Smart Cities, Volume 9, Issue 1 (January 2026) – 19 articles

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

Air pollution is a growing environmental issue in densely populated urban areas worldwide. Rapid population growth and the consequent increase in energy demand, emissions from industrial activities and vehicular traffic, and the reduction in vegetation cover have in recent years led to increasing concerns about quality of life, especially due to serious health problems associated with respiratory diseases. This study focuses on air quality in the city of Turin in north-western Italy. Continuous one-year monitoring, which collected approximately two million georeferenced data points, was possible using specific devices—palm-sized, wearable, and commercially available sensors—in different parts of the city. This enabled the assessment of the geographical and seasonal distributions of the most commonly studied air pollutants, namely particulate matter (PM) of three size fractions, nitrogen dioxide (NO₂), and total volatile organic compounds (TVOCs). The results highlight that the north-western zone and the urban centre are the most polluted areas. In particular, seasonal variations suggest that space heating and cooling systems, together with industrial activities, are the main contributors, more so than vehicular traffic. In this context, handheld devices in an IoT context can provide a reliable description of the spatial and temporal distribution of common air pollutants. Full article

Metropolitan service-placement optimization is computationally challenging under strict evaluation budgets and regulatory constraints. Existing approaches either neglect capacity constraints, producing infeasible solutions, or employ population-based metaheuristics requiring hundreds of evaluations—beyond typical municipal planning resources. We introduce a two-stage optimization framework combining Bayesian optimization with domain-informed heuristics to address this constrained, mixed discrete–continuous problem. Stage 1 optimizes continuous service area allocations via the Tree-structured Parzen Estimator with empirical gradient prioritization, reducing effective dimensionality from 81 services to 10–15 per iteration. Stage 2 converts allocations into discrete unit placements via efficiency-ranked bin packing, ensuring regulatory compliance. Evaluation across 35 benchmarks on Saint Petersburg, Russia (117–3060 decision variables), demonstrates that our method achieves 99.4% of the global optimum under a 50-evaluation budget, outperforming BIPOP-CMA-ES (98.4%), PURE-TPE (97.1%), and NSGA-II (96.5%). Optimized configurations improve equity (Gini coefficient of 0.318 → 0.241) while maintaining computational feasibility (2.7 h for 109-block districts). Open-source implementation supports reproducibility and facilitates adoption in metropolitan planning practice. Full article

Cloud-based platforms form the backbone of smart city ecosystems, powering essential services such as transportation, energy management, and public safety. However, their operational complexity generates vast volumes of system logs, making manual anomaly detection infeasible and raising reliability concerns. This study addresses the challenge of data scarcity in log anomaly detection by leveraging Large Language Models (LLMs) to enhance domain-specific classification tasks. We empirically validate that domain-adapted classifiers preserve strong natural language understanding, and introduce a Proximal Policy Optimization (PPO)-based approach to align semantic patterns between LLM outputs and classifier preferences. Experiments were conducted using three Transformer-based baselines under few-shot conditions across four public datasets. Results indicate that integrating natural language analyses improves anomaly detection F1-Scores by 5–86% over the baselines, while iterative PPO refinement boosts classifier’s “confidence” in label prediction. This research pioneers a novel framework for few-shot log anomaly detection, establishing an innovative paradigm in resource-constrained diagnostic systems in smart city infrastructures. Full article

On-demand public transport systems are increasingly adopted to improve service flexibility, reduce operating costs, and meet emerging mobility needs. Evaluating their performance under realistic demand and operational conditions, however, remains a complex challenge. This study presents a hybrid simulation framework that integrates deep learning-based demand forecasting, behavioural survey data, and agent-based simulation to assess system performance. A BiLSTM neural network trained on real-world smartcard data forecasts short-term passenger demand, which is embedded into an agent-based model simulating vehicle dispatch, routing, and passenger interactions. The framework is applied to a case study in Melbourne, Australia, comparing a baseline fixed-route service with two on-demand scenarios. Results show that the most flexible scenario reduces the average passenger trip time by 32%, decreases the average wait time by 34%, increases vehicle occupancy from 12.1 to 18.6 passengers per vehicle, lowers emissions per passenger trip by 72%, and cuts the service cost per trip from AUD 6.82 to AUD 4.73. These findings demonstrate the potential of hybrid on-demand services to improve operational efficiency, passenger experience, and environmental outcomes. The study presents a novel, integrated methodology for scenario-based evaluation of on-demand public transportation using real-world transportation data. Full article

Smart cities generate vast multimodal data from IoT devices, surveillance systems, health monitors, and environmental monitoring infrastructure. The seamless integration and interpretation of such multimodal data is essential for intelligent decision-making and adaptive urban services. Multimodal machine learning (MML) provides a unified framework to fuse and analyze diverse sources, surpassing conventional unimodal and rule-based approaches. This review surveys the role of MML in smart city sensing across mobility, public safety, healthcare, and environmental domains, outlining key data modalities, enabling technologies and state-of-the-art fusion architectures. We analyze major methodological and deployment challenges, including data alignment, scalability, modality-specific noise, infrastructure limitations, privacy, and ethics, and identify future directions toward scalable, interpretable, and responsible MML for urban systems. This survey serves as a reference for AI researchers, urban planners, and policymakers seeking to understand, design, and deploy multimodal learning solutions for intelligent urban sensing frameworks. Full article

Cities increasingly utilise digital technologies to tackle climate risks and urban shocks, yet their real impact on resilience remains uncertain. This paper systematically reviews 115 peer-reviewed studies (2012–2024) to explore how smart city technologies engage with planning instruments, governance arrangements, and social processes, following PRISMA 2020 and combining bibliometric co-occurrence mapping with a qualitative synthesis of full texts. Three themes organise the findings: (i) urban planning and design, (ii) smart technologies in resilience, and (iii) strategic planning and policy integration. Across these themes, Internet of Things (IoT) and geographic information system (GIS) applications have the strongest empirical support for enhancing absorptive and adaptive capacities through risk mapping, early warning systems, and infrastructure operations, while artificial intelligence, digital twins, and blockchain remain largely at pilot or conceptual stages. The review also highlights significant geographical and hazard biases: most cases come from high-income cities and concentrate on floods and earthquakes, while slow stresses (such as heat, housing insecurity, and inequality) and cities in the Global South are under-represented. Overall, the study promotes a “smart–resilience co-production” perspective, demonstrating that resilience improvements rely less on technology alone and more on how digital systems are integrated into governance and participatory practices. Full article

A sustainable city requires a sustainable means of transportation. This ambition is leading towards higher penetration of electric vehicles (EVs) in our cities, in both the private and commercial sectors, putting an ever greater burden on the existing power grid. Modern deregulated power grids vary electricity tariffs from location to location and from time to time to compensate for any additional burden. In this paper, we propose a profit-aware solution to strategically manage the movements of EVs in the city to support the grid while exploiting these locational, time-varying prices. This work is divided into three parts: (M1) profit-aware charging location and optimal route selection, (M2) profit-aware charging and discharging location and optimal route selection, and (M2b) profit-aware charging and discharging location and optimal route selection considering demand-side flexibility. This work is tested on the MATLAB programming platform using the Gurobi optimisation solver. From the extensive case studies, it is found that M1 can yield profits up to 2 times greater than those of its competitors, whereas M2 can achieve profits up to 2.5 times higher and simultaneously provide substantial grid support. Additionally, the M2b extension makes M2 more efficient in terms of grid support. Full article