A Study on Transport Infrastructure Policy in Smart

A Study on Transport Infrastructure Policy in Smart Knowledge Society:Focusing on the Impact of Information and Communication Technology (ICT) on Travel Behavior

Copyright 2013 Korea Research Institute for Human Settlements

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A Study on Transport Infrastructure Policy in Smart Knowledge Societyby Lee Baek-jin, Oh Sung-ho, Kim Joon-ki. -- Anyang : Korea Research Institute for Human Settlements, 2013 p. ; cm. -- (KRIHS special report ; 2013-20)

Includes bibliographical referencesISBN 978-89-8182-096-1 94300 : Not for saleISBN 978-89-8182-991-9(set) 94300

326.31-KDC5388-DDC21 CIP2013015355

ContentsChapter 1. Introduction _ 1

1. Background 22. Purpose of the study 33. Analysis framework of the study 4

Chapter 2. Urban and Transport Changes Brought by Smart ICT _ 5

1. Urban changes brought by smart ICT 62. Transport changes brought by smart ICT 10

Chapter 3. Empirical Study: Impact of Smart ICT on Travel Behavior _ 15

1. Analysis overview 162. Basic statistics analysis 173. ICT-enabled changes in the activity generation frequency: Tobit model 29

Chapter 4. Empirical Study: Impact of Smart ICT on the Choice of Transportation Mode _ 37

1. Analysis overview 382. Basic statistical analysis 413. Model analysis result 47

Chapter 5. Conclusion & Follow-Up Study _ 59

References _ 64

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Table 3.1 Number of survey samples……………………………………………………… 17Table 3.2 Classification of activities… …………………………………………………… 19Table 3.3 Tobit model’s dependent variables and explanatory variables… ………………… 30Table 3.4 Tobit model’s estimation results for activity generation frequency… …………… 33Table 4.1 Survey design for this study……………………………………………………… 40Table 4.2 Survey overview………………………………………………………………… 41Table 4.3 Pieces of survey paper by travel purpose………………………………………… 41Table 4.4 Change in the number of activities during travel by travel purpose and

transportation mode……………………………………………………………… 42Table 4.5 Frequency of emotional experience during travel by travel purpose and

transportation mode……………………………………………………………… 45Table 4.6 Distribution of gap between actual commute time (ACT) and desired commute

time (DCT) by transportation mode……………………………………………… 46Table 4.7 Analysis on the impact of activity during travel on emotional experience

(summary)… …………………………………………………………………… 49Table 4.8 Result of analysis on mutual dependence between transportation modes for

positive emotion during travel…………………………………………………… 52Table 4.9 Latent variables & identified factors……………………………………………… 53Table 4.10 Result from the transportation mode choice model by travel purpose…………… 55Table 4.11 Simulation result for the share of public transportation modes by scenario

(commute to work/school)……………………………………………………… 58

FigureFigure 2.1 Emergence of virtual stores (subway line no.2 Seolleung station) ……………………8Figure 2.2 Relationship between ICT and traffic volume ……………………………………… 10Figure 2.3 The notion of change in travel time productivity by transport mode……………… 12Figure 3.1 Hours of using information communication devices (during a weekday)………… 20Figure 3.2 Change in the proportion of users of information and communication devices

by age…………………………………………………………………………… 21Figure 3.3 Shopping activity generation rate per hour (during a weekday)………………… 24Figure 3.4 Personal networking generation rate per hour (during a weekday)……………… 25Figure 3.5 Work activity and travel generation rate per hour (during a weekday)…………… 26Figure 3.6 Average activity hour by activity type (during a weekday) ……………………… 27Figure 3.7 Daily average travel frequency for personal networking and non-daily activities

(during a weekday) … ………………………………………………………… 28Figure 3.8 Tobit model’s probability density distribution for activity generation frequency

(during a weekday) … ………………………………………………………… 35Figure 3.9 Tobit model’s probability density distribution for activity generation frequency

(during a weekend) … ………………………………………………………… 36Figure 4.1 Percentage of use of information communication devices by travel purpose and

transportation mode … ………………………………………………………… 43Figure 4.2 Change in the value of time for activities during travel, depending on the length

of travel time …………………………………………………………………… 44Figure 4.3 Change in the gap between ACT and DCT by travel time ……………………… 46Figure 4.4 Change in the public transport utility due to the increase in use of information

communication devices during travel … ……………………………………… 56Figure 4.5 Change in the probability of choosing public transport by scenario … ………… 57Figure 4.6 PDF change in the probability of choosing transportation modes due to

information communication devices …………………………………………… 57

Lee Back-jin Research Fellow. Doctor of Engineering, Hiroshima University. Major research field includes transportation planning and ITS. Recent research works are 「The Strategies of Equity Improvement toward Fair Society-Focusing on the Equity in Transportation Policy」 (2010), 「An Introduction of Mobility

Management: Voluntary Reduction of a Passenger Car Use for Environmentally Sustainable Transport」 (2009), 「Green Growth and National Territorial Development: Transportation Sector Analysis」 (2009), and 「The Development of Nationwide Road Network System(II)」 (2008).

Oh Sung-ho Research Fellow. Ph.D. in Transportation Engineering Studies, University of Wisconsin-Madison, USA. Major research fields include transportation information systems(ITS) and transportation planning. Major research projects are 「Guideline for Selection of New Transit Mode System」 (2012), 「The Appraisal of

Smart-Infrastructure Investments」 (2011), 「Transportation Plan for Wide Linkage with Saemangeum Area」 (2010), 「Building of the Standard Platform for ITS Integration Services」 (2010), 「Corresponding to the Transport Policy with Demographic Change」 (2010), and 「Cooperation for Development Research for Spatial Planning Systems in Developing Countries」 (2009).

Kim Joon-Ki Associate Research Fellow. D.Sc. in Transportation System Engineering, Washington University in St. Louis. His major research fields are behaviour analysis and traffic safety, and his recent works include 「An Introduction of Mobility Management: Voluntary Reduction of a Passenger Car Use for Environmentally

Sustainable Transport」 (2009), 「Green Growth and National Territorial Development: Transportation Sector Analysis」 (2009), 「Smart Highway Mid & Long Term Plan」

(2008, 2009), 「The Development of National-wide Road Network (Ⅱ)」 (2008), and 「Improvement on the Connection of Express Highways for the Preparation of Emergency Situation」 (2008).

Authors

This study focuses on the impact of Information & Communication Technology (ICT) on the transport sector. Advancement of ICT has transformed the individual daily lives, consequently bringing changes to the important basic elements of transport such as activity pattern and travel behavior (behavior in use of time and space.) In terms of transport planning, the ICT affects the existing transport infrastructure planning elements (travel generation, travel distribution, and transportation modes.) This implies that changes are required in the operation or investment policies of transport infrastructure. Therefore, we need to understand how the ICT development affects the transport infrastructure planning elements in a more accurate and quantitative manner. Based on this understanding, we need to proactively respond to the rapid and widespread social changes brought by the ICT evolution. Purpose of this study is to perform a quantitative analysis on how the individual travel behavior transformed by the advancement of smart knowledge society affects the transport planning elements in the transport infrastructure policy. In this study, we first focused on identifying the key factors that drive changes, among the characteristics of smart knowledge society. To this end, we described the current usage of smart devices in Korea and how smart device usage will affect transport areas, through a review of the existing studies. Secondly, for empirical analysis, we analyzed the impact of smart device usage on the transport planning elements (such as changes in travel demand, travel distribution, and choice of transportation mode.) ① As for the change in travel demand, we referred to the time series data of the Time Use Survey (1999, 2004, 2009) conducted by the Statistics Office every 5 year. We also used the questionnaire survey on the ‘Current Usage of Smart Devices’ conducted for office workers in Seoul. Based on the survey results, we additionally analyzed how the use of Social Network Service (SNS) affects social relations among people and consequently their travel frequency. ② As for the change in travel distribution, we referred to the Time Use Survey done by the Statistics Office and analyzed the data based on the indicator of travel generation rate per unit hour. Using the survey data, we additionally analyzed how the urban spatial information and transport information shared through smart devices contribute to the spatial distribution of activities. ③ As for the change in choice of transportation mode, we analyzed how the travel utility is changed by the use of smart devices.

Abstract

KRIHSSPECIAL REPORT2013

Introduction

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Introduction

1. Background

The development of Information & Communication Technology (ICT) has enabled low-cost and high-speed information communication and increased the penetration of smart devices that can be used anywhere at any time. Smart devices are not just a new convenient information communication device but a key driver of change in the daily lives of people. Going further, smart devices are transforming our society into a smart knowledge society. In this study, smart knowledge society is defined as a process where the evolution of ICT and the spread of smart devices change the way people/logistics/information are networked with one another and consequently create a new value.

This study focuses on the impact of ICT evolution on the transport sector of the smart knowledge society. Smart devices are characterized by ‘3R’, which refers to ‘Real-time’, ‘Reach (limitless expansion of information and communication)’, and ‘Reality (transcending spatial constraints.)’ With these characteristics, smart devices are expected to bring changes to the individual people, companies and society (SERI Report, 2010.) In the statistics survey (2010) conducted by Korea Communications Commission, 11.2% of the office workers answered that their working environment has rapidly changed with the increasing use of smart devices, saying that they have a mobile office system in place. The widespread penetration of smart devices may work positively for public transport. For example, people use smartphones the most

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A Study on Transport Infrastructure Policy in Smart Knowledge Society

when they are using public transport (33.3%.) In terms of smartphone applications, transport information is the second-most used application category (41.5%) following entertainment applications (63.9%) (Google Korea, 2010; Industrial Training Institute, 2010.) This implies that ICT development in smart knowledge society changes the individual daily lives and consequently transforms the activity pattern in the transport sector, which then leads to changes in the travel behavior (behavior in use of time and space.)

The evolution of ICT will transform the individual travel behavior, thus bringing changes to the existing transport planning elements (transport demand, travel distribution, transportation mode.) Therefore, we need to proactively respond to the widespread social changes made by the ICT evolution, through an accurate and quantitative analysis of the impact of smart knowledge society on the transport infrastructure planning elements. However, it has not been easy to analyze the impact of the ICT evolution on the transport sector.

2. Purpose of the study

Purpose of this study is to perform a quantitative analysis on how the individual travel behavior transformed by the advancement of smart knowledge society affects the transport planning elements in the transport infrastructure policy.

To this end, we defined and conducted two key research tasks. The first research task was to identify the key drivers of change in smart knowledge society. In other words, we identified the basic factors that drive changes in smart knowledge society and how those factors influence which part of the transport sector. The second research task was to analyze how the transport infrastructure planning elements will be changed in the smart knowledge society and to identify how such information can be quantified. In other words, we analyzed whether the travel frequency will increase or decrease, or whether the urban traffic congestion will still occur in the same place at the same time.

