Search Results (633)

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

This study examines public acceptance of autonomous shuttle services in a real-world urban context by integrating expectation–experience dynamics, system characteristics, and configurational analysis. Based on survey data collected from users of Seoul’s self-driving shuttle operating along the Cheonggyecheon corridor (n = 566), a mixed-method approach combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) is employed. The results confirm that pre-use expectations significantly shape post-use experiences, supporting the expectation–confirmation framework. Notably, perceived autonomy exhibits a significant negative effect on user attitudes, suggesting that users may prefer partial automation rather than full autonomy during early deployment stages. In contrast to prior research, trust and satisfaction do not significantly influence attitudes, suggesting a context-specific pattern in which user evaluations may be shaped more by system-related considerations than by psychological responses in this early-stage pilot setting. Furthermore, perceived human backup plays a dual role by enhancing experienced safety while simultaneously reducing perceived autonomy, highlighting a human backup paradox in early-stage deployment. Contextual factors, including integration value and fare acceptability, significantly influence continuation intention, highlighting the importance of system-level integration in public transport. The fsQCA results further uncover multiple configurational pathways leading to high acceptance, demonstrating causal complexity and equifinality. These findings advance understanding of user acceptance in early-stage autonomous mobility systems and provide both practical and policy-relevant insights for designing safe, trustworthy, and system-integrated AI-enabled transport services, thereby supporting the sustainable deployment of autonomous transport systems in smart cities. Full article

Rapid urbanization in Saudi Arabia puts increasing pressure on energy, water, mobility, and waste-management systems, strengthening the need for evidence-based smart-city policy under Vision 2030. Rather than offering a descriptive inventory of projects, this paper develops a verification-oriented framework for assessing smart-city initiatives in the Kingdom. The framework is built on four principles: (i) distinguishing national contextual indicators from city-level evidence, (ii) separating stated ambitions from observed outcomes, (iii) applying an evidence-grading rubric that prioritizes publicly verifiable mechanisms and performance indicators over anecdotal or promotional claims, and (iv) introducing a readiness–impact matrix adapted to Saudi climatic, infrastructural, and institutional conditions. The framework is applied to major Saudi smart-city cases, including NEOM, KAEC, Riyadh, Jeddah, Makkah, and Madinah. The analysis shows that the strongest publicly documented evidence is concentrated in selected sectoral applications, particularly demand response and smart-building control in electricity systems, leak detection and pressure management in water networks, and intelligent traffic management in urban transport. These cases indicate plausible pathways for improving service efficiency and reducing resource waste; however, publicly verifiable city-level outcome data remain limited, fragmented, and uneven across cases. In response, the paper proposes a policy playbook centered on KPI transparency, interoperable data governance, cybersecurity safeguards, and public–private partnership templates to improve the measurability, comparability, and scalability of smart-city outcomes. By formalizing verification and cross-case assessment, the study contributes a reproducible methodological basis for evaluating smart-city progress and prioritizing future investments in Saudi Arabia. Full article

As climate risks intensify and urbanization accelerates, cities face growing challenges in safeguarding infrastructure, livelihoods, and public well-being. Sustainable insurance has emerged as a key tool for mitigating climate-related risks; however, existing models often lack integration with smart city frameworks and climate resilience strategies. This study conducts a bibliometric review to map the global research landscape of sustainable insurance in climate-resilient smart cities, providing insights into emerging trends, thematic clusters, and knowledge gaps. Using data from the Scopus database and VOSviewer-based keyword co-occurrence analysis, this study identifies four key research clusters: economic-policy integration, climate risk governance, digital urban innovation, and health within the SDG framework. The findings reveal that emerging models such as parametric insurance, microinsurance, and data-driven pricing can align financial protection with real-time climate risks, incentivizing resilience investments and expanding coverage to vulnerable communities. These clusters illustrate the field’s transition toward systems-based approaches, highlighting the need for integrated solutions that blend financial, technological, and social dimensions of resilience. Study recommendations emphasize the integration of insurance into urban planning, the expansion of public–private partnerships, regulatory modernization, and the use of smart city data for dynamic risk pricing. This research offers implications for insurers, governments, urban planners, and development agencies, and positions insurance as a cross-cutting enabler that bridges ESG principles, digital governance, and inclusive sustainability. Full article

