DOI QR코드

DOI QR Code

Metro Station Clustering based on Travel-Time Distributions

통행시간 분포 기반의 전철역 클러스터링

  • Gong, InTaek (Procurement DX Team, LG CNS) ;
  • Kim, DongYun (Graduate School of Logistics, Incheon National University) ;
  • Min, Yunhong (Graduate School of Logistics, Incheon National University)
  • Received : 2022.04.18
  • Accepted : 2022.04.28
  • Published : 2022.05.31

Abstract

Smart card data is representative mobility data and can be used for policy development by analyzing public transportation usage behavior. This paper deals with the problem of classifying metro stations using metro usage patterns as one of these studies. Since the previous papers dealing with clustering of metro stations only considered traffic among usage behaviors, this paper proposes clustering considering traffic time as one of the complementary methods. Passengers at each station were classified into passengers arriving at work time, arriving at quitting time, leaving at work time, and leaving at quitting time, and then the estimated shape parameter was defined as the characteristic value of the station by modeling each transit time to Weibull distribution. And the characteristic vectors were clustered using the K-means clustering technique. As a result of the experiment, it was observed that station clustering considering pass time is not only similar to the clustering results of previous studies, but also enables more granular clustering.

스마트교통카드 데이터는 대표적인 모빌리티 데이터로 이를 이용하여 대중교통 이용행태를 분석하고 정책 개발에 활용할 수 있다. 본 논문은 이러한 연구의 하나로 전철 이용패턴을 이용하여 전철역들을 분류하는 문제를 다룬다. 전철역의 클러스터링을 다룬 기존 논문들은 이용행태 중 통행량만을 고려하였기에 본 논문은 이에 대한 보완적인 방법의 하나로 통행시간을 고려한 클러스터링을 제안한다. 각 역의 승객들을 출근 시간 출발, 출근 시간 도착, 퇴근 시간 출발, 퇴근 시간 도착 승객들로 분류한 다음 각각의 통행시간을 와이블 분포로 모형화하여 추정한 형상모수를 역의 특성값으로 정의하였다. 그리고 특성 벡터들을 K-평균 클러스터링 기법을 사용하여 클러스터링하였다. 실험결과 통행시간을 고려하여 역의 클러스터링을 수행하면 기존 연구의 클러스터링 결과와 유사한 결과가 나올 뿐만 아니라 더 세분화 된 클러스터링이 가능함을 관찰하였다.

Keywords

Acknowledgement

본 연구는 인천대학교 교내연구 (2019-0083)의 지원으로 수행되었음.

