Search Results (954)

The integrated usage of metro and bus services plays a key role in long-distance trips in big cities. Revealing the nonlinear relationship between the intermodal transfer demand and the built environment is significant for building a sustainable public transport system. This paper proposes a stacking ensemble explainable machine learning framework, which uses meta-learner to learn the prediction results of diverse base learners to improve performance, to detect how the impact factors impact the intermodal demand, including metro-to-bus and bus-to-metro directions. In this framework, the ensemble model is the stacking model; the ridge regression model is the second model. The base learners contain tree-based models (e.g., Random Forest, XGBoost and CatBoost) and non-tree-based models (e.g., SVR and KNN). The framework is applied to the case study of Beijing, China, based on one weekday (13 May 2019) and one weekend day (18 May 2019) of smart card data covering the main urban districts within the Sixth Ring Road. The results indicate that the stacking ensemble learning model outperforms the base learning models. For the metro-to-bus direction, transfer time, bus station count, and degree centrality are the top three influential factors; for the bus-to-metro direction, transfer time, bus station count, and shopping POI count are the top three, with lower predictive performance due to greater variability in this direction. However, the interaction effect of transfer time and bus station count is negative. This study could provide new insights into public transport planning and management. Full article

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article

Smart city planning involves a wide range of knowledge domains. However, general intelligent Question Answering systems often fall short when applied to this domain, and the relevant studies are not yet sufficient. To this end, this paper constructs an intelligent QA system that combines a large language model with a domain-specific knowledge graph. Capable of understanding questions accurately and generating professional answers, this system is designed to provide efficient knowledge services for smart city planning by following four steps. First, based on four authoritative planning guidelines, a domain-specific knowledge graph with a four-layer framework is constructed using Neo4j Community Edition 5.26.24. The framework includes top-level goals, knowledge modules, standard terminology and community scenarios. Subsequently, natural language questions are classified and matched with the templates before being converted into structured queries. Finally, the system performs Cypher query language queries and invokes ChatGLM4 to generate professional answers. The knowledge graph contains 100 entity nodes and 44 relations, and its ontology layer defines 28 entity types and 12 relation types. Therefore, the domain knowledge is structured and visualized, and planning professionals can intuitively retrieve diverse planning elements. In addition to its intelligent knowledge query function, this system assists planning professionals in preparing planning schemes and verifying compliance, reducing the time spent on reviewing regulations and comparing clauses, improving the efficiency of scheme preparation, and facilitating the refined implementation of urban renewal projects. It has high application value in smart city planning practices. Its construction approach can also serve as a reference for intelligent knowledge services in other fields. Full article

The present study provides a multi-faceted validation and refinement of a distributed system architecture designed to improve emergency response in resource-constrained urban areas. The architecture integrates IoT sensors, edge computing, field-programmable gate arrays and distributed shortest-path algorithms to enhance resilience and operational efficiency. As a primary validation strategy, a survey of 78 Cameroonian experts in software engineering, distributed systems, urban planning and emergency technologies was conducted. The survey yielded quantitative and qualitative data across multiple analytical dimensions, including subgroup analysis and a transferability assessment covering Nigeria, Senegal, and Kenya. The statistical analysis confirmed that the architecture is technically feasible, adaptable to local constraints, and has the potential to reduce response times. As a secondary validation strategy, a simulation-based study was conducted using iFogSim on smart-city models ranging from 25 to 100 nodes, encompassing five experiments: result consistency, geographic sensitivity, concurrent incident management, path-caching efficiency, and scalability analysis. The simulation results quantitatively corroborate the expert assessments, demonstrating low end-to-end latency and sustained throughput with realistic urban load conditions. Key challenges identified include interoperability, urban data structuring, financial sustainability and inter-institutional coordination. Experts have proposed a hierarchical structure of priority actions and concrete recommendations for engineers, researchers and policymakers. The combined findings validate the architecture and establish a replicable expert-simulation evaluation framework applicable to analogous distributed emergency-response systems in comparable resource-constrained contexts. The empirical results further constitute a reference baseline for the design and implementation of similar architectures. Full article

