Robustness of densely populated urban networks in relation to the spread of traffic
DOI:
https://doi.org/10.37467/gka-revtechno.v8.2042Keywords:
Robustness, Vulnerability, Graphs, Intermediate Centrality, Close Centrality, Random Attack, Directed AttackAbstract
Analyzing, the morphology, robustness or vulnerability of densely populated cities is a challenge for contemporary researchers. Studies on the resilience of urban infrastructures are given by the presence of recurrent adverse events or sporadic disasters. These events force the interruption of intersections or sections of streets momentarily or permanently. For measurements we use network graph properties and computational algorithms, simulating random and targeted attacks. Finally, in the results we identify the location of critical places that contain intersections and sections of street with greater centrality of intermediation and lower average of proximity.
References
Yaoli, W., Song, G., & Yu, L. (2013). Exploration into urban street closeness centrality and its application methods:A case study of Qingdao. GEOGRAPHICAL RESEARCH,2013, 32(3): 452-464.
Yin H., H. B. (2016). Evaluating Disruption in Rail Transit Network: A Case Study of Beijing Subway. Procedia Engineering . DOI: https://doi.org/10.1016/j.proeng.2016.01.233
Barros, J. X. (2014). Urban Growth in Latin American Cities. Published by ProQuest LLC 2014 .
Glabowski, M., Musznick, B., Nowa, P., & Zwierzykowsk, P. (2014). Review and Performance Analysis of Shortest Path Problem Solving Algorithms. International Journalon Advancesin Software, vol7no1&2 .
Wang J., L. S. (2017). Research on the Robustness of Interdependent Networks under Localized Attack. Applied Sciences . DOI: https://doi.org/10.3390/app7060597
Wang, J. (2015). Resilience of Self-Organised and Top-Down Planned Cities—A Case Study on London and Beijing Street Networks. PLOS ONE 10(12): e0141736. DOI: https://doi.org/10.1371/journal.pone.0141736
Wang, K., & Fu, X. (2017). Research on Centrality of Urban Transport Network Nodes. : AIP Conference Proceedings 1839, 020181 (2017); doi: 10.1063/1.4982546 . DOI: https://doi.org/10.1063/1.4982546
Wehmuth, K., Fleury, É., & Ziviani , A. (2017). MultiAspect Graphs: Algebraic Representation and Algorithms. Algorithms . DOI: https://doi.org/10.3390/a10010001
Zhang, K., & Batterman , S. (2013). Air pollution and health risks due to vehicle traffic. Science of the Total Environment 450–451 (2013) 307–316 . DOI: https://doi.org/10.1016/j.scitotenv.2013.01.074
Zou, Z., Xiao , Y., & Gao, J. (2013). Robustness analysis of urban transit network based on complex networks theory. Kybernetes . DOI: https://doi.org/10.1108/03684921311323644
Arcaute, E., Molinero, C., Hatna, E., Murcio, R., Vargas-Ruiz, C., & Masucci, A. P. (2016). Cities and regions in Britain through hierarchical percolation. The Royal Society/doi: 10.1098/rsos.150691 . DOI: https://doi.org/10.1098/rsos.150691
Askarian, A., Xu, R., & Faragó, A. (2016). Utilizing Network Structure to Accelerate Markov Chain Monte Carlo Algorithms. Algorithms . DOI: https://doi.org/10.3390/a9030050
Bader D.A., K. S. (2007). Approximating Betweenness Centrality . In: Bonato A., Chung F.R.K. (eds) Algorithms and Models for the Web-Graph. WAW 2007. Lecture Notes in Computer Science, vol 4863. Springer, Berlin, Heidelberg .
