OPTIMIZATION OF PASSENGER FLOW IN MEDIUM-SIZED AIRPORTS = 중형 공항의 승객 흐름 최적화 연구

다국어 초록 (Multilingual Abstract)

The steady rise in global air travel is placing new demands on medium-sized airports. The latter now faces the difficult task of handling more passengers despite having limited space, staff, and equipment. Unlike large international hubs, they often lack the flexibility and spare capacity needed to handle demand peaks. As a result, bottlenecks frequently appear at check-in, security screening, passport control, and baggage claim. Such delays slow operations and affect passenger comfort and the overall image of the airport. In response, many airports have expanded their terminals and adopted modern systems such as e-gates, self-service kiosks, and automated security lanes. Yet, despite these improvements, there is still little concrete evidence on how much these upgrades have enhanced day-to-day performance. This thesis examined how infrastructure expansion and technological innovation affect passenger flow optimization in medium-sized airports. Using statistical analysis and queueing theory (M/M/s), the study evaluated actual waiting times across multiple checkpoints using longitudinal data from 2019-2024. Tirana International Airport (TIA) was selected as the representative example. It was selected based on the surge in passenger numbers from 3.3 million in 2019 to over 10 million in 2024 and their choice of mitigation measures which were terminal growth and automation. TIA’s case provides a realistic example of how a rapidly developing regional airport manages growth while aiming to sustain service quality under limited resources. This study used statistics and queueing simulations to track waiting-time trends and predict future demand. The collected data were checked with queueing models to make sure the results were consistent. Findings were compared with IATA Level of Service (Los) Code C standards to show what is acceptable. Both descriptive and inferential analyses were conducted, including ANOVA and post-hoc tests, to verify whether the observed improvements were statistically significant across all five processing domains: check-in, security control, passport control (departure and arrival), and baggage claim. Results show that terminal expansion and new technologies helped cut delays, especially at security and passport control. Year-over-year (YoY) changes were clear. The queueing theory analysis revealed reduced utilization rates (ρ) and shorter queue lengths (Lq) in most domains, indicating better operational balance between arrival and service rates. Simulations suggest that when TIA handles more than 13 million passengers a year, service quality may drop unless more capacity is added. This also applies to airports of a similar size. These findings imply that while short-term congestion can be effectively mitigated, long-term sustainability will depend on continuous investment, adaptive scheduling, and data-driven resource management. Overall, the study finds that infrastructure upgrades paired with new technologies can ease congestion and improve flows in medium-sized airports, while stressing the need for careful planning, data-informed choices, and continuous monitoring. Beyond TIA, the methodological framework presented here, combining statistical evaluation, queueing modeling, and international benchmarking, can serve as a practical tool for airport managers seeking to anticipate congestion and design evidence-based capacity plans. The research contributes both academically, by validating the application of M/M/s modeling in airport flow studies, and practically, by offering measurable insights into how terminal development and automation improve passenger experience and operational efficiency.
Keywords: passenger flow management, medium-sized airports, queueing theory, airport capacity optimization, waiting time analysis, terminal expansion, technological innovation, operational efficiency.

목차 (Table of Contents)

