Urban Sci., Volume 10, Issue 6 (June 2026) – 45 articles

This study investigates the determinants of driver acceptance of “M-Flow”, Thailand’s first Advanced Traffic Management solution utilizing Multi-Lane Free Flow (MLFF) technology. While designed to eliminate toll plaza bottlenecks through AI-driven automated billing, the system’s operational efficiency is hindered by a “trust gap” caused by a stringent ten-fold penalty for late payment compliance. By integrating the Technology Readiness Index (TRI 2.0) with the Technology Acceptance Model (TAM), this research explores how psychological readiness dictates the success of smart traffic infrastructures. Data from 485 drivers were analyzed using Structural Equation Modeling (SEM). The results reveal that while technological optimism and innovativeness act as motivators, Insecurity (β = −0.723) emerges as the dominant psychological barrier, directly suppressing the perceived ease of use and triggering behavioral resistance. The findings demonstrate that technical efficiency and diverse payment options alone are insufficient to ensure mass adoption if the regulatory climate fosters financial anxiety. To maximize system throughput, this study recommends that policymakers shift from punitive enforcement to “trust engineering.” By enhancing financial transparency, simplifying the registration-to-payment workflow, and mitigating the “penalty trap” perception, authorities can achieve the psychological seamlessness that is a strict prerequisite for a fully trusted smart transportation infrastructure in Thailand. Full article

Housing is a crucial determinant of population migration. However, the mechanisms through which urban housing influences floating-population re-migration, as well as its role in guiding the efficient spatial allocation of populations, remain underexplored. This study investigated the impact of urban housing status on population re-migration based on the spatial equilibrium theory, and empirically tested this relationship using nearly 370,477 individual migration intentions records from the China Migrants Dynamic Survey (CMDS). The key findings are as follows. First, urban housing status is related to shaping population re-migration intentions. In particular, owner-occupied housing and government-provided low-rent housing are associated with lower re-migration intentions. Second, institutional constraints on migrant populations can vary somewhat depending on household registration status. Rural-registered floating populations may sometimes face somewhat more restrictions in accessing urban housing and public services. By contrast, high-wage areas has less re-migration intentions primarily through labor income gains, leading to heterogeneous housing status effects on migration intentions. Further analysis reveals spatial and individual heterogeneity in how urban housing status shapes population re-mobility. Floating populations residing in first-tier, second-tier, and provincial capital cities prioritize employment opportunities. In comparison, first-generation floating populations, those with local spouses, and individuals engaged in low-risk occupations exhibit stronger demand for stable residence. Full article

João António de Aguiar was one of the most prolific Portuguese architect-planners of the twentieth century, producing an extensive body of work within the framework of the 1934 legislative reform. He employed Urban Development Plans as a key scientific and technical instrument for territorial intervention, both in mainland Portugal and in the overseas territories. Despite his significance, Aguiar’s contribution remains relatively understudied, frequently overshadowed by the reformist ministry of Duarte Pacheco and by the dominant ideological narratives of the period. This article advances a critical analysis centred on urban composition and city design, with particular emphasis on the transformation of coastal urban structures and on Aguiar’s interventions in the Portuguese colonial context. Through a comparative and interpretative methodology, the study examines the formal and spatial principles underpinning his plans, while addressing the cultural challenges involved in adapting European urban models to non-European contexts. By shifting the focus from a merely descriptive inventory of planning instruments to a deeper investigation of urban form, this research offers a more nuanced reading of urban transformation processes in overseas coastal settlements. It contributes to a clearer and more structured understanding of Aguiar’s influence on African and Asian urbanism and on colonial planning practices more broadly. Full article

Smart cities encounter numerous risks, including technical and non-technical risks, on account of exchanging large volumes of data for different services; therefore, understanding and assessing risks for their management becomes essential. In this paper, a risk assessment framework for smart cities, based on a BLOC-ICE systems approach, is proposed. The framework outcomes are analyzed based on the data collected from a sample smart city to understand the importance of risks and the ways to mitigate or avoid such risks. The analysis of risk in the framework is based on the Dempster–Shafer and Bayesian theories, which can be used to assess the risk and its impact based on a particular smart city environment. In this paper, the focus is on design phase risks. The outcome of the analysis shows that strategic risks, stakeholder engagement, regulatory compliance, business continuity, and financial risks are important during the design phase and decision makers should develop measures to address these risks, which, otherwise, can have consequential impacts during the development and operation phase. The paper also provides some research direction on risk assessment. Full article

