- Email: [email protected]
Comparative analysis of government forecasts for the Lisbon Airport
Journal of Air Transport Management 16 (2010) 213–217
Contents lists available at ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Note
Comparative analysis of government forecasts for the Lisbon Airport Anto´nio Samagaio a, *, Mark Wolters b a b
Department of Management, ISEG/School of Economics and Management, Technical University of Lisbon, Rua Miguel Lupi 20, 1249-078 Lisbon, Portugal Economics Faculty, New University of Lisbon, Campus de Campolide, 1099-032 Lisbon, Portugal
a b s t r a c t Keywords: Forecasting Airline passenger forecasts Government forecasts Planning infrastructure capacity
The study examines the official forecasts for airline passenger numbers for the Lisbon metropolitan area. Auto-regressive and exponential smoothing models are used to develop independent forecasts for passenger numbers. The forecasts show that the government forecasts are at the top end of estimates and should be considered overly optimistic. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction There are plans to construct of a new airport for Portugal’s capital, Lisbon and with this has come considerable debate over the passenger and aircraft forecasts commissioned by NAER – Novo Aeroporto, SA, which concluded that traffic growth would exceed the capacity of the existing airport. The long-term forecasts for passenger numbers are a key variable in infrastructure projects related to the airport, as well as investments by civil aviation companies (Biederman, 1993). Flyvbjerg et al. (2005), however, showed that forecasts used to approve 210 transportation related infrastructure projects in 14 countries were more often than not done very poorly. The managers responsible for the projects frequently took an overly positive attitude, giving decision makers an undue feeling of urgency. Thus, the main objective of this study is to find a forecasting model that provides more realistic air passengers forecasts for the period 2008–2020. The decision to construct a new airport is based partially on the data attained from econometric modeling developed by Parsons – FCG (2002). It requires that the errors are not correlated between themselves if there is a normal distribution with an average of zero and constant variance. Also, it is necessary to consider the model’s exogenous variable, the per capita GDP of 18 western countries, that represent an increasing risk factor in the quality of the forecasts of endogenous effects. There are questions, for example over the range of countries considered given that in 2005/2006 traffic from African and Latin America countries represented 15% of Lisbon’s passengers. Finally, the observed trend in the historical data is key in the forecast of passenger numbers but this was estimated at a times of strong growth (Grubb and
* Corresponding author. E-mail address: [email protected] (A. Samagaio). 0969-6997/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2009.09.002
Mason, 2001) and alternative trends may be more appropriate (Njegovan, 2005). 2. Passenger numbers Historical data for the Lisbon Airport were collected together by the Instituto Nacional de Aviaça˜o Civil (INAC) and ANA – Aeroportos de Portugal and includes embarking/disembarking passengers but does not take into account transit traffic. Seasonal peaks characterize passenger numbers in the spring and summer (Adrangi et al., 2001) and to deal with this monthly data was collected for August 1995 to December 2008 (Fig. 1). Although this appear to only provide a relatively small sample of 161 observations other studies utilized similar numbers, Box and Jenkins (1970), for example, use 12 years and Nam et al. (1997) nine. The figure shows the seasonality in demand and the general tendency for sustained growth; characteristics identical to those found by Box and Jenkins and Nam et al. Over the period we find that the annual variance intervals are is not constant like those found by of Grubb and Mason; the ratio between the maximum and minimum values between 1996 and 2008 decreases over the period from 1.97 to 1.66. This difference in seasonality amplitude suggests that the variance depends on the level in which the series is found. Consequently, to transform the series and stabilize the variance we use a generic Box-Cox transformation having verified that the parameter l ¼ 0.6 is the most appropriate for stabilizing the seasonal variance1. 3. Forecasting models and results There is no consensus on the ‘‘best’’ forecasting model. In the opinion of Huth and Eriksen (1987) causal models present some
1
Parameter value was chosen by minimizing MSE.
