Search Results (2,150)

Social media platforms generate large volumes of unsolicited textual content that reflect users’ perceptions of urban transportation. This study proposes a fine-grained annotation framework that classifies social media text into three constructs: attitude, intention, and reported behavior. Each statement is linked to a specific transportation mode and sentiment polarity. Two large language models (LLMs), GPT-5.5 and DeepSeek-Flash, were evaluated using few-shot in-context learning on 200 manually annotated English–French Reddit discussions from Quebec City, Canada. GPT-5.5 was selected based on its mean joint relaxed F1-score and applied to 2475 remaining discussions. The model extracted 23,634 transportation-mode mentions and 10,760 mode-specific construct statements after deduplication. Public transportation was the most frequently mentioned category and had the highest within-mode proportion of negative statements, although its positive statements outnumbered its negative statements. Attitude was the most frequently identified construct, while intention was the least frequent. The framework distinguishes evaluations, future plans, and self-reported actions or experiences, providing a more detailed description of transportation discourse. Given the Reddit sample and remaining extraction errors, these findings are exploratory and may complement other evidence used in sustainable urban mobility planning. Full article

Sustainable urban mobility requires school routes that support active travel while limiting traffic-related pollution and infrastructure barriers. This study presents a low-cost, open-source pipeline for assessing air quality and cycling infrastructure to inform sustainable school-mobility planning. The platform integrates environmental sensing with on-device computer vision, complemented by structured field observation. An exploratory campaign covered five primary schools and connecting bicycle routes in Bolzano, Italy, collecting 9879 records over 9.2 h. The recorded mean PM_2.5 concentrations ranged from 0.40 to 9.45 µg m⁻³ at school sites and reached 24.14 µg m⁻³ during bicycle-mounted transit, highlighting the importance of examining routes alongside school surroundings. Field observations identified cycling provision at all sites, with unidirectional access at one school. Computer vision demonstrated integration feasibility rather than validated infrastructure assessment. The sustainability contribution is an accessible approach to generating local evidence for healthier active travel, school-street interventions, and cycling-network improvements. With calibration and repeated term-time monitoring, the approach could support municipalities in integrating environmental quality into child-friendly, sustainable urban planning. Full article

This paper presents a LoRa-based system for real-time urban water quality monitoring. The proposed platform integrates several sensors, including ultraviolet (UV), infrared (IR), red-green-blue (RGB), pH, turbidity, and temperature sensors, to monitor different water quality parameters. A total of 1443 sensor records were collected during the experiments. Among these records, 75 UV measurements, corresponding to approximately 5.2% of the dataset, were missing or invalid while the Red, Green, and Blue (RGB) and IR measurements were available. Several regression and machine learning models were evaluated to reconstruct these UV values. Multiple Linear Regression showed limited performance in the initial random train–test evaluation (R² = 0.12), while second-order Polynomial Regression achieved a considerably higher R² of 0.963. Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Random Forest (RF) were also evaluated. Random Forest obtained the highest R² in the random train–test evaluation (R² = 0.991). However, the results were less consistent when the models were evaluated using Leave-One-Session-Out (LOSO) validation. In this evaluation, KNN obtained the lowest mean absolute error (MAE) and root mean squared error (RMSE), while the mean R² values were negative for all evaluated models, showing limited generalization between experimental sessions. The possible energy benefit of UV reconstruction was also estimated. Based on an active power of 2.995 W and an acquisition time of 10 s, avoiding 75 additional acquisition cycles would correspond to approximately 0.624 Wh of acquisition energy, or 4.94% under the assumptions used in the energy analysis. These results show that machine learning can be useful for reconstructing individual missing or invalid UV measurements when the corresponding optical measurements are available. At the same time, the differences observed between experimental sessions show that further experiments with more independent sessions and longer field deployments are needed. Full article

