Construction of New-Type Urbanization (NTU) is one of China's key development strategies, significantly affecting low-carbon and sustainable land use. This study employs the Epsilon-Based Measure (EBM) model to calculate the Efficient, Low-Carbon Use of Urban Land (ELUUL), and empirically test the driving effect of the New-Type Urbanization Pilot Policy (NTUPP) on ELUUL using the Difference-in-Differences (DID) model. The study's findings show that NTUPP has significantly promoted ELUUL, and a series of robustness and endogeneity analyses support this conclusion. On average, the implementation of NTUPP has increased ELUUL by 3.03 percent. NTUPP primarily benefits ELUUL via income effects, transportation infrastructure, and economic agglomeration. Compared to the eastern regions, small- and medium-sized cities, and non-resource-based cities, NTUPP promotes ELUUL in the central and western regions, large cities, and resource-based cities. The Chinese government's recognition of NTU's low-carbon characteristics can significantly strengthen NTUPP's role in promoting ELUUL, whereas recognition of NTU's intelligent and civilized characteristics has a less enhancing effect. Public awareness of NTU's green attributes can also help NTUPP promote ELUUL. Furthermore, compared to cities with high levels of educational intervention, low levels of financial development, non-land inspection pilot cities, and planning coordination pilot cities, NTUPP promotes ELUUL more effectively. The study's results provide theoretical and practical knowledge for urbanizing developing countries to promote efficient land use.
Gen AI is transforming city and urban governance in a way that enhances decision-making, management of infrastructure, and delivery of services to the population. This article considers the role of Gen AI in Singapore and Barcelona and determines that AI has a positive effect in Singapore in terms of traffic movement and healthcare and improving waste management and energy consumption in Barcelona. Governance models have a significant impact on the efficiency of AI: a centralized governmental body in Singapore would allow achieving the desired effect more quickly, and the participatory benefit of Barcelona can decelerate its implementation. Using the Diffusion of Innovations (DOI) Theory, the adoption behaviors and ethical consequences of AI were determined. Although the efficiency gains, which are made by AI, are impressive, there are instances where its impact is excessive to the extent of putting issues of trust in AI and fairness. Some of the recommendations are to enhance AI risk assessment, empower the people and hold the algorithms accountable in order to bring future smart city projects to democratic values and social justice.
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.
This article presents the Smart Participation Protocol (SPP), a socio-technical methodology designed to support participatory urban governance by integrating design thinking with Social Urban Digital Twin (SUDT) technologies. Developed through action research in Hadar, a historically aging and socioeconomically challenged neighborhood in Haifa, Israel, the SPP addresses the urgent need to align smart city tools with social and welfare priorities in vulnerable urban settings.The protocol unfolds through five milestones, aligned with the phases of design thinking, and involves immersive, collaborative engagement with local stakeholders. The SUDT component integrates spatialized data from municipalities, civil society, and residents, enabling shared analysis of complex urban conditions. This article focuses on two key insights from the reflexive phase of the study. First, it unpacks the power dynamics and negotiations of authority that surfaced during the formation of the cross-sectoral partnership. Second, it examines how participants' initial resistance to unfamiliar technologies evolved into meaningful engagement as they recognized the embedded social-scientific logics behind the tools.These findings demonstrate the transformative potential of digital technologies when embedded in co-creative, place-based urban policymaking. The SPP offers a model for grounding smart city innovation in local knowledge and inclusive governance, particularly in underrepresented urban contexts.
The operation of automated buses (ABs) as a feeder transport service has been advocated as one of the most suitable ways to integrate shared automated vehicles into the current public transportation system. Previous studies based on pilot demonstrations have explored public attitudes and perceptions toward ABs, but most of those studies have relied on cross-sectional data, which cannot capture changes in attitudes over time. To address this gap, a one-year, three-wave panel survey was conducted in Stockholm, Sweden, with a focus on passengers' real-world experiences in relation to an automated bus operating in mixed traffic on public roads. Latent curve analysis was applied to the longitudinal data to examine the evolution of service evaluations. Although the findings indicated that passengers initially rated speed and travel time unsatisfactorily, these evaluations showed steady improvement over time. In contrast, perceptions of safety, comfort, and convenience were positive at the outset but underwent a moderate decline throughout the study period. Current public transport users and tech-savvy individuals were identified as the primary user groups most likely to adopt ABs in the future.
