Search Results (477)

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Urban air-quality monitoring networks are often sparse, leaving coverage gaps where particulate matter (PM) concentrations cannot be directly observed. This paper extends the CityAirQ pollution tracking platform and its mobile air-quality device prototype by introducing an AI-based benchmark for two Bucharest station networks across three deployment-oriented tasks: multi-station temporal forecasting (Task A), leave-one-station-out same-day spatial estimation (Task B), and a preliminary mobile-site prediction pilot at an uncalibrated location (Task C). The benchmark compares machine-learning models, including ensemble tree methods, recurrent neural networks, and lightweight graph-inspired architectures, evaluated under a unified time-aware rolling protocol. In Task A, the proposed Advanced Stage 0–3 pipeline achieves the best overall MAE (7.12 $\mathsf{\mu }$g/m³), a 4.7% reduction relative to Random Forest (7.47 $\mathsf{\mu }$g/m³), while the Seasonal naïve (10.41 $\mathsf{\mu }$g/m³), Persistence (11.51 $\mathsf{\mu }$g/m³), neural, and graph-inspired references perform worse under recursive forecasting. In Task B, the neighbour-only Random Forest reaches a mean $R^2$ of 0.873 on the classic four-station network and a median $R^2$ of 0.734 on the ten-station city-scale extension. Task C is reported as an exploratory six-day prediction pilot, not as deployment-grade validation: no co-located EPA FRM/FEM or equivalent reference monitor was available at the mobile location ℓ. The historical-transfer Random Forest retained a sample-limited positive PM_2.5 association with the raw mobile readings ($r=0.432$, $n=6$), while a strict one-day-ahead online persistence predictor reduced PM_2.5 MAE from 40.58 to 20.00 $\mathsf{\mu }$g/m³ on the five forecastable mobile days. Ultimately, accurate PM monitoring empowers sustainable urban planning, helping to mitigate exposure risks and supporting long-term public health and environmental sustainability initiatives. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. Full article

Vehicle ad hoc networks (VANETs) can help prevent traffic accidents through wireless communication; however, most studies are based on simulations or static evaluations. This research paper presents the design, implementation, and experimental evaluation of a prototype early-warning system for vehicle proximity based on VANETs using ESP-NOW. The prototype utilizes five ESP32-CAM nodes equipped with MaxSonar sensors installed in vehicles and an RSU unit with a Raspberry Pi for vehicle-to-infrastructure (V2I) communication. Field tests were conducted in Quito, Ecuador, at speeds ranging from 10 to 70 km/h, measuring latency, packet loss, and received signal strength (RSSI). The results show average latencies of 9.9 ms at 10 km/h and 114.5 ms at 70 km/h, with packet loss rates of 2% and 60%, respectively. Statistical analysis reveals 95% confidence intervals for latency ranging from ±0.98 ms to ±6.90 ms, while obstacles introduce marginal attenuation (p = 0.051) with significant dispersion (σ = 5.85 dB). The Doppler shift is negligible (155.6 Hz), but the channel coherence time (2.7 ms) explains the observed degradation. Models were obtained that relate speed to latency (R² = 0.994) and packet loss (R² = 0.991). The prototype is viable for early collision warning at urban speeds (up to 60 km/h), outperforming human reaction time (1.5 s). Full article

