A Fuzzy Decision Tree Model for Airport Terminal Departure Passenger Traffic Forecasting

Cheng, Shaowu; Mu, Qiu; Zhang, Haibo; Zhang, Yaping

doi:10.1061/9780784413623.002

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…Air passenger traffic flow modelling and analysis have been studied in the past for many different regional markets (Blinova, 2007; Carson et al, 2011; Grubb & Mason, 2001). The modelling process employs classical time series approach (Grubb & Mason, 2001), econometric modelling (Fildes et al, 2011; Nicholas, 2021), neural networks (Tsai et al, 2005), decision tree (Cheng et al, 2014), or a combination of several modelling methods (Bao et al, 2012). Blinova (2007) demonstrates the applicability of neural networks for air passenger traffic flows' forecasting in Russia.…”

Section: Related Workmentioning

confidence: 99%

“…The accurate forecasting of air passenger traffic is challenging because the passengers' flow data possess a considerable amount of nonlinearity, nonstationarity, and seasonality (Olmedo, 2016). Recently, some methods for analysis of air passenger traffic have been proposed (Blinova, 2007; Cheng et al, 2014; Phyoe et al, 2016; Tsui et al, 2014). These methods employ either of these three types of models: the econometric model, time series model, and artificial neural network (ANN) model.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting air passenger travel: A case study of Norwegian aviation industry

Anupam,

Lawal

2023

Journal of Forecasting

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Accurate forecasting of airline passenger traffic is important for facilitating the effective management and planning of aviation resources. In this study, we explore the air passenger traffic in the Norwegian aviation industry by collecting the passenger flow data and the corresponding measurements of the weather conditions affecting the flow from the different airports in Norway. We then proposed nonlinear autoregressive with exogenous input (NARX) forecasting models to predict air passenger traffic in advance. The NARX models account for the nonlinearity and nonstationarity in the passenger flow and allow the accurate forecasting of air passenger traffic. We perform experiments to demonstrate the effectiveness of two variants of the NARX model and compare their performances against long short‐term memory (LSTM), a deep learning method. We show that the proposed NARX model achieves the best prediction accuracy compared to LSTM, which is considered as a state‐of‐the‐art approach for fitting sequential data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting air passenger travel: A case study of Norwegian aviation industry

Anupam,

Lawal

2023

Journal of Forecasting

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“…This is necessary to create a strategy for the airports operation with a satisfactory level of passenger service quality and the organization of operational activities of transport means. Similar questions and approaches to their solution have already been covered in the works of contemporaries [430][431][432][433]. The authors of the paper [433] proposed an approach to determine the airport capacity and passenger flow fluctuations using the air passenger index introduced by them.…”

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confidence: 91%

Improvement of Scientific Approaches to the Development of Engineering

Vladlenov¹,

Babyak²,

Gladilin³

et al. 2022

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All rights reserved. Printed in the United States of America. No part of this publication may be reproduced, distributed, or transmitted, in any form or by any means, or stored in a data base or retrieval system, without the prior written permission of the publisher. The content and reliability of the articles are the responsibility of the authors. When using and borrowing materials reference to the publication is required. Collection of scientific articles published is the scientific and practical publication, which contains scientific articles of students, graduate students, Candidates and Doctors of Sciences, research workers and practitioners from Europe and Ukraine. The articles contain the study, reflecting the processes and changes in the structure of modern science.

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“…This is necessary to create a strategy for the airports operation with a satisfactory level of passenger service quality and the organization of operational activities for transport. Similar questions and approaches to their solution have already been covered in the works of contemporaries [1][2][3][4]. The authors of the paper [4] proposed an approach to determining airport capacity and passenger flow fluctuations using the air passenger index introduced by them.…”

Section: Introductionmentioning

confidence: 97%

Study of the Passenger Traffic Parameters in Air Transport

Dolia,

Dolia

2023

Zb. nauk. pracʹ Odes. derž. akad. teh. regul. Âkostì

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The article is an overview of the issue of calculating. The study mainly used the publications of scientists in the chosen field of research, published in recent years, which provides the research with modern scientific achievements in this area. All the publications used are recognized by the scientific community and included in scientometric databases, including Scopus. To obtain the results, the method of system analysis is applied to the methods and approaches used by the authors in solving the problems of calculating the parameters of passenger flows, determining the factors of their formation and modeling such flows. The article formulates the approaches and methods outlined by the authors and reveals certain advantages of the solutions proposed by the authors. The results of the analysis of the development of scientific thought on the issues under consideration are the formation of generalized conclusions regarding the methods of modeling certain transport processes. It is proved that modern scientists pay considerable attention to the modeling of passenger flows in time using mathematical modeling, in particular. Popular methods include neural network methods, computer modeling, and methods for solving problems inherent in engineering networks. As a result, a model was obtained for calculating such parameters as: payback period of the project for the purchase of transport for the route, net and net discounted profit flows, costs of maintaining personnel for route maintenance, passenger correspondence matrix, passenger traffic on the route between network nodes, transferability parameters, and many others. The obtained results make it possible to make systemic decisions on the feasibility of certain actions in the route system and provide probable options for the operation of the route system in its management

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A Fuzzy Decision Tree Model for Airport Terminal Departure Passenger Traffic Forecasting

Cited by 5 publications

References 7 publications

Forecasting air passenger travel: A case study of Norwegian aviation industry

Forecasting air passenger travel: A case study of Norwegian aviation industry

Improvement of Scientific Approaches to the Development of Engineering

Study of the Passenger Traffic Parameters in Air Transport

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