Performance Evaluation of Conflict-Free Trajectory Taxiing in Airport Ramp Area Using Fast-Time Simulations

Okuniek, Nikolai; Jung, Yoon; Gerdes, Ingrid; Zhu, Zhifan; Gridnev, Sergei; Lee, Hanbong

doi:10.1109/dasc.2018.8569228

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Okuniek [26] [27] et al applied the management tool Taxi Routing for Aircraft: Creation and Controlling (TRACC) to trajectory-based ramp traffic management, where TRACC generated conflict-free aircraft trajectories in a congested ramp area.…”

Section: Literature Reviewmentioning

confidence: 99%

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

Jiang

Liu

et al. 2021

IEEE Access

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Section: Literature Reviewmentioning

confidence: 99%

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

Jiang

Liu

et al. 2021

IEEE Access

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“…In the state of art of automatic CD&R, most of the researchers focus on the cruising phase of the aircraft [20,21], ground taxing [22,23], and conflicts between taking off aircraft and landing aircraft on the intersecting runway [24,25] and CD&R between unmanned aerial vehicles (UAVs) [26,27], but the flight speed and maneuvering mode of drones are very different from civil aircraft. There are also CD&R algorithms such as OCRA [28,29] that could be applied to MMDP problems with an extremely large magnitude of agents, the speed of the agent can be reduced to zero in the OCRA model, and each agent is only responsible for the half of the minimum safety interval, which is not applicable to CD&R in civil aviation.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Aircraft Conflict Detection and Resolution Method Based on an Improved Reinforcement Learning Framework

Xu,

Chen,

et al. 2023

International Journal of Aerospace Engineering

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With the steady increase of air traffic column, an auxiliary decision tool is required to compensate the operation redundancy deficiency of more sectors of air traffic control. To solve the problem of nonconflict high-density departure and arrival traffic flow, this method is expected to rapidly establish and maintain safe separation with more flexible changing strategies for aircraft heading and speed. This paper proposes an improved reinforcement learning framework to achieve conflict detection and resolution. The proposed framework includes the first development of an air traffic flow model based on a multiagent Markov decision process. The goal reward function was then maximized by improved Monte-Carlo tree search combined with an upper confidence bound tree. Three simulation scenarios were designed for illustrating the improvements of the proposed algorithm, with the results indicating that the algorithm could establish and maintain safe separation between 20 agents in the simplified hexagon-shaped airspace of Huadong, China. Furthermore, the proposed method was demonstrated to reduce the number of conflicts between aircraft agents by up to 26.32% compared to previous research.

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“…Flow-based macroscopic simulation environments like FACET (NASA) [1], SAAM (EU-ROCONTROL) [2], TAAM [3], Bluesky [4], and TOMATO [5], which primarily focus on network effects, are widely used, though they can lack higher levels of detail concerning ground operations. On the other hand, highly detailed simulations, such as the discrete event-based SIMMOD [6] or the Surface Operations Simulator and Scheduler (NASA) [7], which are applied for capacity assessments at airports or gate allocation problems, do not cover network effects. In contrast to flow-based methods, they focus on a specific airport with its surrounding airspace.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Gate-to-Gate Simulation Environment for the Air Traffic System

Yildiz,

Förster,

Langner

et al. 2023

Aerospace

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This paper presents a concept of a fast-time gate-to-gate simulation environment. The implementation is divided into an air traffic part that uses BADA performance parameters and a simulation of ground processes. The main objective of the flow-based hybrid simulation environment is to cover commercial European air traffic, in order to investigate network-related effects when exposed to disturbances. Based on historic traffic scenarios, the hybrid simulation platform enables the investigation of the local and global effects of a variety of disruptions. With respect to current flow-based models, it is intended to gain better insights into the underlying interdependencies by modelling higher levels of detail for selected parts, whilst covering the whole European air traffic network. After a validation and first calibration of the approach, Monte Carlo simulations, based on flight plans, are performed as proof of concept. This aims to illustrate the local effects of network-wide disturbances and is applied by means of stochastic influences of ground processes, gained from real operational data.

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Performance Evaluation of Conflict-Free Trajectory Taxiing in Airport Ramp Area Using Fast-Time Simulations

Cited by 3 publications

References 11 publications

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

A Priority-Based Conflict Resolution Strategy for Airport Surface Traffic Considering Suboptimal Alternative Paths

An Efficient Aircraft Conflict Detection and Resolution Method Based on an Improved Reinforcement Learning Framework

A Hybrid Gate-to-Gate Simulation Environment for the Air Traffic System

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