Search Results (2,254)

The rapid digitalization of cities is reshaping urban planning practices; however, significant gaps persist between technological investments and institutional governance capacity, particularly in transition economies. This study investigates how digital tools can be systematically embedded within planning processes to improve decision-making quality, coordination, and administrative efficiency. Drawing on urban governance theory and an empirical implementation study conducted in Yerevan, Armenia (population 1.1 million) between 2019 and 2023, the paper develops and operationalizes a multi-layer governance framework that aligns digital instruments—including geospatial information systems, performance dashboards, and decision-support platforms—with strategic, tactical, and operational levels of city management. The framework is evaluated through institutional analysis of municipal policy documents, planning databases, and semi-structured interviews with planning officials. The results reveal substantial governance barriers, including data fragmentation, organizational silos, and limited digital capacity. Framework-based implementation produced measurable improvements: planning decision cycles shortened by 43%, GIS utilization increased from 18% to 68% of eligible projects, inter-agency data sharing rose sixfold, and annual cost savings of approximately $1.2 million were achieved through reduced duplication and faster approvals. By combining conceptual design with empirical validation, the study advances digital urban governance research and offers a transferable, evidence-based model for implementing resilient and efficient data-driven planning systems in resource-constrained contexts. Full article

Station-based car-sharing has been shown to reduce resource-intensive private car ownership. However, only a small proportion of the population uses station-based car-sharing, which could be improved by redesigning the service to reduce walking distances and increase availability. We developed a method for designing an efficient and cost-effective station-based car-sharing network for smart cities that emphasizes user comfort and convenience, while reducing the number of needed cars. To quantify the placements, we created a high-resolution synthetic population for Munich, Germany as a case study. The population was based on census and OpenStreetMap data, and each person was assigned to a suitable mobility plan derived from two mobility surveys. Since car ownership and station-based car-sharing are particularly associated with trips for vacations, we supplemented the mobility plans with long-distance travel data from a one-year tracking dataset. This allowed us to perform a spatial and temporal analysis of the theoretical potential of various station placements for station-based car-sharing. The tested station networks varied in user comfort, especially in the distance to the nearest station and the group size of car-sharing users. Our findings indicate that the best trade-off between convenience and efficiency is a station design with a group size of 217–949 people. We further found that the car-sharing fleet size is strongly influenced by long-distance trips, and that a substitution rate of 1:1.25 to 3.3 with private cars is possible. Full article

Medium-term 5G/NB-IoT planning is made difficult by simultaneous uncertainty in device adoption and per-device traffic behavior because deterministic point forecasts do not quantify overload risk or support reliability-based capacity decisions. A diffusion-to-dimensioning workflow is proposed in which S-curve adoption modeling, bounded usage priors, scenario stress testing, and Monte Carlo uncertainty propagation are combined to generate predictive demand distributions, exceedance curves, and quantile-based capacity rules. The framework is applied to a Great Britain case study for 2025–2029 using smart meter deployment data and an M2M-based proxy for asset-tracking adoption. Analysis shows that planning-year upper-tail outcomes are driven primarily by asset-tracking usage uncertainty rather than by proxy scale alone. A ±30% perturbation of the AT adoption anchor changes $Q_{0.95}$ by approximately ±29.8%, whereas stressed AT usage increases $Q_{0.95}$ by 74.4%. Plausible positive dependence among key AT operational inputs further raises $Q_{0.95}$ by 18.3–22.5%. Limited hold-out evaluation provides strong out-of-sample support for the smart meter adoption stage and plausibility-only support for the shorter AT proxy. The framework is intended for medium-term, data-lean planning settings and is designed to support transparent risk-based capacity decisions rather than deterministic point sizing. Full article

