air passenger demand: holiday effects 7; long-term forecasting for 8, 26; medium-term forecasting for 7–8, 25–26; seasonal adjustment 7; short-term forecasting for 6–7, 25

air transportation: industry 1–6; role in China 5

air travel demand: of airport 19–20; demographic factors 22, 23; determinants of 21–25, 27; economic factors in 21–23; emergencies 22, 24–25; forecasting methods 19–21, 26–27; forecasting problems 6–8; geographic factors 22, 23; government policy 22, 24; integrated forecasting framework 45–46, 46; market structure 22, 24; moving holiday effect 95–96; nationwide 20–21; of O-D (origin-destination) pairs 19; social factors 22, 23–24

Akaike Information Criteria (AIC) 96, 99

ARDL (Autoregressive Distributed Lag) bounds testing approach: cointegration relationship 127–128, 130–133; model specifications 134; see also long-term forecasting

artificial intelligence (AI): short-term demand forecasting 6, 7; techniques 51–53

artificial neural networks (ANN) 13; ANN/back-propagation NN (BPNN) techniques 51–53; ANN-based nonlinear forecasting module 43; genetic programming 56; support vector machines 54–55

Australia 134, 141; historical air passenger traffic and logistic curve fitting 135; logistic curve fitting 133; parameters of logistic curve fitting 136

autoregressive conditional heteroscedasticity (ARCH) model, econometrical model 50–51

Autoregressive Integrated Moving Average (ARIMA) 6, 11–12, 18, 83; econometrical linear forecasting module 41–43, 48–49

autoregressive moving average (ARMA) model, forecasting comparison 118–119, 120, 120

back-propagation neural network (BPNN) techniques: artificial intelligence 51–53; forecasting comparison 118–119, 120, 120; forecasting process with 52

bases and bases management module, TEI@I methodology 43–45

Bayesian model averaging (BMA) 111

Boeing Company 22

calendar: factor 101, 106; Gregorian 95, 107; holiday 96, 100; moving holiday effect 95–96, 102, 108

Canada: historical air passenger traffic and logistic curve fitting 135; logistic curve fitting 133; parameters of logistic curve fitting 136

China: air passenger number vs GDP per capital 4; air passenger traffic and growth rate 3; air passenger traffic and logistic curve fitting 135, 136; air transportation markets 2–6; gross domestic product (GDP) 116, 117; growth of air transportation and economy 4; oil price 116, 117; parameters of logistic curve fitting 136; role of air transportation in 5; SiChuan earthquake 86; trade value 116, 117; urban population 116, 117

Chinese New Year (CNY) 94–96, 102, 107–108

CiteSpace 8–9; evolution of demand forecasting research 73–78; scientometric analysis for demand forecasting 66, 69, 70; software 79

Civil Aviation Administration of China (CAAC) 5, 116, 124, 131

combination forecasting process, TEI@I methodology 57–59, 61–62

Consumer Confidence Index (CCI) 20

demand forecasting methods 10–18; actual passenger traffic vs estimated demand 120; air travel 18–25; electricity demand 11–12; long-term 15–18; medium-term 13–15; multivariate methods 17; multivariate time series models 14–15; production function approach 15; research background 110–111; scenario-based methods 16; short-term 11–13; simulation-based methods 16–17; stochastic frontier analysis (SFA) model 111, 112–115; tourism demand 12–13; univariate statistical filters 14; univariate methods 17–18; see also air travel demand

demand forecasting research: average citations per year 72, 73; clusters of cited references 75; co-citation clusters in time series 75–77; co-citation network of references 74; co-occurrence network of disciplines for 70; disciplines involved in 70, 71; evolution of 73–78; hybrid intelligent forecasting system 76–77; intellectual structure of 74–75; keywords and references with surging citation 77–78; landmark articles in 78, 79; main keywords in 71–72; most cited articles in literature 72, 72; yearly citations in, on Web of Science 67

econometrical models: ARIMA/SARIMA models 48–49; autoregressive conditional heteroscedasticity (ARCH)/generalized ARCH (GARCH) model 50–51; common categories 47; TEI@I methodology 47–51; vector autoregressive (VAR)/vector error correction (VEC) models 49–50

electricity demand forecasting 11–12

empirical mode decomposition (EMD) method, air travel demand 84–85, 85

error correction models (ECM) 13

European Commission 15, 26

expert knowledge and judgment: demand forecasting 59–61; methods of incorporating 59; modeling process 60

forecasting models: artificial intelligence techniques 51–56; combination methods 57–59, 61–62; econometrical models 47–51; expert knowledge and judgmental adjustment 59–61; linear combination approach 57–58; nonlinear combination approach 58–59; TEI@I methodology 46–61; see also demand forecasting methods

forecasting problems: air travel 6–8; long-term 8; medium-term 7–8; short-term 6–7

