Search Results (4,039)

University dormitories in cold climate regions face the dual challenges of high heating energy consumption and poor outdoor pedestrian comfort during winter. Existing studies on university dormitories have primarily focused on individual building performance optimization, while insufficient attention has been paid to the optimization of dormitory cluster layouts and their multi-objective performance. To address this gap, this study establishes a parametric multi-objective optimization framework to simultaneously minimize building energy use intensity, minimize wind speed at pedestrian height, and maximize outdoor thermal comfort. Based on three floor area ratio scenarios, 24 dormitory prototypes are extracted from three building typologies: row-type buildings, detached buildings, and enclosed buildings. The optimization process was implemented on the Grasshopper platform using the NSGA-II algorithm. Cluster analysis is conducted on the Pareto front, and Pearson correlation analysis is applied to investigate the relationships between six urban morphological parameters and the three optimization objectives. The results indicate that: (1) enclosed buildings (E-1 type) and detached buildings (D-1 type) dominate the Pareto-optimal solution set; (2) high-FAR buildings are predominantly distributed in the northeastern part of the site, while public spaces are concentrated in the central-southern area; (3) correlation analysis indicates that shape coefficient (SC) exhibits the strongest correlations with the three objectives; and (4) compared with dominated solutions, Pareto-optimal solutions reduce WS by 10.46% and EUI by 6.57%, while improving UTCI by 0.05 °C. This study provides quantitative decision-making support for efficient planning and low-carbon design of university dormitory clusters in cold climate regions. Full article

Multi-intersection traffic signal coordination and control improves urban traffic efficiency by coordinating signal timing across suitable corridors or groups of intersections. However, most existing work focuses on optimizing the signal timing plan for predefined intersection groups such as all intersections on an arterial, whereas the question of which intersections should be coordinated together for maximum efficiency is often overlooked. Moreover, as traffic patterns vary throughout the day, the most suitable intersection groups may not be fixed across different Time-of-Day (ToD) demand conditions. To address this gap, this study proposes a network partitioning approach to adaptively identify effective groups of intersections for traffic signal coordination. A Signal Coordination Network (SCN) is constructed to quantify the coordination benefit between intersections based on traffic volume, spatial proximity, and cycle length compatibility. By solving a maximum set-packing problem, the proposed approach partitions the SCN into the most effective intersection groups, including isolated intersections, arterial progression groups, and network progression groups. Compared with the best-performing baselines, the proposed method reduces average travel time by 2.7% and average delay by 7.0% across the tested network–ToD scenarios, while the experiments under demand variation show statistically significant improvements in all AM and PM peak scenarios and comparable performance during off-peak periods. These results suggest that explicit coordination-group selection can provide additional operational benefits beyond local retiming, adaptive control, and predefined corridor or network coordination. The proposed framework offers a practical planning-level tool for designing ToD-sensitive signal coordination plans in urban networks. Full article

Visual clutter can affect attention, cognitive load, and environmental evaluation, yet streetscape studies often treat visual complexity as a broad construct without distinguishing visual richness from competition among visual features. Shanghai was selected because its dense, heterogeneous urban fabric and central–peripheral variation make it well suited to city-scale analysis. Using 69,244 street-view images, this study combined computer vision, deep learning, and modeling to quantify edge, color, semantic, and depth clutter and estimate beauty, wealth, liveliness, safety, boredom, and depressiveness. After controlling for built-environment characteristics, 23 of 24 linear associations were significant, although their directions differed. Edge and color clutter were associated with higher positive perceptions and lower negative perceptions. Semantic clutter was positively associated with wealth, liveliness, and safety but negatively associated with beauty; depth clutter was negatively associated with beauty, wealth, and liveliness and positively associated with boredom and depressiveness. Stable inverted U-shaped patterns occurred in only four relationships. Streetscape management in Shanghai should avoid uniformly increasing or reducing visual information. Planning and design may retain architectural detail, vegetation edges, coordinated color variation, and semantic richness while better coordinating facades, signage, street furniture, vehicles, and foreground elements, improving sightline continuity, and reducing obstruction and abrupt spatial discontinuity. Full article

