Search Results (593)

Autonomous Vehicles (AVs) have been widely recognized as a promising solution to urban commuting congestion. However, quantitative evidence based on city-scale simulations of complete road networks in megacities remains limited. This study uses the complete urban road network of Beijing to investigate the influence of autonomous driving on commuting efficiency. Eleven autonomous vehicle penetration scenarios ranging from 0% to 100% at 10% intervals are established within the Simulation of Urban MObility (SUMO) microscopic traffic simulation platform. Human-driven vehicles are modeled using the Krauss car-following model, whereas autonomous vehicles are represented by the Cooperative Adaptive Cruise Control (CACC) model. The vehicle behavioral parameters are literature-based, adopted from published studies and open test data rather than calibrated against empirical Beijing traffic data, while the road network and commuting demand are constructed from Beijing-specific OpenStreetMap and mobile-signaling data. The simulation results reveal three major findings. First, autonomous driving exhibits a gradual efficiency transition over an approximate penetration range of 30% to 50% (identified qualitatively from the simulation trend rather than by a formal statistical change-point estimate). Below this threshold, behavioral heterogeneity between autonomous and human-driven vehicles intensifies traffic flow instability, whereas above it, the cooperative control capability of CACC becomes dominant and substantially improves overall network performance. Second, under full autonomous vehicle penetration, the city-wide average commuting speed increases from 7.20 m/s to 8.27 m/s, representing a 15% gain in the trip-weighted mean commuting speed (distinct from the 16% gain in the flow-weighted network speed reported in the Results), while the mean in-network simulated travel time per completed trip decreases from 561 s to 270 s. This travel-time value is an operational in-network measure and is not directly comparable to a full perceived door-to-door commute. Third, the efficiency benefits of autonomous driving display significant spatial heterogeneity. Speed improvements reach 16% to 20% on expressways and radial commuting corridors but remain between 4% and 8% on urban arterial roads. These findings indicate that the potential efficiency gains associated with autonomous driving, estimated here under fixed commuting demand and therefore as an upper bound, are constrained by the spatial characteristics of the road network. The results provide quantitative evidence supporting priority deployment of autonomous vehicles on expressways and major commuting corridors in megacities. Full article

Building-use information underpins urban building energy modeling, disaster risk assessment, and evidence-based planning, yet semantic labels are missing for most buildings in open databases; the most widely available proxies are OpenStreetMap (OSM) building-type tags, which largely denote type or physical form rather than independently observed current use. Machine learning models that infer such building-type tags from footprint morphology promise scalable label enrichment, but it remains unclear how well such models travel between cities. This exploratory study benchmarks the cross-city transferability of morphology-based OSM building-type-tag inference using an interpretable, openly documented, and computationally lightweight machine learning pipeline; inference of actual current use would require external validation against authoritative records and is treated here as a downstream hypothesis only. From OpenStreetMap extracts of five European cities (Berlin, Amsterdam, Vienna, Barcelona, and Budapest), 3.22 million building footprints were processed and 197,747 labeled buildings were sampled, each described by 22 footprint- and context-level morphometric indicators. Gradient boosting classifiers achieved within-city macro-F1 of 0.831–0.914 (binary residential/non-residential) and 0.595–0.699 (four-class) under spatially blocked cross-validation. On the class-enriched benchmark samples, pairwise transfer retained 85.7% of within-city performance on average, with a minimum of 61.5%; when the same transferred predictions are reweighted to each target’s observed tagged-subset prevalence, mean transferred macro-F1 falls to 0.621 and non-residential precision to 0.09–0.50, and source rankings can reorder, so the class-enriched matrix does not by itself support operational source selection. Transfer degradation was positively associated with the Wasserstein distance between the morphological feature distributions of city pairs (Spearman ρ = 0.66 descriptively; the association is dominated by Amsterdam and largely disappears when Amsterdam pairs are excluded, ρ = 0.13). SHAP analysis indicated that building-size heterogeneity was a recurring predictive signal across the five models, whereas adjacency- and density-related signals varied more across cities. Learning-curve analyses on the class-enriched benchmark indicate that a pooled multi-city model is able to rival locally trained models on comparable class-enriched samples when the target city is morphologically similar to the source pool, but small local samples quickly dominate otherwise; these fractions refer to the class-enriched benchmark samples and do not translate directly into city-level annotation budgets. The proposed pipeline runs end-to-end on a standard computer with exclusively free and open data and software, providing an openly documented benchmark for AI-assisted urban analytics and hypothesis-generating evidence on when morphology-based tag models may be reused across cities. Full article

