• Title/Summary/Keyword: 대중교통카드 자료

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Development of an Algorithm for Estimating Subway Platform Congestion Using Public Transportation Card Data (대중교통카드 자료를 활용한 도시철도 승강장 혼잡도 추정 알고리즘 개발)

  • Lee, Ho;Choi, Jin-Kyung
    • Journal of the Korean Society for Railway
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    • v.18 no.3
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    • pp.270-277
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    • 2015
  • In some sections of the Seoul Metropolitan Subway, severe congestion can be observed during rush hours and on specific days. The subway operators have been conducting regular surveys to measure the level of congestion on trains: the results are then used to make plans for congestion reduction. However, the survey has so far focused just on train' congestion and has been unable to determine non-recurring congestion due to special events. This study develops an algorithm to estimate the platform congestion rate by time using individual public transportation card data. The algorithm is evaluated by comparison of the estimated congestion rate and the ground truth data that are actually observed at non-transfer subway stations on Seoul subway line 2. The error rates are within ${pm}2%$ and the performance of the algorithm is fairly good. However, varying walking times from gates to platforms, which are applied to both non-peak periods and peak time periods, are needed to improve the algorithm.

Analysis of Transit Passenger Movements within Seoul-Gyeonggi-Incheon Area using Transportation Card (대중교통카드자료를 활용한 수도권 통행인구 이동진단)

  • Lee, Mee Young;Kim, Jong Hyung
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.15 no.5
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    • pp.12-19
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    • 2016
  • An average of 20 million individual transit unit activities per day on the Seoul-Gyeonggi-Incheon public transportation network are provided as transportation card analysis data by the metropolitan district (99.02% by 2014 standard, Humanlive, 2015.4). The metropolitan transportation card data can be employed in a comprehensive analysis of public transportation users' current transit patterns and by means of this, an effective use plan can be explored. In enhancing the existing information on the bus and rail integrated network of the metropolis with public transportation card data, the constraints in the existing methodology of metropolitan transit analysis, which functions on a zone unit origin and destination basis, can be overcome. Framework for metropolitan public transportation card data based integrated public transportation analysis, which consists of bus and rail integrated transport modes, is constructed, and through this, a single passenger's transit behavior transit volume can be approximated. This research proposes that in the use of metropolitan public transportation card data, integrated public transportation usage, as a part of individual passenger spatial movements, can be analyzed. Furthermore, metropolitan public transportation card usage data can provide insights into understanding not only movements of populations taking on transit activities, but also, characteristics of metropolitan local space.

Estimating Station Transfer Trips of Seoul Metropolitan Urban Railway Stations -Using Transportation Card Data - (수도권 도시철도 역사환승량 추정방안 -교통카드자료를 활용하여 -)

  • Lee, Mee-Young
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.38 no.5
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    • pp.693-701
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    • 2018
  • Transfer types at the Seoul Metropolitan Urban Railway Stations can be classified into transfer between lines and station transfer. Station transfer is defined as occurring when either 1) the operating line that operates the tag-in card-reader and that operating the first train boarded by the passenger are different; or 2) the line operating the final alighted train and that operating the tag-out card-reader are different. In existing research, transportation card data is used to estimate transfer volume between lines, but excludes station transfer volume which leads to underestimation of volume through transfer passages. This research applies transportation card data to a method for station transfer volume estimation. To achieve this, the passenger path choice model is made appropriate for station transfer estimation using a modified big-node based network construction and data structure method. Case study analysis is performed using about 8 million daily data inputs from the metropolitan urban railway.

The Spatial Correlation of Mode Choice Behavior based on Smart Card Transit Data in Seoul (교통카드 자료를 이용한 서울시 지역별 대중교통 수단 선택 공간상관성 분석)

  • Park, Man Sik;Eom, JinKi;Heo, Tae-Young
    • The Korean Journal of Applied Statistics
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    • v.26 no.4
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    • pp.623-634
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    • 2013
  • In this study, we provide empirical evidence of whether a spatial correlation among mode choices at the TAZ(Traffic Analysis Zone) level exists based on transit smart card data observed in Seoul, Korea. The results show that the areas with a higher probability that passengers choose to take a bus are clustered and that those regions have fewer metro stations than bus stations. We also found that the spatial correlation turned out to be statistically meaningful and provided an opportunity for the potential use of the spatial correlation in modeling mode choices. A reliable spatial interaction would constitute valuable information for transportation agencies in terms of their route planning and scheduling based on the transit smart card data.

