Search Results (13,627)

This paper presents a comprehensive review of the integration of Intelligent Transportation Systems (ITS) and Digital Twin (DT) technologies for intelligent traffic management. It examines the role of key enabling technologies, including the Internet of Things (IoT), machine learning (ML), deep learning (DL), reinforcement learning (RL), Graph Neural Networks (GNNs), vehicle-to-everything (V2X) communication, and edge computing. These technologies support real-time traffic monitoring, traffic prediction, and adaptive control in ITS. The review synthesizes recent research on conventional traffic control methods, optimization-based approaches, learning-based techniques, and DT-enabled traffic management solutions. Particular attention is given to the integration of DTs with intelligent traffic signal control, real-time synchronization, multi-intersection coordination, communication latency, sensing uncertainty, and scalability. The reviewed literature demonstrates the potential of DT-enabled ITS to improve traffic efficiency, reduce congestion, enhance transportation safety, and support sustainable mobility through data-driven decision-making. However, significant challenges remain regarding communication delays, sensor and data uncertainty, computational complexity, scalability, and validation under realistic urban conditions. Based on the reviewed literature, this paper identifies key research gaps and outlines future research directions toward scalable, reliable, adaptive, and real-time DT-enabled ITS architectures for next-generation smart cities. Full article

This study investigates how integrated smart urban systems enhance resource efficiency in Saudi Arabia’s rapidly urbanising cities. Despite growing global interest in smart cities, there remains a critical gap in empirical research that simultaneously examines smart energy, water, and waste systems within a unified analytical framework, particularly in emerging urban contexts, while the mediating role of governance efficiency remains underexplored. Adopting a quantitative survey design, this study employs Structural Equation Modelling (SEM) to analyse 384 valid data from 384 stakeholders across selected urban areas. A stratified sampling approach ensures representation of policymakers, urban planners, and infrastructure managers, and measurement constructs are adapted from validated scales to ensure reliability and validity. The study examines relationships between smart energy systems, smart water management, smart waste monitoring, governance efficiency, and resource efficiency outcomes, with governance efficiency conceptualised as a mediating variable enhancing the effectiveness of smart urban systems. The study hypothesises that integrated smart technologies significantly improve resource efficiency, with smart infrastructure as a key predictor; structural model robustness is assessed using standard SEM fit indices, with CFI (0.93), TLI (0.91), and RMSEA (0.052). This research contributes to theory by integrating smart urban systems and governance into a unified framework, extending smart city and circular economy literature, while offering practical insights aligned with Saudi Vision 2030 to support sustainable urban development. It is recommended that policymakers prioritise integrated smart infrastructure, strengthen institutional frameworks, and promote public awareness to maximise resource optimisation. Future research should adopt longitudinal designs, expand across multiple cities, and incorporate behavioural and policy variables to enhance generalizability and deepen insights into smart urban resource efficiency. Full article

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. Full article

Open government data is a key institutional arrangement in market-oriented data factor reforms. Using the staggered rollout of city-level government data platforms in China as a quasi-natural experiment and panel data of listed manufacturing firms (2011–2024), we employ a staggered difference-in-differences design to examine the effect of open government data on firms’ resource allocation efficiency. We find that government data platforms significantly reduce resource misallocation, a result robust to propensity score matching, exclusion of concurrent policy shocks, and double machine learning. Channel-level tests do not find statistically significant transmission through government transparency, firm digital innovation, or fiscal subsidy reallocation. Directional identification shows that the effect operates primarily as a corrective force on capital-overallocated firms by curbing inefficient investment, rather than as a relief effect on constrained firms, and is more pronounced in high-digital-intensity industries, smaller cities, and regions with lower digital development. By reducing the wasteful use of capital, labour, and energy and improving the information environment in factor markets, open government data offers a low-cost, institutionalized instrument for reconciling productivity growth with sustainable resource use, with direct relevance to the United Nations (UN) Sustainable Development Goals (SDGs). Full article

