Search Results (311)

Smart campuses employ advanced digital technologies and intelligent communication systems to enhance educational, operational, and living environments. This study investigates stakeholder perceptions of smart campus priorities in Saudi Arabia through a structured questionnaire administered to students and faculty. The study considered King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran as a case study in this regard. The survey examined 22 smart campus aspects grouped into six domains: smart education, smart mobility, smart energy and waste management, smart buildings and work environment, smart safety and security, and smart open spaces. The results indicated strong consensus regarding the importance of all domains, with an overall mean rating of 4.3 out of 5.0 and Relative Importance Index (RII) values ranging from 0.77 to 0.91. The highest-ranked aspects included IoT-enabled cooling energy optimization, smart public transportation, smart lighting systems, smart workflow management, e-libraries, and fire prevention and detection systems, reflecting a pronounced emphasis on infrastructure quality, energy efficiency, and operational effectiveness. The findings suggest that smart campus development in Saudi Arabia should prioritize high-impact, user-valued initiatives that align with Vision 2030 objectives including digital transformation. Strategic early investments in smart buildings, energy management, and mobility systems can deliver measurable benefits in this regard. Further research is recommended to consider additional case studies in the Saudi context to ensure that smart campuses remain contextualized and responsive to user needs. Full article

Research on road transport infrastructures is rapidly evolving as electrification, automation, and digital connectivity reshape how systems are designed, operated, and managed. This study presents a combined bibliometric, scientometric, and network analysis of 2755 publications published between 2021 and 2025 to map the intellectual structure, main contributors, and dominant technological themes shaping contemporary road transport research. Using data from the Web of Science Core Collection, co-occurrence mapping, thematic analysis, and collaboration networks were generated using Bibliometrix and VOSviewer. The results reveal strong growth in research output, with China, the United States, and Europe forming the core of high-impact publication and collaboration networks. Six bibliometric clusters were identified and consolidated into three overarching domains: road transport systems, emphasizing vehicle dynamics, control, and real-time computational frameworks; energy and efficiency-oriented mobility research, focusing on electrification, optimization, and infrastructure integration; and emerging digital technologies, including IoT, AI, and autonomous vehicles. The analysis highlights persistent research gaps related to interoperability, cybersecurity, large-scale deployment, and governance of intelligent transport infrastructures. Overall, the findings provide a data-driven overview of current research priorities and structural patterns shaping next-generation road transport systems. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

Efficient and reliable road damage detection is a critical component of intelligent transportation and infrastructure control systems that rely on visual sensing technologies. Existing road damage detection models are facing challenges such as missed detection of fine cracks, poor adaptability to lighting changes, and false positives under complex backgrounds. In this study, we propose an enhanced YOLO-based framework, YOLO-ERCD, designed to improve the accuracy and robustness of sensor-acquired image data for road crack detection. The datasets used in this work were collected from vehicle-mounted and traffic surveillance camera sensors, representing typical visual sensing systems in automated road inspection. The proposed architecture integrates three key components: (1) a residual convolutional block attention module, which preserves original feature information through residual connections while strengthening spatial and channel feature representation; (2) a channel-wise adaptive gamma correction module that models the nonlinear response of the human visual system to light intensity, adaptively enhancing brightness details for improved robustness under diverse lighting conditions; (3) a visual focus noise modulation module that reduces background interference by selectively introducing noise, emphasizing damage-specific features. These three modules are specifically designed to address the limitations of YOLOv10 in feature representation, lighting adaptation, and background interference suppression, working synergistically to enhance the model’s detection accuracy and robustness, and closely aligning with the practical needs of road monitoring applications. Experimental results on both proprietary and public datasets demonstrate that YOLO-ERCD outperforms recent road damage detection models in accuracy and computational efficiency. The lightweight design also supports real-time deployment on edge sensing and control devices. These findings highlight the potential of integrating AI-based visual sensing and intelligent control, contributing to the development of robust, efficient, and perception-aware road monitoring systems. Full article

