DOI QR코드

DOI QR Code

Characteristics and Efficiency Analysis of Evolutionary Seoul Metropolitan Subway Network

진화하는 서울 지하철 망의 특성과 효율성 분석

  • Zzang, See-Young (Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology) ;
  • Lee, Kang-Won (Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology)
  • Received : 2016.04.20
  • Accepted : 2016.05.16
  • Published : 2016.06.30

Abstract

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.

서울 수도권 지하철 망은 과거 수십 년 동안 도심지의 인구 분산, 교통 정체 해소 그리고 인접 도시의 활성화 등 다수의 목적을 위하여 여러 번의 진화 과정을 거쳐 왔다. 본 연구에서는 서울 수도권 망의 동적인 진화에 따라 지하철 망의 특성과 망의 효율이 어떻게 변화해 왔는지를 분석하였다. 아울러 본 연구에서는 지하철 망의 효율을 보다 현실적으로 나타낼 수 있는 새로운 척도를 제안하였다. 서울 지하철 망의 효율성은 74%로 외국의 값들보다 높게 나타났으며 승객의 실질적인 흐름을 고려하면 효율성은 85% 이상으로 더 높게 나타났다. 9호선과 신분당선, 의정부선과 에버라인 노선들은 2013년 9월 이후로 수송 실적 관련 자료를 공개하지 않기 때문에 본 연구에서는 분석 범위를 데이터가 존재하는 2008년 9월부터 2013년 9월로 국한하였다.

Keywords

References

  1. S. Derrible, C. Kennedy (2010) Evaluating, Comparing, and Improving Metro Networks: Application to Plans for Toronto, Canada, Transportation Research Record: Journal of the Transportation Research Board, 2146, pp.43-51. https://doi.org/10.3141/2146-06
  2. M.L. Mouronte (2014) Topological Analysis of the Subway Network of Madrid, International Multi-Conference on Computing in the Global Information Technology, Seville, Spain, pp.9-13.
  3. V. Latora, M. Marchiori (2001) Efficient Behavior of Small-World Networks, Physical Review Letters, 87(19), p.198701. https://doi.org/10.1103/PhysRevLett.87.198701
  4. http://www.ktdb.go.kr (Accessed 30 June 2015).
  5. http://data.seoul.go.kr (Accessed 28 September 2015).
  6. R. Bullock (2007) Great circle distances and bearings between two locations, MDT. June 5.
  7. http://kosis.kr (Accessed 28 September 2015).
  8. L. Biao, Z. Xiaoxi, X. Zhang. (2014) Evaluating the evolution of subway networks: evidence from beijing subway network, EPL(Europhysics Letters), 105(5), p.58004. https://doi.org/10.1209/0295-5075/105/58004
  9. V. Latora, M. Marchiori. (2002) Is the Boston subway a small-world network?, Physica A: Statistical Mechanics and its Applications, 314(1), pp.109-113. https://doi.org/10.1016/S0378-4371(02)01089-0
  10. C. Han, L. Liu. (2009) Topological vulnerability of subway network in China, International Conference on Management and Service Science, Shanghai, China, pp.1-4.
  11. T. Majima, M. Katuhara, K. Takadama (2007) Analysis on Transport Networks of Railway, Subway and Waterbus in Japan, Studies in Computational Intelligence, 56, pp.99-113.

Cited by

  1. Network Betweenness Centrality and Passenger Flow Analysis of Seoul Metropolitan Subway Lines vol.41, pp.2, 2018, https://doi.org/10.11627/jkise.2018.41.2.095