서울도시연구 제8권 제4호 2007. 12, 논문 -Original Paper- pp. 127～138. 127

Analysis of Characteristics and Reliability of Data in Metropolitan Seoul1) Jin-Young Park*⋅Dong-Jun Kim**

서울시 교통카드자료의 특성 및 신뢰성 분석 박 진 영*⋅김 동 준**

ABSTRACT：The use of smart cards for fare payment in public transit has grown in Korea since their introduction in 1996. Currently, the proportion of smart card use in Seoul is more than 90% for buses and 75% for metros. In 2004, the Seoul metropolitan government introduced a new smart card system with a distance based fare system, which requires detailed data of users such as boarding time and GPS-based vehicle location. To investigate the reliability of smart card data, the number of users of every in Seoul gathered from smart card data were compared with data directly from the Seoul Metro Company(SMC). With two simple manipulations to include daily variations and the number of cash users, the smart card data appears statistically indifferent with surveyed data from the Seoul Metro Company. Analyzing line specific proportions of smart card use instead of average smart card use improves the accuracy of the results. From the results, therefore, smart card data show potential as a basis for describing characteristics of public transit users, such as number of transfers, boarding time, hourly trip distribution of number of trips for different transit modes, and travel time distribution for all transit modes and user types. Key Words：smart card, public transit, statistical analysis

요약：1996년 서울시를 필두로 도입된 교통카드는 국내 많은 지역에서 대중교통 이용 편의와 요금징수 용이성 제고를 위해 사용되고 있으며, 이용률도 전국적으로 증가하고 있는 추세이다. 현재 교통카드는 주로 요금징수를 위한 목적으로 사용되고 있으나 교통카드에는 탑승위치와 탑승시간 등 활용가능성이 높은 다양한 정보들이 기록되고 수집되고 있어 이러한 정보의 활용에 대해 많은 교통관련 연구자들이 관심을 가지고 있다. 본 연구에서는 서울시 교통카드 이용자료를 활용하여 대중교통과 관련된 지표들을 산출해 보고, 교통카드 이용자료의 전수화와 지하철 공사 발표 자료와의 비교를 통해 교통카드 이용자료 의 신뢰성을 분석해 보았다. 분석결과 지하철공사자료와 교통카드 이용자료는 차이가 없는 것으로 나타 났으며, 특히 지하철 호선별 교통카드 이용비율을 적용한 결과 교통카드 이용자료의 신뢰성이 높아지는 것으로 나타났다. 본 연구를 통해 교통카드 이용자료의 잠재적 활용가능성을 파악할 수 있었으며, 교통카 드 이용자료 활용시 지하철 및 버스 노선별 자료의 적용 필요성 및 향후 연구주제 등을 제시하였다. 주제어：교통카드, 대중교통, 통계분석

* Research Associate, Department of Metropolitan & Urban Transportation Research, The Korea Transport Institute(한국교통연구원 광역도시 교통연구실 책임연구원) ** Researcher, Department of Metropolitan & Urban Transportation Research, The Korea Transport Institute(한국교통연구원 광역도시교통연 구실 연구원) 128 서울도시연구 제8권 제4호 2007. 12