France: historical air passenger traffic and logistic curve fitting 135; logistic curve fitting 133; parameters of logistic curve fitting 136

frequentist model averaging (FMA) 111

Frisch, Ragnar 47

Garfield, Eugene 68

GDP (gross domestic product) growth 128–129; assumptions for 138; long-term forecasting 132–133; variable 131

generalized autoregressive conditional heteroscedasticity (GARCH) model, econometrical model 50–51

genetic algorithms (GA) 13, 56

genetic programming (GP), artificial intelligence 56

Germany 134, 141; historical air passenger traffic and logistic curve fitting 135; logistic curve fitting 133; parameters of logistic curve fitting 136

Google Trends 7, 9, 94, 144; Genhol vs 96–99, 99, 102, 103; nowcasting 104–108; proposed method with 102, 103

Harvey, Andrew 95

Hodrick-Prescott filter 7, 14

holidays: Chinese New Year (CNY) 94–96, 102, 107–108; Easter 99, 106, 107; moving holiday effect 95–96, 102, 108; Thanksgiving holiday 107

Holt-Winters forecasting methods 12, 21, 27, 125

Hong Kong International Airport (HKIA) 82–83, 85–86, 92, 144; historical air passenger traffic 98, 107–108; monthly passenger traffic of 86

hybrid intelligent forecasting system 76–77, 144

Improved Particle Swarm Optimization (IPSO) algorithm 17

integrated short-term forecasting framework: adoption of SARIMA models 84–85; data description and evaluation criteria 86–87; empirical analysis 85–91; empirical mode decomposition (EMD) method in 84–85, 89, 91; general framework for short-term air travel demand 85; model comparisons 89, 90, 91, 91; modeling the linear component 87–88, 89; modeling the nonlinear component 88–89; proposed framework 83–85; TEI@I methodology basis in 82–83, 91–92

Intergovernmental Panel on Climate Change 130

International Air Transport Association (IATA) 1–2, 125, 138

International Energy Agency 130

International Monetary Fund (IMF) 15, 26, 130, 138

Japan 134, 141; historical air passenger traffic and logistic curve fitting 135; logistic curve fitting 133; parameters of logistic curve fitting 136

Kalman-filter algorithm 15

Lagrange’s theorem 58

least squares support vector regression (LSSVR) 20, 54–55, 100, 101; demonstration of 55; nonlinear modeling 84–85, 88–89; seasonal decomposition (SD)_LSSVR model 101, 102, 103

long-term forecasting: air travel demand 8; ARDL bounds testing approach 132–133, 140–141; ARDL model specifications 134; cointegration relationship 126, 127–128, 130–133; economic globalization and urbanization 124–126; empirical analysis 130–138; empirical data 130, 131; fuzzy linear regression equations 18; general framework 126; irregular event effects 127, 129, 135–137; logistic growth curve fitting 133–134, 135, 136; long-term evolution pattern 126–127, 128–129; Markov-switching regime 129, 135–137; methods 15–18; multivariate methods 17; proposed framework for 126–130; scenario-based methods 16; scenario planning 130, 137–138, 139, 140; simulation-based methods 16–17; unit root testing 131–132, 133; univariate methods 17–18

MAPE (Mean Absolute Percentage Error) 87, 98; equation 87; forecasting performance 91, 99, 102, 103, 106, 119–120, 120, 131, 132, 141; forecasting vs nowcasting 106

Markov-switching (MS) regime approach 129, 135–137, 141

medium-term forecasting: actual passenger traffic vs estimated demand 120; air travel demand 7–8; methods 13–15; multivariate time series models 14–15; production function approach (PFA) 15; univariate statistical filters 14; see also stochastic frontier analysis (SFA) model

MIDAS (mixed data sampling) model 96; MATLAB toolbox 106; methodology of 104–105; nowcasting results 105–106, 107

model average (MA): comparison of stochastic frontier analysis (SFA) model and 118–121; appendices for calculations 147–150

Monte Carlo simulation, long-term forecasting 16–17

multivariate methods: long-term forecasting 17; time series models for medium-term forecasting 14–15

nationwide air travel demand: long-term forecasting for 8; medium-term forecasting for 7–8; short-term forecasting for 6–7

