Identifying human mobility patterns using smart card data

References

• Ahmed, A., & Stopher, P. (2014). Seventy minutes plus or minus 10 – A review of travel time budget studies. Transport Reviews, 34(5), 607–625. https://doi.org/10.1080/01441647.2014.946460
   Web of Science ®Google Scholar
• Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., Menezes, R., Ramasco, J., Simini, F., & Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734, 1–74. https://doi.org/10.1016/j.physrep.2018.01.001
   Web of Science ®Google Scholar
• Briand, A.-S., Come, E., Trepanier, M., & Oukhellou, L. (2017). Analyzing year-to-year changes in public transport passenger behaviour using smart card data. Transportation Research Part C: Emerging Technologies, 79, 274–289. https://doi.org/10.1016/j.trc.2017.03.021
   Web of Science ®Google Scholar
• Cats, O., & Ferranti, F. (2022a). Unravelling individual mobility temporal patterns using longitudinal smart card data. Research in Transportation Business & Management, 43, 100816. https://doi.org/10.1016/j.rtbm.2022.100816
   Web of Science ®Google Scholar
• Cats, O., & Ferranti, F. (2022b). Unravelling the spatial properties of individual mobility patterns using longitudinal travel data. Journal of Urban Mobility, 2, 100035. https://doi.org/10.1016/j.urbmob.2022.100035
   Google Scholar
• Cats, O., & Ferranti, F. (2022c). Voting with one’s feet: Unraveling urban centers attraction using visiting frequency. Cities, 127, 103773. https://doi.org/10.1016/j.cities.2022.103773
   Web of Science ®Google Scholar
• Cats, O., Wang, Q., & Zhao, Y. (2015). Identification and classification of public transport activity centres in Stockholm using passenger flows data. Journal of Transport Geography, 48, 10–22. https://doi.org/10.1016/j.jtrangeo.2015.08.005
   Web of Science ®Google Scholar
• Chan Chun, K., Bahk, J., Kim, H., Jeong, H.-C., & Kim, G. (2023). Classification of the metropolitan subway stations and spheres of influence of main commercial areas in Seoul. Physica A: Statistical Mechanics and its Applications, 609, 128387. https://doi.org/10.1016/j.physa.2022.128387
   Web of Science ®Google Scholar
• Cui, Z., & Long, Y. (2019). Perspectives on stability and mobility of transit passenger's travel behaviour through smart card data. IET Intelligent Transport Systems, 13(12), 1761–1769. https://doi.org/10.1049/iet-its.2019.0212
   Web of Science ®Google Scholar
• Deschaintres, E., Morency, C., & Trepanier, M. (2019). Analyzing transit user behavior with 51 weeks of smart card data. Transportation Research Record: Journal of the Transportation Research Board, 2673(6), 33–45. https://doi.org/10.1177/0361198119834917
   Web of Science ®Google Scholar
• Egu, O., & Bonnel, P. (2020). Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon. Travel Behaviour and Society, 19, 112–123. https://doi.org/10.1016/j.tbs.2019.12.003
   Web of Science ®Google Scholar
• El Mahrsi, M. K., Come, E., Oukhellou, L., & Verleysen, M. (2017). Clustering smart card data for urban mobility analysis. IEEE Transactions on Intelligent Transportation Systems, 18(3), 712–728. https://doi.org/10.1109/TITS.2016.2600515
   Web of Science ®Google Scholar
• El Shoghri, A., Liebig, J., Jurdak, R., Gardner, L., & Kanhere, S. S. (2020, August 31–September 3). Identifying highly influential travellers for spreading disease on a public transport system. Proceedings - 21st IEEE international symposium on a world of wireless, mobile and multimedia networks, WoWMoM, 30–39.