Ⅰ. Introduction implemented smart card for public transit fare payment currently used. In Europe and Asia, the A smart card, or integrated circuit card (ICC), is smart card is a more popular option for fare defined as any pocket-sized card with embedded payment. Including London("Oyster" card) and Paris integrated circuits which can process information. ("Navigo"), smart cards are currently used in 22 This implies that the card can receive input which cities in Europe. Asia has been a pioneer for using is processed - by way of the ICC applications - smart card for fare payment in public transit from and delivered as an output. After first being used the mid-1990’s. Currently more than 30 cities are for bank application, smart cards with contactless using smart cards for public transit fare payment. interfaces are becoming increasingly popular for While the use of smart card data is spreading payment and ticketing applications such as for through out the world, studies related to smart card riding mass transit. Across the world, contactless data are just in the early stages of work. TCRP fare collection systems are being implemented to Report 10 by TRB(1996) discussed smart cards as improve efficiencies in public transit. The various a new method for fare payment in public transit. standards emerging are local and are not Most of papers have focused on technology and compatible, though the MIFARE card from field implementation(Giuliano et al., 2000; Barth et has a considerable market share in the US, Europe al., 2003; Cheung, 2004; Espinosa et al., 2005; and Asia. The , in which the Iseki et al., 2007). Recent work by Bagchi and chip communicates with the card reader through White(2005) performed three case studies in Radio Frequency IDentification (RFID) induction British networks and discussed the potential of technology(at data rates of 106 to 848 kbit/s), smart card data for transit planning. They stated requires only close proximity to an antenna to that smart cards should not replace conventional complete transaction. They are often used when data collection methods. Also Utsunomiya et al. transactions must be processed quickly or (2006) discussed potential use of smart card to hands-free, such as on mass transit systems, where improve transit planning. Trepanier and Chapleau smart cards can be used without even removing (2006) suggested if a smart card system could them from a wallet. locate boarding and alighting locations, it would Because of the convenience of quick and provide transit planners with interesting contactless processing, these smart cards have been information on route profiles. Also Morency et wide spread a method all over the world for use in al.(2007) measured spatial and temporal variability public transit fare payment. In the USA, the of transit users from smart card data through an "Breeze" card in , "Translink" card in the San object-oriented approach. But a lack of boarding Francisco Bay area, "Charlie" card in , and information on smart card data restricted the scope "Smart Trip" card in Washington D.C. are currently of the study. Analysis of Characteristics and Reliability of Smart Card Data in Metropolitan Seoul 129

TABLE 1. Smart Cards for Fare Payment in 2007

Cards Issued Terminals Date Introduced No. of sites No. of sites Area No. (month/year) % No. % selling cards recharging cards (10thousand) Total 5,780 100 54,646 100 13,214 15,736 Bus 01/96 Seoul 3,370 56.8 32,273 57.7 5,908 6,453 Metro 06/98 Busan Bus, Metro 02/98 892 15.0 7,347 13.1 2,558 2,664 Bus 11/00 Daegu 376 6.3 2,432 4.3 675 823 Metro 07/02 Bus 09/98 Incheon - - 595 1.1 611 678 Metro 12/99 Bus 02/98 Gwangju 93 1.6 1,181 2.1 263 289 Metro 10/04 Daejeon Bus 09/03 72 1.2 1,084 1.9 182 632 Ulsan Bus 09/02 86 1.4 655 1.2 165 165 Gyeonggi Bus 05/96 567 9.6 1,985 3.5 1,329 1,679 Wonju Bus 06/01 Gangwon 4 0.1 565 1.0 58 808 ChunCheon Bus 08/02 Chungbuk Bus 07/03 50 0.8 704 1.3 147 147 Chungnam Bus 07/04 34 0.6 1,084 1.9 168 168 Jeonbuk Bus 01/02 66 1.1 970 1.7 405 405 Jeonnam Bus 11/02 33 0.6 870 1.6 159 159 Gyeongbuk Bus 02/02 14 0.2 859 1.5 202 267 Gyeongnam Bus 07/02 101 1.7 1,608 2.9 304 319 Jeju-city Bus 02/98 Jeju 22 0.4 434 0.8 80 80 Seogwipo-city Bus 05/03

TABLE 2. Proportion of Smart Card Use in 2005

Area Seoul Busan Daegu Incheon Gwangju Daejeon Boardings Using Smart Card (%) 81 70 46 80 64 44 Area Ulsan Gangwon Chungnam Jeonnam Gyeongnam - Boardings Using Smart Card (%)) 68 20 41 18 17 -

