A Bibliometric Mapping of Airport Security: Research Trends, Thematic Structure, and Practical Implications

Abstract This study presents a bibliometric mapping of the academic literature on airport security, aiming to chart the intellectual structure, thematic evolution, and collaboration patterns of the field and to translate these patterns into implications for security practitioners and policymakers. A total of 1,207 documents indexed in the Scopus database between 2010 and March 2025 were retrieved and analyzed using bibliometric mapping techniques implemented in VOSviewer. Co-authorship, keyword co-occurrence, publication-trend, and subject-area analyses were conducted to identify the most influential authors, the dominant research themes, and the disciplinary distribution of the field. The co-authorship network revealed ten major clusters comprising 44 authors and 102 connections, with Adrian Schwaninger emerging as the most central author, reflecting a distinct human-factors research tradition alongside a fast-growing computer-vision tradition. The keyword co-occurrence analysis identified 49 keywords occurring at least six times among 3,062 unique keywords, with “airport security,” “security,” “deep learning,” and “aviation security” as the most frequent terms. Publication output rose markedly after 2019, driven mainly by research on deep learning, X-ray imaging, object detection, and threat-recognition systems. Engineering and computer science were the leading subject areas. Beyond mapping the field, the study translates these findings into concrete implications for airport security operators and regulators, and identifies underexplored themes—including passenger experience, policy design, and the integration of human-factors and automated-detection research—that merit further investigation.