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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.

Reducing fuel consumption and emissions of aircrafts during taxiing on airport surfaces is crucial to decrease the operating costs of airline companies and construct green airports. At present, relevant studies have barely investigated the influences of the operation environment, such as low visibility and traffic conflict in airports, reducing the assessment accuracy of fuel consumption and emissions. Multiple aircraft ground propulsion systems on airport surfaces, especially the electric green taxiing system, have attracted wide attention in the industry. Assessing differences in fuel consumption and emissions under different taxiing modes is difficult because environmental factors were hardly considered in previous assessments. Therefore, an innovative study was conducted based on practical running data of quick access recorders and climate data: (1) Low visibility and taxiing conflict on airport surfaces were inputted into the calculation model of fuel consumption to set up a modified model of fuel consumption and emissions. (2) Fuel consumption and emissions models under full- and single-engine taxiing, external aircraft ground propulsion systems, and electric green taxiing system could accurately estimate fuel consumption and emissions under different taxiing modes based on the modified model. (3) Differences in fuel consumption and emissions of various aircraft types under four taxiing modes under stop-and-go and unimpeded aircraft taxiing conditions were obtained through a sensitivity analysis in Shanghai Pudong International Airport under three thrust levels. Research conclusions provide support to the airport management department in terms of decision making on taxiway optimization.

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