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교통카드 자료를 이용한 서울시 지역별 대중교통 수단 선택 공간상관성 분석

The Spatial Correlation of Mode Choice Behavior based on Smart Card Transit Data in Seoul

  • 박만식 (성신여자대학교 통계학과) ;
  • 엄진기 (한국철도기술연구원 교통체계분석연구단) ;
  • 허태영 (충북대학교 정보통계학과)
  • Park, Man Sik (Department of Statistics, Sungshin Women's University) ;
  • Eom, JinKi (Transport Systems Research Team, Korea Railroad Research Institute) ;
  • Heo, Tae-Young (Department of Information and Statistics, Chungbuk National University)
  • 투고 : 2013.04.18
  • 심사 : 2013.08.02
  • 발행 : 2013.08.31

초록

본 연구에서는 교통 분석존(서울시 행정동) 단위별로 대중교통 수단(버스, 도시철도)선택에 있어서 공간 상관성이 존재하는지 여부를 대중교통카드 자료를 기반으로 제시한다. 분석결과 버스를 탑승한 비율이 높은 지역들이 서로 이웃하여 그룹을 형성하고 있으며, 이들 지역은 도시철도 역사의 수가 버스 정류장에 비해 매우 적기 때문인 것으로 분석되었다. 버스에 탑승한 비율이 비슷한 그룹 간에는 공간 상관성이 존재하는 것으로 통계분석결과 나타났으며, 이러한 공간상관성은 향후 대중교통 수단선택 모형 구축에 고려할 수 있을 것으로 판단된다. 대중교퉁 수단선택에 있어 공간상관성의 존재는 대중교통 운영기관이 향후 대중교통카드를 기반으로 대중교통 노선계획, 운영계획을 수립함에 있어 중요한 정보가 될 것으로 기대된다.

In this study, we provide empirical evidence of whether a spatial correlation among mode choices at the TAZ(Traffic Analysis Zone) level exists based on transit smart card data observed in Seoul, Korea. The results show that the areas with a higher probability that passengers choose to take a bus are clustered and that those regions have fewer metro stations than bus stations. We also found that the spatial correlation turned out to be statistically meaningful and provided an opportunity for the potential use of the spatial correlation in modeling mode choices. A reliable spatial interaction would constitute valuable information for transportation agencies in terms of their route planning and scheduling based on the transit smart card data.