This paper presents a novel system-oriented counterfactual deep learning framework, termed Hybrid Prediction–Intervention Neural Architecture (HPINA) for intelligent traffic accident risk prediction and proactive safety intervention in smart transportation systems. Unlike conventional data-driven models that rely solely on observational correlations, the proposed system integrates multi-domain data fusion, temporal deep representation learning, a continuous spatio-temporal risk field, and a latent-space counterfactual reasoning module within a unified decision-support architecture. The framework enables accurate prediction of traffic accident risk and simulation of “what-if” intervention scenarios to support real-time safety optimization in intelligent transportation environments. By leveraging heterogeneous inputs, including traffic dynamics, environmental conditions, road attributes, and temporal patterns, the system constructs a high-dimensional representation that captures complex nonlinear dependencies and evolving risk propagation across the network. A key innovation lies in the integration of a causal intervention mechanism and policy-guided decision layer, which jointly quantify intervention impact and identify optimal strategies for minimizing risk. The experimental results demonstrate that HPINA achieves a Test F1-score of 0.958 and an AUC of 0.989, outperforming strong baselines by up to 5.0% and 3.4%, while achieving a relative risk reduction of 0.091 and improved convergence stability with a validation loss of 0.042. These findings highlight the effectiveness of the proposed framework as an intelligent, scalable, and deployable system for real-world traffic safety management and smart city applications. Full article

This article analyses how capacity building programmes interact with structural constraints in mission-oriented climate policy, focusing on the Italian pilot Let’sGOv (GOverning the Transition through Pilot Actions) within the EU Mission “100 Climate-Neutral and Smart Cities by 2030”. Using an iterative, reflexive methodology (document analysis, direct observation, and qualitative analysis of questionnaires, workshop outputs, and online training feedback), it examines how municipal actors experience and reinterpret capacity building across three coupled dimensions: internal organisational capacity, external stakeholder relations, and multilevel governance interfaces. The empirical setting is a network of nine Italian Mission Cities (Bergamo, Bologna, Florence, Milan, Padua, Parma, Prato, Rome, Turin) supported by technical partners. The bench-learning pathway combined barrier diagnosis, an intensive in-person workshop, and a codesigned online curriculum structured around three thematic clusters (engagement, data, climate finance). Findings indicate that persistent barriers—departmental silos, resource and time scarcity, rigid human resources and procurement routines, asymmetric data access, and regulatory instability—are not removed by capacity building; rather, they are progressively articulated, specified, and reframed into actionable organisational and policy demands. Bench-learning strengthens diagnostic and relational capacities and enables modest institutional innovations (templates, protocols, internal task forces, shared policy briefs), while “hard” governance infrastructures largely remain unchanged. The paper argues that networked capacity building contributes to the emergence of nascent, project-dependent multilevel interfaces only when it supports collective negotiation with national actors and translates local experimentation into durable multilevel interfaces, mitigating risks of projectification and downward responsibility shifting. Full article

Driven by the global agenda of low-carbon urban development, local governments in China have implemented targeted policies requiring new energy vehicle adoption in the ride-hailing industry. This study focuses on a key issue in the development of sustainable smart public transportation systems: the factors affecting the acceptance of electric vehicles (EVs) in ride-hailing services among full-time and part-time drivers. Using 432 valid samples of ride-hailing drivers from Zhangzhou, a third-tier city in China, we compared the basic personal attributes of full-time and part-time drivers. Ordered logit models were developed to explore differences in factors influencing their acceptance of electric ride hailing (ER). Findings reveal: (1) Drivers’ perceived significance of EVs in green transportation is positively associated with their acceptance of ER. (2) Endurance mileage and charging efficiency have no significant effect on acceptance among drivers in underdeveloped cities. (3) Full-time drivers exhibit relatively low concern for subsidy policies, whereas part-time drivers express a pressing need for vehicle purchase subsidies and operational subsidies. (4) Overall, part-time drivers demonstrate higher acceptance of ER than full-time drivers. Based on these findings, this paper offers policy recommendations for governments to enhance ER acceptance among both driver groups. It is important to note that the present study utilizes survey data collected from Zhangzhou. The research conclusions should be treated with caution when applied to other cities, and further studies can be conducted in different regions to verify the results. Full article

The shift toward Smart Cities heavily relies on adopting energy-efficiency strategies to meet ambitious decarbonization targets. However, the rebound effect, where improvements in technical efficiency are partly offset by increased energy consumption, often reduces the expected environmental and economic benefits. Traditional Marginal Abatement Cost Curves (MACC) often ignore this behavioral feedback, which can lead to an overestimation of mitigation potential. This paper introduces a data-driven probabilistic framework for assessing the influence of the rebound effect on a portfolio of urban mitigation strategies by integrating behavioral feedback into a bottom-up MACC. By combining Monte Carlo (MC) simulations to address parametric uncertainty with Bayesian Networks (BN) for conditional inference, the robustness of nine strategies is examined across residential, commercial, and transportation sectors. The results demonstrate that even a moderate rebound effect ($\eta =0.5$) causes a $10.09\%$ decrease in total net abatement, dropping from 24.86 to 22.35 tCO₂e, and significantly raises costs. Notably, the number of strictly cost-effective strategies ($MAC<0$) decreases from six to three, highlighting the fragility of certain “win–win” measures. This framework introduces the concepts of Financial Backfire Probability (FBP) and Environmental Backfire Probability (EBP) as new metrics for urban planning. These findings emphasize that rebound tolerance is a critical factor in climate policy, indicating that additional measures, such as Internet of Things (IoT)-based monitoring and demand-side management, may be necessary to prevent performance erosion amid behavioral uncertainty. Full article

