Accelerating agent-based computation of complex urban systems

Despite its popularity, agent-based modeling is limited by serious barriers that constrain its usefulness as an exploratory tool. In particular, there is a paucity of systematic approaches for extracting coarse-grained, system-level information as it emerges in direct simulation. This is particularly problematic for agent-based models (ABMs) of complex urban systems in which macroscopic phenomena, such as sprawl, may manifest themselves coarsely from bottom-up dynamics among diverse agent-actors interacting across scales. Often these connections are not known, but treating them is nevertheless crucial in enabling prediction, in supporting decisions, and in facilitating the design, control, and optimization of urban systems. In this article, we describe and implement a metasimulation scheme for extracting macroscopic information from local dynamics of agent-based simulation, which allows acceleration of coarse-scale computing and which may also serve as a precursor to handle emergence in complex urban simulation. We compare direct ABM simulation, population-level equation solutions, and coarse projective integration. We apply the scheme to the simulation of urban sprawl from local drivers of urbanization, urban growth, and population dynamics. Numerical examples of the three approaches are provided to compare their accuracy and efficiency. We find that our metasimulation scheme can significantly accelerate complex urban simulations while maintaining faithful representation of the original model.