Dooring accidents remain a major urban traffic safety hazard, often caused by insufficient visual attention to the lateral environment before door opening. Despite growing research on driver hazard perception, how the approach speed of lateral vulnerable road users modulates drivers’ visual attention allocation and risk perception–decision-making in stationary pre-door-opening scenarios remains poorly understood. This study investigates the visual and decision-making responses of stationary drivers under lateral threats of varying speeds. A static real-vehicle experimental platform was established to reproduce a natural roadside parking scenario, in which electric bicycles approached from the left rear at five constant speeds ranging from 5 to 25 km/h. Eye movement data from 20 drivers were collected and analyzed using a Markov transition model, Shannon entropy, and one-way ANOVA to extract Area of Interest (AOI) fixation, gaze sequence entropy, and decision time. The results show that drivers’ visual strategies exhibited a strong non-linear modulation by approach speed, shifting from direct window observation at low speeds, to structured mirror-based checking at intermediate speeds, and to distributed scanning under the highest speed; gaze sequence entropy decreased to a minimum at 20 km/h and rebounded at 25 km/h. Although mean decision time decreased with increasing speed, spatial–temporal analysis revealed that at higher speeds drivers made the “Dangerous” judgment only after the electric bicycle had already passed them, showing that a shorter decision time does not reflect a more effective response. These findings offer a methodological reference for future research on driver attention and risk perception–decision-making in pre-door-opening scenarios. Full article

Urban spatial structure represents a fundamental determinant of urban thermal dynamics; however, the nonlinear responses of different Local Climate Zones (LCZs) and their spatial extents to thermal environments remain insufficiently understood. This study focuses on the central urban area of Qingdao, China, and establishes an integrated analytical framework for urban thermal environments by coupling remote sensing-based classification, WRF–SLUCM numerical simulation, and interpretable machine learning approaches. Based on 10 m Sentinel-2A imagery acquired in 2016, 2020, and 2025, a 17-class LCZ classification scheme was developed using the Google Earth Engine (GEE) platform and an improved Random Forest algorithm. The derived LCZ maps were subsequently incorporated into the WRF 4.7–SLUCM model with a four-level nested configuration to simulate 24 h near-surface meteorological conditions at a finest spatial resolution of 0.333 km, from which the Wet-Bulb Globe Temperature (WBGT) was calculated. Furthermore, the LightGBM model combined with SHAP interpretation was employed to quantitatively examine the nonlinear statistical response between LCZ area and WBGT and to identify the associated critical scales. The overall classification accuracy of the LCZ maps for the three study years ranged from 74.4% to 75.0%, with Kappa coefficients ranging from 0.7333 to 0.7446. The SHAP analysis identified four response patterns: negative-to-positive, positive-to-negative, consistently negative, and consistently positive. Different LCZ types exhibited distinct area thresholds, and some types showed a reversal in their thermal-environment response after exceeding a critical scale. These results indicate that the relationship between LCZ and WBGT is not simply linear but represents a scale-dependent nonlinear statistical association jointly shaped by land-cover type and patch size. This study provides methodological support for the quantitative and fine-scale analysis of urban thermal environments in coastal hilly cities. Full article

Against the backdrop of the deepening digital economy and accelerated green and low-carbon transformation, data factors have emerged as a core strategic resource driving the formation and allocation of new quality productive forces. Whether data can transition from closure to openness, thereby converting data dividends into inclusive green growth momentum that balances efficiency and equity, constitutes a strategic imperative in the process of Chinese-style modernization. Based on city-level data in China from 2010 to 2024, this paper measures the inclusive green growth (IGG) levels of 295 prefecture-level and above cities, and employs the double/debiased machine learning (DDML) model to examine the impact and underlying mechanisms of public data open platforms on urban IGG, treating the launch of these platforms as a quasi-natural experiment. The findings reveal that the launch of public data open platforms exerts a significant positive effect on urban IGG and is associated with improvements across multiple dimensions, including economic development, social equity, green production and consumption, and environmental protection. In terms of mechanisms, public data open platforms promote IGG through three pathways: advancing digital inclusive finance development, mitigating factor market distortions, and improving urban information circulation. The impact effects exhibit significant heterogeneity across cities with different endowments: cities with larger economic scales, more abundant human capital, and higher initial IGG levels benefit more from public data openness. Furthermore, the effect of public data open platforms on urban IGG displays a regional gradient pattern of “strongest in the east, moderate in the west, and weakest in the central region,” indicating that policy dividends are fully realized in information-intensive economies but remain insufficiently realized in regions with relatively lagging digital infrastructure. Full article

