Mobile Broadband Traffic Forecasts in Korea

Chanwoo Cho and Sungjoo Lee

Department of Industrial and Information Systems Engineering, Ajou University,

San5, Woncheon-dong, Yeongtong-gu, Suwon, Korea

Keywords: Mobile Traffic, Forecast, Smart-Phone, Smart-TV, PC, Patterns of Use.

Abstract: During many years, the dominant traffic in mobile broadband networks was voice. However, with the

introduction of diverse mobile broadband equipment, the situation has changed. Since mobile broadband

devices can allow users to access information instant and connect to web quickly, the mobile world has been

revolutionized, where global mobile data traffic has been increasing dramatically. And the changes in the

patterns of usage for mobile devices have started to cause traffic jams on the mobile broadband networks.

As a result, forecasting the future traffic needs is in urgent need to provide high-quality mobile broadband

services. To meet this need, this research aims to suggest a new forecasting method for future mobile

broadband traffic. For the purpose, three-round Delphi survey was conducted to identify devices and

applications that would affect in the future mobile broadband traffic, and their expected growth rates of

users and changes in the patterns of use for each device. Then the total amount of mobile broadband traffic

was forecasted based on survey results. The research results are expected to provide the basic research data

for a further study.

1 INTRODUCTION

Recently, as various mobile broadband equipments –

smart-phones, laptop PCs, tablet PCs, smart TVs and

more– have been widely diffused, global mobile

broadband traffic has been rapidly increasing (Cisco,

2011), and network traffic jams have resulted. Thus,

to maintain the service quality of mobile

communications, accurate forecasting results for

future mobile broadband traffic are required. When

the dominant traffic of mobile broadband networks

was voice, the number of subscribers of mobile

communication services affected patterns of traffic.

Therefore, forecasting results for mobile service

subscribers using diffusion models such as Bass

model (Bass, 1969), Logistic model (Mansfield,

1968) can be utilized as an alternative way for

mobile broadband traffic forecasting (Chu et al.,

2009). However, since new mobile broadband

services–web browsing, video streaming, online

game and more –which can be served by various

equipments have emerged and service usage patterns

have been diversified, it is hard to derive accurate

forecasting results using existing forecasting

methods. The need to reflect these changes to

forecasting has arisen. Therefore, this research aims

to suggest a new traffic forecasting method targeted

at the Korean mobile broadband market, considering

the number of users and patterns of use for mobile

broadband equipment.

The suggested forecasting method is based on a

three-round Delphi survey, and relevant actual data

was used to complement the survey result. The first

Delphi survey was conducted to identify mobile

broadband equipment and applications which are

expected to affect mobile broadband traffic in the

future. The second and third Delphi survey were

conducted to acquire further information on the

expected increase in of users and usage of

applications for each kind of equipment. Mobile

broadband traffic in the future was forecasted using

various demand forecasting methods that are

suitable for the characteristics of each kind of

equipment. The research results are expected to

provide the basic data for futher studies.

The remainder of this paper is organized as

follows. Section 2 reviews existing studies on the

demand diffusion model and Delphi survey. The

overall research framework and detailed processes are

presented in Section 3. Section 4 presents mobile

broadband traffic forecasting results. Finally, Section

5 discusses the summary, contributions, limitations,

and suggestions for further study of this research.

Cho C. and Lee S..

Mobile Broadband Trafﬁc Forecasts in Korea.

DOI: 10.5220/0004066200410045

In Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems (DCNET-2012),

pages 41-45

ISBN: 978-989-8565-23-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 LITERATURE REVIEW

2.1 Demand Diffusion Models

The purpose of a diffusion model is to exhibit

aspects of innovation spreading among adopters in

the specific groups (Mahajan and Muller, 1979).

Since Bass (1969) suggested the concept, diffusion

models have been widely used for demand

forecasting. Diffusion models usually reflect internal

influence and external influence to forecasting. The

logistic model (Mansfield, 1968) and Gompertz

model (Steel, 1977) reflect internal influences by

imitations such as word-of-mouth to forecasting.

The bass model (Bass, 1969) reflects internal and

external influences (e.g. advertising effect)

simultaneously. Diffusion of mobile broadband

equipment is affected by internal and external

influences. Therefore, for the purpose of this study,

various demand diffusion forecasting models

considering mobile equipment’ characteristics were

used in forecasting.

