Smart Cities

One of the key aspects of a smart city is to reduce CO₂ emissions by adopting different strategies that can also improve the quality of life of citizens. Current metropolises present additional issues compared to traditional cities, such as extremely heavy traffic and abandoned spaces. This paper, therefore, proposes two interventions aimed at improving the smartness of the municipality of Rome: the implementation of a photovoltaic field in an abandoned space used to charge electric buses and the implementation of smart traffic lights that optimise the traffic flow. To measure the impact and effectiveness of those interventions, key performance indicators (KPI) were defined to point out the benefits of the analysed strategies, and a quantitative matrix approach was applied. The aim was to establish a correlation between the different scenarios proposed, assigning numerical indices to each of them that can comprehensively express their impact on the identified smart axes. The results obtained showed the importance of selecting appropriate performance indicators to assess the impact of interventions. Furthermore, the findings revealed that the scenarios with the greatest number of indicators are not necessarily the most advantageous. Overall, the simulations indicated that the proposed interventions could produce a significant reduction in emissions due to the implementation of renewable energy production. Full article

This article examines differences between conventional manual measurements of tram operations and data extracted automatically using the REWIZOR program, based on the Yolo10s algorithm. The study addresses the broader question of how artificial intelligence can support analyses of passenger exchange processes in public transport and improve the efficiency of data collection. Measurements conducted in four Polish cities included tram types, stop times, and detailed boarding and alighting durations, while the REWIZOR software enabled automatic detection of stop times and passenger flows based on video recordings. The results show that, although both approaches yield consistent qualitative information regarding doors and passenger counts, significant quantitative discrepancies arise. These differences stem mainly from methodological inconsistencies and varying definitions of boarding, alighting, and stop times, as well as from software-related detection errors. The findings indicate that AI-based measurements require calibration against reference methods to allow reliable comparison with conventional datasets. As currently implemented, REWIZOR can be used effectively for internal analyses of passenger flows, if all compared data come from the same system. Further development—such as implementing simultaneous tracking of people and heads—may considerably improve accuracy and facilitate wider applicability in public transport studies. Full article

Urban air pollution remains a critical public health issue, especially in densely populated cities where pedestrians experience direct exposure to traffic-related and environmental emissions. This study develops and tests a pollution-aware pedestrian routing framework for Thessaloniki, Greece, designed to minimize environmental exposure while maintaining route efficiency. The framework combines high-resolution air-quality data and computational techniques to represent pollution patterns at pedestrian scale. Air-quality is expressed as a continuous European Air Quality Index (EAQI) and is embedded in a network-based routing engine (OSRM) that balances exposure and distance through a weighted optimization function. Using 3000 randomly sampled origin-destination pairs, exposure-aware routes are compared with conventional shortest-distance paths across short, medium, and long walking trips. Results show that exposure-aware routes reduce cumulative AQI exposure by an average of 4% with only 3% distance increase, while maintaining stable scaling across all route classes. Exposure benefits exceeding 5% are observed for approximately 8% of medium-length routes and 24% of long routes, while short routes present minimal or no detours, but lower exposure benefits. These findings confirm that integrating high-resolution environmental data into pedestrian navigation systems is both feasible and operationally effective, providing a practical foundation for future real-time, pollution-aware mobility services in smart cities. Full article

Urban Digital Twins (UDTs) are emerging as a new paradigm in smart city strategies, enabling real-time interaction with urban environments and supporting data-driven decision-making. By expanding beyond traditional smart functions, UDTs facilitate the analysis and simulation of urban resilience and sustainability indicators within a virtual city ecosystem, addressing both immediate urban challenges and long-term planning goals. This paper introduces TwinCity, a city-scale Urban Digital Twin framework developed and validated through a case study of the Green City of Benguerir, Morocco. The framework incorporates a technical architecture based on semantic 3D city models, data integration, and simulation scenarios to analyse the solar energy potential of the rooftop, the energy consumption of the building and the morphological indicators. A user-friendly web interface was developed to visualise and interact with the UDT, ensuring its accessibility. By bridging the gap between technical challenges (such as data scarcity) and practical applications, this work offers a replicable model for cities in the Global South. Full article

