Search Results (145)

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Vehicular Communication Networks (VCNs) are essential to intelligent transportation systems, where real-time data exchange between vehicles and infrastructure supports safety, efficiency, and automation. However, achieving high Quality of Service (QoS)—especially under delay-sensitive conditions—remains a major challenge due to the high mobility and dynamic topology of vehicular environments. While some efforts have explored routing protocol optimization, few have systematically compared multiple optimization approaches tailored to distinct traffic and delay conditions. This study addresses this gap by evaluating and enhancing two widely used routing protocols, QOS-AODV and GPSR, through their improved versions, CM-QOS-AODV and CM-GPSR. Two distinct optimization models are proposed: the Traffic-Oriented Model (TOM), designed to handle variable and high-traffic conditions, and the Delay-Efficient Model (DEM), focused on reducing latency for time-critical scenarios. Performance was evaluated using key QoS metrics: throughput (rate of successful data delivery), packet delivery ratio (PDR) (percentage of successfully delivered packets), and end-to-end delay (latency between sender and receiver). Simulation results reveal that TOM-optimized protocols achieve up to 10% higher PDR, maintain throughput above 0.40 Mbps, and reduce delay to as low as 0.01 s, making them suitable for applications such as collision avoidance and emergency alerts. DEM-based variants offer balanced, moderate improvements, making them better suited for general-purpose VCN applications. These findings underscore the importance of traffic- and delay-aware protocol design in developing robust, QoS-compliant vehicular communication systems. Full article

This study presents a data-driven analysis of navigator stress and workload levels in simulated ship encounters within restricted waters, leveraging real-world automatic identification system (AIS) data from Makassar Port, Indonesia. Six close-quarter scenarios were recreated to reflect critical encounter geometries, and 24 Indonesian seafarers were evaluated using heart rate variability (HRV), perceived stress scale (PSS), and task load index (NASA-TLX) workload assessments. The results indicate that crossing angles, particularly 135° port and starboard encounters, significantly influence physiological stress levels, with age being a moderating factor. Although no consistent relationship was found between workload and HRV metrics, the findings underscore key human factors that may impair navigational performance under cognitively demanding conditions. By integrating AIS-derived traffic data with simulation-based human performance monitoring, this study supports the development of intelligent maritime training frameworks and adaptive decision support systems. The research contributes to broader efforts toward enhancing navigational safety and situational awareness amid increasing automation and traffic densities at sea. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article

Unmanned ships, equipped with self-navigation and image processing capabilities, are progressively expanding their applications in fields such as mining, fisheries, and marine environments. Along with this development, issues concerning waterborne traffic safety are gradually emerging. To address the challenges of navigation and obstacle detection on the water’s surface, this paper presents CDS-YOLOv7, an enhanced obstacle-detection framework for aquatic environments, architecturally evolved from YOLOv7. The proposed system implements three key innovations: (1) Architectural optimization through replacement of the Spatial Pyramid Pooling Cross Stage Partial Connections (SPPCSPC) module with GhostSPPCSPC for expanded receptive field representation. (2) Integration of a parameter-free attention mechanism (SimAM) with refined pooling configurations to boost multi-scale detection sensitivity, and (3) Strategic deployment of depthwise separable convolutions (DSC) to reduce computational complexity while maintaining detection fidelity. Furthermore, we develop a Spatial–Channel Synergetic Attention (SCSA) mechanism to counteract feature degradation in convolutional operations, embedding this module within the Extended Effective Long-Range Aggregation Network (E-ELAN) network to enhance contextual awareness. Experimental results reveal the model’s superiority over baseline YOLOv7, achieving 4.9% mean average precision@0.5 (mAP@0.5), +4.3% precision (P), and +6.9% recall (R) alongside a 22.8% reduction in Giga Floating-point Operations Per Second (GFLOPS). Full article

As autonomous vehicles (AVs) are gradually integrated into existing traffic systems, understanding their impact on freeway operations becomes essential for effective infrastructure planning and policy design. This study explores how AV penetration rates, behavior profiles, and freeway geometry interact to influence traffic performance and safety. Using microscopic simulations in VISSIM (a high-fidelity traffic simulation tool), four typical freeway segment types—basic sections, weaving zones, on-ramp merging areas, and AV-exclusive lanes—were modeled under diverse traffic demands and AV behavior settings. The findings indicate that, while AVs can improve flow stability in simple environments, their performance may deteriorate in complex merging scenarios without supportive design or behavior coordination. AV-exclusive lanes offer some mitigation when AV share is high. These results underscore that AV integration requires context-specific strategies and cannot be universally applied. Adaptive, behavior-aware traffic management is recommended to support a smooth transition toward mixed autonomy. Full article

Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers’ failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage and environments with limited visibility continue to present elevated risks. The scarcity and difficulty of collecting data under such complex conditions pose significant challenges to the development of accurate detection systems. This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. This model is then applied to automatically annotate newly generated synthetic images, which are incorporated into the training dataset for subsequent rounds of model retraining and image generation. Through this iterative process, the model progressively refines its ability to simulate and recognize diverse contextual features, thereby enhancing detection performance under varying environmental conditions. The experimental results show that environmental variations—such as daytime, nighttime, and rainy conditions—substantially affect the model performance in terms of F1-score. Training with a balanced mix of real and synthetic images yields an F1-score comparable to that obtained using real data alone. These results suggest that CycleGAN-generated images can effectively augment limited datasets and enhance model generalization. The proposed system may be integrated with in-vehicle assistance platforms as a supportive tool for pedestrian-crossing detection in data-scarce environments, contributing to improved driver awareness and road safety. Full article

The accuracy of object detection is crucial for the safety and efficiency of traffic management in monitoring systems. Existing detectors, however, struggle significantly within complex urban scenarios where high-density occlusions among the targets occur, as well as extreme scale variations resulting from the size differences of vehicles and distance variations to the camera. To remedy these issues, we introduce DFE-YOLO, an enhanced multi-scale detection framework built upon YOLOv8 that fuses features from various layers at different scales through our ‘four adaptive spatial feature fusion’ module, which performs adaptive spatial fusion via learnable weights normalized by softmax and thereby allows effective feature aggregation across scales. The second contribution is DySample, which uses a lightweight, content-aware, point-based upsampling method to improve multi-scale feature representation as well as reduce imbalance across different object scales. The experiments conducted on the VisDrone-2019 and BDD100K benchmarks showed significantly superior performance against state-of-the-art detectors. Specifically, DFE-YOLO achieved a +4% and +5.1% boost over YOLOv10 in AP and APsmall. This study offers a useful fix for smart transport systems. Full article

The water environment is a dynamic domain critical to global transportation and commerce, where seaplanes operate during take-offs, landings, and ground operations, often near maritime traffic. Canada’s vast remote regions and unique geography increase reliance on seaplanes, especially for private and recreational purposes. This article examines the intersection of aviation and maritime operations through a mixed-methods approach, analyzing seaplane safety on waterways using quantitative and qualitative methods. First, data from 1005 General Aviation (GA) seaplane accidents in Canada (1990–2022) are analyzed, revealing 179 fatalities, 401 injuries, and 118 destroyed aircraft—significant given that seaplanes comprise under 5% of GA aircraft. Of these, 50.35% occurred while the seaplane was not airborne. Second, insights from interviews, focus groups, and questionnaires involving 136 participants are explored through thematic and content analysis. These capture pilot concerns that are not evident in accident data, such as hazards from jet ski interactions and disruptive boat wakes. The findings highlight risks like limited visibility and maneuverability during waterborne take-offs, worsened by seaplanes’ lack of priority over maritime vessels in shared spaces. This article concludes with recommendations for both the seaplane and maritime communities, including increasing awareness among boaters about the presence and operations of seaplanes, as well as regulatory adjustments, particularly considering the right of way. Full article

The Internet of Things (IoT) offers transformative capabilities in enhancing public safety, disaster response, and emergency management by leveraging interconnected devices and real-time data. Through the IoT, smart sensors and networks are deployed across cities and environments to monitor critical parameters including air quality, structural integrity, and environmental changes. These systems provide early warnings for natural disasters such as earthquakes, floods, and wildfires, enabling authorities to respond proactively. In emergency management, IoT devices help coordinate resources and improve situational awareness during crises. Real-time data from wearable devices, smart infrastructure, and communication systems allow responders to track people, manage evacuations, and deploy resources more effectively. For example, IoT-enabled drones and autonomous vehicles are used to deliver supplies or assess damage in hazardous areas without risking human lives. IoT technologies improve post-disaster recovery by continuously monitoring areas for safety hazards and supporting infrastructure restoration. Smart traffic management systems assist in controlling traffic flow for emergency vehicles, while IoT-based communication networks ensure connectivity when traditional systems fail. The IoT significantly enhances the speed, accuracy, and effectiveness of disaster response and public safety operations, leading to the better protection of communities and faster recovery from emergencies. Full article

