• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.145-150
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• 2023

The subway is a public transportation that many people use every day. Line 2 especially has the most crowded stations during the day. However, the risk of crush accidents is increasing due to high congestion during rush hour and this reduces the safety and comfort of passengers. Subway congestion prediction is helpful to forestall problems caused by high congestion. Therefore, this study proposes machine learning classification models that predict subway congestion during commuting time. To predict congestion in Line 2 based in machine learning, we investigate variables that affect subway congestion through previous research and collect a dataset of subway congestion on Line 2 during rush hour from PUBLIC DATA PORTAL. The proposed model is expected to establish the subway operation plane to make passengers safe and satisfied.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.289-295
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• 2016

Subway is a future-oriented means of transportation that can be safely and quickly mass transport many passengers than buses and taxis. Congestion growth due to the increase of the metro users is one of the factors that hinder citizens' rights to comfortably use the subway. Accordingly, congestion prediction in the subway is one of the ways to maximize the use of passenger convenience and comfort. In this paper, we monitor the level of congestion in real time via the existing congestion on the metro using multiple regression analysis and big data processing, as well as their departure station and arrival station information More information about the transfer stations offer a personalized congestion prediction system. The accuracy of the predicted congestion shows about 81% accuracy, which is compared to the real congestion. In this paper, the proposed prediction and recommendation application will be a help to prediction of subway congestion and user convenience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.71-73
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• 2015

2009년 7월 개화에서 신논현까지 서울 지하철 9호선이 개통했다. 2010년 9호선 일평균 통행량은 예측 통행량 대비 97%수준이었으나, 2013년은 110%를 달성했다. 2015년 3월 2단계구간이 개통되어 평일 평균 이용객이 15만명 정도가 더 늘어났다. 국회 자료에 따르면, 출근시간 염창역에서 당산역까지의 혼잡도가 237%로 나타났다. 이는 다른 지하철 혼잡도 2배 뛰어넘는 수치이다. 당산역에서 여의도역(234%), 여의도역에서 노량진역(212%), 노량진역에서 동작역(216%)으로 기록이 될 만큼 특정 구간의 혼잡도가 높게 나타났고 급행노선을 선호하는 인원이 많아 시간이 지날수록 정체현상이 가중되고 있다. 따라서 본 연구는 혼잡도의 주 원인인 정체현상을 감소시키고 여객 수송율을 증가시키기 위해 기존의 급행 프로세스를 변경하는 방안을 제시한다. 여기에 적용된 연구방법은 혼잡도 수준을 낮추기 위해 필요한 프로세스 설정하고 아레나 시뮬레이션 프로그램 분석을 통해 본 연구에서 제시한 방안에 대해 검증한다. 본 연구에서 제안한 방식을 통해 지하철의 혼잡도 해소에 도움을 줄 수 있을 것이다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.49-63
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• 2019

Crowdedness management at subway platforms is essential to improve services, including the prevention of train delays and ensuring passenger safety. Establishing effective crowdedness mitigation measures for platforms requires accurate estimation of the congestion level. There are temporal and spatial constraints since crowdedness on subway platforms is assessed at certain locations every 1-2 years by hand counting. However, smart cards generate real-time big data 24 hours a day and could be used in estimating congestion. This study proposes a model based on data from transit cards to estimate crowdedness dynamically. Crowdedness was defined as demand, which can be translated into passengers dynamically moving along a subway network. The trajectory of an individual passenger can be identified through this model. Passenger flow that concentrates or disperses at a platform is also calculated every minute. Lastly, the platform congestion level is estimated based on effective waiting areas for each platform structure.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.421-422
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• 2015

본 논문은 객차 내 혼잡도 안내 서비스를 제안하고 그 효율성과 도입 시 최적 표시방안에 대해 말하고자 한다. 우리는 실제 지하철 이용객에 대한 관찰과 인터뷰를 통해 지하철 내의 칸별 혼잡도 차이가 발생하는 원인을 찾고, 이를 바탕으로 칸별 탑승 인원을 분산시킬 방안으로서의 객차 내 혼잡도 안내 서비스와 표시방안을 제시한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.153-155
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• 2024

본 논문은 진접선 개통 이후 급증하는 서울 지하철 4호선의 혼잡 문제와 현재 진행 중인 장애인 차별 반대 시위를 다룬다. 네이버의 지도 API를 활용해 위도와 경도 데이터를 추출하고 지하철 노선별 장애인 승객 수와 최대 지연시간을 시각화한다. 2호선과 4호선의 혼잡도가 표시되어 문제의 심각성을 알 수 있다. 평균 출퇴근 시간 탑승 및 하차 수치를 분석하여 4호선 편의시설 개선, 2·4호선 열차 운행 횟수 늘리기, 환승역 운영 최적화 등 전략적 권장 사항을 제시한다. 제안된 대책은 서울시 지하철 시스템의 접근성 향상, 혼잡완화, 전반적인 효율성 제고를 통해 보다 폭넓은 교통시설 개선과 승객 편의 증진에 기여하는 것을 목표로 하고 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.74-79
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• 2016

The Seoul Metropolitan Subway, which started operating in 1974, plays a major role in transporting 7,289 thousands passengers daily. This trend of a steadily increase in passengers from 2012 has increased the congestion rate because of the limited capacity and time. To solve this problem, Seoul city is consistently working on improving the subway facilities, such as the construction of a detour path. This project, however, has only a slight effect on improving the congestion rate and is too expensive to construct the facilities. Hence, this study suggests The Congestion Standardization System based on the IoT for improving the subway congestion rate. Based on the system, the expected effect analysis was performed, which resulted in a decrease in ride passengers from 34 to 20. In addition, this expected effect analysis shows that the number of subway vehicles can increase from 20 to 24. The suggested system will have a significant effect on the efficiency of the management system.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.35-42
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• 2015

As the value of Bigdata has been recognized importantly, public agencies including the government, private sector, etc. began to have an interest in Big Data. As there are sources of various data, and a variety of planning and analysis methods based on these sources has emerged, It is true that Bigdata will become a tool for creation of the new high qualitied information and decision making based on new insights. The purpose of this study is to find an alternative to the subway congestion problem that is not improved even though the various measures. In this study, we tried to explore approaches for ways to improve the congestion of the Seoul Subway using Seoul Metropolitan public data. Lastly, this study derived a policy alternative to establish new bus route that runs around the metro station that have a high level of congestion.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.199-204
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• 2008

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.183-188
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• 2024

As time progresses and technology advances, many modes of transportation have emerged compared to the past. People use various means of transportation including personal cars, subways, buses, and taxis, among which public transport is utilized by people of all ages and genders. Public transportation has the advantage of being affordable and convenient, but with the increase in population compared to the past, traffic congestion has also been increasing, making it increasingly uncomfortable. Especially during specific times or on certain dates, traffic congestion can become significantly worse than usual. Among these, the subway is the mode of transportation used most frequently. Therefore, in this study, we will discuss solutions and analyze the causes of congestion by subway section using the R program.