This study examines the relationship between the financial sustainability of local government units (LGUs) and their investment capacity, focusing on differences between municipalities and cities with county rights in Poland. It investigates how operating surplus, debt-servicing costs, and revenue autonomy are associated with investment capacity and whether these relationships differ across LGU types. The analysis uses data for Polish municipalities and cities with county rights for 2018–2024 obtained from the Local Data Bank of Statistics Poland. Various panel regression specifications were employed, supplemented by descriptive Pearson correlation analysis. The results indicate a consistent positive tendency in the relationship between operating surplus and investment capacity, whereas higher debt-servicing costs were significantly associated with lower investment capacity. Cities with county rights exhibited higher investment capacity than municipalities, although the strength of the evidence varied depending on the measure used. Revenue autonomy, in contrast, was found to be negatively associated with investment capacity. This study provides a comparative assessment of the relationship between financial sustainability and investment capacity, offering implications for local fiscal policy and future research. Full article

In the context of the global implementation of the United Nations 2030 Sustainable Development Goals (SDGs), urban green development serves as a core practical pathway to advance SDG8 (Decent Work and Economic Growth), SDG11 (Sustainable Cities and Communities), and SDG15 (Life on Land). This study constructed a three-dimensional evaluation framework encompassing the green economy, green ecology, and green society, and utilized panel data from 282 Chinese prefecture-level cities from 2009 to 2021. By integrating the CRITIC weighting method, spatial autocorrelation, the Standard Deviational Ellipse (SDE) approach, and the Dagum Gini decomposition, we systematically quantified multi-scale spatiotemporal evolution and disparity patterns at the national scale, as well as across four major economic zones and five core urban agglomerations. Nationwide, the composite green development index rose from 0.422 to 0.522 (+23.70%), while the overall Dagum Gini coefficient declined from 0.072 to 0.053 (−26.39%), with the spatial center of gravity gradually shifting toward the southwest. Eastern China persistently maintained the highest development level; Western China achieved the fastest cumulative growth (26.43%), and Northeastern China lagged behind with the lowest growth rate (20.10%). Among five major urban agglomerations, the Yangtze River Delta (YRD) and Pearl River Delta (PRD) shared an identical average score of 0.582, whereas the Chengdu–Chongqing (CC) agglomeration recorded the highest growth of 33.07%. This research contributes multi-scale empirical evidence for localized SDG implementation in China and provides differentiated policy insights for cross-regional green transition governance. Full article

Urbanisation has become a significant driver of ecological transformation, resulting in the degradation of urban green and blue spaces (UGSs and UBSs) and a subsequent decrease in ecosystem services (ESs). It is imperative to evaluate the spatio-temporal dynamics of UGS and UBS in order to develop sustainable urban planning strategies, particularly in the swiftly expanding cities of the Global South. In the Siliguri Planning Area (SPA), India, this study examines the long-term variations in UGS and UBS and their associated ecosystem service values (ESVs) from 1991 to 2021. The Normalised Difference Vegetation Index (NDVI) and Modified Normalised Difference Water Index (MNDWI) were employed to delineate UGS and UBS using multi-temporal Landsat imagery, respectively). The benefit transfer method was employed to quantify ESV, and sensitivity analysis was conducted to assess the valuation’s reliability. Also, adjusted value coefficients were employed. The findings indicated that landscapes such as tea garden (58.74% reduce) and agricultural land (46.09 increase) have undergone a substantial transformation as a result of urbanisation, with the built-up areas increasing from 5798.61 ha in 1991 to 8500.23 ha in 2021 (46.59% increase). Simultaneously, UGS decreased (by 40.26%) from 12,533.04 ha (47.81% oftotal area)in 1991 to 7486.29 ha (28.55% oftotal area) in 2021, while UBS decreased (by 79.47%) from 255.69 ha (0.94% oftotal area) in 1991 to 52.48 ha (0.19% oftotal area) in 2021. Subsequently, the ESV of UGS and UBS fell significantly from 1183.05 crores (INR) to 706.67 crores (INR) and 34.72 crores (INR) to 7.12 crores (INR), respectively. This confirms the elasticity of the valuation estimates, as the sensitivity coefficients remained below one. The study contributes to understanding the crucial role of urban planners, private property owners, and builders in promoting green–blue infrastructure conservation, wetland restoration, and ecological zoning. Such ecosystem service-based planning is essential for achieving sustainable development in rapidly urbanising regions and enhancing urban ecological resilience. Full article