214
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217
1,500,000 1,300,000 1,100,000 900,000 700,000 500,000
Feb-08
Aug-08
Feb-07
Aug-07
Feb-06
Aug-06
Feb-05
Aug-05
Feb-04
Aug-04
Feb-03
Aug-03
Feb-02
Aug-02
Feb-01
Aug-01
Feb-00
Aug-00
Feb-99
Aug-99
Feb-98
Aug-98
Feb-97
Aug-97
Feb-96
Aug-96
Aug-95
300,000
Fig. 1. Monthly air passengers. Source: INAC and ANA.
advantages over the non-causal in the forecasting of air traffic because they are not sensitive to political variables that can alter the relationship between the variables in the future – i.e. the past does not tell us what the future will hold. Accordingly, non-causal models have been shown to have better results in the short-term. Njegovan disagrees arguing that the econometric models used have had problems in their forecasts of the sector when there is a need to insert new trend data. The trend component is a vital factor in longrun forecast projections, because aeronautical infrastructure projects use forecasts for a period of 10–15 years. Since we are only able to obtain Portuguese monthly data from mid-1995, we use the non-causal models to forecast for156 months, i.e. monthly data from August 1995 to December 2007, leaving out 2008 to use as a quality test. As noted by Makridakis et al. (1998), although factors do not maintain a constant relationship, in some aspects the past does repeat itself in the future and with this in mind, we are able to include a trend. Huth and Eriksen (1987) proposed a decomposed time series model with the trend being part of a deterministic factor of the series; randomness thus resided in the error and seasonality. In the first phase we used a Holt-Winters model given the characteristics (trend and seasonality) for the series. Seasonality is given a lag of 12 and is additive, therefore by using a Box-Cox transformation we obtain the seasonal factor that does not change with the level of the series. The parameters that minimize the forecasting errors are: a ¼ 0.33, b ¼ 0.01 and g ¼ 0.45. With a basis in these estimates it was possible to determine the historical values of the trend, level, and seasonality and utilize them in linear
forecasts of passenger numbers for the period 2008–2020 (Fig. 2). The forecasts are considerably above the Parsons –FCG (2002) method; 23.4 million passengers in 2020, compared to 20.2 using the Parsons – FCG method. Additionally, the passenger series is not white noise. In this context the ARIMA models could be adequate for forecasting. To allow for this, we use difference to establish series averages, or fluctuations around a constant (Fig. 3). We can identify in the figure that a SARIMA (0,1,1) (0,1,1)12 would be an adequate model; it is the same a assumption Box and Jenkins applied to US air passenger data. Using maximum likelihood estimation, the equation with the estimated coefficients is;
V12 VYt ¼ 3t 0:6150833t1 0:9136953t12 þ 0:5619983t13 (1) The coefficients are both statistically significant at the 1% level. On the other hand the residual results of the application of the model show evidence of a series of white noise. Which is consistent with the Ljung-Box Q Statistic in which the first 24 coefficients of autocorrelation are considered; the observed value being 15.61. At a significance level of 5% the hypotheses of autocorrelation coefficients of the residuals is different from zero is not rejected. In short the model adjusts itself to the data that gives the forecasts seen in Fig. 4. In the short-run, forecasts produced by the ARIMA model appear acceptable, but over the medium-term the series starts to assume larger values; yielding a forecast of 24.8 million passengers
2,500,000 2,300,000 2,100,000 1,900,000 1,700,000 1,500,000 1,300,000 1,100,000 900,000
Fig. 2. Forecasts using the Holt-Winters method.
Jul-20
Jan-20
Jul-19
Jan-19
Jul-18
Jan-18
Jul-17
Jan-17
Jul-16
Jan-16
Jul-15
Jan-15
Jul-14
Jan-14
Jul-13
Jan-13
Jul-12
Jan-12
Jul-11
Jan-11
Jul-10
Jan-10
Jul-09
Jan-09
Jul-08
500,000
Jan-08
700,000
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217
215
a
1000. 800. 600. 400. 200. 0. -200. -400. -600. -800. -1000.
