Smart Cities, Volume 8, Issue 5 (October 2025) – 31 articles

Rural communities face persistent transport disadvantages due to low population density, limited-service availability, and high operational costs, restricting access to essential services and exacerbating social inequality. Autonomous public transport systems offer a transformative solution by enabling flexible, cost-effective, and inclusive mobility options. This study investigates the operational determinants for autonomous road-based transit systems in rural and peri-urban South-East Queensland (SEQ), employing a structured survey of 273 residents and analytical approaches, including General Additive Model (GAM) and Extreme Gradient Boosting (XGBoost). The findings indicate that small shuttles suit flexible, non-routine trips, with leisure travelers showing the highest importance (Gain = 0.473) and university precincts demonstrating substantial influence (Gain = 0.253), both confirmed as significant predictors by GAM (EDF = 0.964 and EDF = 0.909, respectively). Minibus shuttles enhance first-mile and last-mile connectivity, driven primarily by leisure travelers (Gain = 0.275) and tourists (Gain = 0.199), with shopping trips identified as a significant non-linear predictor by GAM (EDF = 1.819). Standard-sized buses are optimal for high-capacity transport, particularly for school children (Gain = 0.427) and school trips (Gain = 0.148), with GAM confirming their significance (EDF = 1.963 and EDF = 0.834, respectively), demonstrating strong predictive accuracy. Hybrid models integrating autonomous and conventional buses are preferred over complete replacement, with autonomous taxis raising equity concerns for low-income individuals (Gain = 0.047, indicating limited positive influence). Integration with Mobility-as-a-Service platforms demonstrates strong, particularly for special events (Gain = 0.290) and leisure travelers (Gain = 0.252). These insights guide policymakers in designing autonomous road-based transit systems to improve rural connectivity and quality of life. Full article

Object detection is a critical technology for smart city development. As request volumes surge, inference is increasingly offloaded from centralized clouds to user-proximal edge sites to reduce latency and backhaul traffic. However, heterogeneous workloads, fluctuating bandwidth, and dynamic device capabilities make offloading and scheduling difficult to optimize in edge environments. Deep reinforcement learning (DRL) has proved effective for this problem, but in practice, it relies on manually engineered reward functions that must be redesigned whenever service objectives change. To address this limitation, we introduce an LLM-driven framework that retargets DRL policies for edge object detection directly through natural language instructions. By leveraging understanding of the text and encoding capabilities of large language models (LLMs), our system (i) interprets the current optimization objective; (ii) generates an executable, environment-compatible reward function code; and (iii) iteratively refines the reward via closed-loop simulation feedback. In simulations for a real-world dataset, policies trained with LLM-generated rewards adapt from prompts alone and outperform counterparts trained with expert-designed rewards, while eliminating manual reward engineering. Full article

The rapid evolution of 5G New Radio networks has introduced a wide range of services with diverse requirements, complicating their coexistence within the shared radio spectrum and posing challenges in traffic scheduling and resource allocation. This study aims to analyze and categorize the methods, approaches, and techniques proposed to ensure efficient joint and dynamic packet scheduling and resource allocation among heterogeneous services—namely eMBB, URLLC, and mMTC—in 5G and beyond, with a focus on Quality of Service and user satisfaction. This scoping review draws from publications indexed in IEEE Xplore and Scopus and synthesizes the most relevant evidence related to packet scheduling across heterogeneous services, highlighting key approaches, core performance metrics, and emerging trends. Following the PRISMA-ScR methodology, 48 out of an initial 140 articles were included for explicitly addressing coexistence, scheduling, and resource allocation. The findings reveal a research emphasis on eMBB and URLLC coexistence, while integration with mMTC remains underexplored. Moreover, the evidence suggests that hybrid and deep learning-based approaches are particularly promising for tackling coexistence and resource management challenges in future mobile networks. Full article