Public transport reliability largely determines the performance of smart urban mobility systems, as it directly affects passenger satisfaction and network efficiency. However, the strategic planning of public transport infrastructure is often carried out without dynamic, data-driven insights into operational performance, instead relying solely on static historical records of network operations. This study develops a data-driven framework based on the XGBoost machine learning algorithm to support the prioritization of infrastructure interventions by predicting delay severity and identifying reliability hotspots along an urban bus route. Delay severity is categorized into three classes (minor, moderate, and severe), using a model that incorporates spatial, temporal, operational, and meteorological variables. The XGBoost framework achieves a high predictive performance, with classification accuracies of 91.5% and 89.7% for the outbound and inbound bus route directions, respectively. Feature importance analysis indicates that seasonal and meteorological variables are critical factors influencing delay severity, highlighting the role of broader external environmental conditions on corridor performance. Furthermore, spatial analysis identifies specific bus stops with high delay probabilities, indicating hotspots where infrastructure upgrades should be prioritized at the stop and corridor levels. This study proposes a decision-support tool that enables targeted infrastructure investments at locations where they are most needed, contributing to more efficient and resilient public transport systems in smart cities. Full article

This study examines how smart city discourse is structurally configured across different contexts from the perspective of policy mobility. To this end, three types of data were analyzed: South Korean policy reports, South Korean academic literature, and global academic literature. Based on these sources, text datasets were constructed and analyzed using text mining-based semantic network analysis to identify key concepts and their relational structures. The results show that while similar keywords appear across datasets, differences are observed in the relative positions and relational patterns of key concepts. In South Korean policy reports, implementation- and operation-related concepts such as “service,” “information,” and “management” exhibit relatively higher centrality. In South Korean academic literature, “planning,” “policy,” “research,” and “technology” appear alongside governance- and actor-related concepts, indicating broader relational configurations. In global academic literature, concepts such as “sustainable,” “social,” “governance,” and “policy” show relatively similar levels of centrality, suggesting the coexistence of multiple dimensions within the discourse. These findings suggest that smart city discourse may be configured differently depending on institutional and discursive contexts, rather than converging into a single uniform structure. However, the observed differences should not be interpreted solely as reflecting national contextual differences, as variations in dataset composition may also have partially influenced the results. By conceptualizing the smart city as a structured policy discourse, this study contributes to understanding how policy-related concepts may be selectively emphasized and reconfigured across contexts. Methodologically, the study demonstrates the applicability of semantic network analysis for examining relational patterns within smart city discourse across different data types and contexts. Full article

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

City Information Modelling (CIM) and Urban Digital Twins (UDT) are pivotal for advancing smart urban planning and city management, yet empirical evidence on their real-world implementation is scarce. Following a sequential mixed-methods design, this study addresses this gap through a global investigation analyzing 33 projects across diverse geographic contexts. Findings reveal that these technologies are predominantly applied in 3D visualization (60.6%) and urban planning (48.5%), with significant underutilization in climate adaptation (9.1%) and AI-driven robotics (3.0%). A pronounced physical–social data divide exists, with infrastructure data prioritized over human-centric inputs. Technology stacks converge on GIS, IoT, and BIM. However, an interoperability paradox persists, as internal integration outpaces cross-organizational connectivity. Governance is predominantly public-sector-led, but multi-actor ecosystems are also involved. The study concludes with actionable recommendations to rebalance implementation portfolios, integrate socio-economic data, and advance both technical and institutional interoperability, thereby harnessing CIM and UDT for transformative urban planning and city management. Full article

