Menu
Publications
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2023-23-3-553-563
Role discovery in node-attributed public transportation networks: the study of Saint Petersburg city open data
Read the full article ';
Article in English
For citation:
Abstract
For citation:
Lytkin Yu.V., Chunaev P.V., Gradov T.A., Boytsov A.A., Saitov I.A. Role discovery in node-attributed public transportation networks: the study of Saint Petersburg city open data. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 3, pp. 553–563. doi: 10.17586/2226-1494-2023-23-3-553-563
Abstract
The work presents results of modeling Public Transportation Networks (PTNs) of Saint Petersburg (Russia) and highlights the roles of stations (stops) in this network. PTNs are modeled using a new approach, previously proposed by the authors, based on weighted networks with node attributes. The nodes correspond to stations (stops) of public transport, grouped according to their geospatial location, while the node attributes contain information about social infrastructure around the stations. Weighted links integrate information about the distance and number of transfers in the routes between the stations. The role discovery is carried out by clustering the stations according to their topological and semantic attributes. The paper proposes a software framework for solving the problem of discovering roles in a PTNs. The results of its application are demonstrated on a new set of data about the PTNs of Saint Petersburg (Russia). The significant roles of the nodes of the specified PTNs were discovered in terms of both topological and infrastructural features. The overall effectiveness of the PTNs was assessed. The revealed transportation and infrastructural shortcomings of the PTNs of Saint Petersburg can be considered by the city administration to improve the functioning of these networks in the future.
Keywords: node-attributed network, public transportation network, role discovery, network node classification, network topology, social infrastructure
Acknowledgements. This study is financially supported by the Russian Science Foundation, Agreement 17-71-30029, with co-financing of the “Bank Saint Petersburg”, Russia.
References
Acknowledgements. This study is financially supported by the Russian Science Foundation, Agreement 17-71-30029, with co-financing of the “Bank Saint Petersburg”, Russia.
References
-
Lytkin Yu.V., Chunaev P.V., Gradov T.A., Boytsov A.A., Saitov I.A. Role discovery in node-attributed public transportation networks: the model description. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 340–351. https://doi.org/10.17586/2226-1494-2023-23-2-340-351
-
Haznagy A., Fi I., London A., Nemeth T. Complex network analysis of public transportation networks: A comprehensive study. Proc. of the 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015, pp. 371–378. https://doi.org/10.1109/mtits.2015.7223282
-
Yang X.-H., Chen G., Chen S.-Y., Wang W.-L., Wang L. Study on some bus transport networks in china with considering spatial characteristics. Transportation Research Part A: Policy and Practice, 2014, vol. 69, pp. 1–10. https://doi.org/10.1016/j.tra.2014.08.004
-
Wang L.-N., Wang K., Shen J.-L. Weighted complex networks in urban public transportation: Modeling and testing. Physica A: Statistical Mechanics and its Applications, 2020, vol. 545, pp. 123498. https://doi.org/10.1016/j.physa.2019.123498
-
Shanmukhappa T., Ho I.W.-H., Tse C.K. Spatial analysis of bus transport networks using network theory. Physica A: Statistical Mechanics and its Applications, 2018, vol. 502, pp. 295–314. https://doi.org/10.1016/j.physa.2018.02.111
-
Rossi R.A., Ahmed N.K. Role discovery in networks. IEEE Transactions on Knowledge and Data Engineering, 2015, vol. 27, no. 4, pp. 1112–1131. https://doi.org/10.1109/tkde.2014.2349913
-
Gupte P.V., Ravindran B., Parthasarathy S. Role discovery in graphs using global features: Algorithms, applications and a novel evaluation strategy.Proc. of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017, pp. 771–782. https://doi.org/10.1109/icde.2017.128
-
Revelle M., Domeniconi C., Johri A. Persistent roles in online social networks. Lecture Notes in Computer Science, 2016, vol. 9852, pp. 47–62. https://doi.org/10.1007/978-3-319-46227-1_4
-
MacQueen J. Some methods for classification and analysis of multivariate observations. Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. V. 1: Statistics, 1967, pp. 281–297.
-
Freeman L.C. A set of measures of centrality based on betweenness. Sociometry, 1977, vol. 40, no. 1, pp. 35–41. https://doi.org/10.2307/3033543
-
Freeman L.C. Centrality in social networks conceptual clarification. Social Networks, 1978, vol. 1, no. 3, pp. 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
-
Onnela J.-P., Saramäki J., Kertész J., Kaski K. Intensity and coherence of motifs in weighted complex networks. Physical Review E, 2005, vol. 71, no. 6, pp. 065103. https://doi.org/10.1103/physreve.71.065103
-
Page L., Brin S., Motwani R., Winograd T. The pagerank citation ranking: Bringing order to the web: Technical Report 1999-66, Stanford InfoLab, November 1999.
-
Van der Maaten L., Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, vol. 9, no. 86, pp. 2579–2605.
-
Welch B.L. The generalization of `student's' problem when several different population variances are involved. Biometrika, 1947, vol. 34, no. 1-2, pp. 28–35. https://doi.org/10.2307/2332510