Spatial analysis of subway passenger traffic in Saint Petersburg
Abstract
The purpose of the paper is to create clear visualization of passenger traffic for Saint Petersburg subway system. This visualization can be used to better understand the passenger flow and to make more informed decisions in future planning. Research was based on officially published information about passenger traffic on subway station for years 2016 and 2018. Visualization was created with the variety of methods and software: Voronoi diagrams (QGIS software), social gravitation potential (R programming language), presentation of gravitation potential as a relief (Blender software), service zones of ground transport accessibility (2GIS, QGIS and Mapbox mapping platform). In this research, authors propose the use of intersection between the service zones and social gravitation potential isolines as an instrument for spatial analysis of traffic data. Analysis shown that current development of subway system does not correspond to passenger distribution. All stations were classified according to their accessibility and propositions about future directions of development were made.
Keyword : passenger traffic, traffic visualisation, social gravitation potential, R, Mapbox, Blender, spatial analysis, QGIS, public transport, subway system
How to Cite
Baltyzhakova, T., & Romanchikov, A. (2021). Spatial analysis of subway passenger traffic in Saint Petersburg. Geodesy and Cartography, 47(1), 10-20. https://doi.org/10.3846/gac.2021.11980
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Zhang, Y., Shi, H., Zhou, F., Hu, Y., & Yin, B. (2020). Visual analysis method for abnormal passenger flow on urban metro network. Journal of Visualization, 23, 1035–1052. https://doi.org/10.1007/s12650-020-00674-7
Andrienko, G., Andrienko, N., Boldrini, C., Caldarelli, G., Cintia, P., Cresci, S., Facchini, A., Giannotti, F., Gio nis, A., Guidotti, R., Mathioudakis, M., Muntean, C. I., Pappalardo, L., Pedreschi, D., Pournaras, E., Pratesi, F., Tesconi, M., & Trasarti, R. (2020). (So) Big Data and the transformation of the city. International Journal of Data Science and Analytics. https://doi.org/10.1007/s41060-020-00207-3
Andrienko, N. Andrienko, G., & Rinzivillo, S. (2016). Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics. Information Systems, 57, 172–194. https://doi.org/10.1016/j.is.2015.08.007
Andrienko, N., Andrienko, G., Patterson, F., & Stange, H. (2019). Visual analysis of place connectedness by public transport. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3198–3208. https://doi.org/10.1109/TITS.2019.2924796
Barnes, T. J., & Wilson, M. W. (2014). Big Data, social physics, and spatial analysis: The early years. Big Data & Society, 1(1), 1–14. https://doi.org/10.1177/2053951714535365
Barry, M., & Card, B. (2014). Visualizing MBTA Data. An interactive exploration of Boston’s subway system. http://mbtaviz.github.io/
Bivand, R. S., Pebesma, E., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R. Springer. https://doi.org/10.1007/978-1-4614-7618-4
Bumgardner, B. (2016). Mapping NYC subway traffic: an interactive. http://bryanbumgardner.com/mapping-nyc-subwaytraffic-an-interactive/
Chong, S. M. (2015). NYC subway traffic. http://piratefsh.github.io/mta-maps/public/
Chopra, S., Dillon, T., Bilec, M., & Khanna, V. (2016). A network-based framework for assessing infrastructure resilience: a case study of the London metro system. Journal of the Royal Society Interface, 13(118), 20160113. https://doi.org/10.1098/rsif.2016.0113
Dataveyes. (2013). Metropolitain. http://metropolitain.io/
Derrible, S. (2012). Network centrality of metro systems. PLoS One, 7(7), e40575. https://doi.org/10.1371/journal.pone.0040575
Flowmap.blue. (2020). Flowmap.blue – flow map visualization tool. https://flowmap.blue/
Gonzalez-Navarro, M., & Turner, M. (2016). Subways and urban growth: evidence from earth. Spatial Economics Research Centre. https://www.gov.uk/research-for-development-outputs/subways-and-urban-growth-evidence-from-earth
Goodwin, P., & Noland, R. B. (2003). Building new roads really does create extra traffic: a response to Prakash et al. Applied Economics, 35(13), 1451–1457. https://doi.org/10.1080/0003684032000089872
Huffman, D. (2019). Creating shaded relief in Blender. https://somethingaboutmaps.wordpress.com/2017/11/16/creating-shaded-relief-in-blender/
Itoh, M., Yokoyama, D., Toyoda, M., Tomita, Y., Kawamura, S., & Kitsuregawa, M. (2014). Visual fusion of mega-city big data: An application to traffic and tweets data analysis of Metro passengers. In 2014 IEEE International Conference on Big Data (Big Data) (pp. 431–440). IEEE. https://doi.org/10.1109/BigData.2014.7004260
Kommet agency. (2019). Passazhiropotok na stancijah metro Sankt-Peterburga [Passenger traffic on Saint Petersburg subway stations] (in Russian). https://kommet.ru/stats
Kozin, E. (2017). Enhancement of organizational and technical solutions regarding anchoring of completed construction facilities of underground railway system to operating control. Zapiski Gornogo instituta, 228, 674–680.
