Search Results (9,603)

Conventional standards for evaluating the Korean Highway Capacity Manual (HCM) and U.S. HCM often inadequately represent the localized macroscopic traffic dynamics inherent in complex urban networks. To address this limitation, this study proposes an adaptive branching framework for level of service (LOS) reclassification, guided by the empirical identifiability of fundamental diagrams (FDs) and vehicular density distribution patterns. The methodology classifies traffic states into four categories: (a) FD-based LOS, (b) segmented FD-based LOS, (c) single-state LOS, and (d) empirical free-flow speed-based LOS. These categories redefine LOS criteria based on the temporal and spatial conditions prevalent in urban environments. The proposed reclassified LOS framework, applied to twenty-eight urban corridors across four distinct urban typologies using a reference free-flow speed, effectively captures region-specific performance variations. Ultimately, this research establishes a robust, data-driven methodological framework for localized LOS recalibration, thereby significantly enhancing the realism of urban traffic evaluation. Full article

Against the backdrop of China’s urban modernization pathway, the core value of urban innovation systems is increasingly shifting toward an inclusive orientation. Grounded in the theoretical connotation of inclusive urban innovation, this study establishes an evaluation index system covering equal participation opportunities, procedural fairness, and outcome sharing, and applies the entropy method, kernel density analysis, and spatial autocorrelation to empirically examine the spatial distribution characteristics and formation mechanisms of inclusive innovation spaces in Changsha. The results show that (1) Changsha’s inclusive innovation level presents a gradient decline from the central urban area to the periphery; (2) high–high clusters mainly in areas with stronger innovation–resource concentration and better public service conditions, such as Yuelu District and other districts associated with major innovation platforms. Low–low agglomeration zones cluster in peripheral urban areas like certain townships in Liuyang City and remote regions of Ningxiang City; (3) the spatial differentiation of inclusive innovation is jointly shaped by multiple factors, among which Cultural Education and Industrial Structure show relatively stronger explanatory power; and (4) improving inclusive innovation requires enhancing not only innovation agglomeration, but also public service accessibility, talent support, employment inclusion, and the local sharing of innovation outcomes. This study provides a systematic framework for evaluating urban inclusive innovation space and offers policy insights for promoting balanced and inclusive innovation development in regional innovation cities. Full article

Major infrastructure investments alter accessibility and urban development patterns, yet their impact on housing prices varies significantly across regions. The prevailing interpretation attributes this heterogeneity to supply differences or regulatory constraints, treating land use regulations as exogenous variables. Nevertheless, even two regions with a nominally similar regulatory framework may produce substantially different outcomes in the housing market, depending on the effectiveness of rule implementation. This paper argues that this approach overlooks a critical variable: the ability of regional authorities to coordinate, regulate, permit, and implement spatial development in a predictable and timely manner. In line with this, a conceptual framework is developed, grounded in the literature on spatial and multi-level governance, in which regional institutional capacity is proposed as a potential mediator of capitalization around project milestones (announcement, funding, construction, operation), rather than as a backdrop. This capacity shapes outcomes through three interrelated dimensions: the responsiveness of supply, which depends on administrative capacity and regulatory consistency; the coherence of governance across jurisdictions within functional urban areas; and the management of land value through land value capture instruments. From this framework, testable propositions are derived regarding the intensity, timing, and spatial distribution of price effects. The study does not empirically estimate changes in housing prices, nor does it test the propositions put forward. Instead, it develops the conceptual framework and organizes the spatial and institutional units of observation required for a subsequent empirical test. The framework is specified spatially through Section A, Line 4 of the Athens Metro to organize the project’s spatial units, administrative jurisdictions, land uses, and milestones for future analysis. The contribution is threefold: conceptual, as it elevates regional institutional capacity from a contextual to an explanatory variable; theoretical, in that it bridges urban economics with the governance literature; and policy-relevant, since it repositions the reform of regional governance as a constituent element of housing policy and as a factor that may shape sustainable spatial development outcomes. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

