Smart Cities

16 pages, 1131 KB

Open AccessArticle

HDRSeg-UDA: Semantic Segmentation for HDR Images with Unsupervised Domain Adaptation

by Huei-Yung Lin and Ming-Yiao Chen

Smart Cities 2026, 9(1), 10; https://doi.org/10.3390/smartcities9010010 - 4 Jan 2026

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 [...] Read more.

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

(This article belongs to the Section Artificial Intelligence and LLM Agents for Data-Driven Decisions in Smart Cities)

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27 pages, 914 KB

Open AccessArticle

Reinforcement Learning for Lane-Changing Decision Making in Autonomous Vehicles: A Survey

by Ammar Khaleel and Áron Ballagi

Smart Cities 2026, 9(1), 9; https://doi.org/10.3390/smartcities9010009 - 3 Jan 2026

Abstract

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 [...] Read more.

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

(This article belongs to the Section Smart Urban Mobility, Transport, and Logistics)

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21 pages, 7371 KB

Open AccessArticle

Enhancing Risk Perception and Information Communication: An Evidence-Based Design of Flood Hazard Map Interfaces

by Jia-Xin Guo, Szu-Chi Chen and Meng-Cong Zheng

Smart Cities 2026, 9(1), 8; https://doi.org/10.3390/smartcities9010008 - 2 Jan 2026

Abstract

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 [...] Read more.

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

(This article belongs to the Section Smart Urban Energies and Integrated Systems)

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18 pages, 14655 KB

Open AccessArticle

Wearable Sensors to Estimate Outdoor Air Quality of the City of Turin (NW Italy) in an IoT Context: A GIS-Mapped Representation of Diffused Data Recorded over One Year of Monitoring

by Jessica Maria Chicco, Enrico Prenesti, Valerio Morando, Francesco Fiermonte and Giuseppe Mandrone

Smart Cities 2026, 9(1), 7; https://doi.org/10.3390/smartcities9010007 - 30 Dec 2025

Abstract

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 [...] Read more.

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

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33 pages, 7428 KB

Open AccessArticle

Constrained Metropolitan Service Placement: Integrating Bayesian Optimization with Spatial Heuristics

by Tatiana Churiakova, Ivan Platonov, Mark Bezmaslov, Vadim Bikbulatov, Ovanes Petrosian, Vasilii Starikov and Sergey A. Mityagin

Smart Cities 2026, 9(1), 6; https://doi.org/10.3390/smartcities9010006 - 26 Dec 2025

Abstract

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 [...] Read more.

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

(This article belongs to the Special Issue City Logistics and Smart Cities: Models, Approaches and Planning)

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21 pages, 15857 KB

Open AccessArticle

LogPPO: A Log-Based Anomaly Detector Aided with Proximal Policy Optimization Algorithms

by Zhihao Wang, Jiachen Dong and Chuanchuan Yang

Smart Cities 2026, 9(1), 5; https://doi.org/10.3390/smartcities9010005 - 26 Dec 2025

Abstract

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 [...] Read more.

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

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications: 2nd Edition)

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32 pages, 8941 KB

Open AccessArticle

AI-Powered Evaluation of On-Demand Public Transport: A Hybrid Simulation Approach

by Sohani Liyanage, Hussein Dia and Gordon Duncan

Smart Cities 2026, 9(1), 4; https://doi.org/10.3390/smartcities9010004 - 25 Dec 2025

Abstract

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 [...] Read more.

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

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45 pages, 3603 KB

Open AccessReview

Sensing in Smart Cities: A Multimodal Machine Learning Perspective

by Touseef Sadiq and Christian W. Omlin

Smart Cities 2026, 9(1), 3; https://doi.org/10.3390/smartcities9010003 - 24 Dec 2025

Abstract

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 [...] Read more.