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3. Analysis framework of the study

Firstly, we identified the key factors that drive change in the smart knowledge society. By reviewing the existing studies, we described how the use of smart devices affects cities and transport sector, and presented the data about current usage of smart devices in Korea.

Secondly, for empirical analysis, we analyzed the impact of the use of smart devices on the transport planning elements. As for the transport demand, we referred to the time series data of the Time Use Survey (1999, 2004, 2009) conducted by the Statistics Office. Applying the methodology of activity-based approach, we analyzed the trend of travel frequency that is changed by the use of smart devices. In this study, we also conducted a survey on the ‘Current Usage of Smart Devices’ for office workers in Seoul. Based on the survey results, we performed an additional analysis on how the use of Social Network Service (SNS) affects social relations among people and consequently their travel frequency.

As for the travel distribution, we used the Time Use Survey done by the Statistics Office and analyzed the data based on the indicator of travel generation rate per unit hour. Travel generation rate, in this study, refers to the rate of doing certain activities (eg. shopping, personal networking) simultaneously per unit hour. In addition, we analyzed how the urban spatial information and transport information shared through smart devices contribute to the spatial distribution of activities, based on the results of the survey on ‘Current Usage of Smart Devices’ mentioned above.

As for the choice of transportation mode, we analyzed how the travel utility is changed by the use of smart devices. In particular, we presented how much contribution the use of smart devices can make to the increase in use of public transport. For example, we performed a qualitative analysis on whether the use of smart devices to do multiple activities during travel could ease the stress of using public transport and could actually contribute to the increase in use of public transport. We did a separate survey for this analysis.


Urban and Transport Changes Brought by Smart ICT

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1. Urban changes brought by smart ICT

We would like to discuss changes in transport conditions, based on the review of the existing studies regrading the impact of ICT on urban spaces. We focused on large cities where the daily lives of people are relatively highly affected by ICT evolution, because transport demand is a derived demand from the daily activities of people in their regions or cities. Urban changes brought by the development of ICT were categorized into changes in urban spaces, changes in urban facilities, and changes in urban lifestyle.

1) Changes in urban spaces

Discussions on urban changes in the information society have focused on macroscopic perspectives, such as changes in urban spaces and in use of land. In the smart knowledge society, however, we should pay more attention to the ‘substitution-generation-enhancement’ of facilities/spaces/functions, as urban changes in smart knowledge society are more closely related to the daily lives and activity pattern of people from microscopic perspectives. This is because smart knowledge society highlights convenience in individual daily lives where people can access information anywhere at anytime, unlike the previous information society.

In order to describe the physical urban space changes caused by ICT, Graham

Urban and Transport Changes Brought by Smart ICT

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& Marvin (1996) presented several types of changes that occur through complex interactions among diverse factors, without using the dichotomous model that focuses on concentration and dispersion.1) The first type of change in physical urban space is physical & developmental synergies. Physical & developmental synergies refer to the inter-linkage and co-evolution of urban functions and ICT utilization, as cities are at the center of communication and ICT is the core means of processing such communication. In particular, they emphasized the relation and synergy effect between urban infrastructure and telecommunication infrastructure. The second type of change is substitution effects. For example, physical flows of goods, people and information in urban spaces are replaced by electronic interactions in virtual spaces such as cyber community, e-mail, video conference, remote medical care and remote education, etc. The third type of change is generation effects, where remote telecommunication generates physical flows while physical concentration in urban areas stimulates the use of remote telecommunication. For example, an increase in Internet shopping leads to an increase in the volume of product delivery traffic. The last one is enhancement effects meaning that the use of ICT enhances the efficiency or attractiveness of physical networks such as roads, railways, airline and energy networks.

2) Changes in urban facilities

With the widespread penetration of wireless Internet, urban facilities have evolved into a new kind of place. For example, offices now serve both as a working space and a relaxing space while parks serve both as a relaxing space and a working space. It has already become a common lifestyle for people to handle their business using wireless Internet at a cafe. The case is true not only for Korea but also for foreign countries. They installed wireless LAN in their parks enabling people both to relax and enjoy leisure activities. Such changes in urban facilities have been accelerated especially by the implementation of mobile office. Mobile office enables employees to handle their work, which was previously done through their PC, on their smartphones. The rapid change of spaces is particularly evident in the logistics/finance/sales/distribution areas,

1) Kim Tae-hwan. Cities & Information Communication. p.125. Kim In, Park Soo-jin 『Interpretation of Cities』. Purungil.

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where it is important to use information and to make decisions on a real-time basis. In the industries mentioned above, a rapidly increasing number of people handle their work immediately through cloud computing, not just in their office but wherever they are. There is an argument that such diversification of work flow caused dispersion of transport flow. In the U.S., the traffic jam between suburbs and urban areas was relatively heavier at 8 a.m. and 5 p.m., due to the commuter traffic in 1986. But, recently the traffic flow has been dispersed depending on time and place.2)

3) Changes in urban lifestyle

There will be dual aspects in urban lifestyle, where unnecessary routine travel will be minimized and rational travel will increase while extempore travel and social networking among people will be expanded. This is attributable to the mobile revolution that enabled people, in their daily lives, to handle diverse tasks simultaneously at a certain point of time across multiple spaces, without spending much cost or time to move from one place to another. For example, virtual stores are being increasingly installed in many places these days. Through virtual stores, people can buy everyday supplies while they are on the move without visiting physical stores.

2) Transport flow analysis results by Pisarky, the U.S. transport policy expert (Source: Samsung Economic Research Institute (SERI. 2010. Future Opened Up by Smartphones (CEO Report).)

Figure 2.1. Emergence of virtual stores (subway line no.2 Seolleung station)

Source: Digital Daily. 2012.3. “[2012/Mobile Innovation] Explosive Market Demands” (Park Ki-rok).

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As mobile technologies enabled real-time communication among people, they can share information and knowledge on a real-time basis and make more rational decisions. When there was a heavy snowfall in Seoul in January of 2010, smartphone users uploaded and shared the weather and traffic information mobile, through their online community, etc. It is especially noteworthy that the ICT evolution generated extempore lifestyle in which individual activities as well as social relationship among people are influenced by spontaneous information-sharing. Sharing information spontaneously, people meet with each other, do their activities and plan their schedules, without any prior arrangements.3) Unlike telephone, mobile phone enables users to ask after each other and check their locations simply through text-messaging, for example, or to call each other just to get out of being bored. A study reported that the increase of mobile communication among people enriches the physical gathering among them.4)

Short Message Service (SMS) offers ‘seamless mobile connection with friends,’ even when they have no special thing to talk about or when it is hard to make a call to each other. This implies that people who have constant mobile communication with each other are highly likely to meet with each other spontaneously. The use of smartphones, in particular, could further spread the extempore lifestyle, since smartphones not only allow users to get information they want anywhere at anytime but also offer a seamless messenger service that enables one-to-many messaging. Users can also plan their activities spontaneously, because they can get the ‘spatial’ information they want anywhere through their smartphones. In fact, many people use their smartphones to get information, during their spare time inside the subway or bus.

3) Koppmma, 2002, p.243

4) Ito & Okabe (2002); Hwang Ju-sung. 2005. Mobile Communication and Change in Public Urban Spaces. Korea Information Society Development Institute. p.36.

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2. Transport changes brought by smart ICT

In the followings, we analyzed the smart ICT-enabled changes in the transport sector by categorizing them into changes in travel behavior, changes in mobility, and changes in travel utility.

1) Changes in travel behavior

Discussion on the impact of ICT on travel behavior is based on the substitution relation, complement relation and change time/mode/destination relation of travel demand.5) Substitution relation means that the use of ICT removes the necessity for physical movement, substituting for the transport demand.

Complement relation means that the use of ICT increases the existing transport demand or creates a new transport demand. Change time/mode/destination relation means that the use of ICT changes travel pattern such as change of departure time or change of transportation mode.

5) Alber(1975), Mokhtarian and Meenakshisundaram(1999)

Figure 2.2 Relationship between ICT and traffic volume

Traffic

Frequency of using ICT

Traffic


Traffic


Substitution relation Complement relation Change time/mode/destination relation

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Initial studies on the relation between ICT and travel behavior mainly suggested that there is a close substitution relationship between the use of ICT and physical activities in the daily lives.6) However, recent studies indicate that the use of ICT complements and increases the travel demand, or changes the travel pattern itself. There is a positive relationship between the use of phone and travel, increasing the mutual demand. However, the use of Internet could contribute to the reduction of transport demand (Meenakshisundaram and Goulias, 2001.) To summarize, ICT has a main effect of substituting travel in the short-term empirical analysis, but the ICT and travel have a meaningful effect of increasing the demand for each other in the long-term perspectives. Senbil and Kitamura (2003) indicated that information communication devices put different influence depending on the type of activities. The use of information communication devices has substitution effects on work-related activities, complement effects on the activities where people can make decisions themselves, and neutral effects on maintenance activities.

2) Changes in mobility and accessibility

ICT brings changes to the individual mobility and travel space. In particular, telecommunication is one of the factors that measure travel intention, while travel - personal characteristics - telecommunication are interacting and interdependent with each other.7) According to the study by researchers including Yuan (2012), people who use mobile phones more often than others had larger space of mobility, while individual age and density of transport network had an influence on determining the relationship between the use of mobile phones and travel behavior. Several studies also suggested that the travel utility is enhanced when people can do multiple activities during travel, which will then lead to an increase in the distance of mobility (Ory and Moktarian, 2005; Dijst, 2004.) Smart devices enable the users to access and share diverse urban spatial information (eg. information about good restaurants, path to certain locations, etc.) anywhere at any time. This implies that users of smart

6) Pendyala, Goulias and Kitamura(1991); Saxena and Mokhtarian(1997)

7) Kuniaki(2010)

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devices can increase their knowledge of urban spatial information by sharing diverse information with each other, thus expanding their accessibility to urban spaces and their activity space. This is why we need to consider not only the physical accessibility based on the existing concept of distance and time, but also the virtual accessibility to urban spaces. Smart devices also contributes to expansion of individual activity space and efficient travel, by offering location-based services (eg. navigation service) that reduce uncertainty on the path to certain destinations.

3) Changes in travel utility

In the field of transport planning, travel time has been measured as negative utility that should be minimized, based on the principle of travel time budget. However, the development of smart devices has enabled the users to do various activities during travel. This could make part of the travel time regarded as having positive utility.