Urban regeneration increasingly uses tourism scenario activation to renew heritage, public space and local economies. Yet the production of urban attractiveness can intensify tourism gentrification, commercial reorientation and the erosion of ordinary urban life. This critical integrative review synthesises diverse perspectives from urban regeneration, touristification, tourism gentrification, place branding, speculative and platform urbanism, liveability, publicness and collaborative governance, integrating them into a coherent conceptual framework through an abductive and integrative process of conceptual development. A second-phase structured verification search in Scopus and Web of Science Core Collection produced 5954 search occurrences and 3865 unique records after deduplication, which were used to test literature coverage, conceptual boundaries and counterarguments. The review formalises touristic desirability as the relational and institutionally organised value logic through which anticipated visitor-oriented attention, experience and value shape what becomes visible, investable, programmable and materially actionable. It develops urban cohabitation as an evaluative criterion centred on the joint viability of heterogeneous urban lives, uses and claims under transformation, and cohabitation-oriented governance as the intervention-oriented component identifying capacities through which tourism-oriented trajectories can be anticipated, shaped and redirected. Four operations of desirability, eight interdependent dimensions of cohabitation and four governance capacities are consolidated through cross-domain comparison. The review concludes that tourism-led regeneration becomes inclusive when visitor orientation strengthens, rather than narrows, the conditions through which cities remain habitable, plural and collectively governable. Full article

Background: Ultraviolet (UV) radiation, urban pollution and depletion of cutaneous antioxidant reserves are interconnected drivers of photoaging, matrix metalloproteinase (MMP) activation, mitochondrial dysfunction and dermal extracellular matrix degradation. Plant polyphenols can counteract these processes, but topical efficacy is limited by chemical instability, polarity-driven partitioning and a hostile stratum corneum delivery interface. Here we report a green nanostructured lipid carrier co-loading a phenolic-rich jabuticaba (Plinia cauliflora) peel extract and caffeine (NLC-JC), engineered with Amazonian lipids (Astrocaryum murumuru butter and Orbignya phalerata oil) and stabilized by a Natural Deep Eutectic Solvent (NaDES)-like microenvironment. Methods: UPLC profiling was used to determine the polyphenolic composition of the extract. Physicochemical characterization included hydrodynamic diameter and PDI, ζ-potential, lipid matrix organization (XRD) and morphology (TEM), as well as colloidal stability over 30 days. Cytotoxicity was assessed in SH-4 melanocytes (ISO 10993-5) and in zebrafish (Danio rerio) embryos (96-h IC₅₀, GHS acute hazard threshold). Functional antioxidant and photoprotective activity was evaluated by DPPH radical scavenging, and by attenuation of UVC- and H₂O₂-induced anion superoxide generation, alongside assessment of cellular and nuclear morphology under combined oxidative stress. Results: UPLC profiling revealed a polyphenolic fingerprint dominated by ellagic acid (476.8 ± 19.7 µg g⁻¹), quercetin-3-rhamnoside (90.9 ± 4.6 µg g⁻¹), quercetin (83.7 ± 1.4 µg g⁻¹), cyanidin-3-glucoside (73.0 ± 1.2 µg g⁻¹) and protocatechuic acid (68.2 ± 2.2 µg g⁻¹), bioactives with documented anti-MMP, NF-κB-suppressing and ROS-scavenging activities. Optimized NLC-JC showed a hydrodynamic diameter of 56.1 nm (PDI = 0.131), negative ζ-potential, amorphous lipid matrix (XRD) and spherical morphology (TEM, ≈150–200 nm electron-dense particles), with 30-day colloidal stability. NLC-JC exhibited low cytotoxicity in SH-4 melanocytes (IC₅₀ = 2304 μg mL⁻¹), preserving ≥70% viability across the cosmetically relevant range (ISO 10993-5), and a 96-h LC₅₀ of 2013 μg mL⁻¹ in zebrafish (Danio rerio) embryos, three orders of magnitude above the GHS acute hazard threshold (100 mg L⁻¹). Functionally, NLC-JC produced ≈50% DPPH radical scavenging at concentrations comparable to ascorbic acid, attenuated UVC- and H₂O₂-induced anion superoxide generation and preserved cellular and nuclear morphology under combined oxidative stress. Conclusion: These data position NLC-JC as a safe-by-design, multifunctional dermocosmetic platform combining agro-industrial valorization with mechanism-anchored photoprotection. Full article

