OD-matrix and passenger flow analysis

Dmitry Namiot, Mariia Nekraplonna, Oleg Pokusaev, Alexander Chekmarev


This article deals with approaches to assessing the use of metro stations based on correspondence matrixes describing passenger movements. Telecom operators currently maintain mobile communication in the metro. This results in operators being able to track the entry and exit of passengers from the metro, determining when their mobile subscriber switches to a base station located in the metro (enters the metro) or to a base station in the city (exits the metro). Such anonymous data can be grouped by time to exclude the tracking of individual subscribers and presented for analysis. The final data are a so-called OD-matrix: for a certain time interval for each pair of stations, the number of passengers moving between these stations is known.  Usually, when analyzing transport systems, restoring such a matrix (i.e. actually forecasting passenger flows) is the main task. In this case, the forecast is not needed - all passenger flows are known. This article is devoted to the discussion of what can be the purpose of analysis under such a structure of source data.

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Sweeney, Latanya. "k-anonymity: A model for protecting privacy." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10.05 (2002): 557-570.

Nekraplonna, Mariia, and Dmitry Namiot. "Metro correspondence matrix analysis." International Journal of Open Information Technologies 7.7 (2019): 68-80.

Zhang, Yi, et al. "Daily OD matrix estimation using cellular probe data." 89th Annual Meeting Transportation Research Board. Vol. 9. 2010.

Djukic, Tamara, et al. "Efficient real time OD matrix estimation based on Principal Component Analysis." 2012 15th International IEEE Conference on Intelligent Transportation Systems. IEEE, 2012.

Pucher, John. "Urban travel behavior as the outcome of public policy: the example of modal-split in Western Europe and North America." Journal of the American Planning Association 54.4 (1988): 509-520.

Bulygin M. V., Namiot D. E. Ob ispol'zovanii dannyh mobil'nyh abonentov v cifrovoj urbanistike //Mezhdunarodnyj nauchnyj zhurnal «Sovremennye informacionnye tehnologii i IT-obrazovanie». – 2019. – T. 15. – #. 3. – S. 755-766.

Pomatilov F. S., Namiot D. E. Ob analize passazhiropotokov Moskovskogo metropolitena //Sovremennye informacionnye tehnologii i IT-obrazovanie. – 2019. – T. 15. – #. 2.

Misharin, A., D. Namiot, and O. Pokusaev. "On Processing of Correspondence Matrices in Transport Systems." 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2019.

Namiot D. E., Pokusaev O. N., Lazutkina V. S. O modeljah passazhirskogo potoka dlja gorodskih zheleznyh dorog //International Journal of Open Information Technologies. – 2018. – T. 6. – #. 3.

OECD transport statistics https://www.oecd-ilibrary.org/transport/data/itf-transport-statistics_trsprt-data-en Retrieved: Mar, 2020

Liao, T. Warren. "Clustering of time series data—a survey." Pattern recognition 38.11 (2005): 1857-1874.

Rani, Sangeeta, and Geeta Sikka. "Recent techniques of clustering of time series data: a survey." International Journal of Computer Applications 52.15 (2012).

Wang, Xiaozhe, Anthony Wirth, and Liang Wang. "Structure-based statistical features and multivariate time series clustering." Seventh IEEE International Conference on Data Mining (ICDM 2007). IEEE, 2007.

Hautamaki, Ville, Pekka Nykanen, and Pasi Franti. "Time-series clustering by approximate prototypes." 2008 19th International Conference on Pattern Recognition. IEEE, 2008..


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