The growing need for efficient and sustainable urban water management has accelerated the adoption of smart monitoring infrastructures based on wireless sensor networks (WSNs). This study proposes a connectivity-aware methodology for the optimal deployment of wireless sensor networks (WSNs) in smart water metering systems. The approach models the wireless sensors as nodes embedded in household water meters and determines the minimal yet sufficient set of Data Aggregation Points required to ensure complete network coverage and transmission reliability. A scalable and hierarchical topology is generated by integrating an enhanced minimum spanning tree algorithm with set covering techniques and geographic constraints, leading to a robust intermediate layer of aggregation nodes. These nodes are wirelessly linked to a single cellular base station, minimizing infrastructure costs while preserving communication quality. Simulation results on realistic urban layouts demonstrate that the proposed strategy reduces network fragmentation, improves energy efficiency, and simplifies routing paths compared to traditional ad hoc designs. The results offer a practical framework for deploying resilient and cost-effective smart water metering solutions in densely populated urban environments. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article

Continuous technological, ecological, and digital transformations reshape urban mobility systems. While sustainable mobility has become a dominant keyword, there are many different approaches and policies to help achieve lasting and properly functioning change. This study applies a comprehensive qualitative policy analysis to influential and leading sustainable mobility approaches (i.a. Mobility Justice, Avoid–Shift–Improve, spatial models like the 15-Minute City and Superblocks, governance frameworks such as SUMPs, and tools ranging from economic incentives to service architectures like MaaS and others). Each was assessed across structural barriers, psychological resistance, governance constraints, and affective dimensions. The results show that, although these approaches provide clear normative direction, measurable impacts, and scalable applicability, their implementation is often undermined by fragmentation, Policy Layering, limited intermodality, weak Future-Readiness, and insufficient participatory engagement. Particularly, the lack of sequencing and pacing mechanisms leads to policy silos and societal resistance. The analysis highlights that the main challenge is not the absence of solutions but the absence of a unifying paradigm. To address this gap, the paper introduces CalmMobility, a conceptual framework that integrates existing strengths while emphasizing comprehensiveness, pacing–sequencing–inclusion, and Future-Readiness. CalmMobility offers adaptive and co-created pathways for mobility transitions, grounded in education, open innovation, and a calm, deliberate approach. Rather than being driven by hasty or disruptive change, it seeks to align technological and spatial innovations with societal expectations, building trust, legitimacy, and long-term resilience of sustainable mobility. Full article

Urban green spaces are essential for mitigating the heat island effect, supporting ecosystem services, and maintaining biodiversity. The distribution, fragmentation, and connection of the green spaces significantly impact the behavior of species in cities, serving as key indicators of environmental resilience and ecological benefits. However, current studies, as well as planning standards, often prioritize green spaces independently through their coverage or density, overlooking the importance of continuity and its impact on thermal regulation and accessibility. In this research, urban “hidden green corridors” refer to the unrecognized but functionally significant pathways that link fragmented green spaces through ecological behaviors, which enhance both biological and human habitats. This research focuses on developing an agent-based simulation (ABS) model based on the Physarealm plugin in Rhino, which can assess the effectiveness of these hidden corridors in different urban settings by integrating geographic information systems (GIS) and space syntax. Based on three case studies in Italy (Lambrate District, Bolognina, and Ispra), the simulation results are further interpreted through the AI agentic workflow “SOFIA”, developed by IMM Design Lab, Politecnico di Milano, and compared using manual analysis as well as mainstream large language models (ChatGPT 4.0 Web). The findings indicate that the “hidden green corridors” are essential for urban heat reduction, enhancement of urban biodiversity, and strengthening ecological flows. Full article

Against a global backdrop of industrialization and urbanization, precise measurement of multifactor flows and systematic identification of barriers and drivers are critical for optimizing resource allocation in smart regional development. This study develops an integrated geospatial analytic framework that incorporates mobile signaling data and POI data to quantify the intensity, barriers, and driving mechanisms of urban–rural factor flows in Huzhou City at the township scale. Key findings reveal the following. (1) Urban–rural factor flows exhibit significant spatial polarization, with less than 20% of connections accounting for the majority of flow intensity. The structure shows clear core–periphery differentiation, further shaped by inner heterogeneity and metropolitan spillovers. (2) Barriers demonstrate complex and uneven spatial distributions, with 45.37% of the integrated flow intervals experiencing impediments. Critically, some nodes act as both facilitators and obstacles, depending on the flow type and direction, revealing a metamodern tension between promotion and impairment. (3) Economic vitality plays a crucial role in driving urban–rural factor flow, with different factors having complex, often synergistic or nonlinear effects on both single and integrated flows. The study advances the theoretical understanding of heterogeneous spatial structures in urban–rural systems and provides a replicable analytical framework for diagnosing factor flows in small and medium-sized cities. These insights form a critical basis for designing targeted and adaptive regional governance strategies. Full article