The safe operation of rail transit systems relies heavily on the efficient and reliable maintenance of their equipment, as any malfunction or abnormal operation may pose serious risks to transportation safety. Traditional manual inspection methods are often characterized by high costs, low efficiency, and susceptibility to human error. To address these limitations, this paper presents a rail transit equipment inspection system based on Edge Intelligence (EI) and 5G technology. The proposed system adopts a cloud–edge–end collaborative architecture that integrates Computer Vision (CV) techniques to automate inspection tasks; specifically, a fine-tuned YOLOv8 model is employed for object detection of personnel and equipment, while a ResNet-18 network is utilized for equipment status classification. By implementing an ETSI MEC-compliant framework on edge servers (NVIDIA Jetson AGX Orin), the system enhances data processing efficiency and network performance, while further strengthening security through the use of a 5G private network that isolates critical infrastructure data from the public internet, and improving robustness via distributed edge nodes that eliminate single points of failure. The proposed solution has been deployed and evaluated in real-world scenarios on Beijing Metro Line 6. Experimental results demonstrate that the YOLOv8 model achieves a mean Average Precision (mAP@0.5) of 92.7% ± 0.4% for equipment detection, and the ResNet-18 classifier attains 95.8% ± 0.3% accuracy in distinguishing normal and abnormal statuses. Compared with a cloud-centric architecture, the EI-based system reduces the average end-to-end latency for anomaly detection tasks by 45% (28.5 ms vs. 52.1 ms) and significantly lowers daily bandwidth consumption by approximately 98.1% (from 40.0 GB to 0.76 GB) through an event-triggered evidence upload strategy involving images and short video clips, highlighting its superior real-time performance, security, robustness, and bandwidth efficiency. Full article

Gamma-aminobutyric acid (GABA) is a non-protein amino acid distributed in nature by different types of organisms and microorganisms. GABA has been widely studied for its different physiological functions and industrial applications. Its production is mainly carried out through fermentation processes using lactic acid bacteria (LAB), which are of particular interest because they are safe and possess high glutamate decarboxylase enzyme activity. However, GABA production can vary among different LAB species and is affected by culture conditions. Therefore, strain development and selection, as well as optimization of fermentation parameters, are essential to increase GABA yields and meet the needs of industrial demand. This review quantitatively analyzes recent advances in fermentative GABA production, showing a sustained increase in publications and a predominance of chromatography-based quantification methods (approximately 68%), mainly using pre-column derivatization. Optimized fermentation strategies, supported by statistical and artificial intelligence models, have achieved GABA concentrations of up to 90 mM. In parallel, in silico genomic and metabolic analyses revealed the widespread distribution of key GABA biosynthesis and transport genes among LAB, supporting their selection and engineering. Overall, the integration of advanced analytical methods, bioinformatics-guided strain selection, and computational process optimization emerges as a key strategy to enhance GABA productivity and support future industrial-scale applications. Full article

Traffic sign detection is crucial for intelligent transportation and autonomous driving, yet faces challenges such as illumination variations, occlusions, and scale changes that impact accuracy. To address these issues, the paper proposes the LSTM-CA-YOLOv11 model. This approach pioneers the integration of a Bi-LSTM (Bi-directional Long-Short Term Memory) into the YOLOv11 backbone network to model spatial-sequence dependencies, thereby enhancing structured feature extraction capabilities. The lightweight CA (Coordinate Attention) module encodes precise positional information by capturing horizontal and vertical features. The MSEF (Multi-Scale Enhancement Fusion) module addresses scale variations through parallel convolutional and pooling branches with adaptive fusion processing. We further introduce the SPP-Plus (Spatial Pyramid Pooling-Plus) module to expand the receptive field while preserving fine details, and employ a focus IoU (Intersection over Union) loss to prioritise challenging samples, thereby improving regression accuracy. On a private dataset comprising 10,231 images, experiments demonstrate that this model achieves a mAP@0.5 of 93.4% and a mAP@0.5:0.95 of 79.5%, representing improvements of 5.3% and 4.7% over the baseline, respectively. Furthermore, the model’s generalisation performance on the public TT100K (Tsinghua-Tencent 100K) dataset surpassed the latest YOLOv13n by 5.3% in mAP@0.5 and 3.9% in mAP@0.5:0.95, demonstrating robust cross-dataset capabilities and exceptional practical deployment feasibility. Full article