   Google Scholar
• Eltved, M., Breyer, N., Blafoss Ingvardson, J., & Anker Nielsen, O. (2021). Impacts of long-term service disruptions on passenger travel behaviour: A smart card analysis from the greater Copenhagen area. Transportation Research Part C: Emerging Technologies, 131, 103198. https://doi.org/10.1016/j.trc.2021.103198
   Web of Science ®Google Scholar
• Faroqui, H., Mesvah, M., Kim, J., & Khodaii, A. (2022). Targeted advertising in the public transit network using smart card data. Networks and Spatial Economics, 22(1), 97–124. https://doi.org/10.1007/s11067-022-09558-9
   Web of Science ®Google Scholar
• Gan, Z., Yang, M., Feng, T., & Timmermans, H. (2020). Understanding urban mobility patterns from a spatiotemporal perspective: Daily ridership profiles of metro stations. Transportation, 47(1), 315–336. https://doi.org/10.1007/s11116-018-9885-4
   Web of Science ®Google Scholar
• Ghaemi, M. S., Agard, B., Trepanier, M., & Partovi Nia, V. (2017). A visual segmentation method for temporal smart card data. Transportmetrica A: Transport Science, 13(5), 381–404. https://doi.org/10.1080/23249935.2016.1273273
   Web of Science ®Google Scholar
• Goulet-Langlois, G., Koutsopoulos, H. N., & Zhao, J. (2016). Inferring patterns in the multi-week activity sequences of public transport users. Transportation Research Part C: Emerging Technologies, 64, 1–16. https://doi.org/10.1016/j.trc.2015.12.012
   Web of Science ®Google Scholar
• Goulet-Langlois, G., Koutsopoulos, H. N., Zhao, Z., & Zhao, J. (2018). Measuring regularity of individual travel patterns. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1583–1592. https://doi.org/10.1109/TITS.2017.2728704
   Web of Science ®Google Scholar
• Gutierrez, A., Domenech, A., Zaragozi, B., & Miravet, D. (2020). Profiling tourists’ use of public transport through smart travel card data. Journal of Transport Geography, 88, 102820. https://doi.org/10.1016/j.jtrangeo.2020.102820
   Web of Science ®Google Scholar
• Halvorsen, A., Koutsopoulos, H. N., Ma, Z., & Zhao, J. (2020). Demand management of congested public transport systems: A conceptual framework and application using smart card data. Transportation, 47(5), 2337–2365. https://doi.org/10.1007/s11116-019-10017-7
   Web of Science ®Google Scholar
• He, L., Agard, B., & Trepanier, M. (2020). A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method. Transportmetrica A: Transport Science, 16(1), 56–75. https://doi.org/10.1080/23249935.2018.1479722
   Web of Science ®Google Scholar
• He, L., Trepanier, M., & Agard, B. (2021). Space–time classification of public transit smart card users’ activity locations from smart card data. Public Transport, 13(3), 579–595. https://doi.org/10.1007/s12469-021-00274-0
   Web of Science ®Google Scholar
• Hörcher, D., Graham, D. J., & Anderson, R. J. (2017). Crowding cost estimation with large scale smart card and vehicle location data. Transportation Research Part B: Methodological, 95, 105–125. https://doi.org/10.1016/j.trb.2016.10.015
   Web of Science ®Google Scholar
• Huang, D., Liu, T., Kong, F., & Guang, R. (2021). Employment centers change faster than expected: An integrated identification method and application to Beijing. Cities, 115, 103224. https://doi.org/10.1016/j.cities.2021.103224
   Web of Science ®Google Scholar
• Huang, J., Levinson, D., Wang, J., & Jin, H. (2019). Job-worker spatial dynamics in Beijing: Insights from smart card data. Cities, 86, 83–93. https://doi.org/10.1016/j.cities.2018.11.021
   Web of Science ®Google Scholar
• Hussain, E., Bhaskar, A., & Chung, E. (2021). Transit OD matric estimation using smartcard data: Recent developments and future research challenges. Transportation Research Part C: Emerging Technologies, 125, article no. 103044. https://doi.org/10.1016/j.trc.2021.103044
   Web of Science ®Google Scholar
• Ji, Y., Cao, Y., Liu, Y., Guo, W., & Gao, L. (2019). Research on classification and influencing factors of metro commuting patterns by combining smart card data and household travel survey data. IET Intelligent Transport Systems, 13(10), 1525–1532. https://doi.org/10.1049/iet-its.2018.5512
   Web of Science ®Google Scholar
• Kaewkluengklom, R., Kurauchi, F., & Iwamoto, T. (2021). Investigation of changes in passenger behavior using longitudinal smart card data. International Journal of Intelligent Transportation Systems Research, 19(1), 155–166. https://doi.org/10.1007/s13177-020-00232-3
   Web of Science ®Google Scholar
• Khan, K., Rehman, S. U., Aziz, K., Fong, S., & Sarasvady, S. (2014, February 17–19). DBSCAN: Past, present and future. IEEE proceedings of the fifth international conference on the applications of digital information and web technologies (ICADIWT 2014), 232–238.
