of multimodal AI into security screening processes. This AI would gather detailed data from individual scanners operating on different physical principles and synthesize and compare comprehensive information about detected suspicious objects with databases of harmless and dangerous items. Subsequently, it would decide on the next steps, potentially considering the passenger’s security profile. This paper demonstrates that implementing AI into airport security screening processes can significantly improve the efficiency and reliability of these processes. AI can help reduce the time spent by individual passengers at security checkpoints while increasing the reliability of detection, contributing to the overall improvement of the airport security screening process.

Název v anglickém jazyce

Increasing the Efficiency and Capacity of Airport Security Checking by Artificial Intelligence

Popis výsledku anglicky

Airports face significant pressure to meet various interconnected performance objectives, including ensuring security and capacity. This aspect of airport operations is crucial regardless of the airport’s size. The solution to the security dilemma is the implementation of new technologies combined with the use of artificial intelligence (AI). Artificial narrow intelligence (ANI), mostly based on machine learning, is being introduced in most areas of security inspections. This usually starts with personal identification with biometric verification, followed by passenger profiling, where ANI is also already being applied. The effective security screening of people, luggage, and shipments relies on imaging and volume detection technologies using electromagnetic radiation spectra. ANI with machine learning is beginning to be used in imaging technologies for image analysis, in metal detectors for signal analysis of wanted contraband, in classical spectrometric trace particle detectors, and in optical spectrometric methods for the spectral detection of wanted substances. At the same time, computerized scanner cooperation with occupant profiling is beginning to be promoted as part of the risk-based approach. This paper analyses the potential for introducing advanced AI into airport security processes and their potential impact on the capacity of a connected queuing system according to Jackson Networks. This study presents a vision of the implementation of multimodal AI into security screening processes. This AI would gather detailed data from individual scanners operating on different physical principles and synthesize and compare comprehensive information about detected suspicious objects with databases of harmless and dangerous items. Subsequently, it would decide on the next steps, potentially considering the passenger’s security profile. This paper demonstrates that implementing AI into airport security screening processes can significantly improve the efficiency and reliability of these processes. AI can help reduce the time spent by individual passengers at security checkpoints while increasing the reliability of detection, contributing to the overall improvement of the airport security screening process.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Transport Problems

ISSN

1896-0596

e-ISSN

2300-861X

Svazek periodika

20

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

12

Strana od-do

171-182

Kód UT WoS článku

001534463900014

EID výsledku v databázi Scopus

2-s2.0-105009512407