Despite extensive investment in smart city infrastructure, adoption remains uneven across urban populations. While existing research has examined individual demographic factors influencing smart city adoption, the role of neighborhood-level context as a distinct determinant remains underexplored. This study addresses this gap by investigating how neighborhood residency, beyond individual demographics, shapes engagement with municipal digital technologies. Using data from a representative telephone survey (n = 489) across four socioeconomically diverse neighborhoods in Tel Aviv, we tested whether neighborhood context provides explanatory power beyond demographic variables alone. Using structural equation modeling (SEM), we found that including neighborhoods in the adoption model improves model fit by nearly 20 percentage points relative to a model including only demographic factors. Neighborhood residency is associated with the use of digital services, technological proficiency (TP), and privacy concerns (PC), which, in turn, mediate attitudes toward smart city adoption. These attitudes were then correlated with administrative data on the use of municipal smart city services. An additional analysis revealed that, although user-facing digital services exhibit similar socioeconomic gradients, municipality-led infrastructure (e.g., bike-sharing) shows a more equitable distribution, suggesting distinct equity dynamics across smart city service types. We conclude the paper by suggesting that neighborhoods can be treated as the central unit for studying, designing, and deploying smart city technologies.
Digital technology has become an integral aspect of contemporary cities, especially with the rise of smart city initiatives. Over the past two decades, researchers and practitioners have studied the potential of large public screens, also known as urban screens, in transforming public spaces and shaping the urban experience. This article contributes to the debate by discussing urban screens in their entwinement with the production of capitalist space. It proposes a conceptual model to explain how different actors, including property owners, public screen owners, and brand holders, collaborate to realize class monopoly rent (CRM), using Times Square as a case study. The article also discusses the challenges associated with urban screen by presenting Nightscreen gasometer in Germany, Federation Square in Australia, and Ituita in Brazil. The exploratory nature of the article invites further research to better understand urban screens as contested elements in the production of just cities.
This article presents the results of an evaluation of the impact of transportation automation on the performance of large-scale regional transportation systems. The methodology integrates capacity improvement estimates from connected and autonomous vehicle (CAV) penetration in the traffic mix, estimated using microscopic traffic simulation, into a regional travel demand model (TDM). Thus, it takes advantage of the accuracy of microscopic traffic simulation models in capturing CAV interactions at a high resolution and the scalability of regional TDMs enabling the evaluation of the impact of CAVs at the regional level. Three scenarios are considered in this analysis, assuming the gradual adoption of CAVs in the Dallas-Fort Worth (DFW) region. The first scenario evaluates the CAVs' impact on roadway network performance, assuming that CAVs will enhance network capacity and reduce the value of time (VOT) for drivers. The second scenario investigates the benefits of the connected vehicle-intersection systems. The last scenario investigates the effect of CAV adoption on population and employment distribution. In this scenario, the increased travel convenience and reduced travel delays might cause travelers to make longer trips, affecting their residence and job location choices. The analysis shows that adopting CAVs generally increases the vehicle-miles traveled, reduces the vehicle hour (VH) traveled, increases the average speed, and reduces the total daily delay.
Integrating deep generative models (DGMs) into architectural and urban-form generation is an innovative approach to support the design process. However, whether these applications are what designers need is rarely considered. To capture the user acceptance and visions of applying DGMs to architectural and urban-form generation, survey research was conducted. Drawing on the technology acceptance model (TAM), this study examines factors affecting user acceptance. TAM in this application technology is validated. The results indicate participants' evaluation of perceived usefulness (PU), attitude towards use (ATU), and intention to use (ITU) are modestly positive, while that of perceived ease of use (PEOU) is neutral, in terms of adopting DGMs in architectural and urban-form generation. Participants further display a mild and positive vision of integrating topology, space syntax, and typology in future adoption. Based on the result, a conceptual framework of DGMs-aided architectural and urban-form generation is proposed. The framework can be developed into different versions for beginners, intermediates, and experts in distinct areas considering their local cultural backgrounds to improve user-friendliness. The findings provide actionable insights for the development of user-focused DGM tools in architectural and urban-form generation.
Urban mode choice is either analyzed by subjective preference surveys, which are often biased, or by travel data processing, which misses revealing the travelers' perceptions and intentions. The current article aims to amplify the classic survey-based mode choice analysis by providing objective thresholds for several tangible criteria of transport modes thus reducing the subjective bias. The proposed approach uses a combination of the best-worst and the preference ranking organization method for enrichment evaluations-geometrical analysis for interactive aid (GAIA) methods to evaluate and outrank the available transport modes in an urban environment, i.e., the city of Budapest. The results demonstrate that underground and tram have prominent preference over the bus mode. Regarding the criteria, "Journey time" and "Waiting time" are the most crucial, while "Comfort at stop" and "Safety of stops" are the least important parameters. Practically, it means that track-based transportation systems should be primarily ameliorated in big cities, especially due to their speed and frequency. The introduced methodology and survey procedure can be adopted to other cities worldwide to help transportation planners in their decision-making for ameliorating urban transportation.