• 1. INTRODUCTION 1
• 1.1. Research Subject 1
• 1.2. Historical Development of Tirana International Airport 2
• 1.2.1. Origins and Early Years (1955-2000) 2
• 1.2.2. Privatization and Concession Agreement (2004-2005) 3
• 1.2.3. Ownership Transitions 3
• 1.2.4. Certification and Compliance 3
• 1.3. Infrastructure and Operational Overview of TIA 4
• 1.4. TIA’s Transatlantic Ambitions and International Connectivity 5
• 1.5. Passenger Traffic at TIA 6
• 1.5.1. Long-Term Historical Growth (2005-2024) 6
• 1.5.2. Masterplan Forecast Scenarios (2022-2050) 8
• 1.5.3. Actual Traffic Versus Forecast Accuracy (2022-2024) 9
• 1.5.4. Interpretation and Policy Implications 10
• 1.5.5. TIA’s Capital Expenditures 2022-2025 10
• 1.6. Albania’s Tourism Boom and Policy Strategy 11
• 1.6.1. Overview of the Tourism Boom 11
• 1.6.2. The National Tourism Strategy 2024-2030 12
• 1.6.3. Strategic Objectives and Implementation Tools 12
• 1.6.4. Air Transport and Infrastructure 13
• 1.6.5. Conclusion 14
• 1.7. IATA Level of Service Concept 14
• 1.7.1. Introduction to Level of Service 14
• 1.7.2. Framework and Purpose 15
• 1.7.3. Space-Time Matrix Approach 15
• 1.7.4. Guidelines by Processing Area 15
• 1.7.4.1 Check-in Counters 16
• 1.7.4.2 Security Control. 16
• 1.7.4.3 Passport Control 17
• 1.7.4.4 Baggage Claim 17
• 1.7.5. Integration with Service Level Agreements 18
• 1.7.6. Strategic Role in Terminal Design and Expansion 19
• 1.7.7. Relevance to TIA 19
• 1.8. Research Problem 19
• 1.9. Research Questions 20
• 1.10. Research Objectives 21
• 2. LITERATURE REVIEW 22
• 2.1. Literature Summary on Research Subject 22
• 2.1.1. Passenger Flow Modeling and Simulation 22
• 2.1.2. Airport Terminal Capacity and Infrastructure Planning 23
• 2.1.3. Self-Service Technologies and Passenger Automation 24
• 2.1.4. Passenger Experience and Service Quality 25
• 2.1.5. Sustainability, Future Airport Design, and Demand Forecasting. 26
• 2.2. Literature Summary on Theoretical Background 27
• 2.2.1. Statistical Analysis 27
• 2.2.1.1. Passenger Satisfaction and Airport Efficiency 27
• 2.2.1.2. Risk Analysis and Credit Management in Banking 28
• 2.2.1.3. Occupational Health and Safety in Construction 28
• 2.2.2. Queueing Theory 29
• 2.2.2.1. Passenger Flow and Airport Efficiency 29
• 2.2.2.2. Railway Operations and Capacity Management 30
• 2.2.2.3. Banking Sector Service Quality and Productivity 30
• 3. RESEARCH DESIGN AND METHODOLOGY 31
• 3.1. Research Model 31
• 3.1.1 Proposed Hypotheses 31
• VIII
• 3.1.2 Conceptual model 32
• 3.2. Research Design 33
• 3.3. Data Collection 34
• 3.4. Analytical Framework 34
• 3.4.1. Descriptive Statistics 34
• 3.4.2. Inferential Statistics 35
• 3.4.3. Queueing Theory Modeling (M/M/s) 35
• 3.4.4. Simulation and Forecasting 36
• 3.4.5. Benchmarking 37
• 3.5. M/M/s Model Assumptions and Limitations 37
• 3.6. Justification for Method Choice 38
• 3.7. Tools Used 38
• 4. RESULTS AND DISCUSSION 39
• 4.1. Descriptive Statistics 39
• 4.1.1 Check-in 40
• 4.1.2 Security Control 42
• 4.1.3 Passport Control (Departure) 44
• 4.1.4 Passport Control (Arrival) 47
• 4.1.5 Baggage Claim 48
• 4.2. Statistical Comparison of Waiting Times Across Years 51
• 4.2.1 ANOVA Analysis – Check-in 51
• 4.2.2 ANOVA Analysis – Security Control 52
• 4.2.3 ANOVA Analysis – Passport Control (Departure) 53
• 4.2.4 ANOVA Analysis – Passport Control (Arrival) 55
• 4.2.5 ANOVA Analysis – Baggage Claim 57
• 4.3. Queueing Theory Analysis of Passenger Flow 59
• 4.3.1 Input Parameters and Their Derivation 59
• 4.3.2 Queueing Theory Results 65
• 4.3.3 Comparative Insights 74
• 4.4. Simulation for 13 million Passengers 77
• 4.4.1 Simulation Methodology 77
• 4.4.2 Simulation Results 78
• 4.4.3 Interpretation 78
• 4.5. Benchmarking Against IATA Standards 84
• 4.5.1 Check-in 85
• 4.5.2 Security Control 85
• 4.5.3 Passport Control (Departure) 86
• 4.5.4 Passport Control (Arrival) 87
• 4.5.5 Baggage Claim 88
• 4.5.6 Comparative Insights 89
• 5. CONCLUSION AND RECOMMENDATIONS 90
• 5.1. Conclusion 90
• 5.2. Recommendations 92
• 5.3. Limitations 93
• 5.4. Future Research 94
• 5.5. Final Reflection 95
• 6. APPENDIX 97
• 7. REFERENCES 114