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Modulated spatiotemporal clustering of smart card users

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Abstract

Smart card data offers an in-depth understanding of the travel behavior of public transport users. An efficient way to analyze public transport users is to group them into different clusters with similar behaviors. However, this clustering process should take into account space and time because both of these dimensions characterize daily trips. Depending on the outcome, we might wish to give more importance to space or to time, or we might wish to balance the two. In this study, we present a spatiotemporal clustering tool that permits modulation regarding the importance of space versus time. We then test this tool with different values for the space–time balance parameter to evaluate the influence of this parameter on the results. The method has been applied to 769,614 smart card transactions of the Réseau de transport de la Capitale (Quebec City, Canada). Results show that the influence of space and time can indeed be controlled, and that the types of clusters obtained vary whether one or both of the dimensions are considered.

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References

  • Ansari MY, Ahmad A, Khan SS, Bhushan G (2019) Spatiotemporal clustering: a review. Artif Intell Rev 53:2381–2423

    Article  Google Scholar 

  • Arana P, Cabezudo S, Peñalba M (2014) Influence of weather conditions on transit ridership: a statistical study using data from Smartcards. Transp Res Part A Policy Pract 59:1–12

    Article  Google Scholar 

  • Asakura Y, Iryo T, Nakajima Y, Kusakabe T (2012) Estimation of behavioural change of railway passengers using smart card data. Public Transp 4(1):1–16

    Article  Google Scholar 

  • Bagchi M, White PR (2005) The potential of public transport smart card data. Transp Policy 12:464–474

    Article  Google Scholar 

  • Blythe P (2004) Improving public transport ticketing through smart cards. Proc Inst Civil Eng Municipal Eng 157:47–54

    Google Scholar 

  • Ceapa I, Smith C, Capra L (2012) Avoiding the crowds: understanding tube station congestion patterns from trip data. In: Proceedings of the ACM SIGKDD international workshop on urban computing, ACM, pp 134–141

  • Chu KA, Chapleau R (2008) Enriching archived smart card transaction data for transit demand modeling. Transp Res Record J Transp Res Board 2063:63–72

    Article  Google Scholar 

  • Devillaine F, Munizaga M, Trépanier M (2012) Detection of activities of public transport users by analyzing smart card data. Transp Res Record J Transp Res Board 2276:48–55

    Article  Google Scholar 

  • El Mahrsi M, Côme E, Baro J, Oukhellou L (2014) Understanding passenger patterns in public transit through smart card and socioeconomic data: a case study in Rennes, France. In: The 3rd International Workshop on Urban Computing (UrbComp 2014)

  • Faroqi H, Mesbah M, Kim J (2019) Comparing sequential with combined spatiotemporal clustering of passenger trips in the public transit network using smart card data. Math Prob Eng 2019:1–16

    Article  Google Scholar 

  • Ghaemi MS, Agard B, Trépanier M, Partovi Nia V (2017) A visual segmentation method for temporal smart card data. Transportmetrica A: Transp Sci 13(5):381–404

    Article  Google Scholar 

  • Giraud A, Légaré F, Trépanier M, Morency C (2016) Data Fusion of APC, Smart Card and GTFS to Visualize Public Transit Use. In: Transportation Research Board 96th Annual Meeting, Washington DC, United States, Jan 8–12

  • He L, Trépanier M (2015) Estimating the destination of unlinked trips in transit smart card fare data. Transp Res Record J Transp Res Board 2535:97–104

    Article  Google Scholar 

  • He L, Agard B, Trépanier M (2018a) A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method. Transportmetrica A: Transp Sci 16:56–75

    Article  Google Scholar 

  • He L, Agard B, Trépanier M (2018b) Space-time classification of public transit smart card users’ activity locations from smart card data. In: Conference on Advanced Systems in Public Transport and TransitData 2018, paper 62

  • He L, Trépanier M, Agard B, Munizaga M, Bustos B (2019) Comparing transit user behaviour of two cities using smart card data. In: Annual Meeting of the Transportation Research Board, Washington, DC. No. 19-05564

  • Kieu LM, Bhaskar A, Chung E (2014) Transit passenger segmentation using travel regularity mined from Smart Card transactions data. In: Transportation Research Board 93rd Annual Meeting, Jan 12–16, Washington, DC

  • Kurauchi F, Schmöcker JD (eds) (2017) Public transport planning with smart card data. CRC Press