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

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26 pages, 1266 KB

Open AccessSystematic Review

Integrating Smart City Technologies and Urban Resilience: A Systematic Review and Research Agenda for Urban Planning and Design

by Shabnam Varzeshi, John Fien and Leila Irajifar

Smart Cities 2026, 9(1), 2; https://doi.org/10.3390/smartcities9010002 - 23 Dec 2025

Abstract

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, [...] Read more.

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

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23 pages, 2656 KB

Open AccessArticle

Profit-Aware EV Utilisation Model for Sustainable Smart Cities: Joint Optimisation over EV System, Power Grid System, and City Road Grid System

by Shitikantha Dash, Dikshit Chauhan and Dipti Srinivasan

Smart Cities 2026, 9(1), 1; https://doi.org/10.3390/smartcities9010001 - 22 Dec 2025

Abstract

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 [...] Read more.

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

(This article belongs to the Special Issue Smart Mobility Integration in Smart Cities)

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24 pages, 3463 KB

Open AccessArticle

Bridging the Information Gap in Smart Construction: An LLM-Based Assistant for Autonomous TBM Tunneling

by Min Hu, Hongzheng Gao, Qing Mi, Bingjian Wu, Jing Lu and Yongchang Liu

Smart Cities 2025, 8(6), 212; https://doi.org/10.3390/smartcities8060212 - 17 Dec 2025

Abstract

The development of autonomous tunneling is crucial for building the intelligent underground infrastructure that smart cities require. However, in complex urban environments, the need for frequent manual intervention during Tunnel Boring Machine (TBM) operation remains a challenge, hindering overall efficiency and safety. To [...] Read more.

The development of autonomous tunneling is crucial for building the intelligent underground infrastructure that smart cities require. However, in complex urban environments, the need for frequent manual intervention during Tunnel Boring Machine (TBM) operation remains a challenge, hindering overall efficiency and safety. To address the human–machine collaboration gap, this study analyzes practical experiences from six tunnel projects that use autonomous driving systems. Building on this foundation, we develop an intelligent assistant powered by a large language model (LLM). The assistant constructs a complete service architecture and intervention mechanism, proposes a phased intention recognition framework, and uses conversational interaction to achieve efficient human–machine communication. Experimental results demonstrate the strong classification performance of our intention recognition model. Furthermore, engineering case studies validate the assistant’s effectiveness in enhancing operational transparency, increasing user trust, bridging the human–machine information gap, and ultimately ensuring safer and more reliable tunneling. This research provides a feasible and innovative technological path for human–machine collaboration in the construction of critical urban infrastructure. Full article

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53 pages, 1902 KB

Open AccessReview

Edge AI for Smart Cities: Foundations, Challenges, and Opportunities

by Krishna Sruthi Velaga, Yifan Guo and Wei Yu

Smart Cities 2025, 8(6), 211; https://doi.org/10.3390/smartcities8060211 - 16 Dec 2025

Abstract

Smart cities seek to improve urban living by embedding advanced technologies into infrastructures, services, and governance. Edge Artificial Intelligence (Edge AI) has emerged as a critical enabler by moving computation and learning closer to data sources, enabling real-time decision-making, improving privacy, and reducing [...] Read more.

Smart cities seek to improve urban living by embedding advanced technologies into infrastructures, services, and governance. Edge Artificial Intelligence (Edge AI) has emerged as a critical enabler by moving computation and learning closer to data sources, enabling real-time decision-making, improving privacy, and reducing reliance on centralized cloud infrastructure. This survey provides a comprehensive review of the foundations, challenges, and opportunities of edge AI in smart cities. In particular, we begin with an overview of layer-wise designs for edge AI-enabled smart cities, followed by an introduction to the core components of edge AI systems, including applications, sensing data, models, and infrastructure. Then, we summarize domain-specific applications spanning manufacturing, healthcare, transportation, buildings, and environments, highlighting both the softcore (e.g., AI algorithm design) and the hardcore (e.g., edge device selection) in heterogeneous applications. Next, we analyze the sources of sensing data generation, model design strategies, and hardware infrastructure that underpin edge AI deployment. Building on these, we finally identify several open challenges and provide future research directions in this domain. Our survey outlines a future research roadmap to advance edge AI technologies, thereby supporting the development of adaptive, harmonic, and sustainable smart cities. Full article