   Google Scholar
• Kholodov, Y., Jenelius, E., Cats, O., van Oort, N., Mouter, N., Cebecauer, M., & Vermeulen, A. (2021). Public transport fare elasticities from smartcard data: Evidence from a natural experiment. Transport Policy, 105, 35–43. https://doi.org/10.1016/j.tranpol.2021.03.001
   Web of Science ®Google Scholar
• Kieu, L.-M., Bhaskar, A., & Chung, E. (2015a). Passenger segmentation using smart card data. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1537–1548. https://doi.org/10.1109/TITS.2014.2368998
   Web of Science ®Google Scholar
• Kieu, L.-M., Bhaskar, A., & Chung, E. (2015b). A modified density-based scanning algorithm with noise for spatial travel pattern analysis from smart card AFC data. Transportation Research Part C: Emerging Technologies, 58B, 193–207. https://doi.org/10.1016/j.trc.2015.03.033
   Google Scholar
• Kieu, L.-M., Ou, Y., & Cai, C. (2018). Large-scale transit market segmentation with spatial-behavioural features. Transportation Research Part C: Emerging Technologies, 90, 97–113. https://doi.org/10.1016/j.trc.2018.03.003
   Web of Science ®Google Scholar
• Kim, K. (2020). Identifying the structure of cities by clustering using a new similarity measure based on smart card data. IEEE Transactions on Intelligent Transportation Systems, 21(5), 2002–2011. https://doi.org/10.1109/TITS.2019.2910548
   Web of Science ®Google Scholar
• Kim, K., Oh, K., Lee, Y. K., Kim, S. H., & Jung, J.-Y. (2014). An analysis on movement patterns between zones using smart card data in subway networks. International Journal of Geographical Information Science, 28(8), 1781–1801. https://doi.org/10.1080/13658816.2014.898768
   Google Scholar
• Kim, M.-K., Kim, S.-P., Heo, J., & Sohn, H.-G. (2017). Ridership patterns at subway stations of Seoul capital area and characteristics of station influence area. KSCE Journal of Civil Engineering, 21(3), 964–975. https://doi.org/10.1007/s12205-016-1099-8
   Web of Science ®Google Scholar
• Kolkowski, L., Cats, O., Dixit, M., Verma, T., Jenelius, E., Cebecauer, M., & Jarlebring Rubensson, I. (2023). Measuring activity-based social segregation using public transport smart card data. Journal of Transport Geography, 110, 103642. https://doi.org/10.1016/j.jtrangeo.2023.103642
   Web of Science ®Google Scholar
• Lei, D., Chen, X., Cheng, L., Zhang, L., Ukkusuri, S. V., & Witlox, F. (2020). Inferring temporal motifs for travel pattern analysis using large scale smart card data. Transportation Research Part C: Emerging Technologies, 120, 102810. https://doi.org/10.1016/j.trc.2020.102810
   Web of Science ®Google Scholar
• Li, C., Bai, L., Liu, W., Yao, L., & Travis Waller, S. (2021). Urban mobility analytics: A deep spatial–temporal product neural network for traveler attributes inference. Transportation Research Part C: Emerging Technologies, 124, 102921. https://doi.org/10.1016/j.trc.2020.102921
   Web of Science ®Google Scholar
• Li, W., Zhou, M., & Dong, H. (2020). Classifications of stations in urban rail transit based on the two-step cluster. Intelligent Automation and Soft Computing, 26(3), 531–538. https://doi.org/10.32604/iasc.2020.013930
   Web of Science ®Google Scholar
• Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461. https://doi.org/10.1016/S0031-3203(02)00060-2
   Web of Science ®Google Scholar
• Liu, K., Yin, L., Ma, Z., Zhang, F., & Zhao, J. (2020). Investigating physical encounters of individuals in urban metro systems with large-scale smart card data. Physica A: Statistical Mechanics and its Applications, 545, 123398. https://doi.org/10.1016/j.physa.2019.123398
   Web of Science ®Google Scholar