20
40
60
80
1.00
100
.60
.40
.40
.20
.20
.00
.00
-.20
-.20
-.40
-.40
-.60
-.60
-.80
-.80 0
5
10
15
20
25
30
35
c
.80
.60
-1.00
140
1.00
b
.80
120
-1.00
40
0
5
10
15
20
25
30
35
40
Fig. 3. Data after taking out the seasonal difference and an order 1 difference: (a) Air passenger series; (b) Autocorrelation function; (c) Partial autocorrelation function.
in 2020 representing an annual increase of 4.8%. In accord with Gardner and Mckenzie (1985, 1989), the models present problems when long-term forecasts are needed. Thus, the ARIMA model is an adequate model for forecasts involving short-term time series with short memory (Parzen, 1982).
Looking at Boeing’s forecast for the next 20 years (cited by Robertson, 2007), the average annual growth for Europe will be 5% annually, additionally in a study by Eurocontrol (2005), the forecasts for Portugal for the 2006–2025 is between 3% and 4% annually. The historical data gives us 6.8% growth over the period,
2,750,000
2,250,000
1,750,000
1,250,000
Fig. 4. Forecasts for the ARIMA model.
Jul-20
Jul-19
Jan-20
Jul-18
Jan-19
Jul-17
Jan-18
Jul-16
Jan-17
Jul-15
Jan-16
Jul-14
Jan-15
Jul-13
Jan-14
Jul-12
Jan-13
Jul-11
Jan-12
Jul-10
Jan-11
Jul-09
Jan-10
Jul-08
Jan-09
Jan-08
750,000
216
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217 2,000,000
a
1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
2,500,000
Jul-20
Jul-19
Jan-20
Jul-18
Jan-19
Jul-17
Jan-18
Jul-16
Jan-17
Jul-15
Jan-16
Jan-15
Jul-14
Jul-13
Jan-14
Jul-12
Jan-13
Jul-11
Jan-12
Jul-10
Jan-11
Jan-10
Jul-09
Jul-08
Jan-09
Jan-08
800,000
b
2,000,000
1,500,000
1,000,000
Jan-20
Jan-19
Jan-18
Jan-17
Jan-16
Jan-15
Jan-14
Jan-13
Jan-12
Jan-11
Jan-10
Jan-09
Jan-08
500,000
Fig. 5. Forecasts considering a smoothing of the trend: (a) Gardner and Mckenzie Model; (b) Grubb and Mason model.
which would seem to be too high. The Parsons – FCG study considered a growth rate of 4.01% for 2001–2020. In Gardner and Mckenzie’s opinion method based on a linear trend typically over predict in the long-term. In the case of exponential smoothing, the last observations are those that assume more influence in the estimation of following periods. If we analyze the trend component we verify that after a few ‘‘dark periods’’ for the industry and economy there is an overall positive trend. Thus, in long-term forecasts the Holt-Winters method may not be adequate for predicting possible future slowing in tends of passenger numbers. In this context it becomes important to consider a factor that smoothes the development of the trend. Gardner and Mckenzie add a smoothing factor that was later adapted by Grubb and Mason testing in the UK. In accord with Gardner and Mckenzie, when the verified trend components in the final observations are strong, then it is necessary to consider a 4 very close to one to smooth the growth trend. The choice of 4, as well as the number of observations included in the calculation of the trend, should minimize the forecasting errors. Taking into account the observations from 2008 we define 4 as equal 0.99 giving the forecasts we see in Fig. 5. The forecasts from the first model are 18.7 million passengers (a 2.7% growth), which is at the lower limit of Eurocontrol’s estimates, while from the second model the forecast increased to 22.4 million passengers (4.1% growth). To test for exactness we utilize 2008 data to which model gives the best forecast. Using root mean squared percentage error we
verify that Gardner and Mckenzie’s model minimized the root mean square percentage error. The resultant forecast for Lisbon for 2020 is 18.7 million passengers; below the 20.2 million passenger predicted in the Parsons – FCG study.
