Visual analysis of passenger traffic data of the Moscow subway

Artem Makhankin, Dmitry Namiot

Abstract


The purpose of this work is to create a clear visualization of passenger traffic for the Moscow metro system. The paper provides preliminary preparation of metadata from the entrance groups of turnstiles located at Moscow metro stations and visual analysis of passenger traffic is performed on their basis. The study was based on information for the first quarter of 2020. Visualization was created using various methods and software: Database Management System (DBMS) in which primary information for analysis is stored – PostgreSQL, visualization tool - Kepler.gl, a framework for the Java programming language – Spring Framework, was used to create a flexible access to the DBMS and obtain results for Kepler.gl. In addition, a brief overview of the use of the selected visualization tool and its technical capabilities is considered. Based on the results of visual analysis, it was possible to determine the basic patterns of passenger movement. From a practical point of view, the results help to identify anomalies in movement at an early stage and act. Therefore, this approach is an important, if not critical, element of intelligent transport systems (ITS) and smart city systems. The results of the work done are of interest to specialists in the field of urban planning and urban studies.

Full Text:

PDF (Russian)

References


Kupriyanovsky V. P. et al. Government, industry, logistics, innovation and intellectual mobility in the digital economy //Modern information technologies and IT education. – 2017. – Vol. 13. – No. 1. – pp. 74-96

I. Ceapa, C. Smith, and L. Capra. Avoiding the Crowds: Understanding Tube Station Congestion Patterns from Trip Data. In Proc. Urb-Comp’12, pages 134–141, 2012

L. Sun, D.-H. Lee, A. Erath, and X. Huang. Using Smart Card Data to Extract Passenger’s Spatio-temporal Density and Train’s Trajectory of MRT System. In Proc. UrbComp’12, pages 142–148, 2012.

Aleshko R. A. et al. Development of methods for visualization and processing of geospatial data //Scientific visualization. – 2015. – Vol. 7. – No. 1.

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

Belyakov S. L., Belyakova M. L., Savelyeva M. N. Spatial data visualization adaptive to changes in the database structure //Devices and systems. Management, control, diagnostics. - 2016. – No. 1. – pp. 25-32.

Card M. Readings in information visualization: using vision to think. – Morgan Kaufmann, 1999.

Ding X. et al. Viptra: Visualization and interactive processing on big trajectory data //2018 19th IEEE International Conference on Mobile Data Management (MDM). – IEEE, 2018. – С. 290-291.

Gonçalves T., Afonso A. P., Martins B. Cartographic visualization of human trajectory data: Overview and analysis //Journal of Location Based Services. – 2015. – Т. 9. – №. 2. – С. 138-166.

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/

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 BigData (Big Data) (pp. 431–440). IEEE. https://doi.org/10.1109/BigData.2014.7004260

Sobral T., Galvão T., Borges J. Visualization of urban mobility data from intelligent transportation systems //Sensors. – 2019. – Т. 19. – №. 2.

Thomas J. J. Illuminating the path: the research and development agenda for visual analytics. – IEEE Computer Society, 2005.

Zeng L. et al. A passenger flow control method for subway network based on network controllability //Discrete Dynamics in Nature and Society. – 2018.

Open photo bank; URL: https://ru.depositphotos.com/stock-photos

RIA Novosti; URL: https://ria.ru

Misharin A., Namiot D., Pokusaev O. On Passenger Flow Estimation for new Urban Railways //IOP Conference Series: Earth and Environmental Science. – IOP Publishing, 2018. – Т. 177. – №. 1. – С. 012012.

Official website Kepler.gl. URL: https://kepler.gl/

Official documentation website PostgresSQL, URL: https://www.postgresql.org/docs/

Official documentation website Spring Framework, URL: https://docs.spring.io/spring-framework/docs/current/reference/html/

Moscow Government Open Data Portal – Moscow Metro stations; URL: https://data.mos.ru/classifier/7704786030-stantsii-moskovskogo-metropolitena

Web API documentation HeadHunter; URL: https://github.com/hhru/api/blob/master/docs/areas.md

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

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

Tanaka, K. (1950). The relief contour method of representing topography on maps. Geographical Review, 40(3), 444–456. https://doi.org/10.2307/211219

Kuprijanovskij V. P. i dr. Cifrovaja transformacija jekonomiki, zheleznyh dorog i umnyh gorodov. Plany i opyt Velikobritanii //International Journal of Open Information Technologies. – 2016. – T. 4. – #. 10. – S. 22-31.

Sokolov I. A. i dr. Proryvnye innovacionnye tehnologii dlja infrastruktur. Evrazijskaja cifrovaja zheleznaja doroga kak osnova logisticheskogo koridora novogo Shelkovogo puti //International Journal of Open Information Technologies. – 2017. – T. 5. – #. 9. – S. 102-118.

Kuprijanovskij V. P. i dr. Cifrovaja jekonomika i Internet Veshhej-preodolenie silosa dannyh //International Journal of Open Information Technologies. – 2016. – T. 4. – #. 8. – S. 36-42.

Kuprijanovskaja Ju. V. i dr. Umnyj kontejner, umnyj port, BIM, Internet Veshhej i blokchejn v cifrovoj sisteme mirovoj torgovli //International Journal of Open Information Technologies. – 2018. – T. 6. – #. 3. – S. 49-94.

Medvedenko S., Namiot D. Visual analysis of railway passenger traffic data //International Journal of Open Information Technologies. – 2021. – T. 9. – #. 6. – S. 51-60.

Nekraplonna M., Namiot D. The Analysis of Trajectories in Moscow Subway. – 2021.

CVTS RUT https://cvts.rut.digital/ (data obrashhenija: 11.05.2022)


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162