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Multidimensional visualization of transit smartcard data using space–time plots and data cubes

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Abstract

Given the wide application of automatic fare collection systems in transit systems across the globe, smartcard data with on- and/or off-boarding information has become a new source of data to understand passenger flow patterns. This paper uses Nanjing, China as a case study and examines the possibility of using the data cube technique in data mining to understand space–time travel patterns of Nanjing rail transit users. One month of smartcard data in October, 2013 was obtained from Nanjing rail transit system, with a total of over 22 million transaction records. We define the original data cube for the smartcard data based on four dimensions—Space, Date, Time, and User, design a hierarchy for each dimension, and use the total number of transactions as the quantitative measure. We develop modules using the programming language Python and share them as open-source on GitHub to enable peer production and advancement in the field. The visualizations of two-dimensional slices of the data cube show some interesting patterns such as different travel behaviors across user groups (e.g. students vs. elders), and irregular peak hours during National Holiday (October 1st–7th) compared to regular morning and afternoon peak hours during regular working weeks. Spatially, multidimensional visualizations show concentrations of various activity opportunities near metro rail stations and the changing popularities of rail stations through time accordingly. These findings support the feasibility and efficiency of the data cube technique as a mean of visual exploratory analysis for massive smart-card data, and can contribute to the evaluation and planning of public transit systems.

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Acknowledgements

This study was sponsored by the International Cooperation and Exchange of the National Natural Science Foundation of China (No. 51561135003) and the Key project of National Natural Science Foundation of China (No. 51338003).

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Authors and Affiliations

  1. Department of Geography, Environment and Society, University of Minnesota, 414 Social Science Building, 267 19th Ave S, Minneapolis, MN, 55455, USA

    Ying Song

  2. Urban and Regional Planning Program, Humphrey School of Public Affairs, University of Minnesota, 295E Humphrey School, 301 19th Avenue South, Minneapolis, MN, 55455, USA

    Yingling Fan

  3. Civil and Environmental Engineering Department, University of Wisconsin-Milwaukee, 1818E Shorewood Blvd #307, Shorewood, WI, 53211, USA

    Xin Li

  4. School of Transportation, Southeast University, No.2, Sipailou, 210096, Nanjing, China

    Yanjie Ji

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  1. Ying Song
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  2. Yingling Fan
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  3. Xin Li
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  4. Yanjie Ji
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Corresponding author

Correspondence to Yanjie Ji.

Appendix: Python modules to process and visualize massive smartcard data

Appendix: Python modules to process and visualize massive smartcard data

The original transaction data recorded within metro transit system is commonly archived as a set of binary files; each file stores transactions records for one day. Therefore, we developed scripts using Python programming language to process the raw data, create data cube, realize operations on data cube, and visualize the two-dimensional plots of the operation results. We uploaded the Python scripts to GitHub which is an online development platform to host code, manage projects, and share with others. We have described the module functions at the beginning of each modules and introduced the project in the README file. We made this repository as an open source at: https://github.com/y7song/Research-Collaborations/tree/2016_UMN_YinglingFan_TransitCube. Figure 13 shows the flowchart for all modules in the repository along with their functions.

Fig. 13
figure 13

Flowchart with Python modules and their functions in GitHub repository

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Song, Y., Fan, Y., Li, X. et al. Multidimensional visualization of transit smartcard data using space–time plots and data cubes. Transportation 45, 311–333 (2018). https://doi.org/10.1007/s11116-017-9790-2

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