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Mr. Katsuhiro Sekine
Department of Industrial Management and Engineering, Tokyo University of Science

Basic Info


Research Keywords & Expertise

0 Air Traffic Control
0 Air Traffic Management
0 Multi-objective Optimization
0 Air transport
0 Air transportation

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Career Timeline

Department of Industrial Management and Engineering, Tokyo University of Science

Graduate Student or Post Graduate

01 April 2019 - 01 September 2021


Department of Industrial Management and Engineering, Tokyo University of Science

Undergraduate Student

01 April 2015 - 01 March 2019




Short Biography

Katsuhiro Sekine received his B.S. degree in Industrial Management and Engineering from Tokyo University of Science, Tokyo, Japan, in 2019. During his career, he visited German Aerospace Center (DLR) as a research trainee and carried out the research on extended arrival management. He is currently a Master’s student at Tokyo University of Science. He is also a research trainee at Air Traffic Management Department of Electronic Navigation Research Institute (ENRI) and National Institute of Maritime, Port and Aviation Technology. His research interests include multi-objective optimization, statistical and machine learning method, modeling and simulation, and their applications in air traffic management.

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Journal article
Published: 13 June 2021 in Aerospace
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Although the application of new wake turbulence categories, the so-called “RECAT (wake turbulence category re-categorization)”, will realize lower aircraft separation minima and directly increase runway throughput, the impacts of increasing arrival traffic on the surrounding airspace and arrival traffic flow as a whole have not yet been discussed. This paper proposes a data-driven simulation approach and evaluates the effectiveness of the lower aircraft separation in the arrival traffic at the target airport. The maximum runway capacity was clarified using statistics on aircraft types, stochastic distributions of inter-aircraft time and runway occupancy time, and the levels of the automation systems that supported air traffic controllers’ separation work. Based on the estimated available runway capacity, simulation models were proposed by analyzing actual radar track and flight plan data during the 6 months between September 2019 and February 2020, under actual operational constraints and weather conditions. The simulation results showed that the application of RECAT would reduce vectoring time in the terminal area by 7% to 10% under the current airspace and runway capacity when following a first-come first-served arrival sequence. In addition, increasing airspace capacity by 10% in the terminal area could dramatically reduce en-route and takeoff delay times while keeping vectoring time the same as under the current operation in the terminal area. These findings clarified that applying RECAT would contribute to mitigating air traffic congestion close to the airport, and to reducing delay times in arrival traffic as a whole while increasing runway throughput. The simulation results demonstrated the relevance of the theoretical results given by queue-based approaches in the authors’ past studies.

ACS Style

Katsuhiro Sekine; Furuto Kato; Kota Kageyama; Eri Itoh. Data-Driven Simulation for Evaluating the Impact of Lower Arrival Aircraft Separation on Available Airspace and Runway Capacity at Tokyo International Airport. Aerospace 2021, 8, 165 .

AMA Style

Katsuhiro Sekine, Furuto Kato, Kota Kageyama, Eri Itoh. Data-Driven Simulation for Evaluating the Impact of Lower Arrival Aircraft Separation on Available Airspace and Runway Capacity at Tokyo International Airport. Aerospace. 2021; 8 (6):165.

Chicago/Turabian Style

Katsuhiro Sekine; Furuto Kato; Kota Kageyama; Eri Itoh. 2021. "Data-Driven Simulation for Evaluating the Impact of Lower Arrival Aircraft Separation on Available Airspace and Runway Capacity at Tokyo International Airport." Aerospace 8, no. 6: 165.

