Analysis of Characteristics and Reliability of Smart Card Data in Metropolitan Seoul
1)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 Metro station 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 요금징수용이성 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)
Ⅰ. Introduction
A smart card, or integrated circuit card (ICC), is defined as any pocket-sized card with embedded integrated circuits which can process information.
This implies that the card can receive input which is processed - by way of the ICC applications - and delivered as an output. After first being used for bank application, smart cards with contactless interfaces are becoming increasingly popular for payment and ticketing applications such as for riding mass transit. Across the world, contactless fare collection systems are being implemented to improve efficiencies in public transit. The various standards emerging are local and are not compatible, though the MIFARE card from Philips has a considerable market share in the US, Europe and Asia. The contactless smart card, in which the chip communicates with the card reader through Radio Frequency IDentification (RFID) induction technology(at data rates of 106 to 848 kbit/s), requires only close proximity to an antenna to complete transaction. They are often used when transactions must be processed quickly or hands-free, such as on mass transit systems, where smart cards can be used without even removing them from a wallet.
Because of the convenience of quick and contactless processing, these smart cards have been wide spread a method all over the world for use in public transit fare payment. In the USA, the
"Breeze" card in Atlanta, "Translink" card in the San Francisco Bay area, "Charlie" card in Boston, and
"Smart Trip" card in Washington D.C. are currently
implemented smart card for public transit fare payment currently used. In Europe and Asia, the smart card is a more popular option for fare payment. Including London("Oyster" card) and Paris ("Navigo"), smart cards are currently used in 22 cities in Europe. Asia has been a pioneer for using smart card for fare payment in public transit from the mid-1990’s. Currently more than 30 cities are using smart cards for public transit fare payment.
While the use of smart card data is spreading through out the world, studies related to smart card data are just in the early stages of work. TCRP Report 10 by TRB(1996) discussed smart cards as a new method for fare payment in public transit.
Most of papers have focused on technology and field implementation(Giuliano et al., 2000; Barth et al., 2003; Cheung, 2004; Espinosa et al., 2005;
Iseki et al., 2007). Recent work by Bagchi and White(2005) performed three case studies in British networks and discussed the potential of smart card data for transit planning. They stated that smart cards should not replace conventional data collection methods. Also Utsunomiya et al.
(2006) discussed potential use of smart card to improve transit planning. Trepanier and Chapleau (2006) suggested if a smart card system could locate boarding and alighting locations, it would provide transit planners with interesting information on route profiles. Also Morency et al.(2007) measured spatial and temporal variability of transit users from smart card data through an object-oriented approach. But a lack of boarding information on smart card data restricted the scope of the study.
Area Date Introduced (month/year)
Cards Issued Terminals
No. of sites selling cards
No. of sites recharging cards No.
(10thousand) % No. %
Total 5,780 100 54,646 100 13,214 15,736
Seoul Bus 01/96
Metro 06/98 3,370 56.8 32,273 57.7 5,908 6,453
Busan Bus, Metro 02/98 892 15.0 7,347 13.1 2,558 2,664
Daegu Bus 11/00
Metro 07/02 376 6.3 2,432 4.3 675 823
Incheon Bus 09/98
Metro 12/99 - - 595 1.1 611 678
Gwangju Bus 02/98
Metro 10/04 93 1.6 1,181 2.1 263 289
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
Gangwon Wonju Bus 06/01
ChunCheon Bus 08/02 4 0.1 565 1.0 58 808
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 Jeju-city Bus 02/98
Seogwipo-city Bus 05/03 22 0.4 434 0.8 80 80
TABLE 1. Smart Cards for Fare Payment in 2007
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 -
TABLE 2. Proportion of Smart Card Use in 2005
Ⅱ. Smart Card Data in Metropolitan Seoul
Smart cards have been in use for fare payment since 1996 in Korea. Table 1 shows the history and current use of smart cards in Korea, including Seoul. Currently more than 80% of public transit users are using smart cards for fare payment in the Seoul area. In 2005, 90% of bus users and 72% of
Metro users were using smart cards for fare payment. About twenty million transactions occurred every day. Table 2 shows the proportion of smart card use in Korea. However, with such an early implementation of smart cards, Seoul’s smart card system had suffered due to a lack of capacity and security. Thus, the Seoul metropolitan government implemented a new smart card system
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
TABLE 3. Recorded Information on Smart Cards in Seoul
named "T-money."
