Forecasting Airline Passenger Demand for the Long-haul Route: The

Case of Garuda Indonesia

Adha Mahmeru Bala Putra and Ratih Dyah Kusumastuti

Department of Management, Faculty of Economics and Business, Universitas Indonesia, Kampus UI Depok 16424, West

Java, Indonesia

Keywords: Forecasting, Airline Passenger, Aviation Industry, Long-haul Route.

Abstract: This paper discusses the forecasting of passenger demand for the long-haul route at Garuda Indonesia, which

is the legacy air carrier of Indonesia. We focus on routes with the largest share, namely China and Saudi

Arabia. We use two forecasting models for this purpose. First is a regression model with the population in

each country as the independent variable, and second is the Winter's model that is suitable for data with trend

and seasonality characteristics, such as airline passenger. The performance of both methods is analysed using

forecast errors, which are a mean squared error (MSE), mean absolute deviation (MAD), mean absolute

percentage error (MAPE) and Tracking Signal. The results show that Winter's model is more suitable for the

China route, while the regression model is more suitable for Saudi Arabia route. The forecasting results for

2019-2028 show a significant growth of passengers for both routes that must be anticipated by the company.

1 INTRODUCTION

The airline's industry normally deals with massive

risks, from the rise and fall in jet fuel price and

currency exchange rates, to enormous capital

expenditures, rivalry from low cost carrier and

instability in passenger demand (Pyke et al., 2018).

The industry is a significantly highly-regulated

industryto be based, it is vital for the decision maker

and policy planner to observe and assess the airline’s

performance by utilizing productivity analysis and

efficiency (Chen et al., 2018).

The aviation authority of every nation distributes

factual indicators each year, for example, cargo and

passenger turnover volume, which demand for

improvement of the nation's aviation industry (Xu et

al. 2019). Accurate forecasting of these indicators is

important for both airlines and airports, to oversee

and build their capability, increment passenger load

rate, decrease operation costs, enhance service

quality, reduce environmental impact affect and

enhance their competitive advantage (Xu et al.,

2019). Demand forecasting enables administrators to

make master plans on investment, management and

construction (Flyvbjerg et al., 2005). For this purpose,

selecting a forecasting model that is suitable for the

aviation business is a valuable task and crucial (Xie

et al., 2014).

Based on Airbus (2013) and Boeing (2013) data,

the aviation industry will experience growth of

passenger demand at the rate of almost 5% annually

for the next 20 years.

The airlines carriers adjust their capacity to deal

with the passenger growth, by either increasing the

aircraft size or frequency, which may prompt

distinctive quantities of aircraft movements, but the

movement of the aircraft, which is the number of

operated flights on one leg, affects the different parts

of the air transport system (Kolker 2016). For

instance, Kolker (2016) describes that one trip with a

wide-body aircraft may prompt less emission and less

immersion of airplane terminals and air space than

two flights with narrow body aircraft that carrying

together a similar number of passengers. For this

reason, Kolker (2016) also explains that forecasting

aircraft movements are essential for assessing future

developments and technologies of the air

transportation framework system.

Other researcher, such as Gelhausen (2018), also

explains that long-term planning of transportation

system requires to know future transport prerequisites

for various financial scenarios.

The objective of this study is to select a forecast

model that is suitable to predict the number of an

flights flying Garuda Indonesia airlines that can be

used as a reference for various strategic and

530

Putra, A. and Kusumastuti, R.

Forecasting Airline Passenger Demand for the Long-Haul Route: The Case of Garuda Indonesia.

DOI: 10.5220/0008433305300537

In Proceedings of the 2nd International Conference on Inclusive Business in the Changing World (ICIB 2019), pages 530-537

ISBN: 978-989-758-408-4

operational decision making, such as selecting the

number of aircraft for certain types of aircraft needed

in the future (or fleet planning). We focus on long-

haul routes with highest revenue share, namely,

China and Saudi Arabia. China is chosen because the

growth of Chinese travellers to Indonesia is the

largest in recent years (The Jakarta Post, 2018), while

Saudi Arabia is selected because the country is the

popular pilgrimage destination for Indonesian

Moslem residents (Susanty, 2017). According to the

data from The Ministry of Religious Affairs (MRA),

there is a 63.6% increase in pilgrimage to 818,000 in

2016 from just 500,000 in 2012 (Susanty, 2017).