To enhance the meteorological forecasting and early warning service capability for urban waterlogging risks in Jinan, this study aims to investigate the relationship between rainfall and urban waterlogging. Based on minute-scale precipitation observations from 38 automatic weather stations and records from 70 waterlogging monitoring sites in the urban area of Jinan from 2011 to 2024, this study systematically analyzes the spatiotemporal characteristics of precipitation and waterlogging events and quantifies their response relationship. The main findings are summarized as follows. Heavy precipitation and waterlogging events are strongly temporally coincident, primarily occurring during the main flood season from June to August. Regarding diurnal variation, short-duration heavy rainfall and waterlogging events are concentrated between 14:00 and 20:00. The water depth of most waterlogging events ranges from 0.11 m to 1.04 m, with a median of 0.26 m, and the distribution of waterlogging exhibits a pronounced right-skewed pattern. A moderate positive spatial autocorrelation was observed in waterlogging depth, suggesting that severe urban waterlogging events are more likely to occur in the northern region of Jinan. The precipitation preceding waterlogging events is predominantly short-duration heavy rainfall. A strong temporal relationship exists between peak precipitation and maximum waterlogging depth. In nearly 90% of the waterlogging events, peak precipitation occurs within 2 h before the maximum waterlogging depth, with an average lead time of approximately 55 min. The relationship between antecedent cumulative precipitation and peak waterlogging depth is strongest at the 120 min timescale. About 90% of maximum rainfall over 10 min, 1 h, and 2 h did not exceed the 1-year return period threshold, indicating that the precipitation causing waterlogging events in Jinan is generally non-extreme. Full article

Cropland and rural settlements are core components of rural human–environment systems, and their coordinated development is crucial for regional sustainability, particularly in China’s major agricultural production regions. Taking the Huang-Huai-Hai region as the study area, this study systematically investigates the spatiotemporal evolution of cropland and its coupling relationship with rural settlements using land use data from 1990 to 2020. Grid-based analysis and multiple spatial modeling methods were employed. The results show that: (1) From 1990 to 2020, the cropland in the region decreased by a net total of 21,021.94 km², with annual dynamic degrees ranging from −0.13% to −0.28%. Cropland conversion to other land uses far exceeded conversion from others, with construction land being the primary destination. Among these, rural settlements and urban construction land accounted for 43.75% and 55.58% of the total cropland loss, respectively. (2) The spatial distribution of cropland exhibited a distinct pattern of “hot in the center and south, cold in the periphery and north” (Moran’s I = 0.232, p < 0.001), indicating significant positive spatial autocorrelation. Hot spot areas clustered in the North China Plain and the Huang-Huai Plain, while cold spot areas were distributed in the Yanshan–Taihang mountains and the hilly regions of the Shandong Peninsula, clearly controlled by topography. (3) Cropland change exhibited stage-specific characteristics. The pattern was relatively stable during 1990–2000. During 2000–2010, cropland conversion to other uses intensified, with high-value conversion areas concentrated around urban agglomerations. In the 2010–2020 period, these high-value conversion areas diffused from the core plain areas to urban fringe zones. (4) The spatial coupling between cropland and rural settlements was predominantly characterized by the Moderately Coordinated Type (MCT), accounting for 48.38–58.44% of the area. However, the proportion of Rural Settlement-Dominant Type (RC) increased from 15.51% to 21.58%, indicating a trend toward intensifying human–environment conflicts. Overall, the Huang-Huai-Hai region experienced significant cropland changes. While its spatial pattern remains relatively stable, the coupling relationship between cropland and rural settlements is deteriorating, posing challenges to regional food security and rural sustainable development. Full article

Atmospheric wet deposition represents a major pathway for the transfer of organic micropollutants into terrestrial and aquatic ecosystems. This study investigates the occurrence and spatial distribution of polycyclic aromatic hydrocarbons (PAHs), phthalate esters (PAEs), and BTEX compounds in rainwater across Northern Serbia (Vojvodina region). Rainwater samples were collected during the 2025–2026 heating season at three locations: a petrochemical site in Kikinda, a traffic- and residentially influenced site in Sremska Mitrovica, and an urban background site in Sombor. Total concentrations showed pronounced spatial variability, with the highest ΣBTEX and ΣPAE levels recorded in Kikinda (∑BTEX = 2.818 μg L^∑1; ∑PAE = 0.930 μg L^∑1). Diagnostic ratios identified a dominant petrogenic signature in Kikinda (LMW/HMW > 1), while pyrogenic sources prevailed in Sremska Mitrovica and Sombor ((Fla/Fla + Pyr) > 0.5). BTEX profiles across all sites were characterised by the absence of benzene and elevated toluene and xylene levels (B/T ≈ 0; T/X > 1). Health risk assessment indicated an acceptable but non-negligible carcinogenic risk from PAHs, particularly for children in industrial areas. These findings highlight the role of precipitation as an efficient scavenger of organic pollutants and emphasise the need for integrated atmospheric–hydrological monitoring frameworks in industrialised regions. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Short-term traffic forecasting is a critical component of intelligent transportation systems. While deep learning architectures for this task have proliferated rapidly, the sensor-level data characteristics—zero-value prevalence, distributional heterogeneity, and cross-sensor correlation structure—that drive architecture-specific failure modes remain insufficiently understood, and their implications for evidence-based model selection in real deployments have not been systematically addressed. This study addresses that question through a sensor-network diagnostic framework applied to the METR-LA dataset (Metropolitan Los Angeles; 207 inductive loop detectors, 5-min resolution). The framework integrates systematic characterization of sensor data properties, a controlled benchmark of four representative architectures—Transformer, Spatio-Temporal Graph Convolutional Network (STGCN), Diffusion Convolutional Recurrent Neural Network (DCRNN), and Gated Temporal Convolutional Network (Gated TCN)—under a unified 12→3 prediction setting, and a novel per-sensor regression analysis that quantitatively links zero-value ratios to model-specific prediction errors across all 207 sensors. Building on these findings, this study further proposes Graph-Enhanced Transformer (GETFormer), a lightweight hybrid architecture that augments the Transformer with a single-hop Graph Convolutional Network (GCN) layer and a gated residual fusion module. The diagnostic findings and condition-dependent model-selection guidelines provide an empirically grounded foundation for principled hybrid architecture development in urban traffic sensing. Full article