Factor Analysis for Transit Transfer using Public Traffic Card Data (대중교통카드를 이용한 환승요인분석)

  • Lee, Da-Eun;Oh, Ju-Taek
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.16 no.1
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    • pp.50-63
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    • 2017
  • While transit is inconvenient, it is also inevitable for the efficient public transportation. Reducing the number of transfers as much as possible is most important in providing the convenience of public transportation and facilitating the public transportation. As for the public transportation card data, 61,986 items on weekdays and 69,100 items on weekends were collected. Pattern analysis and traffic influence factors were analyzed using traffic data card. Trip chain results revealed that people have more transit transfers for shopping and leasure than commuting purposes on weekends and that commuting distance and time increase by 10 km and 9.9 minutes, respectively. Besides, results of the structural equation model showed that factor 1(total travel time, total travel distance), factor 2(number of people getting on and off), factor 3(transit time), and factor 4(number of bus connections, number of operations) were found to have significant effects on the number of transfers.

Inferring the Transit Trip Destination Zone of Smart Card User Using Trip Chain Structure (통행사슬 구조를 이용한 교통카드 이용자의 대중교통 통행종점 추정)

  • SHIN, Kangwon
    • Journal of Korean Society of Transportation
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    • v.34 no.5
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    • pp.437-448
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    • 2016
  • Some previous researches suggested a transit trip destination inference method by constructing trip chains with incomplete(missing destination) smart card dataset obtained on the entry fare control systems. To explore the feasibility of the transit trip destination inference method, the transit trip chains are constructed from the pre-paid smart card tagging data collected in Busan on October 2014 weekdays by tracing the card IDs, tagging times(boarding, alighting, transfer), and the trip linking distances between two consecutive transit trips in a daily sequences. Assuming that most trips in the transit trip chains are linked successively, the individual transit trip destination zones are inferred as the consecutive linking trip's origin zones. Applying the model to the complete trips with observed OD reveals that about 82% of the inferred trip destinations are the same as those of the observed trip destinations and the inference error defined as the difference in distance between the inferred and observed alighting stops is minimized when the trip linking distance is less than or equal to 0.5km. When applying the model to the incomplete trips with missing destinations, the overall destination missing rate decreases from 71.40% to 21.74% and approximately 77% of the destination missing trips are the single transit trips for which the destinations can not be inferable. In addition, the model remarkably reduces the destination missing rate of the multiple incomplete transit trips from 69.56% to 6.27%. Spearman's rank correlation and Chi-squared goodness-of-fit tests showed that the ranks for transit trips of each zone are not significantly affected by the inferred trips, but the transit trip distributions only using small complete trips are significantly different from those using complete and inferred trips. Therefore, it is concluded that the model should be applicable to derive a realistic transit trip patterns in cities with the incomplete smart card data.

Analysis of Public Transport Ridership during a Heavy Snowfall in Seoul (기상상황에 따른 서울시 대중교통 이용 변화 분석: 폭설을 중심으로)

  • Won, Minsu;Cheon, Seunghoon;Shin, Seongil;Lee, Seonyeong
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.39 no.6
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    • pp.859-867
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    • 2019
  • Severe weather conditions, such as heavy snowfall, rain, heatwave, etc., may affect travel behaviors of people and finally change traffic patterns in transportation networks. To deal with those changes and prevent any negative impacts on the transportation system, understanding those impacts of severe weather conditions on the travel patterns is one of the critical issues in the transportation fields. Hence, this study has focused on the impacts of a weather condition on travel patterns of public transportations, especially when a heavy snowfall which is one of the most critical weather conditions. First, this study has figured out the most significant weather condition affecting changes of public transport ridership using weather information, card data for public transportation, mobile phone data; and then, developed a decision-tree model to determine complex inter-relations between various factors such as socio-economic indicators, transportation-related information, etc. As a result, the trip generation of public transportations in Seoul during a heavy snowfall is mostly related to average access times to subway stations by walk and the number of available parking lots and spaces. Meanwhile, the trip attraction is more related to business and employment densities in that destination.