• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.58-71
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• 2021

The number of elderly people worldwide is rapidly increasing and the mobility handicapped suffering from inconvenient public transportation service is also increasing. In Korea and abroad, various policies are being implemented to provide high-quality transportation services for the mobility handicapped, and budget support and investment related to mobility facilities are being expanded. The mobility handicapped spends more time for transit transfer than normal users and their satisfaction with transit service is also lower. There exist transfer inconvenience points of the mobility handicapped due to various factors such as long transfer distances, absence of transportation facilities like elevators, escalators, etc. The purpose of this study is to find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. This study process traffic card transaction data and construct transfer travel data by user groups using smart card big data and analysis of the transfer characteristics for each user group ; normal, children, elderly, etc. Finally, find transfer inconveniences points by comparing transfer patterns between normal users and the mobility handicapped. This study is significant in that it can find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. In addition, it can be applicated of Smart card Big data for developing public transportation polices in the future. It is expected that the result of this study be used to improve the accessibility of transit transportation for mobility handicapped.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.101-116
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• 2017

This Study estimates the actual night traffic using the smart card data used by most of the public transportation users, and compares it with the current night bus routes by KT Telecom based on the night time call volume. In order to compare the current night bus and night trips evaluated by smart card data, we presented indicators related to the degree of matching, and estimated the volume of service currently provided. The unique approach of the study is that we chose subway station instead of bus stop for the unit of the study. Bus stops has their complexity in a way that stops with same name could belong to different administrative area depending on its direction. For this reason, we decided to use subway station and defined its adjacent administrative district as the scope of influence. Since night bus is the primary means of transportation during the late night, it is anticipated that they will be able to provide better service by calculating the actual traffic and selecting the routes.

• The Magazine of the IEIE
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• v.22 no.12
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• pp.27-44
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• 1995

초고속 통신망에 근간이 되는 ATM 통신망에 SUN W/S, PC 컴퓨터들을 접속시키는 ATM Adapter Card은 하드웨어, 시스템 소프트웨어 및 응용 소프트웨어로 구성된다. 하드웨어의 구조 및 시스템 소프트웨어의 구조, 성능에 따라서 ATM Adapter Card 위에서 수행되는 응용 소프트웨어의 성능을 결정할 것이다. ATM Adapter Card는 효율적으로 Native ATM 응용 소프트웨어를 지원할 뿐 아니라, 기존의 TCP/IP 응용 소프트웨어를 수용하는 IPoverATM, LAN Emulation 기능도 지원하여야 한다. 또한 PVC(Permanent Virtual Channel), SVC(Siganlling Virtual Channel)을 위하여 ATM Forum UNI 3.1에 준한 Signalling도 필요하다. 이러한 조건들을 만족시키기위하여, 현재 개발 완료된 SUN W/S SBus용 디바이스 드라이버는 DLPI(Data Link Provider Interface), STREAM 방식으로 구현하였으며, 현재 개발 중인 PC PCI용 ATM Adapter Card의 디바이스 드라이버는 NDIS 3.0 방식으로 구현 다양한 Transport Layer를 ATM망에 수용하도록 하였다. Signalling은 Daemon Process를 이용하여 구현하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.1-11
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• 2007

Seoul's transportation system was changed drastically starting the first of June in two thousand. This policy includes integrated distance-based fare system and public transportation card system called smart card. Especially, as public transportation card data contains individual travel, transfer and using modes information it is possible to catch the characteristics of path-based individuals and mass transit. Thus, public transportation card data can contribute to evaluate the mass transit service in integrated public transportation networks. In addition, public transportation card data are able to help to convert previous researches and analyses with link-based trip assignment models to path-based mass transit service analysis. In this study, an algorithm being suitable for path-based trip assignment models is suggested and proposed algorithm can also contribute to make full use of public transportation card data. For this, column generation algorithm hewn to draw the stable solution is adopted. This paper uses the methodology that is to take local approximate equilibrium from partial network and expand local approximate equilibrium to global equilibrium.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.28-38
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• 2019

The smart card data stores the transit usage records of individual passengers. By using this, it is possible to analyze the traffic demand by station and time. However, since the purpose of the trip is not recorded in the smart card data, the demand for each purpose such as commuting, school, and leisure is estimated based on the survey data. Since survey data includes only some samples, it is difficult to predict public transport demand for each purpose close to the complete enumeration survey. In this study, we estimates the purposes of trip for individual passengers using the smart card data corresponding to the complete enumeration survey of public transportation. We estimated trip purposes such as commute, school(university) considering frequency of O-D, duration, and departure time of a passenger. Based on this, the passengers are classified as workers and university students. In order to verify our methodology, we compared the estimation results of our study with the patterns of the survey data.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.692-702
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• 2017