Cities are increasingly expected to achieve environmentally sustainable outcomes while simultaneously adapting to rapid technological transformation and growing governance complexity. However, sustainability performance in urban systems cannot be explained by technological infrastructure alone. Institutional capacity and algorithmic governance capabilities play a critical role in shaping coherent environmental policy implementation and green urban performance, particularly in transition city contexts. This study proposes the ISAG-G Governance Framework (Institutional and Smart Algorithmic Governance for Green Performance), a governance-oriented analytical framework designed to assess green urban governance capacity. The framework integrates four governance dimensions: institutional governance capacity, algorithmic and digital governance enablement, green urban governance performance, and citizen sustainability interaction. Methodologically, the study develops a composite governance index based on a structured indicator system. Indicator weights are determined using the Best–Worst Method (BWM) through expert consultation, while Min–Max normalization and weighted aggregation are applied to construct the composite index. The framework is empirically applied through a comparative analysis of five transition municipalities (evidence from Armenia) representing different levels of administrative capacity and urban development. The findings reveal distinct governance profiles across municipalities and highlight the importance of institutional coherence and algorithmic governance capacity in shaping green urban performance. By moving beyond infrastructure-centric approaches, the proposed framework provides both an analytical and policy-oriented tool for evaluating urban sustainability governance in transition city contexts. Full article

The shortage of interdisciplinary talent is a critical bottleneck constraining the development of smart agriculture. Taking Jiangsu Province as a case study, this research constructs and empirically validates an ecosystem model for cultivating interdisciplinary talent oriented toward smart agriculture. In the theoretical construction phase, an initial three-dimensional model covering “core actors,” “supportive environment,” and “resource elements” was proposed based on ecosystem theory and literature review. This model was subsequently refined through in-depth interviews (March–August 2024, 60–120 min each) and thematic analysis with 58 diverse stakeholders across 13 prefecture-level cities in Jiangsu Province, encompassing universities, agribusinesses, government agencies, research institutes, and frontline practitioners. In the empirical testing phase, structural equation modeling was employed to analyze 382 valid questionnaire responses covering six dimensions: policy environment, market environment, university–enterprise collaboration, curriculum resources, platform resources, and talent cultivation effectiveness (20 items in total). The findings indicate that: (1) the ecosystem model demonstrates good fit and strong explanatory power, with a pronounced “university–enterprise” dual-core driving effect; (2) government policy guidance and platform construction play pivotal supportive roles; (3) market demand and industrial policy constitute critical external driving forces; and (4) “industry–education integrated practice platforms” together with “modular interdisciplinary curricula” exert the most direct positive influence on cultivation outcomes. Based on these findings, this study offers systematic recommendations from three perspectives—mechanism coordination, policy optimization, and resource allocation—providing a theoretically grounded and practically referenced solution for cultivating interdisciplinary talent in smart agriculture. Full article

The aim of this paper is to conceptualize smart culture as an important yet under-researched dimension of smart cities, and to empirically demonstrate the extent to which cultural events in Vilnius’ public spaces align with the key principles of smart culture. The theoretical section of the article provides a definition of smart culture in a smart city, based on which four categories of analysis are identified: accessibility, the integration of technology into the cultural experience, engagement of the population, and promotion of community building. The methodology consists of an instrumental case study, analysis of secondary sources, and directed content analysis. The research findings reveal that Culture Night festival events in Vilnius not only reduce social and geographical barriers to culture but also create spaces for active participation of the population, fostering community and the application of technological solutions in cultural activities. Culture Night represents a clear example of smart culture, highlighting the importance of this dimension in smart city policies. The study shows that the identified characteristics of smart culture may support inclusive and sustainable urban development trends associated with SDG 11 (Sustainable Cities and Communities) and SDG 10 (Reduced Inequalities). Full article

