Search Results (84)

Electric bicycles in elevators pose serious safety hazards. Fires in the confined space make escape difficult, and recent accidents involving e-bike fires have caused casualties and property damage. To prevent e-bikes from entering elevators and improve public safety, this design employs the Nezha development board as the upper computer for visual detection. It uses deep learning algorithms to recognize hazards like e-bikes. The lower computer orchestrates elevator controls, including voice alarms, door locking, and emergency halt. The system comprises two parts: the upper computer uses the YOLOv11 model for target detection, trained on a custom e-bike image dataset. The lower computer features an elevator control circuit for coordination. The workflow covers target detection algorithm application, dataset creation, and system validation. The experiments show that the YOLOv11 demonstrates superior e-bike detection performance, achieving 96.0% detection accuracy and 92.61% mAP@0.5, outperforming YOLOv3 by 6.77% and YOLOv8 by 15.91% in mAP, significantly outperforming YOLOv3 and YOLOv8. The system accurately identifies e-bikes and triggers safety measures with good practical effectiveness, substantially enhancing elevator safety. Full article

Background and Clinical Significance: Traumatic brain injury (TBI) represents a major public health concern due to its profound neurological, psychological, and socioeconomic consequences. Effective management is essential to optimize patient outcomes and reduce healthcare burden. In cases involving extensive bone loss or complex fractures, particularly when decompressive craniectomy (DC) is considered, secondary cranial reconstruction is typically required. However, DC is associated with prolonged hospitalization, multiple surgical interventions, an increased risk of complications, and higher costs. Case Presentation: We present the case of a 59-year-old male involved in a high-energy bicycle accident, sustaining severe craniofacial trauma with multiple midface fractures, a multifragmented left cranial fracture, and a left-sided epidural hematoma with brain compression. Hematoma evacuation and immediate primary reconstruction of the fractured skull using autologous bone were successfully performed, avoiding the need for DC. The patient recovered under intensive care and was transferred to a neurorehabilitation center. Conclusions: Primary reconstruction of large skull fractures using autologous bone should remain the goal, whenever possible, in order to avoid additional costs, risks, and complications. Full article

Bicycle safety remains a critical concern as cycling gains popularity, especially in urban areas where traffic conditions pose significant risks. The increasing presence of bicycles and derivatives of them further complicates traffic dynamics, raising the potential for accidents and injuries. This study examines bicycle accident trends in Poland from 2016 to 2023 using data provided by the Polish Police, supplemented with medical insights on injury mechanisms. The analysis highlights key patterns, such as the higher incidence of accidents during summer, fewer accidents on weekends compared to weekdays, and the disproportionate fatality rate among cyclists over 60. Failure to yield the right of way emerges as a leading cause of accidents. These findings underscore the need for a multifaceted approach to improving cyclist safety, including infrastructural enhancements, public education, stricter enforcement of traffic laws, and advancements in medical response and protective gear. Addressing these factors can contribute to a safer environment that supports the continued growth of sustainable and active transportation. Full article

Micromobility is a topic of growing interest, powered by the introduction of shared electric bicycles and, especially, e-scooters. This type of mobility has recently gained a lot of popularity in large cities, bringing many benefits, such as greener mobility, a connection for first- and last-mile trips, and on-demand transportation alternatives. However, it also comes at the cost of inadequate infrastructure and laws. This created problems, mainly a concerning rise in accidents and consequent injuries. This study first identifies the main causes of accidents and injuries by defining key aspects such as vehicle types, user demographics, and prevalent injuries. Head injuries emerge as the most critical concern, largely due to low helmet usage across various studies. To address this issue, the barriers to helmet adoption are explored in order to develop a new concept aligned with micromobility needs. The proposed helmet design also prioritises sustainability by replacing petroleum-based materials with expanded cork. This alternative reduces carbon emissions while maintaining the desired performance. Additionally, the design follows principles of disassembly, eliminating adhesives and permanent joints to enhance recyclability. The result is a malleable structured helmet that adapts to user requirements while supporting the United Nations’ 2030 sustainability development goals. Full article