Natural Bureau of Statistics of China 3, 131

nonlinear autoregressive (NAR) model 53

nowcasting: forecasting vs 106; Google Trends 104–108; MIDAS (mixed data sampling) model 104–105; MIDAS with leads 105; results 105–106, 107

Organization for Economic Cooperation and Development (OECD) 15, 26

Price, Derek J. de Solla 68

production function approach (PFA), medium-term forecasting 15

Quantum-Behaved Particle Swarm Optimization (QPSO) algorithm 17

research see demand forecasting research

RMSE (Root Mean Squared Error) 87, 98, 102; equation 87; forecasting performance 91, 102, 103, 106, 119–120, 120

rule-based expert system model, TEI@I methodology 40–41

scenario-based methods, long-term forecasting 16

Science Citation Index 68

Science Citation Index Expanded (SCI-EXPANDED) 70

scientometric analysis 66–69; bibliographic records collection 69–70; co-citation clusters in time series 75–77; disciplines involved in demand forecasting 70, 71; keywords and references with surging citation 77–78; main keywords in demand forecasting 71–72; most cited articles in demand forecasting literature 72, 72; overview of bibliography 69–72, 73; see also demand forecasting research

Seasonal Autoregressive Integrated Moving Average (SARIMA) 6, 11, 13, 83, 95, 101; econometrical forecasting model 48–49; forecasting linear component 84, 87–88

seasonal decomposition (SD) forecasting: empirical analysis 101–103; Genhol vs Google Trends 96–99; hybrid forecasting method 99–103; moving holiday effect 95–96; overall forecasting process 101; proposed framework 100; X-13-ARIMA-SEATS method 96–97, 100, 101

seasonality 2, 7, 11–12, 47; air travel demand and 87, 94–96; definition 94; historical passenger traffic at HKIA 86; moving holiday effect 95–96, 107

short-term forecasting: air travel demand 6–7; electricity demand 11–12; methods 11–13; nowcasting 104–106, 107–108; seasonality and 94–96; tourism demand 12–13; see also integrated short-term forecasting framework

Shuffled Frog-Leaping (SFL) algorithm 17

SiChuan earthquake 86

simulation-based methods, long-term forecasting 16–17

Social Sciences Citation Index (SSCI) 70

STAMP method 95

statistical filters: Hodrick-Prescott filter 7, 14; univariate 14, 25

statistical learning theory (SLT), support vector machines (SVM) 54

stochastic frontier analysis (SFA) model: appendices for calculations 147–150; application to air travel demand forecasting 116–121; data for 116–118; demand estimation 115; demand forecasting and 111, 115; evaluation and comparison to model average (MA) 118–121; methodology 112–115; model average 112–114; model construction 115; proposed demand forecasting framework 115; raw data collection 114–115; summary of variables 116

support vector machines (SVM) 13

support vector regression (SVR) model 54–55

TEI@I methodology 8, 34–35, 61–62, 124, 143; ANN-based nonlinear forecasting module 43; ARIMA-based econometrical linear forecasting module 41–43; back-propagation neural network (BPNN) and forecasting process 43, 44; bases and bases management module 43–45; common forecasting models 46–61; general framework of 35–45, 36; integrated air travel demand forecasting framework 45–46; integrated short-term forecasting 82–83, 91–92; man-machine interface (MMI) module 36; rule-based expert system module 40–41; web-based text mining (WTM) module 37, 37–40; see also integrated short-term forecasting framework

tourism, short-term demand forecasting 12–13

TRAMO/SEATS method 7, 95–96, 107

United Nations 130

United States 134, 141; historical air passenger traffic and logistic curve fitting 135; logistic curve fitting 133; parameters of logistic curve fitting 136

univariate methods: long-term forecasting 17–18; medium-term forecasting 14; statistical filters 7, 14

US Department of Defense 130

vector autoregressive (VAR) models 13; econometrical model 49–50

vector error correction (VEC) model 127; econometrical model 49–50

web-based text mining (WTM) module: component of TEI@I methodology 37–40; feature extraction phase 37, 38; main processes of 37; structure analyzing phase 37, 38; text classification phase 37, 39; see also TEI@I methodology

Web of Science database 8, 66, 69–70; yearly citation in demand forecasting 67; yearly published articles about demand forecasting 67

Web of Science™ Core Collection 70, 79

World Bank 116, 131, 139

World War II 130