2.2 Delphi Survey

A Delphi survey is developed as a systematic,

interactive forecasting method which relies on a

panel of experts. It usually consists of several rounds

to build a consensus on unclear things through

feedback processes. Thus, it has been used to derive

backup data in emerging industries which do not

have enough data for quantitative forecasting

(Bengisu and Nekhili, 2006; Gerdsri, 2003), or to

forecast the future of industries that need experts’

opinion to support quantitative forecasting results

such as the tourism industry (Yong et al., 1989) and

aerospace industry (English and Kernan, 1976).

Because mobile broadband equipment such as laptop

PCs, tablet PCs, and smart TVs have a lack of

relevant data, it is hard to apply existing forecasting

methods to them to predict demand or traffic.

Furthermore, since they are affected by external

factors (e.g. relevant policy) more easily than other

kinds of equipment, this fact needs to be considered.

Thus, this study utilized a Delphi survey to derive

backup data for equipment types which have a lack

of actual data.

3 RESEARCH FRAMEWORK

3.1 Overall Research Processes

The overall research processes of this study are as

follows (See figure 1.). First of all, the first Delphi

survey was conducted to identify devices (e.g. smart-

phones, tablet PCs, smart TVs) and applications (e.g.

voice, video streaming) that would affect the future

traffic needs. Then for the target devices, the

second- and third-round Delphi were carried out to

gather further information. Finally, based on the

results of the second- and third- Delphi survey, the

total amount of mobile broadband traffic in the

future was derived by summing up expected traffic

for each target device.

Figure 1: Overall research processes.

3.2 The First-round Delphi Survey

The first Delphi survey investigated the opinion of

18 experts in mobile communication industry. We

posed open questions to identify devices and

applications of each device. As a result, five mobile

broadband devices and three primary applications

for each device were identified (See Table 1.).

Table 1: Identified devices and applications.

Devices Applications

Feature phone Voice, Online game, VoIP

Smart phone

Online game, Web browsing, Video

streaming

Laptop PC

Web browsing, Video streaming,

Download

Tablet PC

Web browsing, Video streaming,

Download

Smart TV

Online game, Web browsing, Video

streaming

DCNET2012-InternationalConferenceonDataCommunicationNetworking

3.3 The Second- and Third- Delphi

Surveys

The second- and third- Delphi survey was conducted

to gather further information about identified

devices and applications in the first Delphi survey,

and 47 experts were involved to both. The second

Delphi survey was an attempt to determine –

information about the expected increase in rate of

users for each device and their patterns of use in

terms of “the type of applications frequently used in

the devices”. In the case of smart TV, due to its

relatively static usage pattern, the expected increase

in the rate of applications reflected the expected

increase in the rate of the number of smart TVs. The

third Delphi survey sought to build a consensus by

asking each participant identical questions, and the

survey results were used to forecast the total amount

of mobile broadband traffic in the future.

3.4 Mobile Broadband Traffic Forecast

The future mobile broadband traffic was forecasted

using the expected increase in the rate of users for

each device and the expected increased rate of usage

for each application. Traffic increases effects by an

increase of users and application usage in this way.

At first, the number of users for each device was

estimated according to their characteristics. Forecast

for the number of users for mobile phones–feature-

phones and smart-phones –was conducted using

actual data. For the other three devices, the expected

increase in the rate of users and the estimated initial

number of users were used. Next, a forecast for the

patterns of use for each device utilized the expected

rate of increase and weight of usage for each

application. Traffic consumptions for each

application were estimated using required

transmission speed ITU-R (International

Telecommunication Union-Radio communication

sector) definition. Last, the total amount of mobile

broadband traffic was calculated by reflecting

forecasts for the number of users and patterns of use.

Mobile broadband traffic was influenced by changes

in the number of users and usage patterns of

applications for each device. Thus, this study

developed a formula for the rate of increase in total

mobile broadband traffic which reflects the rate of

traffic increase by the number of users increase and

usage patterns of applications change (See formula

(1)).

