Smart Cities, Volume 8, Issue 5 (October 2025) – 15 articles

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

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article