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects. Full article

Deep reinforcement learning (DRL) with self-play has emerged as a promising paradigm for solving combinatorial optimization (CO) problems. The recently proposed Gumbel AlphaZero Plan-to-Play (GAZ PTP) framework adopts a competitive training setup between a learning agent and an opponent to tackle classical CO tasks such as the Traveling Salesman Problem (TSP). However, in complex and multi-constrained environments like the Electric Vehicle Routing Problem (EVRP), standard self-play often suffers from opponent mismatch: when the opponent is either too weak or too strong, the resulting learning signal becomes ineffective. To address this challenge, we introduce Two-Stage Self-Play GAZ PTP (TSS GAZ PTP), a novel DRL method designed to maintain adaptive and effective learning pressure throughout the training process. In the first stage, the learning agent, guided by Gumbel Monte Carlo Tree Search (MCTS), competes against a greedy opponent that follows the best historical policy. As training progresses, the framework transitions to a second stage in which both agents employ Gumbel MCTS, thereby establishing a dynamically balanced competitive environment that encourages continuous strategy refinement. The primary objective of this work is to develop a robust self-play mechanism capable of handling the high-dimensional constraints inherent in real-world routing problems. We first validate our approach on the TSP, a benchmark used in the original GAZ PTP study, and then extend it to the multi-constrained EVRP, which incorporates practical limitations including battery capacity, time windows, vehicle load limits, and charging infrastructure availability. The experimental results show that TSS GAZ PTP consistently outperforms existing DRL methods, with particularly notable improvements on large-scale instances. Full article

This article presents a modular and replicable framework to assess spatial accessibility to essential public services in the Lazio Region (Italy). Current policies, framed within the EU Urban Agenda and the UN Sustainable Development Goals, emphasize improving accessibility rather than mobility, integrating land-use and transport planning, and supporting sustainable modes. The study adopts urban centres, densely populated sub-municipal units, as the main spatial unit to capture intra-municipal variability. Accessibility is measured as distance and travel time to the nearest education and healthcare facilities, for both private car and public transport, considering traffic conditions. Distances and times are computed using routing APIs and aggregated into service-specific indicators at urban-centre and municipal levels. Due to GTFS availability, the public transport analysis is restricted to the Province of Rome. Indicators are published as Linked Open Data following DCAT-AP, exposed via a SPARQL endpoint, and visualized through an interactive web map viewer. Results highlight pronounced disparities: car accessibility is relatively uniform, while public transport shows critical gaps in peripheral and mountainous areas. The framework enables transparent benchmarking and supports evidence-based, place-sensitive planning across different European contexts. Full article

Pedestrian safety in developing countries faces critical challenges from rapid urbanization and infrastructure deficiencies. This study investigates how visual context influences pedestrian crossing preferences through a controlled stated preference experiment in multiple Ecuadorian cities. A sample of 875 participants was randomly assigned to view either non-compliant (mid-block crossing) or compliant (signalized crosswalk) imagery before evaluating six hypothetical scenarios involving three crossing alternatives. Multinomial logit models reveal that waiting time, traveling with a minor, and walking distance are primary determinants of choice. Visual context showed systematic associations with choice patterns: compliant imagery was associated with increased preference for safer alternatives (50.5% versus 43.8% prediction accuracy) and larger safety-related parameter magnitudes. Principal Component Analysis identified two latent perception constructs, safety/security and bridge-specific convenience, providing behavioral interpretation of choice patterns. Substantial spatial heterogeneity emerged across cities (χ² = 124.10 and 84.74, p < 0.001), with larger urban centers showing stronger responsiveness to formal infrastructure cues. The findings demonstrate that visual stimuli systematically alter choice distributions and attribute sensitivities through normative activation and perceptual recalibration. This research contributes methodologically by establishing visual framing effects in stated preference frameworks and provides actionable insights for pedestrian infrastructure design, emphasizing alignment of objective safety improvements with perceived risk and contextual behavioral cues. Full article

This study proposes a smart energy management framework for a university campus microgrid aimed at reducing dependence on the main power grid and increasing the utilization of photovoltaic (PV) generation under dynamic load and environmental conditions. The core contribution is a two-stage approach that combines a genetic algorithm (GA) for static day-ahead optimization with a soft actor-critic (SAC) reinforcement learning (RL) agent performing adaptive supervisory management of microgrid active and reactive power flows via battery control. The GA provides an optimal reference schedule under forecasted conditions, while the SAC agent is trained on eight representative scenarios derived from measured PV generation and campus load data to adapt battery operation and grid exchange under uncertainty. The results show that the benefit of RL does not lie in reproducing the static GA solution, but in learning economically rationally adaptive behavior. In particular, the SAC agent exploits low-tariff periods and hedges against adverse PV conditions by proactively adjusting battery charging strategies in real time. This adaptive behavior addresses a key limitation of static optimization, which cannot respond to deviations from forecasted operation, and represents the main added value of the proposed framework. From a practical perspective, the GA-SAC architecture operates at a supervisory level with low computational requirements, making it suitable for scalable deployment in smart campus and smart city energy management systems. Full article

