Data-driven integrated approach to airport demand management

• Main Author: Cheung, Wai Lun
• Other Authors: Rajesh Piplani
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Industrial engineering::Operations research; Engineering::Civil engineering::Transportation
• Online Access: https://hdl.handle.net/10356/152888
• Institution: Nanyang Technological University

The combination of air traffic growth and limitations in airport capacity result in significant congestion throughout the world’s busiest airports. This imposes huge costs on airlines, passengers, and society. Without the option of capacity expansion, which is costly and has a long lead-time, the alleviation of air traffic congestion requires improvements in the utilization of capacity to enhance airport operation efficiency, and/or schedule adjustment to limit over-capacity scheduling. Motivated by the challenge of this important problem, this thesis comprising of three essays pertaining to one analytical capacity model and two variants of strategic demand management model are proposed to increase the utilization of capacity and efficient allocation of capacity. The first essay addresses the limitation of existing analytical capacity models which are not adequate in representing mixed mode operations that could take on any arbitrary sequence as they are unable to consider changes in the operation sequence. The proposed macroscopic analytical runway capacity model to quantify and forecast the maximum operational capacity of an airport provides an easy-to-use decision-support tool for airport operators and can accommodate practical constraints and parameters; model different modes of operations; be efficient in testing out various hypotheses; and provide sufficiently accurate capacity values. As quantifying airport capacity is a critical step towards determining the supply of slots available for allocation to airlines, the new capacity estimates will allow airport operators to design efficient schedules and operational strategies. Computations demonstrate the improvement in capacity estimates and enable quantifying capacity in mixed mode operations with an arbitrary aircraft sequence. The second essay considers the strategic demand management problem with the objective to minimize the number of flights displaced from its original schedule. The proposed model addresses peak traffic with a levelling effect through minor adjustment of the demand with respect to dynamic capacity derived from the earlier analytical capacity estimation model. The proposed approach also allows for exploration of runway configurations, arrival/departure priority, and operational modes (segregated/ mixed) to ensure that the higher levels of demand during the strategic planning phase do not lead to excessive delays on the day of operations. Computational results show the benefits of using dynamic capacity estimation and variable runway configuration. Real-life schedules of the busiest days at Singapore Changi Airport are used in the experiments. The analysis shows that in the case of a segregated mode of operations with variable runway configuration existing infrastructure can even handle 20% higher traffic with no cancelled flights. The investigation then extends to evaluate the improvement in efficiency with a mixed mode of operation over segregated mode. In mixed mode, the number of displaced flights is significantly reduced as compared to the segregated mode operation; the number of flights displaced from all scenarios is 79.6% lower on average. Moreover, the total number of slot displacements for all the flights is also reduced by 82.3%, on average. The third essay considers strategic demand management and its impact on tactical operations. The objective of the proposed two-stage optimization model minimizes the total cost of slots to airlines, the cost of slot reallocation to airlines and passengers, and the cost of expected delays resulting from the optimized schedule. The problem is decomposed into a sequential decision-making exercise. In the first stage, the model determines the slots in which the flight should operate; the quality of the decision made in the first stage is then assessed in the second stage. The solution provides an optimal strategic schedule that should result in minimal delays during the tactical stage. Real-life schedules of the busiest days at Singapore Changi Airport and Hong Kong International Airport are used in the experiments. Further experiments are carried out with a synthetic schedule comprising of more flights than any of the busiest days in the real-life schedule. The experiments demonstrate that the two-stage model is superior to a sequential optimization model. The smallest improvement in the total cost of rescheduling is 4.04%, while the smallest improvement in expected delays in the second stage is 31.55% across all instances. This highlights the benefit of considering the tactical impact (second stage) when making changes to the schedule in demand management (first stage) problem.