Advances in ICT and its Implications for Travel Behavior: A Case Study of Korea

Transcription

1 I- TED 2014 April , Dallas U.S. Advances in ICT and its Implications for Travel Behavior: A Case Study of Korea Sungwon LEE, Gyeng Chul KIM, Seung Kook WU and Jieun OH

2 Contents Ⅰ Ⅱ Ⅲ Ⅳ Introduction (ICT Connectivity and Transport) Stated Preference Survey and Analysis Policy Implications from SP Discussions (Q & A)

3 Introduction Advances in ICT and its Implications Proliferation of ICT devices in Korea and in the world The world is becoming more and more connected 3

4 Changes in our lifestyle and its implications for transport Intelligent multi-purpose devices with ICT convergence Provide various transport services integrated with ICT 4

5 Literature Review Advances in ICT and travel demand models 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. 5

6 Literature Review Advances in ICT and its implications for travel 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 6

7 Mass Transport in Korea: Intracity Bus Intracity Bus Transportation Statistics City bus patronage is increasing Passenger kilometer traveled is also increasing Table 1 Intracity Bus Transportation Statistics Year Passenger Passenger-km ,523,037 24,295, ,408,604 22,730, ,451,840 25,756, ,536,634 25,902, ,615,930 27,587, ,768,437 28,730, ,068,671 30,792, ,168,492 32,037, ,246,965 31,684, ,535,686 32,557,560 *Source: National Bus Transport Association of Korea ( Bus Statistics. (Units: Thousand people, passenger-km) 7

8 Intracity Bus Intracity Bus Company and Vehicles Total 348 bus companies operate 32,575 intracity buses in Korea Table 2 Intracity Bus Company and Vehicles City/Province Company Vehicles Vehicles per company Total , Seoul 67 7, Busan 33 2, Daegu 26 1, Incheon 41 2, Gwangju Daejeon Ulsan Gyeonggi-do 52 9, Gangwon-do Chungcheongbuk-do Chungcheongnam-do Jeollabuk-do Jeollanam-do Gyeongsangbuk-do 15 1, Gyeongsangnam-do 27 1, Jeju *Source: National Bus Transport Association of Korea ( Bus Statistics. 8

9 Intercity and Express Buses Intercity Bus and Express Bus Transportation Statistics Intercity bus patronage is decreasing while passenger-km is increasing Express bus is in decreasing trend Table 3 Intercity Bus and Express Transportation Statistics Year Intercity Bus Express Bus Passenger Passenger-km Passenger Passenger-km ,192 10,985,630 42,132 9,413, ,782 9,821,406 40,140 8,790, ,051 10,232,042 38,877 8,432, ,678 9,861,530 37,687 8,125, ,359 11,955,403 38,873 8,460, ,668 13,667,261 38,569 8,470, ,121 16,726,963 40,451 8,799, ,761 16,881,312 38,095 8,235, ,111 16,590,512 38,204 8,247, ,121 16,364,201 37,005 8,111,058 *Source: National Bus Transport Association of Korea ( Bus Statistics. 9

10 Intercity and Express Buses Intercity Bus and Express Bus Company and Vehicles Intercity Bus Express Bus Province Vehicles per <Table> Intercity Bus and Company Express Bus Vehicles Company and Vehicles company Company Vehicles Vehicles per company Total 83 7, , Gyeonggi-do 16 1, Gangwon-do Chungcheongbuk-do Chungcheongnam-do Jeollabuk-do Jeollanam-do Gyeongsangbuk-do 8 1, Gyeongsangnam-do 21 1, Jeju *Source: National Bus Transport Association of Korea ( Bus Statistics. 10

11 ICT amenities in public transport in Korea Information Utility in Intracity Bus City/Province Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan - Gyeonggi-do Gangwon-do - Chungcheongbuk-do Chungcheongnam-do - Jeollabuk-do - Jeollanam-do Wi-Fi availability in Bus 97 Bus Metro Bus, Gyunggi Province Bus 63 bus lines 11 bus lines Circular 7 Lines Metro 18 Lines 17 lines Kyungick Bus KT Wi-Fi available Other lines SK Wi-Fi available Gyunggi buses Metro Bus Woojin and Dongil Bus KT Wi-Fi available Gyangyang City Bus KT Wi-Fi available Mokpo City KT Wi-Fi available Figure 1 Seongnam City Bus Wi-Fi Gyeongsangbuk-do Gumi City KT Wi-Fi available Gyeongsangnam-do Machang and Dongyang Bus KT Wi-Fi partial Jeju 12 Bus lines *Source: Figure 2 Wi-Fi users in bus 11