Sustainable public transport in South Africa has become a policy priority over the past few decades. Safety is recognised as a fundamental component of this agenda. In many cities, public transport systems are often characterised by poor safety standards, which undermine ridership and public confidence. In the City of Polokwane, despite the successful recent completion of the first phase of Leeto La Polokwane bus system, an Intelligent Transportation System designed for sustainable transport management, the safety performance of the system and its subsequent influence on travel behaviour remain underexplored. This study addresses this gap by investigating the safety performance of the Leeto La Polokwane Phase 1A and its relationship with commuters’ sustainable travel mode choice. Commuter surveys (n = 344), field observations, and actual ridership data were used to collect data. Principal component analysis extracted two key factors (commuter safety and crime prevention), which together explained 63.14% of the total variance in commuters’ perceptions of safety. Commuters reported a neutral perception of commuter safety (mean = 4.03) and a negative perception of crime prevention (mean = 3.90). Multinomial logistic regression revealed that commuters’ perceptions of safety, particularly concerns related to crime prevention, were statistically significant predictors of sustainable travel mode choice. Multiple linear regression further showed that operational continuity and system maturation were positively associated with ridership growth, with operating days (β = 0.307, p = 0.044) and time in operation (β = 0.670, p < 0.001) emerging as significant predictors. Although taxi protests (β = −0.036, p = 0.804) and school holidays (β = −0.199, p = 0.181) were negatively correlated with ridership. The study concludes that safety is a critical determinant of sustainable public transport use that extends beyond the onboard environment to include the entire commuter journey. To improve public transport safety requires a holistic strategy that integrates safe pedestrian infrastructure, effective crime prevention, operational reliability, and strengthened institutional collaboration. Full article

Urban floods pose an escalating threat to sustainable urban development, particularly in densely populated regions where demographic heterogeneity and differential responses to warnings shape flood vulnerability. However, the existing flood risk assessments of humans often neglect age-specific susceptibility and education-dependent behavioral responses. This study develops an agent-based flood risk model (AFRM) that couples the agent model with a hydrodynamic model to support sustainable urban flood management. Human agents are characterized by spatial distributions derived from population heatmap data, daily mobility patterns governed by a probabilistic finite-state machine, age-differentiated vulnerability thresholds (minors, adults, and seniors), and education-dependent probabilities of responding to warnings. The AFRM is established by integrating human agents and the urban flood process based on the NetLogo platform. Subsequently, the study assesses the dynamic human flood risk under various return periods, and explores the effects of age structures and early warning on urban flood risk assessment. Taking the Huayuan Road area of Zhengzhou City, China as a case study, the results demonstrate the following: (1) the AFRM achieves reasonable accuracy, with 7.17% mean absolute percentage errors across all risk levels; (2) neglecting age structures underestimates the flood risk assessment by 18% to 29%, as minors and seniors exhibit a higher susceptibility to flooding; and (3) early warnings reduced the medium- and high-risk exposure by 45% to 58%, highlighting the potential of warning lead time strategies to enhance urban flood resilience. This research provides the support for urban flood safety and sustainable development in a changing climate. Full article

Understanding the spatial distribution and associated factors of sustainable environmental development (SED) is crucial for effectively formulating action plans aimed at achieving the environment-related Sustainable Development Goals (SDGs). Because of spatial heterogeneity characterized by non-uniform distributions of SED within a region, the use of a single or aggregated value at the national or provincial scale in existing research obscures internal variabilities and fails to adequately reveal the spatial disparities in the progress of environment-related SDGs. Consequently, this study aims to investigate the spatially heterogeneous patterns of SED in prefecture-level cities within the Yangtze River Economic Belt (YREB), China—a critical economic zone characterized by stark intra-regional disparities and pressing environmental challenges. To achieve this objective, the SED index is first constructed using principal component weighted aggregation to quantify the SED status, local Moran’s I is then employed to identify heterogenous patters of the spatial distribution patterns of SED, and geographical random forest is utilized to explore spatially varying association patterns between SED and influences factors. In the YREB, the findings indicate the following key insights: (1) SED has generally shown a positive trend across most cities from 2013 to 2021, with approximately 30% experiencing a downward trend, particularly in Jiangxi, Hubei, and Hunan Provinces; (2) the number of high- or low-value aggregation clusters has decreased, concurrent with an increase in spatial variability; (3) human activity disturbance and population density have been identified as significant factors influencing SED, with a notable impact in cities across Sichuan, Chongqing, Guizhou, and Hubei Provinces. This research contributes technical support and a scientific foundation for evaluating and enhancing SED. Full article

