Search Results (88)

China’s rapid urbanization has driven tremendous socioeconomic development while posing new forms of social–spatial inequalities that challenge environmental sustainability and spatial justice. This study investigates urban park-accessibility patterns across 10 s-tier provincial capital cities in China, examining how these patterns relate to housing-price dynamics to reveal diverse manifestations of social–spatial (in)justice. Using comprehensive spatial analysis grounded in distributive justice principles, we measure park accessibility through multiple metrics: distance to the nearest park, park size, and the number of parks within a 15 min walk from residential communities. Our findings reveal significant variation in park accessibility across these cities, with distinctive patterns emerging in the relationship between housing prices and park access that reflect different forms of social–spatial exclusion and inclusion. While most cities demonstrate an unbalanced spatial distribution of parks, they exhibit different forms of this disparity. Some cities show consistent park access across housing-price categories, while others display correlations between high housing prices and superior park accessibility. We argue that these divergent patterns reflect each city’s unique combination of economic development trajectory, politically strategic positioning within national urban hierarchies, and geographical constraints. Through this comparative analysis of second-tier cities, this study contributes to broader understandings of social–spatial (in)justice and urban environmental inequalities within China’s urbanization process, highlighting the need for place-specific approaches to achieving equitable access to urban amenities. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

Urban green spaces (UGS) are essential for residents’ well-being, environmental quality, and social cohesion. However, previous studies have typically employed undifferentiated analytical frameworks, overlooking UGS types and failing to adequately measure the structural disparities of different UGS types within residents’ walking distance. To address this, this study integrates Gaussian Two-Step Floating Catchment Area models, Simpson’s index, and the Gini coefficient to construct an accessibility–diversity–equality assessment framework for UGS. This study conducted an analysis of accessibility, diversity, and equity for various types of UGSs under pedestrian conditions, using the high-density city of Shenzhen, China as a case study. Results reveal high inequality in accessibility to most UGS types within 15 min to 30 min walking range, except residential green spaces, which show moderate-high inequality (Gini coefficient: 0.4–0.6). Encouragingly, UGS diversity performs well, with over 80% of residents able to access three or more UGS types within walking distance. These findings highlight the heterogeneous UGS supply and provide actionable insights for optimizing green space allocation to support healthy urban development. Full article

Natural and man-made disasters are occurring more frequently, making household emergency preparedness essential for an effective response. Enrollees of the World Trade Center Health Registry have been found to be less prepared than the US national average despite their prior disaster exposure. The purpose of this study was to evaluate and compare the effectiveness of two interventions—a mailed brochure and a structured phone call—for increasing emergency preparedness knowledge among this population. We conducted a two-arm parallel group trial between February 2019 and August 2020. Participants were Registry enrollees who completed the Wave 4 Registry (2015–2016) survey, whose primary language was English or Spanish, who lived in New York City, and who did not report being a rescue and recovery worker affiliated with FDNY or NYPD. Enrollees were randomized to receive either a brochure by mail summarizing the components of emergency preparedness or a 15 min phone call describing the same. The primary outcome measure was the number of “yes” responses to the ten-item CDC CASPER emergency preparedness questionnaire, measured at baseline and post-intervention. Enrollees were sequentially alternatively assigned to either the brochure or phone call groups. In total, 705 enrollees were assigned to the brochure (n = 353) or phone call (n = 352) groups, and a total of 702 enrollees were analyzed. The Incident Rate Ratio (IRR) for the effect of time was 1.17 (95% CI = (1.14, 1.20)) and for intervention was 1.00 (95% CI = (0.95, 1.05)) Both the brochure and phone call interventions improved knowledge of emergency preparedness from baseline to post-intervention assessment, and to the same extent. Full article