Urban planners need scalable ways to monitor pedestrian conditions across heterogeneous cities, but conventional Level-of-Service (LOS) methods are often locally calibrated and difficult to transfer. This study proposes a city-adaptive framework for pedestrian LOS mapping using spatio-temporal graph models and few-shot transfer learning. Pedestrian count data from Melbourne, Dublin, and Zurich were converted into six ordinal LOS classes using city-specific percentile thresholds computed from the training data, yielding a relative congestion measure rather than an absolute cross-city standard. We developed a spatio-temporal graph transformer with an ordinal prediction head and evaluated it under in-domain, zero-shot, few-shot, and domain-adaptive settings. The results show strong in-domain performance in Melbourne (accuracy 79.7%; Acc ± 1 99.1%) and effective adaptation to the city-adaptive ordinal classification task. Few-shot fine-tuning with only 5% labeled target city data recovered 95–99% of in-domain performance, suggesting that small amounts of local supervision can substantially reduce calibration requirements in data-scarce environments. KernelSHAP analysis indicates that short-term temporal lag features dominate predictions across cities, whereas spatial and contextual features vary more strongly with local urban structure. The findings suggest that few-shot transfer learning can support pedestrian LOS estimation in cities with limited labeled data; however, the proposed LOS formulation should be interpreted as a city-specific relative indicator rather than an absolute measure of pedestrian comfort, crowding, or service quality. While the framework was evaluated across three cities, additional validation in diverse urban contexts and against perceptual measures of pedestrian experience remains necessary. Overall, the study contributes a city-adaptive framework for transferable relative LOS prediction rather than a universal cross-city LOS standard. Full article

Quasi-Industrial Districts (QIDs) in Japan allow the coexistence of industrial, residential, and commercial functions. However, under pressures such as deindustrialization, demographic decline, and urban restructuring, their functional balance has been increasingly disrupted. This study investigates the spatiotemporal evolution of QIDs in Kitakyushu and develops a GIS-based framework to quantify changes in land-use structure. Using historical zoning and building floor-area data from 1986 to 2024, ternary diagram analysis is applied to examine relationships among the three functional types and identify transformation trajectories. Results show that while the total QID area expanded by 38.8%, internal structures changed significantly. Industry-dominant districts declined, commerce-oriented districts increased, and residential–industrial mixed types largely disappeared, indicating a shift toward commercial and residential functions. These findings reveal a growing mismatch between zoning designations and actual land use. To address this, the study proposes combining industrial concentration with clearer residential zoning, supported by periodic evaluation based on functional deviation thresholds. The framework provides a quantitative tool for adaptive land-use governance in shrinking industrial cities. Full article

Multi-day travel diaries are often associated with high rates of partial completion, limiting their value for activity-based demand modeling. This paper develops a probabilistic framework that encodes daily activity sequences, clusters them with a Gaussian Mixture Model (GMM) to obtain soft (probabilistic) memberships, and predicts missing days through a constrained Lagrangian regression that guarantees valid probability distributions. Applied to the New York City Citywide Mobility Survey for 2019 and 2022, the soft-clustering approach achieves an RMSE as low as 0.17—substantially outperforming hard-clustering baselines (16–36% accuracy)—and reconstructs population-level time-use profiles with approximately 5–6% mean absolute error. Results show that post-pandemic activity patterns are more home-anchored and less varied, with pronounced socioeconomic divergence in recovery trajectories. Full article

Disruptions in urban supply chains increase the risk of reduced access to medicines whose continuous availability is important for public health. This article develops an open-data decision support system (DSS) framework for assessing medicine availability under shortage and node-failure scenarios. The empirical application combines redeemed e-prescription data from the Ukrainian reimbursement program for 2022–2025 with the registry of dispensing points under National Health Service of Ukraine contracts and applies a unified scenario design to Kyiv and Lviv. The results show that demand is more concentrated in Lviv: the top 10 dispensing nodes account for 29.7% of redeemed e-prescriptions, compared with 14.2% in Kyiv. The proposed DSS supports the redistribution of limited available volume across spatial zones; it does not generate additional supply. Its value lies in identifying where lower-tail coverage, service coverage gaps, and redistribution-distance constraints should be monitored under explicitly defined stress-test assumptions. The framework is therefore positioned as a scenario-based planning tool rather than as a real-time inventory-management system. Full article

