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Advances in ICT and its Implications for
Travel Behavior: A Case Study of Korea
I- TED 2014 April 9-11 2014, Dallas U.S.
Sungwon LEE, Gyeng Chul KIM, Seung Kook WU and Jieun OH
Contents
Ⅰ
Ⅱ
Ⅲ
Ⅳ
Introduction (ICT Connectivity and Transport)
Stated Preference Survey and Analysis
Policy Implications from SP
Discussions (Q & A)
Proliferation of ICT devices in Korea and in the world The world is becoming more and more connected
Advances in ICT and its Implications
Introduction
3
Intelligent multi-purpose devices with ICT convergence Provide various transport services integrated with ICT
Changes in our lifestyle and its implications for transport
4
Travel costs and travel time are the main attributes in modal choice models
A number of studies found that Information technology development and penetration of ICT devices (like smartphone) change the life, challenging the notion about travel (Lyons and Urry, 2005) and the disutility of transit modes.
Sustainable Development Commission (2010) reported that “the ability to stay connected and turn travel time into productive work time can be a significant attraction for business travelers.
Advances in ICT and travel demand models
Literature Review
5
Travel time can be productive with ICT Lee-Gosselin and Miranda-Moreno (2009)
A survey of Chicago Transit Authority train riders suggests that the riders use time and money in better way than drivers (Frei and Mahmassani, 2011)
Connolly, Caulfield, and O’Mahony (2009) found that multitasking is extremely common while traveling by rail and smartphone users would benefit from internet connection in trains
Gamberini et al. (2012) found that even if the travel length is relatively short, train riders engage in several activities especially for those using mobile ICT devices
Advances in ICT and its implications for travel
Literature Review
6
7
• City bus patronage is increasing
• Passenger kilometer traveled is also increasing
Intracity Bus Transportation Statistics
Year Passenger Passenger-km
2002 4,523,037 24,295,022
2003 4,408,604 22,730,884
2004 4,451,840 25,756,855
2005 4,536,634 25,902,141
2006 4,615,930 27,587,636
2007 4,768,437 28,730,674
2008 5,068,671 30,792,310
2009 5,168,492 32,037,348
2010 5,246,965 31,684,079
2011 5,535,686 32,557,560
Table 1 Intracity Bus Transportation Statistics
*Source: National Bus Transport Association of Korea (http://www.bus.or.kr), 2011 Bus Statistics.
Mass Transport in Korea: Intracity Bus
(Units: Thousand people, passenger-km)
8
Intracity Bus
• Total 348 bus companies operate 32,575 intracity buses in Korea
Intracity Bus Company and Vehicles
Table 2 Intracity Bus Company and Vehicles
City/Province Company Vehicles Vehicles per company
Total 348 32,575 94 Seoul 67 7,525 112 Busan 33 2,511 76 Daegu 26 1,561 60
Incheon 41 2,323 57 Gwangju 10 972 97 Daejeon 13 965 74 Ulsan 8 674 84
Gyeonggi-do 52 9,883 190 Gangwon-do 9 564 63
Chungcheongbuk-do 10 540 54 Chungcheongnam-do 9 731 81
Jeollabuk-do 13 822 63 Jeollanam-do 11 677 62
Gyeongsangbuk-do 15 1,118 75 Gyeongsangnam-do 27 1,637 57
Jeju 4 172 43
*Source: National Bus Transport Association of Korea (http://www.bus.or.kr), 2011 Bus Statistics.
9
Intercity and Express Buses
• Intercity bus patronage is decreasing while passenger-km is increasing
• Express bus is in decreasing trend
Intercity Bus and Express Bus Transportation Statistics
Table 3 Intercity Bus and Express Transportation Statistics
Year Intercity Bus Express Bus
Passenger Passenger-km Passenger Passenger-km
2002 329,192 10,985,630 42,132 9,413,822
2003 282,782 9,821,406 40,140 8,790,072
2004 262,051 10,232,042 38,877 8,432,356
2005 245,678 9,861,530 37,687 8,125,011
2006 246,359 11,955,403 38,873 8,460,067
2007 239,668 13,667,261 38,569 8,470,892
2008 242,121 16,726,963 40,451 8,799,997
2009 235,761 16,881,312 38,095 8,235,563
2010 226,111 16,590,512 38,204 8,247,025
2011 222,121 16,364,201 37,005 8,111,058
*Source: National Bus Transport Association of Korea (http://www.bus.or.kr), 2011 Bus Statistics.