The Seoul metropolitan government introduced a new distance-based fare system in public transit with the new smart card system. The new fare system varies by mode of transportation and total distance traveled. The fare for public transit starts at 800 Korean Won (about US$0.80) for the first ten km and increases by 100 Korean Won (about US$0.10) for increments of five kilometers. The base fare also includes up to four free transfers applicable to both bus and Metro for 30 minutes after last alighting. Hence, in Seoul, users should contact fare collection terminals twice with smart cards: when one enters the station or vehicle and when one exits.
To accommodate the distance-based fare system, the new smart card is able to calculate distance traveled based on GPS data for each bus and
Metro stop. Also, boarding and alighting time is recorded to give free transfer among different modes. Table 3 shows the types of data recorded on the smart card system in Seoul. Because over 80% of public transit users are using the smart card and with such detailed information about public transit users, the smart card data have potential as a new information source for public transit-related studies.
Ⅲ. Characteristics of Smart Card Data of Metropolitan Seoul
To investigate the potential of smart card data, some important public transportation figures are derived from Seoul’s smart card data. Smart card data were collected on two days: October 27, 2004 (Wednesday) and November 11, 2005 (Thursday).
Mode 2004 2005 Difference (%) Local mini bus 928,974 1,037,522 11.68
Main line bus 1,641,208 2,095,121 27.66 Feeder line bus 2,510,751 2,547,986 1.48 Metropolitan express bus 148,351 122,943 -17.13 Circle bus 15,434 14,615 -5.31 Bus total 5,244,718 5,818,187 10.93 Metro 4,843,438 5,034,531 3.95 Total 10,088,156 10,852,718 7.58 TABLE 4. Number of Smart Card Users of Different
Modes in Metropolitan Seoul
Number of
transfers Trips (2004) Trips (2005) Difference (%) 1 1,861,650 (88.05%) 2,167,887 (86.26%) 16.45 2 213,533(10.10%) 288,204 (11.47%) 34.97 3 31,733(1.50%) 45,968 (1.83%) 44.86 4 7,414(0.35%) 11,011 (0.44%) 48.52 Total 2,114,330(100.00%) 2,513,070 (100.00%) 18.86 TABLE 5. Number of Transfers
Until now smart card data have been mainly used for fare calculation and distribution among different operators, but they have been occasionally used for transportation studies with permission from the Seoul metropolitan government. Two days of data are expected to give some information about the daily variation of smart card data and the passing of one year between 2004 and 2005. As more than twenty million transactions occur every day, the data should be treated by a database management system. In this study, data are analyzed by ORACLE Relational Data Base Management System (RDBMS) version 10.2.
Structured Query Language (SQL) is used for manipulation of the data. In particular, the Multi-Dimensional Analysis technique is employed for data analysis. In the smart card data analysis, user type, travel mode, and travel time are the main dimensions of the data set.
In Seoul smart card data set consists of three user types (adult, student, and children), six travel
modes (local, main, feeder, metropolitan express, and circle buses, as well as Metro), and 24 hours of time. Table 4 shows the number of users of the six modes in two different days. Between 2004 and 2005, the use of smart cards on buses increased 11%, while smart card users on the Metro increased about 4%.
From the data, trips consisting of transfers are investigated. To get a distance-based fare cheaper than the maximum possible fare, users should swipe smart cards on every trip start and end.
Because the new fare system allows four free transfers, it records up to four linked segments for each trip. Most trips consist of less than two transfers(Table 5).
Figure 1 shows the hourly distribution of total public transit trips from the 2004 dataset based on boarding times derived from smart card data. It reveals a conventional pattern of daily public transit travel patterns: 20% of total daily trips are 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 peak period is higher than at any other time of day.
0.00 5.00 10.00 15.00 20.00 25.00 30.00
4~ 5~ 6~ 7~ 8~ 9~ 10~ 11~ 12~ 13~ 14~ 15~ 16~ 17~ 18~ 19~ 20~ 21~ 22~ 23~ 0~ 1~ 2~ 3~
Boarding Time(hr)
Average Travel Time(min.) a
0 200 400 600 800 1000 1200 1400
No. of Trips(1,000tirp) a
Average Travel Time No. of Trips
FIGURE 1. Hourly variation of travel time and number of trips
0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 45 0
4:00 6:00
8:00 10:00
12:00 14:00
16:00 18:00
20:00 22:00
0:00 2:00 Boarding time (hr)
Trip distribution index
Main line bus feeder bus Met. Ex p. Bu s Metro
FIGURE 2. Hourly trip distribution for different modes
Figure 2 shows the details of the hourly trip distribution index by modes based on boarding time. The number of trips at 11 AM is set as 100, and trips produced during another periods are adjusted according to the difference between that period and from the difference between 11 AM and the period. Metro ridership reveals the highest difference between peak and off-peak time trips.
