The Airline Passenger Load Factor Prediction with Machine Learning - Linear Regression Method and Modeling with Fractional Calculus and Deep Assessment Methodology

The Airline Passenger Load Factor Prediction with Machine Learning - Linear Regression Method and Modeling with Fractional Calculus and Deep Assessment Methodology

Author: Kevser Simsek

Publisher:

Published: 2022

Total Pages: 0

ISBN-13:

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Forecasting is used in air transportation to make short-term judgments and longer-term decisions to figure out how demand patterns will vary over time. In our paper, the passenger load factor (PLF) is forecasted using a variety of models so that a preliminary structure for both the economy and business classes can be obtained. To solve this problem, when each flight is assumed to have its own characteristics, a method that can reflect both the flight profile and the flight time dimension is needed. As a result, we used the least-squares method to create a continuous curve that is valid for any time interval by using the least-squares method with the 2015 daily reservation data of the 19-market areas of Turkish Airlines. Making predictions with machine learning was aimed at solving this large-scale problem in a computationally efficient way. Forecasting PLF is done by using information from two years of reservations, group sales data, calendar information, weekly dates, trend differences between the current year and the previous year, load factor information from the same period of the previous year and panel data to allow individual heterogeneity control. The findings of the fractional model when coefficient N is equal to 15 are approximately 2.4 times better than the DAM model and superior to regression analysis. With this study, it is expected to maximize revenue, modify capacity, increase flight route efficiency, optimize air traffic operation, and make projections for specific flying days in terms of revenue management.


Discrete Choice Modelling and Air Travel Demand

Discrete Choice Modelling and Air Travel Demand

Author: Professor Laurie A Garrow

Publisher: Ashgate Publishing, Ltd.

Published: 2012-10-01

Total Pages: 327

ISBN-13: 1409486338

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In recent years, airline practitioners and academics have started to explore new ways to model airline passenger demand using discrete choice methods. This book provides an introduction to discrete choice models and uses extensive examples to illustrate how these models have been used in the airline industry. These examples span network planning, revenue management, and pricing applications. Numerous examples of fundamental logit modeling concepts are covered in the text, including probability calculations, value of time calculations, elasticity calculations, nested and non-nested likelihood ratio tests, etc. The core chapters of the book are written at a level appropriate for airline practitioners and graduate students with operations research or travel demand modeling backgrounds. Given the majority of discrete choice modeling advancements in transportation evolved from urban travel demand studies, the introduction first orients readers from different backgrounds by highlighting major distinctions between aviation and urban travel demand studies. This is followed by an in-depth treatment of two of the most common discrete choice models, namely the multinomial and nested logit models. More advanced discrete choice models are covered, including mixed logit models and generalized extreme value models that belong to the generalized nested logit class and/or the network generalized extreme value class. An emphasis is placed on highlighting open research questions associated with these models that will be of particular interest to operations research students. Practical modeling issues related to data and estimation software are also addressed, and an extensive modeling exercise focused on the interpretation and application of statistical tests used to guide the selection of a preferred model specification is included; the modeling exercise uses itinerary choice data from a major airline. The text concludes with a discussion of on-going customer modeling research in aviation. Discrete Choice Modelling and Air Travel Demand is enriched by a comprehensive set of technical appendices that will be of particular interest to advanced students of discrete choice modeling theory. The appendices also include detailed proofs of the multinomial and nested logit models and derivations of measures used to represent competition among alternatives, namely correlation, direct-elasticities, and cross-elasticities.


Aimms Optimization Modeling

Aimms Optimization Modeling

Author: Johannes Bisschop

Publisher: Lulu.com

Published: 2006

Total Pages: 318

ISBN-13: 1847539122

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The AIMMS Optimization Modeling book provides not only an introduction to modeling but also a suite of worked examples. It is aimed at users who are new to modeling and those who have limited modeling experience. Both the basic concepts of optimization modeling and more advanced modeling techniques are discussed. The Optimization Modeling book is AIMMS version independent.


Airport Systems

Airport Systems

Author: Richard De Neufville

Publisher:

Published: 2003

Total Pages: 883

ISBN-13: 9781601199812

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"This is a premier text by leading technical professionals, known worldwide for their expertise in the planning, design, and management of airports"--Provided by publisher.


Emerging Technologies for Smart Cities

Emerging Technologies for Smart Cities

Author: Prabin K. Bora

Publisher: Springer Nature

Published: 2021-06-11

Total Pages: 209

ISBN-13: 9811615500

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This book comprises the select proceedings of the International Conference on Emerging Global Trends in Engineering and Technology (EGTET 2020), held in Guwahati, India. The chapters in this book focus on the latest cleaner, greener, and efficient technologies being developed for the implementation of smart cities across the world. The broader topical sections include Smart Buildings, Infrastructures and Disaster Management; Smart Governance; Technologies for Smart Cities, and Wireless Connectivity for Smart Cities. This book will cater to students, researchers, industry professionals, and policy making bodies interested and involved in the planning and implementation of smart city projects.


Sensitivity Analysis for Neural Networks

Sensitivity Analysis for Neural Networks

Author: Daniel S. Yeung

Publisher: Springer Science & Business Media

Published: 2009-11-09

Total Pages: 89

ISBN-13: 3642025323

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Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.


Deep Learning for Time Series Forecasting

Deep Learning for Time Series Forecasting

Author: Jason Brownlee

Publisher: Machine Learning Mastery

Published: 2018-08-30

Total Pages: 572

ISBN-13:

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Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.


An Introduction to Mathematical Modeling

An Introduction to Mathematical Modeling

Author: Edward A. Bender

Publisher: Courier Corporation

Published: 2012-05-23

Total Pages: 273

ISBN-13: 0486137120

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Employing a practical, "learn by doing" approach, this first-rate text fosters the development of the skills beyond the pure mathematics needed to set up and manipulate mathematical models. The author draws on a diversity of fields — including science, engineering, and operations research — to provide over 100 reality-based examples. Students learn from the examples by applying mathematical methods to formulate, analyze, and criticize models. Extensive documentation, consisting of over 150 references, supplements the models, encouraging further research on models of particular interest. The lively and accessible text requires only minimal scientific background. Designed for senior college or beginning graduate-level students, it assumes only elementary calculus and basic probability theory for the first part, and ordinary differential equations and continuous probability for the second section. All problems require students to study and create models, encouraging their active participation rather than a mechanical approach. Beyond the classroom, this volume will prove interesting and rewarding to anyone concerned with the development of mathematical models or the application of modeling to problem solving in a wide array of applications.


Linear Models with R

Linear Models with R

Author: Julian J. Faraway

Publisher: CRC Press

Published: 2016-04-19

Total Pages: 284

ISBN-13: 1439887349

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A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models