Journal article
Published: 27 May 2021 in IEEE Access
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Although schedule design has further potential to reduce airline operation costs and flight delay, the effectiveness of the globally optimal schedule design integrating air traffic flow has not been discussed thus far. This paper presents a global multi-objective takeoff time optimization to design efficient flight schedules that lead to minimal congestion and provide sufficient resilience against traffic problems. NSGA-II is adopted as the multi-objective optimization technique in this study. The objective functions include minimization of the total arrival delay and total fuel consumption because these are key performance indicators of air traffic management (ATM). The design variable used in this study is the takeoff time offset of each flight landing at the Tokyo International Airport. 607 design variables were used in this study. The range of the design variables was ±300 s to investigate the effect of a minor variation in the takeoff time. A cellular automaton-based model was utilized to simulate the interaction of the flights with each other. The results of the simulations demonstrated that the obtained optimal solutions could drastically reduce the total arrival delay and total fuel consumption by 1500 min and 80 tons, respectively. The spacing adjustments of one of the optimum flight schedules, in comparison to the original flight schedule, were reduced by 80% in the en-route and terminal airspaces. Additional analyses suggest that it is preferable to have longer takeoff time intervals for flights originating from the same point during congestion hours than those during non-congestion hours. This indicates that the optimization of ground movements in airports improves the efficiency of air traffic operations.

ACS Style

Katsuhiro Sekine; Tomoaki Tatsukawa; Eri Itoh; Kozo Fujii. Multi-Objective Takeoff Time Optimization Using Cellular Automaton-Based Simulator. IEEE Access 2021, 9, 79461 -79476.

AMA Style

Katsuhiro Sekine, Tomoaki Tatsukawa, Eri Itoh, Kozo Fujii. Multi-Objective Takeoff Time Optimization Using Cellular Automaton-Based Simulator. IEEE Access. 2021; 9 ():79461-79476.

Chicago/Turabian Style

Katsuhiro Sekine; Tomoaki Tatsukawa; Eri Itoh; Kozo Fujii. 2021. "Multi-Objective Takeoff Time Optimization Using Cellular Automaton-Based Simulator." IEEE Access 9, no. : 79461-79476.

Conference paper
Published: 01 November 2018 in 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
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The performance of Multi-Objective Evolutionary Algorithms (MOEAs) depends on the various parameter settings such as population size, generation size, crossover, mutation and so on. It is often difficult to know the appropriate parameter setting for a real-world optimization problem in advance. Besides, the optimal parameter values might depend on each optimization problem and MOEA itself. However, there are few studies for investigating the effect of parameters even in benchmark problems. Therefore, in this study, the effects on performance due to the crossover operators and MOEAs are widely investigated by using eight benchmark problems, including DTLZ and WFG benchmark problems. The number of objectives is set to three and six. We consider five major crossover operators: Simulated Binary crossover (SBX), Simplex crossover (SPX), Differential Evolution operator (DE), Parent Centric crossover (PCX), and Unimodal Normal Distribution crossover (UNDX). As MOEAs, we adopt Non-dominated sorting genetic algorithm-II (NSGAII), Non-dominated sorting genetic algorithm-III (NSGA-III),-Dominance-based Evolutionary Algorithm (-MOEA), Indicator-Based Evolutionary Algorithm (IBEA) and Multi-Objective Evolutionary Algorithm with decomposition (MOEA/D) in this study. The experimental results on benchmark problems show that the effect of the crossover operator on each MOEA is almost the same in both three and six objectives. This indicates that the knowledge has been obtained so far could adapt to the other MOEAs and more than three objectives. In addition, parameters of some crossover operators such as SBX have little impact on the performance. This indicates that these crossover operators can be set to a value used so far without the need of tuning.

ACS Style

Katsuhiro Sekine; Tomoaki Tatsukawa. A Parametric Study of Crossover Operators in Multi-objective Evolutionary Algorithm. 2018 IEEE Symposium Series on Computational Intelligence (SSCI) 2018, 1196 -1203.

AMA Style

Katsuhiro Sekine, Tomoaki Tatsukawa. A Parametric Study of Crossover Operators in Multi-objective Evolutionary Algorithm. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). 2018; ():1196-1203.

Chicago/Turabian Style

Katsuhiro Sekine; Tomoaki Tatsukawa. 2018. "A Parametric Study of Crossover Operators in Multi-objective Evolutionary Algorithm." 2018 IEEE Symposium Series on Computational Intelligence (SSCI) , no. : 1196-1203.