Batty, M. (2013). Resilient Cities, Networks, and Disruption. Environment and Planning B: Planning and Design, 40(4), 571–573 . DOI: https://doi.org/10.1068/b4004ed
Boeing, G. (2017). OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Computers, Environment and Urban Systems. 65, 126-139 . DOI: https://doi.org/10.1016/j.compenvurbsys.2017.05.004
BRYAN R., R. (2005). Globalization and Latin American Cities. Volume 29.1 110–23International Journal of Urban and Regional Research . DOI: https://doi.org/10.1111/j.1468-2427.2005.00573.x
Carra, G., & Barthelemy, M. (2017). The fundamental diagram of urbanization. arXiv:1609.06982 [physics.soc-ph] . DOI: https://doi.org/10.1177/2399808317724445
da Cunha, J., & Rodríguez Vignoli, J. (2009). Crecimiento urbano y movilidad en América Latina. Revista Latinoamericana de Población, 3 (4-5), 27-64 . DOI: https://doi.org/10.31406/relap2009.v3.i1.n4-5.1
Flamino, J., Norman, A., & Wyatt, M. (2017). Modeling smart growth of cities through entropy and logistics. arXiv:1707.02360 [physics.soc-ph] . DOI: https://doi.org/10.1137/17S015914
Ganin, A. A., Kitsak, M., Marchese, D., Keisler, J. M., Seager, T., & Linkov, I. (2017). Resilience and efficiency in transportation networks. Science Advances . DOI: https://doi.org/10.1126/sciadv.1701079
Jun-qiang L, L.-h. Y. (2017). Medición de vulnerabilidad de la red de carreteras con análisis de sensibilidad. PLoS ONE 12 (1): e0170292. https://doi.org/10.1371/journal.pone.0170292 . DOI: https://doi.org/10.1371/journal.pone.0170292
Ji, S., & Yan, Z. (2017). Refining Approximating Betweenness Centrality Based on Samplings. arXiv: 1608.04472 [cs.SI] .
Lemes A. and Sacomato M. (2016). Actor centrality in Network Projects and scientific performance: an exploratory study. RAI Revista de Administração e Inovação .
Li K., & He, Y. (2017). The Complex Network Reliability and Influential Nodes. AIP Conference Proceedings 1864, 020144 (2017); doi: 10.1063/1.4992961 . DOI: https://doi.org/10.1063/1.4992961
Li, D., Fu, B., Wang, Y., Lu, G., Berezin, Y., & Stanley, H. (2014). Percolation transition in dynamical traffic network with evolving critical bottlenecks. National Academy of Sciences/doi: 10.1073/pnas.1419185112 . DOI: https://doi.org/10.1073/pnas.1419185112
Liu, Z., & Zhao, S. (2015). Characteristics of road network forms in historic districts of Japan. Frontiers of Architectural Research . DOI: https://doi.org/10.1016/j.foar.2015.08.003
Masucci, A. P., & Molinero, C. (2016). Robustness and Closeness Centrality for Self-Organized and Planned Cities. The European Physical Journal B .
Mohamad, W., & Said, I. (2014). A review of variables of urban street connectivity. IOP Conf. Ser.: Earth Environ. Sci. 18012173 . DOI: https://doi.org/10.1088/1755-1315/18/1/012173
Pratt, G. C. (2015). Traffic, Air Pollution, Minority and Socio-Economic Status: Addressing Inequities in Exposure and Risk. International Journal of Environmental Research and Public Health, 12(5), 5355–5372. http://doi.org/10.3390/ijerph120505355 . DOI: https://doi.org/10.3390/ijerph120505355
Pratt, G., Vadali, M., Kvale, D., & Ellickson, K. (2015). Traffic, Air Pollution, Minority and Socio-Economic Status: Addressing Inequities in Exposure and Risk. International Journal of Environmental Research and Public Health, 12(5), 5355–53 . DOI: https://doi.org/10.3390/ijerph120505355
Roy Chowdhury, I. (2015). Traffic Congestion and Environmental Quality: A Case Study of Kolkata City. International Journal of Humanities and Social Science Invention .
Shauhrat, S., Chopra, T., & Melissa , M. (2016). A network-based framework for assessing infrastructure resilience: a case study of the London metro system. DOI: 10.1098/rsif.2016.0113 . DOI: https://doi.org/10.1098/rsif.2016.0113
Shao, S., Huang, X., Stanley, H., & Havlin, S. (2015). Percolation of localized attack on complex networks. New Journal of Physics . DOI: https://doi.org/10.1088/1367-2630/17/2/023049
Solé-Ribalta, A., Gómez, S., & Arenas, A. (2016). A model to identify urban traffic congestion hotspots in complex networks. Royal Society Open Science . DOI: https://doi.org/10.1098/rsos.160098
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