Research on Flight Phase Division Based on Decision Tree Classifier

Tian, Feng; Cheng, Xiaoke; Meng, Guang; Xu, Yimin

doi:10.1109/ciapp.2017.8167242

Cited by 12 publications

(8 citation statements)

References 2 publications

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“…Recently, many contributions have framed the problem of flight phase detection as a machine learning task. The use of decision trees classifiers to segment flight phases has been explored in [18]. Some machine learning methods are compared in [8].…”

Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

“…For instance, the engine fan speed is used in [20]. In any case, many steps seem necessary in the machine learning literature: selection of the parameters, implementation of a decision tree classifier and clustering of the results in [18], transformation of trajectory data into fixed length sequential data before using an LSTM neural network in [19]. The difficulty of obtaining a reliable training dataset leads some authors to use simulated data [19].…”

Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

“…• The traditional metrics for classification problems such as the error rate, precision and recall [13,14,18,19,20] • Metrics that focus on the total duration of each phase [7] • Metrics that examine the transitions that are incorrectly predicted between phases as well as the total number of transitions [9] In all contributions, the results are, of course, initially visualized. Because it is easy to find a degenerate segmentation that would provide an exact value for the duration of each phase while alternating the flight phases very randomly, it seems reasonable to consider that at least two metrics should be used.…”

Section: Performance Metricsmentioning

confidence: 99%

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Hidden Markov Models and Flight Phase Identification

Perrichon,

Gendre,

Klein

2024

JOAS

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The use of Hidden Markov Models (HMMs) in segmenting flight phases is a compelling approach with significant implications for aviation and aerospace research. It leverages the temporal sequences of flight data to delineate various phases of an aircraft’s journey, making it a valuable tool for enhancing the anal- ysis of flight performance and safety. In this work, we implement a multivariate HMM to identify 6 flight phases: taxi, takeoff, climb, cruise, approach and rollout. We reach a median global accuracy of about 97% over a sample of several thousand flights with a very low number of decoded unlikely transitions. Regarding several performance metrics, our method is competitive with existing methods in the litera- ture, such as fuzzy logic. Additionally, it provides, for each point of the flight, a probability of belonging to each phase. Even in situations where there are missing values in the data, HMMs remain effective, ensuring that no critical information is lost during the segmentation process. We show that HMMs work seamlessly with the fine granularity of Flight Data Recorder (FDR) data. HMMs offer remarkable flex- ibility and adaptability, proving particularly effective when the number or order of phases is unknown or not predetermined, as is often the case with complex flight scenarios such as helicopter flights. This adaptability is crucial for handling the diverse range of flight operations that differ from one aircraft to another. An example is given with the segmentation of an Automatic Dependent Surveillance–Broadcast (ADS-B) helicopter flight operated by the Swedish National Police.

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Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

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Hidden Markov Models and Flight Phase Identification

Perrichon,

Gendre,

Klein

2024

JOAS

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“…Several recent research approaches attempt to solve the problem in different ways. Tian et al explored a method of dividing the flight phase based on a decision tree ( 17 ). Although the classifier achieved respectable results, the small scale of flight experiments leads to concerns over the construction of the decision tree.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Framework for Classification of Flight Phases of General Aviation Aircraft

Zhang

Mott

2022

Transportation Research Record: Journal of the Transportation R

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Flight data mining enables airport owners, operators, and governmental entities to explore more intelligent management strategies; in particular, cost-effectively obtaining accurate operational data is beneficial for general aviation (GA) airports and their associated communities. The current data collection modus operandi, however, does not meet future needs, as aircraft operations are counted manually or estimated by sampling methods. The increasing traffic flow and limited available personnel at most GA airports make it unrealistic to continue using traditional methods to analyze aircraft operational statistics; therefore, a customized approach is needed to address this problem. Since different flight phases have different levels and types of impact on the environment, acquiring information related to the duration of each flight phase at the airport and within its surrounding airspace is critical to the assessment of emissions and noise pollution from aircraft. The primary goal of the research is to provide quantified inputs for the environmental evaluation model, such as the Aviation Environmental Design Tool (AEDT). This paper demonstrates a programmed framework that successfully achieves satisfactory performance in solving flight phase identification problems by testing the synthetic flight data as well as validating the empirical Automatic Dependent Surveillance-Broadcast (ADS-B) data. The experimental results suggest that the proposed methods achieve promising classification accuracy, leading to feasible deployment in airport operations.

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“…Decision tree is easy to understand as compare to other algorithms and solve problem by using the tree representation. Where each node of the tree corresponds to an attribute and each lead node corresponds to a class label [38], [39].…”

Section: Decision Treementioning

confidence: 99%

Machine Learning for Strategic Decision Making during COVID-19 at Higher Education Institutes

Ahmed

Malik

2020

2020 International Conference on Decision Aid Sciences and Application (DASA)

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Machine learning is becoming driving force for strategic decision making in higher educational institutions and it calls for cooperation between stakeholders and the use of efficient computation methods. Contrariwise, making decisions might consume much time, if there is no use of data and computational methods during the process of decision making.The utilization of machine learning is essential when coming up with an ultimate analysis of data and decision making. Besides, the technology which is under artificial intelligence could facilitates incredible output for educational institutes when it came to decision making. This paper analyses the output generated using machine learning algorithms that help in prediction of no detriment policy applicability rate in the case of e-learning during COVID-19. The study investigates the performance of machine learning algorithms for strategic decision making in the higher educational institutes, Global College of Engineering and Technology in particular, whether no detriment policy will be applicable for a particular student based on students performance before COVID-19. The study shown that Random Forest machine learning algorithm performance is higher as compare to Support Vector Machine, Decision Tree and Navie Bayes.

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Research on Flight Phase Division Based on Decision Tree Classifier

Cited by 12 publications

References 2 publications

Hidden Markov Models and Flight Phase Identification

Hidden Markov Models and Flight Phase Identification

Improved Framework for Classification of Flight Phases of General Aviation Aircraft

Machine Learning for Strategic Decision Making during COVID-19 at Higher Education Institutes

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