Children’s physical and cognitive development plays a crucial role in their ability to react appropriately in dynamic traffic situations. One key aspect of traffic safety is the ability to stop movement quickly and accurately after receiving a stop signal. Distraction is a major contributor to road accidents, especially among children who are easily distracted and may not be fully aware of the traffic situation. It is crucial to understand that children up to a certain age may struggle to halt their movement once initiated. This study indicates that the stopping distance, time, and deceleration of children aged six to ten years after a specific stop signal at different speeds are strongly influenced by the speed of movement and the age of the children. The results show that in the “walking” test configuration, the children were able to stop within a range of 0.47 m to 0.63 m, with a shorter distance for older children. The stopping time ranges from 0.84 s to 1.21 s and correlates positively with age. The stopping time and distance of children were measured in both “running” and “walking” test configurations across different age groups. However, in the “running” test configuration, stopping distance is almost the same across all age groups, with children requiring between 1.72 m and 1.84 m and a stopping time ranging from 1.17 s to 1.28 s. In the “walking” test configuration, children are able to decelerate between 0.91 m/s² and 1.57 m/s², while in the “running” test configuration, they are able to decelerate between 2.24 m/s² and 3.19 m/s². Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

While traffic levels are predicted to rise, nearly all European air navigation service providers suffer from staff shortages. In most cases, two air traffic controllers are deployed to control one airspace sector. Enabling the deployment of one controller per sector could be a solution to staff shortage problems. For this Single-Controller Operation (SCO) concept, a demonstrator with integrated support tools based on advanced information technology was developed. These partially automate some controller tasks to allow one controller to work off the same traffic amount as a controller team. The system was tested in a human-in-the-loop real-time simulation under varying traffic loads using a 2 × 2 within-subjects design. The variables assessed include separation minima infringements, exit flight level deviations, instantaneous self-assessment, voice communication, flight distance, and fuel burn. The results show no negative influence on safety, workload, situational awareness, operational efficiency, and environment, with 80% of maximum allowed declared capacity. Thus, SCO has the potential to mitigate staff shortages and raise cost efficiency by 40%. These results showcase the feasibility of the SCO concept under nominal conditions. Assessments with different traffic levels, non-nominal conditions, and an interdependent multi-sector SCO layout are recommended for further investigations. Full article

With the rapid growth in maritime traffic, navigational safety has become a pressing concern. Some vessels deliberately manipulate their type information to evade regulatory oversight, either to circumvent legal sanctions or engage in illicit activities. Such practices not only undermine the accuracy of maritime supervision but also pose significant risks to maritime traffic management and safety. Therefore, accurately identifying vessel types is essential for effective maritime traffic regulation, combating maritime crimes, and ensuring safe maritime transportation. However, the existing methods fail to fully exploit the long-term sequential dependencies and intricate mobility patterns embedded in vessel trajectory data, leading to suboptimal identification accuracy and reliability. To address these limitations, we propose MESTR, a Multi-Task Enhanced Ship-Type Recognition model based on Automatic Identification System (AIS) data. MESTR leverages a Transformer-based deep learning framework with a motion-pattern-aware trajectory segment masking strategy. By jointly optimizing two learning tasks—trajectory segment masking prediction and ship-type prediction—MESTR effectively captures deep spatiotemporal features of various vessel types. This approach enables the accurate classification of six common vessel categories: tug, sailing, fishing, passenger, tanker, and cargo. Experimental evaluations on real-world maritime datasets demonstrate the effectiveness of MESTR, achieving an average accuracy improvement of 12.04% over the existing methods. Full article

Road safety for vulnerable road users, particularly cyclists, remains a critical global issue. This study explores the potential of multimodal visual and haptic interaction technologies to improve cyclists’ perception of and responsiveness to their surroundings. Through a systematic evaluation of various visual displays and Haptic Feedback mechanisms, this research aims to identify effective strategies for recognizing and localizing potential traffic hazards. Study 1 examines the design and effectiveness of Visual Feedback, focusing on factors such as feedback type, traffic scenarios, and target locations. Study 2 investigates the integration of Haptic Feedback through wearable vests to enhance cyclists’ awareness of peripheral vehicular activities. By conducting experiments in realistic traffic conditions, this research seeks to develop safety systems that are intuitive, cognitively efficient, and tailored to the needs of diverse user groups. This work advances multimodal interaction design for road safety and aims to contribute to a global reduction in traffic incidents involving vulnerable road users. The findings offer empirical insights for designing effective assistance systems for cyclists and other non-motorized vehicle users, thereby ensuring their safety within complex traffic environments. Full article