Improving city crossroads safety is a critical problem for modern smart transportation systems (STS). This article presents the results of developing, upgrading, and comprehensively experimentally testing an acoustic monitoring system prototype designed for rapid accident detection. Unlike conventional camera- or lidar-based approaches, the proposed solution uses passive sound source localization to operate effectively with no direct visibility and in adverse weather conditions, addressing a key limitation of camera- or lidar-based systems. Generalized Cross-Correlation with Phase Transform (GCC-PHAT) algorithms were used to develop a hardware–software complex featuring four microphones, a multichannel audio interface, and a computation module. This study focuses on the gradual upgrading of the algorithm to reduce the mean localization error in real-life urban conditions. Laboratory and complex field tests were conducted on an open-air testing ground of a university campus. During these tests, the system demonstrated that it can accurately determine the coordinates of a sound source imitating accidents (sirens, collisions). The analysis confirmed that the system satisfies the V2X infrastructure integration response time requirement (<200 ms). The results suggest that the system can be used as part of smart transportation systems. Full article

As a high-fidelity digital mapping of the physical built environment, the Building Digital Twin (BDT) relies on physical–virtual interaction as a core enabler for lifecycle management. However, existing BDT conceptual models predominantly focus on unidirectional or single-threaded physical–virtual interactions, neglecting the dynamic, concurrent exchanges among multiple digital twins and human users. To overcome this limitation, the Multi-Interactive-Object BDT (MIO-BDT) framework is proposed. The central hypothesis is that explicitly modeling concurrent, multi-party interactions within a formalized conceptual structure can address a key representational gap in current BDT paradigms. The work pursues two testable objectives: (1) to formally define the components, relationships, and rules of the MIO-BDT framework and (2) to validate through a representative use case that the framework can model complex interaction scenarios that are inadequately supported by existing approaches. A systematic analysis of the state of the art is first conducted to ground the framework’s design. The MIO-BDT is then elaborated at both the system level (supporting dynamic interactions among twins, users, and physical entities) and the component level (integrating visual, physical, and interaction sub-models). Formal modeling and verification demonstrate that the framework is logically consistent and deadlock-free and effectively coordinates multi-entity data flows. These findings confirm that the MIO-BDT framework provides enhanced representational capacity, structural clarity for system design, and a unified model for diverse interaction types, thereby establishing a validated conceptual foundation for next-generation, interaction-aware BDT systems. Full article

Smart city initiatives increasingly rely on interoperable and high-quality urban data, yet many cities lack systematic methods for prioritizing which datasets should be developed first. This study proposes an evidence-based framework for smart city data prioritization that integrates data need, data availability, and policy urgency into a unified decision-support model. Using standardized data elements across seven nationally defined smart city domains, the framework was applied to thirty-six certified smart cities in Thailand. Data were collected from municipal authorities and national platforms and structured using ISO-based data element and metadata principles. For each data element, a Need Priority Index, Coverage score, and Policy Readiness indicator were computed to assess governance-relevant data readiness. The results reveal a persistent imbalance between high data demand and low data availability across all domains, with Smart Mobility, Smart Living, Smart Energy, and Smart Economy showing the highest urgency. A Core Common Data Set representing 6.7% of assessed properties was identified, centered on population data, geospatial infrastructure, and plans and performance indicators. The framework provides a scalable approach for guiding investments in interoperable smart city data systems. Full article

Historic city centers host dense ensembles of heritage buildings where conservation goals must coexist with sustainable and smart urban development, yet the semi-outdoor “liminal” spaces of these complexes, such as cloisters, loggias and courtyards, are rarely included in microclimate monitoring networks. This study develops and tests the Liminal Environmental Monitoring System (LEMS), a flexible environmental data acquisition architecture designed for long-term monitoring in such spaces. The LEMS is based on a custom, low-cost data acquisition board able to handle multiple analogue and digital sensors, combined with a daisy-chain communication layout using the MODBUS RS485 protocol and a commercial datalogger as master, in order to meet the technical and visual constraints of historic buildings. Board calibration and sensor characterisation are reported, and the system is deployed in the cloister of Palazzo Costabili, a renaissance complex in the historic city center of Ferrara (Italy). This case study illustrates how the LEMS captures spatial and temporal variation in air temperature, relative humidity and solar irradiance and how an annual solar-shading indicator derived from 3D ray-tracing simulations supports the interpretation of irradiance measurements. The results indicate that the LEMS is a viable tool for heritage-compatible microclimate monitoring and can be adapted to other historic courtyards and loggias. Full article

Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference framework (LLM-SSIF), a lightweight semantic–spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled spatial diagnostics at a 500 m resolution. A key advantage of LLM-SSIF is its deployability: LoRA-based parameter-efficient fine-tuning of an open LLM enables lightweight adaptation under limited compute while scaling fine-label coverage. Trained on a nationwide cuisine-labeled dataset (~220,000 records), the model achieves strong multi-class short-text recognition (macro-F1 = 0.843) and, in the Guangzhou–Shenzhen demonstration, expands usable fine-category labels by ~14–15× to support grid-level inference under long-tail sparsity. The spatial module then isolates cuisine-specific over/under-representation beyond overall restaurant intensity, revealing contrasting cultural configurations between Guangzhou and Shenzhen. Overall, LLM-SSIF provides a reproducible and transferable way to translate unstructured POI texts into spatial–statistical evidence for comparative urban analysis. Full article

