Generative Land Use for Human-centric Communities

Generative Land Use in Volpe

The Generative Land Use project aims to revolutionize urban planning by developing a cutting-edge algorithm that generates the ideal land use distribution based on optimizing a given area's economic, cultural, social, and environmental aspects. Our approach is human-centric, meaning that we prioritize the needs and preferences of local communities in our optimization process. By incorporating these factors, we aim to create urban environments that are not only functional but also sustainable and enjoyable places to live, work, and play.

Once we gather metrics about the area's social, economic, environmental, and cultural aspects, we use that information to model an objective function that will be optimized using genetic algorithms and particle swarm optimization techniques. Our data-driven and evidence-based approach allows us to make informed decisions about the optimal distribution of land use. Additionally, we consider any existing land restrictions, such as roads or building placement, to ensure that our plans are feasible and realistic.

We utilize a genetic algorithm approach to generate diversity in our land use distribution. In each generation, the algorithm selects the best individuals from the population and uses them to create the next generation of possible solutions through crossovers and mutations. The mutations involve moving land uses around the area, creating new ones, and adjusting their size. Throughout this process, we ensure no collisions between the different land uses. This allows us to explore various possibilities and find the optimal solution for our human-centric urban planning approach.

In addition to the genetic algorithm approach, we incorporate particle swarm optimization into our land use distribution modeling. In this technique, each land use is represented as a particle, and these particles move around the area in search of an optimal configuration. The particles are attracted to the fixed points of interest, such as parks or elevator shafts. By grouping the land uses around these points of interest, we can create a more cohesive and efficient urban environment that maximizes the benefits for local communities. These techniques also allow us to consider the dynamic nature of urban environments and adapt to changing needs and circumstances over time.

After we have found the optimal distribution of land uses using our genetic algorithm and particle swarm optimization techniques, we need to translate this solution into a 3D model. In this model, we determine the best position for each land use and decide which floor it should be located on, considering any restrictions on the maximum number of floors. Once the 3D model is complete, we can geolocate the results onto a map, providing a more intuitive and visual representation of the land use distribution.

To test and validate the model, we will use agent-based simulations that model the behavior of individuals in the urban environment. These simulations provide valuable insights into how land use distribution impacts the community's social, economic, and environmental aspects, allowing us to refine and improve the model as needed. Ultimately, our approach to human-centric urban planning using genetic algorithms and particle swarm optimization provides a comprehensive and innovative framework for creating sustainable, livable, thriving urban environments.