Enriquecendo Modelos de Mobilidade Urbana com Padrões Espaço-Temporais e Estratos Socioeconômicos
Resumo
Este trabalho analisa a mobilidade urbana de Curitiba por meio do uso do transporte público. Com a população dividida em subconjuntos correspondentes a estratos socioeconômicos, foram estabelecidas redes onde os vértices correspondem aos bairros da cidade e as arestas representam a existência de um deslocamento entre dois bairros. Para estudar as principais propriedades estruturais dessas redes de mobilidade foram analisados os seus padrões espaço-temporais. Os resultados mostram que padrões espaciais e temporais variam entre os diferentes grupos socioeconômicos. Em particular, o conjunto de dados analisado mostra que, com o aumento da riqueza, a atividade matinal é adiada (em média por 2h) e a distribuição espacial das viagens torna-se mais localizada, com diferenças maiores que 10 km entre as classes.
Palavras-chave:
Mobilidade Urbana, Transporte Público, Classes Socioeconômicas
Referências
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Xu, Y., Belyi, A., Bojic, I., and Ratti, C. (2018). Human mobility and socioeconomic status: Analysis of Singapore and Boston. Computers, Environment and Urban Systems, 72:51 – 67.
Yang, Y., Heppenstall, A., Turner, A., and Comber, A. (2019). Who, where, why and when? using smart card and social media data to understand urban mobility. ISPRS International Journal of Geo-Information, 8(6).
Araujo, M., Melo de Oliveira, J., Jesus, M., Sa, N., Santos, P., and Lima, T. (2011). Collective public transportation: Discussing accessibility, mobility and quality of life. Psicologia Sociedade, 23:574–582.
Balcan, D., Gonçalves, B., Hu, H., Ramasco, J. J., Colizza, V., and Vespignani, A. (2010). Modeling the spatial spread of infectious diseases: The global epidemic and mobility computational model. Journal of Computational Science, 1(3):132 – 145.
Braz, T., Maciel, M., Mestre, D. G., Andrade, N., Pires, C. E., Queiroz, A. R., and Santos, V. B. (2018). Estimating inefficiency in bus trip choices from a user perspective with schedule, positioning, and ticketing data. IEEE Transactions on Intelligent Transportation Systems, 19(11):3630–3641.
Cardillo, A., Zanin, M., Gómez-Gardeñes, J., Romance, M., del Amo, A. J. G., and Boccaletti, S. (2013). Modeling the multi-layer nature of the european air transport network: Resilience and passengers re-scheduling under random failures. The European Physical Journal Special Topics.
Ganin, A. A., Kitsak, M., Marchese, D., Keisler, J. M., Seager, T., and Linkov, I. (2017). Resilience and efficiency in transportation networks. Science Advances, 3(12).
Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. SIGMOD Rec., 14(2):47–57.
Huang, L., Yang, Y., Gao, H., Zhao, X., and Du, Z. (2018). Comparing community detection algorithms in transport networks via points of interest. IEEE Access, 6:29729– 29738.
Inman, J. (1849). Navigation and Nautical Astronomy, for the Use of British Seamen. F. J. Rivington, 7 edition.
Instituto Brasileiro de Geografia e Estatı́stica - IBGE (2010). Censo 2010. https://censo2010.ibge.gov.br. [Online; acessado em 07-Novembro-2019].
Labrinidis, A. and Jagadish, H. V. (2012). Challenges and opportunities with big data. Proc. VLDB Endow., 5(12):2032–2033.
Lotero, L., Hurtado, R. G., Florı́a, L. M., and Gómez-Gardeñes, J. (2016). Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes. Royal Society Open Science, 3(10):150654.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. [Online; accessed 13-November-2019].
Marques-Neto, H. T., Xavier, F. H. Z., Xavier, W. Z., Malab, C. H. S., Ziviani, A., Silveira, L. M., and Almeida, J. M. (2018). Understanding human mobility and workload dynamics due to different large-scale events using mobile phone data. Journal of Network Systems Management, 26(4):1079 – 1100.
Mervis, J. (2012). Agencies rally to tackle big data. Science, 336(6077):22–22.
Miranda, H. d. F. and Silva, A. N. R. d. (2012). Benchmarking sustainable urban mobility: The case of Curitiba, Brazil. Transport Policy, 21:141 – 151.
Nature Editorial (2008). Community cleverness required. Nature, 455(7209):1–1.
Newman, M. (2010). Networks: An Introduction. Oxford University Press.
Newman, M. E. J. (2002). Assortative mixing in networks. Phys. Rev. Lett., 89:208701.
Ninis, A. B., Paes de Barros, R., Grosner, D., Franco, S., Rosalém, A., Bugarin, Karina Sayuri Sataka, G., and Jorge, J. (2012). SAE - Relatorio de definicao da classe media.
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., and Mascolo, C. (2012). A tale of many cities: Universal patterns in human urban mobility. PLOS ONE, 7(5):1–10.
Taniguchi, G. and Duarte, F. (2012). Personal smart cards: From transportation to a city smart card—the database integration of public services in Curitiba. In City Competitiveness and Improving Urban Subsystems: Technologies and Applications, pages 217–232. IGI Global.
Urbanização de Curitiba S/A - URBS (2018). URBS em números. https://www.urbs.curitiba.pr.gov.br/institucional/urbs-em-numeros. [Online; acessado em 07-Novembro-2019].
Van den Broeck, W., Gioannini, C., Gonçalves, B., Quaggiotto, M., Colizza, V., and Vespignani, A. (2011). The gleamviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC infectious diseases, 11:37.
Vila, J., Kozievitch, N., Fonseca, K., Gadda, T., Rosa, M., and Gomez-Jr, L. (2016). Urban mobility challenges – an exploratory analysis of public transportation data in Curitiba. Revista de Informática Aplicada, 12:1.
Xu, Y., Belyi, A., Bojic, I., and Ratti, C. (2018). Human mobility and socioeconomic status: Analysis of Singapore and Boston. Computers, Environment and Urban Systems, 72:51 – 67.
Yang, Y., Heppenstall, A., Turner, A., and Comber, A. (2019). Who, where, why and when? using smart card and social media data to understand urban mobility. ISPRS International Journal of Geo-Information, 8(6).
Publicado
07/12/2020
Como Citar
SANTIN, Priscila Louise Leyser; GUBERT, Fernanda Regina; FONSECA, Mauro; MUNARETTO, Anelise; SILVA, Thiago Henrique.
Enriquecendo Modelos de Mobilidade Urbana com Padrões Espaço-Temporais e Estratos Socioeconômicos. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 910-923.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12334.
