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研究生： 許婉柔
Hsu, Wan-Jou
論文名稱： 飛航管制員排班模式對疲勞程度之影響－以飛航管制塔臺為例
The Effects of Scheduling Model on Fatigue Levels for Air Traffic Controllers – Case Study of Aerodrome Control Tower
指導教授： 張有恆
Chang, Yu-Hern
學位類別： 碩士
Master
系所名稱： 管理學院 - 經營管理碩士學位學程(AMBA)
Advanced Master of Business Administration (AMBA)
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 95
中文關鍵詞： 疲勞風險管理系統 、Samn-Perelli疲勞量表 、排班模式 、生理時鐘
外文關鍵詞： FRMS, Samn-Perelli fatigue scale, scheduling model, circadian body clock
相關次數： 點閱：106  下載：15 
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    • 疲勞是一項人為疏失很重要的潛在因子，然而因為疲勞是一種人體身、心理的複雜狀態，難以精準衡量，因此時常遭到忽略。近期由於出國旅遊人口增加以及低成本航空崛起等因素導致航情量倍增，讓原本因人才培訓不易而導致航空人力不足之問題更為嚴重。現有人員在人力尚未補足的情況下，皆須延長工時，於是航空人員疲勞問題也日趨嚴重。
    過去針對飛行員疲勞之相關研究在國外時有所聞，ICAO於2011年將疲勞風險管理系統 (Fatigue Risk Management System, FRMS)之概念納入第六號附約 (Annex 6)中，內容針對飛行員之疲勞管理提供一系列建議與改善。然飛航管制員，被暱稱為「守護天空的隱形人」，在飛航安全中佔有重要影響力，卻往往是被忽略的一員，國內相關研究也僅限於回顧型問卷分析。然而，2016年，CANSO、ICAO、與IFATCA共同出版了飛航服務人員疲勞管理指南 (Fatigue Management Guide for Air Traffic Service Providers)，首次針對飛航管制員之疲勞議題做特別介紹，為飛航管制員疲勞管理議題一項重大突破。
    本研究參考該手冊之建議，以國內某機場管制塔臺為例，依據塔臺現行班表，使用主觀性疲勞量測工具Samn-Perelli Fatigue Scale，針對飛航管制員進行線上疲勞程度量測。分析結果顯示，夜班之疲勞程度明顯高於日班；一日當中各時段之疲勞程度有顯著差異；各班型休息時間之分配會顯著影響疲勞程度；並於分析結果發現，連班天數並不影響疲勞程度；國內機場起降架次由於差異較小，對於疲勞程度也無顯著影響。本研究結果發現現行排班制度之缺失，並過濾出非影響飛航管制員疲勞之因子，以期改善飛航管制員之疲勞，落實飛航安全文化。

    Fatigue is one of the important issues since it is considered to be a potential risk factor for human error. Despite this, fatigue is often ignored because it is defined as a mental or physical state that is difficult to measure. The ICAO (International Civil Aviation Organization) announced the concept of FRMS (Fatigue Risk Management System) in Annex 6 in 2011, which provided suggestions to reduce aircrew fatigue. In 2016, CANSO (Civil Air Navigation Services Organization), ICAO, and IFATCA (International Federation of Air Traffic Controllers’ Associations) published the Fatigue Management Guide for Air Traffic Service Providers, and for the first time proposed recommendations for air traffic controllers to address this issue. Based on the above management guide, this research used the Samn-Perelli fatigue scale to measure air traffic controllers’ fatigue levels in an international aerodrome control tower. The results indicated that there are significant differences between day and night shifts, time periods and various work shifts. It should be noted that the possible impacts of successive work days and aircraft movements on fatigue were also examined in this research, but they had no significant effect to ATC’s fatigue. This research wishes to shed some new light on aviation safety by investigating air traffic controllers’ fatigue levels with current scheduling models, thus helping governmental departments concerning this subject improve and adjust the current scheduling models to further reduce the risks relating to fatigue.

    Table of Contents i List of Tables iv List of Figures v Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Motivation 2 1.3 Research Purpose 5 1.4 Research Framework 6 Chapter 2 Literature Review 8 2.1 Fatigue Background and Definition 8 2.1.1 Definition of Fatigue 8 2.1.2 Classification of Fatigue 9 2.1.3 Circadian Body Clock 11 2.1.4 Effects of Fatigue 12 2.2 Fatigue Risk Management Systems 13 2.2.1 Definition of Fatigue Risk Management 13 2.2.2 Definitions of Fatigue Risk Management Systems 13 2.2.3 Fatigue Risk Management Systems Policy 14 2.3 Risk Factors on Air Traffic Controllers’ Fatigue 15 2.3.1 Duty in Night Shift 15 2.3.2 Duty in Different Time Periods 16 2.3.3 Duty in Irregular Shifts 17 2.3.4 Successive Work Days 17 2.3.5 Workload of Air Traffic Controllers 17 2.4 Air Navigation and Weather Services 18 2.4.1 Organizations of ANWS 18 2.4.2 Introduction of Air Traffic Controllers 19 2.4.3 Directions of Air Traffic Controllers’ Work Shift 23 2.5 Samn-Perelli Fatigue Scale 23 2.5.1 Origins and Introduction 24 2.5.2 Instructions of Using Samn-Perelli Fatigue Scale 25 2.5.3 Samn-Perelli Fatigue Scale Case Study 26 2.6 Case Studies 28 2.6.1 USAir Flight 1493 28 2.6.2 ATC Service Interruption at Washington National Airport 30 2.7 Summary 31 Chapter 3 Methodology 33 3.1 Research Hypotheses 33 3.2 Questionnaire Design 35 3.3 Statistical Analysis 41 3.3.1 Independent Sample t Test 41 3.3.2 Analysis of Variance (ANOVA) 41 3.3.3 Regression Analysis 42 3.3.4 Summary of Hypothesis Test Methods 43 3.4 Samn-Perelli Fatigue Scale Pretest 43 3.4.1 Method of Pretest 43 3.4.2 Results of Pretest 45 Chapter 4 Empirical Analysis 48 4.1 Basic Profiles 48 4.1.1 Descriptive Analysis 48 4.1.2 Independent Sample T-Test 51 4.2 Comparison Between Day and Night Shift 52 4.3 Comparison Between Time Periods 53 4.3.1 Comparison between Day Shift Periods 54 4.3.2 Comparison between Night Shift Periods 56 4.4 The Effects of Fatigue on Cumulative Work Hours 58 4.4.1 Cumulative Work Hours in Day Shifts 58 4.4.2 Cumulative Work Hours in Night Shifts 60 4.5 Comparison Between Different Shifts 65 4.5.1 Comparison between Different Day Shifts 66 4.5.2 Comparison between Different Night Shifts 68 4.6 The Effects of Fatigue Levels on Successive Work Days 70 4.7 The Effects of Aircraft Movements on Fatigue 71 4.8 Summary 73 Chapter 5 Conclusions and Suggestions 74 5.1 Research Hypotheses Verification 74 5.2 Research Conclusions 76 5.3 Research Suggestions 77 5.4 Research Contributions 78 5.5 Future Research Directions 78 References 80 Appendix I Samn-Perelli Fatigue Scale Pretest 85 Appendix II Samn-Perelli Fatigue Scale For ATC 88 Appendix III Aircraft Movements during Data Collection Period 91

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