Author ORCID Identifier

https://orcid.org/0009-0001-3218-8277

Semester

Summer

Date of Graduation

2026

Document Type

Thesis (Campus Access)

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Civil and Environmental Engineering

Committee Chair

V. Dimitra Pyrialakou

Committee Member

James Bryce

Committee Member

Zeyu Liu

Abstract

Airports are complex socio-technical systems that serve as critical transport infrastructure and key drivers of global economic development. The continued growth of global air traffic generates mounting environmental, operational, and technological pressures that demand coordinated, evidence-based responses from airport planners and decision-makers. As artificial intelligence and advanced digital tools become increasingly embedded in aviation operations, airport managers face new opportunities to leverage these technologies for investment prioritization, capacity planning, and the design of next-generation passenger experiences, alongside the challenge of measuring whether such investments translate into demonstrable performance gains. This study introduces a two-stage Data Envelopment Analysis (DEA) framework designed to support airport performance assessment and benchmarking across economic, operational, and digital dimensions. Three model specifications of increasing comprehensiveness are estimated: a Technical Airside model, a Strategic Land-side model, and a Full Model combining all available input and output dimensions. The framework is applied to a cross-sectional sample of 30 international airports (17 North American and 13 European) using 2023 and 2024 reference-year data, demonstrating how the methodology can benchmark system performance, identify peer leaders, and map performance gaps across diverse airport systems. The novelty of this study lies in the construction of a composite Digital Adoption Index (DI), aggregating eight technology adoptions including biometrics, automated gate systems, generative AI, digital twins, and capacity optimization tools, and its integration into DEA airport efficiency measurement. Second-stage analyses reveal that biometric passenger processing and automated gate infrastructure are the technology dimensions most consistently associated with superior airside efficiency, confirming that technology and AI-driven automation can measurably enhance operational performance. The findings provide actionable insights for planners, policymakers, and airport managers seeking to prioritize digital investments, close benchmarked performance gaps, and guide the design of smarter, more adaptive, and resilient airport systems.

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