Urban Air Mobility development depends on overcoming several challenges. Noise emission is one of the most critical, making Unmanned Air Vehicles’ acoustic characterisation a scientific and regulatory priority. Through an on-board outdoor measurement campaign on three commercial multirotor drones (DJI Mavic 3M, Mavic 3 Classic, and DJI Matrice 4T) during hovering, this study analyses and shows the effects that the most important flight variables: altitude, propeller design and payload, have on the overall noise emission, its directivity and the psychoacoustic annoyance, more specifically through the descriptor developed by Boucher. The results showed that different altitudes (10, 15 and 20 m) do not affect noise emissions. Moreover, overall sound pressure levels may fail to discriminate drones’ noise annoyance. In fact, despite the lower sound pressure levels, for the specific platform and propeller configuration tested (DJI Mavic 3M with manufacturer low-noise blades), the low-noise-propeller configuration showed higher psychoacoustic annoyance than the standard-propeller one, driven primarily by higher roughness (and, to a lesser extent, tonality). Additional payloads (50–150 g) did not increase psychoacoustic annoyance monotonically. The heaviest Matrice 4T showed the most stable psychoacoustic profile, with psychoacoustic annoyance remaining below its unloaded baseline at all payload levels, while the lightest Mavic 3 Classic showed the largest swing. Moreover, considering the drone’s structural symmetry, the angular asymmetries revealed by the directivity analysis suggest hovering-stabilisation dynamics attributions. Overall sound pressure levels alone proved an unreliable predictor of drones’ noise annoyance across all tested flight conditions. Full article

Light Detection and Ranging (LiDAR) has become a benchmark sensing modality for autonomous unmanned aerial vehicle (UAV) navigation in GPS-denied and obstacle-dense environments such as forests, urban canyons, and indoor structures. This paper presents a structured review of the LiDAR-based UAV autonomy pipeline, spanning raw point-cloud processing, three-dimensional environment representation and mapping, simultaneous localization and mapping (SLAM), multi-sensor fusion, and real-time obstacle avoidance and trajectory planning. Reactive geometric methods, volumetric and distance-field mapping frameworks, tightly coupled LiDAR–inertial and LiDAR–inertial–visual odometry systems, gradient- and sampling-based trajectory optimizers, and learning-based end-to-end policies are compared with respect to computational cost, robustness, and applicability to resource-constrained micro-UAV platforms. The review further synthesizes current technical bottlenecks, including onboard computational limits, LiDAR performance degradation under adverse atmospheric conditions, and the difficulty of tracking fast-moving dynamic obstacles, as well as emerging research directions such as solid-state LiDAR integration, kinodynamic trajectory optimization, multi-sensor fusion (including radar- and event-camera-assisted schemes), learning-based exploration and foundation-model-based control, multi-UAV collaborative mapping, and simulation-to-reality transfer. The synthesis indicates that LiDAR remains a strong perceptual backbone for UAV autonomy, but that state-of-the-art systems increasingly combine it with inertial, visual, radar, and learning-based components rather than relying on LiDAR in isolation. Full article

This article investigates the Thessaloniki International Fair (TIF) as a socio-spatial, symbolic and cultural formation within the urban landscape of Thessaloniki and considers its implications for socially sustainable urban development. The study examines how the Fair is collectively represented, what meanings residents, visitors, exhibitors and institutional actors attribute to it, and how institutional narratives interact with everyday urban experiences. Drawing on socio-spatial dialectics and cultural hegemony, the research adopts a sequential mixed-methods design combining a survey of 1066 residents conducted in 2019, 103 semi-structured interviews with exhibitors and visitors conducted in 2019, and 15 interviews with institutional and political stakeholders conducted in 2020. The findings reveal the hybrid character of the TIF as an economic platform, cultural event and political stage. Exhibition participation and perceived impacts vary significantly across demographic and residential groups, while qualitative evidence highlights both the Fair’s contribution to urban vitality and the pressures it generates in relation to mobility and everyday urban life. The study demonstrates that recurring, centrally located exhibitions operate simultaneously as spaces of institutional representation and lived negotiation. By linking these processes to questions of accessibility, inclusion, public-space use and urban governance, the paper contributes to debates on the social and spatial dimensions of urban sustainability and on the sustainable integration of major event infrastructures into host cities. Full article

Research on urban interiors has concentrated on shopping malls, transport buildings, and public institutions, leaving industrial settlements, which integrate production and everyday life within a single boundary, largely outside this discussion. This study examines the Malatya Textile and Yarn Factory campus, established in Türkiye during the early Republican period, as an urban interior and compares its threshold regime with that of its successor urban interior on the same site. The threshold is operationalized as an analytical framework disaggregated into five dimensions: permeability, control regime, temporality, transformation, and exclusion. Local newspaper archives (1934–1951) were examined through qualitative content analysis, while reader comments published on a local digital platform (2002–2023) were examined through discourse analysis; the corresponding news items provided contextual material, and cartographic material enabled component-level matching across the two periods. The findings show that the settlement established a graded order of openness within a closed boundary, and that this order was reconfigured after the site was divided into two distinct ownership regimes (private capital and local government), with the most open components becoming enclosed and the most restricted ones freely accessible. Threshold regimes thus appear to be restructured by ownership arrangements rather than determined by architectural configuration alone. Openness did not disappear; it relocated, detaching from the components that carried collective urban experience. Full article