Communities across the Southwestern United States (US) and Northern Mexico are making critical decisions regarding how they create long-term water resilience, including by reducing water demand and diversifying water supplies in the face of scarcity. There are several emerging frameworks encouraging collaborative governance approaches to water scarcity, such as Collaborative Water Governance and Adaptive Water Governance; however, examples of ongoing implementation of these frameworks by local governments in academic literature are less prevalent. This paper addresses this gap in the literature by sharing case studies and practitioner recommendations resulting from Growing Water Smart (GWS)—a training and assistance program for local communities to conduct collaborative water resilience action planning across jurisdictional borders, as well as between the historically separated disciplines of water resources management and land use planning. This paper presents and assesses the GWS curriculum as a model for local, cooperative responses to water scarcity, grounded in Collaborative Water Governance, Adaptive Governance, and related frameworks. This paper utilizes primary GWS program documents, firsthand participant perspectives, and direct practitioner experiences to present three case studies of GWS communities working across disciplinary and jurisdictional borders: a regionally collaborative facilitation process and intergovernmental agreement regarding water exports in the San Luis Valley of Colorado; a regional GWS workshop and emerging county-wide convening of jurisdictions within the Verde Watershed of central Arizona; and binational collaboration across the US-Mexico border through a workshop between the cities of Douglas, Arizona and Agua Prieta, Sonora, resulting in a deepened understanding of shared effluent flows. Finally, this paper posits that the GWS model initiates more collaborative and informed decision-making, builds capacity for localities through the support of third-party conveners and facilitators, and maximizes the limited financial and human resources available to local jurisdictions—resulting in a valuable and replicable process to advance water resilience across disciplinary and jurisdictional borders. Full article

This systematic review examines the global sustainable urban planning strategies used worldwide and whether they are applicable to the United Arab Emirates. This study reviewed 150 peer-reviewed articles and identified 14 of the most significant sustainable urban planning strategies in use today, including green infrastructure, smart city technologies, compact urban development, transit-oriented development, circular economy principles, mitigation of urban heat island effects, renewable energy integration, sustainable drainage systems, biophilic design, fifteen-minute city concepts, mixed-use development, vertical farming, participatory planning, and urban resilience frameworks. The methodologies applied by the authors to identify the sustainable urban planning strategies employed in the research were thematic analysis and the classification of the strategies into five main categories: environmental sustainability, technological innovation, social equity, economic viability, and cross-cutting. Case studies from Singapore, Copenhagen, Melbourne, and Amsterdam, and examples of current sustainable urban planning initiatives underway in Dubai and Abu Dhabi show how the models can be successfully implemented. The results indicate that multi-strategy approaches produce better results than the application of single strategies. Based on the results of the research, green infrastructure, smart city technologies, and the mitigation of urban heat island effects have been identified as strategies whose characteristics are closely aligned with the UAE’s arid climate conditions, while emphasizing that all fourteen strategies contribute to comprehensive sustainability outcomes and that their relative importance depends on local relevance. The researchers also concluded that for sustainable urban planning to be successful in the UAE, it will require the best practices from around the world be adapted to the unique environmental conditions, cultural contexts, and economic structures of each country. The findings of this study will contribute to the growing body of knowledge related to sustainable urbanism and provide practitioners with useful information and practical guidance when implementing sustainable urban planning practices in the UAE and other arid regions. Full article

This study develops a mobile-supported system that municipalities can use in their irregular waste collection services within the scope of smart cities. Irregular waste refers to waste that individuals or organizations produce non-periodically, which arises unexpectedly or in an unusual manner. Unlike small-volume household waste collected at routine times, irregular waste is generally large-volume waste such as construction rubble, vegetable oil, mineral oil, and garden waste. In the irregular waste collection system developed in this study, waste locations are marked on the map of an application running on mobile devices, and notifications are sent to the municipality. The Google Distance Matrix API was used for processing and visualizing the notification locations on the map. Daily or 4 h planning is carried out using this data. In this study, a genetic algorithm and a differential evolution algorithm were used for vehicle routing and vehicle type optimization. To compare the efficiency of both methods, four different scenarios were designed with different numbers of waste locations and different types and amounts of waste, and the successes of the methods were compared. Differential evolution is found to be on average 0.8% better. Optimizations performed with actual road distances were found to be 8.0% more successful than optimizations performed with Euclidean distances. Full article

