Abstract
Congestion and delays occur on airport surfaces as a result of a rapid increase in the demand for air transport. The aim of this study is to determine the differences between optimized and observed operations to improve airport surface operation at Tokyo International Airport by using mixed-integer linear programming to minimize the total ground movement distance and time based on real-time flight information. Receding horizon schemes are considered to adapt to dynamic environments. The model obtains results that reduce the taxi distance by 18.54% and taxi time by 29.77% compared with the observed data. A comparison of taxiway usage patterns between the optimization results and observed data provides insight into the optimization process, for example, changes in runway cross strategies and taxiway direction rules. Factors such as the objective function weights and airline–terminal relationship were found to significantly affect the optimization result. This study suggests improvements that can be made at airports to achieve a more efficient surface operation.
Highlights
An increasing number of hub airports have reached their capacity owing to a rapid increase in the demand for air transport, resulting in congestion and delays on the airport surface, which can cause a series of major problems [1]
The advantage is that it has rich aircraft taxiing details, such as push-back and taxi route, pause time, and runway selection, with which we can better apply dynamic optimization and, more importantly, all aircraft movements in the optimization results are compared with the observed operations in CARATS Open Data to determine the differences between them, to understand the optimization process and improve airport surface operations
This study developed a dynamic optimization model of surface operation to apply for the Tokyo International Airport
Summary
An increasing number of hub airports have reached their capacity owing to a rapid increase in the demand for air transport, resulting in congestion and delays on the airport surface, which can cause a series of major problems [1]. We apply an optimization approach for airport surface operations to minimize the total ground movement distance and time at Tokyo International Airport. The advantage is that it has rich aircraft taxiing details, such as push-back and taxi route, pause time, and runway selection, with which we can better apply dynamic optimization and, more importantly, all aircraft movements in the optimization results are compared with the observed operations in CARATS Open Data to determine the differences between them, to understand the optimization process and improve airport surface operations. The model emulates the actual operation rules of the airport more closely than in previous studies; it includes details such as aircraft push-back area, multi-runway operations, and runway crossing rules at the Tokyo International Airport Factors such as objective function weights and airline–terminal relationships that affect the optimization results are discussed.
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