Plataforma Computacional para Construção de um Banco de Dados de Grafo do Sistema de Transporte de Curitiba
Resumo
O estudo das relações dinâmicas entre as estruturas topológicas de uma rede de transporte e os padrões de mobilidade nesta rede se faz importante para a criação de soluções inovadoras para problemas de confiabilidade, otimização, vulnerabilidade e previsão de tráfego. Todavia, um dos maiores desafios da área de gerência, planejamento e operação do transporte é a manipulação de um grande volume de dados, geralmente provenientes de sensores de localização instalados nos veículos. Este trabalho tem por objetivo modelar o sistema de transporte coletivo da cidade de Curitiba usando uma base de dados de grafos gerada a partir de um repositório de dados abertos com informações da operação diária do transporte. A principal contribuição é o desenvolvimento de uma plataforma computacional para a geração automática da base de grafo a partir do repositório de dados abertos. Resultados ilustrativos de diversas métricas do transporte de Curitiba são apresentados, mostrando o potencial de análise da ferramenta.
Palavras-chave:
banco de dados de grafos, grafo variante no tempo, rede de transporte, cidade inteligente
Referências
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Cattuto, C., Quaggiotto, M., Panisson, A., and Averbuch, A. (2013). Time-varying social networks in a graph database: A Neo4j use case. In First Int. Workshop on Graph Data Management Experiences and Systems, GRADES "13, NY, USA. ACM.
Curzel, J. L., Liiders, R., Fonseca, K. V. O., and Rosa, M. O. (2019). Temporal performance analysis of bus transportation using link streams. Math. Problems in Eng.,2019:6139379.
Ferraz, A. C. P. and Torres, I. G. E. (2004). Transporte público urbano. Rima Editora.
Flocchini, P., Mans, B., and Santoro, N. (2009). Exploration of periodically varying graphs. In Proc. of 20th Int. Symp. on Algo. and Comp. (ISAAC), pages 534-543.
Freeman, L. C. (1977). A set of measures of centrality based on betweeness. Sociometry,40(1):35-41.
Freeman, L. C. (1979). Centrality in social networks: I. conceptual clarification. SocialNetworks 1, page 215-239.
Gallotti, R. and Barthelemy, M. (2015). The multilayer temporal network of public transport in Great Britain. Sci. Data, 2:1-8.
Holme, P. and Saramãki, J. (2012). Temporal networks. Physics Reports, 519(3):97 —125. Temporal Networks.
Izawa, M., Oliveira, F., Cajueiro, D., and Mello, B. (2017). Pendular behavior of public transportation networks. Phys. Rev. E, 96:012309.
Larson, R. C. and Odoni, A. R. (1981). Urban Operations Research. Prentice-Hall.
Maduako, I., Wachowicz, M., and Hanson, T. (2019). STVG: an evolutionary graph framework for analyzing fast-evolving networks. J Big Data, 6(55):1-24.
Martinek, V. and Zemlicka, M. (2009). Speeding up shortest path search in public transport networks. In CEUR Workshop Proceedings, volume 471.
Nicosia, V., Tang, J., Musolesi, M., Russo, G., Mascolo, C., and Latora, V. (2012). Components in time-varying graphs. Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(2):023101.
Reed, D. (2015). How Curitiba's BRT stations sparked a transport revolution — a history of cities in 50 buildings, day 43. The Guardian.
Rodriguez, M. A. and Neubauer, P. (2010). Constructions from dots and lines. Bulletin ofthe American Society for Information Science and Technology, 36:35-41.
Sienkiewicz, J. and Holyst, J. (2005). Statistical analysis of 22 public transport networks in Poland. Phys. Rev. E, 72:046127.
Tang, J., Musolesi, M., Mascolo, C., and Latora, V. (20104). Characterising temporal distance and reachability in mobile and online social networks. ACM Computer Communication Review, 40:118-124.
Tang, J., Scellato, S., Musolesi, M., Mascolo, C., and Latora, V. (2010b). Small-world behavior in time-varying graphs. Phys. Rev. E, 81:055101.
URBS (2019). URBS em números. hnttps://www.urbs.curitiba.pr.gov.br/transporte/rede-integrada-de-transporte acesso em 09/03/2019.
Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y., and Wilkins, D. (2010). A comparison of a graph database and a relational database: A data provenance perspective. In Proc. of the 48th Annual Southeast Regional Conf., ACM SE "10, NY, USA. ACM.
Wessel, N., Allen, J., and Farber, S. (2017). Constructing a routable retrospective transit timetable from a real-time vehicle location feed and GTFS. J. Transp. Geo., 62:92-97.