4. Conclusions This paper forecast Lisbon Airport’s passenger numbers for 2008–2020 to determine the needs of a new airport using a number of non-causal methods. The model proposed by Gardner and Mckenzie offered the best results in terms of estimation performance and forecasted 18.7 million passengers in 2020. This is a lower number than Parsons – FCG’s econometric study’s, 20.2 million; the growth forecasts lie on the upper limit of analysts’ forecasts. These results fit with prior studies that have showed forecasts used to determine the validity of transportation infrastructure projects are optimistic.
Acknowledgments The first author gratefully acknowledges financial support from the Fundaça˜o para a Cieˆncia e a Tecnologia, Portugal (SFRH/BD/ 35850/2007).
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217
References Adrangi, B., Chatrath, A., Raffiee, K., 2001. The demand for US air transport service: a chaos and nonlinearity investigation. Transportation Research E 37, 337–353. Biederman, P.S., 1993. The role of forecasting at trans world airlines. The Journal of Business Forecasting Methods & Systems 12, 3–4. Box, G.E.P., Jenkins, G.E.M., 1970. Time Series: Forecasting and Control. W.H. Freeman and Co, New York. Eurocontrol, 2005. Long-term Forecast: Flight Movements 2006–2025. www. eurocontrol.int. Flyvbjerg, B., Holm, M.K.S., Buhl, S., 2005. How (in)accurate are demand forecasts in public works projects? The case of transportation. Journal of the American Planning Association 71, 131–146. Gardner, E., Mckenzie, E., 1985. Forecasting trends in time series. Management Science 31, 1237–1246. Gardner, E., Mckenzie, E., 1989. Seasonal exponential smoothing with damped trends. Management Science 35, 372–376.
217
Grubb, H., Mason, A., 2001. Long lead-time forecasting of UK air passengers by Holt-Winters methods with damped trend. International Journal of Forecasting 17, 71–82. Huth, W., Eriksen, S.E., 1987. Airline traffic forecasting using deterministic and stochastic time series decomposition. Logistics and Transportation Review 23, 401–409. Makridakis, S., Wheelwright, S.C., Hyndman, R.J., 1998. Forecasting – Methods and Applications, third ed. John Wiley & Sons, Inc. Nam, K., Yi, J., Prybutok, V.R., 1997. Predicting airline passenger volume. The Journal of Business Forecasting Methods & Systems 16, 14–16. Njegovan, N., 2005. A leading indicator approach to predicting short-term shifts in demand for business travel by air to and from the UK. Journal of Forecasting 24, 421–432. Parsons – FCG, 2002. Previso˜es e ana´lise da procura. www.naer.pt. Parzen, E., 1982. ARARMA models for time series analysis and forecasting. Journal of Forecasting 1, 67–82. Robertson, D., 2007. Boeing Warns of Limits to Airline Growth. www.timesonline.co.uk.
Contents lists available at ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Note
Comparative analysis of government forecasts for the Lisbon Airport Anto´nio Samagaio a, *, Mark Wolters b a b
Department of Management, ISEG/School of Economics and Management, Technical University of Lisbon, Rua Miguel Lupi 20, 1249-078 Lisbon, Portugal Economics Faculty, New University of Lisbon, Campus de Campolide, 1099-032 Lisbon, Portugal
a b s t r a c t Keywords: Forecasting Airline passenger forecasts Government forecasts Planning infrastructure capacity