Rapid urbanization has intensified congestion, environmental pressures, and transport inequities, thereby increasing interest in Smart Urban Mobility (SUM) as an approach that combines digital technologies, sustainable transport strategies, and data-informed decision-making to respond to these challenges. However, the evaluation of SUM remains fragmented due to the absence of harmonized assessment frameworks and the diversity of methodologies applied across smart city contexts. This study presents a systematic literature review of evaluation approaches and indicator architectures for SUM in smart city contexts. Using a PRISMA-guided screening process, 33 eligible studies were selected from 412 retrieved records. Three main methodological groups were identified: quantitative approaches, multi-criteria decision-making methods, and qualitative or participatory frameworks. A total of 273 indicators were organized into eight factor categories, confirming the multidimensional nature of smart mobility assessment while also revealing limited consistency in indicator selection and application across studies. Across the selected studies, current evaluation practices are increasingly linked to project prioritization, planning, and decision support; however, their effectiveness remains constrained by data inconsistencies, governance fragmentation, and insufficient user inclusion. These findings highlight the need for assessment frameworks that are sufficiently comparable to enable cross-city learning, yet flexible enough to reflect local contexts and institutional realities. Full article

Urban planning increasingly depends on methods capable of capturing citizen perspectives in forms that are both inclusive and analytically useful for decision-making. Conventional participation mechanisms, such as public meetings, paper questionnaires, and online platforms, often suffer from low reach, strong self-selection effects, and weak suitability for structured comparative analysis. This paper presents XRCity, a decision support system that combines extended reality, conversational artificial intelligence, and a planner-side backend to support participatory urban planning in public spaces. The system is centered on Olivia, a life-sized virtual assistant deployed on outdoor interactive screens, and on a backend environment that enables planners to prepare knowledge resources, configure interaction scripts, validate conversational behavior, process transcripts, and analyze elicited opinions. The contribution of the paper is not just the presentation of an XR interface, but the description and validation of a complete decision-support pipeline that connects campaign design, citizen interaction, opinion structuring, and planner-side analytics. The system was validated through real-world deployment in Torres Vedras, Portugal. Across more than 250 interactions and over 740 min of conversation, 191 usable sessions were analyzed, showing an average of 6.7 messages per user and 2.8 min per interaction. Of these sessions, 14.7% produced at least one structured response to an urban planning question, exceeding the project target of 10%. These results indicate the operational feasibility of using public-space conversational XR to elicit analyzable planning input, while a formal validation of the opinion-matching step remains future work. Full article

The expansion of accessible, fine-grained city data has significantly increased opportunities for evidence-based and informed policy-making. Despite this evolution, extracting actionable insights from heterogeneous data sources and effectively communicating findings remain persistent challenges. Most existing visualisation approaches and research prioritise technical implementation by focusing on how to visualise, often neglecting the importance of policy-driven visualisation questions and data contexts. This led to flawed analyses, particularly in complex domains such as smart cities and urban policy-making using digital twins. This article presents a novel, practical, step-by-step policy visualisation methodology grounded in empirical smart city research, shifting the emphasis toward policy-element-based questions informed by data-informed evidence. The methodology was successfully applied, tested, and adapted, resulting in an implementable, structured, and integrative approach that aligns with policymakers’ established policy design, implementation, and evaluation cycles. Through this approach, 20 user-driven smart city policy visualisations were operationalised and implemented in strategic policy decision-making contexts across smart city domains, including mobility, spatial planning, and environment. The results demonstrate how dashboards, algorithmic simulations, and digital twins visualisations can be systematically deployed to support evidence-informed decision-making. Full article

Change detection is a fundamental task in remote sensing with broad applications in urban monitoring, agriculture, watershed management, and land use and land cover analysis. In urban environments, accurate change detection is particularly critical for resource management, urban planning, and smart city development. Rapid urbanization has led to frequent and complex changes in buildings, which constitute key structural components of cities. Consequently, continuous and precise monitoring of building dynamics is essential for informed decision-making related to urban growth, environmental assessment, traffic management, and sustainable development. This paper presents a comprehensive review of two-dimensional (2D) and three-dimensional (3D) change detection methods applied to urban areas. Conventional and advanced approaches are systematically analyzed, and their strengths and limitations are critically discussed from a holistic perspective. Special emphasis is placed on recent learning-based techniques, which demonstrate enhanced robustness and accuracy in complex urban environments. Finally, current challenges and future research directions are identified to support the further development of effective 2D and 3D urban change detection methods. Full article