It is expected that this study will provide better

understanding related to the implementation of

forecasting decisions related to the operation

planning that is very important in the aviation

industry. Specifically, this study gives insights on the

reality of airline passengers in Indonesia, especially

for the long haul. It can be used as a reference for

Garuda Indonesia to choose the right operational

decision to maximize its profitability and customer

service level.

The paper is structured as follows. A relevant

literature review is presented in Section 2, relevant

literature review in Section 3, findings and discussion

in Section 4, and conclusion and recommendation in

Section 5.

2 LITERATURE REVIEW

Forecasting is very important in demand

management, because forecasting provides an

estimation of future demand which is the basis of

many business decisions (Wiesner et al., 2019).

Forecasting methods are basically can be classified

into quantitative and qualitative methods (Wisner et

el., 2018; Heizer et al., 2017). Qualitative techniques

are used when the available data is very limited, or

even irrelevant, for this reason a qualitative technique

is needed to be based on intuition or judgment from

an expert in their field, while quantitative techniques

use mathematical methods that utilize historical data

and can also include a number of relevant variables

(Wiesner et al., 2019).

According to Heizer et al. (2017), the quantitative

approaches are basically can be classified into time-

series models (such as moving average and

exponential smoothing models, which forecast only

based on past data) and associative models (such as

regression model, that use changes in one or more

variables to predict the changes in dependent

variables). Concerning, the the time series models,

they usually have the following components: trend

variations (persistent upward and downward pattern),

cyclical variations (repeating up and down

movements that are more than one year and

influenced by external factors such as political or

macroeconomic factors), seasonal variations (regular

up and down fluctuations, such as monthly or yearly),

and random variations (erratic, unsystematic

fluctuations due to random variations or unforeseen

events) (Heizer et al., 2017).

Regarding airline passenger demand forecasting,

Carreira et al. (2017) forecast passenger demand of TAP

Portugal airline, the legacy airlines of Portugal, to

predict the demand in several cities in Brazil. They use

a regression model by looking at the relationship

between the passenger demand, the city’s population,

and whether there is a direct flight to the destination.

Kolker et al. (2016), on the other hand, implement the

forecast of aircraft movement (FoAM) method, which

basically divides each flight segment into a quantity of

passengers, distance and aircraft type category, and then

predicts the passenger growth rate each year as the input

parameter from the data obtained from airbus (2013).

The FoAM model is done using Java and the forecast

process is done automatically.

Hsu et al. (2011), on the other hand, conduct

forecasting on demand that is very fluctuating using

Grey topological and Markov-chain models carried

out on EVA air in Taiwan by considering several

different economic conditions.

Lastly, Xu et al. (2019) use a mixture of

autoregressive, integration, moving average,

seasonal autoregressive, seasonal integration, and

seasonal moving average (SARIMA) and support

vector regression (SVR). In their research, Xu et al.

(2019) include the white gaussian noise in the

forecast model and the proposed procedure are as

follows. First, time series data is used in SARIMA

models to get the parameters. Second, the SARIMA

results are obtained based on the parameters

specified. Third, the white noise gaussian is

recalculated based on the results of SARIMA.

Fourth, four variable combinations are combined to

be processed into a mixed model to predict statistical

indicators in the airline's industry. Finally, the results

of the forecast can be obtained.

As can be seen, there are different forecasting

models that are used to predict airline passenger

Forecasting Airline Passenger Demand for the Long-Haul Route: The Case of Garuda Indonesia

531

demand, however the models are a bit complex and

may not be practical to be used in the real world.

Therefore, in this study, we choose to compare two

forecasting models that are practical and can be used

to forecast Garuda Indonesia’s airline passengers.