Time series and spatio-temporal forecasting are fundamental tasks for complex system modeling and intelligent decision-making, with broad applications in transportation, meteorology, finance, healthcare, and public safety. Compared with simple univariate time series, real-world spatio-temporal data exhibit rich temporal dynamics and intricate spatial interactions, leading to heterogeneity, non-stationarity, and evolving topologies. Addressing these challenges requires modeling frameworks that can simultaneously capture temporal evolution, spatial correlations, and cross-domain regularities. This survey provides a comprehensive synthesis of forecasting methods, spanning statistical algorithms, traditional machine learning approaches, neural architectures, and recent generative and causal paradigms. We review the methodological evolution from classical linear models to deep learning–based temporal modules and emphasize the role of attention-based Transformers as general-purpose sequence architectures. In parallel, we distinguish these architectural advances from pre-trained foundation models for time series and spatio-temporal data (e.g., large models trained across diverse domains), which leverage self-supervised objectives and exhibit strong zero-/few-shot transfer capabilities. We organize the review along both data-type and architectural dimensions—single long-term time series, Euclidean-structured spatio-temporal data, and graph-structured spatio-temporal data—while also examining advanced paradigms such as diffusion models, causal modeling, multimodal-driven frameworks, and pre-trained foundation models. Through this taxonomy, we highlight common strengths and limitations across approaches, including issues of scalability, robustness, real-time efficiency, and interpretability. Finally, we summarize open challenges and future directions, with a particular focus on the joint evolution of graph-based, causal, diffusion, and foundation-model paradigms for next-generation spatio-temporal forecasting. Full article

A large share of the global population now lives in urban areas, which creates growing challenges for city life. Local authorities are seeking ways to enhance urban livability, with transportation emerging as a major focus. Developing smart public transit systems is therefore a key priority. Accurately estimating travel times is essential for managing transport operations and supporting strategic decisions. Previous studies have used statistical, mathematical, or machine learning models to predict travel time, but most examined these approaches separately. This study introduces a hybrid framework that combines statistical regression models and machine learning algorithms to predict public bus travel times. The analysis is based on 1410 bus trips recorded between November 2021 and July 2022 in Kayseri, Turkey, including detailed meteorological and operational data. A distinctive aspect of this research is the inclusion of weather variables—temperature, humidity, precipitation, air pressure, and wind speed—which are often neglected in the literature. Additionally, sensitivity analyses are conducted by varying k values in the K-nearest neighbors (KNN) algorithm and threshold values for outlier detection to test model robustness. Among the tested models, CatBoost achieved the best performance with a mean squared error (MSE) of approximately 18.4, outperforming random forest (MSE = 25.3) and XGBoost (MSE = 23.9). The empirical results show that the CatBoost algorithm consistently achieves the lowest mean squared error across different preprocessing and parameter settings. Overall, this study presents a comprehensive and environmentally aware approach to travel time prediction, contributing to the advancement of intelligent and adaptive urban transportation systems. Full article

The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-based methodologies have made progress, they are fundamentally limited by treating damages as independent, isolated entities, thereby ignoring the intrinsic spatial symmetry and topological organization inherent in complex damage patterns like alligator cracking. This conceptual asymmetry in modeling leads to two major deficiencies: “context blindness,” which overlooks essential structural interrelations, and “temporal inconsistency” in video analysis, resulting in unstable, flickering predictions. To address this, we propose a Spatio-Temporal Graph Mamba You-Only-Look-Once (STG-Mamba-YOLO) network, a novel architecture that introduces a symmetry-informed, hierarchical reasoning process. Our approach explicitly models and integrates contextual dependencies across three levels to restore a holistic and consistent structural representation. First, at the pixel level, a Mamba state-space model within the YOLO backbone enhances the modeling of long-range spatial dependencies, capturing the elongated symmetry of linear cracks. Second, at the object level, an intra-frame damage Graph Network enables explicit reasoning over the topological symmetry among damage candidates, effectively reducing false positives by leveraging their relational structure. Third, at the sequence level, a Temporal Graph Mamba module tracks the evolution of this damage graph, enforcing temporal symmetry across frames to ensure stable, non-flickering results in video streams. Comprehensive evaluations on multiple public benchmarks demonstrate that our method outperforms existing state-of-the-art approaches. STG-Mamba-YOLO shows significant advantages in identifying intricate damage topologies while ensuring robust temporal stability, thereby validating the effectiveness of our symmetry-guided, multi-level contextual fusion paradigm for structural health monitoring. Full article