T = U * A

the rate of increase in total mobile traffic (T)

the rate of increase in the number of users (U)

the rate of increase by usage pattern changing (A)

(1)

4 TRAFFIC FORECASTING

4.1 The Number of Users

The number of users for each device was forecasted

by diverse methods suitable for each in terms of

their characteristics. First of all, demand diffusion

models were used for mobile phones because

enough data for a quantitative forecast was available.

Among various demand diffusion models, those

which could provide powerful prediction and

interpretation were selected. The Bass model was

selected for feature-phones, and the Gompertz model

was chosen for smart-phones. Forecasts for the other

three devices were carried out by a panel of experts.

This method has been used in cases when relevant

data was not fulfilled, precise forecasting results

using data-based methods can not be provided, or

influences of external factors such as policy and

relevant law are extremely. Laptop PC usually has

functions which enable use of WiFi and Wireless

LAN networks that are easily affected by changes of

network policy. Tablet PCs and smart TVs have a

lack of data because they have not been widespread.

Therefore, a panel of experts attempted to forecast

the traffic for these three devices.

All forecasts were developed for five years from

2011 to 2016. Table 2 shows the forecasting results for

the increasing rate of users, and Figure 2 represents the

expected number of users for each device. While the

number of feature-phone users was expected to

decrease continuously, the number of the other four

devices’ users was expected to increase gradually.

Table 2: Forecasting results: the increasing rate of users.

Devices 2011 2012 2013 2014 2015 2016

Feature-

phone

1.00 0.69 0.70 0.82 0.93 0.99

Smart-

phone

1.00 1.91 1.32 1.12 1.05 1.02

Laptop 1.00 1.03 1.03 1.03 1.03 1.03

Tablets 1.00 1.15 1.15 1.15 1.15 1.15

Smart-TV 1.00 1.21 1.21 1.21 1.21 1.21

Figure 2: Forecasting results: the expected number of

users for each device.

MobileBroadbandTrafficForecastsinKorea

4.2 The Patterns of Use

The forecasting of the patterns of use for each device

was developed using the expected increased rate and

weight of use for each application. The initial

weights of use for each application were assumed to

be equal. Table 3 shows the forecasting results for

the increasing rate of application usages. For

feature-phones, use of VoIP was expected to grow

faster than others, as was while video streaming for

smart-phones. Video streaming and download were

expected to be the primary applications for laptop

PCs in the future, as are web browsing and video

streaming for tablet PC.

Table 3: Forecasting results: the expected increased rate of

application usages.

Applications

Voice

Online game

VoIP

Web browsing

Video

Streaming

Download

Devices

Feature-

phone

1.01 1.05 1.10 - - -

Smart-phone - 20 - 1.25 1.30 -

Laptop PC - - - 1.10 1.15 1.15

Tablet PC - - - 1.20 1.20 1.15

With the expected increased rate of application

usages, estimated traffic consumption of each

application was used to forecast the growth rates of

traffic due to the changes of usage patterns in

applications from 2011 to 2016. Traffic consumption

of each application is shown in Table 4. Among six

applications, voice and VoIP need the smallest level

of traffic, and online games, web browsing, and

downloads consume the middle level of traffic. The

largest amount of traffic is required for video

streaming.

Table 4: Estimated traffic consumption of each application

(ITU-R definition, kbps/second).

Applications Traffic consumption

VoIP 14

Voice 16

Online game 384

Web browsing 384

Download 384

Video streaming 2000

The expected growth rates of traffic due to the

changes of usage patterns in applications from 2011

to 2016 are as follows (See Table 5.). The expected

growth rates of feature-phones, laptops, and tablet

PCs were expected to be fixed while that of smart-

phones was expected to change dynamically. The

growth rate of traffic due to smart TV was replaced

by the growth rate of smart TV sales. It was

expected to grow continuously.

Table 5: Forecasting results: the growth rates of traffic due

to the changes of usage patterns in applications.

Devices 2011 2012 2013 2014 2015 2016

Feature-

phone

1.00 1.05 1.05 1.05 1.05 1.05

Smart-

phone

1.00 1.48 1.29 1.10 1.65 1.17

Laptop 1.00 1.14 1.14 1.14 1.14 1.14

Tablet 1.00 1.19 1.19 1.19 1.19 1.19

Smart

1.00 1.30 1.30 1.30 1.10 1.10

4.3 Traffic Forecasting Results

To calculate the total amount of mobile broadband

traffic with formula (1), the increasing rate of users

in Table 2 and the growth rates of traffic due to the

changes of usage patterns in applications in Table 5

were used. Actual data were used as the initial traffic

for feature-phones and smart-phones. Others used

estimated traffic data. Table 6 describes how the

initial traffic data was acquired or estimated.