Textile dyeing wastewater is one of the most challenging industrial effluents to treat due to its high concentrations of persistent organic compounds and nitrogenous substances. Conventional treatment methods often fall short in achieving both sufficient removal efficiency and environmental safety. In this study, we aimed to remove the total nitrogen (T-N) and total organic carbon (TOC) of dyeing wastewater from an industrial complex in D City, Korea, by applying bipolar and packed bipolar electrolysis using aluminum (Al) electrodes and activated carbon (AC). The system was operated for 60 min under varying conditions of applied voltage (5–15 V), electrolyte type and concentration (non-addition, NaCl 5 mM, NaCl 10 mM, Na₂SO₄ 5 mM, Na₂SO₄ 10 mM), and AC packing amount (non-addition or 100 g/L). The highest T-N and TOC removal efficiencies were observed at 15 V, reaching 69.53% and 63.68%, respectively. Electrolyte addition significantly improved initial treatment performance, with NaCl 10 mM showing the best results. However, Al leaching also increased, from 549.83 mg/L (non-addition) to 623.06 mg/L (NaCl 10 mM). When AC was used without electrolysis (control experiment), the T-N and TOC removal efficiencies were limited to 30.24% and 29.86%, respectively. In contrast, AC packing combined with 15 V electrolysis under non-addition achieved 86.04% T-N and 77.98% TOC removal, while also reducing Al leaching by 40.12%. These results suggested that electrochemical treatment with AC packing under non-addition conditions offers the best balance between high treatment efficiency and low environmental impact. These findings demonstrate that the synergistic use of packed activated carbon and electrochemical treatment under additive-free conditions can overcome the limitations of conventional methods. This study contributes to the development of more sustainable and effective technologies for treating high-strength industrial wastewater. Full article

Pedestrian safety is explored within the framework of 15 min cities, with a focus on behavioural differences between blind and sighted individuals. Utilising the pedestrian behaviour scale (PBS), self-reported pedestrian behaviours were analysed using a 5-point Likert scale. A sample of six blind pedestrians was compared with 502 sighted individuals, identifying distinct behavioural patterns across four dimensions: transgression, lapses, aggressive behaviours, and positive behaviours. It was found that blind pedestrians reported higher frequencies of positive behaviours and lower frequencies of aggressive behaviours, aligning with previous studies on vulnerable users. The small sample size of blind pedestrians limits statistical generalizability; however, the study highlights the need for inclusive infrastructure and targeted safety measures to mitigate risks for blind pedestrians in urban areas, particularly in the context of the 15 min city. The implications for policy and urban planning are discussed. Full article

A lack of public vehicular traffic data for a city limits our understanding of the traffic occurring in the street networks of that city; however, there are free tools to extract street network graphs from digital maps and to assess the static properties associated with those graphs. This study proposes a two-stage modeling method to describe dynamic traffic data with static street network features. A quadratic polynomial is used to fit the average travel speed (ATS) pattern observed in the city center. Then, the relationship between the polynomial parameters and street network variables is analyzed through multiple linear regression. Descriptive geometric and topological measurements of downtown areas are obtained with the OSMnx tool (from OpenStreetMap), and with these data, independent variables are defined. The speed of vehicles, assessed every 15 min (from 6:00 a.m. to 10:00 p.m.) on the downtown street networks of twelve major cities, is obtained with the distance_matrix service of GoogleMaps, and with these data, the ATS (the dependent variable) is calculated. The ATS (presenting a U-shape) is modeled with a polynomial equation of order two, so there are three parameters for each city; in turn, each parameter is modeled with a multiple linear regression equation with the independent variables. For training purposes, the ATS equation parameters of ten cities are calculated, and the parameters, in turn, are explained with the proposed method. For validation purposes, the parameters of two cities not considered in the training process are calculated with the multiple linear regression equations. The ATS equation parameters of the twelve cities are correctly modeled so that each city’s ATS can be adequately described. It was concluded that the method selects the independent variables that are suitable to explain the ATS equation parameters. In addition, with the Akaike information criterion, the variable selection case presenting the best trade-off between accuracy and complexity is identified. Full article

With the acceleration of urbanization, the accessibility and equity of urban green spaces have become crucial issues in urban planning and public health. In the context of the 15-min living circle, whether residents can conveniently reach parks within a walkable or bikeable time frame directly impacts their quality of life and social well-being. Traditional park accessibility evaluation methods, such as the G2SFCA, effectively measure accessibility but fail to fully account for the diversity of travel modes and the impact of regional disparities on equity. This study employs the TB-G2SFCA method, integrating the concept of the 15-min living circle, to analyze the equity of park accessibility in the Dianchi Lake ring area of Kunming under different travel modes. The findings reveal significant disparities in park accessibility for walking and cycling, particularly in suburban communities distant from the city center, where many areas cannot reach a park within 15 min. Although accessibility improves under driving and public transit modes, resources remain concentrated in well-connected areas, leaving peripheral regions with insufficient access. Compared to the traditional G2SFCA method, the TB-G2SFCA approach more accurately reflects spatial differences and equity issues across travel modes. This study suggests that future urban park planning should optimize resource allocation, improve transportation networks, and enhance park accessibility in peripheral areas—especially for walking and cycling—to achieve a more equitable and sustainable distribution of urban green spaces. Full article