10
Intercity and Express Buses
<Table> Intercity Bus and Express Bus Company and Vehicles Province
Intercity Bus Express Bus
Company Vehicles Vehicles per company
Company Vehicles Vehicles per company
Total 83 7,636 92 8 1,891 236
Gyeonggi-do 16 1,815 113 4 878 220
Gangwon-do 10 719 72 1 113 113
Chungcheongbuk-do 5 478 96 1 85 85
Chungcheongnam-do 5 865 173 - - -
Jeollabuk-do 5 493 99 - - -
Jeollanam-do 8 592 74 1 637 637
Gyeongsangbuk-do 8 1,018 127 - - -
Gyeongsangnam-do 21 1,399 67 1 178 178
Jeju 5 257 51 - - -
Intercity Bus and Express Bus Company and Vehicles
*Source: National Bus Transport Association of Korea (http://www.bus.or.kr), 2011 Bus Statistics.
11
ICT amenities in public transport in Korea
Information Utility in Intracity Bus
City/Province Wi-Fi availability in Bus
Seoul ◦ 97×× Bus ◦ Metro Bus, Gyunggi Province Bus
Busan ◦ 63 bus lines
Daegu ◦ 11 bus lines
Incheon ◦ Circular 7 Lines ◦ Metro 18 Lines
Gwangju ◦ 17 lines
Daejeon ◦ Kyungick Bus KT Wi-Fi available ◦ Other lines SK Wi-Fi available
Ulsan -
Gyeonggi-do ◦ Gyunggi buses ◦ Metro Bus
Gangwon-do -
Chungcheongbuk-do ◦ Woojin and Dongil Bus KT Wi-Fi available
Chungcheongnam-do -
Jeollabuk-do -
Jeollanam-do ◦ Gyangyang City Bus KT Wi-Fi available ◦ Mokpo City KT Wi-Fi available
Gyeongsangbuk-do ◦ Gumi City KT Wi-Fi available
Gyeongsangnam-do ◦ Machang and Dongyang Bus KT Wi-Fi partial
Jeju ◦ 12 Bus lines
*Source: http://gall.dcinside.com/board/view/?id=bus&no=266582
Figure 1 Seongnam City Bus Wi-Fi
Figure 2 Wi-Fi users in bus
12
Rail Transport in Korea
• Rail passenger transport at 1,149 million and distance traveled is 42,492
million passenger-km in 2012
• Both regional rail and metro rail patronage have been increasing since 2005
Korail Passenger Transportation Statistics
Table 4 Korail Passenger Transportation Statistics
*Source: KORAIL (http://www.korail.com), 2012 Rail Statistics.
Year
Total Regional rail Subway in
Metropolitan Region Airport rail
Passenger Passenger-
km Passenger
Passenger-km
Passenger Passenger-
km Passenger
Passenger-km
2005 950,995 31,004,212 115,002 19,076,105 835,993 11,928,106 - -
2006 969,145 31,415,976 114,331 19,078,533 854,814 12,337,443 - -
2007 989,294 31,595,987 110,631 18,680,339 878,664 12,915,648 3,752 91,812
2008 1,018,977 32,026,528 113,098 18,671,356 905,879 13,355,172 6,078 145,159
2009 1,020,319 31,299,106 107,733 17,816,751 912,586 13,482,355 7,340 163,964
2010 1,060,941 33,012,479 112,093 19,018,617 948,848 13,993,862 10,044 211,489
2011 1,118,621 36,784,264 121,769 21,603,203 996,852 15,181,061 32,521 656,660
2012 1,149,340 42,492,561 125,817 22,111,981 1,023,523 20,380,580 49,138 977,392
(Units: thousand people, thousand passenger-km)
13
Railroad
• KTX (Express Rail)
- KTX provides free wifi service
- KTX car number 5 and 13 have PC’s for internet
• Subway
- Metro rail stations and vehicles provide Wi-Fi services (SKT and KT)
ICT Amenities in Rail
Figure 3 KTX Wi-Fi (Left) and KTX-Sancheon business room (Right)
Figure 4 Wi-Fi use case in subway(Left) and Wi-Fi equipment in passenger car (Right)
14
Stated Preference Survey: Base Statistics
Survey Statistics
• Gender • Occupation
• Generation • Residential
15
Base Statistics Car User Characteristics
• Vehicle characteristics • Types of fuels
• Frequency of using private vehicle for commuting
• Reasons for using private vehicles
Average 4.3times
16
Commuting Travel Behavior
• Average time required for commuting • Average travel cost per month
(Unit: minutes) (Units: thousand won)
Average 38.5 min
Average 200,804 won
• Modes of transportation seperate from commuting
17
• Recognition of free Wi-Fi service in public transportation
• Use of ICT devices on public transportation
• Network services when using ICT devices on public transportation
Based on 219 ICT
device users
• Wi-Fi condition of ICT devices on public transportation
Based on 52
Wi-Fi users
Not well connected
but always available
Well connected and fast
Well connected, but often slow
ICT Device Usage in Public Transportation
18
Unstable network connection
Provided with unlimited data
Not sensitive to data usage
Inconvenience of connecting to Wi-Fi
Not familiar with the connection procedures
Too slow
• Reasons for not using Wi-Fi on public transportation
Using SNS (Facebook, Twitter etc.)
