Mathematical Modelling of Urban and Smart Cities
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Abstract
Rapid urban growth places pressure on resources, infrastructure, housing, and public services, creating challenges for sustainable development and urban governance. This study extends a classical urban dynamics model by incorporating smart city resource allocation and examines the contribution of mathematical modeling to urban planning and management. The model includes time-dependent smart city resource allocation, S(t), resource availability, R, the resource replenishment rate, α, and the resource depletion rate, β. These components provide a framework for examining relationships among urban growth, resource availability, and allocation decisions. A mathematical modeling and simulation approach was employed, with the model implemented and explored through numerical simulations in MATLAB. Lagos served as the case-study context for interpreting the implications of urban expansion and resource management. The analysis considered the potential role of mathematical models in anticipating urban growth, assessing sustainability challenges, and informing governance decisions. The findings suggest that uncontrolled urban expansion can contribute to resource depletion and associated social challenges. They highlight the importance of aligning resource allocation with population-related demands and the capacity to replenish available resources. The study recommends that government authorities pursue coordinated smart city initiatives addressing infrastructure development, housing provision, public service delivery, and decentralization to support urban stability. Mathematical modeling can assist these efforts by making assumptions about growth and resource dynamics explicit and providing a structured basis for examining planning options. The study concludes that integrating resource considerations into urban dynamics models offers a useful framework for investigating sustainable city development. Further empirical calibration and validation would strengthen confidence in the model’s predictive performance and clarify its applicability to Lagos and other urban contexts.
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References
Ahn, K., & Rakha, H. (2008). The effects of route choice decisions on vehicle energy consumption and emissions. Transportation Research Part D: Transport and Environment, 13(3), 151–167. https://doi.org/10.1016/j.trd.2008.01.005
Almeida, C. M., Monteiro, A. M., & Câmara, G. (2003). Modeling urban land use dynamics: A review [Publication record unverified]. Landscape and Urban Planning, 62(3), 219–234.
Anas, A., Arnott, R., & Small, K. A. (1998). Urban spatial structure. Journal of Economic Literature, 36(3), 1426–1464.
Barabási, A.-L. (2002). Linked: The new science of networks. Perseus Publishing.
Batty, M. (2013). The new science of cities. MIT Press. https://doi.org/10.7551/mitpress/9399.001.0001
Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6. https://doi.org/10.1177/2399808317751169
Chen, C., Xu, X., & Zhang, Y. (2011). Energy modeling and forecasting for smart cities [Publication record unverified]. Energy and Buildings, 43(10), 2893–2901.
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In 2012 45th Hawaii International Conference on System Sciences (pp. 2289–2297). IEEE. https://doi.org/10.1109/HICSS.2012.615
Crooks, A. T., Castle, C. J. E., & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417–430. https://doi.org/10.1016/j.compenvurbsys.2008.09.004
Edwards, D. (2001). Mathematical modeling: A practical approach [Publication record unverified]. Pearson Education.
Fellows, L., & Hipel, K. (2007). Modeling and simulation for sustainable development [Publication record unverified]. Journal of Environmental Management, 85(4), 1063–1075.
Forrester, J. W. (1969). Urban dynamics. MIT Press.
Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanović, N., & Meijers, E. J. (2007). Smart cities: Ranking of European medium-sized cities. Final report. Centre of Regional Science, Vienna University of Technology. https://doi.org/10.34726/3565
González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782. https://doi.org/10.1038/nature06958
Goodchild, M. F. (2010). Twenty years of progress: GIScience in 2010. Journal of Spatial Information Science, (1), 3–20. https://doi.org/10.5311/JOSIS.2010.1.2
Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162. https://doi.org/10.1057/jos.2010.3
Mitchell, W. J. (1999). E-topia: “Urban life, Jim—but not as we know it”. MIT Press.
Newman, M. E. J. (2010). Networks: An introduction. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199206650.001.0001
Sheffi, Y. (1985). Urban transportation networks: Equilibrium analysis with mathematical programming methods. Prentice-Hall.
United Nations, Department of Economic and Social Affairs, Population Division. (2019). World urbanization prospects: The 2018 revision (ST/ESA/SER.A/420). United Nations. https://population.un.org/wup/assets/WUP2018-Report.pdf
Zhang, Y., Yang, H., & Wang, J. (2017). Machine learning algorithms for smart city data analytics [Publication record unverified]. IEEE Access, 5, 5975–5989.
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