Artificial intelligence systems increasingly mediate high-stakes human activities, yet urban navigation remains highly challenging for blind and visually impaired individuals. Although digital navigation technologies have significantly improved route planning and accessibility, many existing systems still rely on generic interaction paradigms that insufficiently account for cognitive load, contextual uncertainty, and the adaptive needs of vulnerable users. This challenge highlights the importance of Human-Centred AI approaches capable of supporting not only functional accessibility, but also cognitively sustainable and trustworthy interaction. This paper introduces LAZAR, a human-centred adaptive AI framework for accessible urban mobility grounded in a user-centred design methodology and formalised through a structured Software Requirements Specification. Rather than focusing exclusively on route optimisation, LAZAR approaches assistive navigation as an adaptive human–AI interaction problem in which instructional granularity, interaction frequency, and feedback mechanisms are designed to support user autonomy and situational awareness whilst limiting unnecessary cognitive burden. The proposed framework integrates high-fidelity prototyping, accessibility-oriented interaction modelling, and a modular multi-agent architecture intended to support adaptive and personalised guidance. Central to the approach is a cognitive load-aware interaction layer designed to regulate the presentation and timing of navigational assistance according to user needs and contextual conditions. The proposed multi-agent architecture is presented as a modular design framework whose interaction principles and interface logic were partially operationalised in the evaluated prototype. The complete integration of all adaptive coordination mechanisms, together with large-scale real-world validation, remains part of ongoing and future development work. This work contributes a structured methodology for the design of adaptive assistive AI systems that integrates accessibility requirements, human-centred interaction principles, and cognitively informed guidance strategies. A formative usability evaluation involving eleven visually impaired participants provides preliminary empirical evidence regarding usability, accessibility, and perceived usefulness of the proposed interaction model. The framework establishes a foundation for future research on inclusive and adaptive AI-based navigation systems in urban environments. Full article

Tunnelling-induced safety risks from adjacent piles have become increasingly severe with the rapid development of urban underground space. Model tests have become essential for revealing the complex pile-tunnel interaction mechanism. This paper reviews the research progress of model tests on the influence of single-line tunnelling on adjacent piles, focusing on test soil materials, tunnel simulation methodologies, analysis of test results, and research prospects. However, current model test studies are constrained by several critical limitations, including insufficient similarity between soil materials and prototype conditions, and overly idealized simulation of tunnel excavation. This paper identifies a significant research gap: the inability of current volume-loss techniques to capture 3D dynamic factors (e.g., face pressure and grouting timing) and the lack of meso-scale observation at the pile-soil interface. This review provides a systematic synthesis of these methodological challenges and proposes future research prospects to provide a more scientific basis for engineering design and risk control. Full article

This paper presents an experimental investigation of a spherical photovoltaic (SPV) system enhanced with an integrated paraboloid reflector and monitored via an Internet of Things (IoT) platform. The SPV’s omnidirectional geometry enables improved light absorption from multiple angles, maximizing energy capture throughout the day and under diverse weather conditions, particularly in extreme climates such as in Qatar. A prototype was developed using photovoltaic cells mounted on a 30 cm diameter spherical frame, paired with a reflector constructed from Styrofoam covered with mini glass mirrors. Performance was benchmarked against a conventional flat photovoltaic (FPV) panel with an equal number of cells. Real-time IoT monitoring captured voltage, temperature, and irradiance data, enabling precise performance evaluation. Results demonstrate that the SPV system achieved a 32.2% higher weekly energy output than the FPV panel, with reflector-assisted gains ranging from 14.8% to 39.7%. The SPV operated at 8–12 °C cooler, producing more stable voltage outputs (24–28 V vs. 17–25 V). Additionally, the design reduced dust accumulation by 27% and required ~35% less installation area per watt. IoT integration facilitated automated monitoring and alerts for critical conditions such as overheating (>50 °C) or voltage drops (<12 V). These findings highlight the SPV system as a compact, efficient, and intelligent solution for next-generation solar energy harvesting in urban and extreme-environment applications. Full article