Ⅱ. Smart Card Data in Metropolitan Seoul Metro users were using smart cards for fare payment. About twenty million transactions Smart cards have been in use for fare payment occurred every day. Table 2 shows the proportion since 1996 in Korea. Table 1 shows the history of smart card use in Korea. However, with such an and current use of smart cards in Korea, including early implementation of smart cards, Seoul’s smart Seoul. Currently more than 80% of public transit card system had suffered due to a lack of capacity users are using smart cards for fare payment in the and security. Thus, the Seoul metropolitan Seoul area. In 2005, 90% of bus users and 72% of government implemented a new smart card system 130 서울도시연구 제8권 제4호 2007. 12 named "T-money." Metro stop. Also, boarding and alighting time is The Seoul metropolitan government introduced a recorded to give free transfer among different new distance-based fare system in public transit modes. Table 3 shows the types of data recorded with the new smart card system. The new fare on the smart card system in Seoul. Because over system varies by mode of transportation and total 80% of public transit users are using the smart distance traveled. The fare for public transit starts card and with such detailed information about at 800 Korean Won (about US$0.80) for the first public transit users, the smart card data have ten km and increases by 100 Korean Won (about potential as a new information source for public US$0.10) for increments of five kilometers. The transit-related studies. base fare also includes up to four free transfers applicable to both bus and Metro for 30 minutes Ⅲ. Characteristics of Smart Card Data after last alighting. Hence, in Seoul, users should of Metropolitan Seoul contact fare collection terminals twice with smart cards: when one enters the station or vehicle and To investigate the potential of smart card data, when one exits. some important public transportation figures are To accommodate the distance-based fare system, derived from Seoul’s smart card data. Smart card the new smart card is able to calculate distance data were collected on two days: October 27, 2004 traveled based on GPS data for each bus and (Wednesday) and November 11, 2005 (Thursday).

TABLE 3. Recorded Information on Smart Cards in Seoul

Information Description Card ID Card number for each smart card Boarding time Boarding time (year/month/day/hour/minutes/second) Type of mode Bus (local/main/feeder/Metropolitan/circle bus), Metro Number of transfers Number of transfers (from 0 to 4) Number of bus routes Given number for every bus route Name of bus route Name of bus route ID of operator Given number of every bus/Metro operator ID of vehicle (bus) Given number of every operated bus Type of user Adult, student, or children ID of boarding location Given number of boarding bus/Metro stop Name of bus stop Name of boarding bus/Metro stop Alighting time Alighting time (year/month/day/hour/minutes/second) ID of alighting location Given number of alighting bus/Metro stop Name of bus stop Name of alighting bus/Metro stop Basic fare Starting (base) fare Additional fare Additional fare with distance Analysis of Characteristics and Reliability of Smart Card Data in Metropolitan Seoul 131

Until now smart card data have been mainly used modes (local, main, feeder, metropolitan express, for fare calculation and distribution among and circle buses, as well as Metro), and 24 hours different operators, but they have been occasionally of time. Table 4 shows the number of users of the used for transportation studies with permission six modes in two different days. Between 2004 from the Seoul metropolitan government. Two and 2005, the use of smart cards on buses days of data are expected to give some information increased 11%, while smart card users on the about the daily variation of smart card data and the Metro increased about 4%. passing of one year between 2004 and 2005. As From the data, trips consisting of transfers are more than twenty million transactions occur every investigated. To get a distance-based fare cheaper day, the data should be treated by a database than the maximum possible fare, users should management system. In this study, data are swipe smart cards on every trip start and end. analyzed by ORACLE Relational Data Base Because the new fare system allows four free Management System (RDBMS) version 10.2. transfers, it records up to four linked segments for Structured Query Language (SQL) is used for each trip. Most trips consist of less than two manipulation of the data. In particular, the transfers(Table 5). Multi-Dimensional Analysis technique is employed Figure 1 shows the hourly distribution of total for data analysis. In the smart card data analysis, public transit trips from the 2004 dataset based on user type, travel mode, and travel time are the boarding times derived from smart card data. It main dimensions of the data set. reveals a conventional pattern of daily public In Seoul smart card data set consists of three transit travel patterns: 20% of total daily trips are user types (adult, student, and children), six travel produced during the morning peak time from 7 AM to 9 AM. Also it shows the hourly variation of travel times. The travel time in the morning TABLE 4. Number of Smart Card Users of Different Modes in Metropolitan Seoul peak period is higher than at any other time of day. Difference Mode 2004 2005 (%) Local mini bus 928,974 1,037,522 11.68 TABLE 5. Number of Transfers Main line bus 1,641,208 2,095,121 27.66 Number of Difference Trips (2004) Trips (2005) Feeder line bus 2,510,751 2,547,986 1.48 transfers (%)