• Liu, S., Yamamoto, T., Yao, E., & Nakamura, T. (2021). Examining public transport usage by older adults with smart card data: A longitudinal study in Japan. Journal of Transport Geography, 93, 103046. https://doi.org/10.1016/j.jtrangeo.2021.103046
   Web of Science ®Google Scholar
• Liu, Y., & Cheng, T. (2020). Understanding public transit patterns with open geodemographics to facilitate public transport planning. Transportmetrica A: Transport Science, 16(1), 76–103. https://doi.org/10.1080/23249935.2018.1493549
   Web of Science ®Google Scholar
• Long, Y., Liu, X., Zhou, J., & Chai, Y. (2016). Early birds, night owls, and tireless/recurring itinerants: An exploratory analysis of extreme transit behaviors in Beijing, China. Habitat International, 57, 223–232. https://doi.org/10.1016/j.habitatint.2016.08.004
   Web of Science ®Google Scholar
• Luo, D., Cats, O., & van Lint, H. (2017). Constructing transit origin-destination matrices with spatial clustering. Transportation Research Record: Journal of the Transportation Research Board, 2652(1), 39–49. https://doi.org/10.3141/2652-05
   Google Scholar
• Ma, X., Liu, C., Wen, H., Wang, Y., & Wu, Y.-J. (2017). Understanding commuting patterns using transit smart card data. Journal of Transport Geography, 58, 135–145. https://doi.org/10.1016/j.jtrangeo.2016.12.001
   Web of Science ®Google Scholar
• Ma, X., Wu, Y., Wang, Y., Chen, F., & Liu, J. (2013). Mining smart card data for transit riders’ travel patterns. Transportation Research Part C: Emerging Technologies, 36, 1–12. https://doi.org/10.1016/j.trc.2013.07.010
   Web of Science ®Google Scholar
• Manley, E., Zhong, C., & Batty, M. (2018). Spatiotemporal variation in travel regularity through transit user profiling. Transportation, 45(3), 703–732. https://doi.org/10.1007/s11116-016-9747-x
   PubMed Web of Science ®Google Scholar
• Medina, S. A. O. (2018). Inferring weekly primary activity patterns using public transport smart card data and a household travel survey. Travel Behaviour and Society, 12, 93–101. https://doi.org/10.1016/j.tbs.2016.11.005
   Web of Science ®Google Scholar
• Moradi, M., & Trepanier, M. (2020). Assessing longitudinal stability of public transport users with smart card data. Transportation Research Procedia, 48, 1364–1375. https://doi.org/10.1016/j.trpro.2020.08.166
   Google Scholar
• Park, Y., Choi, Y., Kim, K., & Keun Yoo, J. (2022). Machine learning approach for study on subway passenger flow. Scientific Reports, 12(1), 2754. https://doi.org/10.1038/s41598-022-06767-7
   PubMedGoogle Scholar
• Pelletier, M. P., Trepanier, M., & Morency, C. (2011). Smart card data use in public transit: A literature review. Transportation Research Part C: Emerging Technologies, 19(4), 557–568. https://doi.org/10.1016/j.trc.2010.12.003
   Web of Science ®Google Scholar
• Pieroni, C., Giannotti, M., Alves, B. B., & Renato, A. (2021). Big data for bus issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city. Journal of Transport Geography, 96, 103203. https://doi.org/10.1016/j.jtrangeo.2021.103203
   Web of Science ®Google Scholar
• Qian, X., Sun, L., & Ukkusuri, S. V. (2021). Scaling of contact networks for epidemic spreading in urban transit systems. Scientific Reports, 11(1), 4408. https://doi.org/10.1038/s41598-021-83878-7
   PubMed Web of Science ®Google Scholar
• Qiu, G., Song, R., He, S., Xu, W., & Jiang, M. (2019). Clustering passenger trip data for the potential passenger investigation and line design of customized commuter bus. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3351–3360. https://doi.org/10.1109/TITS.2018.2875466
   Web of Science ®Google Scholar
• Rokach, L., & Maimon, O. (2005). Clustering methods. In O. Maimon, & L. Rokach (Eds.), Data mining and knowledge discovery handbook (pp. 321–352). Springer.
