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by Yafei Zheng, Kin Keung Lai, Shouyang Wang
Forecasting Air Travel Demand
Cover
Title
Copyright
Contents
List of figures
List of tables
Acknowledgments
1 Introduction
2 Existing research
3 Theoretical basis – TEI@I methodology
4 A scientometric analysis of demand forecasting (1975–2015): a visual description
5 An integrated short-term forecasting framework with empirical mode decomposition method
6 A novel seasonal decomposition-based short-term forecasting framework with Google Trends data
7 A medium-term demand forecasting method based on stochastic frontier analysis and model average
8 Long-term air travel demand forecasting: an integrated method with ARDL bounds testing approach and scenario planning
9 Conclusions and future research
Appendices
Index
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List of tables
Figures
1.1
Historical air passenger traffic and growth rate since 1978
1.2
The relationship between air passenger number and GDP per capita in China
1.3
The growth of air transportation and economy in China
2.1
Determinants of air travel demand
3.1
A general framework based on TEI@I methodology
3.2
The main processes of a WTM module
3.3
The syntax of an individual pattern
3.4
The syntax of a combination pattern
3.5
The structure of BPNN and the process of BPNN-based forecasting
3.6
The integrated air travel demand forecasting framework
3.7
The forecasting process with a BPNN forecasting model
3.8
A simple demonstration of a LSSVR model
3.9
The tree of the expression of
x
×
x
+
y
/2
3.10
The basic methods of incorporating expert knowledge
3.11
The modeling process of combining expert knowledge
4.1
Yearly published articles about demand forecasting on the Web of Science
4.2
Yearly citations in demand forecasting research on the Web of Science
4.3
The co-occurrence network of disciplines for demand forecasting
4.4
Keywords co-occurrence for demand forecasting research
4.5
The co-citation network of references
4.6
Timeline version of co-citation network for demand forecasting research
4.7
Time zone version of keywords co-occurrence in demand forecasting research
5.1
General forecasting framework for short-term air travel demand
5.2
The monthly passenger traffic of HKIA
5.3
The original series (blue) and the adjusted series (grey) with corresponding outlier effects
5.4
The IMFs and residue for the nonlinear component
5.5
Forecasts of various methods
6.1
Historical air passenger traffic in HKIA
6.2
The proposed SD-based forecasting framework
6.3
Performance comparison of different methods in terms of MAPE
6.4
Performance comparison of different methods in terms of RMSE
7.1
Our proposed SFA-based demand forecasting framework
7.2
Historical trends of explanatory variables
7.3
The actual passenger traffic and estimated demand series
8.1
General framework for the long-term demand forecasting method
8.2
Estimated coefficients of various estimation windows
8.3
Historical air passenger traffic (black circle) and logistic curve fitting (grey dashed line)
8.4
The logistic curve fitting of the Chinese air travel market
8.5
Time path of smoothed probabilities of both regimes
8.6
Long-term air travel demand forecasts in various scenarios
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