키워드

참고문헌

  1. Bagchi, M. and White, P. R. (2005). The potential of Public Transport Smart Card Data, Transport Policy, 12, 464-474. https://doi.org/10.1016/j.tranpol.2005.06.008
  2. Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data, Chapman & Hall/CRC, Boca Raton, Florida.
  3. Besag, J. E. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion), Journal of the Royal Statistical Society B, 36, 192-225.
  4. Best, N. G., Arnold, R. A., Thomas, A., Waller, L. A. and Conlon, E. M. (1999). Bayesian models for spatially correlated disease and exposure data, Bayesian Statistics, axford University Press, Oxford, 6, 131-156.
  5. Blythe, P. (2004). Improving Public Transport Ticketing through Smart Cards, Proceedings of the Institute of Civil Engineers: Municipal Engineer, 157, 47-54.
  6. Bryan, H. and Blythe, P. (2007). Understanding behavior through Smart card Data Analysis, Proceedings of the Institute of Civil Engineers: Transport, 160, 173-177.
  7. Chu, K. K. and Chapleau, R. (2008). Enriching Archived Smart Card Transaction Data for Transit Demand Modeling, Transportation Research Record: Journal of the Transportation Research Board, No. 2063, Transportation Research Board of the National Academies, Washington, D.C., 63-72.
  8. Cliff, A. and Ord, J. K. (1972). Testing for Spatial Autocorrelation among Regression Residuals, Geographical Analysis, 4, 267-284.
  9. Cliff, A. and Ord, J. K. (1981). Spatial Processes, Models and Applications, Pion Limited, Las Vegas.
  10. Conlon, E. M. and Waller, L. A. (2000). Flexible spatial hierarchical models for mapping disease rates", American Statistical Association Proceedings of the Section on Statistics and the Environment, 82-87.
  11. Cressie, N., Kaiser, M. S., Daniels, M. J., Aldworth, J., Lee, J., Lahiri, S. N. and Cox, L. H. (1999). Spatial analysis of particulate matter in an urban environment, in Gomez-Hernandez, J., Soares, A. and Froidevaux, R. (Eds.): geoENV II - Geostatistics for Environmental Applications, Kluwer, Dordrecht, 41-52.
  12. Ding, Y. and Fotheringham, A. S. (1992). The integration of spatial analysis and GIS, Computers, Environment and Urban Systems, 16, 3-19. https://doi.org/10.1016/0198-9715(92)90050-2
  13. Eom, J. K., Choi, M. H., Kim, D. S., Lee, J. and Song, J. Y. (2009). Evaluation of Metro Services based on Transit Smart Card Data (A Case Study of Incheon Line 1), Journal of The Korean Society for Railway, 15, 80-87. https://doi.org/10.7782/JKSR.2012.15.1.080
  14. Eom, J. K., Lee, J. and Lee, K. S. (2013). Access and Egress Patterns of Travel to a Regional Railway Station Based on Transit Smart Card Data (Case study: Seoul Station during Chuseok Holiday), Journal of The Korean Society for Railway, 16, 59-64. https://doi.org/10.7782/JKSR.2013.16.1.059
  15. Eom, J. K., Park, M. S. and Heo, T. Y. (2012). Estimating Probability of Mode Choice at Regional Level by Considering Spatial Association of Departure Place, Journal of The Korean Society for Railway, 12, 656-662.
  16. Geary, R. C. (1954). The contiguity ratio and statistical mapping, The Incorporated Statistician, 5, 115-145. https://doi.org/10.2307/2986645
  17. Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulations using multiple sequences, Statistical Science, 7, 457-511. https://doi.org/10.1214/ss/1177011136
  18. Goodchild, M. F. (1986). Spatial Autocorrelation, CATMOG 47, Geobooks: Norwich UK.
  19. Goodchild, M. F. (1987). A spatial analytical perspective on geographical information systems, International Journal of Geographical Information Systems, 1, 335-354. https://doi.org/10.1080/02693798708927821
  20. Goodchild, M. F., Haining, R. P. and Wise, S. M. (1992). Integrating GIS and spatial data analysis: Problems and possibilities, International Journal of Geographical Information Systems, 6, 407-423. https://doi.org/10.1080/02693799208901923
  21. Jang, W. (2010). Travel Time and Transfer Analysis Using Transit Smart Card Data, CD-ROM. Proceedings of 89th Annual Transportation Research Board Meeting, Washington, D.C.
  22. Kaiser, M. S., Daniels, M. J., Furakawa, K. and Dixon, P. (2002). Analysis of particulate matter air pollution using Markov random field models of spatial dependence, Environmetrics, 13, 615-628. https://doi.org/10.1002/env.534
  23. Lim, Y. T., Park, C., Kim, D. S., Eom, J. K. and Lee, J. (2012). Estimating Trip Distribution Model by Using Transit Card Data, Journal of Transport Research, 19, 1-11.
  24. Moran, P. (1950). Notes on continuous stochastic phenomena, Biometrika, 37, 17-23. https://doi.org/10.1093/biomet/37.1-2.17
  25. R Development Core Team. (2005). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, (Available from www.R-project.org).
  26. Sokal, R. R. and Oden, N. L. (1978). Spatial autocorrelation in biology, I. Methodology, Biological Journal of the Linnen Society, 10, 199-228. https://doi.org/10.1111/j.1095-8312.1978.tb00013.x
  27. Spiegelhalter, D. J., Thomas, A. and Best, N. G. (2000). WinBUGS Version 1.4 User Manual, Cambridge: Medical Research Council Biostatistics Unit, 2000. (Available from http://www.mrc-bsu.cam.ac.uk/bugs)
  28. Utsunomiya, M., Attanucci, J. and Wilson, N. H. (2006). Potential Uses of Transit Smart Card Registration and Transaction Data to Improve Transit Planning, Transportation Research Record: Journal of the Transportation Research Board, No. 1971, Transportation Research Board of the National Academies, Washington, D.C., 119-126.

피인용 문헌

  1. On the Geometric Anisotropy Inherent In Spatial Data vol.27, pp.5, 2014, https://doi.org/10.5351/KJAS.2014.27.5.755
  2. Analysis of Relative Preference on Transit Mode by Administrative district for Accessibility of Seoul Station vol.51, pp.7, 2016, https://doi.org/10.17208/jkpa.2016.12.51.7.135