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Search Results (775)

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Keywords = traffic diversion

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22 pages, 2053 KiB  
Article
Enhanced Real-Time Method Traffic Light Signal Color Recognition Using Advanced Convolutional Neural Network Techniques
by Fakhri Yagob and Jurek Z. Sasiadek
World Electr. Veh. J. 2025, 16(8), 441; https://doi.org/10.3390/wevj16080441 - 5 Aug 2025
Abstract
Real-time traffic light detection is essential for the safe navigation of autonomous vehicles, where timely and accurate recognition of signal states is critical. YOLOv8, a state-of-the-art object detection model, offers enhanced speed and precision, making it well-suited for real-time applications in complex driving [...] Read more.
Real-time traffic light detection is essential for the safe navigation of autonomous vehicles, where timely and accurate recognition of signal states is critical. YOLOv8, a state-of-the-art object detection model, offers enhanced speed and precision, making it well-suited for real-time applications in complex driving environments. This study presents a modified YOLOv8 architecture optimized for traffic light detection by integrating Depth-Wise Separable Convolutions (DWSCs) throughout the backbone and head. The model was first pretrained on a public traffic light dataset to establish a strong baseline and then fine-tuned on a custom real-time dataset consisting of 480 images collected from video recordings under diverse road conditions. Experimental results demonstrate high detection performance, with precision scores of 0.992 for red, 0.995 for yellow, and 0.853 for green lights. The model achieved an average mAP@0.5 of 0.947, with stable F1 scores and low validation losses over 80 epochs, confirming effective learning and generalization. Compared to existing YOLO variants, the modified architecture showed superior performance, especially for red and yellow lights. Full article
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17 pages, 6471 KiB  
Article
A Deep Learning Framework for Traffic Accident Detection Based on Improved YOLO11
by Weijun Li, Liyan Huang and Xiaofeng Lai
Vehicles 2025, 7(3), 81; https://doi.org/10.3390/vehicles7030081 (registering DOI) - 4 Aug 2025
Abstract
The automatic detection of traffic accidents plays an increasingly vital role in advancing intelligent traffic monitoring systems and improving road safety. Leveraging computer vision techniques offers a promising solution, enabling rapid, reliable, and automated identification of accidents, thereby significantly reducing emergency response times. [...] Read more.
The automatic detection of traffic accidents plays an increasingly vital role in advancing intelligent traffic monitoring systems and improving road safety. Leveraging computer vision techniques offers a promising solution, enabling rapid, reliable, and automated identification of accidents, thereby significantly reducing emergency response times. This study proposes an enhanced version of the YOLO11 architecture, termed YOLO11-AMF. The proposed model integrates a Mamba-Like Linear Attention (MLLA) mechanism, an Asymptotic Feature Pyramid Network (AFPN), and a novel Focaler-IoU loss function to optimize traffic accident detection performance under complex and diverse conditions. The MLLA module introduces efficient linear attention to improve contextual representation, while the AFPN adopts an asymptotic feature fusion strategy to enhance the expressiveness of the detection head. The Focaler-IoU further refines bounding box regression for improved localization accuracy. To evaluate the proposed model, a custom dataset of traffic accident images was constructed. Experimental results demonstrate that the enhanced model achieves precision, recall, mAP50, and mAP50–95 scores of 96.5%, 82.9%, 90.0%, and 66.0%, respectively, surpassing the baseline YOLO11n by 6.5%, 6.0%, 6.3%, and 6.3% on these metrics. These findings demonstrate the effectiveness of the proposed enhancements and suggest the model’s potential for robust and accurate traffic accident detection within real-world conditions. Full article
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31 pages, 1737 KiB  
Article
Trajectory Optimization for Autonomous Highway Driving Using Quintic Splines
by Wael A. Farag and Morsi M. Mahmoud
World Electr. Veh. J. 2025, 16(8), 434; https://doi.org/10.3390/wevj16080434 - 3 Aug 2025
Viewed by 39
Abstract
This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using [...] Read more.
This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article
(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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26 pages, 2843 KiB  
Article
A CDC–ANFIS-Based Model for Assessing Ship Collision Risk in Autonomous Navigation
by Hee-Jin Lee and Ho Namgung
J. Mar. Sci. Eng. 2025, 13(8), 1492; https://doi.org/10.3390/jmse13081492 (registering DOI) - 1 Aug 2025
Viewed by 141
Abstract
To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at [...] Read more.
To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article
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22 pages, 580 KiB  
Article
The Choice of Training Data and the Generalizability of Machine Learning Models for Network Intrusion Detection Systems
by Marcin Iwanowski, Dominik Olszewski, Waldemar Graniszewski, Jacek Krupski and Franciszek Pelc
Appl. Sci. 2025, 15(15), 8466; https://doi.org/10.3390/app15158466 - 30 Jul 2025
Viewed by 270
Abstract
Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained [...] Read more.
Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained on such datasets. This issue is crucial given the application of such a model to actual internet traffic because high-performance measures obtained on datasets do not necessarily imply similar efficiency on the real traffic. We propose a procedure consisting of cross-validation using various sets sharing some standard traffic classes combined with the t-SNE visualization. We apply it to investigate four well-known and widely used datasets: UNSW-NB15, CIC-CSE-IDS2018, BoT-IoT, and ToN-IoT. Our investigation reveals that the high accuracy of a model obtained on one set used for training is reproducible on others only to a limited extent. Moreover, benign traffic classes’ generalizability differs from harmful traffic. Given its application in the actual network environment, it implies that one needs to select the data used to train the ML model carefully to determine to what extent the classes present in the dataset used for training are similar to those in the real target traffic environment. On the other hand, merging datasets may result in more exhaustive data collection, consisting of a more diverse spectrum of training samples. Full article
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21 pages, 2255 KiB  
Article
Cloud-Based Architecture for Hydrophone Data Acquisition and Processing of Surface and Underwater Vehicle Detection
by Francisco Pérez Carrasco, Anaida Fernández García, Alberto García, Verónica Ruiz Bejerano, Álvaro Gutiérrez and Alberto Belmonte-Hernández
J. Mar. Sci. Eng. 2025, 13(8), 1455; https://doi.org/10.3390/jmse13081455 - 30 Jul 2025
Viewed by 252
Abstract
This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports [...] Read more.
This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance. Full article
(This article belongs to the Section Ocean Engineering)
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15 pages, 4592 KiB  
Article
SSAM_YOLOv5: YOLOv5 Enhancement for Real-Time Detection of Small Road Signs
by Fatima Qanouni, Hakim El Massari, Noreddine Gherabi and Maria El-Badaoui
Digital 2025, 5(3), 30; https://doi.org/10.3390/digital5030030 - 29 Jul 2025
Viewed by 354
Abstract
Many traffic-sign detection systems are available to assist drivers with particular conditions such as small and distant signs, multiple signs on the road, objects similar to signs, and other challenging conditions. Real-time object detection is an indispensable aspect of these detection systems, with [...] Read more.
Many traffic-sign detection systems are available to assist drivers with particular conditions such as small and distant signs, multiple signs on the road, objects similar to signs, and other challenging conditions. Real-time object detection is an indispensable aspect of these detection systems, with detection speed and efficiency being critical parameters. In terms of these parameters, to enhance performance in road-sign detection under diverse conditions, we proposed a comprehensive methodology, SSAM_YOLOv5, to handle feature extraction and small-road-sign detection performance. The method was based on a modified version of YOLOv5s. First, we introduced attention modules into the backbone to focus on the region of interest within video frames; secondly, we replaced the activation function with the SwishT_C activation function to enhance feature extraction and achieve a balance between inference, precision, and mean average precision (mAP@50) rates. Compared to the YOLOv5 baseline, the proposed improvements achieved remarkable increases of 1.4% and 1.9% in mAP@50 on the Tiny LISA and GTSDB datasets, respectively, confirming their effectiveness. Full article
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21 pages, 5817 KiB  
Article
UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification
by Yu Shen, Ge Cao, Huan-Yu Dong, Bo Dong and Chang-Myung Lee
Appl. Sci. 2025, 15(15), 8413; https://doi.org/10.3390/app15158413 - 29 Jul 2025
Viewed by 141
Abstract
With the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise management. To address this [...] Read more.
With the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise management. To address this issue, this study presents two key contributions. First, we construct a new urban noise classification dataset, namely the urban noise 15-category dataset (UN15), which consists of 1620 audio clips from 15 representative categories, including traffic, construction, crowd activity, and commercial noise, recorded from diverse real-world urban scenes. Second, we propose a novel deep neural network architecture based on a residual network and integrated with a time–frequency attention mechanism, referred to as residual network with temporal–frequency attention (ResNet-TF). Extensive experiments conducted on the UN15 dataset demonstrate that ResNet-TF outperforms several mainstream baseline models in both classification accuracy and robustness. These results not only verify the effectiveness of the proposed attention mechanism but also establish the UN15 dataset as a valuable benchmark for future research in urban noise classification. Full article
(This article belongs to the Section Acoustics and Vibrations)
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22 pages, 6926 KiB  
Article
Exploring Heavy Metals Exposure in Urban Green Zones of Thessaloniki (Northern Greece): Risks to Soil and People’s Health
by Ioannis Papadopoulos, Evangelia E. Golia, Ourania-Despoina Kantzou, Sotiria G. Papadimou and Anna Bourliva
Toxics 2025, 13(8), 632; https://doi.org/10.3390/toxics13080632 - 27 Jul 2025
Viewed by 928
Abstract
This study investigates the heavy metal contamination in urban and peri-urban soils of Thessaloniki, Greece, over a two-year period (2023–2024). A total of 208 composite soil samples were systematically collected from 52 sites representing diverse land uses, including high-traffic roadsides, industrial zones, residential [...] Read more.