The study examines the official forecasts for airline passenger numbers for the Lisbon metropolitan area. Auto-regressive and exponential smoothing models are used to develop independent forecasts for passenger numbers. The forecasts show that the government forecasts are at the top end of estimates and should be considered overly optimistic. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction There are plans to construct of a new airport for Portugal’s capital, Lisbon and with this has come considerable debate over the passenger and aircraft forecasts commissioned by NAER – Novo Aeroporto, SA, which concluded that traffic growth would exceed the capacity of the existing airport. The long-term forecasts for passenger numbers are a key variable in infrastructure projects related to the airport, as well as investments by civil aviation companies (Biederman, 1993). Flyvbjerg et al. (2005), however, showed that forecasts used to approve 210 transportation related infrastructure projects in 14 countries were more often than not done very poorly. The managers responsible for the projects frequently took an overly positive attitude, giving decision makers an undue feeling of urgency. Thus, the main objective of this study is to find a forecasting model that provides more realistic air passengers forecasts for the period 2008–2020. The decision to construct a new airport is based partially on the data attained from econometric modeling developed by Parsons – FCG (2002). It requires that the errors are not correlated between themselves if there is a normal distribution with an average of zero and constant variance. Also, it is necessary to consider the model’s exogenous variable, the per capita GDP of 18 western countries, that represent an increasing risk factor in the quality of the forecasts of endogenous effects. There are questions, for example over the range of countries considered given that in 2005/2006 traffic from African and Latin America countries represented 15% of Lisbon’s passengers. Finally, the observed trend in the historical data is key in the forecast of passenger numbers but this was estimated at a times of strong growth (Grubb and
* Corresponding author. E-mail address: [email protected] (A. Samagaio). 0969-6997/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2009.09.002
Mason, 2001) and alternative trends may be more appropriate (Njegovan, 2005). 2. Passenger numbers Historical data for the Lisbon Airport were collected together by the Instituto Nacional de Aviaça˜o Civil (INAC) and ANA – Aeroportos de Portugal and includes embarking/disembarking passengers but does not take into account transit traffic. Seasonal peaks characterize passenger numbers in the spring and summer (Adrangi et al., 2001) and to deal with this monthly data was collected for August 1995 to December 2008 (Fig. 1). Although this appear to only provide a relatively small sample of 161 observations other studies utilized similar numbers, Box and Jenkins (1970), for example, use 12 years and Nam et al. (1997) nine. The figure shows the seasonality in demand and the general tendency for sustained growth; characteristics identical to those found by Box and Jenkins and Nam et al. Over the period we find that the annual variance intervals are is not constant like those found by of Grubb and Mason; the ratio between the maximum and minimum values between 1996 and 2008 decreases over the period from 1.97 to 1.66. This difference in seasonality amplitude suggests that the variance depends on the level in which the series is found. Consequently, to transform the series and stabilize the variance we use a generic Box-Cox transformation having verified that the parameter l ¼ 0.6 is the most appropriate for stabilizing the seasonal variance1. 3. Forecasting models and results There is no consensus on the ‘‘best’’ forecasting model. In the opinion of Huth and Eriksen (1987) causal models present some
1
Parameter value was chosen by minimizing MSE.
214
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217
1,500,000 1,300,000 1,100,000 900,000 700,000 500,000
Feb-08
Aug-08
Feb-07
Aug-07
Feb-06
Aug-06
Feb-05
Aug-05
Feb-04
Aug-04
Feb-03
Aug-03
Feb-02
Aug-02
Feb-01
Aug-01
Feb-00
Aug-00
Feb-99
Aug-99
Feb-98
Aug-98
Feb-97
Aug-97
Feb-96
Aug-96
Aug-95
300,000
Fig. 1. Monthly air passengers. Source: INAC and ANA.