KRTI [Transport infrastructure development committee of Saint Petersburg]. (2018). Stroitel’stvo metropolitena [The construction of subway] (in Russian). https://krti.gov.spb.ru/stroitelstvo-metropolitena/
Levinson, D. (2012). Network structure and city size. PLoS One, 7(1), e29721. https://doi.org/10.1371/journal.pone.0029721
Lin, E., Park, J., & Züfle, A. (2017). Real-time Bayesian microanalysis for metro traffic prediction. In UrbanGIS’17: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics (pp. 1–4). https://doi.org/10.1145/3152178.3152190
Mapbox. (2020). About maps | Mapbox. https://www.mapbox.com/about/maps/
Massobrio, R., & Nesmachnow, S. (2020). Urban mobility data analysis for public transportation systems: a case study in Montevideo, Uruguay. Applied Sciences, 10(16), 5400. https://doi.org/10.3390/app10165400
Pebesma, E., & Bivand, R. S. (2005). Classes and methods for spatial data: the sp package. https://cran.r-project.org/web/packages/sp/vignettes/intro_sp.pdf
Pérez-Messina, I., Graells-Garrido, E., Jesús Lobo, M., & Hurter, C. (2020). Modalflow: cross-origin flow data visualization for urban mobility. Algorithms, 13(11), 298. https://doi.org/10.3390/a13110298
Saint Petersburg metro. (2017). Statisticheskie dannye metro [Subway statistic data] (in Russian). http://www.metro-spb.ru/statisticheskie-dannye/2016/
Shin, H. (2020). Analysis of subway passenger flow for a smarter city: knowledge extraction from Seoul metro’s ‘Untraceable’ big data. IEEE Access, 8, 69296–69310. https://doi.org/10.1109/ACCESS.2020.2985734
Stewart, J. Q. (1942). A measure of the influence of a population at a distance. Sociometry, 5(1), 63–71. https://doi.org/10.2307/2784954
Stewart, J. Q. (1947). Empirical mathematical rules concerning the distribution and equilibrium of population. Geographical Review, 37(3), 461–485. https://doi.org/10.2307/211132
Tanaka, K. (1950). The relief contour method of representing topography on maps. Geographical Review, 40(3), 444–456. https://doi.org/10.2307/211219
Xiao, F., & Yu, G. (2018). Impact of a new metro line: analysis of metro passenger flow and travel time based on smart card data. Journal of Advanced Transportation, 2018, 9247102. https://doi.org/10.1155/2018/9247102
Yang, J., Chen, S., Qin, P., & Lu, F. (2015). The Effects of subway expansion on traffic conditions: evidence from Beijing (Environment for Development Discussion Paper EfD DP 15–22). www.jstor.org/stable/resrep15032
Zhang, Y., Shi, H., Zhou, F., Hu, Y., & Yin, B. (2020). Visual analysis method for abnormal passenger flow on urban metro network. Journal of Visualization, 23, 1035–1052. https://doi.org/10.1007/s12650-020-00674-7