With urban populations steadily rising, densification processes become increasingly inevitable. The creation of dense urban settings has the potential to influence the wellbeing of residents, with potential variations across countries and cultures. This study aims to assess the differences and similarities in subjective wellbeing (SWB) assessments among residents of three cities: Haifa, Israel; Nantes, France; and New York City, USA, utilizing behavioral experiments in virtual environments. The results indicate that participants from different countries exhibit comparable spatial needs and preferences, although disparities were observed in street types and building morphologies. The insights gleaned from this study have the potential of informed design relating to locality of future urban environments, placing a significant emphasis on enhancing wellbeing.
This article presents a deep learning-based model to analyze and predict parking durations in public parking lots within Hangang Park, thereby enhancing the design and management efficiency of urban parking facilities. While public parking lots are essential for managing urban traffic and user flow, existing systems often lack the ability to incorporate spatial variation and accurately forecast demand. To address these limitations, a prediction model was developed using real-world parking data collected over five years, combined with temporal and meteorological variables such as day of the week, time of day, temperature, and solar radiation. A Transformer-based model was employed for its ability to handle complex, multivariate time-series data. The model outperformed the benchmark long short-term memory (LSTM) model in all performance metrics, achieving lower mean absolute error (MAE; 5.262), root mean square error (RMSE; 5.295), and a higher R-square (0.501), highlighting its superior predictive accuracy and generalization. Attention weight analysis further revealed that weather conditions and regional characteristics were the most influential factors in predicting parking duration. The proposed framework enables more precise demand forecasting and supports dynamic space allocation strategies. These findings offer valuable insights for optimizing the operation of public parking facilities and contribute to the development of smarter, more responsive urban mobility systems.
Drawing on 16 interviews across the Brno Metropolitan Area (BMA) in the Czech Republic, this article clarifies local actors' perceptions of the smart city (SC) concept, examines the roles of metropolitan and regional governance in advancing it, and documents the implementation of, and experience with, smart projects in both the core city of Brno and its hinterland municipalities. We distinguish between predominantly technocentric and holistic perceptions of the SC concept. While many actors view "smart city" as an overhyped term, tainted by corporate interests and deliberately omitted from the BMA's strategic document, it still emerges in practice through EU-funded Integrated Territorial Investments and other local initiatives. Our analysis highlights the joint regional platform for developing smart measures, compensating for the BMA's limited institutional and financial capacity. Individual municipal SC projects are motivated by grant availability, cost savings, environmental and social objectives, and the goal of enhancing residents' quality of life. COVID-19 accelerated digital adoption, improving communication and access to public services; however, these initiatives sometimes suffer from low citizen uptake and face fiscal and staffing constraints in hinterland municipalities. The article contributes to debates on smart cities and smart territories by offering insights for policymakers and practitioners.
As digital technology becomes deeply integrated into urban life, research on the urban experience that is based on user-generated content (UGC) has emerged as an important pathway for understanding the production of urban space in the digital age. However, existing research lacks frameworks for assessing urban experience and an in-depth understanding of the mechanisms of physical-digital space interactions. A Citywalk is a hybrid practice that integrates urban exploration, visual documentation, and society sharing via digital technologies. This study takes Shanghai's Citywalk practices as a case study, integrating 14,213 posts from Weibo and Xiaohongshu, employing structured topic modeling (STM) to identify 14 core experience themes, and developing an Experience-Quality Index (EQI) that combines five dimensions: communication heat, interaction depth, sentiment score, Point of Interest (POI) diversity, and thematic contribution. The findings reveal that multi-thematic content demonstrates significantly higher experience-quality than single-theme content (0.1333 vs. 0.1055), weekday experience-quality exceeds that of weekends and holidays (0.120 vs. 0.117), and experience-quality shows a significant distribution of spatial clustering (Moran's I = 0.092). This study proposes a "physical-digital bidirectional interaction" theoretical framework, extending spatial production theory to the digital age, providing new methodological pathways for UGC data-based urban experience assessment, and offering data-driven strategic support for digitalized urban planning.
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.
Urban construction faces increasing pressure to meet complex regulatory requirements and ambitious sustainability goals, especially within the context of rapidly evolving smart cities. This paper introduces a novel AI-powered Sustainable Building Bot (SBB), a modular system designed to enhance regulatory intelligence (RI) and sustainability assessment intelligence (SAI). Leveraging generative AI technologies, notably LangChain and GPT-3.5, the SBB comprises 10 bot variants and a replicable evaluation framework. Results demonstrate significant gains over manual methods, including faster response times, fewer errors, and improved productivity. However, human oversight remains essential to ensure contextual accuracy, ethical use, and alignment with evolving regulatory landscapes.
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.