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores FL as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures. Full article

To address the scientific gap concerning optimal urban wind turbine morphology, this work presents an experimental performance comparison between two small-scale wind turbine designs: a conventional three-blade horizontal-axis wind turbine (HAWT) and a duct-equipped Ayanz-inspired screw-blade turbine. Both configurations were tested in a controlled wind tunnel under steady and transient wind conditions, including synthetic gusts designed to emulate urban wind patterns. The analysis focuses on power output, aerodynamic efficiency (via the power coefficient $C_P$), dynamic responsiveness, and integration suitability. A key novelty of this study lies in the full-scale experimental comparison between a non-conventional Ayanz screw-blade turbine and a standard three-blade turbine, since experimental data contrasting these two geometries under both steady and gusty urban wind conditions are extremely scarce in the literature. Results show that while the three-blade turbine achieves a higher $C_P$ peak and greater efficiency near its optimal operating point, the Ayanz turbine exhibits a broader performance plateau and better self-starting behavior under low and fluctuating wind conditions. The Ayanz model also demonstrated smoother power build-up and higher energy capture under specific gust scenarios, especially when wind speed offsets were low. Furthermore, a methodological contribution is made by comparing the $C_P$ vs. tip speed ratio λ curves at multiple wind speeds, providing a novel framework (plateau width analysis) for realistically assessing turbine adaptability and robustness to off-design conditions. These findings provide practical insights for selecting turbine types in variable or urban wind environments and contribute to the design of robust small wind energy systems for deployments in cities. Full article

Accurate detection and localization of traffic objects are essential for autonomous driving tasks such as path planning. While semantic segmentation is able to provide pixel-level classification, existing networks often fail under challenging conditions like nighttime or rain. In this paper, we introduce a new training framework that combines unsupervised domain adaptation with high dynamic range imaging. The proposed network uses labeled daytime images along with unlabeled nighttime HDR images. By utilizing the fine details typically lost in conventional SDR images due to dynamic range compression, and incorporating the UDA training strategy, the framework effectively trains a model that is capable of semantic segmentation across adverse weather conditions. Experiments conducted on four datasets have demonstrated substantial improvements in inference performance under nighttime and rainy scenarios. The accuracy for daytime images is also enhanced through expanded training diversity. Full article

Autonomous lane-changing is one of the most critical and complex tasks in automated driving. Recent progress in reinforcement learning (RL) has shown strong potential to help autonomous vehicles (AVs) make safe and flexible lane-change decisions in real time under uncertain traffic conditions. In the current studies, there is a lack of a common structure that links RL algorithms, simulation tools, and performance evaluation methods. This paper presents a detailed examination of RL-based lane-changing systems in AVs, tracing their development from early rule-based models to modern learning-based approaches. It introduces a clear classification of lane-changing types—discretionary, mandatory, cooperative, and emergency—and connects each to the most suitable RL methods, including value-based, policy-based, actor–critic, model-based, and hybrid algorithms. Each method is examined for its performance, safety, and computational demands. Furthermore, it reviews major simulation environments, such as SUMO, CARLA, and SMARTS, and summarizes key evaluation measures related to safety, efficiency, comfort, and real-time performance. The comparison shows open research challenges, including model adaptation, safety assurance, and transfer from simulation to real-world driving. Finally, it outlines promising directions for future work, such as cooperative decision-making, safe and explainable RL, and lightweight models for real-time use. This review provides a clear foundation and practical guide for developing reliable and understandable RL-based lane-changing systems for future intelligent transportation. Full article

Floods are among the most destructive natural disasters, posing major challenges to human safety, property, and urban resilience. Effective communication of flood risk is therefore crucial for disaster preparedness and the sustainable management of smart cities. This study explores how interface design elements of flood hazard maps, including interaction modes and legend color schemes, influence users’ risk perception, decision support, and usability. An online questionnaire survey (N = 776) and a controlled 2 × 2 experiment (N = 40) were conducted to assess user comprehension, cognitive load, and behavioral responses when interacting with different visualization formats. Results show that slider-based interaction significantly reduces task completion and map-reading times compared with drop-down menus, enhancing usability and information efficiency. Multicolor legends, although requiring higher cognitive effort, improve users’ risk perception, engagement, and memory of flood-related information. These findings suggest that integrating cognitive principles into interactive design can enhance the effectiveness of digital disaster communication tools. By combining human–computer interaction, visual cognition, and smart governance, this study provides evidence-based design strategies for developing intelligent and user-centered flood hazard mapping systems. The proposed framework contributes to the advancement of smart urban resilience and supports the broader goal of building safer and more sustainable cities. Full article