Subclavian artery pseudoaneurysms are rare but potentially life-threatening vascular injuries frequently associated with trauma such as clavicle fractures. In this paper we describe the case of a 49-year-old male who developed a post-traumatic pseudoaneurysm of the subclavian artery after a bicycle accident. The diagnosis was delayed due to non-specific symptoms and an initially missed aneurysm on computed tomography imaging. Persistent pain, swelling, and erythema in the subclavian region prompted further detailed diagnostics, which ultimately revealed the pseudoaneurysm. The patient was successfully treated with endovascular stent–graft implantation. We screened the PubMed database to identify similar cases managed exclusively through endovascular intervention. Reports of iatrogenic pseudoaneurysms and those treated with open surgery were excluded. Variables such as time to diagnosis, clinical presentation, features of pseudoaneurysms, and complications were analyzed to highlight the role of endovascular techniques as a minimally invasive and effective treatment option. These cases pose both a diagnostic and a therapeutic challenge, as early recognition of symptoms is crucial to prevent serious complications including thrombosis, neurological deficits, and even limb loss. Full article

Chinese cities are increasingly developing exclusive bicycle paths to improve the safety and efficiency of bicycle transit. The width of bikeways is a critical factor influencing cyclists’ safety and comfort, with insufficient width identified as a major contributor to bicycle accidents. Therefore, determining the minimum operational width for human-powered bicycles is essential for bikeway design. While some countries’ design manuals consider speed as a factor in determining width, there is a lack of field experiments to validate these specifications from the perspective of cyclists’ safety and comfort. This study addresses this gap by conducting a field experiment to measure cycling workload, which reflects safety and comfort under different widths and cycling speeds. The experiment involved 12 cyclists on a test road, where cycling workload was measured at various preset widths and cycling speeds for a single human-powered cyclist. The results were further validated using conventional lateral distance measurement techniques, which are used in the existing literature to determine the cycling width. The results show that wider bikeway widths lead to a lower cycling workload, enhancing comfort and safety. However, both very high (over 20 km/h) and very low (under 5 km/h) speeds significantly increase cyclists’ workload, which in turn requires a wider path to maintain a safe and comfortable cycling experience. The study found that a minimum width of 0.90 m may be adequate for cyclists traveling at speeds between 10 and 15 km/h, while a width of 1.0 m is sufficient for speeds ranging from 5 km/h to 25 km/h, provided the bicycle width does not exceed 0.62 m. Given that cyclists typically progress from slower to faster speeds, a minimum operational width of 1.0 m is recommended for most cases. This study highlights the importance of considering cyclists’ workload in determining appropriate bikeway widths. It provides valuable insights for designing safer, more comfortable bike paths and reducing bicycle accidents, contributing to the sustainable development of urban cycling infrastructure. Full article

Accidents involving cyclists and trucks are among the most severe road accidents. In 2021, 199 cyclists were killed in accidents involving a truck in the EU. The main accident situation is a truck turning right and a cyclist going straight ahead. A large proportion of these accidents are caused by the inadequate visibility in an HGV (Heavy Goods Vehicle). The blind spot, in particular, is a significant contributor to these accidents. A BSD (Blind Spot Detection) system is expected to significantly reduce these accidents. There are only a few studies that estimate the potential of assistance systems, and these studies include a combined assessment of cyclists and pedestrians. In the present study, accident simulations are used to assess a warning and an autonomously intervening assistance system that could prevent truck to cyclist accidents. The main challenges are local sight obstructions such as fences, hedges, etc., rule violations by cyclists, and the complexity of correctly predicting the cyclist’s intentions, i.e., detecting the trajectory. Taking these accident circumstances into consideration, a BSD system could prevent between 26.3% and 65.8% of accidents involving HGVs and cyclists. Full article

(1) Background: As cycling gains popularity as a mode of transportation, the frequency of accidents involving cyclists also rises. This has become a major concern for traffic safety, sustainability, and city planning. Identifying the risk factors that contribute to bicycle road accidents remains a significant challenge. This study aims to figure out which risk factors make some road segments more dangerous for cyclists than others. (2) Methods: This study introduces the use of a bicycle simulator to test different road segments involving thirty-nine participants. The impact of demographics and some risk factors related to infrastructure were analyzed in terms of their influence on the perceived level of risk through pre- and post-surveys. (3) Results: The findings showed that the bicycle facility type affects the perceived level of risk. Shared-use roads were ranked as riskiest, while separated bike lanes were least risky. Bicycle roads with no separated safety barriers had higher risks. Heavy traffic jams increased danger among cyclists. Women gave higher risk ratings than men. The perceived levels of risk were then compared with the previously developed risk index and they correlated well. (4) Conclusions: This confirms that the risk index can reliably evaluate the degree of risk of each road segment. Full article