Conventional international rankings assess urban efficiency via context-blind metrics, a design that the literature suggests may structurally disadvantage Mediterranean centres and obscure the sustainable policy pathways mandated by the Sustainable Development Goals, most notably the governance of urban transitions under SDG 11. This paper proposes the Context-Weighted Liveability Index (CWLI), which introduces context-sensitive weights into the aggregation of standard smart city KPIs, bridging the global comparability of IMD-style indices with the Mediterranean-specific assessment logic of the ASCIMER framework. Weights derive from five geographic coefficients—climate, culture, economy, historical density, and demography—through a transparent weighted additive formulation with an explicit sensitivity matrix, whose robustness is verified through Monte Carlo uncertainty analysis over 5000 perturbed configurations spanning parameters, coefficients, measurements and benchmarks. Coefficients and KPIs are computed from open spatial data through a replicable GIS protocol (QGIS; OpenStreetMap, Copernicus land cover and land surface temperature, and ISTAT/ELSTAT census data at sub-municipal scale). Applied comparatively to Bologna and Athens, the framework shows that contextual weighting concentrates over 60% of the total weight on climate-sensitive indicators and yields, through the decomposition of contributions, a policy diagnosis that differs from the one suggested by reading an overall smart city rank in isolation: Athens’ largest contribution is digital and its liveability deficit territorial—a profile with direct consequences for sustainable urban transition and talent attraction. Implications for SDG 11 monitoring, equitable access to urban green space, and digital twin integration are discussed. Full article

Air pollution is a major environmental risk factor, particularly for children, who experience repeated exposure during daily school commuting. Exposure-aware routing has been proposed as a strategy to reduce contact with environmental pollutants; however, limited evidence exists regarding how optimization opportunities vary across pollutants and urban environments. This study presents a GeoAI-based framework for pollutant-aware route optimization using OpenStreetMap street networks and air quality data from the Basque Government environmental monitoring network. Approximately 2000 simulated school commuting trajectories were generated across Donostia-San Sebastián and Bilbao’s metropolitan area. For each origin–destination pair, the shortest-path route was compared with alternative routes optimized for PM₁₀ and NO_x exposure. The results revealed substantial differences between pollutants and cities. In Donostia-San Sebastián, NO_x optimization produced the greatest benefits, with more than 10% of routes achieving exposure reductions above 5% and a maximum reduction of 54.9%. In contrast, Bilbao exhibited the highest optimization potential for PM₁₀, with a maximum reduction of 32.8% and 8.2% of routes achieving reductions greater than 5%. Meaningful reductions were generally achieved with moderate increases in traveled distance. These findings suggest that exposure-aware routing may benefit a subset of school commuting trajectories and that optimization potential strongly depends on the local environmental conditions. Full article

Background: Transport costs in industrial waste management can account for up to 60% of total expenditures, yet existing optimization models often rely on simplified distance metrics and treat facility location and routing separately. This paper addresses these gaps. Methods: A simulation model is developed integrating real OpenStreetMap road networks, differentiated environmental risk coefficients by waste hazard class, and joint optimization of the number, location, and capacity of sorting stations and recycling plants. A nearest-available-facility heuristic is applied for routing. The model is implemented as an agent-based simulation in AnyLogic and validated on real data from the Republic of Tatarstan. Results: The optimized configuration (five sorting stations and two new recycling plants) increased the recycling rate from 29.8% to 69.1%, reduced waste sent to storage from 59.3% to 21.5%, and achieved a positive net present value. Transport costs became the dominant cost item (47% of total costs). Conclusions: The model provides a practical decision-support tool for transport planners and logisticians, enabling an assessment of infrastructure decisions on transport work, mileage, and emissions. Integrating real road networks and environmental risk coefficients significantly improves the accuracy of logistics optimization in waste management systems. Full article