Background: Urban road-traffic fatalities are a public health burden and a governance challenge because protection is uneven across urban space and time. Methods: We analyzed 702,540 administrative road-incident records from Medellín, Colombia (2008–2025), identified 2762 fatal cases, standardized incident categories, and harmonized time and coordinate fields. Spatial analyses were based on 2507 geocoded fatalities. We combined descriptive profiling, chi-square tests, logistic regression comparing pedestrian-strike and collision fatalities, sensitivity analyses using grouped time periods and a pandemic-period indicator, and spatial autocorrelation measures using Moran’s I and Getis–Ord $G_i^{*}$. Results: Incident type composition did not differ significantly between daytime and nighttime, but it varied across districts (comunas). Each later hour was associated with slightly higher odds that a fatality would be classified as a pedestrian strike rather than a collision (OR = 1.033), and fatalities in the urban core had nearly threefold higher odds of being classified as pedestrian strikes (OR = 2.953). Sensitivity analyses did not materially alter these associations. Spatial statistics showed strong clustering among the dominant fatality classes and identified 129 significant hotspot cells. Conclusions: Fatal road-traffic harm in Medellín is spatially concentrated and varies by incident mechanism, with pedestrian fatalities disproportionately concentrated in central areas of intense pedestrian–vehicle interaction. These findings show that transparent surveillance analytics can inform governance prioritization while also underscoring the need to improve data completeness, incorporate exposure measures, and interpret pandemic-period patterns with caution. Full article

Road transport is one of the most significant sources of environmental pollution and greenhouse gas emissions; therefore, the development of sustainable mobility is becoming an important direction of urban transport policy. The objectives of the European Union’s transport policy encourage cities to plan and implement measures that reduce the environmental impact of transport, improve transport conditions, and increase the availability of mobility alternatives. The aim of this study is to evaluate the planning of sustainable mobility development in Lithuanian cities by analysing sustainable urban mobility plans, the measures proposed in them, and their links to the needs of urban transport systems. The study applied descriptive statistics, comparative analysis, and document content analysis methods. The urban plans of Lithuanian cities were evaluated according to the following criteria: the time scope and relevance of the plan, the completeness of the analysis of the existing transport system, the assessment of the environment and quality of life in cities, and the compliance of the planned sustainable mobility measures with the needs of the city. The results of the study show that only a portion of Lithuanian cities have prepared sustainable urban mobility plans, and their contents and analytical bases differ. Some of the plans do not provide a sufficiently detailed and relevant analysis of the current situation; therefore, the need for the selected measures is not always clearly justified. The cities analysed generally envisage or apply measures to improve public transport, develop pedestrian and bicycle infrastructure, regulate traffic, create electric vehicle infrastructure, and promote multimodality. It was concluded that sustainable mobility planning in Lithuanian cities is uneven, and its assessment depends not only on the diversity of the envisaged measures but also on the analytical quality of planning documents, the justification of measures, and the consistency of envisaged implementation measures. The study highlights the need to strengthen data-based sustainable mobility planning and to more clearly link the measures envisaged in the plans with the specific challenges of urban transport systems. Full article

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

Urban mobility is undergoing a rapid transition driven by digitalization, electrification, and automation. However, current research remains largely fragmented across specific technological domains, obscuring the interactions required for city-scale deployment. To address this gap, we conducted a literature review (2018–2026) adhering to the PRISMA 2020 guidelines. Using Google Scholar as an aggregate search engine, we screened and synthesized 162 peer-reviewed studies across four foundational pillars: intelligent transportation systems, resilient infrastructure, electric mobility, and autonomous/connected vehicles. The methodological evaluation of the literature reveals a prevalent overreliance on simulation models compared to large-scale field trials. Through a narrative synthesis of the selected studies, we derive a comprehensive five-layer conceptual framework that integrates the infrastructure, mobility, energy, digital, and governance layers. The findings indicate that scaling smart mobility is frequently constrained by institutional fragmentation and infrastructure rigidity, which often act as bottlenecks equal to or greater than technological capability. The review concludes by outlining targeted research priorities to guide the integration of sustainable urban mobility. Full article