In other words, using travel time to do multiple activities during travel could bear productive value from personal perspectives, regardless of the economic perspective

Figure 2.3 The notion of change in travel time productivity by transport mode

counter-productive

unpr

oduc

tive

productive

bus

cartrain

‘fully

’ pro

duct

ive

‘ultra’productive

f

productivity

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that views the existing travel time as disutility (researchers including Lyons, 2005.) The use of smart devices during travel changes the utility of transportation mode (private car vs public transport), and can contribute particularly to the increase in use of public transport. The reason is that public transport gives the users more opportunities to do productive activities during travel, compared to their private cars that should be driven by themselves.

Travel time is something people have no choice but to spend for their daily activities. As people can do multitasking using their smart devices during travel, there is a time-saving effect for doing other activities. This means that productive use of travel time could help improve the quality of individual life, along with positive influence on their life in the qualitative terms.


Empirical Study: Impact of Smart ICT on Travel Behavior

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In this study, we categorized the smart ICT-enabled changes in the transport sector into changes in travel behavior, changes in mobility and accessibility (activity space), and changes in travel utility. In the followings, we presented empirical study results regarding travel behavior changes as well as mobility and accessibility changes brought by smart ICT.

1. Analysis overview

In this part, we analyzed travel behavior changes caused by the use of information communication devices. We used the Time Use Survey data of Statistics Office to have an empirical analysis on ‘whether the use of information communication devices changes travel behavior’ and ‘the level of influence on travel behavior changes by activity type.’ The empirical analysis is then followed by microscopic analysis on the travel behavior changes caused by the use of information communication devices. Put more specifically, we analyzed how the use of smart devices (basically smartphones, and especially SNS and urban spatial information (virtual accessibility)) influences travel behavior changes. We divided the empirical analysis into several parts, since the available data covering the entire country (or metropolitan areas) was in fact limited to 2009 and the full-scale penetration of smart devices (smartphones, smart pads, etc.) started throughout Korea in 2010. Considering such data constraints, we conducted a questionnaire survey on ‘Current Usage of Smart Devices’ for office workers in

Empirical Study: Impact of Smart ICT on Travel Behavior

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Seoul. Based on the survey results, we performed an additional analysis on how the use of SNS influences social relationship among people and consequently their travel frequency.

2. Basic statistics analysis

1) Analysis data

Analysis scope of travel behavior changes includes the analysis on whether the use of information communication devices has a meaningful influence on the change of actual daily activities, and if it has, what the influence on the increase/decrease of travel generation is. For the analysis, we used the raw data of Time Use Survey conducted by

Table 3.1 Number of survey samples

Region1999 2004 2009

Number of samples

Proportion(%)Number of samples

Proportion(%)Number of samples

Proportion(%)

Seoul 10,437 12.15 8,072 12.76 5,220 12.88

Busan 6,902 8.03 4,886 7.72 2,918 7.20

Daegu 5,538 6.45 3,990 6.31 2,884 7.12

Incheon 5,463 6.36 4,070 6.43 2,424 5.98

Gwangju 4,398 5.12 3,212 5.08 2,198 5.42

Daejeon 4,186 4.87 3,034 4.80 2,182 5.38

Ulsan 3,455 4.02 2,990 4.73 2,202 5.43

Gyeonggi 8,535 9.94 7,076 11.18 4,924 12.15

Gangwon 4,548 5.29 2,984 4.72 1,818 4.49

Chungbuk 4,442 5.17 3,116 4.93 2,074 5.12

Chungnam 4,702 5.47 3,290 5.20 1,964 4.85

Jeonbuk 4,594 5.35 3,228 5.10 1,988 4.91

Jeonnam 4,825 5.62 3,364 5.32 1,974 4.87

Gyeongbuk 5,622 6.54 3,966 6.27 1,904 4.70

Gyeongnam 5,265 6.13 3,902 6.17 2,062 5.09

Jeju 2,994 3.49 2,088 3.30 1,790 4.42

Total 85,906 100 63,268 100 40,526 100

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the Statistics Office for the entire country. Using the time series data of the Time Use Survey8) conducted in 1999, 2004, and 2009, we performed a macroscopic analysis on the use of information communication devices and travel behavior changes. In the Time Use Survey of Statistics Office, respondents recorded at 10 minute interval all activities they engaged in for 24 hours a day. This survey data has personal category (gender, age, occupation, etc.), household category (whether they have pre-schoolers, type of housing, type of housing tenure, etc.), and travel-related category (travel time, transportation mode, etc.) The number of survey samples was 85,906 people in 1999, 63,268 people in 2004, and 40,526 people in 2009. We used the data of all regions for analysis, but excluded people who are engaged in agricultural and fishing industries to suit the analysis purpose of this study. In order to analyze the impact of information communication devices on the travel behavior, we reclassified daily activities into the activity of using information communication devices and the activity of daily living, as seen in the table below. Activity of daily living can be classified in diverse ways, depending on the analysis purpose. In this study, we classified the activity of daily living into essential activity (sleeping), maintenance activity, obligatory management activity, free time, shopping activity (daily/non-daily), work, personal networking, and learning. As for the activity of using information communication devices, however, it should be noted that the use of Internet shopping and SNS in 1999 was not widespread among people, and hence the data of 1999 does not have a separate category of Internet shopping and SNS.

2) Changes in the behavior of using information communication devices

Regarding the changes in the behavior of using information communication devices, the hours of using Internet shopping and SNS increased while the hours of information search on the Internet and personal networking through phone calls decreased in 2009, compared to 2004. It means that the users went beyond the existing passive behavior of searching for information on the Internet to use their information communication

8) Time Use Survey is conducted by the Statistics Office every 5 year, to identify how the people spend 24 hours a day.

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Table 3.2 Classification of activities

Type of activities Key details

1. Sleep Sleeping

2. Daily shoppingActivity of directly buying everyday supplies including groceries at a store or a mart

3. Non-daily shoppingActivity of directly buying non-daily supplies including clothing and furniture at a relevant store

4. WorkWaged work, self employment (including day job and side job), unpaid family work, etc.

5. Personal networking Networking with family, friends, and relatives, etc. outside home

6. Learning Regular classes for students at school, private tutoring institute, etc.

7. Maintenance at homePersonal maintenance activity at home (having meals, washing face, doing hair and makeup, etc.)

8. Obligatory management at home

Management activity at home (cleaning the house, cooking meals, taking care of family, etc.)

9. Free time at home Personal hobby, relaxing at home (reading books, watching TV, etc.)

10. Maintenance outside home

Personal maintenance activity outside home (hair, makeup, skin care, etc.)

11. Obligatory management outside home

Management activity outside home (visiting public offices, learning activity of non-students)

12. Free time outside homePersonal hobby, relaxing outside home (climbing mountains, reading books, etc.)

Activity of using information

communication devicesKey details

1. Internet shopping Purchase of daily/non-daily supplies on the Internet

2. Information search on the Internet

Search of information such as news and life information on the Internet

3. SNS Use of personal homepage, blog, chat, and e-mail on the Internet

4. Networking through phone calls

Personal networking through phone calls or text messaging (excluding phone calls for business)

Travel activity Key details

Transportation modesWalk, intra-city bus, subway, private cars, and others (taxi, subway, inter-city bus, train, etc.)

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devices in a more active manner (for Internet shopping and SNS), and that the hours of phone calls decreased because they do personal networking through text messaging more than phone calls. We classified the survey respondents into users and non-users, depending on whether they use information communication devices or not. Then we identified changes in the percentage of using information communication devices by year, region and age. In this survey, users of information communication devices refer to the respondents who said that they do shopping, information search and SNS on the Internet at least once or more per day. Firstly, in terms of the change in the overall use of information communication devices, the percentage of users increased by year (average rate of using information communication devices from 15.89% in 2004 to 21.57% in 2009.) As for the change in the proportion of users by age, younger generation took up the largest portion as 71.92% in 1999 and users in their 40s or older accounted for a very small portion. In 2009, however, the number of middle-aged users in their 30s or 40s increased. This implies that the use of information communication devices has become widespread among more diverse people.

Figure 3.1 Hours of using information communication devices (during a weekday)

10 minutes7

6

5

4

3

2

1

0

Internet shopping Information search on the Internet Personal networking through phone calls(including text messaging)

SNS

1999 2004 2009

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3) ICT-enabled changes in the activity generation rate per hour

For analysis on the activity pattern of respondents, we defined the activity generation

rate per hour as P(a )i , as below. In other words, P(a )i is the percentage of certain activities respondents are engaged in simultaneously per unit hour, which is related to the rate of using transport facilities (eg. use density of shopping centers.)

1

n

jP(a )=[freq(a )/ freq(a j=1,2,3)] ,.*100 ..,i, Ni i j ,...

=∑

P(a )i means the rate of activity ‘ ai ’ generation to the total activity generation frequency per hour. ‘ freq(a )j ’ refers to the frequency of activity ‘ ai ’ generation per hour.

(1) Changes in the activity generation rate: shopping activity

Shopping activity is divided into daily shopping and non-daily shopping, based on the concept of direct shopping where consumers directly visit relevant stores and buy necessary goods. The rate of daily shopping activity generation per hour was similar among 1999, 2004 and 2009 whereas the rate of non-daily shopping activity

Figure 3.2 Change in the proportion of users of information and communication devices by age

2009

2004

1999

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Users in their 10s Users in their 20s Users in their 30s Users in their 40s Users in their 50s Users in their 60s or older

21.20% 25.85% 25.32% 18.42%

6.54%

23.34% 31.51% 25.83% 14.25%

3.94%

34.29% 37.63% 18.99% 7.33%

1.26%

22

generation per hour showed a meaningful decrease from 1999, 2004 to 2009. This proves that the rate of Internet shopping activity per hour increased in 2009, compared to 2004. According to this analysis, the Internet shopping activity has an effect of substituting for direct shopping activity and influences the decrease of non-daily shopping rather than daily shopping.

(2) Changes in the activity generation rate: personal networking

Personal networking activity refers to directly meeting with others such as acquaintances, family and relatives, etc. somewhere outside home, for the purpose of personal networking. In the case of direct personal networking, the rate of activity generation showed a meaningful decrease, as time changes by year. However, the activity generation rate per hour, in terms of personal networking through phone calls (including text messaging), had a meaningful increase in 2004 and 2009, compared to 1999. The percentage of SNS activity also showed a meaningful increase in 2009, compared to 2004. This indicates that personal networking through phone calls and utilization of SNS, based on the use of information communication devices, have an effect of reducing the activity generation rate per hour, in the case of direct personal networking.