Today, due to the widening of the wealth gap, the intensification of climate change, and the acceleration of both population growth and population aging, our cities are being tested by multiple economic, environmental, and social challenges, including, but not limited to, urban sprawl, urban gentrification, marginalization, housing crisis, tent city, urban flooding, urban heat island, environmental migrants, urban slums, tent cities, urban aging, and empty nesters [...] Full article

Climate change and urbanization are intensifying the urban heat island effect and negatively impacting outdoor thermal comfort in cities. Innovative planning strategies are required to design more livable and resilient urban spaces. Building on a state of the art of current Urban Digital Twins (UDTs) for outdoor thermal comfort analysis, this paper presents the design and implementation of a functional UDT prototype. Developed for a pilot area in Huelva, Spain, the system integrates real-time environmental data, spatial modeling, and simulation tools within an open-source architecture. The literature reveals that while UDTs are increasingly used in urban management, their application to outdoor thermal comfort remains limited and technically challenging, especially in terms of real-time data, modeling accuracy, and user interaction. The case study demonstrates the feasibility of a modular, open-source UDT capable of simulating mean radiant temperature and outdoor thermal comfort indexes at high resolution and visualizing the results in a 3D interactive environment. UDTs have strong potential for supporting microclimate-sensitive planning and improving outdoor thermal comfort. However, important challenges remain, particularly in simulation efficiency, model detail, and stakeholder accessibility. The proposed prototype addresses several of these gaps and provides a basis for future improvements. Full article

This study examines the global mobility of researchers in the smart city domain from 2000 to 2020, using inter-country and intercity affiliation data from the Web of Science. Employing network analysis and spatial econometric models, the paper maps the structural reconfiguration of scientific labor circulation. The results show that the international mobility network is dense yet asymmetric, dominated by a small set of high-frequency corridors such as China–United States, which intensified markedly over the two decades. While early networks were fragmented and polycentric, the later period reveals a multipolar configuration with significant growth in South–South and intra-European exchanges. At the city level, Beijing, Shanghai, Wuhan, and Nanjing emerged as central nodes, reflecting the consolidation of East Asian hubs within the global knowledge system. Mesoscale community detection highlights the coexistence of territorially embedded ecosystems and transregional corridors sustained by thematic and reputational affinities. Growth decomposition indicates that high-income countries benefit from both talent retention and international inflows, while upper-middle-income countries rely heavily on inbound mobility. Spatial regression and quantile models confirm that economic growth and baseline scientific visibility remain robust drivers of urban smart city performance. In contrast, mobility effects are context-dependent and heterogeneous across city positions. Together, these findings demonstrate that researcher mobility is not only a vector of knowledge exchange but also a mechanism that reinforces spatial hierarchies and reshapes the geography of global smart city innovation. Full article

The effective management of smart cities relies on real-time data from urban environments, where Unmanned Aerial Vehicles (UAVs) are critical sensing platforms. However, deploying high-performance detection models on resource-constrained UAVs presents a major challenge, particularly for identifying small, dense targets like pedestrians and vehicles from high altitudes. This study aims to develop a lightweight yet accurate detection algorithm to bridge this gap. We propose YOLOv10-DSNet, an improved architecture based on YOLOv10. The model integrates three key innovations: a parallel dual attention mechanism (CBAM-P) to enhance focus on small-target features; a novel lightweight feature extraction module (C2f-LW) to reduce model complexity; and an additional 160 × 160 detection layer to improve sensitivity to fine-grained details. Experimental results demonstrate that YOLOv10-DSNet significantly outperforms the baseline, increasing mAP50-95 by 4.1% while concurrently decreasing computational costs by 1.6 G FLOPs and model size by 0.7 M parameters. The proposed model provides a practical and powerful solution that balances high accuracy with efficiency, advancing the capability of UAVs for critical smart city applications such as real-time traffic monitoring and public safety surveillance. Full article