Music, Videos, etc.
Playing games
Business purposes (checking -mail etc.)
Instant messaging (Kakaotalk, etc.)
Internet searching
• Purposes of using ICT devices on public transportation (Multiple responses)
* Based on 219 ICT device users
* Based on 3G, 4G (LTE) users
ICT Usage Behavior in Public Transportation
19
ICT Service Usage in Public Transportation
• Monthly average mobile phone bill (Unit: Won)
• Monthly data allowance
• Average time of using mobile phones per day
Average 3.25 GB
Units : hour
Very Sufficient
Sufficient
Just OK
Somewhat Insufficient
Not-sufficient
Quantitative Policy Impact Analysis
Stated preference methodology for impact analysis of hypothetical transport policy measures
- Bases for scientific transport policy intervention Econometric testing of transport policy related
hypotheses
- Perceived vs. real cost of transport
Stated Preference Analysis for ICT’s Impact on Travel Behavior
20
SP Methodology and Estimation Results
If variables are too numerous and too widely varied impossible to create all possible sets of SP
questionnaires Use fractional factorial plan which analyzes only main
effects and guarantee the orthogonality of variables following Kocur et al. (1982) and Hensher (1994)
SP design of mode choice between passenger cars and alternative modes of bus and subway
Explanatory variables travel expense, travel time, and service levels (ICT
amenity levels: free Wi-Fi and etc.)
21
where mass = bus or subway, Surveyed on 240 car users binary choice with
multiple levels of attributes 3,840 effective data points
Ct i meCfuelUcar ⋅⋅ ++= 21 ββα
Mt i meMi ctU ⋅⋅ += 43 ββmass
Utility functions
22
Coefficient Estimation for Respondents Who Declared Bus as Alternative
Coefficient Standard Error Prob. |z|>Z*
CFUEL -.14258D-05*** .4319D-06 -3.30
CTIME -.04316*** .00761 -5.67
MICT .33463*** .06910 4.84
MTIME -.04156*** .00666 -6.24
A_CAR .25930 .17834 1.45
Note: nnnnn.D-xx or D+xx ⇒ multiply by 10 to -xx or +xx. Note: ***, **, * ⇒ Significant at 1%, 5%, 10% level
23
Elasticity Estimates for Respondents Who Declared Bus as Alternative
CFUEL CAR MASS
Elasticity -.2036 .1069
CTIME CAR MASS
Elasticity -1.0322 .5769
MICT CAR MASS
Elasticity -.4390 .2302
MTIME CAR MASS
Elasticity 1.0965 -.6464
24
Most attribute variables are statistically significant, except car constant
Positive car constant But not statistically significant: No intrinsic car preference
Time related demand elasticity is much higher than cost related ones
ICT amenity related elasticity is positive Implying fairly positive role of attracting car users into public transport
Coefficient Estimation for Respondents Who Declared Subway as Alternative
Note: nnnnn.D-xx or D+xx ⇒ multiply by 10 to -xx or +xx. Note: ***, **, * ⇒ Significant at 1%, 5%, 10% level
Coefficient Standard Error Prob. |z|>Z*
CFUEL -.94589D-06*** .3209D-06 -2.95
CTIME -.02434*** .00530 -4.60
MICT .23738*** .06988 3.40
MTIME -.02399*** .00463 -5.18
A_CAR -.11309 .18785 -.60
26
Elasticity Estimates for Respondents Who Declared Subway as Alternative
CFUEL CAR MASS
Elasticity -.1976 .0902
CTIME CAR MASS
Elasticity -.9036 .4363
MICT CAR MASS
Elasticity -.3245 .1502
MTIME CAR MASS
Elasticity .9875 -.4979
27
Most attribute variables are statistically significant, except car constant
Negative car constant But not statistically significant: No intrinsic car preference
Time related demand elasticity is much higher than cost related ones
ICT amenity related elasticity is positive Implying fairly positive role of attracting car users into public transport
Similar result to Bus User case
Demand elasticity of ICT amenity lies just between cost and time variable
Implying higher response than price related policy options Providing higher ICT connectivity in mass
transit could attract car users to MT by making travel time more enjoyable or productive
Reducing travel time is still most powerful policy measure for modal shift towards MT
Policy Implications and Conclusion
No intrinsic car preference in our survey unlike previous ones
ICT revolution will change the way people
travel Riding MT could be productive
Policy Implications and Conclusion
Thank You. (swlee@koti.re.kr)
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