The equitable allocation of health resources is fundamental to building healthy cities and advancing sustainable regional development. Using panel data for 122 county-level units in Hunan Province, China, from 2011 to 2022, this study constructs an evaluation index system for healthcare resource allocation and applies trend surface analysis, spatial autocorrelation, the Dagum Gini coefficient, and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal evolution, equity, and driving factors of health resource allocation. The results show the following. (1) The level of health resource allocation in Hunan Province rose steadily, with the composite score increasing by 74%, yet a persistent spatial pattern of higher allocation in the east and north than in the west and south remained; hot spots clustered in Changsha, while cold spots concentrated in parts of Southern Hunan and Western Hunan. (2) Regional disparities narrowed gradually, and the Dagum decomposition identified transvariation density as the dominant source of inequality, with an average contribution of 53.58%, exceeding intra-group and inter-group differences. (3) The effects of the drivers exhibited marked spatiotemporal heterogeneity. Per capita GDP promoted health resource allocation mainly in developed regions, while urbanization exerted stronger positive effects in less-developed regions. The positive effect of per capita disposable income gradually shifted from less-developed to developed regions over time, and population density generally showed a positive effect, with stronger influences concentrated in the Greater Western Hunan region. This study contributes to a deeper understanding of how regional disparities and heterogeneous driving mechanisms shape health resource allocation, providing evidence for more adaptive and equitable healthcare governance. Full article

Ecological sensitivity zoning is widely used in conservation planning, yet the interaction between land-use structure and landscape ecological risk across different sensitivity levels remains underexplored. We propose a dual-coupling framework—integrating (i) land-use composition and (ii) landscape patterns with ecological sensitivity—to assess landscape ecological risk. The Analytic Hierarchy Process (AHP) weighted sensitivity factors across five units (Levels 1, 3, 5, 7, and 9). The entropy weight method derived the Landscape Ecological Risk Index (LERI), while ridge regression and partial least squares regression (PLSR) identified key explanatory land-use variables. When land use is included in the sensitivity zoning, unexpectedly, the low-sensitivity zone (Level 3) exhibited the highest LERI (0.7588), whereas the non-sensitive zone (Level 1) recorded the lowest (0.1732). Ridge regression at the sensitivity-level scale revealed grassland (β = 0.255, VIP = 1.596) as the strongest positive correlate of LERI. Construction land showed a negative association at this scale (β = −0.433, VIP = 1.299). Grid-scale validation, however, indicated that the construction-land relationship is scale dependent, with grassland remaining the only factor consistently positive across both scales. PLSR validated these sensitivity-level trends, with grassland ranking highest in VIP across both models. Mechanistically, low-sensitivity zones feature a mixed mosaic of cropland, grassland, and construction land, intensifying landscape fragmentation. Conversely, non-sensitive zones, dominated by uniform construction land, exhibit landscape homogenization and correspondingly lower risk. This study challenges the traditional assumption that high ecological sensitivity inherently dictates high risk, emphasizing that land-use-driven landscape fragmentation in low-sensitivity zones warrants priority in ecological risk assessments. Full article

