ABSTRACT This article presents LTS-BikePlan, a data-driven tool based on the Level of Traffic Stress (LTS) methodology, which we developed to improve the planning of bicycle infrastructure and assess the comfort and safety of cyclists. This application provides essential insights into the cyclability of cities (particularly in Italy, where it was developed) by integrating data from public administration sources and GIS resources, including OpenStreetMap (OSM) and the Digital Elevation Model. We showcase LTS-BikePlan use in a range of cycling situations with case studies in the Italian cities of Trento and Bolzano. This study addresses the limited integration of stress-level mapping and safety analysis in current planning tools and demonstrates that many routes and intersections have intermediate traffic stress levels (LTS 3), making it difficult for beginning cyclists to navigate dense traffic and diverse topography. In contrast, urban and residential environments tend to have lower stress levels. However, the findings show that low-stress situations are not always associated with fewer collisions, emphasizing the need of high-quality infrastructure in improving the safety of urban cycling safety. They take a novel approach to identifying critical high-risk areas with high collision potential in low-stress zones, employing predictive models for infrastructure improvement and collision risk assessment to advance urban cycling research and assist policymakers in improving cycling safety and sustainability.

ABSTRACT Despite ample criticisms, cities are increasingly using closed-circuit television (CCTV) cameras and artificial intelligence (AI) technologies, justified by citing urban safety. How safety is evoked within such technology-laden projects is little studied. To address this gap, we empirically investigated the Bengaluru Safe City Project, an AI-powered surveillance system. We qualitatively analyzed key documents describing the surveillance system’s intended purposes and technological capabilities. Our analysis uncovers three themes: gendered notions of safety, the cameras’ gaze, and the need for human engagement with the system. We show how socio-politically informed priorities influence the safety notions within the surveillance system.

ABSTRACT The rapid advancement of large language models (LLMs) has sparked growing interest in their potential applications within urban planning. These models offer novel capabilities in natural language processing tasks, potentially providing advanced support across various aspects of urban planning. Despite the promises, little is known about how and to what extent LLMs have been applied in the planning literature. This study addresses this gap by conducting a comprehensive literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The review depicts five key domains where LLMs are being applied in urban planning: (a) planning and management; (b) public services and participation; (c) transportation and urban mobility; (d) environmental monitoring and sustainability; and (e) urban design and architecture. Additionally, the review identifies four essential characteristics that LLMs should possess to be effective in urban planning: (a) professionalism; (b) inclusivity; (c) trustworthiness; and (e) convenience. Considering the findings, a conceptual framework is developed that illustrates how these characteristics enhance the flexibility and effectiveness of LLMs in supporting urban planning tasks. This framework offers a theoretical foundation for urban planners to effectively integrate LLMs into practice and provides a roadmap for future research and technological innovations in the application of LLMs within urban environments.

ABSTRACT Smart city technologies present opportunities for local governments, while also exposing them to new risks. While privacy threats and algorithmic biases have received significant attention within urban studies, cybersecurity has received far less. Our study examines systematic variation across smart city technologies in cyber-risks. We administered a survey to cybersecurity experts in which they ranked the vulnerabilities, threats, and consequences of potential cyberattacks for nine different smart city technologies. Analysis using a Markov-based approach indicates that cyber-risks vary greatly across technologies. Notably, technologies that are more vulnerable in technological terms are also viewed as most likely to be impactful if an attack were to occur, and most likely to be targeted by nation-states. Case studies of emergency or security alerts and smart water meters—ranked as most risky and less risky respectively—underscore and help explain this variation, while also highlighting that local governments vary in their ability to vet vendors, and that cyber-protections are typically subordinated to other concerns in procurement decisions. Our study illustrates the utility of disaggregating the concept of the “smart city,” and the analytic utility of comparisons across technologies rather than geographic units and provides a useful framework for local government decision-making.

ABSTRACT This article explores the relationship between artistic experimentation of technology and blockchain-based governance systems. It is based on a case study of the BeeDAO project. Imagining new forms of interspecies urban dwelling, the artists and activists behind BeeDAO proposed to create a blockchain-based governance model (a Distributed Autonomous Organization [DAO]) dedicated to improving the living conditions of bees. We analyze how artistic imaginaries of technology may envision new governance frameworks to shape sustainable futures. We characterize as techno-ecological agentivity the reliance on blockchain systems to design organizational models where other-than-human living entities would be integrated into the participatory governance of natural ecosystems. Our analysis highlights that BeeDAO enacts a cybernetic vision of governance, emulating a techno-determinist utopia of social transformation. The project faced practical challenges due to the intricacies of human collaboration, technological dependencies, and the complexities of urban ecologies. Yet as an artistic initiative, it significantly contributed to the urban renovation project of Haus der Statistik by using blockchain as a narration-building tool.