Understanding the penetrability of synthetic aperture radar (SAR) signals into near-surface materials is a prerequisite for using SAR observations to infer subsurface physical properties. However, direct measurements of penetration depth under controlled material conditions remain limited, especially for comparisons across radar bands, soil water content, sand, and hard ground surfaces. This study provides direct laboratory measurements of L-band and S-band SAR signal penetration using a ground-based SAR system in a microwave anechoic chamber. Unlike penetration depth inversion studies, dihedral corner reflectors were buried at known depths. We identified the depth at which each reflector response became indistinguishable from the background. The reported values represent effective signal penetration intervals under the laboratory geometry. The chamber effectively reduced thermal noise and electromagnetic interference. At an incidence angle of 40°, the L-band signal penetrated 45–50 cm and 30–35 cm in clay samples prepared at 4% and 18% volumetric soil water content (SWC), respectively. The L-band penetration depth was 85–90 cm in dry sand with 3% SWC, about 9 cm in asphalt, 5 cm in gravel, and 2 cm gravel plus 7 cm asphalt in a composite hard-surface layer. The S-band penetration depth was 20–25 cm in loose clay, 15–20 cm in compacted clay, and 15–20 cm in sandy loam under the tested conditions. These results show that SAR penetration depends strongly on wavelength, water content, material type, and compaction/surface condition. We also demonstrate that thin hard ground surfaces can allow measurable L-band penetration. Our findings provide experimental benchmarks for interpreting SAR signals in subsurface parameter retrieval and sand-layer characterization. Full article

Purpose: This study conducts a systematic literature review on the intersection of real estate exposure and geotechnical hazards, focusing specifically on seismic and subsurface risks. The objective is to synthesize key thematic trends, methodologies, and governance frameworks that inform risk-informed planning in seismically vulnerable urban areas. Design/methodology/approach: A Boolean search query was implemented on Lens.org, identifying 55 peer-reviewed articles published between January 2020 and May 2025. Inclusion criteria required explicit focus on property exposure to seismic or ground instability risks. Thematic analysis was conducted based on title and abstract data, supported by a Python (version 3.11)-generated word cloud to inductively identify five core clusters: (1) seismic assessment and earthquake risk, (2) building vulnerability and structural performance, (3) subsurface hazards and ground instability, (4) urban areas, heritage, and social vulnerability, and (5) risk mitigation, planning, and resilience frameworks. Findings: The review reveals a shift from hazard-centric, engineering-based models toward integrated, multi-scalar frameworks that embed risk within socio-economic, spatial, and institutional contexts. While consensus exists on the importance of probabilistic modeling, retrofitting, and GIS-based tools, divergences persist around behavioral valuation, policy uptake, and equity in implementation. Heritage cities and informal settlements emerge as under-addressed but critically vulnerable domains. Originality/value: This study systematically maps interdisciplinary research on real estate exposure to seismic and subsurface risks post-2020. By bridging engineering, planning, behavioral economics, and disaster governance, the review provides a unique synthesis relevant for academics, urban planners, and policymakers seeking to design equitable and resilient urban futures. The five-cluster thematic taxonomy introduced in this review represents an original synthesis that bridges engineering vulnerability assessment, behavioral economics, heritage preservation, and resilience governance. Unlike previous reviews that have typically focused on single disciplinary perspectives, this taxonomy integrates multi-scalar approaches spanning asset-level diagnostics to national exposure modeling, providing a comprehensive framework for understanding real estate exposure to seismic and subsurface risks. Full article