The measurement and improvement of urban land use efficiency (ULUE) are crucial for sustainable development in China’s Canal Cities (CCCs). Drawing on the theories of production factors, spatial externalities, and agglomeration economy, this study proposes a framework that explicitly addresses the trade-offs and synergies of sustainable land use. A comprehensive ULUE evaluation index system was established. The super-SBM (Slack-Based Measure) and Global Malmquist–Luenberger (GML) index models were employed to assess the green efficiency of urban land use from 2002 to 2023, while Kernel Density Estimation (KDE) and the optimal parameters-based geographical detector (OPGD) model were used to investigate the spatiotemporal evolution and influencing factors of ULUE. The results reveal a distinctive V-shaped trend in efficiency, marked by significant spatial disequilibrium and predominantly technology-driven sustainable growth. Furthermore, ULUE exhibits a spatial distribution characterized by bipolar and multipolar differentiation, accompanied by concurrent concentration and dispersion, with high-value clusters dominating the spatial clustering type. Government regulation emerges as the dominant factor influencing ULUE, underscoring the pivotal role of policy intervention in guiding the sustainable development of land use. The interactions among pairs of influencing factors strengthened over time; notably, the interaction between government regulation and other factors is the strongest. Four-quadrant analysis profoundly reveals the underlying mechanism, distinguishing a high-quality, sustainable development model driven by technological innovation and a resource-dependent economic growth model. The findings provide valuable insights for promoting green development and formulating sustainable land use policies in CCCs. Full article

Objective: To evaluate the impact of urban–rural health insurance integration on the health outcomes and financial burden of rural older adults. Methods: Utilizing panel data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2013 to 2018, we employed a staggered difference-in-differences model coupled with propensity score matching (PSM-DID) for rigorous causal identification. Results: The policy significantly reduced out-of-pocket medical expenditures for rural households by approximately 5.6% (p = 0.034). Concurrently, significant improvements were observed in both physical health (a 0.092-point reduction in ADL impairment scores) and mental health (a 0.725-point reduction in CES-D depression scores). Mechanism analyses revealed that the integration did not significantly increase the probability of outpatient or inpatient visits—thereby ruling out supplier-induced demand and moral hazard—while effectively reducing the incidence of catastrophic health expenditure by 1.9% (p = 0.004). Heterogeneity analyses indicated that while the financial relief was universally distributed across varying educational levels, the policy dividends were predominantly captured by the younger-old demographic. Notably, the reduction in financial burden was not statistically significant for the oldest-old cohort (aged 75 and older). Conclusions: The urban–rural health insurance integration has achieved a dual dividend of financial protection and health enhancement without triggering the overutilization of medical services. Nevertheless, the unmet care expenses for older adults with severe disabilities underscore the urgent necessity for a secondary safety net, such as long-term care insurance. Full article

Accurate reconstruction of complete tree point clouds is essential for estimating ecosystem structural characteristics from LiDAR data. In urban forestry environments, however, terrestrial laser scanning (TLS) and mobile laser scanning (MLS) frequently produce incomplete observations. Occlusion caused by neighboring trees, together with interference from surrounding urban objects such as buildings and vehicles, often leads to missing regions within scanned point clouds. These defects may further affect the reliability of tree structural analysis and parameter estimation. Although recent learning-based point cloud completion methods have improved reconstruction performance, several limitations remain when they are applied to complex tree structures. Many existing networks depend on farthest point sampling (FPS) for feature extraction, which can result in the loss of fine-scale branching information. Furthermore, local feature aggregation methods based on the traditional k-nearest neighbor (KNN) strategy are highly sensitive to regions with uneven point cloud distribution, such as the canopy region where density variations are significant in tree point clouds. To alleviate these issues, this study proposes GS-TreeAttn, an attention-guided framework specifically for tree point cloud completion. This network models density and structural representation as a coupled problem and employs a structure-guided density-adaptive attention mechanism to jointly capture global structural dependencies and local geometric features. We comprehensively evaluate the proposed method using publicly available datasets and urban forestry data collected under real-world scanning conditions. Experimental results show that even in complex scenarios with severe occlusion and uneven sampling density, GS-TreeAttn generates more complete reconstruction results. This improvement is particularly evident in regions where the canopy and branches mutually occlude each other, where information loss is very common in real-world urban forestry. Full article