The form of a city evolves as the complexity of its systems increases. This study discusses how urban growth challenges have contributed to the deterioration of built environments and cultural heritage assets. It investigates how smart sustainable city (SSC) strategies have become significant policy instruments in regenerating Baghdad’s future built heritage and advancing the conservation of the city’s architectural heritage, infrastructure systems, and quality of life. The study aims to investigate how SSC methods can serve as the main element for managing complex urban data and advancing heritage, socio-economic, and environmental sustainability. The research employs mixed methods such as mapping, serial vision, and walking tools to survey Baghdad’s heritage centre (Old Rusafa) natural and built environment and cultural heritage condition. Together, these methods provide a comprehensive understanding of the heritage area’s physical and socio-cultural dimensions. It is argued that achieving smart urban heritage requires the adoption of sustainable strategies that promote the conservation of architectural heritage. Accordingly, the research outcomes enhance understanding of the smart sustainable city concept (SSC) impact on Baghdad city’s cultural heritage regeneration and allow for the creation of an Index Wheel, which provides city stakeholders with a range of strategies and indicators to conserve Baghdad’s built heritage sustainably. Full article

With the rapid development of applications such as smart cities and the industrial internet, the computation-intensive tasks generated by massive sensing devices pose significant challenges to traditional cloud computing paradigms. Unmanned aerial vehicle (UAV)-assisted edge computing systems, leveraging their high mobility and wide-area coverage capabilities, offer an innovative architecture for low-latency and highly reliable edge services. However, the practical deployment of such systems faces a highly complex multi-objective optimization problem featured by the tight coupling of task offloading decisions, UAV trajectory planning, and edge server resource allocation. Conventional optimization methods are difficult to adapt to the dynamic and high-dimensional characteristics of this problem, leading to suboptimal system performance. To address this critical challenge, this paper constructs an intelligent collaborative optimization framework for UAV-assisted edge computing systems and formulates the system quality of service (QoS) optimization problem as a mixed-integer non-convex programming problem with the dual objectives of minimizing task processing latency and reducing overall system energy consumption. A multi-agent joint task association and trajectory optimization (MA-JTATO) algorithm based on hybrid reinforcement learning is proposed to solve this intractable problem, which innovatively decouples the original coupled optimization problem into three interrelated subproblems and realizes their collaborative and efficient solution. Specifically, the Advantage Actor-Critic (A2C) algorithm is adopted to realize dynamic and optimal task association between UAVs and edge servers for discrete decision-making requirements; the multi-agent deep deterministic policy gradient (MADDPG) method is employed to achieve cooperative and energy-efficient trajectory planning for multiple UAVs to meet the needs of continuous control in dynamic environments; and convex optimization theory is applied to obtain a closed-form optimal solution for the efficient allocation of computational resources on edge servers. Simulation results demonstrate that the proposed MA-JTATO algorithm significantly outperforms traditional baseline algorithms in enhancing overall QoS, effectively validating the framework’s superior performance and robustness in dynamic and complex scenarios. Full article

We explored the application of spatial information technology in the assessment and optimization of cultural heritage resources within traditional settlements in Meizhou City, a core area of Hakka culture in China. By integrating methods such as geographic information systems and Kernel density estimation, it systematically evaluates the spatial distribution and socioeconomic conditions of these settlements. A multi-criteria evaluation model is constructed to quantify resource endowment across cultural, historical, and ecological dimensions, with particular emphasis on key factors influencing conservation effectiveness, such as infrastructure and economic vitality. Combining field investigations and literature review, we propose adaptive reuse strategies and policy recommendations to enhance settlement resilience and balance cultural preservation with regional development. Their expected outcomes include the engineering of a multidimensional geographic database for traditional settlements, the establishment of a spatial decision-support framework for heritage infrastructure conservation, and the development of systematic optimization protocols integrated with China’s rural revitalization technical policies. These results provide a computational and methodological foundation for interdisciplinary research in sustainable cultural heritage management and smart rural engineering. Full article

The Air-front Smart City (ASC) concept is proposed to address the stagnation of industries in developed countries and stimulate economic growth in developing countries while maintaining a higher quality of life for people and contributing to decarbonization and overall United Nations SDGs in an existing study. However, no studies have been conducted to assess ASC policies. Therefore, this study integrates the integrated accessibility index into the quality of life (QOL) and quality of business (QOB) evaluation models to assess the startup ecosystem in Aichi, Singapore, and Munich within the ASC concept. The study uses survey data conducted in Aichi to estimate monetary values of QOL and QOB component indicators, calculates the integrated accessibility indices, and estimates QOL and QOB. Furthermore, the study sets scenarios to assess the impacts of living and business urban policies in Aichi. Additionally, the study using Aichi parameters compares the startup ecosystem in Singapore and Munich. The result shows that the key drivers of startup attraction are corporate tax rate, economic growth, and safety; enhancing these indicators directly increases startups’ QOB, business partners, and residents’ QOL. It was found that QOB in Singapore is comparatively higher, whereas QOL is higher in Aichi. Full article