Peri-urban rural regeneration is often assessed through completed projects or administrative indicators, while the governance mechanisms that sustain rural sustainability receive less attention. This article examines how project-based participation can be translated into governance capacity in Hsinchu City, northwestern Taiwan. Adopting an evidence-bound reflective documentary case-study design, it analyzes policy, spatial planning, training, community project, and rural regeneration records from the 2025 Hsinchu City Community Rural Regeneration Guidance Program. The findings show that regeneration operates through municipal coordination, functional clusters, community participation, and site-based interventions. Six intermediate capacities are identified: proposal-making, coordination, maintenance, interpretive, sustainability-translation, and resilience capacities. The study contributes a transferable framework linking rural sustainability, SDG localization, ESG-oriented governance, and peri-urban regeneration through explicit evidence-use boundaries. It does not claim verified long-term impacts on income, carbon reduction, biodiversity, demographic change, or resident satisfaction; rather, it clarifies how governance capacity may be generated and where future independent verification is required. Full article

When planning 30 km/h zones within a selected urban street network in a city centre, the effectiveness of applicable traffic-calming measures (TCMs) is analysed to support sustainable urban mobility and the design of safer urban streets. One of the possible TCMs is the mid-block narrowing (MBN), which has been reported in previous studies as having limited effectiveness. However, those studies were constrained by several limitations, including the lack of speed v₈₅ analyses at different distances from the MBN and the absence of assessments of other contextual variables affecting speed. To address these gaps, speed measurements were conducted within an urban street network in the city centre of Szczecin, Poland, where several MBNs had been implemented. Speeds were recorded using SR4 sensors at nine measurement points along 12 study areas during three weekdays under dry weather conditions. The collected data were analysed and speed profiles were developed for each study area. Free-flow speed was the dependent variable, while independent variables included junction spacing, different types of entrance and exit junctions, and other contextual factors. The main objective was not only to estimate the operating speed and zone of influence of MBNs but also to identify other factors determining speed profiles between junctions. The analyses showed that speeds depend on junction spacing, street type, the type of entrance and exit junctions together with their traffic arrangement, and the streetscape elements located at the MBN. The outcome of the study was the development of a comparative framework for assessing the speed-related effectiveness of MBNs under specific contextual conditions, providing a basis for the sustainable redevelopment of selected city-centre street networks. This framework may support the implementation of 30 km/h zones in city-centre street networks when planning new interventions, comparing designs, or aiding decision-making. Full article

Smart mobility strategies often emphasize digital information, applications, and data-driven transport management, while giving less attention to the stop-level conditions that shape passengers’ everyday experience. This study develops and applies a Smart Bus Stop Readiness Index (SBSRI) to assess seven interchange bus stops in Jeddah, Saudi Arabia. The index integrates five weighted dimensions: Passenger Information and Digital Readiness; Physical and Thermal Comfort Provision; Pedestrian Accessibility and Universal Design; Safety and Security; and Land-Use and Activity Integration. Data were collected through field audits, spatial mapping, passenger observations, and a short survey of 71 users. The results indicate that the selected stops have operational interchange importance but generally limited readiness. The mean SBSRI score was 39.07/100; under the adopted planning-oriented classification scheme, only Al-Balad Main Station A achieved moderate readiness, while the remaining stops were classified as showing low or very low readiness. Physical and Thermal Comfort Provision was the weakest dimension, particularly in relation to shade, seating, shelter, and shaded waiting areas. Passenger information and pedestrian accessibility also showed substantial deficiencies. Sensitivity analysis indicated that the principal stop rankings remained stable under alternative weighting scenarios, although category labels were more responsive to threshold selection. This study concludes that smart bus stop readiness should be assessed as a socio-technical condition integrating digital systems with climate-responsive waiting provision, pedestrian accessibility, safety, and the surrounding urban context. Full article

