Abstract
High-efficiency taxiing for safe operations is needed by all types of aircraft in busy airports to reduce congestion and lessen fuel consumption and carbon emissions. This task is a challenge in the operation and control of the airport’s surface. Previous studies on the optimization of aircraft taxiing on airport surfaces have rarely integrated waiting constraints on the taxiway into the multi-objective optimization of taxiing time and fuel emissions. Such studies also rarely combine changes to the airport’s environment (such as airport elevation, field pressure, temperature, etc.) with the multi-objective optimization of aircraft surface taxiing. In this study, a multi-objective optimization method for aircraft taxiing on an airport surface based on the airport’s environment and traffic conflicts is proposed. This study aims to achieve a Pareto optimized taxiing scheme in terms of taxiing time, fuel consumption, and pollutant emissions. This research has the following contents: (1) Previous calculations of aircraft taxiing pathways on the airport’s surface have been based on unimpeded aircraft taxiing. Waiting on the taxiway is excluded from the multi-objective optimization of taxiing time and fuel emissions. In this study, the waiting points were selected, and the speed curve was optimized. A multi-objective optimization scheme under aircraft taxiing obstacles was thus established. (2) On this basis, the fuel flow of different aircraft engines was modified with consideration to the aforementioned environmental airport differences, and a multi-objective optimization scheme for aircraft taxiing under different operating environments was also established. (3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes. A case study based on the Shanghai Pudong International Airport was also performed in the present study. The taxiway from the 35R runway to the 551# stand in the Shanghai Pudong International Airport was optimized by the non-dominant sorting genetic algorithm II (NSGA-II). The taxiing time, fuel consumption, and pollutant emissions at this airport were compared with those of the Kunming Changshui International Airport and Lhasa Gonggar International Airport, which have different airport environments. Our research conclusions will provide the operations and control departments of airports a reference to determine optimal taxiing schemes.
Highlights
Air traffic flow has sharply increased with the continuous development of the air transport industry
(3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes
In 2010, the 37th Conference of International Civil Aviation Organization (ICAO) was focused on environmental protection, and the Committee on Climate Change estimated that carbon emissions caused by air transport in the middle of the 21st century will increase by 7–8 times compared to those in 1990 [9]
Summary
Air traffic flow has sharply increased with the continuous development of the air transport industry. This study aims to propose a multi-objective optimization method for aircraft taxiing on an airport surface that considers both the airport’s environment and traffic conflicts. (1) Previous calculations of aircraft taxiway have excluded the waiting points on the taxiway from the multi-objective optimization of taxiing time and fuel emissions in order to avoid taxiing conflicts. (2) Previous studies have rarely considered the taxiing multi-objective optimization problem under different airport environments. A multi-objective optimization model of aircraft taxiing under different airport environments was constructed. Through a multi-objective optimization of different aircraft models, the optimal values of the Pareto fronts were intuitively determined via a comparison of the calculation results, which provided references for the operational control of airport surfaces.
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