Metro Station Clustering based on Travel-Time Distributions

InTaek Gong, DongYun Kim, Yunhong Min


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.

Full Text:



Bagchi, M. and White, P. R., “The potential of public transport smart card data,” Transport Policy, Vol. 12, No. 5, pp. 464-474, 2005.

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.

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.

Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A-L., “Understnading individual human mobility patterns,” Nature, Vol. 453, No. 7196, pp. 779-782, 2008.

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.

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.

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.

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.

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.

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.

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.

Kim, S. K., “Plans for raising the utilization of smart card data,” KRIHS Monthly Magazine, Vol. 205, pp. 18-24, 2015.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.


  • There are currently no refbacks.