ABSTRACT This article examines the evolving perceptions of cities in the context of burgeoning artificial intelligence (AI) technologies. First, the work explores recently developed theories, such as those on urban assemblage, AI urbanism, and autonomous cities that offer useful interpretations for the recent material transformations and in conceiving cities as increasingly hybrid and complex entities. Secondly, we aim to reflect, both from a theoretical and an empirical point of view, on urban narratives that emerge around the relationship between “AI and the City.” Through the semantic and network analysis applied to a sample of data extracted from the X.com (formerly Twitter) archive, this article investigates the visions of future cities considering the opinions of people. Results reveal a spectrum of views about technological and socioeconomic aspects and narratives, which oscillate between techno-euphoria and technophobia. These findings underscore the importance of incorporating public sentiment into urban policy decisions. While AI investments can offer benefits for the functioning of urban assemblages, understanding and integrating people’s experiences and perspectives are vital for the conceptualization of new and inclusive urban models. This approach paves the way for policies that resonate more closely with the perceptions and expectations of urban dwellers in an AI-integrated future.

ABSTRACT Geospatial Artificial Intelligence (GeoAI) systems are increasingly used by local governments to manage urban planning activities. However, there is a lack of clear guidelines for responsible artificial intelligence (AI) implementation. We address this gap by applying the task-data-user-technology fit theory. First, we create a conceptual framework that translates ethical AI principles into practical system requirements. Second, we apply this framework through a case study analysis. We examine the geospatial AI system that the city of eThekwini, South Africa, has deployed to monitor their informal settlements. Based on expert interviews, our analysis highlights the ethical trade-offs inherent in the interactions between fits, data, tasks, users, and AI interactions, especially in an inherently localized and multi-stakeholder field such as urban planning. Additionally, we show how these fits are interconnected and cross-dependent. For example, the technical skills and resources of users can influence all other fits. Our study also reveals how task reconfiguration, user adaptability, and data improvement can enhance or hinder alignment with technology. From these insights, we introduce practical and theoretical recommendations for responsible AI development, adoption, and use.

ABSTRACT This article examines the implications of integrating Artificial Intelligence (AI) into urban infrastructures, using Vienna's Intelligent Pedestrian Traffic Light (IPTL) as a focal point. As cities globally embrace AI, Urban AI (UAI) emerges, reshaping urban life. The central inquiry is: How does AI in urban governance alter urban spaces, and what new political dynamics arise? The IPTL, a UAI exemplar, uses advanced surveillance and algorithms to enhance pedestrian signaling, aiming to reduce jaywalking and optimize traffic. Through mixed-methods, combining theoretical and empirical approaches, the study delves into expert narratives, public perceptions, and the technology's design. It uncovers the transformative effects of such technologies on urban spaces and human–vehicle interactions. Findings highlight that while the IPTL promises urban governance advancements, it may overlook diverse urban needs, as seen in the neglect of the visually impaired. The research accentuates the imperative for a more encompassing, inclusive lens in AI-centric urban endeavors, underscoring the significance of recognizing and addressing the myriad rhythms and requirements of all urban residents. The article culminates in a call for a reconstructivist perspective on UAI, championing the democratization of urban realms and foregrounding pedestrian-centric visions in upcoming mobility ventures.

ABSTRACT While smart city initiatives have characterized global urbanization from the 1990s to the 2020s, nowadays a novel artificial intelligence (AI) enabled approach to urban governance is rapidly emerging, thereby shaping the governance and planning of present and future cities. This urban phenomenon can be understood theoretically through the notion of anticipatory governance, and empirically through so-called City Brain systems. This is particularly evident in China where a wide range of urban AI solutions are being experimented at different scales which this paper seeks to illustrate. First, by building a database of AI-urban policy texts associated with Chinese cities, we capture and discuss the national network of discourses surrounding urban AI. Second, we draw on empirical research conducted in Beijing to examine an existing city brain project and explain its impact on urban governance. Our study reveals the multi-scalar policy landscape of urban AI transitions in China and sheds light on the extent to which emerging AI technologies such as city brains can proactively address urban problems, thus developing an understanding of anticipatory governance in the age of urban AI. We conclude the paper by reflecting on the complex corporate-state relations embedded in the co-production of city brains, their diffusion and impact beyond China.