Evolutionary Computing for Multi-Objective Sustainable Urban Spatial Planning

Abstract. The processes of urbanisation and climate change are necessitating the transformation of cities towards sustainable cities that are robustly adapted to natural hazards, while simultaneously reducing energy and resource usage to mitigate further climatic change. Frequently such objectives conflict with each other, negatively affecting sustainability as a whole. For example, urban intensification with the intention of lowering transport energy costs has been found to exacerbate urban heat islands, increase flood risk and lead to poor health outcomes. This paper presents the use of an evolutionary computing spatial optimisation framework as one method by which multiple positively and negatively correlated sustainability objectives can be evaluated in time and space to assist urban planning. A coupled genetic algorithm and pareto optimisation approach is used to evaluate spatial configurations of future development against sustainability objectives (e.g., reduced heat risk, minimal flood risk, brown field development, optimal mobility). The developed approach is evaluated in a Greater London Authority (GLA) case study that simulates future urban development patterns that satisfy projected population growth whilst being sensitive to climate induced hazards and current planning policies. The spatial optimization framework developed significantly improves upon the existing urban development plan with the Pareto-front found to be 35% better than the proposed spatial plan for London. However, trade-offs between objectives were found to exist, most notably it was not possible to achieve a pairwise optimization between heat and flood risk and urban sprawl and heat risk.