References

  1. Bagchi, M. and White, P. R., "The potential of public transport smart card data," Transport Policy, Vol. 12, No. 5, pp. 464-474, 2005. https://doi.org/10.1016/j.tranpol.2005.06.008
  2. Ebrahimpou, Z., Wan, W., Cervantes, O., Luo, T., and Ullah, H., "Comparison of main approaches for extracting behavior features from crowd flow analysis," ISPRS International Journal of Geo-Information, Vol. 8, No. 10, p. 440, 2019. https://doi.org/10.3390/ijgi8100440
  3. El Mahrsi, M. K., Come, E., Oukhellou, L., and Verleysen, M., "Clustering smart card data for urban mobility analysis," IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 3, pp. 712-718, 2017. https://doi.org/10.1109/TITS.2016.2600515
  4. Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A-L., "Understnading individual human mobility patterns," Nature, Vol. 453, No. 7196, pp. 779-782, 2008. https://doi.org/10.1038/nature06958
  5. Gordon, J., Koutsopoulos, H., Wilson, N., and Attanucci, J., "Automated inference of linked transit journeys in London using fare-transaction and vehicle location data," Transportation Research Record: Journal of the Transportation Research Board, Vol. 2343, No. 1, pp. 17-24, 2013. https://doi.org/10.3141/2343-03
  6. Ha, J. and Lee, S., "The estimation of commuting patterns and the analysis of the commuting network structure using smart card data: Focused on the possibility of application through the validation process with household travel survey data," Journal of Korea Planning Association, Vol. 51, No. 4, pp. 123-143, 2016. https://doi.org/10.17208/jkpa.2016.08.51.4.123
  7. Hofmann, M. and O'Mahony, M., "Transfer journey identification and analyses from electronic fare collection data," In the Proceedings of IEEE Intelligent Transportation Systems Conference, pp. 34-39, 2005.
  8. Hong, S.-P., Min, Y.-H., Park, M.-J., Kim, K. M., and Oh, S. M., "Precise estimation of connections of metro passengers from smart card data," Transportation, Vol. 43, pp. 749-769, 2016. https://doi.org/10.1007/s11116-015-9617-y
  9. Jun, M. J., Choi, K., Jeong, J. E., Kwon, K. H., and Kim, H. J., "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Vol. 48, pp. 30-40, 2015. https://doi.org/10.1016/j.jtrangeo.2015.08.002
  10. Kieu, L. M., Bhaskar, A., and Chung, E., "Passenger segmentation using smart card data," IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 3, pp. 1537-1548, 2015. https://doi.org/10.1109/TITS.2014.2368998
  11. Kim, K., Oh, K., Lee, Y., and Jung, J., "Discovery of travel patterns in Seoul Metropolitan subway using big data of smart card transaction systems," The Journal of Society for e-Business Studies, Vol. 18, No. 3, pp. 211-222, 2013.
  12. Kim, S. K., "Plans for raising the utilization of smart card data," KRIHS Monthly Magazine, Vol. 205, pp. 18-24, 2015.
  13. Lee, M., Han, J., and Lee, H., "Analysis of the transit ridership pattern using transportation card data: Focusing on Ganghwa," The Journal of Korea Institute of Intelligent Transportation Systems, Vol. 17, No. 2, pp. 58-72, 2018. https://doi.org/10.12815/kits.2018.17.2.58
  14. Ma, X. L., Wu, Y. J., Wang, Y. H., Chen, F., and Liu, J. F., "Mining smart card data for transit riders' travel patterns," Transportation Research Part C: Emerging Technologies, Vol. 36, pp. 1-12, 2013. https://doi.org/10.1016/j.trc.2013.07.010
  15. Min, M. K., "Classification of seoul metro stations based on boarding/alighting patterns using machine learning clustering," The Journal of the Institute of Internet, Broadcasting and Communication, Vol. 18, No. 4, pp. 13-18, 2018. https://doi.org/10.7236/JIIBC.2018.18.4.13
  16. Morency, C., Trepanier, M., and Agard, B., "Analysing the variability of transit users ehaviour with smart card data," In Proceedings of IEEE Intelligent Transportation Systems Conference, pp. 44-49, 2006.
  17. Mudholkar, G. S. and Srivastava, D. K., "Exponentiated Weibull family for analyzing bathtub failure-rate data," IEEE Transactions on Probability, Vol. 42, No. 2, pp. 299-302, 1993.
  18. Munizaga, M. and Palma, C., "Estimation of a disaggregate multi-modal public transport origin-destination matrix from passive smartcard data from Santiago, Chile," Transportation Research Part C: Emerging Technologies, Vol. 24, pp. 9-18, 2012. https://doi.org/10.1016/j.trc.2012.01.007
  19. Park, J. S. and Lee, K., "Classification of the seoul metropolitan subway stations using graph partitioining," Journal of the Economic Geographical Society of Korea, Vol. 15, No. 3, pp. 343-357, 2012. https://doi.org/10.23841/egsk.2012.15.3.343
  20. Seaborn, C., Attanucci, J., and Wilson, N., "Analyzing multimodal public transport journeys in London with smart card fare payment data," Transportation Research Record: Journal of the Transportation Research Board, Vol. 2121, No. 1, pp. 55-62, 2009. https://doi.org/10.3141/2121-06
  21. Trepanier, M., Tranchant, N., and Chapleau, R., "Individual trip destination estimation in a transit smart card automated fare collection system," Journal of Intelligent Transportation Systems, Vol. 11, No. 1, pp. 1-14, 2007. https://doi.org/10.1080/15472450601122256
  22. Utsunomiya, M., Attanucci, J., and Wilson, N., "Potential uses of transit smart card registration and transaction data to improve transit planning," Transportation Research Record: Journal of the Transportation Research Board, Vol. 1971, No. 1, pp. 119-126, 2006. https://doi.org/10.1177/0361198106197100114
  23. Zhou, Q., Liu, S., and Wang, Y., "A study on the coordinative relation of land use and transport around the metro station," Railway Transport and Economy, Vol. 40, No. 4, pp. 100-106, 2018.