Providing pedestrian accessibility to urban services is a big challenge and a key factor in creating more walkable urban areas. Moreover, it is a critical aspect of climate-resilient urban planning as it is broadly assumed that neighborhoods with greater walkability discourage automobile use and reduce CO₂ emissions. The idea of 15-minute cities, defined as urban environments where most places that residents need to access are within a 15-minute walk, is gaining increasing attention worldwide. Because aspects of urban performance are increasingly quantifiable, generative, and data-driven design approaches can explore broader sets of potential solutions, while optimization can help identify designs with desired properties. This work demonstrates and tests a new approach that combines shape grammars, a formal method for shape generation that facilitates the elaboration of complex patterns and meaningful solutions, with multi-objective optimization. The goal was to optimize the design of urban fabric layouts and the location of amenities to provide 15-minute neighborhood configurations that minimize infrastructure cost (as estimated by cumulative street length) and the number of amenities, while maximizing pedestrian accessibility to urban services (as assessed by overall integration and the average distance from all plots to nearest amenities).

How can computational tools support the proposition of 15-minute cities? This paper examines the implementation of computer-aided approaches that support the analysis and proposition of urban areas where residents can access all their basic needs within a 15-minute walk, bike ride, or public transportation ride—the ultimate goal of a 15-minute city. Although this concept has gained significant attention as a sustainable and equitable urban development model, more efficient implementations require complex spatial and temporal analysis. At the same time, computational approaches such as those within the generative and parametric logic can utilize algorithms to create and test design solutions quickly and efficiently, allowing designers to address a variety of scenarios and options for producing more walkable, integrated, and resilient urban environments. Exploring a systematic review of the scientific literature (n = 244 peer-reviewed empirical studies) on Scopus and Web of Science databases, this paper synthesizes approaches to support the analysis and design of 15-minute cities, emphasizing the use of computer-aided techniques. The work contributes to developing a knowledge base on computational tools for 15-minute cities, highlighting their strengths, limitations, and potential for further development.

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.

Designing urban areas that provide smaller distances to their amenities is a key factor toward more walkable environments. Moreover, this is a critical aspect of climate-resilient urban planning since it is broadly assumed that areas with greater walkability discourage automobile usage and reduce CO₂ emissions. Generative and data-driven design approaches, in turn, increase designers’ ability to explore wider sets of potential solutions. In this sense, identifying designs with an optimized performance out of the vast possibilities that computation can provide is crucial. Shape grammars are a formal method of shape generation that facilitate the elaboration of complex patterns and meaningful designs. This paper hypothesizes that coupling shape grammars with multi-objective optimization can help address trade-offs and decision-making in urban design. It focuses on the pedestrian accessibility and infrastructure cost (as estimated by cumulative street length) trade-off in urban fabrics as a case study to verify the suitability of a grammar-based optimization approach for more dynamic and efficient solution-finding in urban design. Our findings suggest that a grammar-based optimization approach is helpful in addressing urban trade-offs as it could be used to filter the design space and provide optimal alternative fabric layouts with increased pedestrian accessibility and decreased infrastructure cost.

This article focuses on the use of computational tools to provide dynamic assessment and optimized arrangements while planning and discussing interventions in urban areas. The objective is to address the use of algorithmic systems for generating and evaluating urban morphologies guided by Transit-Oriented Development principles. Transit-Oriented Development is an urban development model that considers geometric and measurable parameters for designing sustainable cities. It advocates compact mixed-use neighborhoods within walking distance to a variety of transportation options and amenities, seeking to result in optimized infrastructure provision and energy-efficient low-carbon districts. This article presents algorithmic experiments for the optimization of a rapid transit district, through its urban morphology and services’ location, providing an accurate Transit-Oriented Development modeling. The main findings of this study highlight that the combination of Transit-Oriented Development and algorithmic–parametric tools has the potential to significantly contribute to a process of responsible planning and, ultimately, to mitigate global warming.