Urban sustainability management increasingly relies on large volumes of heterogeneous environmental data generated by smart city infrastructures. While these data streams offer significant potential for evidence-informed policymaking, environmental governance, and public engagement, their effective use is often constrained by technical barriers and persistent data-skills gaps among non-specialist stakeholders. Using urban air quality as a policy-relevant and data-rich sustainability domain, this paper presents a proof-of-concept dashboard that investigates how large language model (LLM)-enabled natural language interfaces can lower barriers to querying, analysing, and visualising urban environmental data. The system translates natural language questions into executable database queries and automatically generates visualisations over air-quality datasets. A controlled comparative benchmark of proprietary and open-source LLMs is conducted to assess their suitability for text-to-SQL generation in this application context. In this benchmark, proprietary GPT-based models achieved the highest observed query accuracy and robustness among the evaluated models, highlighting practical trade-offs between performance, transparency, reproducibility, and long-term governance. This paper makes a twofold contribution: First, it demonstrates the technical feasibility of an LLM-enabled natural language access layer for smart-city environmental data. Second, it uses the implemented system as a concrete case through which to analyse the trust, transparency, inclusivity, vendor-dependency, and data-quality challenges that arise when such systems are incorporated into sustainability-oriented decision-support workflows. The study provides a transferable design contribution for urban sustainability data access by showing how natural language interfaces, model benchmarking, automated visualisation, and governance-aware system design can be combined to support more inclusive interaction with complex environmental datasets. Full article

The accurate characterization and geo-localization of objects using image data and LiDAR are important for forestry, agriculture, urban planning, infrastructure monitoring, and related geospatial applications. However, reliability is affected by uncertainty introduced during sensor acquisition, LiDAR-image projection, segmentation, object-parameter estimation, and final geo-localization. This paper presents a proof-of-concept and method prototype for an uncertainty-aware LiDAR-image workflow in a forestry setting. The novelty of the work does not lie in proposing a new segmentation architecture, but in integrating image-based segmentation, LiDAR-image projection, DBH-level geometric estimation, stage-wise uncertainty propagation, and uncertainty-aware reconciliation of alternative estimates within a single modular workflow. The experimental evaluation was conducted on a limited pilot dataset consisting of 12 individual trees, multiple LiDAR acquisition viewpoints, and 18 high-resolution photographs. The number of trees is the number of independent analyzed objects, whereas the scans and photographs represent acquisition observations. Dense LiDAR point clouds provide many object-level geometric measurements, but these points are not interpreted as independent biological samples. Under the tested acquisition and processing conditions, the uncertainty-aware reconciliation step reduced the estimated spatial uncertainty to approximately 2.5 ± 0.4 cm. This value should be interpreted as a pilot result for the analyzed dataset, not as a general performance guarantee across forest types, tree species, stand densities, lighting conditions, or occlusion patterns. The contribution of this study is therefore positioned as a modular engineering-oriented uncertainty propagation and reconciliation workflow for DBH-level forestry localization. Potential use in robotics, infrastructure monitoring, or other high-precision geospatial applications is discussed only as a future direction requiring separate validation, larger datasets, and real-time implementation work. Full article

Stray-current corrosion from DC-electrified railways drives premature failure of buried metallic infrastructure (pipelines, foundations, tunnel reinforcement), causing resource waste, repair-driven carbon emissions and service disruptions that undermine the sustainability of urban transit corridors. Conventional impressed-current cathodic protection (ICCP) design relies on uniform-anode rules of thumb or closed commercial codes that cannot quantify the trade-off between protection uniformity, energy use and hardware cost. We present an open MATLAB framework that couples a custom 3D finite element method (FEM) solver with multi-objective particle swarm optimisation (MOPSO) and minimises three competing objectives simultaneously: total impressed current, RMS deviation from the protection target, and number of active anodes. A laboratory-calibrated coupling factor ($\mathrm{CF}=1.98$, consistent with the image-method prediction of 2 for a highly conductive pipe inclusion) absorbs the pipe–soil interface kinetics into a single direct FEM solve, and a pre-computed Green’s-function basis accelerates each MOPSO evaluation by more than two orders of magnitude. The solver is validated against an instrumented prototype with RMSE $=14.9$ mV across ten Cu/CuSO₄ saturated reference electrode (CSE) measurements, and applied to a 500 m DC traction line. At an identical total current of 20.30 A across five anodes, the optimised design achieves an RMSE of 86.6 mV against the $-850$ mV NACE target, whereas a conventional uniform layout produces severe over-protection (RMSE $=1107$ mV)—a twelve-fold reduction. The framework is recommended as a transparent, reproducible engineering tool that simultaneously extends pipeline service life and reduces rectifier energy demand, supporting UN Sustainable Development Goals 9 and 11 for sustainable urban-rail infrastructure. Full article