Short-term traffic load prediction is a fundamental component of intelligent transportation systems (ITSs), supporting real-time monitoring, congestion mitigation, and adaptive traffic management in smart cities. Owing to the dynamic and nonlinear nature of urban traffic, identifying prediction models that align with real-world traffic dynamics remains a key challenge. This study presents a comparative evaluation of data-driven traffic load prediction models using high-resolution one-minute traffic data collected from a major urban roundabout in Jeddah, Saudi Arabia. The evaluated models include regression-based machine learning approaches and recurrent deep learning architectures, which are assessed under consistent preprocessing and evaluation conditions. Model performance is evaluated using standard error metrics and complemented by temporal and residual analyses to examine prediction behavior under different traffic regimes. The optimized GRU model achieved the best predictive accuracy with an RMSE of 149.12 veh/h, followed closely by the optimized LSTM model (RMSE = 150.85 veh/h). The results indicate that while conventional machine learning models can effectively capture overall traffic trends under relatively stable conditions, recurrent deep learning models demonstrate stronger capability in modeling nonlinear temporal dependencies and rapid traffic fluctuations when properly configured. In addition, a variability-based regime analysis was conducted to evaluate model robustness under different traffic demand dynamics, revealing that model performance advantages are context-dependent rather than universal. The findings highlight the importance of systematic comparative evaluation and data-driven model selection for developing reliable traffic prediction components in real-time ITS applications and sustainable urban mobility planning. Full article

Multi-view 3D reconstruction is essential for smart city, supporting applications such as smart city planning and autonomous navigation. While traditional reconstruction pipelines and recent neural implicit methods, such as NeRF, achieve high visual fidelity, they often struggle with geometric accuracy and sparse-view scenarios. To address this challenge, we present DiffLiGS, a novel multi-modal 3D reconstruction framework that integrates LiDAR point clouds and LiDAR-guided diffusion-based priors into the 3D Gaussian Splatting (3DGS) pipeline, enabling high-fidelity and geometrically accurate models. Our method first densifies sparse LiDAR depths using a diffusion model and refines them through multi-view geometric constraints, producing dense LiDAR depth maps that provide robust supervision for 3DGS optimization. Leveraging these dense depth maps, we guide a Stable Video Diffusion model to synthesize novel view images, which are incorporated into training to enhance reconstruction completeness and visual realism. By jointly fusing rich appearance cues from multi-view images with precise LiDAR-derived geometry and diffusion priors, DiffLiGS achieves unified, geometry-aware 3D scene representations. Our extensive experiments demonstrate that our approach significantly improves both geometric accuracy and rendering quality compared to existing 3D reconstruction methods, enabling real-time, high-precision modeling of complex urban environments. Full article

Jordan is among the most water-stressed countries globally, with renewable freshwater availability falling below 100 m³ per capita per year. The Jordan Valley (JV), the country’s primary irrigated agricultural corridor, faces interconnected pressures across water, energy, food, and ecosystem (WEFE) systems under intensifying climatic and demographic stressors. This study evaluates the integrated performance of the WEFE nexus in the Jordan Valley using updated evidence (2018–2023) to quantify cross-sector interactions, performance gaps, and intervention priorities. A mixed-methods empirical assessment integrated quantitative sectoral data on water supply–demand and quality, electricity supply–demand and renewable deployment, agricultural productivity, and ecosystem pressure indicators, complemented by Living Lab–based stakeholder interviews. Sectoral indices were calculated based on supply–demand adequacy and aggregated into an overall WEFE Nexus Index. Results indicate persistent water scarcity, with a domestic supply of 23.48 MCM yr⁻¹ versus demand of 26.00 MCM yr⁻¹ (deficit −2.52 MCM yr⁻¹) and irrigation supply of 206 MCM yr⁻¹ relative to approximately 400 MCM yr⁻¹ demand (deficit −194 MCM yr⁻¹). Water services account for 14% of national electricity consumption, while solar pumping provides approximately 40% of daytime irrigation energy. Agricultural productivity is constrained by salinity and water quality, resulting in yield gaps (e.g., greenhouse vegetables: 4.7 vs. 10.0 t/dunum). Sectoral performance is uneven (Water 0.71; Energy 1.00; Food 0.45; Ecosystem 0.50), yielding an overall WEFE Nexus Index of 0.63 (0.50 after efficiency adjustment). Climate projections indicate continued warming (+1.8 °C) and declining precipitation (−11%) by 2060. Water harvesting, integrated renewable-powered water services, wastewater reuse, salinity management, climate-smart agriculture, and ecosystem restoration are critical to enhancing climate-resilient resource security in the Jordan Valley. The WEFE index developed here offers a tool for integrated planning and underscores that achieving climate-resilient resource security in the Jordan Valley will require strategic, cross-sector interventions and adaptive governance rather than sector-specific fixes. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

