A Safety-Aware Real-Time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties

Sandamali, Gammana Guruge Nadeesha; Su, Rong; Sudheera, Kalupahana Liyanage Kushan; Zhang, Yicheng

doi:10.1109/tits.2021.3083964

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…The mathematical model presented in this section serves as an excellent representation of the flights-to-delay (ATFM slot) allocation problem encountered in the real world and it is commonly utilised in academic literature (Castelli et al, 2011). However, it is worth noting that if the ILP method is employed to solve the DCB problem while considering uncertainty, the size and complexity of the model could potentially increase further (Sandamali et al, 2021;Chen et al, 2017). Hence, the primary challenges for the optimisation method in addressing the actual DCB problem lie in the instability of computing time and the difficulties associated with handling uncertainty.…”

Section: Comparison With An Optimisation Methodsmentioning

confidence: 99%

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

CHEN

2022

SSRN Journal

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Section: Comparison With An Optimisation Methodsmentioning

confidence: 99%

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

CHEN

2022

SSRN Journal

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“…ATFM has several stakeholders, such as airlines, pilots, local flow administration centers, and the national flow administration center. Superior techniques and proficient traffic controllers are pertinent to ATFM [60,61]. Weather situations, aircraft operational restrictions, and controllers ' skills have enormous implications on the proper functioning of ATFM systems.…”

Section: Ai For Atfmmentioning

confidence: 99%

Review on artificial intelligence techniques for improving representative air traffic management capability

Tang

Liu

Pan

2022

J. of Syst. Eng. Electron.

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The use of artificial intelligence (AI) has increased since the middle of the 20th century, as evidenced by its applications to a wide range of engineering and science problems. Air traffic management (ATM) is becoming increasingly automated and autonomous, making it lucrative for AI applications. This paper presents a systematic review of studies that employ AI techniques for improving ATM capability. A brief account of the history, structure, and advantages of these methods is provided, followed by the description of their applications to several representative ATM tasks, such as air traffic services (ATS), airspace management (AM), air traffic flow management (ATFM), and flight operations (FO). The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages: (i) these methods provide alternative approaches to conventional physical modeling techniques, (ii) these methods do not require knowing relevant internal system parameters, (iii) these methods are computationally more efficient, and (iv) these methods offer compact solutions to multivariable problems. In addition, this review offers a fresh outlook on future research. One is providing a clear rationale for the model type and structure selection for a given ATM mission. Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission. These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

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“…When satellite navigation services are unable to meet aircraft operational requirements, ground navigation systems, such as Very High‐Frequency Omnidirectional Range (VOR), Distance Measuring Equipment (DME), and Non‐Directional Beacon (NDB), would have to be employed (Jakšić & Janić, 2020). This may result in a significant reduction of airspace capacity and cause an imbalance problem between flight demand and airport capacity (Ball & Lulli, 2004; Sandamali et al., 2021). As a result, certain flights will need to be rescheduled or even canceled to address this imbalance.…”

Section: Introductionmentioning

confidence: 99%

Forward‐Looking Study of Solar Maximum Impact in 2025: Effects of Satellite Navigation Failure on Aviation Network Operation in the Greater Bay Area, China

Xue,

Yang,

Liu

et al. 2023

Space Weather

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Satellite navigation based on the Global Navigation Satellite System can provide aircraft with more precise guidance and increase flight efficiency. However, severe space weather events can cause satellite navigation failure due to the dramatic increase in total electron content and irregularities in the ionosphere. Consequently, ground navigation has to be used to replace satellite navigation, increasing aircraft separation standards and reducing airspace capacity. As a result, numerous flights may be delayed or even canceled, incurring significant financial losses. The occurrence peak of space weather events generally coincides with the 11‐year‐cycle solar maximum, and 2025 is expected to be the upcoming solar maximum. The Greater Bay Area (GBA), located in the equatorial ionization anomaly region of China, is particularly vulnerable to space weather impacts. To explore the effects of satellite navigation failure on flight operation, we conduct this looking‐forward study and propose solution methods from the standpoint of Air Traffic Management, by simulating satellite navigation failure scenarios. Based on the projected flight volume in 2025 related to the GBA airports, simulation results show that the economic costs can be tens of millions of Euros, which is dependent on the duration of satellite navigation failure and the time interval of ground navigation‐based landing. We believe that this study can be a benchmark for evaluating the potential economic effects of forthcoming space weather on flight operations.

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A Safety-Aware Real-Time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties

Cited by 7 publications

References 21 publications

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

General Multi-Agent Reinforcement Learning Integrating Heuristic-Based Delay Priority Strategy for Demand and Capacity Balancing

Review on artificial intelligence techniques for improving representative air traffic management capability

Forward‐Looking Study of Solar Maximum Impact in 2025: Effects of Satellite Navigation Failure on Aviation Network Operation in the Greater Bay Area, China

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