A demand model for domestic air travel in Sweden

Kopsch, Fredrik

doi:10.1016/j.jairtraman.2011.11.006

Cited by 58 publications

(16 citation statements)

References 8 publications

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“…It could be that short-haul flying has started to displace automobility for example, a development that can hardly be interpreted as environmentally beneficial. There is some evidence to suggest that, at least for business travellers, some substitution from other modes into air travel is occurring (Kopsch, 2012).Perhaps cars are being used for more but shorter trips within urban areas, in situations where the eco-efficiency of the car is at its lowest. Alternatives to travel, such as online social networking and telework, may have substituted some driving trips, as may home delivery services from supermarkets (Lu and Peeta, 2009;Sivak and Schoettle, 2012).…”

Section: The Indications Of Post-automobilitymentioning

confidence: 99%

From ‘freedom of the open road’ to ‘cocooning’: Understanding resistance to change in personal private automobility

Wells

Xenias

2015

Environmental Innovation and Societal Transitions

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Section: The Indications Of Post-automobilitymentioning

confidence: 99%

From ‘freedom of the open road’ to ‘cocooning’: Understanding resistance to change in personal private automobility

Wells

Xenias

2015

Environmental Innovation and Societal Transitions

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“…This manual was originally developed in 1985 using traditional modelling techniques (Alekseev, Seixas 2009). Indeed, in the past, multiple linear regression (MLR) models have been generally used to forecast air traffic demand (see, for example, Abed et al 2001;Aderamo 2010;Ba-Fail et al 2000;Bhadra 2003;International Civil Aviation Organization 2006;Kopsch 2012;Sivrikaya, Tunç 2013).…”

Section: Traditional Air Travel Demand Forecasting Approachesmentioning

confidence: 99%

Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand Using a Genetic Algorithm Approach

et al. 2016

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This study has proposed and empirically tested for the first time Genetic Algorithm (GA) models for forecasting Australia’s domestic low cost carriers’ demand, as measured by enplaned passengers (GAPAXDE Model) and revenue passenger kilometres performed (GARPKSDE Model). Data was divided into training and testing data sets, 36 training data sets were used to estimate the weighting factors of the GA models and 6 data sets were used for testing the robustness of the GA models. The genetic algorithm parameters used in this study comprised population size (n): 1000, the generation number: 200, and mutation rate: 0.01. The modelling results have shown that both the linear GAPAXDE and GARPKSDE models are more accurate, reliable, and have a slightly greater predictive capability compared to the quadratic models. The overall mean absolute percentage error (MAPE) of the GAPAXDE and GAR-PKSDE models are 3.33 per cent and 4.48 per cent, respectively.

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“…This manual was originally developed in 1985 using traditional modelling techniques (Alekseev and Seixas 2009). Historically, multiple linear regression (MLR) models have generally been used to forecast airline passenger traffic demand (see, for example, Aderamo 2010;Ba-Fail et al 2000;Bhadra 2003;Kopsch 2012;Sivrikaya and Tunç 2013).…”

Section: Traditional Air Travel Demand Forecasting Approachesmentioning

confidence: 99%

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

Srisaeng

Baxter

Richardson³

et al. 2015

J.Aerosp. Technol. Manag.

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This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia's domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively.

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A demand model for domestic air travel in Sweden

Cited by 58 publications

References 8 publications

From ‘freedom of the open road’ to ‘cocooning’: Understanding resistance to change in personal private automobility

From ‘freedom of the open road’ to ‘cocooning’: Understanding resistance to change in personal private automobility

Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand Using a Genetic Algorithm Approach

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

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