Machine Learning Prediction of Airport Delays in the US Air Transportation Network

2018 ◽  
Author(s):  
Keshav Ram Chandramouleeswaran ◽  
David Krzemien ◽  
Kevin Burns ◽  
Huy T. Tran
Keyword(s):  
Machine Learning ◽  
Transportation Network ◽  
Air Transportation ◽  
The Us

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

2010 ◽  
Vol 20 (03) ◽  
pp. 795-804 ◽  
Author(s):  
L. DA F. COSTA ◽  
F. A. RODRIGUES ◽  
P. R. VILLAS BOAS
Keyword(s):  
Complex Networks ◽  
Transportation Network ◽  
Major Change ◽  
Air Transportation ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Maximum Likelihood Classification ◽  
Scale Free ◽  
The Us ◽  
Transportation Routes

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modifications in their intrinsic organization. For instance, the network of transportation routes between cities and towns (hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways (paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation (in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Possibility of Using in Russia the Experience of LED Lighting Application at the USA Airfields

2019 ◽  
pp. 71-77
Author(s):  
Vladimir V. Vorozhikhin ◽  
Eugenia L. Moreva ◽  
Vladimir G. Starovoytov ◽  
Igor G. Tyutyunnik
Keyword(s):  
Air Transportation ◽  
Improve Quality ◽  
Led Lighting ◽  
The Us ◽  
The Usa ◽  
Quality And Reliability ◽  
Lighting Application

The purpose of this paper is an investigation of LEDs illumination experience at US-based aerodromes with an assessment of its feasibility and its necessity in Russia. The following methods were used: the analysis of aerodrome lighting requirements; the review and the analysis of development features in aerodrome LEDs illumination; the experience analysis of LEDs illumination of US-based aerodromes; the deductive analysis and the assessment synthesis of feasibility and necessity of US experience in LEDs illumination at Russian-based aerodromes. The following results were achieved: – The analysis of issues and opportunities was conducted for development of LEDs illumination at US-based aerodromes and of American experts’ recommendations for its use; – The cases were taken for use and assessment of development in LEDs illumination at US-based aerodromes; – The review and the analysis were conducted in relation to a developing market of LEDs illumination at Russian-based aerodromes. The main conclusion is that the US experience will improve quality and reliability of service provided in air transportation, comfort, and safety of Russian flights, as well as competitiveness of Russian- based airports and airlines (indirectly).

Predicting the price of Vietnamese shrimp products exported to the US market using machine learning

2021 ◽  
Author(s):  
Nguyen Minh Khiem ◽  
Yuki Takahashi ◽  
Khuu Thi Phuong Dong ◽  
Hiroki Yasuma ◽  
Nobuo Kimura
Keyword(s):  
Machine Learning ◽  
The Us
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