High-resolution mapping of urban surface CO₂ is essential for refined carbon monitoring, emission management, and low-carbon urban planning. Mobile monitoring provides dense street-level observations, but raw CO₂ measurements are often affected by transient traffic disturbances, vehicle idling, and localized plume events, which limits their direct use as stable spatial mapping targets. This study developed an integrated framework for predicting, mapping, and interpreting stable surface CO₂ patterns in Shenzhen by combining vehicle mobile observations, CSF processing, multiscale remote sensing predictors, machine learning. A CSF-based lower-envelope filter was used to suppress short-duration positive peaks and extract a more stable CO₂ accumulation signal from mobile observations. Multiscale predictors representing transportation, urban activity, surface environment, and built form were constructed to characterize both local and surrounding urban contexts. Compared with raw CO₂, the CSF-processed target substantially improved prediction performance. The best validation R² across the candidate models increased from 0.59 to 0.90 in April and from 0.62 to 0.93 in November. The predicted maps identified persistent high-CO₂ areas in central and southwestern Shenzhen. SHAP results showed that transport networks and urban activity reinforced surface CO₂ accumulation, whereas vegetation and open-surface contexts weakened accumulation at broader spatial ranges. These findings provide an interpretable framework for high-resolution urban CO₂ mapping and refined low-carbon governance. Full article

Open urban data portals are increasingly proposed as infrastructures for citizen-facing locative services, yet there are no explicit criteria for deciding which georeferenced entities qualify as urban digital locative elements, nor a principled basis for organising them. This hinders semantic interoperability, discovery, and reuse. This article pursues three objectives: to define eligibility criteria distinguishing such elements from other georeferenced data; to derive a faceted, multi-label typology organising them for discovery and reuse; and to assess operational feasibility in a real setting. Following a design-science approach, the artefact, five eligibility criteria and a nine-category typology, was derived by synthesising three evidence sources: a structured literature review, a comparative analysis of six CKAN-based portals in Portugal and Brazil, and an exploratory pilot in Fortaleza, with explicit mappings to FIWARE, schema.org/Place, OpenStreetMap, and CityGML/INSPIRE. The framework was then exercised on the portal corpus and in a working platform, complemented by a small in-situ user study (n = 17). The criteria filtered locative elements effectively, all categories were populated by real datasets, and category-based discovery was understandable in situ, though these findings are preliminary. Open government data thus emerges as an active layer for operationalising locative services, with broader validation left to future work. Full article

Urban Building Energy Modeling (UBEM) offers a scalable, physics-based method to simulate energy demand at the district level, enabling data-driven district energy demand planning and optimization. However, translating UBEM into a reliable, openly replicable workflow remains a significant methodological gap. In particular, limited attention has been devoted to the development of transparent and transferable UBEM workflows capable of systematically quantifying the impact of modeling assumptions on district-scale thermal demand accuracy. This paper presents and validates a five-step UBEM pipeline integrating freely available geospatial data from OpenStreetMap (OSM), archetype-based building characterization, multi-scenario EnergyPlus simulation via the Urban Modeling Interface (UMI) within a structured validation framework. To improve interpretability and reproducibility, a dedicated three-scenario simulation protocol was developed to isolate and quantify the influence of geometry simplifications, archetype assumptions, and weather data fidelity on model accuracy. The workflow is demonstrated through application to a real district heating system (DHS) in northern Italy, encompassing UBEM results validated against monitored consumption data at different temporal resolutions. The refined model achieves a district-scale annual magnitude error of 1.30% between real and simulated data. Persistent limitations in domestic hot water representation and peak load estimation are identified as priorities for future development. Full article

This study proposes a hybrid computational model for diagnosing such biases using groundwater observation data across Kazakhstan. The analytical dataset included 2402 georeferenced observations, including 492 natural springs from OpenStreetMap (OSM), 109 boreholes from OSM, and 1801 spatially filtered pseudo-absence observations. Springs and boreholes together formed 601 positive groundwater observations, while pseudo-absence samples represented a spatially filtered background level rather than confirmed groundwater absence. Each observation was characterized by 89 environmental predictors extracted from Google Earth Engine. The proposed hybrid observation source bias index (HOSBI) combines a normalized robust effect size based on the median absolute value of the Cliff delta, multivariate distribution divergence quantified using RBF-MMD, and spatially confirmed source distinctiveness. These components were assigned fixed weights of 0.40, 0.35, and 0.25 to emphasize statistical and distributional data while maintaining spatial validation. Spatial cross-validation achieved a balanced accuracy of 0.855 for distinguishing OSM sources from OSM wells and 0.846 for separating positive observations from background pseudo-absences. Climate showed the strongest source-related bias (HOSBI = 0.923), while Sentinel-1 SAR contributed the most to the contrast between positive and background data (HOSBI = 0.923). The proposed framework provides an interpretable and replicable preliminary assessment of source bias in heterogeneous geospatial datasets. Full article

