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Urban green infrastructure, especially trees, are widely regarded as one of the most effective ways to reduce urban temperatures in heatwaves and alleviate the adverse impacts of extreme heat events on human health and well-being. Nevertheless, urban planners and decision-makers are still lacking methods and tools to spatially evaluate the cooling effects of urban green spaces and exploit them to assess greening strategies at the urban agglomeration scale. This article introduces a novel spatially explicit approach to simulate urban greening scenarios by increasing the tree canopy cover in the existing urban fabric and evaluating their heat mitigation potential. The latter is achieved by applying the InVEST urban cooling model to the synthetic land use/land cover maps generated for the greening scenarios. A case study in the urban agglomeration of Lausanne, Switzerland, illustrates the development of tree canopy scenarios following distinct spatial distribution strategies. The spatial pattern of the tree canopy strongly influences the human exposure to the highest temperatures, and small increases in the abundance of tree canopy cover with the appropriate spatial configuration can have major impacts on human health and well-being. The proposed approach supports urban planning and the design of nature-based solutions to enhance climate resilience.

Motorcycle taxis operate in a non-regulated, uncontrolled environment, raising public health, environmental and economic issues. Consideration of such issues requires an in-depth understanding of motorcycle taxis’ organizational and operational features. Urban sprawl, a difficult economic context and demographic pressure have led to an increase in motorcycle taxi activities in the Republic of Haiti. This study presents a novel approach to analyzing informal modes of transport using qualitative data from semi-directive interviews and observations with a spatial analysis of motorcycle taxi activities by GPS tracking. The analysis is comprised of three phases. First, a suitable tracking device and fixing method were tested and selected. In the second, 12 drivers were equipped with charged devices on a daily basis. The interviews were conducted simultaneously. Finally, we draw four main results using the Haversine method and calculating the inactivity rate. Given a median action range of

Modern cities dynamically face several challenges including digitalization, sustainability, resilience and economic development. Urban planners and designers must develop urban forms that address these challenges. With the integration of new communication and information technologies (Smartphone, GIS, Drones, IoT, Sensors, etc.), urban activities have generated large volumes of urban data. The rapid growth in terms of collection and big data storage capacities combined with the ever-increasing computational power of modern machines have made possible their efficient treatment using machine (ML) and deep learning (DL) algorithms. The emergence of such groundbreaking methods has in turn helped to address the challenges of modern-day cities in several domains (health, security, mobility, etc). ML algorithms have been proposed to model the urban form’s indicators for intelligent urban planning decision making. They have been proven to perform better than the traditional methods. However, the potential of ML has not yet been fully explored in research for urban planning decision support. This paper presents a comprehensive review of ML applications for mitigating the challenges of modern cities planning. First and foremost, an overview of the urban forms, sources of urban data, the ML and DL techniques as well as their potential in solving the aforementioned challenges. For each ML method, we will highlight it working principle, advantages, disadvantages and potential applications using comparative tables. Finally, we will discuss the issues and challenges of ML methods in urban form’s modeling while ultimately advocating some future research directions.