Participatory multi-objective optimization for planning dense and green cities

The consideration of urban ecosystem services becomes increasingly important when planning compact cities. We implement a multi-objective optimization approach to support decision-makers in their efforts to develop green and dense cities. Embedded in a participatory process, the applied genetic algorithm allows us to assess spatial tradeoffs between urban ecosystem services and compactness. The optimization model is embedded in a decision support system for interactive analysis and communication of the results, facilitating the engagement of planners to support sustainable development. We illustrate the process in a multi-level case study in Singapore, a tropical city state aiming to pursue its distinct greening strategy. The whole process, from the problem definition to the obtained solution set, is evaluated using a feedback loop with stakeholders. Using this approach, we identify robust and best-suited urban development locations as well as temporal prioritization schemes evolving around future public transportation nodes.

Automated summary

Considering urban ecosystem services is increasingly important in planning compact cities. A multi-objective optimization approach using genetic algorithm is used to assess spatial tradeoffs between urban ecosystem services and compactness for sustainable development. The process is illustrated through a case study in Singapore where stakeholders' feedback loop is utilized for evaluation.