• Journal of the Economic Geographical Society of Korea
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• v.14 no.2
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• pp.116-128
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• 2011

Some subway passengers may want to have their fresh vegetables purchased through internet at a service facility within the subway station of the Metropolitan Seoul subway system on the way to home, which raises further questions about which stations are chosen to locate service facilities and how many passengers can use the facilities. This problem is well known as the pickup problem, and it can be solved on a traffic network with traffic flows which should be identified from origin stations to destination stations. Since flows of the subway passengers can be found from the smart card transaction database of the Metropolitan Seoul smart card system, the pickup problem in the Metropolitan Seoul subway system is to select subway stations for the service facilities such that captured passenger flows are maximized. In this paper, we have formulated a model of the pickup problem on the Metropolitan Seoul subway system with subway passenger flows, and have proposed a fast heuristic algorithm to select pickup stations which can capture the most passenger flows in each step from an origin-destination matrix which represents the passenger flows. We have applied the heuristic algorithm to select the pickup stations from a large volume of traffic network, the Metropolitan Seoul subway system, with about 400 subway stations and five millions passenger transactions daily. We have obtained not only the experimental results in fast response time, but also displayed the top 10 pickup stations in a subway guide map. In addition, we have shown that the resulting solution is nearly optimal by a few more supplementary experiments.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.304-314
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• 2015

Detour of metro (subway) in metropolitan area has been regarded as one of inherent elements that make entire metro system less competitive than passenger cars. However, factors affecting detour of metro line is not specified clearly when a new subway system is planned or an existing system is improved. Previous detour-related studies was reviewed for developing 'distance efficeincy evaluation model' for metro as well as its counterpart (i.e. road). Metro line 3 (orange line) of Seoul metropolitan area was applied as a case study. As a results, the most detoured OD segment is Daechi - Apgujeong and its detour factor is 1.93 which means that the distance of the metro is 1.93 times longer distance compared to the distance of the road. The metro line 3 has averagely 20% longer distance compared to road for the identical O/D pairs.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.743-744
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• 2017

수도권 뿐만 아니라 지방에서도 대중교통의 발달이 가속화되고 있다. 수도권 지하철의 경우 지하철 스크린도어가 생겼지만 그럼에도 불구하고 문 끼임 사고가 많이 발생하고 있는 상황이다. 그렇지만 국내 지하철 사업에서 문 끼임 사고 방지 예산에는 전혀 투자가 되지 않고 있다. 국민 보건의 측면에서 지하철 문 끼임 사고의 심각성과 인간이 지닌 잠재적 가치를 고려할 때 이에 대한 대책이 시급하다. 이에 본 시스템에서는 RF(Radio Frequency)을 기반으로 한 지하철 끼임 사고 방지 모델을 대안으로 제시하며, 국민 보건과 인간의 잠재성이라는 사회적 가치를 문제해결에 접목하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.38-39
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• 2010

수도권 대중교통 이용자는 2004년 서울시의 대중교통 체계 개편에 따라 교통 카드를 사용하여 버스와 지하철을 이용하게 되었다. 교통 카드를 사용하는 각 승객의 승차와 하차에 관한 데이터가 하나의 트랜잭션으로 구성되고, 하루 천만 건 이상의 트랜잭션들로 구성된 대용량 교통카드 트랜잭션 데이터베이스가 만들어지고 있다. 대중교통을 이용하는 승객들의 승차와 하차에 관한 여러 정보를 담고 있는 교통카드 트랜잭션 데이터베이스에서 유용한 패턴이나 정보를 탐사해내는 연구가 계속 진행되고 있다. 이런 연구 결과는 수도권 대중교통 정책을 입안하는데 중요한 기초 자료가 되고 수도권 승객들에게 대중교통을 보다 잘 이용할 수 있는 정보로 제공된다. 교통카드 이용률은 2006년 79.5%, 2007년 80.3%, 2008년 81.6%로 점차적으로 증가하고 있다. 대용량의 교통카드 트랜잭션 데이터베이스에 대한 연구를 살펴보면 하루 동안의 교통카드 트랜잭션 데이터베이스에서 순차 패턴을 탐사하는 알고리즘을 연구하였고[1], 승객들의 통행 패턴에 대한 분석연구를 확장하여 일 년에 하루씩 2004년에서 2006년까지 3일간의 교통카드 트랜잭션 데이터베이스로부터 승객 시퀀스의 평균 정류장 개수와 환승 횟수 등을 연도별로 비교하였다[2]. 수도권 지하철 시스템의 특성에 관한 연구로는 네트워크 구조 분석이 있었고[3], 승객의 기종점 통행 행렬(Origin-Destination trip matrix)에 의한 승객 흐름의 분포가 멱함수 법칙(power law)임을 보여주는 연구가 있었고[4], 지하철 교통망에서 모든 링크상의 승객들의 흐름을 찾아내는 연구가 있었다[5]. 본 논문에서는 교통카드 트랜잭션 데이터베이스에서 지하철 승객들의 통근 패턴을 탐사해내는 방법을 연구하였다. 수도권 지하철 네트워크에 대한 정보를 입력하고 하루치의 교통카드 트랜잭션 데이터베이스에 연구된 방법을 적용하여 8가지 통근 패턴들을 탐사해내고 분석하였다. 탐사된 패턴들 중에서 많은 승객들이 지지하는 출퇴근 패턴에 대해서는 시간대별로 승객수를 그래프로 보여주었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.64-79
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• 2023

