• 한국IT서비스학회지
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• 제19권2호
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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.

• 한국ITS학회 논문지
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• 제21권3호
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• pp.1-17
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• 2022

수도권 도시철도 혼잡으로 인해 다양한 문제가 발생되고 있어 도시철도 혼잡을 정확히 분석하는 연구 또한 해결방안 중 하나일 것이다. 현재 계수법, 중량검지법, CCTV, 모바일 Wi-Fi 등을 활용한 혼잡도 분석방법론은 결과의 정확성이 낮거나 단일 노선 및 열차에만 국한되는 한계점이 존재한다. 본 연구에서는 도시철도 승객의 다중경로 이용행태와 수도권 도시철도 열차운행계획을 반영한 열차 혼잡도 분석모형을 제시하였다. 특히 열차 용량 초과시 승객이 대기한 후 다음 열차에 탑승하는 통행패턴을 고려하여 열차 혼잡도 산정의 정확성을 제고하였다. 열차 혼잡도는 10분 단위로 갱신되어 시민에게 혼잡정보 제공이 가능하므로 사회적 편익증대에 기여할 것으로 기대된다.

• 한국가시화정보학회지
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• 제19권2호
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• pp.26-31
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• 2021

The spanwise growth of turbulence structures in turbulent pipe flow was investigated using the direct numerical simulation data of Re_𝜏 = 544, 934 and 3008. Two-point correlations and pre-multiplied energy spectra of streamwise velocity fluctuations were examined along the spanwise direction. The arclength direction is defined as r𝛳, which is useful for an analogy with the spanwise direction for channels or boundary layers; here, r and 𝛳 are the radial distance from the core and the azimuthal angles, respectively. Both analyses showed that the arclength scales increased with increasing the wall-normal distance. It showed that the coherent structures were confined in the core region due to the crowding effect of a circular pipe geometry. The pipe flow simulation could describe a realistic geometrical flow along the azimuthal direction, unlike the simulations of turbulent channel or boundary layer flow using periodic boundary conditions along the spanwise direction. The present results provided the spanwise organization of energy-containing motions over a broad range of scales in turbulent pipe flow.