Metropolitan express bus 148,351 122,943 -17.13 1 1,861,650 (88.05%) 2,167,887 (86.26%) 16.45

Circle bus 15,434 14,615 -5.31 2 213,533(10.10%) 288,204 (11.47%) 34.97

Bus total 5,244,718 5,818,187 10.93 3 31,733(1.50%) 45,968 (1.83%) 44.86

Metro 4,843,438 5,034,531 3.95 4 7,414(0.35%) 11,011 (0.44%) 48.52

Total 10,088,156 10,852,718 7.58 Total 2,114,330(100.00%) 2,513,070 (100.00%) 18.86 132 서울도시연구 제8권 제4호 2007. 12

30.00 1400 Average Travel Time No. of Trips 25.00 1200

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FIGURE 1. Hourly variation of travel time and number of trips

Trip distribution index 45 0 Main line bus 40 0 feeder bus 35 0 Met. Ex p. Bus 30 0 Metro 25 0 20 0 15 0 10 0 5 0 0

0 0 0 0 0 0 0 0 0 0 0 0 :0 :0 :0 :0 :0 :0 :0 :0 :0 :0 :0 :0 4 6 8 0 2 4 6 8 0 2 0 2 1 1 1 1 1 2 2 Boarding time (hr)

FIGURE 2. Hourly trip distribution for different modes

Figure 2 shows the details of the hourly trip And also, the number of trips of most modes distribution index by modes based on boarding except metropolitan express bus quickly decreases time. The number of trips at 11 AM is set as 100, after 8 PM. But the distribution of trips by and trips produced during another periods are metropolitan express buses, which link Seoul’s adjusted according to the difference between that downtown with suburban bedroom communities, period and from the difference between 11 AM shows a long tail until 12 AM. This may suggest and the period. Metro ridership reveals the highest that people with long trip distances from bedroom difference between peak and off-peak time trips. communities prefer the Metro in the morning Analysis of Characteristics and Reliability of Smart Card Data in Metropolitan Seoul 133

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FIGURE 3. Cumulative travel time of different modes because of its punctuality but prefer buses in the related studies, they should fully represent the evening because of the increased comfort from the characteristics of every public transit user. As coach bus style design, even though they have to shown in Table 2, the proportion of smart card pay higher fares. users, particularly in Seoul, is more than 90% for Figure 3 shows the cumulative travel time of buses and 72% for the Metro. Still, 10% of bus each mode. About 90% of the trips on the Metro users and 30% of Metro users do not use smart and metropolitan express buses take less than one cards. If these people’s travel characteristics are hour. For other modes such as main line buses and different from smart card users, they should be feeder buses, 90% of the trips take less than 30 analyzed independently and added to the result minutes. This suggests that the Metro works as the from smart card users. The result may be that cash main public transit mode for long distance travel users are not frequent users of public transit, and and buses work as feeders for short distance trips. they may have different trip patterns from smart card user. Ⅳ. Reliability of Smart Card Data 2. Alighting information To use data from smart cards for any purpose, reliability of the data should first be investigated. Smart cards are only processed at the time of During this study, some important issues related to boarding in most cities. Thus, smart card data the reliability of the data were found. generally do not have complete information on each trip. Unlike other cities, however, because the 1. Characteristics of cash users Seoul area implemented a distance-based fare To use smart card data in policy decisions and system, users also must process their smart cards 134 서울도시연구 제8권 제4호 2007. 12