Most urban areas have Traffic Management Centers that rely partially on communication with 9-1-1 centers for incident detection. This level of awareness is often lacking for rural interstates spanning several 9-1-1 centers. This paper presents a novel approach to extending TMC visibility by automatically monitoring regional 9-1-1 dispatch channels using off-the-shelf hardware and open-source speech-to-text libraries. Our study presents a proof-of-concept study servicing 71 miles of rural I-65 in Indiana, successfully monitoring four county dispatch centers from a single location, and efficiently transcribing live audio within 60 s of broadcast. This work’s primary contribution is demonstrating the feasibility and practical value of automated incident detection systems for rural interstates. This technology is implementation-ready for extending the visibility of Traffic Management Centers in rural interstate segments. Further work is underway for developing scalable procedures for integrating multiple remote sites, extracting more diverse keyword sets, investigating optimal speech-to-text models, and assessing the technical aspects of the experimental procedures of this manuscript. Full article

Climate change is intensifying the frequency and severity of extreme weather events, threatening the resilience of buildings and urban infrastructure. While technical solutions for climate adaptation in buildings are well documented, their economic viability remains a critical, yet underexplored, dimension of decision-making. This novel systematic review analyzes publications with an exclusive focus on climate adaptation strategies for buildings using cost-based evaluation methods. This review categorises the literature into three methodological clusters: Cost–Benefit Analysis (CBA), Life Cycle Costing (LCC), and alternative methods including artificial intelligence, simulation, and multi-criteria approaches. CBA emerges as the most frequently used and versatile tool, often applied to evaluate micro-scale flood protection and nature-based solutions. LCC is valuable for assessing long-term investment efficiency, particularly in retrofit strategies targeting energy and thermal performance. Advanced methods, such as genetic algorithms and AI-driven models, are gaining traction but face challenges in data availability and transparency. Most studies focus on residential buildings and flood-related hazards, with a growing interest in heatwaves, wildfires, and compound risk scenarios. Despite methodological advancements, challenges persist—including uncertainties in climate projections, valuation of non-market benefits, and limited cost data. This review highlights the need for integrated frameworks that combine economic, environmental, and social metrics, and emphasises the importance of stakeholder-inclusive, context-sensitive decision-making. Ultimately, aligning building adaptation with financial feasibility and long-term sustainability is achievable through improved data quality, flexible methodologies, and supportive policy instruments. Full article

A significant portion of home energy consumption is due to concealed faults and the inefficient usage of home appliances, usually because of user ignorance and a lack of proactive maintenance strategies. In this paper, ENACT, a digital-twin-based system, is proposed as the solution that facilitates better user understanding, encourages sustainable maintenance practices for appliances, and provides prescriptive maintenance recommendations. With the integration of smart plugs, behavioral analysis, and a 3D spatial interface, ENACT offers real-time device monitoring while providing context-aware suggestions. The system was installed in 20 households over a 12-month period, with users engaging with both 2D and 3D models of their surroundings. The quantitative results, including an average System Usability Scale score of 80.5, and qualitative feedback demonstrated intense user engagement, with strong evidence of mindset shifts towards proactive maintenance behavior. The findings confirm that digital twin technologies, when combined with targeted guidance, can significantly improve appliance lifespans, energy efficiency, and user empowerment within homes. Full article

Efficient transportation management is essential for the sustainability and safety of modern urban infrastructure. Traditional road inspection and transport management methods are often labor-intensive, time-consuming, and prone to inaccuracies, limiting their effectiveness. This study presents a UAV-based transport management system that leverages machine learning techniques to enhance road anomaly detection and severity assessment. The proposed approach employs a structured three-tier model architecture: A unified obstacle detection model identifies six critical road hazards—road cracks, potholes, animals, illegal dumping, construction sites, and accidents. In the second stage, six dedicated severity classification models assess the impact of each detected hazard by categorizing its severity as low, medium, or high. Finally, an aggregation model integrates the results to provide comprehensive insights for transportation authorities. The systematic approach seamlessly integrates real-time data into an interactive dashboard, facilitating data-driven decision-making for proactive maintenance, improved road safety, and optimized resource allocation. By combining accuracy, scalability, and computational efficiency, this approach offers a robust and scalable solution for smart city infrastructure management and transportation planning. Full article