12 Rail Transport in Korea Korail Passenger Transportation Statistics 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 Table 4 Korail Passenger Transportation Statistics Year Passenger Total Passenger Regional rail (Units: thousand people, thousand passenger-km) Subway in Metropolitan Region Passenger Passenger Airport rail Passengerkm Passengerkm Passengerkm Passengerkm ,995 31,004, ,002 19,076, ,993 11,928, ,145 31,415, ,331 19,078, ,814 12,337, ,294 31,595, ,631 18,680, ,664 12,915,648 3,752 91, ,018,977 32,026, ,098 18,671, ,879 13,355,172 6, , ,020,319 31,299, ,733 17,816, ,586 13,482,355 7, , ,060,941 33,012, ,093 19,018, ,848 13,993,862 10, , ,118,621 36,784, ,769 21,603, ,852 15,181,061 32, , ,149,340 42,492, ,817 22,111,981 1,023,523 20,380,580 49, ,392 *Source: KORAIL ( Rail Statistics. 12

13 Railroad ICT Amenities in Rail KTX (Express Rail) - KTX provides free wifi service - KTX car number 5 and 13 have PC s for internet Subway Figure 3 KTX Wi-Fi (Left) and KTX-Sancheon business room (Right) - Metro rail stations and vehicles provide Wi-Fi services (SKT and KT) Figure 4 Wi-Fi use case in subway(left) and Wi-Fi equipment in passenger car (Right) 13

14 Stated Preference Survey: Base Statistics Survey Statistics Gender Occupation Generation Residential 14

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 15

16 Commuting Travel Behavior Average time required for commuting (Unit: minutes) Average 38.5 min Average travel cost per month (Units: thousand won) Average 200,804 won Modes of transportation seperate from commuting 16

17 ICT Device Usage in Public Transportation 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 Not well connected but always available Based on 52 Wi-Fi users Well connected and fast Well connected, but often slow 17

18 ICT Usage Behavior in Public Transportation Reasons for not using Wi-Fi on public transportation Too slow Not familiar with the connection procedures * Based on 3G, 4G (LTE) users Inconvenience of connecting to Wi-Fi Not sensitive to data usage Provided with unlimited data Unstable network connection Purposes of using ICT devices on public transportation (Multiple responses) Using SNS (Facebook, Twitter etc.) Music, Videos, etc. * Based on 219 ICT device users Playing games Business purposes (checking -mail etc.) Instant messaging (Kakaotalk, etc.) Internet searching 18

19 ICT Service Usage in Public Transportation Monthly average mobile phone bill (Unit: Won) Average time of using mobile phones per day Units : hour Monthly data allowance Average 3.25 GB Not-sufficient Somewhat Insufficient Very Sufficient Sufficient Just OK 19

20 Stated Preference Analysis for ICT s Impact on Travel Behavior 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 20

21 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

22 Utility functions Ucar β 1 Cf uel + β = α + 2 Ct i me U mas s = β 3 Mi ct + β 4 Mt i me where mass = bus or subway, Surveyed on 240 car users binary choice with multiple levels of attributes 3,840 effective data points 22

23 Coefficient Estimation for Respondents Who Declared Bus as Alternative Coefficient Standard Error Prob. z >Z* CFUEL D-05***.4319D CTIME *** MICT.33463*** MTIME *** A_CAR Note: nnnnn.d-xx or D+xx multiply by 10 to -xx or +xx. Note: ***, **, * Significant at 1%, 5%, 10% level 23

24 Elasticity Estimates for Respondents Who Declared Bus as Alternative CFUEL CAR MASS Elasticity CTIME CAR MASS Elasticity MICT CAR MASS Elasticity MTIME CAR MASS Elasticity

25 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

26 Coefficient Estimation for Respondents Who Declared Subway as Alternative Coefficient Standard Error Prob. z >Z* CFUEL D-06***.3209D CTIME *** MICT.23738*** MTIME *** A_CAR Note: nnnnn.d-xx or D+xx multiply by 10 to -xx or +xx. Note: ***, **, * Significant at 1%, 5%, 10% level 26

27 Elasticity Estimates for Respondents Who Declared Subway as Alternative CFUEL CAR MASS Elasticity CTIME CAR MASS Elasticity MICT CAR MASS Elasticity MTIME CAR MASS Elasticity

28 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

29 Policy Implications and Conclusion 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

30 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

31 Thank You.