advantages over the non-causal in the forecasting of air traffic because they are not sensitive to political variables that can alter the relationship between the variables in the future – i.e. the past does not tell us what the future will hold. Accordingly, non-causal models have been shown to have better results in the short-term. Njegovan disagrees arguing that the econometric models used have had problems in their forecasts of the sector when there is a need to insert new trend data. The trend component is a vital factor in longrun forecast projections, because aeronautical infrastructure projects use forecasts for a period of 10–15 years. Since we are only able to obtain Portuguese monthly data from mid-1995, we use the non-causal models to forecast for156 months, i.e. monthly data from August 1995 to December 2007, leaving out 2008 to use as a quality test. As noted by Makridakis et al. (1998), although factors do not maintain a constant relationship, in some aspects the past does repeat itself in the future and with this in mind, we are able to include a trend. Huth and Eriksen (1987) proposed a decomposed time series model with the trend being part of a deterministic factor of the series; randomness thus resided in the error and seasonality. In the first phase we used a Holt-Winters model given the characteristics (trend and seasonality) for the series. Seasonality is given a lag of 12 and is additive, therefore by using a Box-Cox transformation we obtain the seasonal factor that does not change with the level of the series. The parameters that minimize the forecasting errors are: a ¼ 0.33, b ¼ 0.01 and g ¼ 0.45. With a basis in these estimates it was possible to determine the historical values of the trend, level, and seasonality and utilize them in linear
forecasts of passenger numbers for the period 2008–2020 (Fig. 2). The forecasts are considerably above the Parsons –FCG (2002) method; 23.4 million passengers in 2020, compared to 20.2 using the Parsons – FCG method. Additionally, the passenger series is not white noise. In this context the ARIMA models could be adequate for forecasting. To allow for this, we use difference to establish series averages, or fluctuations around a constant (Fig. 3). We can identify in the figure that a SARIMA (0,1,1) (0,1,1)12 would be an adequate model; it is the same a assumption Box and Jenkins applied to US air passenger data. Using maximum likelihood estimation, the equation with the estimated coefficients is;
V12 VYt ¼ 3t 0:6150833t1 0:9136953t12 þ 0:5619983t13 (1) The coefficients are both statistically significant at the 1% level. On the other hand the residual results of the application of the model show evidence of a series of white noise. Which is consistent with the Ljung-Box Q Statistic in which the first 24 coefficients of autocorrelation are considered; the observed value being 15.61. At a significance level of 5% the hypotheses of autocorrelation coefficients of the residuals is different from zero is not rejected. In short the model adjusts itself to the data that gives the forecasts seen in Fig. 4. In the short-run, forecasts produced by the ARIMA model appear acceptable, but over the medium-term the series starts to assume larger values; yielding a forecast of 24.8 million passengers
2,500,000 2,300,000 2,100,000 1,900,000 1,700,000 1,500,000 1,300,000 1,100,000 900,000
Fig. 2. Forecasts using the Holt-Winters method.
Jul-20
Jan-20
Jul-19
Jan-19
Jul-18
Jan-18
Jul-17
Jan-17
Jul-16
Jan-16
Jul-15
Jan-15
Jul-14
Jan-14
Jul-13
Jan-13
Jul-12
Jan-12
Jul-11
Jan-11
Jul-10
Jan-10
Jul-09
Jan-09
Jul-08
500,000
Jan-08
700,000
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217
215
a
1000. 800. 600. 400. 200. 0. -200. -400. -600. -800. -1000.
20
40
60
80
1.00
100
.60
.40
.40
.20
.20
.00
.00
-.20
-.20
-.40
-.40
-.60
-.60
-.80
-.80 0
5
10
15
20
25
30
35
c
.80
.60
-1.00
140
1.00
b
.80
120
-1.00
40
0
5
10
15
20
25
30
35
40
Fig. 3. Data after taking out the seasonal difference and an order 1 difference: (a) Air passenger series; (b) Autocorrelation function; (c) Partial autocorrelation function.
in 2020 representing an annual increase of 4.8%. In accord with Gardner and Mckenzie (1985, 1989), the models present problems when long-term forecasts are needed. Thus, the ARIMA model is an adequate model for forecasts involving short-term time series with short memory (Parzen, 1982).
Looking at Boeing’s forecast for the next 20 years (cited by Robertson, 2007), the average annual growth for Europe will be 5% annually, additionally in a study by Eurocontrol (2005), the forecasts for Portugal for the 2006–2025 is between 3% and 4% annually. The historical data gives us 6.8% growth over the period,
2,750,000
2,250,000
1,750,000
1,250,000
Fig. 4. Forecasts for the ARIMA model.
