Understanding the factors affecting airport level demand (arrivals and departures) using a novel modeling approach

Tirtha, Sudipta Dey; Bhowmik, Tanmoy; Eluru, Naveen

doi:10.1016/j.jairtraman.2022.102320

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…The present analysis synthesizes the findings of Daldoul et al (2016), Gundelfinger-Casar and Coto-Millán (2017), Yan and Chai (2017), Huang et al (2013), Hanson et al (2022), Tirtha et al (2023) . Collectively, these studies identify a total of fourteen primary factors that exert influence on the air passenger transportation market.…”

Section: Influential Factor Selection and Variable Framework Developmentsupporting

confidence: 83%

Forecasting Post-Epidemic Air Passenger Flow Among Hub Cities in China Based on PLS and GA-SVR Model

Li,

Liu,

Tsai

2024

International Journal of Gaming and Computer-Mediated Simulations

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For stakeholders and companies involved in civil aviation to plan and make wise decisions, accurate estimates of air passenger flow are necessary. The conclusions drawn from this model serve as an invaluable resource for pertinent choices. The top hub cities in mainland China's air travel network are predicted using a variable weight combination model that combines PLS and GA-SVR. According to the test results, the model developed in this study improves prediction accuracy. This demonstrates how well detailed information about social and economic development can be gleaned from linear development patterns and nonlinear fluctuation rules. Predictions can be made with more accuracy and a better fit as a result. Over the next five years, it is predicted that over 300 million passengers will fly between the top hub cities in mainland China, an increase of 6.51% per year. The growth in passenger traffic varies significantly between different routes. The routes from Beijing to Shanghai and Shanghai to Shenzhen saw the most travellers, while the Beijing-Chengdu route saw the fastest growth in traveller numbers. The study's findings provide useful advice for civil aviation businesses and people involved in their decision-making, fostering growth in the sector during the post-pandemic period.

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Section: Influential Factor Selection and Variable Framework Developmentsupporting

confidence: 83%

Forecasting Post-Epidemic Air Passenger Flow Among Hub Cities in China Based on PLS and GA-SVR Model

Li,

Liu,

Tsai

2024

International Journal of Gaming and Computer-Mediated Simulations

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“…Multiple linear regression model (MLRM, hereafter is called MR) is a statistical estimation tool that provides explanation of a dependent variable with more than one independent variable. In the literature, there are many studies on the application of the MLRM method on aviation problems [ 34 ]. Thanks to the model, the parameters to be used in the estimation are determined and the effect of the independent variables on the dependent variable is found.…”

Section: Solution Methodologymentioning

confidence: 99%

Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

Ucar

Aygün

Tanyeri

2023

J Therm Anal Calorim

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As a kind of gas turbine engines, turbofan engines have powered a number of aero-vehicles in aviation sector. The necessity of turbofan with higher energy efficiency has been greatly drawn attention since these are operating dependent to fossil fuels. In this study, energy and emission metrics of fifty-one mixed flow turbofan engines (MFTE) with different bypass ratio, overall pressure ratio and fuel flow are modeled with multi-regression (MR) method. The obtained models are subjected to metaheuristic approaches involving genetic algorithm (GA) and simulated annealing (SA) so as to decrease error of the models. According to MR findings, rated thrust of MFTEs is estimated with 1.4877 of minimum square error (MSE) whereas GA and SA make it lower as 1.3404 and 1.2524, respectively. On the other hand, NO x emission index of MFTEs is predicted with relatively low coefficient of determination (R 2 ) as 0.8620. However, its accuracy is enhanced to 0.8633 (with GA) and 0.8655 (with SA). Finally, exergy efficiency of MFTEs is estimated the highest model correctness with GA. Namely, R 2 of the model is computed as 0.9280 with GA and 0.9277 with SA. Without applying these methods, its R 2 is obtained as 0.9263 with MR. When considering these outcomes, thanks to modeling and optimization methods, prediction of performance and emission indexes of mixed flow turbofan engines could be performed with lower error values. It is thought that the study helps in prediction of environmental effect regarding turbofan engines that are utilized at busy airports.

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“…The reader should note that the proposed discrete outcome model system can be employed to predict a continuous measure of delay by generating the estimate of y qk * based on model results. Thus, the proposed hybrid approach allows us to handle the presence of rounded delays (see Tirtha et al [ 29 ] for implementation details).…”

Section: Econometric Methodologymentioning

confidence: 99%

“…On the other hand, employing a continuous variable representation is not appropriate with rounded values. Thus, in our proposed research we employ a hybrid framework that ties the continuous delay measure to a categorical variable allowing us to estimate the model as a discrete outcome system with the inherent ability to predict as a continuous variable ( 29 – 31 ) (more details in the Econometric Methodology section).…”

Section: Contributions Of the Current Studymentioning

confidence: 99%

Flight-Level Analysis of Departure Delay and Arrival Delay Using Copula-Based Joint Framework

Tirtha

Bhowmik

Eluru

2022

Transportation Research Record: Journal of the Transportation R

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The main goal of the current study is to identify the factors affecting flight-level airline delay by jointly modeling departure and arrival delays. Toward this end, we develop a novel copula-based group generalized ordered logit (GGOL) model system that accommodates for the influence of common observed and unobserved effects on flight departure and arrival delays. The proposed model is estimated using 2019 marketing carrier on-time performance data compiled by the Bureau of Transportation Statistics (BTS) for 67 airports in the continental U.S. The delay data is augmented with a comprehensive set of independent variables including traffic conditions at the origin and destination airports in the hours preceding flight departure and arrival, trip-level attributes, weather variables for the entire flight duration, and spatial and temporal factors. The model estimation results highlight that the Joe copula model with parameterization provides the best data fit. The model performance is further established to be excellent using a holdout sample. Finally, to illustrate the applicability of the model for prediction and highlight the impact of independent variables, we perform a prediction exercise under a host of hypothetical scenarios. The illustration provides a mechanism for employing the proposed model as a tool for airline-carrier-level or airport-level delay prediction analysis using weather forecasts while controlling for a host of independent variables.

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Understanding the factors affecting airport level demand (arrivals and departures) using a novel modeling approach

Cited by 5 publications

References 29 publications

Forecasting Post-Epidemic Air Passenger Flow Among Hub Cities in China Based on PLS and GA-SVR Model

Forecasting Post-Epidemic Air Passenger Flow Among Hub Cities in China Based on PLS and GA-SVR Model

Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

Flight-Level Analysis of Departure Delay and Arrival Delay Using Copula-Based Joint Framework

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