Adverse weather and low-illumination conditions in urban road scenarios substantially degrade license plate image quality, posing a major challenge to robust automatic license plate recognition for urban intelligent transportation systems and smart city construction. To address the limitations of conventional pipelines that optimize detection, enhancement, and recognition in isolation, this study proposes CLEI, a unified framework integrating YOLOv12-based detection, GAN-based image enhancement, and a novel CNN–Mamba network (CMN) for character recognition. Using a curated dataset of 3000 license plate images captured under rain, snow, fog, and nighttime urban roadside conditions, we first benchmarked several mainstream detectors and identified YOLOv12s as the most effective model in terms of accuracy, inference speed, and computational efficiency. To mitigate blur and low-quality degradation in cropped plate regions, DeblurGAN-v2 was employed for adaptive enhancement, achieving PSNR of 16.61 dB, SSIM of 0.8776, and LPIPS of 0.1151. For recognition, the proposed CMN replaces the recurrent module in CRNN with a Mamba-based state-space model, improving sequence modeling efficiency and robustness. CMN achieved 93.3% plate accuracy, outperforming CRNN (91.0%) and LPRNet (88.5%), while the full CLEI framework reached 93.67% accuracy after enhancement. These results demonstrate that collaborative optimization across detection, restoration, and recognition enables accurate and efficient license plate recognition in severely degraded urban traffic environments, providing a reliable technical support for urban traffic monitoring, public security governance and smart city infrastructure construction. Full article

The increasing integration of renewable energy sources, smart appliances, and distributed energy technologies has significantly increased the complexity of residential energy systems, necessitating advanced Home Energy Management Systems (HEMS). Optimization techniques play a critical role in achieving key objectives, including energy cost reduction, load balancing, minimizing the peak-to-average ratio, and enhancing user comfort. This paper presents a comprehensive review and critical analysis of optimization techniques employed in HEMS, including mathematical methods, metaheuristic algorithms, artificial intelligence (AI)-based approaches, and rule-based strategies. These techniques are systematically classified and compared based on scalability, computational complexity, uncertainty handling, and real-time applicability. The analysis reveals that while conventional methods provide reliable solutions for structured problems, AI-based techniques offer superior adaptability and performance in dynamic and data-driven environments. Furthermore, key research gaps are identified, including limited multi-objective optimization, inadequate consideration of uncertainty and electric vehicle integration, and the lack of real-world implementation. Finally, future research directions are outlined, emphasizing hybrid optimization frameworks and intelligent, IoT-enabled energy management systems. Full article

Groundwater quality assessment in urban and peri-urban environments is often constrained by incomplete monitoring records, irregular sampling frequencies, and heterogeneous environmental datasets. The primary objective of this study is to predict the Water Quality Index (WQI) in the Attica River Basin, Greece, using advanced machine learning (ML) techniques. A groundwater quality dataset comprising 958 observations from 80 monitoring stations was analyzed using six physicochemical parameters, namely electrical conductivity, ammonium, nitrate, nitrite, chloride, and sulphate. Three modeling approaches, namely TabNet (with Winsorization), SVM, and Gradient Boosting Machines (GBM), were implemented to estimate groundwater quality conditions. To address the challenge of missing data, Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching (PMM) was implemented and systematically compared against conventional imputation approaches, including smoothed averages, interpolation, and forward-fill methods. The novelty of this study lies in the integration of open-access groundwater chemistry data, advanced multivariate imputation (MICE-PMM), and attention-based deep learning (TabNet) for groundwater quality prediction in a Mediterranean peri-urban area under data-scarce conditions. Using a multi-year groundwater monitoring dataset, the results indicate that the integrated MICE-PMM and TabNet framework achieved the highest predictive performance, with R² = 0.91, NSE = 0.91, RMSE = 52.21, and MAE = 25.68. Feature importance and sensitivity analyses identified nitrate as the dominant driver of WQI variability, highlighting the strong influence of anthropogenic nutrient loading on groundwater quality. Overall, the proposed framework provides a transferable, data-driven approach for groundwater quality prediction, environmental monitoring, and groundwater resource management in urban and peri-urban aquifer systems characterized by incomplete environmental datasets. Full article

In the context of economic disparities, housing as a fundamental right highlights processes of social differentiation and stratification. From a complexity perspective, factors such as location, distance from development hubs, and designs that standardize needs exacerbate weaknesses in its conception. The new realities of living in housing prompt us to rethink design approaches that integrate housing and work. This research analyzes the Ciudad Alegría Social Housing Program, located in the city of Loja, Ecuador. The diagnostic method indicated that 24% of homes have commercial projections as a survival strategy. While these spatial patterns diminish the levels of habitability in the homes, they also provide benefits such as proximity between home and work, savings in transportation costs, interaction with neighbors, and mixed uses. These observations reveal gaps in the architectural design process, which fails to consider both service providers and users in decision-making related to the design of VIS programs, highlighting the need for this phenomenon to be elevated to public policy. Full article