With the development of technologies and the growing need for accurate positioning inside buildings, the localization method based on Received Signal Strength Indicator (RSSI) fingerprinting is becoming increasingly popular. Its popularity is explained by the relative simplicity of implementation, low cost and the ability to use existing wireless infrastructure. This review article covers all the key aspects of building such systems: from the wireless communication technology and the creation of a radiomap to data preprocessing methods and model training using machine learning (ML) and deep learning (DL) algorithms. Specific recommendations are provided for each stage that can be useful for both researchers and practicing engineers. Particular attention is paid to such important issues as RSSI signal instability, the impact of multipath propagation, differences between devices and system scalability issues. In conclusion, the review highlights the most promising areas for further research. For smart cities, the approaches and recommendations presented in the review contribute to the development of urban services by combining indoor positioning systems with IoT platforms for automation, transport and energy management. Full article

Technological advancements are reshaping New Mobility Services (NMS) by enhancing trip planning, booking, and payment processes, while also improving fleet management, infrastructure utilization, and data-driven decision-making. Despite these developments, challenges persist in integrating technologies into cohesive and interoperable mobility systems. This study draws insights from 163 stakeholders across the NMS ecosystem to examine both the opportunities and barriers associated with the effective integration of technology into NMS, particularly within urban and metropolitan contexts. Using statistical methods, these responses were analyzed across eight stakeholder groups to determine whether their views converge or diverge. Findings reveal a broad consensus on the technologies expected to have the greatest impact, as well as on the main challenges of integrating these technologies into NMS. Divergences arise in the perceived influence on specific mobility attributes, such as environmental sustainability, security, safety, equity, and social inclusion, and in the services considered most likely to benefit. Notably, investors express a more optimistic view across nearly all technologies, prioritizing shared vehicle services and anticipating the strongest impacts in environmental sustainability. The rest of the stakeholder groups emphasize the potential of technology to enhance modal integration and identify Mobility-as-a-Service (MaaS) as the NMS with the greatest expected benefits. These insights help identify strategic priorities and redirect efforts toward promoting investment in technologies with the highest potential to deliver transformative benefits across the NMS ecosystem. Full article

Multimodal transportation systems, particularly those combining metro and bikesharing, have become central to addressing the first- and last-mile connectivity challenges in urban environments. This study presents a comprehensive data-driven framework to analyze the spatiotemporal interplay between metro and dockless bikesharing usage using real-world data from Tianjin, China. Two primary methods are employed: K-means clustering is used to categorize metro stations and bike usage zones based on temporal demand features, and non-negative Tucker decomposition is applied to a three-way tensor (day, hour, station) to extract latent mobility modes. These modes capture recurrent commuting and leisure behaviors, and their alignment across modes is assessed using Jaccard similarity indices. Our findings reveal distinct usage typologies, including mismatched (misalignment of jobs and residences), employment-oriented, and comprehensive zones, and highlight strong temporal coordination between metro and bikesharing during peak hours, contrasted by spatial divergence during off-peak periods. The analysis also uncovers asymmetries in peripheral stations, suggesting differentiated planning needs. This framework offers a scalable and interpretable approach to mining multimodal travel patterns and provides practical implications for station-area design, dynamic bike rebalancing, and integrated mobility governance. The methodology and insights contribute to the broader effort of data-driven smart city planning, especially in rapidly urbanizing contexts. Full article

A major challenge for modern transit systems relying on traditional fixed-route designs is providing broad accessibility to users. Flex-route transit can enhance accessibility in low-density areas, since it combines the directness of fixed-route transit with the coverage of on-demand mobility. Although deviating for optional pickups can increase ridership and transit accessibility, it also deteriorates the service performance for fixed-route riders. To balance this inherent trade-off, this paper proposes a reinforcement learning approach for deviation decisions. The proposed model is used in a case study of a proposed flex-route service in the city of Boston. The performance on competing objectives is evaluated for reward configurations that adapt to peak and off-peak scenarios. The analysis shows a significant improvement of our method compared to a heuristic derived from industry practice as a baseline. To evaluate robustness, we assess performance across scenarios with varying demand compositions (fixed and requested riders). The results show that the method achieves greater improvements than the baseline in scenarios with increased request ridership, i.e., where decision-making is more complex. Our approach improves service performance under dynamic demand conditions and varying priorities, offering a valuable tool for smart cities to operate flex-route services. Full article