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30 pages, 3321 KB

Open AccessArticle

An Attempt to Assess the Impact of AI as a Modern Tool for Regional Policy in the Process of Innovative and Economic Development of European Regions

by Nikolay Tsonkov and Miroslav Zlatev

Smart Cities 2025, 8(6), 210; https://doi.org/10.3390/smartcities8060210 - 15 Dec 2025

Abstract

The beginning of the 21st century is associated with a significant technological leap on a global scale, which has had a substantial impact on production and economic processes at the national and regional levels. This radical technological change in the economy is linked [...] Read more.

The beginning of the 21st century is associated with a significant technological leap on a global scale, which has had a substantial impact on production and economic processes at the national and regional levels. This radical technological change in the economy is linked to the emergence and development of artificial intelligence and effective knowledge management, which are the main drivers of economic growth. The use of AI today can be traced in many different areas of applied science—medicine, physics, mathematics, and engineering design, including modeling, planning, and management of territorial systems. The accumulation of large databases and other information necessary for AI to function is directly related to the spatial aspects of economic development, which is also based on local potential (a place-based approach). At the same time, local knowledge resources and innovation potential are not fully utilized in the context of technology diffusion and AI implementation in individual countries and regions. In this regard, this study aims to analyze the role of regional innovation systems, with a focus on AI development, and to track their impact across individual European regions, using NUTS2 spatial-level data to ensure objectivity. The authors consider AI innovation a modern tool for decision-making in the implementation of regional policy, with a specific impact on cohesion between EU regions. The results of the study show a direct link between the localization of regional innovation systems, R & D expenditure, AI implementation, and the economic development of European regions. Important factors influencing this process are the degree of Internet coverage, the capacity to generate innovation, the degree of AI implementation in the individual economic sectors of the countries, the growth of the ICT sector in relation to the overall development of GDP and the economy, and the result of the smart specialisation of regional innovation systems. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

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29 pages, 1156 KB

Open AccessReview

A Conceptual Framework for Integrating IoT and Blockchain for Smart and Sustainable Urban Development

by Abdulaziz I. Almulhim

Smart Cities 2025, 8(6), 209; https://doi.org/10.3390/smartcities8060209 - 14 Dec 2025

Abstract

Cities increasingly face urban sustainability challenges due to rapid urbanization, climate pressures, and infrastructure demands. In response, smart city frameworks have emerged as transformative strategies that promote sustainability, efficiency, and resilience. Among the enabling technologies, the integration of the Internet of Things (IoT) [...] Read more.

Cities increasingly face urban sustainability challenges due to rapid urbanization, climate pressures, and infrastructure demands. In response, smart city frameworks have emerged as transformative strategies that promote sustainability, efficiency, and resilience. Among the enabling technologies, the integration of the Internet of Things (IoT) and blockchain is gaining traction for supporting data-driven, transparent, and inclusive forms of sustainable smart cities. This systematic review analyzes peer-reviewed studies to examine how IoT and blockchain contribute to smart and sustainable urban development. The findings are organized into five thematic areas: (1) applications of IoT and blockchain for sustainable urban development; (2) operational applications across urban sustainability sectors such as energy, mobility, waste, and environmental management; (3) blockchain-enabled urban governance mechanisms including smart contracts, identity systems, and emergency response; (4) direct citizen engagement through transparent participation platforms and incentive-based systems; and (5) challenges and opportunities associated with IoT and blockchain technologies in the context of sustainable city development. In addition, the study proposes a conceptual framework that illustrates how IoT and blockchain integration support sustainable urban innovation. The review highlights the transformative potential of IoT–blockchain convergence in shaping future smart and sustainable cities and aligns with the United Nations Sustainable Development Goal 11. Full article

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19 pages, 1211 KB

Open AccessArticle

Hub, Bridge, or Channel? Role Selection and Evolution of Urban Green Innovation Networks Under Climate Risk

by Pengfei Zhao and Qingfeng Luo

Smart Cities 2025, 8(6), 208; https://doi.org/10.3390/smartcities8060208 - 12 Dec 2025

Abstract

Physical climate risks are reshaping economic geography and pose a direct threat to the collaborative networks of green innovation that underpin mitigation and adaptation. This paper examines how climate risk differentially affects three core structural roles that cities occupy in green innovation collaboration [...] Read more.

