Simulação de trânsitos intraurbanos em cidades médias brasileiras:inferences for sustainable urban planning in Uberlândia, Minas Gerais, Brazil
DOI:
https://doi.org/10.29393/UR15-6STRA30006Palavras-chave:
Modelagem Baseada em Agentes, Planejamento urbano, Cidades inteligentes, Densidade Urbana, Inteligência ArtificialResumo
Os caóticos processos de urbanização no Brasil acarretaram em inúmeros problemas no espaço urbano atual, como o espraiamento das cidades. A partir desta problemática, emerge a necessidade de se aperfeiçoar métodos de planejamento urbano capazes de garantir o desenvolvimento sustentável. Nesse sentido, entendendo que uma possível solução seja a densificação urbana, o presente trabalho com recorte no bairro Santa Maria em Uberlândia-MG, Brasil, simula com base em agentes a repercussão desta densificação, mais especificamente seus impactos diretos no trânsito local. Os resultados obtidos expressam que a infraestrutura urbana atual não seria capaz de comportar tal crescimento populacional, ocasionando novos problemas como conurbação e sobrecarregamento dos serviços. Portanto, apesar de assertivo, o adensamento deverá ocorrer em etapas e com distribuição uniforme pela cidade, garantindo que nenhuma região sobrecarregue outra para compensar.
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Referências
Adirbas, A. (2019). Façade form-finding with swarm intelligence. Automation in Construction, 99, 140-151.
Batty, M. (2005). Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals. Cambridge: The MIT Press.
Batty, M. (2018). Digital twins. Environment and Planning B. Urban Analytics and City Science, 45.
Bingöl, A., & Çolakoglu, B. (2016). Agent-Based Urban Growth Simulation: A case study on Istanbul. 34th Conference of Education and Research in Computer-Aided Architectural Design in Europe, 41-48, August 2016 Oulu: Ecaade.
Lopes, M., Antunes, C., & Janda, K. (2019). Energy and behaviour: Challenges of a low-carbon future. Academic Press, 321-351.
Cheliotis, K. (2020). An agent-based model of public space use. Computers, Environment and Urban Systems, 81, 101476.
Coates, P., Healy, N., Lamb, C., & Voon, W. (1996). The use of cellular automata to explore bottom-up architectonic rules. Eurographics UK Chapter 14th Annual Conference, 26-28 March 1996 London: Eurographics Association UK.
Dembski, F., Wössner, U., & Yamu, C. (2019, July). Digital twin. Em Virtual Reality and Space Syntax: Civic Engagement and Decision Support for Smart, Sustainable Cities: Proceedings of the 12th International Space Syntax Conference, Beijing, China (pp. 8-13).
Dresch, A., Lacerda, D., & Antunes, J. (2014). Design Science Research: A Method for Science and Technology Advancement. Springer.
Fonseca, M. (2007). Forma Urbana e Uso do Espaço Público. As transformações no centro de Uberlândia. (Tese doutoral). Universidade Politécnica de Cataluña.
Fouladvand, J., Rojas, M. A., Hoppe, T., & Ghorbani, A. (2022). Simulating thermal energy community formation: Institutional enablers outplaying technological choice. Applied Energy, 306, 117897.
França, I. (2007). A cidade média e suas centralidades: O exemplo de Montes Claros no norte de Minas Gerais. (Dissertação de mestrado). Universidade Federal de Uberlândia.
Gilbert, N. (2008). Agent-Based Models. (Quantitative Applications in the Social Sciences). SAGE Publications. https://doi.org/10.4135/978 1412983259
Glock, J. & Gerlach, J. (2023). Berlin Pankow: a 15-min city for everyone? A case study combining accessibility, traffic noise, air pollution, and socio-structural data. European Transport Research Review, 15, 1. https://doi.org/10.1186/s12544-023-00577-2
González-Méndez, M., Olaya, C., Fasolino, I., Grimaldi, M., & Obregón, N. (2021). Agent-Based Modeling for Urban Development Planning based on Human Needs. Conceptual Basis and Model Formulation. Land Use Policy, 101, 105110. https://doi.org/10.1016/j.landusepol.2020.105110
Groat, L., & Wang, D. (2013). Architecture Research Methods. John Wiley & Sons.
Gurram, S., Stuart, A., & Pinjari, A. (2019). Agent-based modeling to estimate exposures to urban air pollution from transportation: exposure disparities and impacts of high-resolution data. Computers, Environment and Urban Systems, 75, 22-34.
Holland, J. (1999). Emergence: from chaos to order. Basic Books.
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA (IBGE). (2021). Censo Brasileiro de 2020. IBGE.
Jencks, C. (1971). Architecture 2000: prediction and methods. Praeguer.