Background: Early childhood nutrition is strongly associated with neurodevelopmental outcomes, particularly in socially vulnerable settings. Limited evidence is available describing the relationship between nutritional status, food security, and neurodevelopment among preschool children in low-income urban areas of Colombia. This study aimed to evaluate nutritional status, household food insecurity, and neurodevelopmental outcomes in children attending early childhood centers in El Codito, Bogotá, and to explore the association between anthropometric indicators and neurodevelopmental performance. Methods: A cross-sectional study was conducted in children enrolled in community childcare centers. Nutritional status was assessed using anthropometric indicators according to World Health Organization growth standards, including weight for age, height for age, and body mass index for age. Neurodevelopment was evaluated using the Escala Abreviada de Desarrollo (EAD). Household food insecurity was measured through a validated questionnaire. Descriptive statistics were performed, and associations between variables were analyzed using correlation tests and group comparisons according to data distribution. Results: Most participants presented adequate nutritional status; however, a proportion of children showed risk of stunting or excess weight. Neurodevelopmental scores were generally within expected ranges, although variability was observed across developmental domains. Significant associations were identified between certain anthropometric indicators and neurodevelopmental outcomes. Moderate to severe household food insecurity was identified in 21.4% of participating households. Conclusions: Nutritional status and household food insecurity represent important contextual factors for child health in vulnerable urban populations. These findings highlight the importance of integrated nutritional and developmental monitoring strategies within early childhood programs. Further longitudinal studies are required to clarify causal pathways and to guide targeted public health interventions in similar contexts. Full article

Against the backdrop of the transition of new-type urbanization towards high-quality development, the triple contradictions of population agglomeration, land constraints, and housing supply-demand imbalance have become increasingly prominent. The conventional binary framework of human–land relations can no longer meet the requirements of coordinated development within human settlement systems, creating an urgent need to examine the multi-system interactions among population, land, and housing in order to resolve spatial mismatch. Taking Guangzhou as a case study, this research integrates 2020 population census data, land-use data from the European Space Agency (ESA), housing-price data from the Anjuke platform, and multi-source data on related influencing factors, and conducts a systematic empirical analysis by combining coupling coordination analysis, a relative development model, and the geographical detector. The findings reveal that the coupling coordination level of population, land and housing in Guangzhou exhibits a concentric, ring-shaped distribution pattern with central agglomeration and peripheral decline. The relative development among the three systems can be classified into matching types including the core-differentiated type, the peripheral-imbalanced type, and the surrounding-equilibrium type. With respect to influencing factors, all pairwise interactions are of the bi-factor enhancement type, and the driving mechanism displays a three-stage dynamic evolution. This study enriches research on human–land relations, provides precise guidance for optimizing spatial allocation and alleviating housing mismatch conflicts in Guangzhou, and offers transferable practical experience for comparable cities in China seeking to advance the high-quality development of new-type urbanization. Full article

Focusing on contrasting climate and land use pathways, this analysis explores the changing trajectories of water conservation function over time. An integrated framework combining the PLUS and InVEST models with Spearman’s correlation analysis and geographically weighted regression (GWR) was applied to examine the spatiotemporal heterogeneity and underlying drivers of water conservation function in the Chengdu–Chongqing Economic Zone during the period 2000–2020. Thus, it further predicted the evolution trend under two scenarios, namely SSP1-1.9 (Sustainable Development Pathway) and SSP2-4.5 (Medium Development Pathway), for the period 2030–2050. The findings reveal that: (1) Between 2000 and 2020, the spatial distribution of water conservation function shifted markedly, with low-value areas contracting and high-value zones expanding, alongside a progressive transition toward a predominantly medium-to-high functional structure. (2) In mountainous and hilly transition zones, precipitation (PRE) and forest cover proportion (FCP) exhibited notably positive effects, whereas evapotranspiration (PET) exerted a negative effect. In contrast, in plain and urbanized areas, built-up land proportion (BUP), population density (POP), and gross domestic product density (GDP) demonstrated pronounced negative effects. (3) Future simulations indicate that under the sustainable development pathway (SSP1-1.9), the combined area of high and extreme functional zones will recover by 2050, whereas under the moderate development pathway (SSP2-4.5), such extreme functional zones will be nearly eliminated. These results underscore the substantial impact of development pathways on regional water security and sustainability. Full article