• 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.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.163-165
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• 2006

The subway passengers are usually alert to the current location of the train in order not to miss the destination or transfer stations. The Global Positioning System (GPS), although expensive, can give an alarm if properly programmed, but cannot receive the satellite signals, underground. Therefore, a novel approach to context-aware location-based subway information system is motivated. The passengers, who are equipped with mobile devices such as laptop, PDA, and mobile phone as clients of the Personal Area Network (PAN), request the Bluetooth connection to the server which is installed in each car of the train. While the sorrel broadcasts the location-based information including the previous station, the current velocity of the train, the distance and time to the next station, the clients provide additional services based on the recognized context of the information, and what the passengers individually want. The services are spontaneous and autonomous rather than passive. The services include not only the information on the nearby stations, exit numbers, connection buses but also the location-based alarms which can be set specific to various personal requests, and sounded by the location of the train rather than time. Whereas the arrival time may not be accurate due to the delays of the train, the location can be accurately traced and broadcast by the server. Also, the clients do not need any expensive systems like GFS. Towards validating the proposed approach, we implemented a Bluetooth PAN including a PC server, two PDA clients and a laptop client. We modeled a train on the Incheon Subway Line #1 and a train on the Seoul-to-Incheon Line on the server, simulated the virtual trains together with the real clients. and verified that all the services were successfully provided.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.103-110
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• 2012

The population growth along with the urbanization, has caused more problems in many public areas, such as subway airport terminals, hospital, etc. Many surveillance systems have been installed in the public areas, but not all of those can be monitored in real-time, because the operators that observe the monitors are very small compared with the number of the monitors. For example, the observer can miss some crucial accidents or detect after considerable delays. Thus, intelligent surveillance system for preventing the accidents are needed, such as Intelligent Surveillance Systems. in this paper, we propose a new crowd density estimation method which aims at estimating moving crowd using images from surveillance cameras situated in outdoor locations. The moving crowd is estimated from the area where using optical flow. The edge information is also used as feature to measure the crowd density, so we improve the accuracy of estimation of crowd density. A multilayer neural network is designed to classify crowd density into 5 classes. Finally the proposed method is experimented with PETS 2009 images.