Fever visit management (FVM) played a critical role in reducing the risk of local outbreaks caused by positive cases during the coronavirus disease 2019 (COVID-19) pandemic under the dynamic zero-COVID-19 policy. Fever clinics were established to satisfy the healthcare needs of citizens with fever symptoms, including those with and without COVID-19. Learning how FVM affects fever medical care accessibility for citizens in different places can support decision making in establishing fever clinics more equitably. However, the dynamic nature of the population at different times has rarely been considered in evaluating healthcare facility accessibility. To fill this gap, we adjusted the Gaussian-based two-step floating catchment area method (G2SFCA) by considering the hourly dynamics of the population distribution derived from mobile phone location data. The results generated from Xining city, China, showed that (1) the accessibility of fever clinics explicitly exhibited spatial distribution patterns, being high in the center and low in surrounding areas; (2) the accessibility reduction in suburban areas caused by FVM was approximately 2.8 times greater than that in the central city for the 15 min drive conditions; and (3) the accessibility of fever clinics based on the nighttime anchor point was overestimated in central areas, but underestimated in suburban areas. Full article

Traffic flow at intersections is influenced by spatial design, control methods, technical equipment, and traffic volume. This article focuses on detecting traffic flows at intersections using video recordings, employing a YOLO-based framework for automated analysis. We compare manual evaluation with machine processing to demonstrate the efficiency improvements in traffic engineering tasks through automated traffic data analysis. The output data include traditionally immeasurable parameters, such as speed and vehicle gaps within the observed intersection area. The traffic analysis incorporates findings from monitoring groups of vehicles, focusing on their formation and speed as they traverse the intersection. Our proposed system for monitoring and classifying traffic flow was implemented at a selected intersection in the city of Zilina, Slovak Republic, as part of a pilot study for this research initiative. Based on evaluations using local data, the YOLOv9c detection model achieved an mAP50 of 98.2% for vehicle localization and classification across three basic classes: passenger cars, trucks, and buses. Despite the high detection accuracy of the model, the automated annotations for vehicle entry and exit at the intersection showed varying levels of accuracy compared to manual evaluation. On average, the mean absolute error between annotations by traffic specialists and the automated framework for the most frequent class, passenger cars, was 2.73 across all directions at 15 min intervals. This indicates that approximately three passenger cars per 15 min interval were either undetected or misclassified. Full article

Enhancing the quality of human settlements in traditional villages is crucial for promoting sustainable development and achieving the broader objectives of rural revitalization. A key challenge in addressing current settlement issues is the development of a scientifically grounded assessment framework for evaluating the quality of the settlement in traditional villages. To fill this gap, this study systematically constructed a theoretical and methodological system of comprehensive assessment of the settlement quality in traditional villages based on land use functions. Specifically, first, a comprehensive framework for assessing the settlement quality in traditional villages is proposed by systematically deconstructing the interrelationships among the key dimensions and elements with the guiding by the objectives of livable community development, the social–ecological system (SES) framework, and the land use functions (encompassing production, living, and ecological functions, PLE function). Second, considering the multi-scalar effects of livable community development (15 min, 25 min, and 35 min living circles), an indicator system that enables a quantitative and refined assessment of settlement quality in traditional villages, as well as a method for identifying barriers indicators are proposed based on the critical tool of land use functions. Finally, an empirical analysis of 757 traditional villages in Guizhou Province, China, is conducted to validate the feasibility and applicability of the theoretical framework and methodological approach presented in this study. The findings reveal that the overall settlement quality in Guizhou’s traditional villages is relatively low, with numerous villages in Qiandongnan Miao and Dong Autonomous Prefecture and Tongren City falling within a low-to-medium quality range, especially in the scale of 35 min living circle. To address these challenges, this study proposes classification-based improvement strategies to enhance the quality of the living environment in traditional villages. The results offer important theoretical, methodological, and empirical insights for the transformation and upgrading of settlements in traditional villages in Guizhou Province and other similar regions. Full article