   Google Scholar
• Roth, C., Kang, S. M., Batty, M., & Barthelemy, M. (2011). Structure of urban movements: Polycentric activity and entangled hierarchical flows. PloS one, 6(1), e15923. https://doi.org/10.1371/journal.pone.0015923
   PubMed Web of Science ®Google Scholar
• Schläpfer, M., Dong, L., O’Keeffe, K., Santi, P., Szell, M., Salat, H., Anklesaria, S., Vazifeh, M., Ratti, C., & West, G. B. (2021). The universal visitation law of human mobility. Nature, 593(7860), 522–527. https://doi.org/10.1038/s41586-021-03480-9
   PubMed Web of Science ®Google Scholar
• Sulis, P., Manley, E., Zhong, C., & Batty, M. (2018). Using mobility data as proxy for measuring urban vitality. Journal of Spatial Information Science, 16(2018), 137–162. https://doi.org/10.5311/JOSIS.2018.16.384
   Google Scholar
• Sun, L., Axhausen, K. W., Lee, D.-H., & Huang, X. (2013). Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences (PNAS), 110(34), 13774–13779. https://doi.org/10.1073/pnas.1306440110
   PubMed Web of Science ®Google Scholar
• Van Oort, N., Brands, T., & de Romph, E. (2015). Short-term prediction of ridership on public transport with smart card data. Transportation Research Record: Journal of the Transportation Research Board, 2535(1), 105–111. https://doi.org/10.3141/2535-12
   Google Scholar
• Viallard, A., Trepanier, M., & Morency, C. (2019). Assessing the evolution of transit user behaviour from smart card data. Transportation Research Record: Journal of the Transportation Research Board, 2673(4), 184–194. https://doi.org/10.1177/0361198119834561
   Web of Science ®Google Scholar
• Wang, J., Kong, X., Rahim, A., Xia, F., Tolba, A., & Al-Makhadmeh, Z. (2017). IS2Fun: identification of subway station functions using massive urban data. IEEE Access, 5, 27103–27113. https://doi.org/10.1109/ACCESS.2017.2766237
   Web of Science ®Google Scholar
• Wang, L., Zhang, Y., Zhao, X., Liu, H., & hang, K. (2020). Irregular travel groups detection based on cascade clustering in urban subways. IEEE Transactions on Intelligent Transportation Systems, 21(5), 2216–2225. https://doi.org/10.1109/TITS.2019.2933497
   Web of Science ®Google Scholar
• Wang, Z., Liu, H., Zhu, Y., & Zhang, Y. (2021). Identifying urban functional areas and their dynamic changes in Beijing: Using multiyear transit smart card data. Journal of Urban Planning and Development, 147(2), 04021002. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000662
   Web of Science ®Google Scholar
• Wang, Z., Luo, D., Cats, O., & Verma, T. (2020, September 20–23). Unravelling the hierarchy of public transport networks. Proceedings of the IEEE 23rd international conference on intelligent transportation systems (ITSC), 1–6.
