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Signature Redacted Analysis of Airside Operations at Singapore MASSACHUSETS INSTITI ITE Changi Airport OF TECHNOLOGY ;0 0 by JUL 1 0 2019 Kai Ling Neo LIBRARIES C/) B.Sc. Economics, London School of Economics and Political Science (2017) Submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of Master of Science in Transportation at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2019 @ Massachusetts Institute of Technology 2019. All rights reserved. Signature redacted A u th o r .............................................................. Department of Civil and Environmental Engineering May 17, 2019 Signature redacted C ertified by ........................ Hamsa Balakrishnan Professor of Aeronautics and Astronautics Thesis Supervisor Signature redacted A ccepted by .......................... Heidi Nepf Donald and Martha Harleman Professor of Civil and Environnental Engineering Chair, Graduate Program Committee 2 Analysis of Airside Operations at Singapore Changi Airport by Kai Ling Neo Submitted to the Department of Civil and Environmental Engineering on May 17, 2019, in partial fulfillment of the requirements for the degree of Master of Science in Transportation Abstract Air travel demand has been on an upward trend in recent years, and airports have thus become increasingly congested. To alleviate airport congestion, building new infrastructure such as runways to improve capacity is an obvious solution but it is highly expensive and has a long lead time. In the short term, airport managers and operators have to learn to utilize current capacity more efficiently instead. This begins with the understanding of the current operations and then identifying areas for improvement to better utilize the available capacity. In this thesis, we present a data-driven approach to analyze airport surface oper- ations. The methodology is presented using data from Singapore's Changi Airport, one of the busiest airports in the world and a major transportation hub for South- east Asia. The current operations at the airport is characterized using multiple data sources to identify inefficiencies such as surface congestion and unsatisfactory runway occupancy times. Using the airport characterization, we develop queuing models for the departure process to estimate congestion-related delays and taxi-out times. The taxi-out time estimates from the queuing models have the potential to improve pre- dictability as well as aid in the decision making process to reduce congestion on the airport surface. In order to reduce congestion, many major airports around the world, including Changi Airport, are improving their capacity by adding additional runways. To better understand the impact of additional runways, we present a detailed capac- ity analysis with Changi Airport as a case study. Using empirical and theoretical capacity estimates, along with historical data on the impact of airport expansion from similar airports such as Charlotte Douglas International Airport, we estimate the short-term and long-term improvements in throughput at Changi Airport. The analysis and models built in this thesis thus aim to aid Changi Airport's efforts in alleviating congestion in both the short term and the long term, by providing insights on areas for improvement for current operations and potential impacts of future operational decisions. Thesis Supervisor: Hamsa Balakrishnan Title: Professor of Aeronautics and Astronautics 3 4 Acknowledgments First and foremost, I'd like to thank my amazing advisor Prof Hamsa Balakrishnan, for her invaluable guidance these two years. Her kindness, patience, brilliance and strength are qualities I can only hope to emulate. I've learnt so much from her, and she has been a source of inspiration for me as a young woman. I'd also like to thank Changi Airport Group. Their generous funding has not only enabled me to learn from the very best, but also allowed me to travel and experience what this world has to offer. I'd also like to thank them for kindly providing the data sets that made this thesis possible. I am incredibly blessed to have beautiful friends, here in Cambridge and back in Singapore, accompanying me on this journey. Without their support, I'd not have survived these two years of graduate school. I'd first like to thank my lab mates for all the laughter and fun these past two years. Max has been my happy pill this past year - he always offered a listening ear so readily and there's never a dull moment with him. I remember fondly our trip to Chicago, and I hope we can go on many more trips in the future. I'd also like to thank Sandeep - he laboriously read through my thesis and offered me so much help these two years. This thesis would not have been possible without him. I'm glad to have met a kind and steady friend like him. I'd also like to thank my best friend back in Singapore, Minchih, for always lending me a listening