This study investigates the heavy metal contamination in urban and peri-urban soils of Thessaloniki, Greece, over a two-year period (2023–2024). A total of 208 composite soil samples were systematically collected from 52 sites representing diverse land uses, including high-traffic roadsides, industrial zones, residential neighborhoods, parks, and mixed-use areas, with sampling conducted both after the wet (winter) and dry (summer) seasons. Soil physicochemical properties (pH, electrical conductivity, texture, organic matter, and calcium carbonate content) were analyzed alongside the concentrations of heavy metals such as Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. A pollution assessment employed the Geoaccumulation Index (Igeo), Contamination Factor (Cf), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI), revealing variable contamination levels across the city, with certain hotspots exhibiting a considerable to very high ecological risk. Multivariate statistical analyses (PCA and HCA) identified distinct anthropogenic and geogenic sources of heavy metals. Health risk assessments, based on USEPA models, evaluated non-carcinogenic and carcinogenic risks for both adults and children via ingestion and dermal contact pathways. The results indicate that while most sites present low to moderate health risks, specific locations, particularly near major transport and industrial areas, pose elevated risks, especially for children. The findings underscore the need for targeted monitoring and remediation strategies to mitigate the ecological and human health risks associated with urban soil pollution in Thessaloniki. Full article
(This article belongs to the Special Issue Distribution and Behavior of Trace Metals in the Environment)
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29 pages, 7518 KiB  
Article
LEDs for Underwater Optical Wireless Communication
by Giuseppe Schirripa Spagnolo, Giorgia Satta and Fabio Leccese
Photonics 2025, 12(8), 749; https://doi.org/10.3390/photonics12080749 - 25 Jul 2025
Viewed by 384
Abstract
LEDs are readily controllable and demonstrate rapid switching capabilities. These attributes facilitate their efficient integration across a broad spectrum of applications. Indeed, their inherent versatility renders them ideally suited for diverse sectors, including consumer electronics, traffic signage, automotive technology, and architectural illumination. Furthermore, [...] Read more.
LEDs are readily controllable and demonstrate rapid switching capabilities. These attributes facilitate their efficient integration across a broad spectrum of applications. Indeed, their inherent versatility renders them ideally suited for diverse sectors, including consumer electronics, traffic signage, automotive technology, and architectural illumination. Furthermore, LEDs serve as effective light sources for applications in spectroscopy, agriculture, pest control, and wireless optical transmission. The capability to choice high-efficiency LED devices with a specified dominant wavelength renders them particularly well-suited for integration into underwater optical communication systems. In this paper, we present the state-of-the-art of Light-Emitting Diodes (LEDs) for use in underwater wireless optical communications (UOWC). In particular, we focus on the challenges posed by water turbidity and evaluate the optimal wavelengths for communication in coastal environments, especially in the presence of chlorophyll or suspended particulate matter. Given the growing development and applications of underwater optical communication, it is crucial that the topic becomes not only a subject of research but also part of the curricula in technical school and universities. To this end, we introduce a simple and cost-effective UOWC system designed for educational purposes. Some tests have been conducted to evaluate the system’s performance, and the results have been reported. Full article
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28 pages, 8266 KiB  
Article
SpatioConvGRU-Net for Short-Term Traffic Crash Frequency Prediction in Bogotá: A Macroscopic Spatiotemporal Deep Learning Approach with Urban Factors
by Alejandro Sandoval-Pineda and Cesar Pedraza
Modelling 2025, 6(3), 71; https://doi.org/10.3390/modelling6030071 - 25 Jul 2025
Viewed by 341
Abstract
Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents [...] Read more.
Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents a fundamental line of analysis in road safety research within the scientific community. Among these efforts, macro-level modeling plays a key role by enabling the analysis of the spatiotemporal relationships between diverse factors at an aggregated zonal scale. However, in cities like Bogotá, predicting short-term traffic crashes remains challenging due to the complexity of these spatiotemporal dynamics, underscoring the need for models that more effectively integrate spatial and temporal data. This paper presents a strategy based on deep learning techniques to predict short-term spatiotemporal traffic crashes in Bogotá using 2019 data on socioeconomic, land use, mobility, weather, lighting, and crash records across TMAU and TAZ zones. The results showed that the strategy performed with a model called SpatioConvGru-Net with top performance at the TMAU level, achieving R2 = 0.983, MSE = 0.017, and MAPE = 5.5%. Its hybrid design captured spatiotemporal patterns better than CNN, LSTM, and others. Performance improved at the TAZ level using transfer learning. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
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18 pages, 1138 KiB  
Article
Intelligent Priority-Aware Spectrum Access in 5G Vehicular IoT: A Reinforcement Learning Approach
by Adeel Iqbal, Tahir Khurshaid and Yazdan Ahmad Qadri
Sensors 2025, 25(15), 4554; https://doi.org/10.3390/s25154554 - 23 Jul 2025
Viewed by 266
Abstract
Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning [...] Read more.
Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article
(This article belongs to the Special Issue Emerging Trends in Next-Generation mmWave Cognitive Radio Networks)
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15 pages, 724 KiB  
Article
Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection
by Qifan Yang, Yu-Ang Chen and Yifan Shi
Appl. Sci. 2025, 15(14), 8094; https://doi.org/10.3390/app15148094 - 21 Jul 2025
Viewed by 234
Abstract
Intrusion detection systems are crucial for cybersecurity applications. Network traffic data originate from diverse terminal sources, exhibiting multi-view feature spaces, while the collection of unknown intrusion data is costly. Current one-class classification (OCC) approaches are mainly designed for single-view data. Multi-view OCC approaches [...] Read more.
Intrusion detection systems are crucial for cybersecurity applications. Network traffic data originate from diverse terminal sources, exhibiting multi-view feature spaces, while the collection of unknown intrusion data is costly. Current one-class classification (OCC) approaches are mainly designed for single-view data. Multi-view OCC approaches usually require collecting multi-view traffic data from all sources and have difficulty detecting intrusion independently in each view. Furthermore, they commonly ignore the potential subcategories in normal traffic data. To address these limitations, this paper utilizes the Broad Learning System (BLS) technique and proposes an intrusion detection framework based on a multi-view cluster structure guided one-class BLS-autoencoder (IDF-MOCBLSAE). Specifically, a multi-view co-association matrix optimization objective function with doubly-stochastic constraints is first designed to capture the cross-view cluster structure. Then, a multi-view cluster structure guided one-class BLS-autoencoder (MOCBLSAEs) is proposed, which learns the discriminative patterns of normal traffic data by preserving the cross-view clustering structure while minimizing the intra-view sample reconstruction errors, thereby enabling the identification of unknown intrusion data. Finally, an intrusion detection framework is constructed based on multiple MOCBLSAEs to achieve both individual and ensemble intrusion detection. Through experimentation, IDF-MOCBLSAE is validated on real-world network traffic datasets for multi-view one-class classification tasks, demonstrating its superiority over state-of-the-art one-class approaches. Full article
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46 pages, 10548 KiB  
Review
A Review of Hybrid LSTM Models in Smart Cities
by Bum-Jun Kim and Il-Woo Nam
Processes 2025, 13(7), 2298; https://doi.org/10.3390/pr13072298 - 18 Jul 2025
Viewed by 650
Abstract
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM [...] Read more.
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM models that integrate LSTM with complementary algorithms, including CNN, GRU, ARIMA, and SVM. These hybrid architectures aim to enhance prediction accuracy, integrate diverse data sources, and improve computational efficiency. This study systematically reviews principles, trends, and real-world applications, quantitatively evaluating hybrid LSTM models using performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2), while identifying key study limitations. The case studies considered include traffic management, environmental monitoring, energy forecasting, public health, infrastructure assessment, and urban waste management. For example, hybrid models have achieved substantial accuracy improvements in traffic congestion forecasting, reducing their mean absolute error by up to 29%. Despite the inherent challenges related to structural complexity, interpretability, and data requirements, ongoing research on attention mechanisms, model compression, and explainable AI has significantly mitigated these limitations. Thus, hybrid LSTM models have emerged as vital analytical tools capable of robust spatiotemporal prediction, effectively supporting sustainable urban development and data-driven decision-making in evolving smart city environments. Full article
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35 pages, 8048 KiB  
Article
Characterization and Automated Classification of Underwater Acoustic Environments in the Western Black Sea Using Machine Learning Techniques
by Maria Emanuela Mihailov
J. Mar. Sci. Eng. 2025, 13(7), 1352; https://doi.org/10.3390/jmse13071352 - 16 Jul 2025
Viewed by 206
Abstract
Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid [...] Read more.
Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article
(This article belongs to the Section Ocean Engineering)
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