Extreme climate events and rapid urbanization pose severe threats to urban water sustainability and environmental resilience. This study presents a surrogate-assisted hydrological simulation framework designed to support sustainable urban drainage planning and multi-objective scenario exploration. The proposed approach integrates a physics-based hydrodynamic model with a machine learning surrogate (XGBoost) to emulate system responses across diverse design configurations. Under the assumed setup, the framework rapidly evaluates hydrological indicators (peak runoff, flood depth, duration, and extent) alongside microclimatic co-benefits (urban cooling and ventilation) driven by blue-green infrastructure. The surrogate model demonstrates high fidelity $\left(R^2\ge 0.93\right)$, achieving a speedup factor of ${10}^5$ to enable sustainable design screening and trade-off analysis between flood mitigation and urban liveability. Furthermore, post-construction water quality baselines demonstrate the framework’s capacity to incorporate holistic environmental metrics. Overall, this research provides a computationally efficient decision-support tool to advance the Sustainable Development Goals (SDGs)—particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action)—by offering an actionable methodology for climate-resilient grey-green infrastructure planning under data-constrained conditions. Full article

The increasing severity of heat waves in cities requires the integration of spatial morphology with temperature-resilient design; however, comparisons of multi-city systems are scarce. The aim of this article is to assess the potential thermal resilience of Poland’s largest cities, taking into account local climate zones (LCZs), which aligns with the framework of sustainable urban development. The use of LCZs allows for a comparison of the resilience of cities without being influenced by their historical development, providing a diagnostic basis for climate adaptation. The proposed potential thermal resilience index ($R_c$) integrates exposure, sensitivity, and adaptive capacity, serving as the operational inverse of structural vulnerability to heat (urban heat vulnerability). The results indicate that 62.5% of the cities are characterized by moderate potential thermal resilience; 31.3% of cities have low resilience, and only 6.2% of cities have high resilience. High potential thermal resilience is associated with a large proportion of LCZs A, B, and D, as well as a reduction in both high-density and low-density development, which provides practical guidance for land-use planning. The index shows geographical variation—higher values were found in southern and southwestern Poland. The results suggest the need to correlate physical interventions with social vulnerability and the macroclimate when designing blue-green infrastructure as a key element of sustainable adaptation. Full article

The rapid growth of e-commerce has intensified last-mile delivery activities in urban areas, creating challenges for city logistics systems related to operational efficiency, congestion, and environmental impacts. In this context, understanding the factors that influence customer satisfaction with logistics operators is increasingly important, as mismatches between customer expectations and delivery service characteristics may lead to operational inefficiencies such as failed delivery attempts and repeated delivery rounds. This study proposes a machine learning framework for predicting customer satisfaction with postal and logistics operators in urban delivery systems using survey data on customer characteristics, preferences, and service perceptions. Several machine learning algorithms were developed and evaluated to identify the key determinants of customer satisfaction and assess their predictive performance. Beyond predictive accuracy, the study interprets customer satisfaction as an indicator of the alignment between customer expectations and delivery service configurations. Improved alignment may support service configurations that reduce delivery mismatches and repeated delivery attempts, which are recognized as a significant source of additional transport activity in urban freight systems. By identifying customer segments whose expectations are not adequately addressed by existing delivery services, the proposed framework can support more informed service design and operational decision-making. From a city logistics perspective, the potential reduction in failed deliveries and repeated delivery rounds may contribute to lower vehicle kilometers travelled, congestion, energy consumption, and emissions associated with urban freight transport, although these operational and environmental indicators were not directly measured in this study. The proposed approach therefore provides a data-driven decision-support tool that can help operators improve service quality and serve as a basis for future integration with operational and environmental indicators in sustainable last-mile delivery planning. Full article

This paper proposes a comprehensive approach for data-driven participatory community monitoring based on “Citizen Science” (CS), ISO 37120, and artificial intelligence (AI). The design integrates AI with the CS six-stage life cycle and citizen data governance principles through an AI-CS framework, aligning with the Copenhagen Social Summit. The framework was developed for local governments in Ecuador, a country where territorial planning lacks citizen data disaggregated by territorial, sociodemographic, and contextual variables. This fact limits the capacity of local governments to make evidence-based decisions. Between October 2025 and February 2026, data from 30,253 events were collected in 22 provinces and 93 cantons of the country. The data were analyzed by means of ordinal logistic regression to identify predictors of perceived severity and by means of DBSCAN, an unsupervised machine learning clustering algorithm, to characterize territorial patterns. The results suggest that citizen perception is organized into systemic and predictable patterns when structured using ISO 37120 categories. The spatial analysis reveals heterogeneous territorial patterns with levels of urgency that differ depending on the canton and the urban–rural context. The proposed approach allows local governments to obtain disaggregated territorial data for participatory planning. Its design may be transferable to other Global South contexts facing similar data gaps and is aligned with SDGs 9, 11, 16, and 17. Full article