Xu, Q., Zu, Z., Xu, Z., Zhang, W., and Zheng, T. (2013). Space p-based empirical research on public transport complex networks in 330 cities of China. J. Transp. Sys Eng. & IT,13:193-198.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst., 30(1-7):107-117.
Casteigts, A., Flocchini, P., Quattrociocchi, W., and Santoro, N. (2012). Time-varying graphs and dynamic networks. Int. Journal of Parallel, Emergent and Distributed Systems, 27(5):387-408.
Cattuto, C., Quaggiotto, M., Panisson, A., and Averbuch, A. (2013). Time-varying social networks in a graph database: A Neo4j use case. In First Int. Workshop on Graph Data Management Experiences and Systems, GRADES "13, NY, USA. ACM.
Curzel, J. L., Liiders, R., Fonseca, K. V. O., and Rosa, M. O. (2019). Temporal performance analysis of bus transportation using link streams. Math. Problems in Eng.,2019:6139379.
Ferraz, A. C. P. and Torres, I. G. E. (2004). Transporte público urbano. Rima Editora.
Flocchini, P., Mans, B., and Santoro, N. (2009). Exploration of periodically varying graphs. In Proc. of 20th Int. Symp. on Algo. and Comp. (ISAAC), pages 534-543.
Freeman, L. C. (1977). A set of measures of centrality based on betweeness. Sociometry,40(1):35-41.
Freeman, L. C. (1979). Centrality in social networks: I. conceptual clarification. SocialNetworks 1, page 215-239.
Gallotti, R. and Barthelemy, M. (2015). The multilayer temporal network of public transport in Great Britain. Sci. Data, 2:1-8.
Holme, P. and Saramãki, J. (2012). Temporal networks. Physics Reports, 519(3):97 —125. Temporal Networks.
Izawa, M., Oliveira, F., Cajueiro, D., and Mello, B. (2017). Pendular behavior of public transportation networks. Phys. Rev. E, 96:012309.
Larson, R. C. and Odoni, A. R. (1981). Urban Operations Research. Prentice-Hall.
Maduako, I., Wachowicz, M., and Hanson, T. (2019). STVG: an evolutionary graph framework for analyzing fast-evolving networks. J Big Data, 6(55):1-24.
Martinek, V. and Zemlicka, M. (2009). Speeding up shortest path search in public transport networks. In CEUR Workshop Proceedings, volume 471.
Nicosia, V., Tang, J., Musolesi, M., Russo, G., Mascolo, C., and Latora, V. (2012). Components in time-varying graphs. Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(2):023101.
Reed, D. (2015). How Curitiba's BRT stations sparked a transport revolution — a history of cities in 50 buildings, day 43. The Guardian.
Rodriguez, M. A. and Neubauer, P. (2010). Constructions from dots and lines. Bulletin ofthe American Society for Information Science and Technology, 36:35-41.
Sienkiewicz, J. and Holyst, J. (2005). Statistical analysis of 22 public transport networks in Poland. Phys. Rev. E, 72:046127.
Tang, J., Musolesi, M., Mascolo, C., and Latora, V. (20104). Characterising temporal distance and reachability in mobile and online social networks. ACM Computer Communication Review, 40:118-124.
Tang, J., Scellato, S., Musolesi, M., Mascolo, C., and Latora, V. (2010b). Small-world behavior in time-varying graphs. Phys. Rev. E, 81:055101.
URBS (2019). URBS em números. hnttps://www.urbs.curitiba.pr.gov.br/transporte/rede-integrada-de-transporte acesso em 09/03/2019.
Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y., and Wilkins, D. (2010). A comparison of a graph database and a relational database: A data provenance perspective. In Proc. of the 48th Annual Southeast Regional Conf., ACM SE "10, NY, USA. ACM.
Wessel, N., Allen, J., and Farber, S. (2017). Constructing a routable retrospective transit timetable from a real-time vehicle location feed and GTFS. J. Transp. Geo., 62:92-97.
Xu, Q., Zu, Z., Xu, Z., Zhang, W., and Zheng, T. (2013). Space p-based empirical research on public transport complex networks in 330 cities of China. J. Transp. Sys Eng. & IT,13:193-198.
Publicado
10/12/2020
Como Citar
PEIXOTO, Altieris Marcelino; ROSA, Marcelo de Oliveira; LÜDERS, Ricardo; FONSECA, Keiko Verônica Ono.
Plataforma Computacional para Construção de um Banco de Dados de Grafo do Sistema de Transporte de Curitiba. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 4. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 125-137.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2020.12358.
