Structural and temporal analysis of metro passenger traffic

Daulet Ospanov, Dmitry Namiot, Oleg Pokusaev


The article is devoted to one approach to the analysis of traffic flows. The initial data for analysis are the so-called correspondence matrices, which describe the number of trips per unit of time between two points. The specific dataset that was analyzed in the work is a matrix of Moscow metro correspondence (passenger trips between metro stations) for February 2018. The purpose of the analysis is a structural-temporal analysis of passenger traffic (how and when passengers move). The paper proposes a method for analyzing transport traffic based on a combination of singular value decomposition and machine learning clustering methods. Singular value decomposition is used here for dimensionality reduction. The concept of using these tools in conjunction is not new, it has been used in other areas, but in this work, it has been successfully adapted specifically for the transport sector. The article presents a library software module for the implementation of each stage of the development of the proposed model. The module is capable of processing large amounts of data and has the potential for easy scaling and expansion. The paper presents an example of the implementation of the proposed methodology in relation to historical data on the passenger flow of the Moscow metro.

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DOI: 10.5281/zenodo.7981178

Van Arem B. et al. Recent advances and applications in the field of short-term traffic forecasting //International journal of forecasting. – 1997. – T. 13. – #. 1. – S. 1-12.

Papageorgiou M. (ed.). Concise encyclopedia of traffic and transportation systems. – Pergamon, 1991. – T. 6.

E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach,” Transportation Research Part C: Emerging Technologies, vol. 13, no. 3, pp. 211–234, 2005.

Y. Wei and M.-C. Chen, “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks,” Transportation Research Part C: Emerging Technologies, vol. 21, no. 1, pp. 148–162, 2012.

Xiong X. et al. Dynamic origin–destination matrix prediction with line graph neural networks and kalman filter //Transportation Research Record. – 2020. – T. 2674. – #. 8. – S. 491-503.

Ashok, K., and M. E. Ben-Akiva. Alternative Approaches for Real-Time Estimation and Prediction of TimeDependent Origin–Destination Flows. Transportation Science, Vol. 34, No. 1, 2000, pp. 21–36.

X. Jiang, L. Zhang, and X. M. Chen, “Short-term forecasting of highspeed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in china,” Transportation Research Part C: Emerging Technologies, vol. 44, pp. 110–127, 2014.

Y. Chen, H. S. Mahmassani, and Z. Hong, “Data mining and pattern matching for dynamic origin–destination demand estimation: Improving online network traffic prediction,” Transportation Research Record: Journal of the Transportation Research Board, no. 2497, pp. 23–34, 2015.

Zhu, Z., Peng, B., Xiong, C., & Zhang, L. (2016). Short-term traffic flow prediction with linear conditional gaussian Bayesian network. Journal of Advanced Transportation, 50(6), 1111–1123.

Zhang, Y., & Zhang, Y. (2016). A comparative study of three multivariate short-term freeway traffic flow forecasting methods with missing data. Journal of Intelligent Transportation Systems, 20(3), 205–218.

Jiang, H., Zou, Y., Zhang, S., Tang, J., & Wang, Y. (2016). Short-term speed prediction using remote microwave sensor data: Machine learning versus statistical model. Mathematical Problems in Engineering, 2016, 1–13.

Wu, Y.-J., Chen, F., Lu, C.-T., & Yang, S. (2016). Urban traffic flow prediction using a spatio-temporal random effects model. Journal of Intelligent Transportation Systems, 20(3), 282–293.

Zhu, Z., Peng, B., Xiong, C., & Zhang, L. (2016). Short-term traffic flow prediction with linear conditional gaussian Bayesian network. Journal of Advanced Transportation, 50(6), 1111–1123.

Shahsavari, B., & Abbeel, P. (2015). Short-term traffic forecasting: Modeling and learning spatio-temporal relations in transportation networks using graph neural networks. University of California at Berkeley, Technical Report No. UCB/EECS-2015-243.

Qing, L., Yongqin, T., Yongguo, H., & Qingming, Z. (2014). The forecast and the optimization control of the complex traffic flow based on the hybrid immune intelligent algorithm. Open Electrical & Electronic Engineering Journal, 8, 245–251.

Dong, C., Shao, C., Richards, S. H., & Han, L. D. (2014). Flow rate and time mean speed predictions for the urban freeway network using state space models. Transportation Research Part C: Emerging Technologies, 43, 20–32.

Dimitriou, L., Tsekeris, T., & Stathopoulos, A. (2008). Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transportation Research Part C: Emerging Technologies, 16(5), 554–573.

Karchevskij E. M., Karchevskij M. M. Lekcii po linejnoj algebre i analiticheskoj geometrii: uchebnoe posobie. – 2012.

Roweis, Sam T., and Lawrence K. Saul. Nonlinear dimensionality reduction by locally linear embedding., Science 290. 5500 (2000): 2323-2326.

S. Sun, C. Zhang, and G. Yu, “A Bayesian network approach to traffic flow forecasting,” IEEE Trans. Intell. Transp. Syst., vol. 7, no. 1, pp. 124-132, Mar. 2006.

S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding", Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000.

Yang C, Yan FF, Xu XD. Clustering Daily Metro Origin-Destination Matrix in Shenzhen China. AMM 2015;743: 422–32.

Frey, Brendan J., and Delbert Dueck. "Clustering by passing messages between data points." science 315.5814 (2007): 972-976.

C. Yang, F. Yan and X. Xu, "Daily metro origin-destination pattern recognition using dimensionality reduction and clustering methods," 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, pp. 548-553.

Nekrapljonnaja M.N., and Namiot D.E.. "Analiz matric korrespondencii metro" International Journal of Open Information Technologies, vol. 7, no. 7, 2019, pp. 68-80.

Duan Z. Lei Z. Zhang M. et al.: ‘Understanding multiple days metro travel demand at aggregate level’, IET Intell. Transp. Syst., 2018, 13, (5), pp. 756– 763.

Zhi Y. Li H. Wang D. et al.: ‘Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data’, Geo Spat. Inf. Sci., 2016, 19, (2), pp. 94–105.

Kuprijanovskaja, Ju. V., et al. "Umnyj kontejner, umnyj port, BIM, Internet Veshhej i blokchejn v cifrovoj sisteme mirovoj torgovli." International Journal of Open Information Technologies 6.3 (2018): 49-94.

Nikolaev, D. E., et al. "Cifrovaja zheleznaja doroga-innovacionnye standarty i ih rol' na primere Velikobritanii." International Journal of Open Information Technologies 4.10 (2016): 55-61.

Kuprijanovskij V. P., Namiot D. E., Sinjagov S. A. Demistifikacija cifrovoj jekonomiki //International Journal of Open Information Technologies. – 2016. – T. 4. – #. 11. – S. 59-63.


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