(3) Changes in the activity generation rate: travel activity

Travel is essentially generated by the needs for doing external activities and is closely related to the usage capacity of transport infrastructure. In other words, travel reflects a combination of effects from diverse external daily activities of people such as shopping, personal networking and work and is directly related to the use of transport infrastructure. During weekdays, work activity has the biggest influence on the travel generation rate. The work activity rate per hour is not much different among 1999, 2004 and 2009 while partially increasing in 1999.

The travel generation rate per hour partially increased in 2009 more than in 1999, but showed no meaningful difference by year. This is in contrast with the previous analysis that the use of information communication devices brought a meaningful decrease in the activity generation rate for shopping and personal

23


networking. The reason would probably be that the use of information communication devices substituting for direct shopping and direct personal networking had no meaningful influence on the decrease of actual travel generation rate, or that the use of information communication devices may have changed the activity pattern from other perspectives.

In terms of the average activity hour by activity type as in <Figure 3.6>, the average hour of daily shopping activity is not much different by year, whereas the average hour of non-daily shopping and personal networking activities shows a meaningful decrease. This implies that the use of information communication devices has potential complement effects, because it saves the time for non-daily shopping and personal networking activities while allowing other activities to be generated during the saved time.

24

Figure 3.3 Shopping activity generation rate per hour (during a weekday)

0.12

0.1

0.08

0.06

0.04

0.02

00:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Daily shopping

(H)

Rate of activity generation to the total activities(%) 1999 2004 2009

0.025

0.02

0.015

0.01

0.005

00:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

(H)

Non-daily shopping


0.014

0.012

0.01

0.008

0.006

0.004

0.002

00:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

(H)

Internet shopping

Rate of activity generation to the total activities(%) 2004 2009

25


Figure 3.4 Personal networking generation rate per hour (during a weekday)

Personal networking

00:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

(H)

0.35

0.4

0.3

0.25

0.2

0.1

0.15

0.05


SNS

00:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

(H)

0.045

0.04

0.035

0.03

0.025

0.02

0.015

0.01

0.005

Rate of activity generation to the total activities(%) 2004 2009

Text messaging

00:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

(H)

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01


26

Figure 3.5 Work activity and travel generation rate per hour (during a weekday)

Work activity

00:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

(H)

2.5

2

1.5

1

0.5


Travel0

0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00(H)

1

0.8

0.6

0.4

0.2


27


Figure 3.6 Average activity hour by activity type (during a weekday)

10 minutes7

6

5

4

3

2

1

0

Daily shopping Non-daily shopping Personal networking

1999 2004 2009

(4) Changes in the daily average travel frequency, depending on the use of information communication devices

We had a brief analysis on the previous statement that the use of information communication devices may have changed activity patterns from other perspectives than the substitution effects for shopping and personal networking activities. To this end, we compared the daily average travel frequency for personal networking and non-daily shopping activities between users and non-users of information communication devices, because the use of information communication devices had a meaningful impact on the personal networking and non-daily shopping generation rate per hour.

In the case of personal networking activity, the user group’s travel frequency by year decreased more than the non-user group’s. This indicates that the use of information communication devices has substitution effects, reducing the travel frequency for direct personal networking activity. In terms of non-daily shopping activity, however, the user group had higher travel frequency than the non-user group. In other words, the use of Internet shopping did not cause a meaningful decrease in the travel frequency for direct shopping activity.

28

Figure 3.7 Daily average travel frequency for personal networking and non-daily activities (during a weekday)

Times/Day

1.4

1

1.2

0.8

0.6

0.4

0.2

0

Personal networking1999



Users Non-users

Times/Day

0.07

0.05

0.06

0.04

0.03

0.02

0.01

0

Non-daily shopping1999



Users Non-users

29


3. ICT-enabled changes in the activity generation frequency: Tobit model

In this part, we analyzed whether the use of information communication devices changes the frequency of activity generation. We set the Tobit model to perform a quantitative analysis on the change of activity generation frequency. Activity generation frequency is defined as the total frequency of certain activities done for a day. We analyzed the frequency of personal networking activity and travel. Considering the increasing use of information communication devices including the Internet, we analyzed the difference between the impact of the existing telephone usage and the impact of the information communication device usage on the activity generation frequency. The existing studies mainly focused on the impact of telephone usage on the activity generation frequency. However, we need to analyze the difference between the impact of telephone and the impact of information communication devices on the activity generation frequency, because an increasing number of people are sharing information through the Internet in the smart knowledge society.

In this study, our sample data includes many respondents who had neither the personal networking activity nor the travel frequency for a day. This may cause a bias in the analysis, if we apply the existing multiple regression model. Therefore, we used Tobit model in this study. We estimated the total frequency of personal networking activity for a day and the total travel frequency for a day as the dependent variables for this model. Explanatory variables include personal attributes of respondents, hours of doing other activities (eg. working hours at company, obligatory management hours at home, etc.), frequency of phone calls, and whether the respondents use information communication devices or not.

1) Validity of model

In the Tobit model, we estimated personal networking activity and travel as the dependent variables for each year (1999, 2004, 2009). The reason is that the estimation result for each year can give a clearer explanation about how the use of information communication devices changed the frequency of activity generation.

30

Table 3.3 Tobit model’s dependent variables and explanatory variables

Variable Unit Remarks

Dependent variable

Total frequency of personal networking activity generation Total frequency of travel generation

(times/day)Continuous

variable

Explanatory variable

Personal attributes

Gender (male=1, female=0)Continuous

variable

Marital status (married=1, unmarried=0)Dummy variable

Younger generation (30 or younger=1, others=0)Dummy variable

Older generation (60 or older=1, others=0)Dummy variable

Education level(college graduate or higher=1, others=0)

Dummy variable

Have pre-schooler children (yes=1, no=0)Dummy variable

Whether they are office workers

(office worker=1, others=0)Dummy variable

Other activities

Working hours at company (10 minutes)Continuous

variable

Maintenance hours at home (10 minutes)Continuous

variable

Obligatory management hours at home

(10 minutes)Continuous

variable

Obligatory management hours outside home

(10 minutes)Continuous

variable

Information communication

Frequency of telephone use (times/day)Continuous

variable

Use of information communication devices

(users=1, non-users=0)Dummy variable

In the analysis result, the value of 2P ,which represents descriptive power of the model, was not high as 0.07~0.164. But, we selected this model because all estimated coefficients were estimated to be rational. Descriptive power of the model that estimated travel generation frequency is relatively lower than that of the model for personal networking activity, because travel generation itself is caused by a

31


combination of more complicated factors. Estimated coefficients have the following meaning, based on the estimation

results of the frequency of personal networking activity generation during a weekday. In the estimation results related to personal attributes, gender had a negative value (-) where women had higher frequency of personal networking activity than men, and married people (+), middle-aged people (in their 30s~60s and office workers) (+) had relatively higher frequency of personal networking activity. In the estimation results related to other activities, working hours at a company had a negative value (-), where the longer the working hours at a company, the lower the frequency of personal networking activity. In addition, respondents, who spend longer hours in doing maintenance and obligatory management activities at home and in doing obligatory management activities outside home, showed relatively lower frequency of personal networking activity. These results explain that the Tobit model produced rational estimates.

2) Impact of the use of information communication devices on the frequency of personal networking activity generation

The use of information communication (Internet) has substitution effects reducing the frequency of personal networking activity, while the use of phone calls has complement effects increasing the frequency of personal networking activity. The variable for the use of information communication (Internet) was estimated to be a negative value (-), regardless of whether it is a weekday or a weekend. This indicates that the use of information communication devices decreases the frequency of personal networking activity. In particular, considering the fact that people mostly used their personal computer to get connected to and use the Internet until 2009, the Tobit model is regarded as valid. The variable for the use of phone calls was estimated to be a positive value (+), regardless of whether it is a weekday or a weekend. This implies that the higher the frequency of phone calls, the higher the frequency of personal networking activity.

32

3) Impact of the use of information communication devices on the frequency of travel generation

The variable for the use of information communication (Internet) was estimated to be a negative value (-), regardless of whether it is a weekday or a weekend. This indicates that the use of information communication devices reduced the travel frequency. The variable for the frequency of using phone calls during a weekday had a meaningful impact on the increase of travel frequency in 1999, whereas it had no meaningful impact in 2004 and 2009. The variable for the frequency of using phone calls during a weekend had a meaningful impact in the decrease of travel frequency only in 2009. This indicates that the use of information communication (Internet) had substitution effects of reducing the travel frequency and that the impact from the use of phone calls continuously decreased.

For a more concrete analysis on how the use of information communication devices influences personal networking frequency and travel frequency, we presented the probability density distribution of the personal networking frequency and travel frequency in <Figure 3.8> and <Figure 3.9>. In the estimation results for 2009, the probability of doing personal networking activity three times during a weekday was around 9.6% for information communication (Internet) users and about 11.0% for non-users. This indicates that the use of information communication contributes to about a 1.4% decrease in the probability of personal networking activity. However, the probability of doing personal networking activity three times during a weekday was about 13.1% for the respondents who use phone calls three times a day and around 10.3% for non-users of phone calls. That is, the use of phone calls contributes to about a 2.3% increase in the probability of personal networking activity.

33


Tabl

e 3.4

Tob

it m

odel

’s es

timat

ion

resu

lts fo

r act

ivity

gen

erat

ion

frequ

ency

Wee

kday

Wee

kend

Pers

onal

net

wor

king

ac

tivity

(ext

erna

l)Tr

avel

Pers

onal

net

wor

king

ac

tivity

(ext

erna

l)Tr

avel

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

Inva

riabl

e2.

720

2.37

4 2.

160

4.88

6 4.

780

4.22

1 2.

239

2.22

0 1.

767

5.19

5 4.

753

4.32

1

Gend

er(m

ale=

1, fe

mal

e=0)

-0.3

39

-0.3

38

-0.3

42

-0.0

80

-0.1

44

-0.0

69

-0.3

48

-0.4

16

-0.2

79

-0.1

26

-0.1

71

-0.0

89

Mar

ital s

tatu

s(m

arrie

d=1,

un

mar

ried=

0)0.

044

0.05

8 0.

151

0.39

9 0.

483

0.41

2 0.

202

0.08

8 0.

170

0.38

1 0.

548

0.46

9

Youn

ger g

ener

atio

n(30

or

youn

ger=

1, o

ther

s=0)

0.19

0 -0

.141

-0

.164

-0

.621

-0

.509

-0

.261

-0

.277

-0

.428

-0

.313

-0

.515

-0

.284

-0

.245

Olde

r gen

erat

ion(

60 o

r old

er=1

, ot

hers

=0)

-0.0

95

-0.0

71

-0.5

20

-0.4

80

-0.4

47

0.28

5 0.