ear and offering me advice so readily. Here's to 11 years of friendship! I'd also like to thank my friends, An and Kevin, for being my two constants in Sidpac. Thank you for always making such good food for me, I'm very lucky to be your regular dinner guest. A big thank you to Kevin, for always being such a great listener and steady friend. Without a soju drinking buddy like you, grad school would have been a lot more sober. Thank you An, for always being there to support me in every way possible. Getting to know you so well this past year has been my utmost pleasure. I'll always remember all the fun crazy times we share together. I hope we can be in touch for years to come, no matter where we end up. My two years at MIT wouldn't have been complete without my program mates (a 5 huge shout out to Akash and Shraddha) and also Leo, who taught me so much and is someone I hold very dear. I'm grateful to all the amazing people I have met at MIT - there's way too many for me to list but you guys know who you are. Thank you all for shaping my MIT experience so positively. Last but not least, I'd like to thank my family. I had the privilege of growing up in such a great family that is always so keen to provide. I never felt limited - I could try anything I wanted and I know they'll always be there to support me. My parents have been the best role models and my brother has been the dorkiest and kindest older brother I could ask for. Thank you so much for everything, I love you guys. 6 Contents 1 Introduction 17 1.1 M otivation. ........................... 17 1.2 Thesis O utline. ................... ...... 18 2 Changi Airport 19 2.1 Overview of Changi Airport ......... ......... 19 2.2 Data Sources from Changi Airport .............. 21 2.2.1 Airport Operational Data ..... .......... 21 2.2.2 Aircraft Track Data ................... 22 3 Characterization of Current Airport Surface Operations 25 3.1 General Statistics of Changi Airport ............. 26 3.2 Surface Operations Analysis: Using Aircraft Track Data . 29 3.2.1 Runway Occupancy Times ......... ...... 30 3.2.2 Surface Congestion Hotspots .............. 32 3.2.3 Runway Queues .................... 34 3.2.4 Rapid Exit Taxiway Usage .......... ..... 36 3.2.5 Taxiway Routes ..................... 37 4 Queue Model for Airport Surface Operations 41 4.1 Literature Review: Airport Departure Process Modeling 42 4.2 Queueing Network Model ................ 44 4.2.1 Single Class Queues ........... ..... 44 7 4.2.2 Queueing Network ..... ........... ....... 46 4.3 Queue Model for Changi Airport . ........... ....... 47 4.3.1 Model Inputs and Outputs ............. ...... 47 4.3.2 Queueing Network .... ................... 48 4.3.3 Analytical Queueing Network Model for Changi Airport .. 50 4.4 Model Performance under North-flow ... .............. 53 4.4.1 Model Performance: Original Model (Model 1) ........ 53 4.4.2 Model Performance: Additional Conditioning on Departure Fleet M ix (M odel 2) . ......... .......... ...... 55 4.4.3 Model Performance: Additional Conditioning on Arrival Fleet M ix (M odel 3) ..... .................... 58 4.5 Model Performance under South-flow ................. 63 4.5.1 Model Performance: Original Model (Model 1) .. ...... 63 4.5.2 Model Performance: Additional Conditioning on Departure Fleet M ix (M odel 2) . ......... .......... ...... 65 4.5.3 Model Performance: Additional Conditioning on Arrival Fleet M ix (M odel 3) ..... .................... 66 5 Airport Capacity Analysis: Impact of Additional Runway 71 5.1 Literature Review: Capacity Estimation and Representation 72 5.2 Current Achieved and Theoretical Capacity of SIN .... ... 74 5.3 Future Achieved and Theoretical Capacity of SIN ..... .. 77 5.3.1 Future Theoretical Capacity of SIN ......... ... 77 5.3.2 Measuring the Difference between Achieved and Theoretical C apacity ........................ 78 5.3.3 Case Study: CLT .............. ..... 82 5.3.4 Case Study: EWR and DFW ........... 90 5.3.5 Inferring Future Achieved Capacity of SIN ..... 94 6 Conclusion 99 6.1 Sum m ary .... ........... ............ ..... 99 8 6.2 Extensions and future work .... ...... ...... ....... 100 9 THIS PAGE INTENTIONALLY LEFT BLANK 10 List of Figures 2-1 Diagram of Changi Airport Terminal and Airside Layout [12] .... 20 2-2 A histogram of fraction of time missing from arrival flight tracks .. 23 2-3 A histogram of fraction of time missing from departure flight tracks . 23 3-1 Percentage of IMC weather throughout the day ... ........ 28 3-2 Runway occupancy time of arrival flights in the dataset .... .. 31 3-3 Runway occupancy time of arrival flights in dataset without the outlier 32 3-4 Average speed (in knots) of arrival flights on the surface . .. .. 33 3-5 Average speed (in knots) of departure flights on the surface .. .. 34 3-6 Runway Queues in Area 1 (02C) ...... ......... .. .. 35 3-7 Runway Queues in Area 1 (02L) ..........
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