Urban navigation in dynamic environments remains a challenging problem for blind and visually impaired users due to the presence of unpredictable obstacles and the limitations of conventional navigation systems, which rely primarily on static map-based information and lack real-time environmental awareness. This paper presents a real-time sensor-driven navigation system based on a multi-agent architecture incorporating a reinforcement-learning navigation policy for assistive mobility in urban environments. The proposed system integrates GPS-based global localization with vision-based perception to enable continuous fusion of global route planning and local obstacle detection. This integration allows the system to dynamically adjust navigation strategies in response to changing environmental conditions. The architecture is designed as a modular multi-agent system comprising agents for perception, navigation, sensor fusion, personalization, safety arbitration, interface management, and system monitoring. The reinforcement learning component formulates local navigation as a sequential decision-making problem, where the navigation policy is trained to balance path efficiency, obstacle avoidance, and safety constraints through interaction with simulated environments. Prototype implementation is developed and evaluated in both simulation and controlled real-world scenarios. Experimental results demonstrate that the proposed system shows improved obstacle avoidance performance and navigation stability under the evaluated conditions while maintaining low-latency responsiveness compared to baseline navigation approaches. The system also exhibits robust behaviour under varying environmental conditions, supporting its potential applicability to assistive navigation tasks in controlled urban environments. The proposed approach contributes to a scalable architecture that integrates a reinforcement-learning navigation policy within a multi-agent coordination framework and real-time sensor perception, providing a foundation for the development of intelligent and deployable assistive navigation systems. Full article

Background: A bidirectional association between diabetes and oral health is well established, yet oral self-care is overlooked in diabetes management. Health Belief Model (HBM)-guided oral care interventions have exhibited promising outcomes in the literature but have not been used to guide oral self-care interventions designed for people with diabetes (PWD). Positioned at the early conceptualization and design stage of such a program, this developmental study was to identify self-perceived needs in oral self-care practices and to obtain preliminary feedback among PWD about the blueprint of a new program—DiaOral^©. Methods: We conducted semi-structured interviews with 15 PWD recruited from a large healthcare system, with a goal to recruit patients from racially/ethnically diverse urban/suburban zip codes. Interviews explored participants’ oral self-care practices in relation to diabetes. Sample DiaOral^© content and images on a blueprint were presented and feedback was solicited. Braun and Clarke’s reflexive thematic analysis was used to code and interpret transcripts, aligning emerging themes with HBM constructs through team-based consensus. Results: Three major themes and 27 sub-themes emerged: (1) lack of knowledge on optimal oral care, (2) low perceived importance of preventive care and oral health in diabetes, and (3) low self-efficacy for performing effective oral self-care. Participants expressed satisfaction with the content and their perceived confidence and interest potentially in using the DiaOral^© program based on their preliminary review of the blueprint. Conclusions: Findings support the relevance of HBM constructs in shaping oral self-care among PWD. This developmental study suggests that the DiaOral^© blueprint is ready to move forward to website prototype development. Future work will finalize the program and evaluate its efficacy among PWD. Full article