This paper demonstrates how detailed acoustic micro-segmentation can serve as a vital decision-support tool, providing localized, evidence-based data required to effectively integrate environmental acoustics and foster community co-design in urban rail modernization planning. Furthermore, while physical noise barriers are widely praised as standard mitigation products in transport engineering, our analysis highlights their severe inherent limitations, including spatial constraints, high costs, and visual pollution, making them highly unsuitable for dense historical fabrics. As viable alternatives, we propose the implementation of active, source-targeted engineering technologies (e.g., rail absorbers, modernized track beds) alongside dynamic operational governance. To be effective, these alternative solutions must be sourced and embedded directly into the earliest stages of infrastructural project documentation. Ultimately, this paper demonstrates that evidence-based acoustic governance is an essential institutional pillar for long-term resilience, providing interdisciplinary perspectives that inform future sustainable transport policy and practice. Full article

Cities face increasingly complex sustainability challenges that demand cross-sector collaboration, yet existing participatory tools rarely integrate internationally recognised sustainability frameworks with systems-based visualisation in a way that supports coordinated action across diverse stakeholders. This paper presents the Placemaking Maptivity, a card-based serious game that operationalises the ISO 37101 cross-analysis matrix—six sustainability purposes against twelve areas of action—as a shared analytical scaffold for participatory neighbourhood planning. Following a Design Science Research approach, this formative artefact-development study iteratively refined the tool through four rounds of beta testing in online, in-person, and mixed-stakeholder international configurations, drawing use cases from the EU-funded REEFLEX, PROBONO, and eFORT projects. Each game mechanic is traced to a defined learning objective using the Learning Mechanics–Game Mechanics (LM-GM) framework. Stakeholder feedback across iterations provides preliminary observations that the Maptivity supports identification of multi-purpose contributions, mapping of interdependencies, and collaborative prioritisation of next steps while highlighting facilitation, timing, and contextual adaptation as critical success factors. Whether the tool improves planning quality, learning outcomes, or decision-making effectiveness compared to alternative approaches remains a question for future, more rigorously designed evaluation studies. We discuss the formative character of these results, set out limitations, and propose a more rigorous summative evaluation protocol to support broader scaling and adoption of the methodology. Full article

Automated 3D city generation, focusing on road networks and building layouts, is in high demand for applications in urban planning, analysis, and simulations. The surge in deep generative models has facilitated automated design in recent years. However, the lack of high-quality datasets and benchmarks hinders the progress of these data-driven methods in generating city configurations. To fill this gap, this study introduces a multimodal dataset designed for the controllable generation of road networks and building layouts (named RoBus), whose public project repository provides release materials, and constitutes a large-scale resource in the field of generative city design. The RoBus dataset comprises aligned images, graphics, labels, and texts, with 72,400 paired samples that cover around 80,000 km² globally. Besides utilizing prevalent generative models, we also introduce baseline models that leverage the multimodal features of RoBus. The experiments establish the dataset’s usability while revealing complementary trade-offs rather than uniform superiority. ControlNet obtains the lowest road network FID ($20.78$), whereas our topology-aware road baseline obtains the highest traffic-convenience score ($0.83$) at the cost of lower fidelity and diversity. For building layouts, our multimodal baseline reduces FID to $17.42$ and building-density Wasserstein distance from $6.12$ to $3.37$ but produces lower diversity and a higher invalid-sample rate than the strongest comparison methods. Full article