Post-war reconstruction in Aleppo requires more than replacing damaged buildings; it demands an understanding of the city’s historically layered urban fabrics, their differing socio-spatial logics, and their unequal capacities for recovery. Following severe conflict-related destruction during the Syrian civil war, particularly between 2012 and 2016, and the additional impact of the February 2023 earthquake, Aleppo’s recovery is further complicated by the heritage significance of its Ancient City, inscribed on the UNESCO World Heritage List in 1986 and included on the List of World Heritage in Danger since 2013. This study examines how urban morphology can guide reconstruction through a comparative analysis of four neighborhoods representing major phases of Aleppo’s development: Jdaideh, Azizieh, Mohafaza, and Jabal Badro. Using a qualitative historical–morphological approach, the research analyzes figure–ground relations, street-network structure, degrees of transition between public, semi-public, semi-private, and private spaces, and landmark–node systems to identify the spatial characteristics, temporal persistence, and planning meaning of each district. The findings show that Aleppo is not a homogeneous urban system but a city composed of distinct fabrics with different strengths, vulnerabilities, and reconstruction needs. The comparison further demonstrates that density alone is not an adequate indicator of urban quality or resilience. The study concludes that reconstruction should be based on fabric-specific strategies, including preservation-sensitive rehabilitation, reinforcement of public nodes, balanced connectivity, governance-aware phasing, and incremental upgrading. Urban morphology is therefore proposed as a practical, but not exhaustive, framework for context-sensitive recovery in conflict-affected and historically layered cities. Full article

Urban sprawl in Iran has previously been examined through spatial measurement, driver classification, and multi-criteria weighting approaches. However, less attention has been given to the hierarchical structure through which governance, market, infrastructure, demographic, and regulatory conditions reinforce one another over time. This study develops a structural interpretation of urban sprawl in Iran’s major metropolitan regions by integrating expert refinement of key drivers with Interpretive Structural Modeling and MICMAC analysis. Rather than ranking drivers by relative importance, the analysis identifies their causal positioning within the wider sprawl system. The findings show that institutional fragmentation, weak enforcement capacity, and limited metropolitan coordination occupy the deepest structural levels, shaping downstream outcomes such as speculative land development, infrastructure-led peripheral expansion, housing pressure, and the growth of outlying settlements. The study contributes to urban-sprawl scholarship by reframing Iranian metropolitan expansion as a governance-embedded spatial process and by identifying leverage points for coordinated intervention. Policy responses should therefore prioritize institutional alignment, enforceable growth-management mechanisms, and infrastructure investment that supports compact rather than dispersed metropolitan development. Full article

The Saudi Green Initiative (SGI) represents a major national effort to enhance environmental sustainability and urban livability in Saudi Arabia. Despite its ambitious targets, limited empirical research has evaluated its spatial performance and social impacts. This study assesses the progress of SGI implementation in Riyadh by examining the spatial distribution, accessibility, and equity of urban green spaces (UGS), alongside residents’ perceptions of their quality. A mixed-methods approach was adopted, integrating Geographic Information Systems (GIS)-based spatial analysis with a structured survey of 180 residents. Spatial indicators were evaluated against the World Health Organization (WHO) benchmark of 9 m² per capita and the SGI target of 28 m² per capita. The results reveal that although total green space has increased between 2018 and 2024, its distribution remains uneven, with high-density neighborhoods consistently falling below recommended standards. Survey findings indicate high satisfaction with recreational and environmental benefits, but lower satisfaction with facilities and public engagement. The study highlights that increasing total green space alone does not ensure equitable access and emphasizes the need for population-sensitive planning strategies. These findings provide practical insights for improving the spatial equity and effectiveness of urban greening initiatives and contribute to broader sustainable urban development goals. Full article

Post-war Aleppo is often framed through destruction, legal constraints, and the technical demands of reconstruction. This article challenges that assumption by re-reading Aleppo’s pre-2011 municipal vision as an analytical resource for post-war recovery. The study adopts a qualitative interpretive methodology based on municipal archival material, including the City Council work programme, strategic planning presentations, project documents, and materials related to the City Development Strategy, Madinatuna initiative, the old city, Bab Antakiya, and major public-space and service initiatives. The analysis followed three steps: identifying repeated municipal priorities and planning concepts; organizing them into thematic axes; and interpreting flagship projects as spatial expressions of a broader municipal vision. To assess post-war relevance, the archive is also read against evidence of damage, displacement, urban functionality, and heritage loss. The results show that Aleppo’s pre-2011 municipal vision can be reconstructed through six interrelated axes: strategic urban development and managed growth; the old city as a living urban fabric; urban repair in the city centre; mobility and accessibility; culture and social development; and development partnerships and international cooperation. The findings reveal that these axes formed a partially integrated municipal urbanism rather than isolated projects, while flagship interventions such as Bab Antakiya, the Green Path, the river corridor, and the Citadel surroundings materialized this logic. The study also finds that this vision remained institutionally vulnerable because of political centralization and limited municipal autonomy. It concludes that post-war recovery should build on critical continuity rather than reconstruction from scratch. Full article