213

0.10

0 -0

.540

-0

.443

-0

.336

Educ

atio

n le

vel (

colle

ge g

radu

ate

or h

ighe

r=1,

oth

ers=

0)-0

.311

-0

.091

-0

.097

0.

487

0.39

7 0.

447

-0.0

74

0.30

9 0.

278

0.23

6

Have

pre

-sch

oole

r chi

ldre

n (y

es=1

, no=

0)0.

113

0.07

0 0.

101

Whe

ther

they

are

offi

ce

wor

kers

(offi

ce w

orke

r=1,

ot

hers

=0)

0.12

9 0.

199

0.27

1 -0

.088

0.

117

0.09

0 0.

184

0.05

7

Wor

king

hou

rs a

t com

pany

(10

min

utes

)-0

.032

-0

.030

-0

.032

-0

.014

-0

.014

-0

.012

-0

.025

-0

.023

-0

.025

-0

.014

-0

.011

-0

.011

34

Tabl

e 3.4

Tob

it m

odel

’s es

timat

ion

resu

lts fo

r act

ivity

gen

erat

ion

frequ

ency

(Con

tinue

d)

Wee

kday

Wee

kend

Pers

onal

net

wor

king

ac

tivity

(ext

erna

l)Tr

avel

Pers

onal

net

wor

king

ac

tivity

(ext

erna

l)Tr

avel

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

2009

Estim

ate

2004

Estim

ate

1999

Estim

ate

Mai

nten

ance

hou

rs a

t hom

e(10

m

inut

es)

-0.0

59

-0.0

56

-0.0

49

-0.0

61

-0.0

54

-0.0

48

-0.0

59

-0.0

55

-0.0

43

-0.0

82

-0.0

74

-0.0

60

Oblig

ator

y m

ange

men

t hou

rs a

t ho

me(

10 m

inut

es)

-0.0

33

-0.0

30

-0.0

26

-0.0

30

-0.0

35

-0.0

29

-0.0

31

-0.0

30

-0.0

24

-0.0

42

-0.0

42

-0.0

35

Oblig

ator

y m

anag

emen

t hou

rs

outs

ide

hom

e(10

min

utes

)-0

.022

-0

.021

-0

.014

0.

070

0.02

4 0.

032

-0.0

06

0.01

9

Freq

uenc

y of

tele

phon

e us

e0.

129

0.18

1 0.

141

0.20

8 0.

202

0.29

7 -0

.045

Use

of in

form

atio

n co

mm

unic

atio

n(In

tern

et)(U

ser=

1, N

on-u

ser=

0)-0

.210

-0

.142

-0

.257

-0

.152

-0

.093

-0

.337

-0

.229

-0

.179

-0

.480

-0

.362

-0

.234

Stan

dard

dev

iatio

n(σ )

1.82

2 1.

783

1.78

5 2.

089

2.17

3 2.

076

1.87

4 1.

774

1.77

6 2.

178

2.26

5 2.

193

Num

ber o

f sam

ples

2425

6.0

3795

5.0

5236

5.0

2425

6.0

3795

5.0

5236

5.0

1627

0.0

2531

3.0

3354

1.0

1627

0.0

2531

3.0

3354

1.0

LL(C

)-2

6147

.2

-389

39.0

-5

3095

.9

-311

37.1

-4

9707

.0

-663

03.0

-1

7128

.1

-255

61.9

-3

3378

.6

-217

87.3

-3

4281

.8

-441

04.0

LL(β

)-2

4004

.3

-366

09.2

-4

9643

.1

-299

46.2

-4

8238

.3

-641

88.9

-1

6358

.8

-243

42.1

-3

1833

.8

-205

39.3

-3

2801

.4

-425

58.7

McF

adde

n’s

(2 P)

0.16

4 0.

120

0.13

0 0.

076

0.05

9 0.

064

0.09

0 0.

095

0.09

3 0.

115

0.08

6 0.

070

Not

e 1)

Sug

gest

ed o

nly

mea

ning

ful v

aria

ble

at th

e si

gnifi

canc

e le

vel o

f 0.5

or h

ighe

r

Not

e 2)

Cal

cula

tion

of th

e m

odel

’s d

escr

iptiv

e po

wer

2 P

=LL

(C)-

(LL(

β)-

K) /

LL(

C).

K re

fers

to th

e nu

mbe

r of v

aria

bles

.

35


Figur

e 3.8

Tob

it m

odel

’s pr

obab

ility d

ensit

y dist

ribut

ion

for a

ctivi

ty g

ener

atio

n fre

quen

cy (d

urin

g a

wee

kday

)

Pers

onal

net

wor

king

act

ivity

(ext

erna

l)Tr

avel

Use

of in

form

atio

n co

mm

unic

atio

n(In

tern

et)

Freq

uenc

y of

tele

phon

e us

eUs

e of

info

rmat

ion

com

mun

icat

ion(

Inte

rnet

)Fr

eque

ncy

of te

leph

one

use

1999

2004

2009

Not

e 1)

The

x a

xis r

epre

sent

s the

freq

uenc

y of

act

ivity

gen

erat

ion,

and

y a

xis r

epre

sent

s the

pro

babi

lity

of a

ctiv

ity g

ener

atio

n.

Not

e 2)

Com

pare

d th

e re

spon

dent

s who

use

pho

ne c

alls

thre

e tim

es a

day

and

non

-use

rs o

f pho

ne c

alls

, for

the

freq

uenc

y of

usi

ng p

hone

cal

ls.

Prob

abilit

y of

act

ivity

gen

erat

ion

Use

rs o

f inf

orm

atio

n co

mm

unica

tion

Non

-use

rs o

f inf

orm

atio

n co

mm

unica

tion

25.0

%

20.0

%

15.0

%

10.0

%

5.0%

0.0%

01

23

45

67

8

Prob

abilit

y of

act

ivity

gen

erat

ion

Use

rs o

f inf

orm

atio

n co

mm

unica

tion

Non

-use

rs o

f inf

orm

atio

n co

mm

unica

tion

25.0

%

20.0

%

15.0

%

10.0

%

5.0%

0.0%

01

23

45

67

8

Prob

abilit

y of

act

ivity

gen

erat

ion

Use

rs o

f inf

orm

atio

n co

mm

unica

tion

Non

-use

rs o

f inf

orm

atio

n co

mm

unica

tion

25.0

%

20.0

%

15.0

%

10.0

%

5.0%

0.0%

01

23

45

67

8

25.0

%

20.0

%

15.0

%

10.0

%

5.0%

0.0%

01

23

45

67

8

User

of p

hone

calls

(text

mes

sagi

ng) -

3 ti

mes

Non

-use

r of p

hone

call

s(tex

t mes

sagi

ng)

Prob

abilit

y of

act

ivity

gen

erat

ion

25.0

%

20.0

%

15.0

%

10.0

%

5.0%

0.0%

01

23

45

67

8

User

of p

hone

calls

(text

mes

sagi

ng) -

3 ti

mes

Non

-use

r of p

hone

call

s(tex

t mes

sagi

ng)

Prob

abilit

y of

act

ivity

gen

erat

ion

25.0

%

20.0

%

15.0

%

10.0

%

5.0%

0.0%

01

23

45

67

8

User

of p

hone

calls

(text

mes

sagi

ng) -

3 ti

mes

Non

-use

r of p

hone

call

s(tex

t mes

sagi

ng)

Prob

abilit

y of

act

ivity

gen

erat

ion

Prob

abilit

y of

act

ivity

gen

erat

ion

Use

rs o

f inf

orm

atio

n co

mm

unica

tion

Non

-use

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Figur

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Empirical Study: Impact of Smart ICT on the Choice of Transportation Mode

38

Empirical Study: Impact of Smart ICT on the Choice of Transportation Mode

In this chapter, we would like to discuss how Smart ICT affects the change of travel utility. Change of travel utility refers to the situation where the use of smart devices enables the users to do multiple productive activities during travel and hence increases the utility of travel itself. Change of travel utility, in particular, is expected to contribute to the increase in use of public transportation modes. In this regard, we performed an empirical study on the impact of smart ICT on the choice of transportation mode.

1. Analysis overview

1) Analysis purpose

In smart knowledge society, mobility no longer means only spatial mobility but should include the potential use of ICT that transcends time and space. Therefore, we need an empirical study on how the use of ICT affects the activities during travel, in order to ensure sustainable mobility. In this study, we reviewed the variables affecting emotion (satisfaction, utility) during travel and the difference among the activities during travel by transportation mode and travel purpose. Besides the traditional factors such as travel time and cost affecting the choice of transportation mode, we analyzed how the use of ICT during travel affects the choice of transportation mode.

39


In this chapter, the analysis purpose is first to demonstrate whether doing multitasking with a smart device during travel could generate positive effects during travel in qualitative terms. Secondly, we aimed to conduct a quantitative analysis on how much contribution the use of smart devices during travel could make to the increase in use of public transport.

2) Survey overview

(1) Composition of survey items

The survey items were created on the basis of the hypothesis built through the review of the existing studies. We prepared the survey questionnaire, classifying activities by travel purpose into travel for commute to work/school and travel for shopping/leisure activities. The survey is composed of a total of 68-72 questions throughout 4 sections including personal & travel attributes, travel through private cars, travel through public transport, and perception on travel behavior.

In order to analyze the satisfaction of users with their activities during travel, we conducted the survey by using Day Reconstruction Method(DRM) for the latest travel for commute to work/school and travel for leisure activities.

(2) Survey

We conducted the survey by dividing the questionnaire into two types according to the two types of travel purpose (commute to work/school and shopping/leisure activities.) In order to secure as many valid respondents as possible for each travel purpose, we additionally conducted field survey. For example, we visited companies for the item of ‘commute to work/school’, and went to large department stores or shopping malls during weekdays and weekends for the item of ‘shopping/leisure activities’. We designated survey areas among the places where people have alternatives to private cars, such as bus and subway, with well-developed public transport services, and where people can reach the secondary center (Gangnam area) of Seoul with not much gap in the travel time among transportation modes. We set differential proportion of survey participants for each age group, considering the age-specific population

40

composition in the metropolitan areas. A total of 400 pieces of survey paper were distributed to the survey respondents.

Table 4.1 Survey design for this study

ClassificationNo. of items

Key details Remarks

Commute to work/school

1. Personal & travel attributes

12

· Gender, age, income, occupation, major transport mode, type of working hour (to identify if the travel is time-dependent), frequency of using Internet during travel, etc.