This study reviewed the process of applying the Monte Carlo simulation technique to the traffic allocation problem of metropolitan subways. The analysis applied the assumption of a normal distribution in which the travel time information of the inter-station sample is the basis of the probit model. From this, the average and standard deviation are calculated by separating the traffic between stations. A plan was proposed to apply the simulation with the weights of the in-vehicle time of individual links and the walking and dispatch interval of transfer. Long-distance traffic with a low number of samples of 50 or fewer was evaluated as a way to analyze the characteristics of similar traffic. The research results were reviewed in two directions by applying them to the Seoul Metropolitan Subway Network. The travel time between single stations on the Seolleung-Seongsu route was verified by applying random sampling to the in-vehicle time and transfer time. The assumption of a normal distribution was accepted for sample sizes of more than 50 stations according to the inter-station traffic sample of the entire Seoul Metropolitan Subway. For long-distance traffic with samples numbering less than 50, the minimum distance between stations was 122Km. Therefore, it was judged that the sample deviation equality was achieved and the inter-station mean and standard deviation of the transport card data for stations at this distance could be applied.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.3
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• pp.343-357
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• 2012

The Seoul metropolitan subway system can be represented by a graph which consists of nodes and edges. In this paper, we study classification of subway stations and trip behaviour of subway passengers through partitioning the graph of the subway system into roughly equal groups. A weight of each edge of the graph is set to the number of passengers who pass the edge, where the number of passengers is extracted from the transportation card transaction database. Since the graph partitioning problem is NP-complete, we propose a heuristic algorithm to partition the subway graph. The heuristic algorithm uses one of two alternative objective functions, one of which is to minimize the sum of weights of edges connecting nodes in different groups and the other is to maximize the ratio of passengers who get on the subway train at one subway station and get off at another subway station in the same group to the total subway passengers. In the experimental results, we illustrate the subway stations and edges in each group by color on a map and analyze the trip behaviour of subway passengers by the group origin-destination matrix.

• Proceedings of the KSR Conference
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• 2001.05a
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• pp.52-57
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• 2001

ㆍ수도권 철도는 우리나라의 경제성장과 함께 증가하는 교통처리 대책으로 1974년 8월 15일 서울지하철 1호선과 함께 경인선 서울∼인천간, 경부선 서울∼수원간, 경원선 용산∼청량리∼성북간을 복선 전철화 하여 도시철도로 전환, 이에 따라 수도권 위성도시의 발전과 인구 폭증으로 안산, 산본, 평촌, 분당, 일산 등 신도시를 개발하고 교통난 해소를 위해 서울지하철은 1호선에서 9호선가지 건설중이며, 수도권 철도는 경부선 영등포∼천안간 2복선전철과 안산선, 분당선, 일산선, 경의선, 경원선, 중앙선, 경춘선, 용산선, 신공항선 등 복선전철화와 경부고속철도 등 수송능력 증강을 위해 건설하고 있다. (중략)

• Journal of the Korean Society for Railway
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• v.19 no.3
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• pp.388-396
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• 2016

The metropolitan subway network of Seoul has gone through many evolutionary processes in past decades to disperse the floating population and improve the traffic flow. In this study, we analyzed how the structural characteristics and the efficiency of the subway network have changed according to the dynamic evolutionary processes of the metropolitan subway network of Seoul. We have also proposed new measures that can be used to characterize the structural properties of the subway network more practically. It is shown that the global efficiency is about 74%, which is higher than those of subway networks of foreign countries. It should also be considered that passenger flow between stations is even higher, at about 85%. Since the private lines, including line 9, the New Bundang line, the Uijeongbu line, and the Ever line do not release their traffic data since September, 2013, only 5 years of data from September, 2008 to September, 2013 is available. So, in this study we limit the analysis period to these 5 years.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.316-323
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• 2010

We propose a method to find flows of transit users in the subway transportation network of the metropolitan Seoul and analyze the passenger flows on some central links of the network. The transportation network consists of vertices for subway stops, edges for links between two adjacent subway stops, and flows on the edges' Each subway transit user makes a passenger flow along edges of the shortest path from the origin stop to the destination stop in his trip. In this paper, we have developed a new algorithm to find the passenger flow of each link in the subway network from a large trip-transaction database of subway transit users. We have applied the algorithm to find the passenger flows from one day database of about 5 million transactions by the subway transit users. As results of the experiments, the travel behavior on 4 central subway links is analyzed in passenger flows and top 10 flows among all subway links are explained in a table.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.67-83
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• 2018

This research is motivated by the question, "Where, when, and through what mode does an individual passenger moving within a subway station use to travel from starting to final destinations ?" To answer this, the stations passed by the individual passenger, the path taken, and modes used need to be known beforehand. In the metropolitan integrated public transportation fare system, Automated Fare Collection System(AFC) can be a source of information on transit modes, stations, and paths of individual passengers. AFC calculates a fare for the passenger based on travel data such as boarding and alighting stations, time, and mode used. In this research, an Selected Station Analysis(SSA) method, in which AFC data is used to observe passenger movement in the metropolitan public transportation subway station from the perspective of subway transfer stations, is proposed. SSA subdivides individual passenger movement in transfer stations and analyzes initial station/time and final destination station/time information using the trip chain perspective.