And also, the number of trips of most modes except metropolitan express bus quickly decreases after 8 PM. But the distribution of trips by metropolitan express buses, which link Seoul’s downtown with suburban bedroom communities, shows a long tail until 12 AM. This may suggest that people with long trip distances from bedroom communities prefer the Metro in the morning
0 10 20 30 40 50 60 70 80 90 100
0~
5 5~10
10~15 15~20
20~25 25~30
30~35 35~40
40~45 45~50
50~55 55~60
60~65 65~70
70~75 75~80
80~85 85~90
90~95 95~100
100~
105 105~110
110~115 115~
120 120 and more Travel time(minu tes)
(%)
M etro M ain line bus Feeder bu s M et. Ex p.bu s
FIGURE 3. Cumulative travel time of different modes
because of its punctuality but prefer buses in the evening because of the increased comfort from the coach bus style design, even though they have to pay higher fares.
Figure 3 shows the cumulative travel time of each mode. About 90% of the trips on the Metro and metropolitan express buses take less than one hour. For other modes such as main line buses and feeder buses, 90% of the trips take less than 30 minutes. This suggests that the Metro works as the main public transit mode for long distance travel and buses work as feeders for short distance trips.
Ⅳ. Reliability of Smart Card Data
To use data from smart cards for any purpose, reliability of the data should first be investigated.
During this study, some important issues related to the reliability of the data were found.
1. Characteristics of cash users
To use smart card data in policy decisions and
related studies, they should fully represent the characteristics of every public transit user. As shown in Table 2, the proportion of smart card users, particularly in Seoul, is more than 90% for buses and 72% for the Metro. Still, 10% of bus users and 30% of Metro users do not use smart cards. If these people’s travel characteristics are different from smart card users, they should be analyzed independently and added to the result from smart card users. The result may be that cash users are not frequent users of public transit, and they may have different trip patterns from smart card user.
2. Alighting information
Smart cards are only processed at the time of boarding in most cities. Thus, smart card data generally do not have complete information on each trip. Unlike other cities, however, because the Seoul area implemented a distance-based fare system, users also must process their smart cards
TABLE 6. Proportions of Number of Users Without Alighting Data
Mode 2004(%) 2005(%)
Local mini bus 7.55 7.20
Main line bus 10.17 10.96
Feeder line bus 10.17 10.32
Metropolitan exp. bus 64.36 80.01
Circle bus 7.89 7.33
Metro 0.50 0.33
Total 6.08 6.29
when they get off to avoid the maximum fare.
Table 6 shows the proportion of riders without alighting information for each public transit mode.
For the Metro, because every user should pass a ticket gate when they are entering or exiting a station, every trip has complete information. But bus data shows a different situation. To get free transfers and a distance-based fare that is less than the maximum fare, the user has to complete his trip data by processing his card; but if the user carries out only a single trip, he does not have to complete his trip data when he alights. Thus, there are missing alighting data for bus users. In particular, smart card data of metropolitan express bus have a high proportion of missing alighting data because users cannot receive a free transfer from the bus and fares are fixed.
To investigate the reliability of smart card data, the number of users of every station in Metro lines one to eight obtained from smart card data is compared with conventional published data from the Seoul Metro Company(SMC). The Seoul Metro
Company publishes monthly the number of users of every Metro station based on data from ticket gates. To compare the two sets of data, the numbers of users from smart card data are converted to monthly values using the number of cash users and daily variations.
First, the number of cash users is added to the smart card data. In October 2004, SMC published the proportion of smart card users as 70.5%.
Total users of each Metro station (smart card users + cash users) on one day = number of smart card users ÷ 0.705 (proportion of smart card users per total Metro users) (1)
Second, there exists daily variation of number of Metro users through weekday. Because smart card data were collected on Wednesday, a Wednesday variation of 1.098 per weekly average proportion of smart card use is implemented to get the weekly average number of users.
Number of total users per day considering daily variation = total users of the day ÷ 1.098 (Wednesday variation) (2)
The number of users at 260 stations of Metro lines one to eight is compared with the daily number of users published by SMC. Figure 4 shows the ratio of each station’s users from smart card data against SMC published data. From the data, three abnormal data points were found: the Sport Complex, World Cup Stadium, and Moran Market stations. User-based smart card data were 40% higher than the monthly average user data at Sport Complex Station because there was a
0 . 4 0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1 1 . 1 1 . 2 1 . 3 1 . 4
FIGURE 4 Ratio of Number of Metro Users from Smart Card Data to Data Published by SMC
Lines No. of Stations Min. Value Max. Value Average Std. Dev. t-test
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
TABLE 7. Results of Statistical Analysis of Differences between Smart Card Data and Published Data(Applying average proportion of smart card use)
sporting event on the day the data were obtained.