   Google Scholar
• Yang, C., Yan, F., & Ukkusuri, S. V. (2018). Unraveling traveller mobility patterns and predicting user behavior in the Shenzhen metro system. Transportmetrica A: Transport Science, 14(7), 576–597. https://doi.org/10.1080/23249935.2017.1412370
   Web of Science ®Google Scholar
• Yap, M., & Cats, O. (2021). Taking the path less travelled: Valuation of denied boarding in crowded public transport systems. Transportation Research Part A, 147, 1–13. https://doi.org/10.1016/j.trb.2021.02.010
   Google Scholar
• Yap, M., Luo, D., Cats, O., van Oort, N., & Hoogendoorn, S. (2019). Where shall we sync? Clustering passenger flows to identify urban public transport hubs and their key synchronization priorities. Transportation Research Part C: Emerging Technologies, 98, 433–448. https://doi.org/10.1016/j.trc.2018.12.013
   Web of Science ®Google Scholar
• Yong, J., Zheng, L., Mao, X., Gao, A., & Weining, L. (2021). Mining metro commuting mobility patterns using massive smart card data. Physica A: Statistical Mechanics and its Applications, 584, 126351. https://doi.org/10.1016/j.physa.2021.126351
   Web of Science ®Google Scholar
• Yue, Y., Lan, T., Yeh, A. G. O., & Li, Q.-Q. (2014). Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies. Travel Behaviour and Society, 1(2), 69–78. https://doi.org/10.1016/j.tbs.2013.12.002
   Web of Science ®Google Scholar
• Zhang, S., Yang, Y., Zhen, F., Losbang, T., & Li, Z. (2021). Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach. Journal of Transport Geography, 90, 102938. https://doi.org/10.1016/j.jtrangeo.2020.102938
   Web of Science ®Google Scholar
• Zhang, X., Xu, Y., Tu, W., & Ratti, C. (2018). Do different datasets tell the same story about urban mobility – A comparative study of public transit and taxi usage. Journal of Transport Geography, 70, 78–90. https://doi.org/10.1016/j.jtrangeo.2018.05.002
   Web of Science ®Google Scholar
• Zhang, Y., Marshall, S., Cao, M., Manley, E., & Chen, H. (2021). Discovering the evolution of urban structure using smart card data: The case of London. Cities, 112, 103157. https://doi.org/10.1016/j.cities.2021.103157
   Web of Science ®Google Scholar
• Zhao, X., Wu, Y.-P., Ren, G., Ji, K., & Qian, W.-W. (2019). Clustering analysis of ridership patterns at subway stations: A case in Nanjing, China. Journal of Urban Planning and Development, 145(2), 04019005. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000501
   Web of Science ®Google Scholar
• Zhao, Z., Koutsopoulos, H. N., & Zhao, J. (2018). Individual mobility prediction using transit smart card data. Transportation Research Part C: Emerging Technologies, 89, 19–34. https://doi.org/10.1016/j.trc.2018.01.022
   Web of Science ®Google Scholar
• Zhong, C., Batty, M., Manley, E., Wang, J., Wang, Z., Chen, F., & Schmitt, G. (2016). Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PloS One, 11(2), e0149222. https://doi.org/10.1371/journal.pone.0149222
   PubMed Web of Science ®Google Scholar
• Zhong, C., Manley, E., Muller Arisona, S., Batty, M., & Schmitt, G. (2015). Measuring variability of mobility patterns from multiday smart-card data. Journal of Computational Science, 9, 125–130. https://doi.org/10.1016/j.jocs.2015.04.021
   Web of Science ®Google Scholar
• Zhou, Y., Zheng, S., Hu, Z., & Chen, Y. (2022). Metro station risk classification based on smart card data: A case study in Beijing. Physica A: Statistical Mechanics and its Applications, 594, 127019. https://doi.org/10.1016/j.physa.2022.127019
   Web of Science ®Google Scholar
• Zhuang, D., Hao, S., Lee, D.-H., & Jin, J. G. (2020). From compound word to metropolitan station: Semantic similarity analysis using smart card data. Transportation Research Part C: Emerging Technologies, 114, 322–337. https://doi.org/10.1016/j.trc.2020.02.017
   Web of Science ®Google Scholar