3 RESEARCH METHOD

3.1 Research Stages and Data

Collection

The purpose of this study is to predict Garuda

Indonesia passenger demand for long-haute routes,

focusing on Saudi and Chinese routes, which will be

valuable for several strategic decisions, such as fleet

planning.

We use two forecasting models that are suitable for

predicting airline passenger demand, namely the

regression model by Carreira et al. (2017), and the

classic Winter’s model that takes into account trend and

seasonality aspects (Chopra and Meindl, 2016). In order

to determine the suitable forecast method, we use

compare passenger demand data and the forecast results

for the period of 2013-2018. Concerning Winter’s

model, we use MS Excel Solver to find the optimal

values of the smoothing constants. The suitability of the

models is then determined by analysing their

performances using the forecast errors (MAD, MSE,

MAPE, and tracking signals). Based on the results, we

then forecast the demand for 2019-2028 using the

forecast method with the least errors.

Data for this study are collected through internal

demand passenger data from the Market Research

Department in Garuda Indonesia. The data is obtained

from the Global Distribution system (GDS) for bi-

direction passenger traffic from all cities with the

origin of all cities in Indonesia and all cities in China

and Saudi Arabia, including transit passengers

specifically for Garuda Indonesia passengers. The

population data of China and Saudi Arabia are

retrieved from http://www.worldometers.info, which

is owned by DADAX, that is run independently by an

international team of volunteers, researchers and

developers (Worldometers, 2019).

3.2 Forecasting Models

As previously mentioned, the first model is adjusted

from Carreira et al. (2017), which formulate a

regression model using comparative demand data

and population data. The advantage of using this

method is to utilize the size effects of the Chinese and

Saudi Arabian populations on demand from Garuda

Indonesia. The regression equation is formulated as

the following:

ln D = a + b. ln P +

(1)

Then, the future demand equation is:

D = e

(a+ )

(2)

Where D is airline passenger demand, P is the

country’s population, a and b are regression

coefficients, and ɛ is the error term.

The second model that we use is Winter’s model.

Chopra and Meindl (2016) explain that Winter’s

model is suitable when the systematic factor of

demand has a trend, a level, and a seasonal factor.

The formulation is just like in Formulation (3).

Systematic Component of Demand =

(3)

(Trend + Level) x Seasonal Component

The forecast equation, on the other hand is

presented in Formulation (4), while the trend equation

is explained in Formulation (5), level equation is

presented in Formulation (6), and the seasonal factor

equation is described in Formulation (7).

t+1

= (L

t+1

and F

t+l

= (L

+ lT

t+l

(4)

Tt+1 = (Lt+1 – Lt) + (1- )Tt

(5)

Lt+1 = (Dt+1 / St+1) + (1- )(Lt + Tt)

(6)

St+p+1 = (Dt+1 / Lt+1) + (1- )St+1

(7)

Where:

forecast at time t

level at time t

trend at the time t

seasonal component at the time t

𝛼 𝛼 is the weight for the level

𝛽 weight for the trend

δ weight for the seasonal component

P seasonal period

l time step ahead to forecast

The value for the (the level’s smoothing constant)

is 0 < < 1. The value for the (trend’s smoothing

constant) is 0 < < 1, while the value for the (is (the

seasonal factor’s smoothing constant) is 0 < < 1).

Using this method, we need at least one-year actual

data to forecast future demand.

ICIB 2019 - The 2nd International Conference on Inclusive Business in the Changing World

532

3.3 Measures of Forecast Errors

In this study the forecast error analysis is carried out

by using several commonly used methods, which are

MSE, MAD, MAPE and Tracking Signal.

Mean Squared Error (MSE) is one of the forecast

error analysis methods that compare whether errors

generated by one forecast method are greater than

other forecast methods that are more accurate. MSE

is related to the of the forecast errors. MSE equation

is in Formulation (8), where Et is the error (the

difference between forecast and actual values) at

period t.

Mean Absolute Deviation (MAD) is used in

estimating the standard deviation from random

components when the assumed random component is

normally distributed. MAD is better at measuring errors

than MSE when the cost of forecast error in forecasting

technique is proportional to the number of errors. The

MAD equation is in Formulation (9), and is the sum of

the absolute deviation At (Formulation 10).