Physical climate risks are reshaping economic geography and pose a direct threat to the collaborative networks of green innovation that underpin mitigation and adaptation. This paper examines how climate risk differentially affects three core structural roles that cities occupy in green innovation collaboration networks: hubs, which aggregate knowledge and are measured by degree centrality; channels, which transmit information and are captured by closeness centrality; and bridges, which link resources and are reflected in betweenness centrality. Using a panel of Chinese cities over the past decade and two way fixed effects models, we estimate the impacts of climate risk on cities’ network roles. The results show that climate risk significantly reduces all three roles, but the negative effects on channels and hubs are substantially larger than the effect on bridges. This pattern is consistent with a defensive structural reconfiguration of the network that emphasizes resilience at the expense of efficiency. The specific pathways and magnitudes of change depend on local financial conditions, regulatory responses, a city’s position in the urban hierarchy, and the type of climate risk encountered. These findings incorporate exogenous environmental pressure into theories of network evolution and provide empirical support for shifting regional innovation policy from an efficiency first orientation toward a resilience oriented innovation ecosystem. Full article

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20 pages, 4207 KB

Open AccessArticle

Urban Micromobility in Practice: Insights from a Full-Year Analysis of Shared Scooter Use in Tel Aviv

by Ada Garus, Gabriel Dadashev, Biagio Ciuffo and Bat-Hen Nahmias-Biran

Smart Cities 2025, 8(6), 207; https://doi.org/10.3390/smartcities8060207 - 12 Dec 2025

Cited by 1

Abstract

This paper investigates the spatiotemporal patterns and accessibility implications of shared e-scooter use in Tel Aviv, drawing on a complete year (2024) of trip-level data from all licensed providers. Shared micromobility services are often promoted as tools for reducing car dependency and improving [...] Read more.

This paper investigates the spatiotemporal patterns and accessibility implications of shared e-scooter use in Tel Aviv, drawing on a complete year (2024) of trip-level data from all licensed providers. Shared micromobility services are often promoted as tools for reducing car dependency and improving urban accessibility, yet their actual usage patterns and equity outcomes remain underexplored, especially outside North America and Western Europe. This study aims to address this gap by integrating over 9 million reconstructed scooter trips with public transport accessibility data, local weather records, and institutional calendar effects. Multivariate regression was applied to quantify temporal and environmental determinants of demand, seasonal-trend decomposition to reveal cyclical usage patterns, and spatial analysis to assess whether scooters extend or reinforce existing mobility hierarchies. Findings indicate that scooter use in Tel Aviv is highly structured, peaking during afternoon hours, dropping during holidays and rain, and reflecting the weekly rhythms of the workweek in Tel Aviv. However, spatial patterns show a strong concentration of usage within already well-connected central areas, with limited activity in low-accessibility zones. These results suggest that shared e-scooters are not currently fulfilling their potential as first- or last-mile connectors; instead, they primarily serve as short-range, intra-core alternatives to walking. Full article

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33 pages, 1750 KB

Open AccessSystematic Review

Quantum and Quantum-Inspired Optimisation in Transport and Logistics: A Systematic Review

by Paloma Liu, Simon Parkinson and Kay Best

Smart Cities 2025, 8(6), 206; https://doi.org/10.3390/smartcities8060206 - 11 Dec 2025

Abstract

Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. [...] Read more.

Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review identifies limited real-world deployment, a lack of standardised benchmarks, and scarce cost–benefit evaluations, highlighting key areas where further empirical work is needed. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration. Full article

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22 pages, 6879 KB

Open AccessArticle

Spatial Analysis on Urban Justice Delivering the Community Parks: A Case of the Saudi Arabian City of Al-Khobar

by Sara Qwaider, Mohammad Sharif Zami, Muhammad Bilal, Riyad Ashmeel and Mohammad A. Hassanain

Smart Cities 2025, 8(6), 205; https://doi.org/10.3390/smartcities8060205 - 10 Dec 2025

Abstract

This study evaluates the spatial equity of community parks in Al-Khobar City, Saudi Arabia, by examining their proximity, availability, distribution, accessibility, and user satisfaction. Ensuring equitable access to public open spaces is vital for promoting urban liveability and achieving the sustainability objectives of [...] Read more.

This study evaluates the spatial equity of community parks in Al-Khobar City, Saudi Arabia, by examining their proximity, availability, distribution, accessibility, and user satisfaction. Ensuring equitable access to public open spaces is vital for promoting urban liveability and achieving the sustainability objectives of Saudi Vision 2030. A mixed-methods approach integrating Geographic Information System (GIS)-based spatial analysis with a structured user survey was applied. GIS was used to map park locations, calculate per capita green space, and assess accessibility within a 500 m walking radius, while survey data from 300 respondents captured user satisfaction and perceptions of community park dimensions and indicators. The results reveal pronounced spatial disparities across neighbourhoods, with more than twenty areas lacking any park access and several others falling below the 5 m² per capita standard. In contrast, centrally located neighbourhoods demonstrate adequate provision and higher satisfaction levels. These findings indicate a fragmented and inequitable park distribution that limits community well-being and social inclusion. The study concludes that integrating GIS-based evidence with community feedback can inform data-driven planning policies and promote equitable, accessible, and sustainable community parks. The proposed framework offers a replicable model for assessing urban green space equity in other Saudi and Middle Eastern cities. Full article

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27 pages, 7377 KB

Open AccessArticle

A Hybrid Control Strategy for Multi-Timescale Air Conditioning Load Demand Response

by Yifan Bai, Jiandong Jiang, Qiangang Jia, Chenghao Liu, Binghao Yang and Peng Zhuang

Smart Cities 2025, 8(6), 204; https://doi.org/10.3390/smartcities8060204 - 9 Dec 2025

Abstract

Globally, the transition of energy structure towards clean and low-carbon is accelerating, with the increasing grid integration ratio of renewable energy. However, the inherent intermittency, volatility and randomness of such energy sources are in fundamental conflict with the traditional operation mode of existing [...] Read more.

Globally, the transition of energy structure towards clean and low-carbon is accelerating, with the increasing grid integration ratio of renewable energy. However, the inherent intermittency, volatility and randomness of such energy sources are in fundamental conflict with the traditional operation mode of existing power systems, which not only restricts the absorption capacity of renewable energy, but also poses severe challenges to the safe and stable operation of power systems. The integration of renewable energy sources into existing power systems poses numerous challenges that can be mitigated through the utilization of demand-side flexible resources. Among these, air-conditioning (AC) loads, as a prominent example, offer significant potential for enhancing flexibility in power systems. Nonetheless, determining an optimal AC control strategy to achieve the desired power response remains challenging, particularly in practical control settings where reliance on a single timescale control strategy may prove inadequate to address fluctuations in power system flexibility requirements. This paper investigates the characteristics of direct start-stop control and duty cycling control within a multi-timescale, source-load coordinated scheduling framework. Furthermore, a hybrid control strategy that combines these two methods is proposed, accompanied by the formulation of a power curtailment model tailored to the hybrid control strategy. Case study results demonstrate that the hybrid control strategy effectively augments AC load flexibility and enhances scheduling feasibility, thereby supporting the stable operation of the power system. Full article

(This article belongs to the Section Smart Grids)

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