A Deep Reinforcement Learning Approach for Airport Departure Metering Under Spatial–Temporal Airside Interactions

Ali, Hasnain; Pham, Duc-Thinh; Alam, Sameer; Schultz, Michael

doi:10.1109/tits.2022.3209397

Cited by 13 publications

(2 citation statements)

References 35 publications

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“…Airport civil engineering is a study of maintenance techniques, regulation, operation of landside and airside facilities at airports, as well as supervision of construction or repair activities of airport buildings (Pasandín & Pérez, 2021). Airside is part of the airport supporting the aircraft activities for landing and take-off including runways, taxiways, and aprons (Ali et al, 2022;Hom & Orman, 1975). Runway is an area used for aircraft landing and take-off activities (Di Mascio et al, 2020).…”

Section: English For Airport Civil Engineering: Airside and Landsidementioning

confidence: 99%

B-ACE Mobile Learning with Outcome-Based Education Approach: Developing ESP Learning Media for Airport Civil Engineering Students

Pratama,

Jazera,

Putri

et al. 2024

JEASP

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This study sought to develop prototype of B-ACE mobile learning application using Android operating system with an outcome-based education approach as a media to learn basic airport civil engineering. This study adopted the ADDIE development model. This study involved 72 aviation cadets of Airport Civil Engineering Department to complete the student needs analysis questionnaire and three lecturers who taught Basic English course in the department to complete the curriculum needs analysis questionnaire and conduct FGD. Results showed that the B-ACE prototype was developed through analysis, design, development, implementation, and evaluation stages. Experts showed valid scores without revision as the result of translational validity test (x̄ = 3.86), thus, the B-ACE mobile learning prototype was in accordance with the airside and landside materials and the outcome-based education curriculum applied in higher education. The students’ responses were positive after learning using B-ACE (x̄ = 98.8%).

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Section: English For Airport Civil Engineering: Airside and Landsidementioning

confidence: 99%

B-ACE Mobile Learning with Outcome-Based Education Approach: Developing ESP Learning Media for Airport Civil Engineering Students

Pratama,

Jazera,

Putri

et al. 2024

JEASP

View full text Add to dashboard Cite

show abstract

“…While the above studies lack consideration of human behavior, they primarily rely on predefined rules to control the aircraft taxiing process, such as the limitation of taxiing speed and the assignment of aircraft priority. For each conflict detected, aircraft priority is determined according to simple rules, including 'first-come-first-served'; a departure aircraft is given priority over an arrival aircraft [16][17][18]. In such cases, the conflicting aircraft with lower priority has to alter its route from its ideal path or slow down to ensure that the aircraft can safely enter the next area [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge

Sui,

Chen,

Zhou

2023

Aerospace

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With the escalating complexity of surface operations at large airports, the conflict risk for aircraft taxiing has correspondingly increased. Usually, the Air Traffic Controllers (ATCOs) generate route, speed and holding instructions to resolve conflicts. In this paper, we introduce a conflict resolution framework that incorporates prior knowledge by integrating a Multi-Layer Perceptron (MLP) neural network into the Monte Carlo Tree Search (MCTS) approach. The neural network is trained to learn the allocation strategy for waiting time extracted from actual aircraft taxiing trajectory data. Subsequently, the action probability distribution generated with the neural network is embedded into the MCTS algorithm as a heuristic evaluation function to guide the search process in finding the optimal conflict resolution strategy. Experimental results show that the average conflict resolution rate is 96.8% in different conflict scenarios, and the taxiing time required to resolve conflicts is reduced by an average of 42.77% compared to the taxiing time in actual airport surface operations.

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A novel bi-objective model for sustainable and efficient airport logistics management: A case study on Copenhagen airport

Ramezanian,

Larizadeh,

Tosarkani

2024

Computers & Industrial Engineering

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A Deep Reinforcement Learning Approach for Airport Departure Metering Under Spatial–Temporal Airside Interactions

Abstract: A deep reinforcement learning approach for airport departure metering under spatial-temporal airside interactions

Cited by 13 publications

References 35 publications

B-ACE Mobile Learning with Outcome-Based Education Approach: Developing ESP Learning Media for Airport Civil Engineering Students

B-ACE Mobile Learning with Outcome-Based Education Approach: Developing ESP Learning Media for Airport Civil Engineering Students

A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge

A novel bi-objective model for sustainable and efficient airport logistics management: A case study on Copenhagen airport

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