TABLE 6. Proportions of Number of Users Without Company publishes monthly the number of users Alighting Data of every Metro station based on data from ticket Mode 2004(%) 2005(%) gates. To compare the two sets of data, the Local mini bus 7.55 7.20 numbers of users from smart card data are Main line bus 10.17 10.96 converted to monthly values using the number of Feeder line bus 10.17 10.32 cash users and daily variations. Metropolitan exp. bus 64.36 80.01 First, the number of cash users is added to the Circle bus 7.89 7.33 smart card data. In October 2004, SMC published Metro 0.50 0.33 the proportion of smart card users as 70.5%. Total 6.08 6.29 Total users of each Metro station (smart card users + cash users) on one day = number of smart when they get off to avoid the maximum fare. card users ÷ 0.705 (proportion of smart card users Table 6 shows the proportion of riders without per total Metro users) (1) alighting information for each public transit mode. Second, there exists daily variation of number of For the Metro, because every user should pass Metro users through weekday. Because smart card a ticket gate when they are entering or exiting a data were collected on Wednesday, a Wednesday station, every trip has complete information. But variation of 1.098 per weekly average proportion bus data shows a different situation. To get free of smart card use is implemented to get the weekly transfers and a distance-based fare that is less than average number of users. the maximum fare, the user has to complete his trip data by processing his card; but if the user Number of total users per day considering daily carries out only a single trip, he does not have to variation = total users of the day ÷ 1.098 complete his trip data when he alights. Thus, there (Wednesday variation) (2) are missing alighting data for bus users. In particular, smart card data of metropolitan express The number of users at 260 stations of Metro bus have a high proportion of missing alighting lines one to eight is compared with the daily data because users cannot receive a free transfer number of users published by SMC. Figure 4 from the bus and fares are fixed. shows the ratio of each station’s users from smart card data against SMC published data. From the To investigate the reliability of smart card data, data, three abnormal data points were found: the the number of users of every station in Metro lines Sport Complex, World Cup Stadium, and Moran one to eight obtained from smart card data is Market stations. User-based smart card data were compared with conventional published data from 40% higher than the monthly average user data at the Seoul Metro Company(SMC). The Seoul Metro Sport Complex Station because there was a Analysis of Characteristics and Reliability of Smart Card Data in Metropolitan Seoul 135

        1 .4

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FIGURE 4 Ratio of Number of Metro Users from Smart Card Data to Data Published by SMC sporting event on the day the data were obtained. for each of Metro lines one through eight. Also, a But smart card data reveals 40% fewer users at paired sample t-test was performed to investigate World Cup Stadium and Moran Market stations the difference between smart card data and SMC than the monthly average number of users because published data. The t-test assesses whether the there were no sporting events and the market was means of two groups are statistically different from not opened on that day. Thus, data from these each other. This analysis is appropriate whenever three stations are removed from the analysis. you want to compare the means of two groups. When we are looking at the differences between Table 7 shows basic statistics of the difference scores for two groups, we have to judge the between smart car data and SMC published data difference between their means relative to the

TABLE 7. Results of Statistical Analysis of Differences between Smart Card Data and Published Data(Applying average proportion of smart card use)

t-test Lines No. of Stations Min. Value Max. Value Average Std. Dev. Value P-value 1 9 0.694 1.030 0.842 0.111 -3.58 0.007 2 48 0.845 1.224 1.016 0.082 2.20 0.033 3 30 0.731 1.113 0.975 0.092 -1.00 0.326 4 26 0.766 1.097 0.968 0.083 -1.73 -0.096 5 50 0.721 1.190 0.974 0.106 -0.20 0.842 6 36 0.864 1.139 1.103 0.068 1.89 0.067 7 42 0.747 1.178 0.998 0.101 0.25 0.806 8 16 0.803 1.071 0.962 0.078 -1.54 0.144 136 서울도시연구 제8권 제4호 2007. 12 spread or variability of their scores. And the t-test statistical results by implementing line-specific does just this. The formula for the t-test is a ratio. smart card use proportions. The top part of the ratio is just the difference between the two means or averages, the bottom V. Conclusion part is a measure of the variability or dispersion of the scores. The t-value will be positive if the first Use of smart cards for fare payment in public mean is larger than the second and negative if it transit has grown in Korea since its introduction in is smaller. Using the t-value, you can conclude the 1997. Currently, the proportion of smart card use difference between two groups. The null in Seoul is more than 90% for buses and 75% for hypothesis is that the difference of the two data Metros. In 2004, the Seoul metropolitan sets is zero. The t-test shows that data sets of line government introduced a new smart card system one and two are statistically different at a 95% with distance-based fares, which requires detailed level of significance. data of users such as boarding time and GPS-based To investigate the error in lines one and two, a location. It is important that with the help of the line-specific monthly average proportion of smart new fare system and technology, the smart card card use is applied in equation (1). The proportion data in Seoul consist of boarding and alighting of smart card users on line one is relatively low information with time. The objective of this study compared to other Metro lines, perhaps because it is to investigate the potential of smart card data includes two intercity and commuter railway and their reliability. Some characteristics of public stations. On the other hand, Metro line two shows transit users that can be calculated from these data a higher proportion of smart card users. By include number of transfers, boarding time, hourly implementing these line-specific smart card use trip distribution of number of trips for different proportions, the accuracy of the results is transit modes, and travel time distribution for all improved, except for line seven. Table 8 lists