Table 6: The initial traffic of each device.

Devices Description

Featurephone Actual data

Smartphone Actual data

Laptop PC

estimated traffic per laptop * the

number of laptop * the number of

service subscribers

Tablet PC

estimated traffic per tablet * the

number of tablet * the number of

service subscribers

Smart TV

estimated traffic per smart TV * sales

of smart TV

Mobile broadband traffic including the growth of

users and changes of application usages by devices

and total traffic is represented in Table 7. The

forecasting results have two remarkable

characteristics. First, mobile broadband traffic in the

future is expected to be centralized to specific

devices. In 2011, smart-phones caused 53.4% of

mobile broadband traffic, followed by smart TVs

(35.4%), tablet PCs (7.4%), feature-phones (1.9%)

and laptop PCs (1.7%). But in 2016, top two devices,

smart-phones and smart TVs, are expected to cause

more than 95% of total mobile broadband traffic

(See Figure 3.). This means that mobile broadband

DCNET2012-InternationalConferenceonDataCommunicationNetworking

traffic forecasting targeted to specific devices can

derive feasible results.

Table 7: Total traffic increases and forecasted traffic of

devices.

Devices 2011 2012 2013 2014 2015 2016

Feature

- phone

T 0.34 0.24 0.18 0.16 0.15 0.16

R 1.00 0.72 0.74 0.86 0.98 1.04

Smart-

phone

T 9.79 27.6 47.1 58.1 100.1 119.6

R 1.00 2.82 1.71 1.23 1.72 1.19

Laptop

T 0.31 0.37 0.43 0.51 0.60 0.71

R 1.00 1.18 1.18 1.18 1.18 1.18

Table

t PC

T 1.35 1.85 2.53 3.46 4.74 5.91

R 1.00 1.37 1.37 1.37 1.37 1.37

Smart

T 6.47 9.31 13.4 19.3 27.7 36.4

R 1.00 1.44 1.44 1.44 1.44 1.31

Total

18.3 39.4 63.7 81.5 133 163

1.00 2.16 1.62 1.28 1.64 1.22

* T: The amount of expected traffic (1000TB)

R: The expected increasing rate of traffic

Second, the expected mobile broadband traffic

for each device was affected by changes in patterns

of use. In the case of smart-phones, although the

growth rate of users was expected to decrease

continuously, the growth rate of traffic was expected

to increase similarly with the growth rate of traffic

due to the changes of usage patterns. This trend also

appeared in the case of smart TVs. Thus, patterns of

usage and their changes should be considered among

the most important aspects for mobile broadband

traffic forecasting.

5 CONCLUSIONS

This study aims to suggest a new traffic forecasting

method considering the number of users and patterns

of use for mobile broadband equipment. For the

purpose of this study, a three-round Delphi survey

was conducted to identify devices and applications

that would affect the future traffic needs. Then,

forecasts of the number of users for devices and the

growth rates of traffic caused by the changes in

usage patterns of applications were accomplished.

As a result, smart-phones and smart TVs were

identified as requiring most of the traffic needs in

the future, and the changes of usage patterns were

expected to influence the total amount of mobile

broadband traffic considerably. There are two main

contributions of this study. First, this study suggests

a forecasting method which reflects the changes of

usage patterns for mobile broadband devices to

support deriving realistic results. Next, this study

can provide the basic research data for future studies

in the mobile communication area and forecasting in

other areas.

However, this study has also a limitation. In the

suggested forecasting method, there exist several

assumptions. This is inevitable because it was

caused by the characteristics of the devices. If actual

data is used in the forecasting, better results can be

acquired. Therefore, in a further study, this will be

complemented to derive more reliable forecasting

results.

ACKNOWLEDGEMENTS

This research was supported by Basic Science

Research Program through the National Research

Foundation of Korea (NRF) funded by the Ministry

of Education, Science and Technology (No. 2009-

0089674).

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MobileBroadbandTrafficForecastsinKorea