This article presents the Bike Urban Analytics Platform (BUAP), an open-source platform integrating historical and real-time cycling data for urban mobility analysis and decision support. BUAP uses historical data covering 2012–2024 (with partial coverage for 2024) from Valencia’s expanding network of inductive loop detectors, currently comprising 134 sensors, and provides interactive temporal and spatial exploration together with a natural-language query interface. Exploration of the historical dataset reveals recurrent temporal patterns, including three daily peaks, weekday dominance, and marked seasonality, with an August trough and an October peak. A stable-panel analysis of 58 detectors showed a 79% increase in recorded cycling intensity between 2014 and 2023. As a complementary statistical case study, cycling mobility during Valencia’s Fallas festival (15–19 March) was examined across eight editions. Cycling intensity was lower during Fallas than in the preceding period in all editions, with statistically significant differences in seven, while hourly patterns shifted from typical commuting periods toward relatively greater nighttime and early-morning activity. These differences are interpreted as patterns associated with the Fallas period rather than as an isolated causal event effect. Overall, BUAP provides a potentially transferable framework for data-informed urban mobility analysis and management during major cultural and tourism-related events. Full article

Public administrations increasingly use social media to communicate urban-planning decisions, creating observable spaces in which platform-engaged users express criticism, support, proposals, local knowledge, and evaluations of public authorities. Because such comments are self-selected and platform-specific, they cannot be treated as representative measures of city-wide public opinion. This study examines 3228 Facebook comment records associated with 82 posts published by the Municipality of Constanța, Romania, on mobility and transport, planning instruments and regulation, and public space and regeneration between 2016 and 2026. Using computationally assisted and manually supervised content analysis, the study distinguishes three complementary dimensions: comment-level social acceptance, modes of online community engagement, and planning legitimacy. Opposition accounted for 49.7% of comments, compared with 11.6% acceptance, while proposals or suggestions represented the most frequent participation mode (23.9%). Trust, corruption, and institutional credibility emerged as the dominant legitimacy frame (59.1%), followed by environmental and green value (44.4%), mobility and traffic (32.7%), and performance and feasibility (23.5%). All three policy domains recorded negative corpus-level Social Acceptance Index values, with the strongest opposition concerning planning instruments and regulation (−0.427). Within this bounded Facebook environment, the findings show that critical responses cannot be understood solely as rejection of urban interventions, because the corpus also contains substantive planning-relevant contributions and distinct claims about institutional and planning legitimacy. Full article

Understanding how pedestrians perceive roadway conditions and vehicle-state communication is important for examining pedestrian interactions with automated vehicles (AVs). This study used an immersive virtual reality experiment to examine three trial-level perception outcomes—perceived crash likelihood, perceived environmental safety, and perceived collision severity—across urban local-street crossing scenarios. The primary analysis included 949 trial-level observations from 104 participants across scenarios varying in site context, roadway-design configuration, vehicle speed, and vehicle condition. The three vehicle conditions represented a conventional vehicle, an AV without an external human–machine interface (eHMI), and an eHMI-equipped AV. Linear mixed-effects models were used to account for repeated observations within participants. Curbside buffer presence was associated with lower perceived crash likelihood and perceived collision severity and higher perceived environmental safety. Relative to the conventional-vehicle condition, the eHMI-equipped AV condition was associated with more favorable ratings across all three outcomes, whereas no statistically significant differences were detected between the AV-without-eHMI and conventional-vehicle conditions. Direct comparison of the two AV conditions showed lower perceived crash likelihood and perceived collision severity for the eHMI-equipped AV. The 50-km/h vehicle-speed condition was associated with higher perceived crash likelihood and perceived collision severity but not with perceived environmental safety. Overall, the findings highlight the importance of distinguishing multiple dimensions of pedestrian safety perception and support the use of immersive VR as a controlled platform for examining perceptual responses to roadway and vehicle conditions. Full article