TABLE 8. Results of Statistical Analysis of Differences between Smart Card Data and Published Data(Applying line-specific proportions of smart card use)

% of card No. of t-test Lines Min. Value Max. Value Average Std. Dev. use Stations Value P-value 1 61.50 9 0.796 1.180 0.965 0.127 -0.68 0.517 2 73.07 48 0.815 1.181 0.980 0.079 -0.04 0.966 3 69.05 30 0.747 1.136 0.995 0.094 -0.28 0.784 4 70.37 26 0.767 1.099 0.960 0.083 -1.61 0.119 5 67.80 50 0.724 1.195 0.979 1.066 0.04 0.966 6 68.60 36 0.858 1.131 1.006 0.067 1.33 0.192 7 69.40 42 0.733 1.158 0.979 0.099 -0.86 0.393 8 64.80 16 0.844 1.126 1.011 0.082 1.49 0.158 Analysis of Characteristics and Reliability of Smart Card Data in Metropolitan Seoul 137 transit modes and user types. To investigate the for Seoul Metropolitan Government to set up reliability of smart card data, the number of users strategy for urban transportation system of Seoul of every Metro station in Seoul from smart card Metropolitan area. Above all, smart card data can data and from Seoul Metro Company statistics be an important source to create future were compared. With two simple manipulations to transportation demand matrix of Seoul include daily variations and number of cash users, Metropolitan area. It can reduce time and cost smart card data reveal itself to be statistically currently required to create it. However, more indifferent with surveyed data from the Seoul work will be required to build reliable models to Metro Company. From the results, smart card data represent all public transit users and their travel show potential to describe characteristics of public patterns with smart card data. It should be transit users. Also, it is clear that smart card data identified whether smart card data could represent can represent all Metro users: smart card users as traffic patterns of whole transportation users. As well as cash users. discussed in this paper, characteristics of metro However, this study also suggests that to users can be fully described by smart card data. represent all Metro users with smart card data, But for bus users, it should be studied as there line-specific proportions of card use should be exists lack of off-boarding information. Also employed. Even though it is not discussed in this regional variation of lack of information should be study, route-specific proportions of smart card use investigated. may be required to estimate all bus users from Another issue for using smart card data is to set smart card data. Methods to include the number of up proper process and managing system for it. bus users who do not have alighting information Currently, in spite of its potential as a new data should also be investigated. source, data use for other purpose than original This study is just the first step of the work fare calculation is not fully set up. If it is not set required to use smart card data for public transit up properly, with considering private information planning and operation. With its potential as a new protection rule, it will be an important issue. source of data, smart card data is expected to play Currently these data have already provided data an important role for Seoul Metropolitan area to some public transportation studies. And with the transportation research. Because smart card data is information included in it, smart card datawill collected as real time base, by linking with become an important part of transportation studies. passenger information system, it can provide public transit related information such as waiting time and Reference ────────── estimated arrival time, to users. Also by building historic data base, smart card data can provide Bagchi, M. and White, P.R., 2005, "The potential of public trend of transportation users’ characteristics, which transport smart card data", Transport Policy, 12, could be used to estimate future trend of 464~474. transportation. This will be valuable information Barth, M., Todd, M., and Shaheen, S., 2003, "Intelligent 138 서울도시연구 제8권 제4호 2007. 12

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