-

2. Travel through private cars

15

·…Travel time, travel cost, financial support for travel, activities and emotion during travel, the most useful activity during travel, loyalty to the use of private cars, etc.

·…In the case of the travel for shopping/leisure activities, there are additional survey items such as compulsion of travel, attributes of travel date (weekend, weekday), whether the travel includes minors, etc.

DRM

3. Travel through public transport

13

·…Travel time, travel cost, frequency of transfer, financial support for the travel, activities and emotion during travel, the most useful activity during travel, conditions inside the bus or subway (taking a seat, crowdedness inside the bus or subway), satisfaction with the arrival service, etc.

·…In the case of the travel for shopping/leisure activities, there are additional survey items such as compulsion of travel, attributes of travel date (weekend, weekday), whether the travel includes minors, etc.

DRM

4. P e r c e p t i o n o n travel behavior

28·…The most desirable travel time, preferred activity

during travel and preference for use of time, perception on use of private cars, etc.

-

41


Table 4.2 Survey overview

Classification Details

Survey period 2012. 5. 31 ~ 6. 11 (for 12 days) ※ Including the period for training surveyors and weekend

Survey areas Gangnam areas in Seoul, Pyeongchon, Bundang

Survey participants


Visited companies and conducted field survey

Shopping/leisure activities

Field survey for customers of large department stores and shopping malls (during weekdays and weekends)

Survey institution GRI, Co., Ltd. (professional survey institution)

Table 4.3 Pieces of survey paper by travel purpose

Travel purpose

Distributed pieces

Valid pieces Areas


230 pieces 200 pieces100 pieces for Seoul, 50 pieces for Bundang and Pyeongchon respectively

Shopping300 pieces

98 pieces50 pieces for Seoul, 24 pieces for Bundang and Pyeongchon respectively

Leisure activities

102 pieces53 pieces for Seoul, 24 pieces for Bundang, 25 pieces for Pyeongchon

2. Basic statistical analysis

1) Possession of information communication devices during travel for commute to work/school

In commuting to work/school, 82% (163 people) of the 200 respondents had smartphones while the average number of information communication devices they carry during travel is 1.12 devices. Since smartphone offers a combination of multiple functions, smartphone owners appeared to have lower needs for other information communication devices. Subway users carry more information communication devices (1.2 devices) than the private car or bus users. The survey also found that respondents, whose travel time is over 1 hour, carry more information communication devices (private car users;1.2 devices, public transport users;1.3 devices) than the respondents

42

whose travel time is within 1 hour (both private car users and public transport users;1.1 devices), regardless of the transportation mode.

2) Number of activities during travel

In the travel for commute to work/school by private cars, the average travel time is 42 minutes during which the driver does an average of 2.5 activities and the passenger does an average of 3.8 activities. In this case, the number of activities using information communication devices was 1.6 for the private car drivers (64%) and 2.3 for the passengers (61%). We also compared the number of activities during travel among users of other transportation modes, excluding the private car drivers who should concentrate on driving. As a result, we found that the number of activities during travel was similar between the passengers (3.8 activities) of private cars and subway users (3.7 activities). However, bus users had relatively lower number of activities (2.9 activities) during travel. Among the activities during travel, the activities using information communication devices accounted for a larger proportion when they were related to work/study/self-development.

Table 4.4 Change in the number of activities during travel by travel purpose and transportation mode

Travel purpose

Transportation mode

Average travel time

Travel condition & mode

No. of activities

during travel

No. of activities related to

work/study

The most useful activity during

travel


Private car 42 min.Driver 2.5 (1.6) 1.2 (1.0)

Listening to radio/music

Passenger 3.8 (2.3) 1.6 (1.1)Text messaging,

conversation

Public transport

46 min. Subway 3.7 (2.1) 2.1 (1.2)Phone calls,

reading

34 min. Bus 2.9 (1.6) 1.5 (0.7)Phone calls, text

messaging

Shopping/leisure

activities

Private car 29 min.Driver 2.3 (1.3) 1.1 (0.7) Conversation with

the person next to themselves Passenger 3.0 (1.6) 1.3 (0.6)

Public transport

33 min. Subway 3.2 (1.7) 1.7 (1.0) Phone calls

32 min. Bus 2.7 (1.3) 1.0 (0.4) Phone calls

※ Numbers in brackets refer to the number of activities using information communication devices.

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Figure 4.1 Percentage of use of information communication devices by travel purpose and transportation mode

Percentage

90%

50%

60%

70%

80%

40%

30%

20%

10%

0Driver Passenger

Private car

Commute to work/school Shopping/leisure activities

Public transport Public transportPrivate car

Subway Bus Driver Passenger Subway Bus

Percentage of activity using ICT during travel

Percentage of work/study related activity using ICT during travel

3) Change of activity during travel, depending on the length of travel time

Private car drivers, whose commute to work/school takes less than 1 hour, said that the most often done and most useful activity during their travel is ‘listening to radio and music’, while private car passengers answered that ‘text messaging’ is the most often done and most useful activity during travel. In the case of the commute to work/school, public transport users responded that the most often done activity during travel is ‘phone calls’, followed by ‘text messaging’ and ‘reading documents such as newspaper and books’, regardless of their travel time. Respondents with longer hours of travel time tend to do more meaningful activities, instead of simple activities, during travel. For example, respondents whose travel time is less than 1 hour answered that the most useful activity during travel is ‘phone calls’ and ‘text messaging’, while respondents whose travel time is 1 hour or longer said that the most useful activity during travel is ‘self-development activity’ and ‘reading documents such as newspaper and books.’

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4) The value of time for activities during travel

In terms of transportation mode, subway users had higher value of time for their activities during travel. An average of 82% of the private car passengers and subway users answered that their activity during the commute to work/school had a value of 5 minutes or more, compared to their working hours. An average of 76% of the private car drivers and bus users gave such answers. Meanwhile, an average of 77% of the private car passengers answered that their activity during the travel for shopping/leisure activities had a value of 5 minutes or more, followed by private car drivers (73%), subway users (71%), and bus users (67%).

Figure 4.2 Change in the value of time for activities during travel, depending on the length of travel time

Passenger SubwayDriver Bus

30%

20%

10%

0%5~10 minNone 10~15 min 15~20 min 20 min. or more

Response rate

The value of activities during travel for commute to work/school, compared to working hours

The value of activities during travel for shopping/leisure activities, compared to working hours

Passenger SubwayDriver Bus

40%

20%

30%

10%

0%5~10 minNone 10~15 min 15~20 min 20 min. or more

Response rate

5) Emotional experience through activities during travel

Our survey found that among the 122 private car owners who commute to work/school by driving, 100 people (82%) experienced positive emotion, whereas only 47% of the private car passengers experienced positive emotion. When commuting to work/school by public transport, half of the users (57.6% of subway users, 54.4% of bus users) experienced positive emotion regardless of the public transportation mode.

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Table 4.5 Frequency of emotional experience during travel by travel purpose and transportation mode

Emotion experienced through travel

Travel for commute to work/school

Travel for shopping/leisure activities

Private car Public transport Private car Public transport

Driver Passenger Subway Bus Driver Passenger Subway Bus

Positive emotion

Happy 14 12 5 6 50 37 23 4

Warm & friendly 5 17 8 3 9 25 9 11

Competent/capable

24 14 30 16 23 19 18 27

Enjoying myself 57 10 33 12 10 0 23 39

Sub-total 82% 43% 62% 30% 75% 66% 60% 66%

Negative emotion

Wishing the travel would soon be

finished2 9 23 8 8 5 5 27

Restless 0 1 2 0 0 0 0 1

Angry 1 0 2 1 0 0 0 0

Depressed 2 2 2 1 1 1 3 0

Anxious 4 2 3 2 0 0 0 0

Exhausted 13 11 24 19 6 6 10 0

Sub-total 16% 12% 25% 19% 6% 6% 11% 0%

Sub-total 122 78 132 68 107 93 91 109

6) Gap between actual commute time and desired commute time9)

In order to identify the satisfaction of respondents with their commute time to work/school, we surveyed the gap between the desired commute time (DCT) and actual commute time (ACT). DCT and ACT are the indicators for measuring satisfaction of commuters with their commute time. The wider the gap between DCT and ACT is, the bigger the complaints about the actual commute time are. In our survey, respondents who mainly use private cars or subway answered that their desired commute time

9) Lothlorien et. al (2010) analyzed the gap between DCT and ACT through the ordered probit model, and suggested that commute time is not a disutility that must definitely be minimized. In this study, the gap between DCT and ACT through use of information communication devices was estimated to be 16 minutes on average.

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to work/school is within 30 minutes. Respondents who mainly use bus said that their desired commute time is within 20 minutes. In addition, 46% of the private car users responded that their actual commute time is shorter than or same as the desired commute time, while only 34% of the public transport users answered that their actual commute time is shorter than or same as the desired commute time.

Table 4.6 Distribution of gap between actual commute time (ACT) and desired commute time (DCT) by transportation mode

Gap with actual commute time Travel by private car Travel by public transport

ⅰ) If DCT > ACT, positive value(+) 54 (27%) 28 (14%)

ⅱ) If DCT = ACT, 0 38 (19%) 39 (20%)

ⅲ) If DCT < ACT, negative value(-) 108 (54%) 133 (66%)

Sub-total 200 200

Figure 4.3 Change in the gap between ACT and DCT by travel time

20

15

10

5

5 10 15 20 25 30 35 40 45 50 55 60

0

-5

-10

-15

-20

-25

-30

DCT - ACT(Minutes) Private car users

Public transport users

Actual travel time(minutes)

DCT > ACT

(+)

DCT < ACT

(-)

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3. Model analysis result

1) Analysis model

We used the following models to analyze changes in the use of ICT (multi-tasking) by travel purpose and transportation mode and to figure out how the personal attributes in terms of social and economic status affect the travel utility (satisfaction). In utilizing the analysis models below, we focused on the activities during travel as well as emotional experience during travel (DRM analysis) and impact of activities during travel on the choice of transportation mode (MNL analysis).

(1) Seemingly Unrelated probit Regression (SUR)

There are two main reasons that we utilized the SUR model to analyze activities during travel and emotional experience during travel that were surveyed through DRM. The first reason is that the emotional status during travel differs by the chosen transportation mode (private cars or public transport). This implies that there would be interdependence between them. For example, when a person, who prefers using his/her private car, uses public transport due to some parking problems, the person may feel negative emotion during his/her travel in the public transport not because of the quality of public transport service (travel time, arrival on time, comfortableness inside the public transport, etc.) but because of his/her personal preference for private cars. Secondly, we applied the SUR model to identify the direction of emotional correlation during the travel by private cars and the travel by public transport.