Recently, there has been a growing interest in using smart card data. However, there are restrictions on the utilization of data in many areas outside the Seoul metropolitan area because the data does not contain alighting information. This paper presents a methodology for estimating alighting stops of smart card data. Estimation results were verified by smart card data from Seoul and Gwangju. The estimation rates were 78.2% and 81.6% in Seoul and Gwangju, respectively. The matching accuracy was 54.2% and 33.4%, respectively. However, if up to two stops of error are allowed, the accuracy values were 93.6% and 94.0%, respectively. We also discussed changes in estimation results due to adjusting the allowable walking distance, which is a key parameter of trip chaining methods. As the allowable walking distance increases, the estimation rate increases, while the accuracy decreases, and it is found that the estimation results change by around 500m.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.40-53
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• 2018

In smart card data, movement of railway passengers appears in order of smart card terminal ID. The initial terminal ID holds information on the entering station's tag-in railway line, the final terminal ID the exit station tag-out railway line, and the middle terminal ID the transfer station tag subway line. During the past, when the metropolitan city rail consisted of three public corporations (Seoul Metro, Incheon Transit Corporation, and Korail), OD data was expressed in two metrics of initial and final smart card terminal ID. Recently, with the entrance of private corporations like Shinbundang Railroad Corporation, and UI Corporation, inclusion of entering transfer line terminal ID and exiting transfer line terminal ID as part of Chain OD has become standard. Exact route construction using Chain OD has thus become integral as basic data for revenue allocation amongst metropolitan railway transport corporations. Accordingly, path detection in railway networks has evolved to an optimal path detection problem using Chain OD, hence calling for a renewed solution method. This research proposes an optimal path detection method between the initial terminal ID and final terminal ID of Chain OD terminal IDs within the railway network. Here, private line transfer TagIn/Out must be reflected in optimal path detection using Chain OD. To achieve this, three types of link-based optimum path detection methods are applied in order of 1. node-link, 2. link-link, 3. link-node. The method proposed based on additional path costs is shown to satisfy the optimal conditions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.105-114
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• 2016

With approximately 20 million transportation card data entries of the metropolitan districts being generated per day, application of the data to management and policy interventions is becoming an issue of interest. The research herein attempts a model of the possibility of dynamic demand change predictions and its purpose is thereby to construct a Dynamic Passengers Trip Assignment Model. The model and algorithm created are targeted at city rail lines operated by seven different transport facilities with the exclusion of travel by bus, as passenger movements by this mode can be minutely disaggregated through card tagging. The model created has been constructed in continuous time as is fitting to the big data characteristic of transport card data, while passenger path choice behavior is effectively represented using a perception parameter as a function of increasing number of transfers. Running the model on 800 pairs of metropolitan city rail data has proven its capability in determining dynamic demand at any moment in time, in line with the typical advantages expected of a continuous time-based model. Comparison against data measured by the eye of existing rail operating facilities to assess changes in congestion intensity shows that the model closely approximates the values and trends of the existing data with high levels of confidence. Future research efforts should be directed toward continued examination into construction of an integrated bus-city rail system model.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.125-138
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• 2020

Currently, subway crowding is estimated by observing a specific point at specific hours once or twice every 1 or 2 years. Given the extensive subway network in Seoul Metropolitan Area covering 588 stations, 11 lines and 80 transfer stations as of 2017, implementing crowding mitigation policy may have its limitations due to data uncertainty. A proposal has recently been made to effectively use smart card data, which generates big data on the overall subway traffic related to an estimated 8 million passengers per day. To mitigate subway crowding, this study proposes two viable options based on data related to smart card used in Seoul Metropolitan Area. One is to create a subway passenger pattern model to accurately estimate subway crowding, while the other is to prove effectiveness of early bird policy to distribute subway demand that is concentrated at certain stations and certain time. A subway passenger pattern model was created to estimate the passenger routes based on subway terminal ID at the entrance and exit and data by hours. To that end, we propose assigning passengers at the routes similar to the shortest routes based on an assumption that passengers choose the fastest routes. In the model, passenger flow is simulated every minute, and subway crowding level by station and line at every hour is analyzed while station usage pattern is identified by depending on passenger paths. For early bird policy, highly crowded stations will be categorized based on congestion level extracted from subway passenger pattern model and viability of a policy which transfers certain traveling demands to early commuting hours in those stations will be reviewed. In particular, review will be conducted on the impact of policy implemented at certain stations on other stations and lines from subway network as a whole. Lastly, we proposed that smart card based subway passenger pattern model established through this study used in decision making process to ensure effective implementation of public transport policy.