The evolution of land-use types in mining cities is critical for balancing resource exploitation and ecological sustainability. Taking Jungar Banner as a case study, this study uses remote sensing imagery and auxiliary data (2010–2025) to examine spatiotemporal land-use changes, applies the OPGD model to detect driving factors, and integrates a Markov chain with an optimized NEGM-MOP-PLUS model to project 2030 land-use patterns under multiple scenarios. Results show that grassland shrank markedly, while cropland and built-up land expanded—the latter reaching 395.75 km² by 2025. After 2020, core mining areas became more contiguous, while peripheral zones showed increased fragmentation, with built-up land expansion becoming the dominant trend. Driving forces shifted from natural constraints to anthropogenic dominance: natural factors prevailed in 2010, mining impacts took the lead by 2015, and a mining–precipitation dual-core structure emerged by 2020. Future projections indicate continued grassland and bare land reduction, alongside water and built-up land expansion across all scenarios. Among them, the CDS, which balances economic and ecological objectives, is identified as the optimal spatial planning direction based on ecosystem service value (ESV) assessment. These findings offer practical guidance for managing land-use transitions in arid and semi-arid resource-based mining regions. Full article

Conventional international rankings assess urban efficiency via context-blind metrics, a design that the literature suggests may structurally disadvantage Mediterranean centres and obscure the sustainable policy pathways mandated by the Sustainable Development Goals, most notably the governance of urban transitions under SDG 11. This paper proposes the Context-Weighted Liveability Index (CWLI), which introduces context-sensitive weights into the aggregation of standard smart city KPIs, bridging the global comparability of IMD-style indices with the Mediterranean-specific assessment logic of the ASCIMER framework. Weights derive from five geographic coefficients—climate, culture, economy, historical density, and demography—through a transparent weighted additive formulation with an explicit sensitivity matrix, whose robustness is verified through Monte Carlo uncertainty analysis over 5000 perturbed configurations spanning parameters, coefficients, measurements and benchmarks. Coefficients and KPIs are computed from open spatial data through a replicable GIS protocol (QGIS; OpenStreetMap, Copernicus land cover and land surface temperature, and ISTAT/ELSTAT census data at sub-municipal scale). Applied comparatively to Bologna and Athens, the framework shows that contextual weighting concentrates over 60% of the total weight on climate-sensitive indicators and yields, through the decomposition of contributions, a policy diagnosis that differs from the one suggested by reading an overall smart city rank in isolation: Athens’ largest contribution is digital and its liveability deficit territorial—a profile with direct consequences for sustainable urban transition and talent attraction. Implications for SDG 11 monitoring, equitable access to urban green space, and digital twin integration are discussed. Full article

Walking tourism is increasingly supported by a wide range of digital technologies that are crucial for mitigating environmental impacts and promoting sustainable territorial development, yet their adoption remains fragmented and rarely interpreted within a unified infrastructure perspective. While systemic infrastructure perspectives are well-established in smart city and transport literature, existing studies on walking trails still tend to focus on individual tools or user-oriented applications. Drawing upon broader smart mobility concepts, this paper proposes a system-level framework, the SmartTrail Framework (STF), for the classification, functional interpretation and integration assessment of digital technologies in walking trail infrastructures. The framework is conceived as a transferable analytical and operational instrument applicable both to academic literature and to real-world trail systems, supporting infrastructure assessment, planning and digital maturity evaluation. Following an initial bibliometric analysis of a broader Scopus dataset comprising 1697 records, a structured literature review of 253 publications was conducted to operationalise and illustrate the framework through the systematic classification of digital technologies, the mapping of functional requirements, and the assessment of Integration Levels (IL). The STF integrates three analytical dimensions: a technology domain taxonomy (six categories), a functional requirement model (five infrastructure functions) and an IL scheme (IL0–IL3) that characterises the degree of systemic coherence among digital components. The results reveal a critical functional imbalance in current digitalisation: while user-oriented navigation and information services are widely adopted, backend infrastructure functions, particularly safety and operational logistics, remain severely underdeveloped, keeping most trail systems trapped in low-integration configurations. Applied to real-world trail systems, the framework enables the identification of architectural gaps, the prioritisation of integration investments and the definition of development pathways towards intelligent trail ecosystems. The paper aims to contribute to field research by providing a structured interpretative framework for understanding digital technologies as infrastructural components of walking trails. This supports a shift from tool-based thinking to system-based infrastructure design and offers practical implications for planners, managers and policymakers involved in the development of smart, resilient and environmentally sustainable trail infrastructures. Full article