Abstract
In the traffic network, the betweenness centrality helps in identification of the most occupied roads and crossroads. Usually, the main roads have the highest betweenness centrality score, given their importance in the traffic flow. The side roads’ score is generally lower and it never takes into account what is happening on the main road. In a case of unusual event happening in the city, the betweenness score of the main road can increase multiplicatively, while the score of the side road is increased only slightly. Thus, we propose an extension to the original betweenness centrality score algorithm that enables the propagation of the betweenness centrality score from the main road to the side roads, allowing us better description of the current traffic situation. This is the continuation of our work on better refinement of the BC score for the purpose of the traffic modelling and the traffic flow control.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xia, J., Sun, J., Jia, P., Zhao, Z.: Do cancer proteins really interact strongly in the human protein-protein interaction network? Comput. Biol. Chem. 35, 121–125 (2011)
Li, M., Wang, J., Chen, X., Wang, H., Pan, Y.: A local average connectivity-based method for identifying essential proteins from the network level. Comput. Biol. Chem. 35, 143–150 (2011)
Hagmann, P., et al.: Mapping the structural core of human cerebral cortex. PLoS Biol. 6, 1479–1493 (2008)
Szell, M., Thurner, S.: Measuring social dynamics in a massive multiplayer online game. Soc. Netw. 32, 313–329 (2010)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Application. Cambridge University Press, Cambridge (1994)
Clifton, A., Turkheimer, E., Oltmanns, T.F.: Personality disorder in social networks: Network position as a marker of interpersonal dysfuction. Soc. Netw. 31, 26–32 (2009)
Zhou, S., Mondragón, R.J.: Accurately modeling the internet topology. Phys. Rev. E70, 066108 (2004)
Kawamoto, H., Igarashi, A.: Efficient packet routing strategy in complex networks. Phys. A Stat. Mech. Appl. 391, 895–904 (2012)
Shang, P., Li, X., Kamae, S.: Chaotic analysis of traffic time series. Chaos Solitons Fractals 25, 121–128 (2005)
Hong, W.C., Dong, Y., Zheng, F., Lai, C.Y.: Forecasting urban traffic flow by SVR with continuous ACO. Appl. Math. Model. 35, 1282–1291 (2011)
Daganzo, C.F.: The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp. Res. Part B Methodol. 28, 269–287 (1994)
Klein, D.J.: Centrality measure in graphs. J. Math. Chem. 47, 1209–1223 (2010)
Galafassi, C., Bazzan, A.L.C.: Analysis of traffic behavior in regular grid and real world networks (2013)
Gao, S., Wang, Y., Gao, Y., Liu, Y.: Understanding urban traffic flow characteristics: a rethinking of betweenness centrality. Environ. Plan. B Plan. Des. 40, 135 (2012)
Zhao, P.X., Zhao, S.M.: Understanding urban traffic flow characteristics from the network centrality perspective at different granularities. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 41, 263–268 (2016)
Hanzelka, J., Běloch, M., Martinovič, J., Slaninová, K.: Vertex importance extension of betweenness centrality algorithm. In: International Conference on Data Management, Analytics and Innovation - ICDMAI, Pune, India (2018)
Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)
Acknowledgments
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPS II) project ‘IT4Innovations excellence in science - LQ1602’, partially supported by the SGC grant No. SP2018/142 ’Optimization of machine learning algorithms for HPC platform II’, VŠB - Technical University of Ostrava, Czech Republic, and by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project ‘IT4Innovations National Supercomputing Center – LM2015070’.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Hanzelka, J., Běloch, M., Křenek, J., Martinovič, J., Slaninová, K. (2018). Betweenness Propagation. In: Saeed, K., Homenda, W. (eds) Computer Information Systems and Industrial Management. CISIM 2018. Lecture Notes in Computer Science(), vol 11127. Springer, Cham. https://doi.org/10.1007/978-3-319-99954-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-99954-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99953-1
Online ISBN: 978-3-319-99954-8
eBook Packages: Computer ScienceComputer Science (R0)