Cities across the globe face a variety of social, economic, and environmental challenges, and building smart city systems has become a popular strategy, through a combination of institutional and organizational systems along with technological innovation. However, smart city projects drastically vary in scope and size, from building infrastructure for data gathering to improve policy, to developing more efficient government services, and even covering aspects of sustainable economic development or citizens’ quality of life. Applying perspectives from social informatics, we developed and tested two hypotheses using a dataset comprising 99 US cities to answer the following question: What is the impact of technical and social aspects of city governance mechanisms such as regulations, plans, and partnerships on the adoption of smart city projects? We study the adoption of smart city initiatives through the lenses of a comprehensive conceptualization of the smart city that includes the dimensions of government, infrastructure, and society. Our findings suggest that governance arrangements positively correlate with smart city projects in all three dimensions. We found, however, that legitimacy and inclusion aspects for governance may have a stronger impact on Smart Infrastructure projects. Future research is necessary to continue exploring the nuanced interactions between governance and smart city policy. Full article

Spatiotemporal modeling and prediction of road network traffic flow are essential components of intelligent transport systems (ITS), aimed at effectively enhancing road service levels. Sustainable and reliable traffic management in smart cities requires the use of modern algorithms based on a comprehensive analysis of a significant number of dynamically changing factors. This paper designs a Crested Porcupine Optimizer (CPO)-CNN-LSTM-Attention time series prediction model, which integrates machine learning and deep learning to improve the efficiency of traffic flow forecasting in the condition of urban roads. Based on historical traffic patterns observed on Paris’s roads, a traffic flow prediction model was formulated and subsequently verified for effectiveness. The CPO algorithm combined with multiple neural network models performed well in predicting traffic flow, surpassing other models with a root-mean-square error (RMSE) of 17.35–19.83, a mean absolute error (MAE) of 13.98–14.04, and a mean absolute percentage error (MAPE) of 5.97–6.62%. Therefore, the model proposed in this paper can predict traffic flow more accurately, providing a solution for enhancing urban traffic management in intelligent transportation systems, and thus offering a research direction for the future development of smart city construction. Full article

Effective speed management at urban entry points is essential for ensuring traffic safety and supporting sustainable mobility in smart cities. This study contributes to urban mobility planning by using a high-fidelity driving simulation to evaluate gateway designs that enhance safety and behavioral compliance at built-up entry zones. Seven gateway configurations, comprising physical (i.e., chicanes, road narrowing) and psychological (i.e., transverse markings, avenue planting) speed calming measures, were evaluated against a reference scenario. A total of 54 participants completed a 14 km simulated route under standardized conditions, with vehicle speed, acceleration/deceleration, and lateral position continuously recorded. The strongest effects were observed in designs featuring chicanes, which achieved the largest speed reductions but also induced abrupt deceleration. In contrast, the combination of road narrowing and transverse markings resulted in a smoother and more gradual deceleration, minimizing driver discomfort and lateral instability. Psychological measures alone, such as avenue planting, had a limited impact on speed behavior. These findings highlight the importance of combining physical and psychological traffic calming measures to create effective, perceptually engaging transitions that promote safer and more consistent driver responses. Full article

Parking space shortages are attributed to an increased density of vehicle presence in the urban context, necessitating the implementation of effective parking management strategies, especially in areas where facility expansion is constrained by limited land availability. Many parking facilities remain operationally inefficient and underutilized due to manual VP methods and having little access to parking resource utilization data. This study develops a DT-based SPMS integrating machine vision, data modeling, and DT technology to automate facility management operations. The system uses YOLOv7 for vehicle and License Plate Detection (LPD), and Deep Text Recognition–Scene Text Recognition (DTR-STR) for license plate recognition (LPR). The findings indicate an 89.89% accuracy for VP- and LPR-based occupancy tracking tasks, and 94.86% for vehicle detection or VD-based occupancy tracking. The system in the built environment comprises three features: (1) automated VP at parking entry and exit points, (2) occupancy monitoring through LPR, (3) Object Detection (OD) for occupancy tracking. The 3D BIM DT model in Autodesk Revit processes inference data from machine vision models to visualize parking activity. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs. Full article