National Forest Trails (NFTs), a key component of forest welfare infrastructure, increasingly require a shift from government-led management to citizen-participatory governance. This study examined the structural relationships among volunteer motivation, activity satisfaction, perceived quality-of-life (QoL) change, and behavioral intention in the context of NFT volunteering. A survey was conducted with 217 adults who had participated in forest trail volunteering programs in Korea, and the data were analyzed using structural equation modeling (SEM). The results showed that volunteer motivation had significant positive effects on reward importance, activity satisfaction, and perceived QoL change. Activity satisfaction positively influenced both Future Participation Intention and Active Participation Intention, whereas perceived QoL change had a significant positive effect only on Active Participation Intention. In addition, activity satisfaction and perceived QoL change mediated the relationship between volunteer motivation and Active Participation Intention. These findings suggest that forest trail volunteers are not merely supplementary labor for trail management, but active participants in forest governance who both contribute to and benefit from the environments they help sustain. Overall, the study indicates that sustainable NFT volunteering depends not only on motivation itself, but also on the quality and personal meaning of the volunteer experience. The findings highlight the importance of experience-centered program design, appropriate recognition systems, and greater attention to participant-centered well-being outcomes in sustainable forest trail governance. Full article

This work proposes a spatial optimization framework based on Monte Carlo Tree Search (MCTS) to support infrastructure planning in complex urban environments. The challenge lies in integrating diverse geospatial and socioeconomic data to balance efficiency, defined as potential demand, with territorial equity, related to mobility needs. The approach formulates the problem as a sequential decision process, capturing the interdependence of location choices and enabling structured exploration of the solution space. Unlike traditional optimization methods that rely on local heuristics or require strong simplifications, this framework accommodates non-linear relationships and competing objectives without sacrificing system complexity. The use of MCTS effectively balances exploration and exploitation, making it well-suited for high-dimensional, non-convex spatial problems. This methodology offers a flexible and scalable tool for urban planning, adaptable to various contexts and constraints. It supports generating solutions that are both efficient and aligned with equity considerations, providing valuable guidance for decision-making in rapidly evolving urban systems. Full article

This study introduces RE-VAL (REal-estate VALuation), an explainable framework for urban real-estate analysis that integrates reproducible data acquisition, geographically informed feature processing, predictive benchmarking, and interpretable outputs suitable for decision-support-oriented analysis. Unlike static automated valuation models, the RE-VAL framework is designed to reflect context-dependent market behaviour across heterogeneous urban areas. The comparative evaluation on 1153 residential listings from Cagliari (Italy) showed that MLP achieved the strongest predictive performance, while Random Forest provided the most convincing balance between predictive competitiveness and interpretability. Beyond point estimation, the framework leverages SHAP-based decomposition to translate algorithmic outputs into transparent, monetary-based “Bonus/Malus” adjustment tables. The analysis highlights the presence of potentially non-linear interactions, including a possible premium associated with energy efficiency in prestigious areas, and suggests that the framework can remain informative when incomplete technical data are preserved as potential proxy signals rather than being discarded as noise. Rather than identifying a single predictor, RE-VAL provides a transparent, extensible and decision-oriented workflow for urban real-estate valuation, advancing the integration of explainable artificial intelligence within complex spatial-economic systems. Full article

This study introduces a multi-source spatial methodology that moves beyond the traditional urban/rural dichotomy to classify the American landscape into detailed, temporally defined settlement types. By combining historical housing unit and population estimates (HHUUD10 and LTDB) standardized to 2010 census tract boundaries with high-resolution, grid-level data on the built environment (HISDAC-US), this research establishes a settlement typology based on the development history of detailed geographic units. This framework classifies areas (from Prewar Cores and 21st-Century Suburbs to exurban fringes, outlying towns and rural areas) based on their era of development and proximity to urban centers. Applying this typology reveals profound spatial and demographic decentralization spanning eighty years of metropolitan expansion. The findings demonstrate a stark geographic sorting: expanding greenfield edges and exurbs have become magnets for high-income, highly educated, and predominantly White populations. However, longitudinal tracking reveals a distinct morphological “life-course” within suburban rings. As older suburbs age and their housing stock depreciates, they open to wider demographic integration, transforming into destinations for Black and foreign-born residents. Furthermore, the data highlight a contemporary polarization of human capital, concentrated in both the newest suburban peripheries and the resurgent urban cores, contrasting with persistent economic decline in outlying towns and rural areas. Ultimately, this methodology provides a flexible, longitudinal framework for understanding the long-term morphological and demographic evolution of American settlement. Full article