In this regard, we compared the analysis results from the SUR model, which reflects the correlation between the choice of private cars and the choice of public transport, and the analysis results from the univariate probit model by transportation mode. Based on this comparison, we analyzed their empirical difference.

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(2) Transportation mode choice model: Multinomial Logit Model (MNL)

Multinomial logit model is the most widely used model for estimating the relation between dependent variables and independent variables when the dependent variables are based on discrete choice (McFadden, 1974). The MNL model is based on the random utility theory that people as a decision maker choose the alternative (transportation mode) that maximizes their personal travel utility.

(3) Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a methodology of analyzing data sets that are used for reducing relatively many variables or for describing the variables (Johnson and Wichern, 1992). PCA would be meaningless, if there is no correlation among variables. Generally, PCA is a very useful way of analyzing data sets, if there are a large number of highly correlated variables.

PCA is usually applied to identifying an individual’s potential propensity, as it enables extraction of new, mutually independent variables, among multiple variables that are observed to have mutual dependency. As the most general type of factor analysis, PCA enables us to find linear combination of variables that could provide as much explanation as possible for variance.

2) Analysis result: model for analyzing the impact of activity during travel on emotional experience

(1) Analysis result from the model of travel for commute to work/school

According to the analysis result, people who prefer to move or travel are more likely to experience positive emotion during travel, regardless of the transportation mode. They have negative emotion during travel usually when their activity is constrained during travel.

Negative emotion tends to increase among the private car users, who use the Internet much frequently, when their activity during travel is limited due to driving.

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In the case of public transport users, negative emotion increases when their activity during travel is limited because of the crowdedness inside the public transportation modes.

(2) Analysis result from the model of travel for shopping/leisure activities

In the case of the travel for shopping/leisure activities, it was difficult to find variables that have implications for each transportation mode. Public transport users are more likely to have positive emotion when they got a seat in the bus or subway, which indicates the importance of available seats in the public transport service. In addition, variables indicating satisfaction with the commute time to work/school had a positive influence on the use of public transport. This implies that we need an additional analysis on the variable of gap between desired travel time (DTT) and actual travel time (ATT.)

Table 4.7 Analysis on the impact of activity during travel on emotional experience (summary)

Transportation mode

Travel for commute to work/school Travel for shopping/leisure activities

Frequency of using Internet during travel

Frequency of using Internet outdoor

Crowdedness in the public

transportation mode

Frequency of using Internet outdoor

Satisfaction with travel

Travel with an

available seat

Gap between DTT and

ATT

Private car users Negative(-) Positive(+) - - Positive(+) - -

Public transport users

- - Negative(-) Negative(-) - Positive (+) Positive(+)

Note: 1) Frequency of using Internet during travel: if the respondent uses Internet during travel 2~3 times;1, others;0

2) Frequency of using Internet outdoor: 2~3 times a week;1, others;03) Crowdedness in the public transportations mode: crowded; 1, others; 04) Satisfaction with travel: ‘satisfied’ or ‘very satisfied’; 1, others; 0 5) Travel with an available seat inside the public transportation mode; 1, others; 0

6) Gap between DTT and ATT: DTT>ATT; 1, DTT≦ATT; 0

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(3) Result of analysis on mutual dependence between transportation modes for positive emotion during travel

We analyzed SUPR model to simultaneously estimate explanatory variables that influence positive emotion felt by users when they do activities during travel by private cars and by public transport. In this analysis, dependent variable is the positive emotion during travel by private cars and the positive emotion during travel by public transport. Positive emotion refers to ‘happy’, ‘warm/friendly’, ‘enjoying myself’, ‘competent/capable’ among the 12 emotion categories presented in DRM. We also performed a separate analysis on the univariate probit model for private cars and public transportation modes respectively, and compared the analysis results. As for the coefficients estimated by bivariate probit model and univariate probit model, the statistical significance and signs of coefficients did not change in all explanatory variables while there was a gap only in terms of their size. We conducted t-test and likelihood ratio test to identify mutual dependence of emotional experience during travel by two transport modes. As a result, we found that there is mutual dependence between the emotional experiences during travel by the two transport modes and that the analysis through univariate probit model could produce biased estimates if the variables are assumed to be independent from each other without any mutual dependence as in the SUR model. This is because t value of , which is the correlation coefficient of SUR model, has statistical significance, and null hypothesis was rejected at the significance level of 1% in the likelihood ratio test. Therefore, it is rational to apply the bivariate probit model to analyze the mutual dependence among transportation modes for positive emotion during travel. Signs of correlation coefficients all turned out to be positive value, which could be interpreted that they have a weak complement relation with each other. In this study, emotional experience during travel by the two transportation modes is in complement relation, because most of the survey respondents are choice riders who have driver’s license (84.5%) and private cars (77.5%). If the majority of the survey respondents had been captive riders whose only available transportation mode was public transport, then the signs of correlation coefficients would have been different from the above.Variables influencing the emotion during travel differ by the transportation mode. In the case of private car users, their perception on travel, rather than their activity during

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travel, influences their emotion during travel. As for the public transport users, their activity during travel had a greater influence on their emotion during travel. In other words, private car drivers, in particular those who use ICT often and prefer to use their own car, experience positive emotion more when they talk with the person sitting next to them in the car or are lost in thought while driving. But, positive emotion for public transport users decreases when they read documents such as books or newspaper inside the bus or subway. This is probably attributable to the fact that they can not continue doing the activity during travel when the nearby passengers get on or off the public transport at a bus stop or a subway station. When the public transport users cannot do any activity during travel due to the crowdedness in the bus or subway, their positive emotion decreased as expected. Variables that put the largest influence on all types of positive emotional experience turned out to be the variables of preference for travel, travel purpose and satisfaction with the travel time, etc. This is in line with the existing study (Mokhtarian, et al., 2001; Ory and Mokhtarian, 2005) that travel itself could become a purpose in the case of the variable regarding preference for travel itself (Trip_Prefer). Positive emotional experience also increased when the travel purpose is shopping/leisure activities. The variable regarding the gap between ATT and DTT decreased the positive emotion when ATT is longer than DTT during travel by public transport, while increasing the positive emotion when ATT is shorter than DTT. The variable regarding satisfaction with the travel time has more influence on public transport users rather than on private car users. Emotion of public transport users was more influenced by the gap between their personally desired travel time and the actual travel time, rather than the absolute length of travel time. An interesting fact is that the variable regarding social and economic attributes of respondents, such as income, age and gender, had no much influence on the emotion during travel. Factor 1 refers to the latent variable indicating that private car means more than just a transportation mode, and was analyzed to have a negative sign. Respondents, who view their private cars as something more than just a transportation mode, showed a decrease in their positive emotion during travel by public transport. This is attributable to their perception on their private cars, rather than to the activity during travel or to any negative view on the public transport service.

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Table 4.8 Result of analysis on mutual dependence between transportation modes for positive emotion during travel

Positive emotional experience during travel (positive; 1, not positive;0)

SUR ModelUnivariate Probit Model

Private car users Public transport usersCoefficient(Coef.) Coefficient(Coef.) Coefficient(Coef.)

Positive experience in use of private carPrivate car as a main transportation mode (if private car is the main transportation mode;1, if not;0)

0.513 ‡ 0.562 ‡ -

Preference for travel (if the user prefers travel itself;1, if not;0) 1.161 ‡ 1.156 ‡ -

Value of the activity during travel (if the user thinks that their activity during travel has a value of 5 minutes or more of their working hours; 1, if not;0)

0.279 † 0.322 † -

Office workers*commute to work/school(if the user is an office worker and the travel purpose is to commute to work;1, if not;0)

0.311 ‡ 0.331 † -

Factor_1 (latent variable) (Private cars mean more than just a transportation mode.)

- -0.153 ‡ -

Constant -1.680 -0.219 -

Positive experience in use of public transportReading (if the user read a book during travel;1) -0.268 † - -0.443 ‡

Conversation (if the user talked with the person sitting next to him/her; 1)

0.438 ‡ - 0.646 ‡

Factor_1 (latent variable) (Private cars mean more than just a transportation mode.)

- - -0.132 †

Crowdedness (if it was crowded inside the public transportation mode;1)

-0.263 - -0.423 ‡

Travel time (if ATT is longer than DTT; 1) - - -0.321 †

On time arrival of public transportation mode (if the public transportation mode arrived on time;1)

0.382 ‡ - 0.371 ‡

Preference for travel (if the user prefers travel itself;1) 1.142 ‡ - 1.161 ‡

Shopping/leisure activities (if the travel purpose is to go shopping or to have leisure activities;1)

0.303 † - -

Constant -0.184 0.041

Correlation coefficient( ) 0.723 ‡

Log pseudo-likelihood -319.735 -161.142 -184.04416

Number of observations 400 400 400

Pseudo 2P 0.178 0.159 0.257

※ ‡ ; statistical significance level 5%, † ; statistical significance level 10%, significance level of crowdedness

variable ; 15%

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3) Analysis result: Impact of activity during travel on public transportation mode

(1) Establishment of transportation mode choice model by travel purpose

We need to reflect personal potential propensity when establishing the transportation mode choice model, because choice of transportation mode could be influenced not only by travel time and travel cost but also by personal potential propensity (including preference for private car.) Through Principal Component Analysis (PCA), we identified three potential factors for choice of transportation mode.

Table 4.9 Latent variables & identified factors

Classification Survey items (factor loading)

Factor 1Perception on

private car

My car protects my privacy. (0.633)

I feel safe when driving my car. (0.633)

My car protects me from bad weather. (0.564)

Private car means more than just a transportation mode to me. (0.606)

I assess people by their cars. (0.695)

My car shows who I am. (0.675)

I envy those who drive foreign-made luxury cars. (0.748)

I think that a private car represents the social status or dignity of the owner. (0.772)

Factor 2 Driving preference

It is fun to drive. (0.790)

My hobby is driving. (0.815)

I enjoy driving. (0.811)

Factor 3Use of information

communication devices

Frequency of using information communication devices during travel by public transport (0.601)

Experience of using public transport, for the purpose of using information communication devices (-0.844)

Note: 1) Factor selection method: PCA, Rotation method: Verimaz rotating method with Kaiser normalization2) Marked only the factor loading result that is higher than 0.5.3) (·) refers to the factor loading value.4) At Factor 3, the experience of using public transport for the purpose of using information

communication devices has negative (-) value, because five-points Likeret scales are in the order that

is opposite to the frequency of using information communication devices.