With the wave of new campus construction gradually receding, the focus of green campus planning and design is shifting toward the low-carbon retrofitting of the existing built environment. University campuses often face challenges such as dispersed land use, inadequate spatial planning, disorganized road layouts, suboptimal landscape design, and low energy efficiency. Grounded in a review of current research on campus carbon emissions, this study integrates green technology indicators with planning and design approaches to establish a multi-scale, context-adaptive planning framework for carbon control, spanning five dimensions: intensive land use, spatial layout, transportation systems, landscape development, and facility integration. Employing a combined approach of bibliometric analysis and case studies, this research examines and compares typical university campuses both domestically and internationally to validate the effectiveness of the synergistic “technology-system-behavior” pathway in mitigating high-carbon lock-in. Through a systematic comparative analysis of representative low-carbon campuses, the synthesized results indicate that under optimal operational conditions, the clustered reorganization of functional zones demonstrates the potential to reduce transportation carbon emissions by approximately 25%; comprehensive retrofitting of building envelopes can decrease building energy consumption intensity by an estimated 30%; a multimodal coordinated transport system can increase the share of non-motorized travel to around 65%; establishing high carbon-sequestration plant communities can enhance carbon sink capacity by up to 30%; and smart facility integration can reduce overall campus carbon emissions by a projected range of 25–40%. It should be noted that these quantitative outcomes represent high-probability potential ranges, with actual performance subject to behavioral and operational fluctuations. This study provides theoretical support and practical pathways for achieving “near-zero carbon campuses” and underscores the important demonstrative role that higher education institutions can play in addressing climate change. Full article

Background: Smart urban logistics has emerged as a key element of sustainable city development, with direct effects on economic performance, environmental quality, and urban livability. Issues with traffic, pollutants, infrastructure strain, and last-mile delivery efficiency have become more pressing due to rapid urbanization and the expansion of e-commerce. In this regard, underground or enclosed corridor-based tube-based freight transit systems have surfaced as a viable smart infrastructure option for automated and low-impact commodities delivery. Methods: This study adopts an analytical literature review complemented by a structured case study analysis to examine the potential role of tube-based freight transport systems in future urban logistics. Key technological concepts, including pneumatic tubes, automated capsule transport, and integration with digital platforms, the Physical Internet, and smart city management systems, are examined through a structured analytical review of the literature. Results: The outcome of the reviewed studies indicates that tube-based systems can contribute to congestion alleviation, emission reduction, and improved delivery reliability by shifting selected freight flows away from surface transport networks. However, governance frameworks, infrastructure integration, and institutional coordination mechanisms continue to have a significant impact on claimed performance outcomes. Conclusions: Tube-based freight systems represent a promising but conditional pathway toward smarter and more sustainable urban logistics. Their large-scale deployment is forced by high capital costs, standardization challenges, regulatory uncertainty, and social acceptance issues. Coordinated investment plans, encouraging legal frameworks, and integrated urban planning techniques in line with smart city goals are needed to overcome these obstacles. Full article

Smart energy networks, including electricity distribution and district heating, are increasingly operated as sensor-enabled infrastructures where maintenance decisions must be made under heterogeneous and time-varying operating conditions. In these settings, time-to-event data are rarely complete; preventive actions and limited observation horizons routinely introduce censoring and truncation, so models and validation procedures must account for partially observed lifetimes to avoid biased inference and misleading performance estimates. This review surveys survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation, spanning non-parametric, semi-parametric, parametric, and learning-based approaches, with emphasis on censoring-aware formulations and the use of static and time-varying covariates derived from sensor, inspection, and contextual information. A structured taxonomy and a systematic mapping of model families to data types, core assumptions (proportional hazards versus parametric distributional structure), and decision-oriented outputs such as risk ranking, horizon failure probabilities, and RUL distributions are presented. Evaluation practice is also synthesized by covering discrimination metrics, censoring-aware RUL accuracy measures, and probabilistic assessment via proper scoring rules, including the time-dependent Brier score and Integrated Brier Score (IBS). The review provides researchers and practitioners with a practical guide to selecting, fitting, and evaluating survival models for risk-informed maintenance planning in smart energy networks. Full article