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. Full article

Electric bicycles offer convenient short-distance travel, but improper battery charging poses a fire risk, especially indoors, potentially causing significant accidents, property damage, and even threats to life. Recognizing the charging state of electric bicycle batteries is crucial for safety. This paper proposes a novel method to identify the charging process of lithium batteries in electric bicycles. Methods that do not require physical alterations to the equipment are used to acquire users’ electricity consumption data, with current signals preprocessed and input into a combined model integrating convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. The proposed model captures complex patterns and features in the charging data, effectively identifying the charging characteristics of lithium batteries. Validation using NASA’s lithium battery dataset and real experimental data shows that the combined model achieves recognition accuracy of 96% and 97% on training data and 93% and 94% on validation data. Further validation under multiple device loads and comparison with other models indicate that the proposed method is highly accurate, outperforming traditional CNN and LSTM models by 4–9%. This research enhances the safety and regulation of electric bicycle battery charging and provides a reliable method for non-intrusive load identification in smart monitoring systems, contributing to improved safety measures and energy management in residential environments. Full article

This work presents the results of an experimental study of the braking properties of bicycles and an e-bicycle. Data on braking decelerations and braking distance under different braking conditions were obtained for different bicycles including an e-bicycle. A satellite receiver was used to receive the data and specialized software was used for their processing. The specialized literature lacks data on the braking properties of bicycles for Bulgarian road conditions, which are necessary for any expert investigations of accidents involving cyclists. An initial database of bicycle braking decelerations for various braking conditions was compiled. The obtained results on the braking properties should be very useful in the preparation of auto-technical examinations for the court and the police, as well as for improvements in road traffic safety. Full article

The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved. Full article

The growing interest in cycling as a means of urban transport has led to an increased focus on cyclist safety as a key aspect of urban planning and transport policy. Simulation studies conducted by the International Transport Forum have demonstrated that reductions in CO₂ and other pollutants can be achieved in the context of urban transport, thus realising the goals of decarbonising road transport. The spread of modal transport in cities is a potential reality within the next decade. Bicycles play a significant role in this context. This article presents an analysis of data on accidents involving cyclists. National and international data were analysed to identify the main risk factors. The aim of the paper is to analyse the risk to cyclists with an attempt to identify and map the five biggest risks to cyclists in urban traffic. The aim of the research is to raise awareness of cyclist safety issues and to identify directions for further action to reduce accidents and improve overall road safety. The results of the conducted analyses indicate that the risk for cyclists in road traffic (including urban traffic) has been on a noticeable downward trend over the past five to ten years. This trend allows for the mapping of the most significant types/risks in cycling in urban environments, thereby enabling the implementation of risk management strategies based on the method of risk mapping. Full article

The study of vulnerable road users (VRUs) behavior is key to designing and optimizing driving assistance systems, such as the autonomous emergency braking (AEB) system. These kinds of devices could help lower the VRU accident rate, which is of particular interest to cyclists, who are the subject of this research. To better understand cyclists’ reaction patterns in frequently occurring collision scenarios in urban environments, this paper focuses on developing a virtual reality (VR) simulator for cyclists (VRBikeSim) that incorporates eye-tracking functionality. The braking and steering systems were calibrated by means of on-track tests with a sensorized bicycle in order to improve the accuracy of the bicycle virtual model. From the data obtained in the virtual tests, a battery of predictive models was built using supervised machine learning classifiers. All of them exhibited an accuracy higher than 85%, especially the K-Nearest Neighbors model. This model allowed us to obtain the best balance between the prediction of avoidance and collision cases, as well as enabling computationally lower times to be incorporated into the decision-making algorithm of an AEB system. Full article

Sustainable development means taking care of the environment, which also means promoting green transport, which involves the systematic development of personal transport in its broadest sense. The positive aspects associated with cheap and convenient electric transport are intertwined with the problem of collisions and accidents. While developing road infrastructure for electric vehicles such as scooters, bicycles, and others, research should be conducted in parallel to ensure the highest possible level of safety for users. There is also an increase in the number of people using bicycles and electric scooters, which develop significant speeds. The problem of accidents among users of classic and electric bicycles and scooters is evident, and post-accident injuries pose a serious challenge to medical practitioners. The literature is rich in statistical analyses of accidents among users of scooters and bicycles, but there are no studies where the behaviour of users of bicycles, scooters, etc. is analysed. The authors of this study set out to develop a measurement system to assess the traffic safety of people using bicycles and scooters. The device uses LIDAR to record the speed of the vehicle and a camera, the images of which are processed by an algorithm in order to classify the user as being on a bicycle or scooter and using or not using head protection with a helmet. It is also possible to analyse the behaviour of the vehicle users under study. The article describes the built measurement device and presents the results of the initial measurements made by the device. Full article