In an increasingly urbanized world, ensuring equitable access to urban green spaces (UGS) is essential for human well-being. Previous studies have largely focused on measuring proximity or availability of UGS, often neglecting the role of the walkable environment and the interaction between supply, demand, and movement flows. To address this gap, we develop a novel modeling framework that integrates the Detour Index (DI) and Local Significance (LS) to jointly capture physical barriers and recreational flows within urban street networks. Using openly available data from OpenStreetMap and Urban Atlas, we model the walkable environment in Leipzig, Germany, at a high spatial resolution. The approach enables the identification of inefficient routes, potential barriers, and areas of high use intensity, providing actionable insights for urban planning. By combining network-based accessibility with flow-based indicators, our method advances existing approaches that rely on static distance measures. The analyses of different planning alternatives further demonstrate how changes in urban structure affect accessibility and crowding patterns. The framework is transferable and based on open data, providing a foundation for future research to integrate behavioral factors and richer datasets to further refine accessibility modeling. Full article

Background: Translating urban environmental health evidence into actionable policies remains challenging in South-Eastern Europe, where environmental epidemiology has yet to reach maturity and institutional capacity and cross-sector coordination are suboptimal. This study assessed stakeholders’ awareness, perceived roles, and prioritization of urban health challenges, alongside the barriers and evidence needs related to healthy and sustainable urban development. Methods: A cross-sectional online survey was conducted between March and May 2025 among 108 stakeholders identified through a collaborative evaluation process. Participants represented national institutions, municipal actors, academia, non-governmental organizations, business, and citizens. They reported on their role and influence, and were asked to identify priority urban health problems, relevant policies and actions, perceived barriers to decision-making, and expected benefits of addressing priority problems. Results: Most respondents reported limited or moderate influence on urban decision-making. Priority problems clustered around air pollution, traffic, and land-use pressures, with climate change and heat also frequently cited. Dominant barriers included lack of coordination and policy continuity, insufficient political support, and limited funding and institutional capacity. Anticipated gains centered on improved public health, cleaner air, and citizen satisfaction, with broader quality-of-life and economic co-benefits also identified. Conclusions: Prioritized urban environmental problems are largely consistent with scientific evidence on their health impacts, though certain risk factors remain underestimated. Access to specific, actionable scientific evidence and the co-production of solutions with broad stakeholder representation are essential prerequisites for effective urban health policy and practice. Full article

This study investigates whether spa settlements can be analytically interpreted through a relational spascape framework that reveals structural and configurational patterns beyond conventional typological classifications. In the context of increasing interest in therapeutic landscapes and heritage-sensitive development, spa settlements represent complex spatial systems shaped by the interplay of natural resources, urban form, and socio-cultural practices, yet they remain insufficiently understood through existing analytical models. The methodology is based on a structured analytical design combining three urbanization dimensions (material transformation, territorial regulation, and everyday life) with six thematic fields, operationalized through graded cross-affiliation scoring. The empirical research is conducted on a sample of 12 spa settlements in Serbia, selected to reflect diverse geographical, morphological, and developmental conditions. Statistical calibration was performed using Principal Component Analysis (PCA) and hierarchical clustering to identify underlying structural relationships and configurational groupings. The results indicate that spa settlements operate as multi-affiliated relational entities rather than fixed typologies, exhibiting dimension-specific structural logics and forming distinct configurational families depending on the analytical perspective applied. PCA reveals differentiated internal structures across dimensions, while clustering confirms the absence of a single stable typology. The findings support a relational understanding of spa settlements as dynamic spatial systems characterized by shifting alignments of material, regulatory, and experiential factors. Beyond the Serbian context, the study offers a transferable methodological framework that connects qualitative urban interpretation with quantitative spatial analysis, contributing to heritage-sensitive planning, territorial governance, and the management of spa systems as relational clusters. Full article

