Estimating contrail formation and proposing effective mitigation strategies have posed significant challenges for the aviation industry in recent years. This study utilizes the Japanese airspace as a case study to address the challenge of assessing and minimizing the environmental impact of contrails. Initially, we introduce a novel combination of meteorological and flight trajectory data sources, followed by a comparative data quality analysis. Drawing on four months of data during different seasons from 2023, we conduct an in-depth analysis of contrail formation within the Japanese airspace, uniquely quantifying contrails with highresolution data to provide insights into their geographical and seasonal variations. Subsequently, we examine the effectiveness of altitude diversions as a mitigation strategy. Our findings identify clear geographical and seasonal influences on contrail formation in the region. We illustrate that altitude diversions can significantly reduce contrail formation with minimal altitude adjustments of up to 2000 ft. We found that minor altitude diversions can mitigate between 70% and 90% of the persistent contrail formed near the Japan region. Notably, the results also highlight a concerning phenomenon: during warmer months, such as July, a higher quantity and percentage of persistent contrails is observed, and a larger proportion of these contrails cannot be mitigated through altitude diversions. This result could intensify positive radiative forcing during warmer periods, underscoring the need for further research into contrail mitigation strategies.

Reducing the length of departure queues at runway entry points is one of the most important requirements for reducing aircraft traffic congestion and fuel consumption at airports. This study designs an aircraft departure model at a runway using a time-varying fluid queue. The proposed model enables us to determine the aircraft waiting time in the departure queue and to evaluate effective control approaches for assigning suitable holds at gates rather than runway entry points. As a case study, this study modeled the departure queue at runway 05 of Tokyo International Airport for an entire day of operations. Using actual traffic data of departures at the airport, the model estimates that aircraft spend a total of 2.5 h departure waiting time in a day at runway 05. Considering the stochastic nature of actual departure traffic, the relevance of the proposed model is discussed using validation criteria. The model estimation shows a reasonable, expected order of magnitude compared with the departure queue recorded in the actual traffic data. Furthermore, ecological and economic benefits are quantitatively evaluated assuming a reduction in the departure queue length. Our results show that about one kiloton of fuel oil per year is wasted due to aircraft waiting to depart from a single departure runway.

This paper proposes to analyze control strategies for arrival air traffic at an airport using a classical queuing model. The parameters of our model are estimated by means of a data-driven analysis of two years of radar tracks and flight plans for arrival flights at Tokyo International Airport from 2016 to 2017. Our results show that increasing the capacity with one or two more aircraft in the airspace up to 60 NM around the airport significantly mitigates arrival delays, even when assuming future, increased arrival traffic volumes. The outcomes of this study provide insights into the effectiveness of arrival control strategies and are seen as a means to recommend scenarios to be further analyzed with human-in-the-loop simulations.

This paper proposes data-driven queuing models and solutions to reduce arrival time delays originating from aircraft arrival processing bottlenecks at Tokyo International Airport. A data-driven analysis was conducted using two years of radar tracks and flight plans from 2016 and 2017. This analysis helps not only to understand the bottlenecks and operational strategies of air traffic controllers, but also to develop mathematical models to predict arrival delays resulting from increased, future aircraft traffic. The queue-based modeling approach suggests that one potential solution is to expand the realization of time-based operations, efficiently shifting from traffic flow control to time-based arrival management. Furthermore, the proposed approach estimates the most effective range of transition points, which is a key requirement for designing extended arrival management systems while offering automation support to air traffic controllers.