Currently, around 60% of the world’s population lives in cities, with about half of Portugal’s population concentrated in the Metropolitan Areas of Lisbon and Porto. In this context, urban spaces need to be planned and managed to accommodate more inhabitants while preserving the urban quality of life and ensuring sustainability for future generations. Responding to these challenges, new urban models are emerging, such as the 15-Minute City model defined by Carlos Moreno. This model is based on four pillars: proximity, diversity, density, and ubiquity, aiming to enable residents to complete daily activities within a maximum of 15 min on foot or by other active transport modes. Through an analysis of two neighborhoods in Lisbon using specific indicators, this research aims to understand how urban planning influences each area’s alignment with the 15-Minute City model. This analysis will guide measures to improve residents’ quality of life by applying the principles of the 15-Minute City while broadening the discussion to include other relevant dimensions beyond travel distances and access, identifying areas that require further urban planning or intervention. Full article

The needs of night communities and the barriers they face in accessing diverse urban amenities are underexplored in urban planning research. Focus is primarily given to the needs of cultural consumers, frequently overlooking the challenges faced by regular nighttime communities, including night workers. Through a GIS-based analysis, the aim of this research is to shed light on differences in accessibility to core urban services between day and night in the city of Milan. The spatiotemporal analysis was performed using a customized version of the 15-min City Score Toolkit, an open-source, Python-based proprietary tool developed to automate the 15 min access metric estimation. Proprietary Point-Of-Interest (POI) data that were retrieved, sorted and filtered from the Google Places API are used to simulate time-variant walkability maps based on opening hour information contained in the dataset. The research reveals significant differences in walkability potential, both in spatial and temporal terms, and highlights gaps in nighttime service availability. The work presents an innovation on the 15 min city approach that highlights the impact of 24-h urban rhythms on real walkability outcomes. The quality limitations of the Google data are extensively explored in the article, providing further insight into the replicability and scalability of the methodology for future research. Full article

The 15-minute city concept, designed to ensure that all essential services and amenities are accessible within a 15 min walk or bike ride from home, presents a transformative vision for urban living. This paper explores the concept of a 15-minute city and its implications, along with its main features and pillars. Furthermore, it elaborates on how the integration of artificial intelligence (AI) and computational tools can be utilized in optimizing the 15-minute city model. We reveal how AI-driven algorithms, machine learning techniques, and advanced data analytics can enhance urban planning, improve accessibility, and foster social integration. Our paper focuses on the practical applications of these technologies in creating pedestrian-friendly neighborhoods, optimizing public transport coordination, and enhancing the quality of life for urban residents. By executing some of these computational models, we demonstrate the potential of AI and computational tools to realize the vision of the 15-minute city, making urban spaces more inclusive, resilient, and adaptive to the evolving needs of their inhabitants. Full article

The process of industrial transformation, becoming the most important for building up sustainable cities, is in urgent need for studying alternative options for land use to ensure sustainable city development. Rapid urbanization requires new built-up design in strong correlation with urban spatial planning issues. The objective of this paper is to contribute for the studies of sustainable land use solutions through investigating the problems of the reorganization of depressed industrial areas. The research methodology is based on the multicriteria decision-making method to provide primarily social functions of depressed industrial areas, depending on environmental and economic aspects of their historical location in urban structure. Since the article highlights the concept of the 15-min city to build a polycentric urban spatial structure, the assessment of supply and demand matching of social facilities is based on the analysis of 15 min accessibility to the serviced within either industrial zone and the city on the whole. The GIS spatial analysis method supports the developed criteria set to evaluate main city functions. The research provides the methodology for defining the priority land use solution for a specific industrial area in a specific location. The proposed methodology suggests a procedure for evaluating the importance of the land use composition for the city community, considering environmental requirements and cost-effectiveness of the project. Three different industrial zones located in the same city are a case study to asses the reliability of the proposed methodology. The research conclusion provides implementing sustainable land-use solutions for improving urban environment quality as well as the quality of life for the population. Full article