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Spatio-Temporal Data Mining for Aviation Delay Prediction

Kai Zhang, Yushan Jiang, Dahai Liu, Houbing Song
2020 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)  
There has been a lot of attempts to apply data-driven methods such as machine learning to forecast flight delay situation using air traffic data of departures and arrivals.  ...  A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay.  ...  ACKNOWLEDGMENT This research was supported by the Center for Advanced Transportation Mobility (CATM), USDOT Grant #69A3551747125.  ... 
doi:10.1109/ipccc50635.2020.9391561 fatcat:jmowlrf2zjapldrayql2hnhnyq

Assessing identifiability in airport delay propagation roles through deep learning classification

Ilinka Ivanoska, Luisina Pastorino, Massimiliano Zanin
2022 IEEE Access  
We finally discuss some operational implications of this approach. INDEX TERMS Air transport, airport identifiability, delays, deep learning.  ...  We show how Deep Learning models are able to recognise airports with high precision, thus suggesting that delays are defined more by the characteristics of each airport than by the global network effects  ...  In more recent years a new trend is emerging: the use of Deep Learning (DL) models [22] , [23] , i.e. machine learning models not requiring an a priori definition of features, to predict the occurrence  ... 
doi:10.1109/access.2022.3158313 fatcat:rj6wi6ydvjc3pey6qrkmnejnvi

A Multi-Agent Approach for Reactionary Delay Prediction of Flights

Yash Guleria, Qing Cai, Sameer Alam, Lishuai Li
2019 IEEE Access  
The current research draws motivation from this behavior and develops a multi-agent based method to predict the reactionary delays of flights, given the magnitude of primary delay that the flights witness  ...  Through the reactionary delay values predicted by the multi-agent based method, the flights are first classified as delayed or un-delayed in terms of departure.  ...  approaches, machine learning and agent based techniques [14] , [17] - [19] .  ... 
doi:10.1109/access.2019.2957874 fatcat:arv4agvskvghbox3fsin7t4iyu

A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction

Weili Zeng, Juan Li, Zhibin Quan, Xiaobo Lu, Jinjun Tang
2021 Journal of Advanced Transportation  
to establish a deep learning framework for delay prediction.  ...  Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an  ...  Acknowledgments is paper was supported by the National Natural Science Foundation of China (nos. 62076126 and 62006041) and the Fundamental Research Funds for the Central Universities (no. NS2018044).  ... 
doi:10.1155/2021/6638130 fatcat:l37dvprcqvgjlawldhjwqutktu

Assessing Interdependencies and Congestion Delays in the Aviation Network

G. Lykou, P. Dedousis, G. Stergiopoulos, D. Gritzalis
2020 IEEE Access  
In addition, using historic flight performance data we provide predictions for flight chains, which are prone to delays.  ...  Based on data collected from the US Bureau of Transportation Statistics, we analyze how flight delay risk propagates inside the aviation network.  ...  A comparative analysis of models for predicting delays in air transportation has been presented by [25] , comparing the performance of different approaches to predict delays in air traffic networks.  ... 
doi:10.1109/access.2020.3045340 fatcat:5nh2lfpf6jbmffdnqmyug27yuu

Misclassification in Big Data Soft Set Environment

Jyoti Arora, Kamaljit Kaur
2017 International Journal of Computer Applications  
The results shows that out of these classifiers, SVM classify 86% of the data correctly and only 14% of data has misclassification.  ...  Because sometimes cleaned data can also affect the prediction accuracy or other testing.  ...  Fig1: Machine Learning approaches in big data Machine Learning in Big Data Supervised Learning Support Vector Machine Random Forest Logistic Regressi on k- nearest neighbor Decision  ... 
doi:10.5120/ijca2017914298 fatcat:yduxkzpxznaltpqqmvbdbbjvyy

Ping-pong beam training for reciprocal channels with delay spread

Elisabeth de Carvalho, Jorgen Bach Andersen
2015 2015 49th Asilomar Conference on Signals, Systems and Computers  
We show that, for maximum target delay of the order of the pulse duration, the MC performance degrades slightly as the range delay increases.  ...  Massive Machine Type Communication characterized by low data-rates and low activity devices requires new physical layer solutions.  ...  Preliminary experiments demonstrate the efficacy of the novel approach in the contexts of energy resource forecasting and network delay prediction.  ... 
doi:10.1109/acssc.2015.7421451 dblp:conf/acssc/CarvalhoA15 fatcat:mqokuvnh3zg45licnfbgxyvxfu

Identification of Weather Influences on Flight Punctuality Using Machine Learning Approach

