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.
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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’.
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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
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DOI: https://doi.org/10.1007/978-3-319-99954-8_24
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