But smart card data reveals 40% fewer users at World Cup Stadium and Moran Market stations than the monthly average number of users because there were no sporting events and the market was not opened on that day. Thus, data from these three stations are removed from the analysis.
Table 7 shows basic statistics of the difference between smart car data and SMC published data
for each of Metro lines one through eight. Also, a paired sample t-test was performed to investigate the difference between smart card data and SMC published data. The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups.
When we are looking at the differences between scores for two groups, we have to judge the difference between their means relative to the
Lines % of card use
No. of
Stations Min. Value Max. Value Average Std. Dev. t-test
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
TABLE 8. Results of Statistical Analysis of Differences between Smart Card Data and Published Data(Applying line-specific proportions of smart card use)
spread or variability of their scores. And the t-test does just this. The formula for the t-test is a ratio.
The top part of the ratio is just the difference between the two means or averages, the bottom part is a measure of the variability or dispersion of the scores. The t-value will be positive if the first mean is larger than the second and negative if it is smaller. Using the t-value, you can conclude the difference between two groups. The null hypothesis is that the difference of the two data sets is zero. The t-test shows that data sets of line one and two are statistically different at a 95%
level of significance.
To investigate the error in lines one and two, a line-specific monthly average proportion of smart card use is applied in equation (1). The proportion of smart card users on line one is relatively low compared to other Metro lines, perhaps because it includes two intercity and commuter railway stations. On the other hand, Metro line two shows a higher proportion of smart card users. By implementing these line-specific smart card use proportions, the accuracy of the results is improved, except for line seven. Table 8 lists
statistical results by implementing line-specific smart card use proportions.
V. Conclusion
Use of smart cards for fare payment in public transit has grown in Korea since its introduction in 1997. 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 distance-based fares, which requires detailed data of users such as boarding time and GPS-based location. It is important that with the help of the new fare system and technology, the smart card data in Seoul consist of boarding and alighting information with time. The objective of this study is to investigate the potential of smart card data and their reliability. Some characteristics of public transit users that can be calculated from these data include 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. To investigate the reliability of smart card data, the number of users of every Metro station in Seoul from smart card data and from Seoul Metro Company statistics were compared. With two simple manipulations to include daily variations and number of cash users, smart card data reveal itself to be statistically indifferent with surveyed data from the Seoul Metro Company. From the results, smart card data show potential to describe characteristics of public transit users. Also, it is clear that smart card data can represent all Metro users: smart card users as well as cash users.
However, this study also suggests that to represent all Metro users with smart card data, line-specific proportions of card use should be employed. Even though it is not discussed in this study, route-specific proportions of smart card use may be required to estimate all bus users from smart card data. Methods to include the number of bus users who do not have alighting information should also be investigated.
This study is just the first step of the work required to use smart card data for public transit planning and operation. With its potential as a new source of data, smart card data is expected to play an important role for Seoul Metropolitan area transportation research. Because smart card data is collected as real time base, by linking with passenger information system, it can provide public transit related information such as waiting time and estimated arrival time, to users. Also by building historic data base, smart card data can provide trend of transportation users’ characteristics, which could be used to estimate future trend of transportation. This will be valuable information
for Seoul Metropolitan Government to set up strategy for urban transportation system of Seoul Metropolitan area. Above all, smart card data can be an important source to create future transportation demand matrix of Seoul Metropolitan area. It can reduce time and cost currently required to create it. However, more work will be required to build reliable models to represent all public transit users and their travel patterns with smart card data. It should be identified whether smart card data could represent traffic patterns of whole transportation users. As discussed in this paper, characteristics of metro users can be fully described by smart card data.
But for bus users, it should be studied as there exists lack of off-boarding information. Also regional variation of lack of information should be investigated.
Another issue for using smart card data is to set up proper process and managing system for it.
Currently, in spite of its potential as a new data source, data use for other purpose than original fare calculation is not fully set up. If it is not set up properly, with considering private information protection rule, it will be an important issue.
Currently these data have already provided data to some public transportation studies. And with the information included in it, smart card datawill become an important part of transportation studies.
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원 고 접 수 일：2007년 8월 10일
1차심사완료일：2007년 10월 12일 최종원고채택일：2007년 10월 22일
