Abstract
Motivated by the constantly growing interest and real-world applicability shown in complex networks, we model and optimize the network formed by road networks in cities from an innovative perspective. We detect traffic hotspots which lead to congestion using the betweenness centrality of the road graph. This is shown to have a power-law distribution which we set out to redistribute and equalize. Optimization at a macro-level is not feasible because of the graph size, and thus we recursively narrow down the methodology to a sub-optimization of city neighborhoods. To that end, the paper introduces a genetic algorithm which redistributes betweenness optimization at a neighborhood level, district level, and city level to reduce and/or eliminate congestion hotspots, by changing street directions, without adding any new roads. Experimental results yield an improvement with a factor of 4 times in terms of reducing load off from hotspots and transferring it to neighboring streets.
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Acknowledgments
This work was partially supported by the strategic grant POSDRU/159/1.5/S/137070 (2014) of the Ministry of National Education, Romania, co-financed by the European Social Fund—Investing in People, within the Sectoral Operational Programme Human Resources Development 2007–2013.
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Topirceanu, A., Iovanovici, A., Cosariu, C., Udrescu, M., Prodan, L., Vladutiu, M. (2016). Social Cities: Redistribution of Traffic Flow in Cities Using a Social Network Approach. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_4
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DOI: https://doi.org/10.1007/978-3-319-18296-4_4
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