    Google Scholar 

  • Kusakabe T, Asakura Y (2014) Behavioural data mining of transit smart card data: a data fusion approach. Transp Res Part C: Emerg Technol 46:179–191

    Article  Google Scholar 

  • Langlois GG, Koutsopoulos HN, Zhao J (2016) Inferring patterns in the multi-week activity sequences of public transport users. Transp Res Part C: Emerg Technol 64:1–16

    Article  Google Scholar 

  • Ma X, Wu YJ, Wang Y, Chen F, Liu J (2013) Mining smart card data for transit riders’ travel patterns. Transp Res Part C: Emerg Technol 36:1–12

    Article  Google Scholar 

  • Morency C, Trepanier M, Agard B (2007) Measuring transit use variability with smart-card data. Transp Policy 14(3):193–203

    Article  Google Scholar 

  • Nishiuchi H, King J, Todoroki T (2013) Spatial-temporal daily frequent trip pattern of public transport passengers using smart card data. Int J Intell Transp Syst Res 11(1):1–10

    Google Scholar 

  • Pelletier MP, Trépanier M, Morency C (2011) Smart card data use in public transit: a literature review. Transp Res Part C: Emerg Technol 19(4):557–568

    Article  Google Scholar 

  • Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  • Seaborn C, Wilson NH, Attanucci J (2009) Analyzing multimodal public transport journeys in London with smart card fare payment data. Transp Res Record J Transp Res Board 2121(1):55–62

    Article  Google Scholar 

  • “Statistics toolbox.” API Reference Documentation. The MathWorks. http://www.mathworks.com/access/helpdesk/help/toolbox/stats/. Accessed 3 Jan 2021

  • Spurr T, Chapleau R, Piché D (2014) Discovery and partial correction of travel survey bias using subway smart card transactions. Transp Res Record J Transp Res Board 2405(1):56–67

    Article  Google Scholar 

  • Trépanier M (2012) Use of smart card data to plan urban public transport. RTS-Recherche Transp Securite 28(2):139

    Google Scholar 

  • Trépanier M, Morency C (2010). Assessing transit loyalty with smart card data. In: Paper presented at the 12th World Conference on Transport Research, Lisbon, Portugal

  • Trépanier M, Tranchant N, Chapleau R (2007) Individual trip destination estimation in a transit smart card automated fare collection system. J Intell Transp Syst 11(1):1–14

    Article  Google Scholar 

  • Utsunomiya M, Attanucci J, Wilson N (2006) Potential uses of transit smart card registration and transaction data to improve transit planning. Transp Res Record J Transp Res Board 1971:119–126

    Article  Google Scholar 

  • Yap M, Cats O, van Arem B (2018) Crowding valuation in urban tram and bus transportation based on smart card data. Transportmetrica A: Transp Sci 16:23–42

    Article  Google Scholar 

  • Zhao J, Qu Q, Zhang F, Xu C, Liu S (2017) Spatio-temporal analysis of passenger travel patterns in massive smart card data. IEEE Trans Intell Transp Syst 18(11):3135–3146

    Article  Google Scholar 

  • Zhou M, Wang D, Li Q, Yue Y, Tu W, Cao R (2017) Impacts of weather on public transport ridership: results from mining data from different sources. Transp Res Part C: Emerg Technol 75:17–29

    Article  Google Scholar 

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Acknowledgements

The authors wish to thank the Réseau de transport de la Capitale (RTC) for providing the data. They also thank the Thales group, Cortex Media, Prompt Quebec and the National Science and Engineering Research Council of Canada (NSERC) for providing funding.

Funding

This research was supported by the following organizations: Thales group, Cortex Media, Prompt Quebec and the National Science and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. CIRRELT/Polytechnique Montréal, Department of Mathematical and Industrial Engineering, Polytechnique Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec, H3C 3A7, Canada

    Rémi Decouvelaere, Martin Trépanier & Bruno Agard

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  1. Rémi Decouvelaere
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  2. Martin Trépanier
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  3. Bruno Agard
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Creation of spatiotemporal clustering tool that permits modulation of the influence of time and space, in the case of smart card users.

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Correspondence to Martin Trépanier.

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Decouvelaere, R., Trépanier, M. & Agard, B. Modulated spatiotemporal clustering of smart card users. Public Transp 16, 657–680 (2024). https://doi.org/10.1007/s12469-022-00305-4

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  • DOI: https://doi.org/10.1007/s12469-022-00305-4

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