In recent years, the use of warm-season species, which are species requiring less water, has been pursued in continental areas, but their dormancy and spring green-up need to be properly defined. In urban green areas, we find that small-scale microclimatic differences, while less intense than classical urban–rural gradients, still influence vegetation performance and spring green-up. This study examines the impact of microclimatic temperature variation on the spring green-up of different cool-season and warm-season turfgrasses in the continental climate of Madrid, Spain. The evaluation of colour change during the spring green-up process has been conducted using different vegetation indices, and mathematical models for correlating temperature with the indices’ values have been obtained. The results indicate that with average temperatures varying by about 1.3 °C and 0.9 °C in January and February, respectively, there have been marked differences in spring green-up, especially in cool-season turfgrasses, of almost one month. In contrast, differences in warm-season turfgrasses were reduced. Among the four vegetation indices, Canopeo has proved to be the best for detecting the early stages of spring green-up, with R² values ranging from 0.43 to 0.92. Meanwhile, the tailored greenness index for turfgrass was the most effective for determining the moment at which warm-season grasses achieve the colouration of cool-season grasses, with R² ranging from 0.79 to 0.85. Finally, the green leaf index was particularly valuable for identifying differences among species and sectors throughout the entire spring green-up process. Models based on this index achieve high R² values (0.57 to 0.94), but these models predict the moment at which warm-season grasses achieve cool-season grasses’ colouration later than it actually occurs. Understanding how turfgrasses respond to these localised microclimatic conditions is essential for selecting resilient species and improving maintenance strategies in parks, sports areas, and other components of urban green infrastructure. Full article

Fragmented transit governance across multiple agencies makes measuring service inequality in large metropolitan regions notoriously difficult. This paper maps schedule-aware transit service intensity—an origin-side, supply-focused component of accessibility—across the Greater Toronto Area (GTA) by integrating General Transit Feed Specification (GTFS) data from six providers within an H3 hierarchical hexagonal grid. The measure does not capture destination access, travel time, transfers, fares, reliability, or crowding, and is therefore framed throughout as a service-intensity indicator rather than a full accessibility evaluation. We operationalize the indicator as the number of cumulative scheduled departures per hour reachable within an 800 m walking catchment for three distinct time windows: weekday peak, weekday midday, and Saturday midday. Across 9635 hexagons and 23,026 stops, our results reveal a sharply hierarchical regional network. When weighted by population, 16.4% of GTA residents have no scheduled service within walking distance during the weekday morning peak; the corresponding area-weighted share, reflecting the extensive greenbelt and agricultural fringe, is 70.6%. Only 22.6% of hexagons reach at least 12 departures per hour, while 75.5% of residents meet that threshold. Median service intensity drops from 234.25 departures per hour in the Urban Core to zero beyond the Inner Suburban Ring, and service thins out on weekends, with retention in the outer rings dropping to roughly 75% of weekday levels. Spearman correlations show that service intensity is concentrated in denser, more diverse, and lower-income census-tract contexts, with population density emerging as the strongest hex-level correlate ($\rho =0.69$); after Clifford–Richardson correction for spatial autocorrelation (effective $n\approx 745$), the principal CT-level correlations remain statistically significant ($p<{10}^{-15}$), and partial correlations controlling for density indicate that socioeconomic composition retains an independent, if attenuated, association. Under one-tract-one-observation aggregation ($n=1144$ unique tracts), the income gradient strengthens to $\rho =-0.74$ and becomes co-equal in magnitude with population density ($\rho =0.74$), confirming that the hex-level coefficients are not artifacts of pseudo-replication. A population-weighted Gini coefficient of 0.60 confirms substantial distributional inequality. Sensitivity analyses confirm that the Inner-to-Outer Suburban break is robust to alternative ring thresholds (10/25/40 and 20/35/50 km), to exclusion of the four Halton municipalities affected by incomplete local-feed coverage, to H3 resolution at the municipal level, and—in a representative shortest-path network sub-analysis for Pickering (not a full GTA-wide network-distance test)—to use of network rather than Euclidean walking distance. These patterns suggest that a substantial gap exists between where suburban residential growth has occurred and where frequent transit service is available, a pattern with historical roots in the 1996–2006 service–need alignment, though the 2006–2023 trajectory is not directly measured here. The results suggest that the transition zone between the inner and outer suburbs may warrant further investigation as a planning focus, and that cross-agency weekend service coordination merits further analysis as a potential equity dimension. This multi-agency H3 framework establishes a reproducible baseline for monitoring schedule-aware service intensity in polycentric metropolitan areas. Full article