Urban farming microinterventions are small, place-based cultivation projects that operate under severe spatial and resource constraints yet can generate social learning and locally embedded resilience. The present paper examines how design decisions shape the effectiveness of such interventions through three design-led case studies: Blooming Structure (2018, Warsaw), a temporary hydroponic “laboratory” installation; Micro-cultivation (2018, Warsaw), a shopfront vertical demonstration farm; and Micro-cultivation 2 (2019), modular “cultivation furniture” for interiors and exhibition deployment. The analysis combines project documentation with practice-based observations and applies five interpretive dimensions: spatial fit, technical feasibility, communicative legibility, replicability, and social programming. Findings highlight that successful microinterventions align legible cultivation infrastructure with high visibility, accessibility and participatory formats that support skills transfer and copying-based scaling. Rather than offering universal claims about urban agriculture outcomes, the paper provides a reference set of design principles that may inform similar micro-scale interventions in other contexts, subject to local constraints. Limitations include the small sample size and the concentration on projects from Poland. Practically, the findings can support designers, municipalities, and civic organisations in structuring microinterventions as replicable, low-threshold prototypes and in aligning technical systems with maintenance capacity and public engagement. Full article

Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating ‘AI slop’, consisting of geometrically inconsistent/unreliable data, into the online map. While the OSM “Code of Conduct for Automated Edits” provides a policy framework for data ingestion, it lacks a machine-enforceable mechanism for real-time quality gating. This paper proposes a GeoAI-Gatekeeper to perform this task—an automated process that applies empirical Acceptable Quality Thresholds (AQT) to address the GeoAI data governance problem. Because the Gatekeeper utilizes an intrinsic, no-reference evaluation of geometric fidelity, it can assess incoming AI-generated data streams in real-time without requiring ground-truth benchmarks. Importantly, it focuses exclusively on the geometric validation of building footprints, acknowledging for now that semantic enrichment, such as tagging, remains a human-centric task. The presented GeoAI-Gatekeeper is a working prototype developed for a specific urban area, systematically triaging incoming AI-generated data into three tiers; Auto-Accept, Manual Review, and Reject. It provides a Web-GIS interface for Human-in-the-Loop (HITL) functionality to ensure the OSM community remains the final arbiter of acceptable data quality. Testing the Gatekeeper in Dublin (Ireland) demonstrates that our solution can auto-ingest 93.6% of features with a 14x reduction in human review effort while still adhering to OSM’s cartographic integrity standards. By implementing qualitative community guidelines into machine-enforceable thresholds, our approach introduces a viable methodology for next-generation hybrid VGI systems. Importantly, it ensures that the transition towards automated data ingestion reinforces, rather than undermines, the reliability of global crowd-source mapping datasets. Full article

Urban stormwater is often viewed as a drainage problem rather than a local water resource, even in areas where runoff capture could simultaneously reduce flooding and promote the reuse of non-potable water. This study develops, installs, and field-tests a decentralized, school-scale stormwater harvesting system that relocates permeable concrete, transforming it from a passive infiltration surface into a purpose-built capture and pretreatment interface. The system integrates a 3 m × 3 m permeable concrete slab with load-bearing sections, an impermeable underlayer to ensure controlled flow, a double-compartment sump for staged sedimentation and hydraulic damping, sequential filtration with sand/gravel and activated carbon, and a 5000 L storage tank. The prototype was implemented at CETis 105 in Querétaro, Mexico, and evaluated during its commissioning and operation in the 2023 rainy season. Field operations demonstrated reduced ponding in the catchment area and a reliable flow of runoff to the pretreatment units. In the sump compartments, apparent color decreased from 221 to 59 Pt-Co, turbidity from 46.8 to 12.9 NTU, and COD from approximately 30–35 to 15–18 mg·L⁻¹, corresponding to approximate pretreatment reductions of 73.3%, 72.4%, and 40–57%, respectively, before post-filtration. Conversely, the elevated pH, electrical conductivity, and total dissolved solids indicated interaction with fresh cementitious materials and dissolved ionic residues during initial operation, highlighting the need for curing, initial washing, and post-filtration verification before declaring compliance with reuse requirements. Therefore, the results support the feasibility of the proposed configuration as a decentralized, low-infrastructure architecture for localized runoff control and pretreatment, while confirming that full reuse validation still requires microbiological and post-filtration evaluation. The study provides a field-proven system design adaptable to school campuses and similar institutional environments for distributed stormwater management and non-potable water storage. Full article