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.
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.
Accurate analysis of the causes of metro rail traffic congestion provides a means of addressing issues arising from metro rail traffic congestion in metropolitan areas. Currently, congestion analysis based on counting, weight detection, CCTVs, and mobile Wi-Fi is limited by poor accuracies or becaus...
Accurate analysis of the causes of metro rail traffic congestion provides a means of addressing issues arising from metro rail traffic congestion in metropolitan areas. Currently, congestion analysis based on counting, weight detection, CCTVs, and mobile Wi-Fi is limited by poor accuracies or because studies have been restricted to single routes and trains. In this study, a train congestion analysis model was used that includes the transfer and multi-path behavior of metro passengers and train operation plans for metropolitan urban railroads. Analysis accuracy was improved by considering traffic patterns in which passengers must wait for next trains due to overcrowding. The model updates train crowding levels every 10 minutes, provides information to potential passengers, and thus, is expected to increase the social benefits provided by the Seoul metropolitan subway
Accurate analysis of the causes of metro rail traffic congestion provides a means of addressing issues arising from metro rail traffic congestion in metropolitan areas. Currently, congestion analysis based on counting, weight detection, CCTVs, and mobile Wi-Fi is limited by poor accuracies or because studies have been restricted to single routes and trains. In this study, a train congestion analysis model was used that includes the transfer and multi-path behavior of metro passengers and train operation plans for metropolitan urban railroads. Analysis accuracy was improved by considering traffic patterns in which passengers must wait for next trains due to overcrowding. The model updates train crowding levels every 10 minutes, provides information to potential passengers, and thus, is expected to increase the social benefits provided by the Seoul metropolitan subway
Azevedo, J. A., Costa, M. E. O. S., Madeira, J. J. E. R. S. K. and Martins, E. Q. V.(1993), "An algorithm from the ranking of shortest paths", European Journal of Operation Research, vol. 69, pp.97-106.
Cho, S. K. and Chung, I. B.(2015), "A Study on the Solution of Train Delay and Congestion on Seoul Subway Line 2", Seoul City Research, pp.123-135.
Eom, J. K., Song, J. Y. and Lee, K. S.(2014), "Load factor decrease in the Seoul metro circle line through analyzing passenger OD demand", Journal of the Korean Society for Railway, vol. 17, no. 6, pp.457-465.
Hong, S. P., Min, Y. H., Park, M. J., Kim, K. M. and Oh, S. M.(2015), "Precise estimation of connections of metro passengers from Smart Card data", Transportation, vol. 43, no. 5, pp.749-769.
Jiao, L., Shen, L., Shuai, C., Tan, Y. and He, B.(2017), "Measuring crowdedness between adjacent stations in an urban metro system: A Chinese case study", Sustainability, vol. 9, no. 12, 2325.
Kim, K. M., Oh, S. M. and Rho, H. L.(2016), "Express Train Choice and Load Factor Analysis as Line Extension in Seoul Metro 9", The Korean Society for Railway, vol. 19, no. 5, pp.663-671.
Lee, S. J. and Shin, S. I.(2020), "A Study on Improving Subway Crowding Based on Smart Card Data: A Focus on Early Bird Policy Alternative", Journal of Information Technology Services, vol. 19, no. 2, pp.125-138.
Lee, S. J., Shin, S. I., Lee, S. H. and Yun, S. J.(2021), "Estimation of Usable Waiting Areas considering Passenger Behavior on Urban Railway Platforms", Journal of Korean Society of Transportation, vol. 39, no. 6, pp.721-736.
Ministry of Land, Infrastructure and Transport(2013), Highway Capacity Manual, South Korea.
Ministry of Land, Infrastructure and Transport(2018), Urban Railway Station and Transfer and Convenience Facilities Design Guidelines.
Nguyen, K. A., Wang, Y., Li, G., Luo, Z. and Watkins, C.(2019), "Realtime tracking of passengers on the London underground transport by matching smartphone accelerometer footprints", Sensors, vol. 19, no. 19, 4184.
Seoul Institute(2016), "Policy utilization for crowdedness costs for subway in Seoul", Policy Report, vol. 208, p.3.
Seoul Open Data Portal, http://stat.seoul.go.kr, 2021.11.22.
Shin, S. I., Lee, S. J. and Lee, C. H.(2019), "A Model for Analyzing Time-Varying Passengers' Crowdedness Degree of Subway Platforms Using Smart Card Data", The Korea Institute of Intelligent Transport Systems, vol. 18, no. 5, pp.49-63.
Tirachini, A., Hensher, D. A. and Rose, J. M.(2013), "Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand", Transportation Research Part A: Policy and Practice, vol. 53, pp.36-52.
AI-Helper
※ AI-Helper는 부적절한 답변을 할 수 있습니다.