Detecção de comunidades em redes complexas para identificar gargalos e desperdício de recursos em sistemas de ônibus
Resumo
Recentemente foi constatado a maioria da população do globo terrestre está nas grandes metrópoles. Esse crescimento populacional traz consigo uma série de desafios e o transporte público aparece como uma solução recorrente para atacar problemas de mobilidade nessas grandes cidades. Neste trabalho, foi realizado um estudo de caso para ajudar a compreender as deficiências do transporte público através da mineração de redes complexas que representam oferta e demanda de transportes públicos. Foi conduzido um processo de caracterização de redes de oferta e demanda do sistema de ônibus de uma grande metrópole brasileira e o mesmo lançou uma luz sobre potencial sobrecarga da demanda e desperdício na oferta de recursos que podem ser mitigados com estratégias de equilíbrio entre oferta e demanda.
Referências
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Gordillo, F. (2006). The value of automated fare collection data for transit planning: an example of rail transit od matrix estimation (Doctoral dissertation, Massachusetts Institute of Technology).
Hamon, R., Borgnat, P., Flandrin, P., and Robardet, C. (2013). Networks as signals, with an application to a bike sharing system. In Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE (pp. 611-614). IEEE.
Hua-ling, R. (2007, August). Origin-destination demands estimation in congested dynamic transit networks. In Management Science and Engineering, 2007. ICMSE 2007. International Conference on (pp. 2247-2252). IEEE.
Kleiber, M. (1961). The fire of life. An introduction to animal energetics. The fire of life. An introduction to animal energetics.
Lenormand, M., Tugores, A., Colet, P., and Ramasco, JJ (2014). Tweets na estrada. PloS one, 9 (8), e105407.
Munizaga, M. A., and Palma, C. (2012). Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C: Emerging Technologies, 24, 9-18.
Nadaraya, E. A. (1964). On estimating regression. Theory of Probability & Its Applications, 9(1), 141-142.
Oppenheim, N. (1995). Urban travel demand modeling: from individual choices to general equilibrium. John Wiley and Sons.
Sienkiewicz, J., and Hołyst, J. A. (2005). Statistical analysis of 22 public transport networks in Poland. Physical Review E, 72(4), 046127.
Wang, J., Mo, H., Wang, F., and Jin, F. (2011). Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach. Journal of Transport Geography, 19(4), 712-721.
Watson, G. S. (1964). Smooth regression analysis. Sankhyā: The Indian Journal of Statistics, Series A, 359-372.
Wilkinson, D. M., and Huberman, B. A. (2004). A method for finding communities of related genes. proceedings of the national Academy of sciences, 101(suppl 1), 52415248.
Barabási, A. L., Albert, R., and Jeong, H. (2000). Scale-free characteristics of random networks: the topology of the world-wide web. Physica A: statistical mechanics and its applications, 281(1), 69-77.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008.
Caminha, C., Furtado, F., Pinheiro, V., and Ponte, P. (2016) Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network. In Smart Cities Conference (ISC2), 2016 IEEE International, pp. 1–6, IEEE.
Caminha C, Furtado V, Pequeno THC, Ponte C, Melo HPM, Oliveira EA, et al. (2017) Human mobility in large cities as a proxy for crime. PLoS ONE 12(2): e0171609. DOI: 10.1371/journal.pone.0171609,
Chang, S. K., and Schonfeld, P. M. (1991). Multiple period optimization of bus transit systems. Transportation Research Part B: Methodological, 25(6), 453-478.
Chaves, L. (2015). Analisando a mobilidade de pesquisadores através de registros curriculares na Plataforma Lattes. IV Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2015)
Clauset, A., Shalizi, C. R., and Newman, M. E. (2009). Power-law distributions in empirical data. SIAM review, 51(4), 661-703.
Domencich, T. A., and McFadden, D. (1975). Urban travel demand-a behavioral analysis (No. Monograph).
Elmasry, G. F., and McCann, C. J. (2003, October). Bottleneck discovery in large-scale networks based on the expected value of per-hop delay. In Military Communications Conference, 2003. MILCOM'03. 2003 IEEE (Vol. 1, pp. 405-410). IEEE.
GAO, Z. Y., Wu, J. J., Mao, B. H., and Huang, H. J. (2005). Study on the complexity of traffic networks and related problems [J]. Communication and Transportati0n Systems Engineering and Information, 2, 014.
Girvan, M., and Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12), 7821-7826.
Gordillo, F. (2006). The value of automated fare collection data for transit planning: an example of rail transit od matrix estimation (Doctoral dissertation, Massachusetts Institute of Technology).
Hamon, R., Borgnat, P., Flandrin, P., and Robardet, C. (2013). Networks as signals, with an application to a bike sharing system. In Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE (pp. 611-614). IEEE.
Hua-ling, R. (2007, August). Origin-destination demands estimation in congested dynamic transit networks. In Management Science and Engineering, 2007. ICMSE 2007. International Conference on (pp. 2247-2252). IEEE.
Kleiber, M. (1961). The fire of life. An introduction to animal energetics. The fire of life. An introduction to animal energetics.
Lenormand, M., Tugores, A., Colet, P., and Ramasco, JJ (2014). Tweets na estrada. PloS one, 9 (8), e105407.
Munizaga, M. A., and Palma, C. (2012). Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C: Emerging Technologies, 24, 9-18.
Nadaraya, E. A. (1964). On estimating regression. Theory of Probability & Its Applications, 9(1), 141-142.
Oppenheim, N. (1995). Urban travel demand modeling: from individual choices to general equilibrium. John Wiley and Sons.
Sienkiewicz, J., and Hołyst, J. A. (2005). Statistical analysis of 22 public transport networks in Poland. Physical Review E, 72(4), 046127.
Wang, J., Mo, H., Wang, F., and Jin, F. (2011). Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach. Journal of Transport Geography, 19(4), 712-721.
Watson, G. S. (1964). Smooth regression analysis. Sankhyā: The Indian Journal of Statistics, Series A, 359-372.
Wilkinson, D. M., and Huberman, B. A. (2004). A method for finding communities of related genes. proceedings of the national Academy of sciences, 101(suppl 1), 52415248.
Publicado
02/07/2017
Como Citar
CAMINHA, Carlos; FURTADO, Vasco; PINHEIRO, Vládia; PONTE, Caio.
Detecção de comunidades em redes complexas para identificar gargalos e desperdício de recursos em sistemas de ônibus. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 6. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 532-543.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2017.3262.
