Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization

Kim, Jung-Hyun; Justin, Cedric Y.; Mavris, Dimitri N.; Briceño, Simon I.

doi:10.2514/1.i010940

Cited by 9 publications

(4 citation statements)

References 50 publications

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“…Machine learning techniques have been previously applied to flight planning primarily for path planning, optimization, and scheduling [8][9][10][11][12]. In this work, we use state-of-the-art machine learning (ML) technologies to enhance flight planning operations by recognizing, predicting, and incorporating plan changes due to undesirable flight routing behaviors, in-mission coordination decisions and Air Traffic Control deviations early into the planning cycle.…”

Section: Figure 1: Current Flight Planning Processmentioning

confidence: 99%

Improving flight planning efficiency through machine learning

Vaidyanathan,

Danet,

Tran

et al. 2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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Flight planning in support of multi-domain operations is a complex and manpower intensive process. Multiple factors such as changing environmental conditions, navigational data and constraints, air-traffic procedures and policies, maturation of routes and coordination with all stakeholders contribute to this complexity. These changing constraints result in multiple updates and deviations to the flight plans which eventually impact mission objectives and timing. This work applies state-of-the-art machine learning technologies to automatically determine the quality of generated flight plans to enable rapid verification and approval for plan filing. It also identifies preferred routes that are input into planner that results in higher flight plan acceptance rates. We leverage voluminous sources of real operational flight planning data and filed planning data to develop the models. Supervised learning is leveraged to predict whether a generated plan will be filed or rejected. The unsupervised learning component identifies flight plan preferences for feedback to the planner system. These models could be eventually deployed in a flight planning operational environment to reduce human effort, cost, and time to generate flyable plans. The results from this work could also be used to improve planner rules and search algorithms.

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Section: Figure 1: Current Flight Planning Processmentioning

confidence: 99%

Improving flight planning efficiency through machine learning

Vaidyanathan,

Danet,

Tran

et al. 2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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“…Machine learning methods, a branch of artificial intelligence [ 16 ], have been applied to solve real-time optimal control problems [ 17 ], [ 18 ]. Machine learning is broadly classified into three categories, namely, supervised learning, unsupervised learning, and reinforcement learning.…”

Section: Machine Learningmentioning

confidence: 99%

Feature-based Learning for Optimal Abort Guidance

Kenny

Hendrix

You

et al. 2023

AIAA SCITECH 2023 Forum

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“…A kernel function k is responsible for transforming the input data into the high-dimensional feature space. (4) where ɸ : X → H is a projection from feature space into high-dimensional feature space.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The World Health Organization (WHO) declared a global pandemic in January 2020 due to the 2019 coronavirus disease outbreak (COVID- 19). The epidemic has had a huge influence on all industries around the world, posing substantial risks [4] .…”

Section: Introductionmentioning

confidence: 99%

Users’ Evaluation of Traffic Congestion in LTE Networks using Machine Learning Techniques

Kuboye

Adedipe

Oloja

et al. 2023

artif. intell. adv.

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Over time, higher demand for data speed and quality of service by an increasing number of mobile network subscribers has been the major challenge in the telecommunication industry. This challenge is the result of an increasing population of human race and the continuous advancement in mobile communication industry, which has led to network traffic congestion. In an effort to solve this problem, the telecommunication companies released the Fourth Generation Long Term Evolution (4G LTE) network and afterwards the Fifth Generation Long Term Evolution (5G LTE) network that laid claims to have addressed the problem. However, machine learning techniques, which are very effective in prediction, have proven to be capable of great importance in the extraction and processing of information from the subscriber’s perceptions about the network. The objective of this work is to use machine learning models to predict the existence of traffic congestion in LTE networks as users perceived it. The dataset used for this study was gathered from some students over a period of two months using Google form and thereafter, analysed using the Anaconda machine learning platform. This work compares the results obtained from the four machine learning techniques employed that are k-Nearest Neighbour, Support Vector Machine, Decision Tree and Logistic Regression. The performance evaluation of the ML techniques was done using standard metrics to ascertain the real existence of congestion. The result shows that k-Nearest Neighbour outperforms all other techniques in predicting the existence of traffic congestion. This study therefore has shown that the majority of LTE network users experience traffic congestion.

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Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization

Cited by 9 publications

References 50 publications

Improving flight planning efficiency through machine learning

Improving flight planning efficiency through machine learning

Feature-based Learning for Optimal Abort Guidance

Users’ Evaluation of Traffic Congestion in LTE Networks using Machine Learning Techniques

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