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The potential factors are individual perception on private car (perception on private car), preference for driving (driving preference), tendency in use of information communication devices during travel (use of information communication devices), etc. We established the transportation mode choice model for each travel purpose by using the multinomial logit model and excluding captive riders. In our analysis on the factors that influence choice of private cars in the commute to work/school, we found that the bigger the gap between the travel time of public transport and the travel time of private car (meaning that public transportation mode gets later than the private car does), the more the use of private cars increased. Respondents, who receive financial support from their companies for their commute to work by private var (eg. fuel expenses), showed a higher tendency in using their private cars. Respondents at or below the age of 30 (aged 20~30) showed a lower tendency in using private cars, compared to other age groups. Men showed a higher tendency in using their private cars to commute to work/school, compared to women. In terms of the factors that influence choice of public transportation modes in commute to work/school, respondents who receive support for public transportation expenses showed a higher tendency of using public transport. The use of information communication devices during travel also increased the tendency of using public transport. In terms of travel for shopping/leisure activities/personal networking, only the variables of gender, age and the use of information communication devices during travel had a statistically significant impact on the choice of public transportation modes.

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Table 4.10 Result from the transportation mode choice model by travel purpose

Variables

Commute to work/schoolShopping/leisure activities/

personal networking

Private carPublic

transportPrivate car

Public transport

Constant0.013(0.330)

-0.793

(0.268)**-

Gap between the travel time of public transportation mode and the travel time of private car(travel time of public transport travel time of private car, minutes)

0.017(0.007)*

- - -

Support for private car’s transportation cost (if receiving support =1, if not = 0)

2.835(1.177)*

- - -

At or below the age of 30(if the user is at or below the age of 30 = 1, if not = 0)

-1.134(0.509)*

--1.251

(0.524)*-

Male (male =1, female = 0)0.974

(0.405)*-

0.956(0.420)*

-

Support for travel cost (if receiving support =1, if not = 0)

-1.544

(0.777)*- -

Use of information communication devices during travel

-0.599

(0.214)*-

0.589(0.202)**

Number of observation data 158 149

LL(0) -109.517 -103.279

LL(β) -82.2597 -75.256

2P 0.214 0.251

Note: 1) Based on the significance level of 0.1 (*≤0.05, **≤0.01)2) (·) refers to standard deviation.3) LL (0) is the Log Likelihood value when all variables are limited to 0, while LL(β) refers to the Log

Likelihood value at the final convergence.4) 2P is defined as 2P = 1-(LL(β)-N) / LL(0). Prevent over-estimation as the number (N) of variables

increases.

(2) Simulation analysis

We set three types of scenarios and performed a simulation analysis to identify their influence on the choice of public transportation modes. Among a total of three scenarios, scenario 1 assumed that travel time of public transport decreases by 10

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minutes and scenario 2 assumed that travel time of public transport decreases by 10%, while scenario 3 assumed that the tendency of using information communication devices increases by 10%. In other words, a 10% increase in the use of information communication devices leads to an average of 9.4% increase in the utility of public transport. This implies that the use of information communication devices increases the utility of public transportation modes.

As for the change in the probability of choosing public transport for each scenario, a 10% decrease in the travel time of public transport (that is, travel time of public transport is 10% shorter than now) and a 10% increase in the use of information communication devices increases the probability of choosing public transport by an average of 4.5% and 2.4% respectively.

Due to the increase in use of information communication devices, the probability density function for the choice of public transport moved to the right side and the probability density function for the choice of private car moved to the left side. The overlapping region between the two types of probability is small but increasing.

As a result of the scenario-specific simulation, we found that the share of public transportation modes could increase through improvement of the environment where people use information communication devices. The share of public transportation

Figure 4.4 Change in the public transport utility due to the increase in use of information communication devices during travel

2.5

1.5

0.5

-0.5

-1.5

2.5

2.0

1.5

1.0

0.5

Base A 10% Increase in ICT use Base A 10% Increase in ICT use

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modes increased by 3.0%, when the travel time of public transport decreases by 10 minutes (scenario 1). A 10% decrease in the travel time of public transport (scenario 2) and a 10% increase in the use of information communication devices (scenario 3) increased the share of public transportation modes by 1.0% and 0.4% respectively.

Figure 4.5 Change in the probability of choosing public transport by scenario

1

0.8

0.6

0.4

0.2

0

0.6

0.5

0.4

Base A 10% increase in ICT use

A 10% decrease in the travel time of public transport

Base A 10% increase in ICT use

A 10% decrease in the travel time of public transport

Figure 4.6 PDF change in the probability of choosing transportation modes due to information communication devices

Private car (BASE) Public transport (BASE) Private car (10% increase in ICT) Public transport (10% increase in ICT)

Increase in the probability of choosing public transport

f(P)

(P)

Decrease in the probability of choosing private car

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Table 4.11 Simulation result for the share of public transportation modes by scenario (commute to work/school)

Classification Case1 Case2 Case3 Case4 Case5 AverageIncrease/decrease

Base 43.7% 40.5% 34.8% 40.5% 34.2% 38.7%

Scenario 1A 10-minute decrease in the travel time of public transport

44.9% 44.3% 36.1% 45.6% 38.0% 41.8% 3.0%

Scenario 2A 10% decrease in the travel time of public transport

44.3% 43.0% 34.8% 41.8% 34.8% 39.7% 1.0%

Scenario 3A 1 0 % i n c r e a s e i n u s e o f information communicat ion devices

44.3% 41.1% 34.8% 40.5% 34.8% 39.1% 0.4%

Note: As the scenario analysis is based on the probability model, we extracted random figures five times and

presented the average value.


Conclusion & Follow-Up Study

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The existing studies on how the use of information communication devices influences the transport sector have made no more progress than making a conceptualization or just presented fragmentary research of partial phenomenon. In this study, we aimed to analyze quantitative impact of the use of information communication devices on the transport sector, despite the constraints in data and analysis methodology. We also intended to discuss such an analysis from the overall perspectives of transport planning elements. To this end, we divided the study into the analysis on the characteristics of smart knowledge society and the empirical analysis on the impact of ICT on the transport sector. Our study results are summarized as below.

Firstly, the analysis on the characteristics of smart knowledge society shows that the development of ICT brings changes to the urban facility, space and lifestyle, and consequently transforms the travel behavior, mobility and travel utility in the transport sector. In other words, the advancement of smart knowledge society could change critical planning elements (travel demand, travel distribution, choice of transportation mode, etc.) for establishing transport infrastructure policy. In terms of transport demand, there are ‘substitution effects’ reducing travel generation, ‘complement (increase) effects’ increasing travel generation, and ‘change time/mode/destination effects’ modifying the travel method itself. However, the travel behavior changes brought by ICT are diverse, complicated and different depending on the type of activity. As for the travel distribution, the development of ICT enhances individual mobility and accessibility, and the urban spatial information shared through smart devices contributes to the expansion of individual activity space and efficient travel. In

Conclusion & Follow-Up Study

61


terms of the choice of transportation mode, ICT has enabled users to do multitasking during travel and to use their travel time in a productive manner. This could contribute to the increase of travel utility and the perceptional change on the use of public transport.

Secondly, the empirical analysis on the impact of ICT upon the transport sector came up with the following results. Based on the Time Use Survey conducted by the Statistics Office, we identified the followings from the macroscopic perspectives. ① The proportion of ICT users and hours of their ICT usage constantly increased. The use of information communication devices, in particular, is spreading from the younger generation to the middle-aged and older generation. ② The use of information communication (Internet) had substitution effects reducing the frequency of personal networking activity generation, while the use of phone calls had complement effects increasing the frequency of personal networking activity generation. This indicates that the higher the frequency of phone calls, the more the frequency of personal networking activity increases. ③ The variable of the use of information communication (Internet) had substitution effects reducing the frequency of travel generation, while the variable of the use of phone calls had complement effects increasing the frequency of travel generation during weekdays. However, the impact of the use of phone calls on the frequency of travel generation constantly decreased. ④ In the analysis of annual changes in the marginal effects of each information communication device on the frequency of travel generation, we found that phone calls contribute to the increase of both personal networking activity and travel generation frequency, but that the impact from the use of phone calls decreases as time changes. The use of information communication (Internet) contributes to a decrease in the travel generation frequency and the impact from the use of information communication (Internet) was on a constant increase. The use of information communication devices had a greater influence on the activity on the weekend, rather than during weekdays.

We also analyzed how the use of information communication devices during travel influences qualitative aspects and the utility of transportation modes, and came up with the following results. ① The entire travel time was not regarded as wasted time, as the users could do activities using their information communication devices during travel. Public transport users, in particular, tended to think that their

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travel time is not wasted, when they got a seat in the public transportation modes. In contrast, they showed a higher tendency of regarding their travel time as wasted time, when they cannot do any activity during travel due to the crowdedness in the public transportation modes or when they have to transfer once or more. ② Utility of time changed, depending on the travel time and whether the information communication devices are used or not during travel. Our survey respondents answered that the most useful activity during their travel of less than 1 hour was online activity such as ‘phone calls’ and ‘text messaging.’ In the case of the travel for 1 hour or longer, the most useful activity turned out to be ‘self-development activities’ and ‘reading documents such as newspaper and books.’ This implies that people tend to do meaningful activities instead of simple activities during travel, if their travel time gets longer. ③ Public transport users did more activities during travel than private car users did. The use of information communication devices during travel had a significant impact on the rate of using public transportation modes. In other words, the use of information communication devices enhances the utility of public transportation modes, contributing to the use of public transport.

In this study, we tried to establish an empirical data-based model to assess how the development of ICT influences the transport sector. Nevertheless, this study has many constraints. First, although we analyzed the impact of the ICT development on the transport planning elements by applying diverse methodologies, we considered only the independent impacts for each stage, without the analysis on the entire impacts (travel generation→travel distribution→choice of transportation mode).

Second, there are not many standardized analysis methodologies available for quantitative analysis of the impact of ICT on the transport sector. In this study, we tried to apply the most rational model available but would like to point out that individual models require more precise review in the future.

Lastly, there were constraints in the data for analysis. The latest data available in the Time Use Survey of Statistics Office is the data as of 2009. However, it is from 2010 that the full-scale penetration of smartphones began in Korea. This creates a time gap between the latest available data and the period of full-scale smartphone penetration. In order to complement this gap, we performed additional survey but should indicate that the number of samples was limited.

Despite the constraints described above, this study has a significant meaning

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in the sense that it tried to perform a quantitative analysis on the impact of ICT development on the transport sector, which was only conceptualized or was not fully discussed by many of the existing studies. We also expect that the findings from this study could be utilized for conducting relevant studies in the future, from academic and policy perspectives.

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