MAPE, on the other hand, is the average of the

absolute error in terms of percentage. MAPE is a

very good measure in measuring forecast error when

the calculated forecast has significant seasonality

and demand related between one period to another.

MAPE equation is in Formulation (11), where Et and

Dt are error and demand in period t respectively.

The last measure that we use is tracking signal

(TS), which is the ratio of bias to MAD. When TS in

a period is outside the ±6 value, it is a sign that the

forecast is biased. The tracking signal equation can be

seen in Formulation (12), and the bias value is

explained in Formulation (11).

4 FINDINGS & DISCUSSIONS

4.1 Adjusted Carreira et al. (2017)

Model

The results of applying the adjusted regression model

by Carreira et al. (2017) to forecast Garuda

Indonesia’s passenger demand to China and Saudi

Arabia can be seen in Tables 1 and 2.

It can be seen that for China route that the adjusted

R square value is 0.815, with the t-value of 4.2, and

the p-value is less than 0.005. For the Saudi Arabia

route, we can see that the adjusted R square value

which is 0.815, with t- value 4.20 and the p-value is

less than 0.005. Thus, we can say that the model can

be used to forecast the passenger demand to both

routes.

Table 1: Regression result for China.

R Square

0.81554618

Adjusted R

0.76943272

Square

Standard

0.16868676

Error

Coefficients

t Stat

P-value

Intercept

-741.017503

-4.1327289

0.01446176

X Variable 1

35.8061659

4.2054303

0.01363614

Table 2: Regression result for Saudi Arabia.

R Square

0.81544393

Adjusted R

0.76930491

Square

Standard

0.10966107

Error

Coefficients

t Stat

P-value

Intercept

-67.7377908

-3.5319395

0.02418827

X Variable 1

4.66742158

4.2040016

0.01365181

Applying the model, the comparisons between

actual and forecasted passenger demand can be seen

in Figures 1 and 2. The results show that the forecasts

look relatively China and Saudi Arabia routes, using

both forecasting models.

Forecasting Airline Passenger Demand for the Long-Haul Route: The Case of Garuda Indonesia

533

Figure 1: Actual and forecast (regression model) for China.

Figure 2: Actual and forecast (regression model) for Saudi

Arabia.

4.2 Winter’s Model

As previously mentioned, in applying Winter’s

model to forecast the passenger demand to China and

Saudi Arabia routes, we use optimal values of

smoothing constants generated by MS Excel Solver,

and the optimal values for the smoothing constants

can be seen in Tables 3 and 4.

Table 3: Smoothing constants for China’s route.

Table 4: Smoothing constants for Saudi Arabia route.

The smoothing constants values are then used in

Winter’s model formulations, and the actual and

forecast values for both routes are presented in

Figures 3 and 4.

The forecast results of Chinese routes (Figure 3)

look very close to the actual demand, even better

than the results of the to route the previous

regression model (Figure 1). However, this is

contrary to the forecast results of Saudi Arabia

(Figure 4). It can be seen that the differences

between actual and forecast in 2014 and 2016 are

significant, although the differences are not that

significant in the other years. Thus, it seems that the

forecast using adjusted Carreira et al. (2017) model

looks better for the Saudi Arabia route than Winter’s

model.

Figure 3: Actual and forecast (Winter’s model) for China.

Figure 4: Actual and forecast (Winter’s model) for Saudi

Arabia.

4.3 Forecast Errors

The forecast errors of both models on both routes are

calculated, and presented in Table 5.

For the Chinese route, the MAD, MSE, and

MAPE values of Winter's model look much smaller

than the regression model, ±while tracking signal for

ICIB 2019 - The 2nd International Conference on Inclusive Business in the Changing World

534

both models is still within 6, with tracking signal.

value of the regression model is closer to zero than

that of Winter’s model.

Contrary to the results for China route, the results

for Saudi Arabia route show that the MAD, MSE,

and MAPE values of the regression model are much

smaller than those of Winter's model, with tracking

the signal of regression model close to zero.