Sakdirat Kaewunruen, Jessada Sresakoolchai, Yue Xiang
2021 Climate  
The scope and emphasis of this study is placed on the machine learning application to practical flight punctuality prediction in relation to climate conditions.  ...  Machine learning has been developed in correlation with the weather and statistical data for operations at Birmingham Airport as a case study.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cli9080127 fatcat:vvwg727b5jdsjmv7sy7g5cc5q4

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

Augustin Degas, Mir Riyanul Islam, Christophe Hurter, Shaibal Barua, Hamidur Rahman, Minesh Poudel, Daniele Ruscio, Mobyen Uddin Ahmed, Shahina Begum, Md Aquif Rahman, Stefano Bonelli, Giulia Cartocci (+4 others)
2022 Applied Sciences  
into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030.  ...  Nowadays, computer science plays a major role in data management and decisions made in ATM.  ...  machine learning-based flight delay and can- Prediction, CTR, CTR Traffic Flying cellation predictions [348] Forecasting air passenger demand with a new hy-brid ensemble approach Prediction, Airspace,  ... 
doi:10.3390/app12031295 fatcat:bo6bkycd7fcz3putjfb4cyw5aa

Machine learning applications to smart city

Badri Narayan Mohapatra, Prangya Prava Panda
2019 ACCENTS Transactions on Image Processing and Computer Vision  
Based on these data generated by different smart devices and smart city applications machine learning approach is the best adaptive solution.  ...  The key contribution of this paper is a machine learning application survey towards smart city.  ...  Conflicts of interest The authors have no conflicts of interest to declare. Supervised Learning Regression Classification ACCENTS Transactions on Image Processing and Computer Vision, Vol 5 (14)  ... 
doi:10.19101/tipcv.2018.412004 fatcat:wzjgeufzujcmfp5a63kfkqz7cq

Emerging Technologies for Smart Cities' Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

Kenneth Li-Minn Ang, Jasmine Kah Phooi Seng, Ericmoore Ngharamike, Gerald K. Ijemaru
2022 ISPRS International Journal of Geo-Information  
The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative  ...  To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi11020085 fatcat:bjkv6cu7zbfqbl7q7ezfhai5ya

Survey of Flight Anomaly Detection Methods: Challenges and Opportunities

Vivian Rowoli Igenewari, Zakwan Skaf, Ian K. Jennions
2019 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
Safety enhancement is a major goal of the aviation industry owing to the predicted increase in air travel.  ...  This paper surveys current flight AD approaches, their strengths and limitations as well as brings to light the benefits of a hybrid AD method to extend previous work and find safety-critical events, particularly  ...  Her current research interests involve big data analytics, machine learning and anomaly detection techniques for aerospace-related datasets.  ... 
doi:10.36001/phmconf.2019.v11i1.898 fatcat:kjy6ldyr5zcvhoo5wx3y6tyowm

Front Matter: Volume 8745

Ivan Kadar
2013 Signal Processing, Sensor Fusion, and Target Recognition XXII  
SESSION 6 MULTISENSOR FUSION METHODOLOGIES AND APPLICATIONS V 8745 0S Sequential testing over multiple stages and performance analysis of data fusion [8745-30] G.  ...  Numbers in the index correspond to the last two digits of the six-digit CID Number.  ...  FA8750-12-C-0315 Advanced Machine Learning & Statistical Inference Approaches for Big Data Analytics and Information Fusion Proprietary & Confidential  ... 
doi:10.1117/12.2031900 fatcat:dwkpv5gaqfemlpqebcgmquu63i

AutoML to Date and Beyond: Challenges and Opportunities [article]

Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, ChengXiang Zhai, Kalyan Veeramachaneni
2021 arXiv   pre-print
As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities  ...  data, defining prediction problems, creating a suitable training data set, and selecting a promising machine learning technique.  ...  For instance, take the following prediction goal: For each airline, predict the maximum delay suffered by any of its flights.  ... 
arXiv:2010.10777v4 fatcat:arixmky6erdvhnmboe2sfgbb7a

Development of A Fully Data-Driven Artificial Intelligence and Deep Learning for URLLC Application in 6G Wireless Systems: A Survey [article]

Adeeb Salh, Lukman Audah, Qazwan Abdullah, Abdullah Noorsaliza, Nor Shahida Mohd Shah, Jameel Mukred, Shipun Hamzah
2021 arXiv   pre-print
Artificial Intelligence provides a new technique to design wireless networks by apply learning, predicting, and make decisions to manage the stream of big data training individuals, which provides more  ...  The full future of the sixth generation will develop a fully data-driven that provide terabit rate per second, and adopt an average of 1000+ massive number of connections per person in 10 years 2030 virtually  ...  By using data-driven ways through treating accessible data and increasingly learning, the ML AI Such as logic, ifthen rules, decision trees that enable the computer Machine Learning Such as statistical  ... 
arXiv:2108.10076v1 fatcat:b753qbfwjrdujca6spguxobaxq
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