A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

2018 ◽  
Vol 32 (2) ◽  
pp. 615-633 ◽  
Author(s):  
Xinyu Zhang ◽  
Yafei Zheng ◽  
Shouyang Wang
Keyword(s):  
Stochastic Frontier Analysis ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Stochastic Frontier ◽  
Air Travel ◽  
Frontier Analysis ◽  
Travel Demand Forecasting ◽  
Model Average ◽  
Forecasting Method ◽  
Air Travel Demand

Long-term air travel demand forecasting

2018 ◽  
pp. 124-142
Author(s):  
Yafei Zheng ◽  
Kin Keung Lai ◽  
Shouyang Wang
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Air Travel ◽  
Travel Demand Forecasting ◽  
Air Travel Demand

Methodological Framework for Air-Travel Demand Forecasting

1996 ◽  
Vol 122 (2) ◽  
pp. 96-104 ◽  
Author(s):  
Matthew G. Karlaftis ◽  
Konstantinos G. Zografos ◽  
Jason D. Papastavrou ◽  
John M. Charnes
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Air Travel ◽  
Methodological Framework ◽  
Travel Demand Forecasting ◽  
Air Travel Demand

Critical Review and Analysis of Air-Travel Demand

2010 ◽  
pp. 71-87
Author(s):  
Matthew G. Karlaftis
Keyword(s):  
Critical Review ◽  
Classification Scheme ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Critical Factor ◽  
Air Travel ◽  
Modeling Approach ◽  
Airline Networks ◽  
Air Travel Demand ◽  
Data Requirements

Demand forecasting may be the most critical factor in the development of airports and airline networks. This chapter reviews various approaches used to forecast air travel and airport demand forecasting. It classifies existing methods according to the modeling approach used to evaluate the available data; then, the forecasting approaches are viewed in relation to data requirements. Finally, a new matrix classification scheme is introduced that combines both the data available and the technique used to evaluate this data in a more concise and manner.

Air Passenger Demand Forecasting Using Particle Swarm Optimization and Firefly Algorithm

2019 ◽  
Vol 16 (9) ◽  
pp. 3735-3743
Author(s):  
Souad Larabi Marie-Sainte ◽  
Tanzila Saba ◽  
Sihaam Alotaibi
Keyword(s):  
Error Rate ◽  
Firefly Algorithm ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Air Travel ◽  
Swarm Optimization ◽  
Passenger Demand ◽  
Optimal Values ◽  
Air Travel Demand ◽  
Prediction Techniques

Air travel demand is a crucial part of planning for airlines and airports. It helps in elaborating decisions and recognizing risks and opportunities. Forecasting air passenger demand is an interesting research study that deserves investigation. This problem requires prediction techniques such that Linear Regression and Neural Network. These techniques are efficient, but they have several parameters that necessitate appropriate values to provide the least error rate of prediction. Some recent air travel demand studies investigated Genetic Algorithms to provide optimal values for these parameters. In this article, we propose to explore the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) to find the optimal values for Linear Regression (LR) coefficients. This study presents two new hybrid prediction techniques (PSO based LR and FA based LR) to handle airline demand forecasting, which researchers have not previously covered. The results of PSO based LR, FA-based LR and LR are compared to find the best model with the lowest prediction error rate. The results showed that PSO based LR achieved the best prediction results with a lower error rate compared to FA based LR and LR alone. This study is performed using the data of Los Angeles International airport.

Stochastic Frontier Analysis

2000 ◽  
Author(s):  
Subal C. Kumbhakar ◽  
C. A. Knox Lovell
Keyword(s):  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Frontier Analysis
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