Jul-20
Jul-19
Jan-20
Jul-18
Jan-19
Jul-17
Jan-18
Jul-16
Jan-17
Jul-15
Jan-16
Jul-14
Jan-15
Jul-13
Jan-14
Jul-12
Jan-13
Jul-11
Jan-12
Jul-10
Jan-11
Jul-09
Jan-10
Jul-08
Jan-09
Jan-08
750,000
216
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217 2,000,000
a
1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
2,500,000
Jul-20
Jul-19
Jan-20
Jul-18
Jan-19
Jul-17
Jan-18
Jul-16
Jan-17
Jul-15
Jan-16
Jan-15
Jul-14
Jul-13
Jan-14
Jul-12
Jan-13
Jul-11
Jan-12
Jul-10
Jan-11
Jan-10
Jul-09
Jul-08
Jan-09
Jan-08
800,000
b
2,000,000
1,500,000
1,000,000
Jan-20
Jan-19
Jan-18
Jan-17
Jan-16
Jan-15
Jan-14
Jan-13
Jan-12
Jan-11
Jan-10
Jan-09
Jan-08
500,000
Fig. 5. Forecasts considering a smoothing of the trend: (a) Gardner and Mckenzie Model; (b) Grubb and Mason model.
which would seem to be too high. The Parsons – FCG study considered a growth rate of 4.01% for 2001–2020. In Gardner and Mckenzie’s opinion method based on a linear trend typically over predict in the long-term. In the case of exponential smoothing, the last observations are those that assume more influence in the estimation of following periods. If we analyze the trend component we verify that after a few ‘‘dark periods’’ for the industry and economy there is an overall positive trend. Thus, in long-term forecasts the Holt-Winters method may not be adequate for predicting possible future slowing in tends of passenger numbers. In this context it becomes important to consider a factor that smoothes the development of the trend. Gardner and Mckenzie add a smoothing factor that was later adapted by Grubb and Mason testing in the UK. In accord with Gardner and Mckenzie, when the verified trend components in the final observations are strong, then it is necessary to consider a 4 very close to one to smooth the growth trend. The choice of 4, as well as the number of observations included in the calculation of the trend, should minimize the forecasting errors. Taking into account the observations from 2008 we define 4 as equal 0.99 giving the forecasts we see in Fig. 5. The forecasts from the first model are 18.7 million passengers (a 2.7% growth), which is at the lower limit of Eurocontrol’s estimates, while from the second model the forecast increased to 22.4 million passengers (4.1% growth). To test for exactness we utilize 2008 data to which model gives the best forecast. Using root mean squared percentage error we
verify that Gardner and Mckenzie’s model minimized the root mean square percentage error. The resultant forecast for Lisbon for 2020 is 18.7 million passengers; below the 20.2 million passenger predicted in the Parsons – FCG study.
4. Conclusions This paper forecast Lisbon Airport’s passenger numbers for 2008–2020 to determine the needs of a new airport using a number of non-causal methods. The model proposed by Gardner and Mckenzie offered the best results in terms of estimation performance and forecasted 18.7 million passengers in 2020. This is a lower number than Parsons – FCG’s econometric study’s, 20.2 million; the growth forecasts lie on the upper limit of analysts’ forecasts. These results fit with prior studies that have showed forecasts used to determine the validity of transportation infrastructure projects are optimistic.
Acknowledgments The first author gratefully acknowledges financial support from the Fundaça˜o para a Cieˆncia e a Tecnologia, Portugal (SFRH/BD/ 35850/2007).
A. Samagaio, M. Wolters / Journal of Air Transport Management 16 (2010) 213–217
References Adrangi, B., Chatrath, A., Raffiee, K., 2001. The demand for US air transport service: a chaos and nonlinearity investigation. Transportation Research E 37, 337–353. Biederman, P.S., 1993. The role of forecasting at trans world airlines. The Journal of Business Forecasting Methods & Systems 12, 3–4. Box, G.E.P., Jenkins, G.E.M., 1970. Time Series: Forecasting and Control. W.H. Freeman and Co, New York. Eurocontrol, 2005. Long-term Forecast: Flight Movements 2006–2025. www. eurocontrol.int. Flyvbjerg, B., Holm, M.K.S., Buhl, S., 2005. How (in)accurate are demand forecasts in public works projects? The case of transportation. Journal of the American Planning Association 71, 131–146. Gardner, E., Mckenzie, E., 1985. Forecasting trends in time series. Management Science 31, 1237–1246. Gardner, E., Mckenzie, E., 1989. Seasonal exponential smoothing with damped trends. Management Science 35, 372–376.
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