Urban coastal cities face compounded flood hazards driven by sea-level rise, intense precipitation, and dense impervious surfaces. This study develops and demonstrates a cloud-native Python 3.12 GIS framework for flood susceptibility screening and critical facility exposure analysis in Miami, Florida, one of the most flood-exposed coastal cities in the United States. Defined here as a geospatial workflow that retrieves data dynamically from cloud-hosted APIs and executes entirely within a hosted computing environment, the framework integrates three open-source spatial indicators: terrain elevation from the USGS 3D Elevation Programme via py3dep; Euclidean distance to water bodies from OpenStreetMap via OSMnx; and building footprint density as an impervious surface proxy, also from OpenStreetMap. Indicators were standardised and combined using literature-informed MCDA weights (water proximity: 0.40; elevation: 0.35; building density: 0.25) into a continuous flood susceptibility index, classified at the 33rd- and 66th-percentile thresholds. In this proof-of-concept application, high-susceptibility zones cover 48.66 km² (34.0%) of the city, concentrated along coastal waterfronts and inland canal corridors. Overlaying critical facility locations on the classified surface indicates that 9 of 16 hospitals (56.2%), 61 of 244 schools (25.0%), and 5 of 17 fire stations (29.4%) fall within high-susceptibility zones; because this overlay uses centroid-based facility points that have not been cross-checked against official municipal or state facility registries, these counts should be read as indicative rather than definitive. Exact binomial testing shows that the school exposure deficit is statistically significant (p = 0.00), while elevated hospital exposure, although substantively notable, does not reach significance at the current sample size (p = 0.07). The susceptibility surface itself has not been quantitatively validated against external benchmarks such as FEMA flood maps or historical inundation records, the MCDA weights have not been sensitivity-tested, and spatial autocorrelation in the index has not been assessed; concrete protocols for each of these steps are specified as subsequent calibration work rather than as prerequisites for the architecture demonstrated here. The contribution of this paper is the reproducible, cloud-native workflow architecture and its proof-of-concept application, not a validated operational assessment tool; we present it explicitly as a methodological protocol and workflow demonstration, not as an evaluation of flood risk. The framework is fully reproducible, low-cost, and transferable to other US coastal cities. Full article

Urban unmanned aerial vehicle (UAV) navigation may require path planning that accounts for geometric obstacles and spatially varying communication-related risk. This paper presents a transformer-guided interference-aware 3D path-planning method for urban voxel environments. A 3D convolutional neural network (CNN)–transformer network predicts a dense route probability field from occupancy, electromagnetic risk, start–goal, and auxiliary planning channels. The field is restored to the raw-map resolution and used only as a search prior for A* on the original occupancy and risk maps. Obstacle avoidance, endpoint correctness, 6-connected motion (each move reaches one of six face-adjacent voxels, with no diagonal motion), and final path cost evaluation are enforced by graph search rather than by the neural model. On 320 synthetic urban cases covering four map sizes and four building density settings, Guided A* achieves a 27.7× speedup over A* and an 11.9× speedup over Weighted A*, while reducing expanded nodes by 91.2% relative to A*. The mean path cost and electromagnetic cost increase by 2.7% and 5.7%, respectively. Compared with the rapidly exploring random tree (RRT), the method reduces path cost by 10.1% and electromagnetic exposure by 12.0% at similar runtime. A post-training sensitivity study further identifies an empirical balance between route-prior guidance, electromagnetic risk avoidance, route length, and search effort, while the Manhattan weight exhibits the expected heuristic inflation efficiency–quality trade-off. An extended model trained on a larger mixture of procedural and Sionna RT ray-traced data, including real OpenStreetMap building geometry, is further evaluated without retraining on two real-geometry benchmarks, UrbanRadio3D and an OpenStreetMap–Sionna RT suite, where Guided A* retains a 100% success rate and reduces expanded nodes by 97–99% relative to A* while increasing mean path cost by at most 1.7%. Full article

This study proposes a reproducible methodological framework for deriving potential sustainable urban mobility indicators from open-source traffic microsimulation in data-scarce cities. The approach integrates OpenStreetMap data, targeted manual traffic counts, and SUMO to estimate potential technical and environmental indicators through a four-stage workflow comprising network construction, model configuration, indicator extraction, and spatial visualization. The downtown area of Bahía Blanca (Argentina) is presented as an illustrative proof of concept demonstrating the implementation of the framework rather than a fully calibrated traffic model. Future work includes origin–destination demand estimation, model calibration and validation, and coupling with atmospheric dispersion models. Full article