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices. Full article

Intelligent learning applied to multidimensional data streams has established itself as a rapidly expanding field, driven by the growth of ubiquitous computing and the Internet of Things. The complexity of these streams, characterized by their high dimensionality, variability, and continuous nature, poses significant challenges for traditional approaches to analysis. This study presents a bibliometric analysis of scientific output indexed in Scopus between 2015 and 2025, with the aim of identifying trends, challenges, and opportunities in this field. The results show sustained growth in publications, a marked interdisciplinary orientation, and a diversity of applications including transportation, biomedicine, energy, and information systems. Likewise, there is a geographical concentration in certain leading countries and uneven development in terms of international collaboration. This work contributes to mapping the current state of the field and points to future lines of research aimed at its consolidation. Full article

This paper investigates the integration of proximity theory (PT) into the management of public transport service disruptions within sustainable Mobility-as-a-Service (MaaS) systems, an area that is largely underexplored. PT provides a multidimensional framework for analyzing relationships and interactions within complex systems, encompassing five dimensions: geographical, cognitive, institutional, organizational, and social, each influencing coordination, learning, and adaptability. Building on this framework, the study introduces temporal proximity as an original sub-dimension of geographical proximity, forming a spatial–temporal proximity theory (PTST), which highlights the critical role of timing, synchronization, and coordinated responses in transport disruption management. To operationalize these principles, a mixed-integer programming (MIP) model was developed to optimize traveler assignments across 50 routes for 10 travelers, minimizing delays, transfers, walking distance, crowding, and CO₂ emissions. Two scenarios were analyzed: one without environmental considerations and another with CO₂ penalties. Results show that emissions were reduced by up to 50% for certain routes, while maintaining feasible travel times and route choices. The case study demonstrates that PTST can be operationalized as a practical tool, bridging mobility resilience and environmental responsibility, and providing actionable insights for sustainable and intelligent MaaS platforms. Full article

Traffic congestion continues to pose a significant challenge to contemporary urban transportation systems, exerting substantial effects on economic productivity, environmental sustainability, and the overall quality of life. This systematic literature review thoroughly explores the development of traffic congestion forecasting methodologies from 2014 to 2024 by analyzing 100 peer-reviewed publications according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examine the technological advancements from traditional machine learning (achieving 75–85% accuracy) through deep learning approaches (85–92% accuracy) to recent large language model (LLM) implementations (90–95% accuracy). Our analysis indicates that LLM-based systems exhibit superior performance in managing multimodal data integration, comprehending traffic events, and predicting non-recurrent congestion scenarios. The key findings suggest that hybrid approaches, which integrate LLMs with specialized deep learning architectures, achieve the highest prediction accuracy while addressing the traditional limitations of edge case management and transfer learning capabilities. Nonetheless, challenges remain, including higher computational demands (50–100× higher than traditional methods), domain adaptation complexity, and constraints on real-time implementation. This review offers a comprehensive taxonomy of methodologies, performance benchmarks, and practical implementation guidelines, providing researchers and practitioners with a roadmap for advancing intelligent transportation systems using next-generation AI technologies. Full article

Intelligent Transportation Systems (ITS) play a pivotal role in addressing traffic congestion, inefficiency, and safety concerns. Emergency lane control on highways is a critical ITS component, yet existing strategies often lack flexibility, theoretical rigor, and the ability to handle dynamic spatiotemporal interactions under uncertain data. To address these limitations, this paper introduces Koopman Graph Mamba (KGM), an innovative framework integrating the Koopman operator with a graph-based state space model for dynamic emergency lane control. KGM leverages multimodal traffic data to predict spatiotemporal patterns, facilitating real-time decisions. An interpretable decision module based on fuzzy neural networks ensures context-sensitive decisions. Evaluated on a real-world dataset from the Changshen Expressway (Nanjing-Changzhou section) and public datasets including NGSIM, PeMS04, and PeMS08, KGM demonstrates superior performance with linear computational complexity, underscoring its potential for large-scale, real-time applications. Full article