Although smart-city benchmarking has produced many indices and rankings, cities still lack a practical way to assess whether successful initiatives can be transferred across institutional contexts and converted into implementable urban roadmaps. In this study, we aimed to develop and empirically test a paired donor–recipient “problem–solution” methodology that bridges comparative city analysis with implementation readiness gap assessment, addressing the persistent disconnect between smart-city benchmarking and actionable transfer guidance. The smart-city ecosystem was decomposed into eight functional dimensions covering digital foundations, service platforms, finance and procurement, innovation capacity, governance, legal adaptability, and citizen participation. The method was applied to the Ottawa-Astana pair using a systematic desk-based analysis of publicly available strategic documents, legislation and policy frameworks, and implementation materials (e.g., roadmaps, program guidelines, departmental plans, and monitoring outputs). Data were analyzed using a structured gap analysis algorithm employing a three-level qualitative compliance scale (Full Compliance, Partial Compliance, and Non-compliance) to assess recipient city status against donor benchmarks across all eight functional dimensions. The results reveal Astana’s partial compliance with the Ottawa benchmark, with moderate readiness and pronounced “hard-soft” asymmetry; that is, greater progress in regard to infrastructure and platforms, but persistent gaps in adaptive regulation, experimentation-friendly legal instruments, and participatory governance. These findings suggest that progressing toward a Smart City 2.0 model requires prioritizing regulatory sandboxes, adaptive procurement pathways for pilots, and scalable civic-tech mechanisms alongside continued investment in talent and innovation ecosystems—understood here as interconnected networks of universities, technology parks, civic-tech communities, and incubation infrastructure that collectively sustain capacity for technology absorption and local adaptation. The proposed paired framework is replicable and supports phased, actionable transfer roadmaps for policymakers. Full article

Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we aim at designing and validating experimentally a low-cost drone-based unmanned autonomous mission patrolling system with waypoint navigation, real-time video backhauling, AI-based human/object detection and GPS path re-planning when an event occurs to ensure the safety of patrol missions under battery constraints. Methods: The proposed architecture combines autonomous navigation and embedded flight-control with online analog video streaming and ground-station-based computer vision processing. Object detection based on deep learning for live aerial video is used, and the proposed system’s performance is tested at different altitudes, lighting states and GPS patrol plans. Results: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system is able to adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Conclusions: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system can adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Full article

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously absent in the literature. We identify optimal powertrain efficiency values that are directly derived from publicly available vehicle specifications, ensuring transparency and accessibility. Our findings demonstrate that this simple, physics-based model accurately estimates fuel consumption and CO₂ emissions for standard EPA cycles and can be effectively generalized to user-defined scenarios. This establishes a computationally efficient, interpretable, and robust method for environmental impact assessment, policy evaluation, and real-time emissions estimation. Full article

This research is an analysis of the relationship between sustainable urbanization and economic inequality through smart city initiatives in developing countries such as India. Rapid urbanization in developing countries tends to have a detrimental impact on socioeconomic inequalities, and the effort to build smart cities may inadvertently increase exclusion when it is not planned with inclusiveness in mind. To reach this goal, an integrated Multi-Criteria Decision-Making (MCDM) approach using a combination of AHP, TOPSIS, and DEMATEL is adopted to systematically identify, assess, and identify the key criteria that affect the inclusive urban development. This study’s results show that infrastructure, governance, digital accessibility, and social inclusion play a key role in mitigating urban disparities and facilitating sustainable development. In particular, good governance and the availability of equitable digital infrastructure appear to be one of the critical factors in the reduction in inequalities and long-term urban resilience. This research provides policy-oriented insights for policymakers in designing inclusive smart city policies in accordance with the Sustainable Development Goals, as well as theoretical contributions to urban sustainability research. Full article

This work presents INTELLECTUM as a reference architecture and design-time evaluation framework for multi-entity XR–AI–digital twin systems. Rather than optimizing a specific implementation, the paper formalizes architectural invariants, event semantics, and coordination mechanisms that precede and inform system realization. INTELLECTUM provides a conceptual framework for structuring interactions across physical and virtual environments, emphasizing human-centered design, immersive digital twins, and collaborative extended-reality workspaces. The technical specification defines core architectural components, human integration modalities via WebXR and heterogeneous sensor networks, and representative usage scenarios within smart city ecosystems. By enabling AI-assisted urban planning, interactive simulation, and multi-actor coordination, INTELLECTUM positions itself as an XR-based architectural foundation for next-generation smart city platforms. Full article