In this publication we investigate in depth the design, architecture and implementation of a bio and cultural guiding system with augmented capabilities. Our platform will be able to provide a flexible and user-friendly application for smart mobile devices with Android OS, which will be able to provide to the user the ability to discover nearby places of interest such as museums, archeological sites, religious sites, natural beauty sites, etc. In addition, our application is able to offer users an enhanced, comprehensive and augmented tour experience, based on visual and audio smartphone services, using asynchronous and real-time user–server communication mechanisms, thus eliminating the need for a human tour guide operator in cases of a remote area, guiding service cost or unavailable time slot. The proposed platform integrates visual overlays, audio narration, Geolocation services, and cloud-based data management to enable users to explore cultural, historical, and natural points of interest without the need for a human tour guide. A modular, layered system architecture is developed, combining Firebase Realtime Database, RESTful web services, OpenStreetMap-based navigation, and Android-native components to ensure scalability, flexibility, and real-time responsiveness. UML modeling, including class and sequence diagrams, is employed to formally describe system structure and behavior, while a mathematical data model validates the consistency of the underlying database schema. The implementation demonstrates how AR guiding systems can enhance spatial understanding, accessibility, and user engagement while supporting sustainable tourism practices and efficient destination management. The results indicate that the proposed solution is technically feasible, user-centered, and adaptable to diverse bio-cultural contexts, contributing to the advancement of intelligent, inclusive, and sustainable digital tourism platforms. Full article

The growing complexity of urban mobility requires datasets that integrate dynamic traffic observations with meteorological, geometric, and urban-context information. This study presents MUTra-CDMX, a multisource urban traffic dataset covering a 14.72 km section of the Insurgentes Sur corridor in Mexico City. Traffic data were obtained from TomTom at five-minute intervals for 20 consecutive road segments from 1 November 2024 to 28 February 2025. Hourly meteorological data were retrieved from Meteosource, while segment-level geometry, topology, signalized locations, and nearby points of interest were derived from TomTom metadata and OpenStreetMap. The primary analytical file contains 691,200 segment–timestamp records and 12 variables describing traffic and free-flow conditions, meteorological information, derived operational indicators, and reconstruction status. Of these records, 682,264 are original observations and 8936 are reconstructed segment–timestamp combinations, identified by the Boolean variable is_imputed. Technical validation confirmed complete temporal coverage, preservation of original traffic observations, consistent weather alignment, and reconstruction performance through artificial masking. Predictive utility was evaluated through chronological travel-time forecasting under a leakage-controlled protocol. At the 30 min horizon, XGBoost achieved a mean absolute error of 12.84 s, a root mean squared error of 37.91 s, and a coefficient of determination ($R^2$) of 0.771, outperforming a persistence baseline. MUTra-CDMX supports congestion analysis, imputation studies, spatiotemporal modeling, and travel-time forecasting. Full article

Public-data electric-vehicle charging-station siting needs a screening workflow that can use spatial proxies while keeping demand, grid-capacity, and implementation claims separate from the score. This study develops an interaction-aware Nonlinear Interaction–Einstein Aggregation (NI-EA) framework for Dubai and extends it with source-conditioned robust candidate-set diagnostics. From 7410 admitted candidate/amenity records, 5097 inside-boundary candidates are scored using a candidate-derived activity-density proxy, a charger-coverage-gap proxy, and a grid-access proxy. The analysis compares NI-EA with WSM, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Einstein aggregation; reconstructs a 63-scenario interaction/curvature/blending rank matrix; evaluates weighting, road-network, and official-DEWA source sensitivities; and reports necessary and possible top-K candidate sets, family-balanced finite-scenario acceptability, rank-displacement summaries, and bounded spatial-evidence context from official community, transport, parking, DEWA, and OpenStreetMap-derived sources. The baseline leader is S1421/Boonmax, while official-DEWA coordinate-source reconciliation changes the leader to S3473. Across the reconstructed interaction, weighting, road-network, and official-DEWA scenario families, the top-15 necessary core contains 12 candidates, and the top-15 possible envelope contains 18 candidates. Activity-radius and charger-count coverage alternatives are reported separately as proxy-definition sensitivities. TOPSIS has 0/15 top-15 overlap with NI-EA because it favors a different profile with much higher coverage-gap scores but low activity density. The reported output is therefore a source-conditioned planning shortlist and robustness audit, not an observed-demand map, feeder-capacity validation, financial feasibility assessment, or construction recommendation. Full article