Jia, M. (2019). A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings. Energy and Buildings, 199, 352–367.
Leach, N. (2009). Swarm Urbanism. Em Architectural Design: Digital Cities. Wiley. 56-63. https://doi.org/10.1002/ad.918
López Baeza, J., Carpio-Pinedo, J., Sievert, J., Landwehr, A., Preuner, P., Borgmann, K., Avakumovi?, M., Weissbach, A., Bruns-Berentelg, J. & Noennig, J.R. (2021). Modeling pedestrian flows: Agent-based simulations of pedestrian activity for land use distributions in urban developments. Sustainability, 13(16).
Mitchell, M. (2009). Complexity: a guide tour. Oxford University Press.
Francisco, A., Mohammadi, N., & Taylor, J. E. (2020). Smart city digital twin–enabled energy management: Toward real-time urban building energy benchmarking. Journal of Management in Engineering, 36(2), 04019045.
Mohammadi, N., Vimal, A., & Taylor, J. (2020). Knowledge Discovery in Smart City Digital Twins. Em Proceedings of the 53rd Hawaii International Conference on System Sciences, 1656-1664.
Prefeitura Municipal de Uberlândia. (2010). Plano Diretor de Transporte e Mobilidade. Uberlândia.
PROCESSING SOFTWARE. (2021). https://processing.org/
Read, G., Salmon, P., & Thompson, J. (2020). Using Cognitive Work Analysis to Inform Agent-Based Modelling of Automated Driving. Em Advances in Social Simulation: Looking in the Mirror, 385-390. Springer.
Santos, M. (1993). A Urbanização Brasileira. EDUSP.
Souza, A. (2021). Recursos Hídricos e a Ecologia da Paisagem. Sabesp. http://Site.Sabesp.Com.Br/Uploads/File/Asabesp_doctos/Ecologia_paisagem_completo.Pdf
Thompson, J., Wijnands, J. S., Mavoa, S., Scully, K., & Stevenson, M. R. (2019). Evidence for the ‘safety in density’ effect for cyclists: Validation of agent-based modelling results. Injury Prevention, 25(5), 379-385.
Thompson, J., Read, G. J., Wijnands, J. S., & Salmon, P. M. (2020). The perils of perfect performance; considering the effects of introducing autonomous vehicles on rates of car vs cyclist conflict. Ergonomics, 63(8), 981-996.
Torrens, P. (2022). Agent models of customer journeys on retail high streets. Journal of Economic Interaction and Coordination, 18(1), 87-128.
Uddin, M. N., Wei, H. H., Chi, H. L., & Ni, M. (2021). Influence of occupant behavior for building energy conservation: A systematic review study of diverse modeling and simulation approach. Buildings, 11(2), 41.
United Nations. (2018). World Urbanization Prospects. The 2018 Revision. Department of Economic and Social Affairs, Population Division.
Veloso, P., & Pratschke, A. (2013). Entre forma e performance: a teoria de projeto de Christopher Alexander. Em D. Barros, M. Tosello & D. Sperling (Eds.), Didáctica proyectual y entornos postdigitales: prácticas y reflexiones en escuelas latinoamericanas de arquitectura y diseño, 223-237. SIGraDI.
Xu, W., Huang, X., & Kimm, G. (2021). “Tear down” the fences: developing ABM informed design strategies for ungating closed residential communities developing abm informed design strategies for ungating closed residential communities. Em Proceedings of the 26th CAADRIA Conference, Hong Kong (Vol. 29).
Yang, L., van Dam, K. H., Anvari, B., & de Nazelle, A. (2019). Evaluating the impact of an integrated urban design of transport infrastructure and public space on human behavior and environmental quality: A case study in Beijing. Em D. Payne, J. A. Elkink, N. Friel, T. U. Grund, T. Hochstrasser, P. Lucas & A. Ottewill. (Eds.). Social Simulation for a Digital Society: Applications and Innovations in Computational Social Science (pp. 121-133). Springer Nature.
Yu, S. (2022). Agent-based modelling using survey data to simulate occupancy patterns and occupant interactions for workplace design. Building and Environment, 224, 109519.
Yurrita, M., Grignard, A., Alonso, L., & Larson, K. (2022, janeiro). Real-Time Inference of Urban Metrics Applying Machine Learning to an Agent-Based Model Coupling Mobility Mode and Housing Choice. Em Multi-Agent-Based Simulation XXII: 22nd International Workshop, MABS 2021, Virtual Event, May 3-7, 2021, Revised Selected Papers. 125-138. Springer.
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Copyright (c) 2022 Raphaela Ferreira, Karen Santini Dias Passos, André Luís de Araujo
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