Traffic congestion remains a major urban challenge, impacting economic productivity, environmental sustainability, and commuter well-being. This systematic review investigates how artificial intelligence (AI) techniques contribute to detecting traffic congestion. Following the SPAR-4-SLR protocol, we analyzed 44 peer-reviewed studies covering three data categories—spatiotemporal, probe, and hybrid/multimodal—and four AI model types—shallow machine learning (SML), deep learning (DL), probabilistic reasoning (PR), and hybrid approaches. Each model category was evaluated against metrics such as accuracy, the F1-score, computational efficiency, and deployment feasibility. Our findings reveal that SML techniques, particularly decision trees combined with optical flow, are optimal for real-time, low-resource applications. CNN-based DL models excel in handling unstructured and variable environments, while hybrid models offer improved robustness through multimodal data fusion. Although PR methods are less common, they add value when integrated with other paradigms to address uncertainty. This review concludes that no single AI approach is universally the best; rather, model selection should be aligned with the data type, application context, and operational constraints. This study offers actionable guidance for researchers and practitioners aiming to build scalable, context-aware AI systems for intelligent traffic management. Full article

This systematic literature review pioneers the synthesis of cybersecurity challenges for automotive digital twins (DTs), a critical yet underexplored frontier in connected vehicle security. The notion of digital twins, which act as simulated counterparts to real-world systems, is revolutionizing secure system design within the automotive sector. As contemporary vehicles become more dependent on interconnected electronic systems, the likelihood of cyber threats is escalating. This comprehensive literature review seeks to analyze existing research on threat modeling and security testing in automotive digital twins, aiming to pinpoint emerging patterns, evaluate current approaches, and identify future research avenues. Guided by the PRISMA framework, we rigorously analyze 23 studies from 882 publications to address three research questions: (1) How are threats to automotive DTs identified and assessed? (2) What methodologies drive threat modeling? Lastly, (3) what techniques validate threat models and simulate attacks? The novelty of this study lies in its structured classification of digital twin types (physics based, data driven, hybrid), its inclusion of a groundbreaking threat taxonomy across architectural layers (e.g., ECU tampering, CAN-Bus spoofing), the integration of the 5C taxonomy with layered architectures for DT security testing, and its analysis of domain-specific tools such as VehicleLang and embedded intrusion detection systems. The findings expose significant deficiencies in the strength and validation of threat models, highlighting the necessity for more adaptable and comprehensive testing methods. By exposing gaps in scalability, trust, and safety, and proposing actionable solutions aligned with UNECE R155, this SLR delivers a robust framework to advance secure DT development, empowering researchers and industry to fortify vehicle resilience against evolving cyber threats. Full article

Frequent transit services in urban areas have the potential to increase their accessibility to transit-dependent riders and reduce congestion by attracting new ridership through a modal shift. However, bus services operating in mixed traffic face operational challenges that reduce reliability and hinder their attractiveness. The sources of unreliability can range from local-level conditions, like the road infrastructure, to higher-level decisions, like the service plan. For the effective planning of improvement strategies, both scales of analysis must be considered. This paper uses a novel modeling framework to understand reliability by analyzing the route and segment factors separately. The Chicago Transit Authority (CTA) bus network is used as a case study for the analysis. The data reflect the operational, demand, and urban conditions of 50 high-frequency bus routes. At the route level, we use the coefficient of headway variation as the dependent variable and diverse route characteristics as explanatory variables. The results indicate that the most significant contributors to the variability of headways are variability in schedules and dispatching at terminals. It is also found that driver experience impacts reliability and that east–west routes are more unreliable than north–south routes. At the segment level, we use data from trips involved in bunching and gaps. As the dependent variable, a novel measure is formulated to capture how quickly bunching or gaps are formed. The bunching and gap events are treated as separate regression models. Findings suggest that link and dwell time variability are the most significant contributors to gap and bunching formation. In terms of infrastructure, bus lane segments reduce gap formations, and left turns increase bunching and gap formations. The insights presented can inform improvements in service and transit infrastructure planning to improve transit level of service (LOS) and support the future of sustainable, smart cities. Full article