DOI QR코드

DOI QR Code

Network Betweenness Centrality and Passenger Flow Analysis of Seoul Metropolitan Subway Lines

서울 수도권 지하철망의 호선별 망 매개 중심성과 승객 흐름 분석

  • Lee, Kang Won (Department of Industrial and Systems Engineering, Seoul National University of Science and Technology) ;
  • Lee, Jung Won (Department of Industrial and Systems Engineering, Seoul National University of Science and Technology)
  • 이강원 (서울과학기술대학교 산업정보시스템공학과) ;
  • 이정원 (서울과학기술대학교 산업정보시스템공학과)
  • Received : 2018.04.27
  • Accepted : 2018.06.18
  • Published : 2018.06.30

Abstract

Using network betweenness centrality we attempt to analyze the characteristics of Seoul metropolitan subway lines. Betweenness centrality highlights the importance of a node as a transfer point between any pairs of nodes. This 'transfer' characteristic is obviously of paramount importance in transit systems. For betweenness centrality, both traditional betweenness centrality measure and weighted betweenness centrality measure which uses monthly passenger flow amount between two stations are used. By comparing traditional and weighted betweenness centrality measures of lines characteristics of passenger flow can be identified. We also investigated factors which affect betweenness centrality. It is the number of passenger who get on or get off that significantly affects betweenness centrality measures. Through correlation analysis of the number of passenger and betweenness centrality, it is found out that Seoul metropolitan subway system is well designed in terms of regional distribution of population. Four measures are proposed which represent the passenger flow characteristics. It is shown they do not follow Power-law distribution, which means passenger flow is relatively evenly distributed among stations. It has been shown that the passenger flow characteristics of subway networks in other foreign cities such as Beijing, Boston and San Franciso do follow power-law distribution, that is, pretty much biased passenger flow traffic characteristics. In this study we have also tried to answer why passenger traffic flow of Seoul metropolitan subway network is more homogeneous compared to that of Beijing.

Keywords

References

  1. Cheng, Y.Y., Lee, R.K.W., Lim, E.P., and Zhu, F., Measuring centralities for transportation networks beyond structures, Applications of Social Media and Social Network Analysis, 2015, pp. 23-39.
  2. Choi, S.H., A Study on the Development of a Simulator for Social Network in Organizations Using ARENA, Journal of Society of Korea Industrial and Systems Engineering, 2012, Vol. 35, No. 3, pp. 62-69.
  3. Derrible, S., Network Centrality of Metro Systems, PloS One, 2012, Vol. 7, No. 7, pp. 1-10.
  4. Haznagy, A., Fi, I., Londo, A., and N'emeth, T., Complex Network analysis of Public Transportation Networks : A Comprehensive Study, Models and Technology for Intelligent Transport Systems, 2015, Budapest, Hungary, pp. 371-378.
  5. Jo, H.W., Lee, S.H., and Shin, K.W., An Empirical Study on the Relationship between Subway Trips and Characteristics of Subway Catchment Area, Journal of the Korea Academia-Industrial Cooperation Society, 2010, Vol. 12, No. 12, pp. 5191-5198.
  6. Korea Transportation Database, (Accessed 30 January 2018), http://www.ktdb.go.kr.
  7. Lee, J.W. and Lee, K.W., Analysis of Seoul Metropolitan Subway Network Characteristics using Network Centrality Analysis, Journal of the Korean Society for Railway, 2017, Vol. 20, No. 3, pp. 413-422. https://doi.org/10.7782/JKSR.2017.20.3.413
  8. Leng, B., Zhao, X., and Zhang, X., Evaluating the Evolution of Subway Networks : Evidence from Beijing Subway Network, A letters Journal Exploring The Frontier of Physics, 2014, Vol. 105, e58004, pp. 1-6.
  9. Monterola, C.P., Ramli, M.A., Khoon, G.A., and Guang, T.H., A Method to Ascertain Rapid Transit System's Throughput Distribution Using Network Analysis, Procedia Computer Science, 2014, Vol. 29, pp. 1621-1630. https://doi.org/10.1016/j.procs.2014.05.147
  10. Mouronte, M.L., Topological Analysis of the Subway Network of Madrid, International Multi-Conference on Computing in the Global Information Technology, 2014, Seville, Spain, pp. 9-13.
  11. Seoul Open Data Plaza, (Accessed 30 January 2018), http://data.seoul.go.kr.
  12. Stoilova, S. and Stoev, V., An Application of the Graph Theory Which Examines the Metro Network, Transport Problems, 2015, Vol. 10, No. 2, pp. 35-48.
  13. Wang, J., Li, Y., Liu, J., He, K., and Wang, P., Vulnerability Analysis and Passenger Source Prediction in Urban Rail Tranit System, Plos One, 2013, Vol. 8, No. 11, pp. 1-8.
  14. Xu, Q., Mao, B.A., and Bai, Y., Network Structure of Subway Passenger Flow, Journal of Statistical Mechanics : Theory & Experiment, 2016, pp. 1-18.
  15. Zang, S.Y. and Lee. K.W., Characteristics and Efficiency Analysis of Evolutionary Seoul Metropolitan Subway Network, Journal of the Korean Society for Railway, 2016, Vol. 19, No. 3, pp. 388-396. https://doi.org/10.7782/JKSR.2016.19.3.388