The difference in the results of China and Saudi

Arabia routes may be due to the fact that the Chinese

route has a seasonal pattern in each year compared to

the Saudi Arabia route, which seasonal pattern

cannot be well described because the Hajj season and

the Ramadan season move in advance approximately

10 days each year.

Table 5: Forecast errors.

CHINA ASSOCIATIVE REGRESSION MODEL

MAD

MSE

MAPE

(0.49)

44,801

2,954,183,230

10.6%

CHINA TIME SERIES WINTER'S MODEL

MAD

MSE

MAPE

(1.40)

21,378

744,040,486

6.0%

SAUDI ASSOCIATIVE REGRESSION MODEL

MAD

MSE

MAPE

(1.35)

9,276

136,243,278

1.8%

SAUDI ARABIA TIME SERIES WINTER'S

MODEL

MAD

MSE

MAPE

(4.87)

77,525

14,603,142,791

26.0%

4.4 Forecasting Future Demand

Based on the results explained in the previous

subsections, we forecast demand for Garuda's airline

passengers for the next 10 years (2019-2028).

Demand for China route is predicted using the

Winter's model forecast method (see Figure 5), while

the demand for Saudi Arabia route is carried out by

the regression model (see Figure 6). The line in

Figures 5 and 6 indicate the growth rate of airline

passengers for both routes.

Figure 5: Forecast (Winter’s model) for China.

The forecast results for the China route for the

next 10 years show an annual average growth of

almost 9 percent with a predicted 750.000 passengers

in 2019 and reach more than 1.6 million passengers

in 2028, while for Saudi Arabia the route shows the

average growth of 6 percent each year with a

prediction of about 550.000 passengers in 2019 and

reaching more than 900.000 in 2028. The growth for

the China and Saudi Arabia routes shows a higher

number than Airbus (2013) and Boeing (2013)

predictions, while they expect only almost 5 percent

growth in passengers for Next 20 years.

Figure 6: Forecast (Regression model) for Saudi Arabia.

To address the results of this forecast, it is highly

recommended that Garuda Indonesia increase its seat

capacity for flights to China and Saudi Arabia. This

can by increasing the frequency of flights to China

and Saudi Arabia, as well as opening routes directly

from secondary cities in Indonesia to China or Saudi

Arabia, and vice versa. The increase in the number of

seats also needs to be supported by good fleet

planning, which is the planning of the number of

aircraft that can meet the demands for both routes,

which must also be balanced with the company's

financial capability.

Forecasting Airline Passenger Demand for the Long-Haul Route: The Case of Garuda Indonesia

535

5 CONCLUSIONS

The paper presents the forecasting analysis of airline

passenger demand for the long-haul routes at Garuda

Indonesia, the legacy airline of Indonesia. We

compare two forecasting methods, which are suitable

for the purpose, namely an adjusted regression model

from Carreira et al. (2017) and Winter’s model with

optimal smoothing constants.

The results show that the regression model is

more suitable for Saudi Arabia route, while Winter’s

model is more suitable for China route as indicated

by the forecast error values.

The results may be due to the fact that China

demand shows a seasonal pattern in each year, while

for Saudi Arabia route, the passenger demand is

mainly depending on the Hajj season and Ramadan

season which are moving forward in approximately

10 days every year.

Forecast results for 2019-2028 show that the

demand for Saudi Arabia and China grow at the rate

of 6% and 9% and reach 900.000 and 1.6 million

passengers in 2028 respectively. The results imply

that Garuda Indonesia must anticipate the growth by

increasing the seating capacity by increasing the

flight frequency and/or using larger aircraft.

This study has limitations. Even though demand

data from Garuda Indonesia is monthly data,

however, the population data in annual data, thus,

forecasting is conducted using annual data, that may

not accurately reflect the actual situation.

Future research may include using the resulting

forecast for strategic fleet planning, to maximize the

profitability of the airline.

ACKNOWLEDGEMENTS

We would like to thank Garuda Indonesia, especially

the Market Research Department for their support in

this study.

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