Carlo Ratti’s research while affiliated with Politecnico di Milano and other places

Energy poverty affects 550,000 homes in the Netherlands yet policy interventions to alleviate this issue are rare. Therefore, we test two energy coaching interventions in Amsterdam: a static information group (n = 67) which received energy efficient products and one energy-use report, and a smart information group (n = 50), which also had a display providing real-time feedback on energy-use. Results across both groups, show a 75% success rate for alleviating energy poverty. On average homes reduced monthly electricity consumption by 62 kWh (33%), gas by 41 m³ (42%), bills by €104 (53%) and percentage of income spent on energy from 10.1% to 5.3%.

The urban heat island (UHI) phenomenon is recognized as a main urban sustainability problem in the face of a changing climate, affecting human health, energy consumption, and other socio-economic considerations. The UHI can be mitigated by urban greenery, but it needs further investigation of detailed impacts across the urban landscape. The aim was to study UHI and model the relation to greenery in combination with urban grey structures, at a high spatiotemporal resolution across the urban landscape, in Stockholm. Temperature data was collected through opportunistic drive-by sensors on electric three-wheeled taxis. Data on greenview and skyview factors were used to inform on greenery and building density along the roads. During night and morning hours, the surface temperature was in general higher than air temperature, indicating that some densely built-up environments stored heat overnight. Hot zones were unevenly distributed throughout the city, while greenery had a cooling effect, especially when combined with skyview as an inverse measure of building density. Our results provide information on the spatiotemporal distribution of heat that can be used to inform efforts to use greenery for mitigating impacts of UHI on urban residents.

Shared autonomous vehicle (SAV) is expected to enhance urban transportation efficiency through innovative mobility resource management. By developing a comprehensive agent-based simulation framework, this study investigates several key factors influencing fleet size and parking demand for adoption of SAVs in future urban mobility systems. The framework examines the joint impact of both operational (e.g., reservation time and maximum waiting time) and demand-side characteristics (e.g., demand rate and origin-destination balance) on SAV system performance. A two-stage simulation procedure is developed. In the first stage, the ideal fleet size and parking demand are estimated such that the specified travel demand and service quality are satisfied. The derived service design is then implemented in the second stage to assess additional SAV system performance indicators including rejection rate and empty meters traveled. To obtain a holistic understanding of the studied factors, we construct various simulation scenarios based on historical taxi data in central areas of Chengdu, Shanghai (China) and Manhattan of New York City (USA), and build a regression model on the simulation outcomes. The results reveal a general mechanism by which operational characteristics and demand patterns influence SAV fleet and parking size, as well as system performance across cities with distinct structures and layouts. A balanced OD distribution shows a particularly significant impact on reducing fleet size, parking space, and empty meters. Several trade-offs such as the balance between fleet size and service quality are also identified, providing insights into the deployment of SAVs from the perspectives of both operators and regulators.

Shared autonomous vehicle (SAV) is expected to enhance urban transportation efficiency through innovative mobility resource management. By developing a comprehensive agent-based simulation framework, this study investigates several key factors influencing fleet size and parking demand for adoption of SAVs in future urban mobility systems. The framework examines the joint impact of both operational (e.g., reservation time and maximum waiting time) and demand-side characteristics (e.g., demand rate and origin-destination balance) on SAV system performance. A two-stage simulation procedure is developed. In the first stage, the ideal fleet size and parking demand are estimated such that the specified travel demand and service quality are satisfied. The derived service design is then implemented in the second stage to assess additional SAV system performance indicators including rejection rate and empty meters traveled. To obtain a holistic understanding of the studied factors, we construct various simulation scenarios based on historical taxi data in central areas of Chengdu, Shanghai (China) and Manhattan of New York City (USA), and build a regression model on the simulation outcomes. The results reveal a general mechanism by which operational characteristics and demand patterns influence SAV fleet and parking size, as well as system performance across cities with distinct structures and layouts. A balanced OD distribution shows a particularly significant impact on reducing fleet size, parking space, and empty meters. Several trade-offs such as the balance between fleet size and service quality are also identified, providing insights into the deployment of SAVs from the perspectives of both operators and regulators.

Global society faces the pressing question of how to eliminate reliance on fossil fuels while meeting increasing energy demand. In comparison to solar and wind energy, nuclear power has been largely ignored in urban studies research. However, nuclear energy has recently regained attention through the emergence of Small Modular Reactors (SMRs), and as the stakes of decarbonization become increasingly essential. To evaluate situations in which SMRs bring value to urban energy mixes, this paper focuses on Nuclear Batteries (NBs), a specific class of SMRs, that can fit in standard shipping containers. First, we outline an evaluation framework for the use and application of NBs; second, we present use cases for NBs in real-world situations, from disaster relief to grid reinforcement; and third, we discuss the social challenges around this technology.

Evaluating sidewalk accessibility is conventionally a manual and time-consuming task that requires specialized personnel. While recent developments in Visual AI have paved the way for automating data analysis, the lack of sidewalk accessibility datasets remains a significant challenge. This study presents the design and validation of Sidewalk AI Scanner, a web app that enables quick, crowdsourced and low-cost sidewalk mapping. The app enables a participatory approach to data collection through imagery captured using smartphone cameras. Subsequently, dedicated algorithms automatically identify sidewalk features such as width, obstacles or pavement conditions. Though not a replacement for high-resolution sensing methods, this method leverages data crowdsourcing as a strategy to produce a highly scalable, city-level dataset of sidewalk accessibility, offering a novel perspective on the city’s inclusivity; fostering community empowerment and participatory planning. This article is part of the theme issue ‘Co-creating the future: participatory cities and digital governance’.

In recent decades, the population boom in Middle Eastern cities has led to increased motorization and consequent carbon emissions. Cities have been expanding their public transportation systems and promoting walking and biking. However, the scorching temperatures make the active-mobility choice challenging. This study investigates the alignment of the shortest and shadiest paths within 500 and 1000-meter walksheds of metro stations in Dubai, where commuters often walk to their destinations. We developed an optimal shading path method that assigns dynamic rewards to buildings, trees, and indoor shade, which better reflects real-world conditions than existing models that only give shade a constant reward. The results reveal that, on average, the shadiest path is 1.3% longer than the shortest path, with an 8.9% increase in building shade and an 8.8% decrease in sun exposure compared to the shortest path. The findings provide valuable information to planning agencies about where to prioritize shade provision, ultimately enhancing pedestrian comfort and promoting active mobility in warmer cities.