Search Results (56)

Motorcyclists remain a disproportionately large group of vulnerable road users, with fatality rates significantly higher than that in other road groups. Additionally, fatal accidents involving motorcyclists have a more slowly decreasing trend in comparison to that of other road users, while the number of this kind of users is growing fast. For all these reasons, there is a need to understand what the key factors leading to fatal accidents are in order to identify the possible measures to minimize the accidents themselves or at least their consequences. This would permit, indeed, to positively impact the road traffic system, leading to the creation of the safest road traffic system possible, as it is the goal of the Sustainable Safety approach. The aim of this study is to dive into the mentioned problem, analyzing fatal motorcycle accidents in Slovenia over a decade, highlighting the key factors contributing to these incidents. By integrating data from four databases, the study evaluated accident trends, infrastructural elements, and rider behavior through a multi-stage analysis. Firstly, data were collected from four national, up-to-date databases that contain information about road accidents themselves, the road infrastructure, additional police data, and media descriptions. After merging this information into one comprehensive database, where each row represents all the data available for one accident, a general analysis of accidents’ trends over the considered 10-year period was developed, considering at first all fatal road accidents, then deepening it to accidents caused by a motorcyclist, and finally to single-vehicle accidents. A statistical analysis followed, aimed at identifying a statistical correlation between the accidents and the factors leading to them. The results of the first accident analysis indicated that excessive speed, incorrect driving direction, and overtaking maneuvers are the primary causes of fatal accidents, especially on non-urban roads preferred by motorcyclists. Single-vehicle accidents frequently involve collisions with roadside objects, including safety barriers and poles, underscoring the need for targeted infrastructural improvements. The following correlation analysis revealed that a total of seven factors were statistically significant: three human factors (age, gender, experience)—which were the ones with the strongest correlations—one infrastructural factor (pavement width), and three factors belonging to external conditions (accident type, cause, and location). Of these, four were positively correlated to the causer, while three, i.e., pavement width, causes, and road location, were negatively correlated. This study provides a foundation for future research on less severe accidents and proactive risk behavior analysis, aiming to improve motorcyclist safety comprehensively. Full article

This paper proposes a methodology based on the fuzzy approach, which provides decision-making support to the organizer of last-mile delivery (LMD) in selecting sustainable delivery models for a specific territory. Solving this task is essential to ensure that the delivery process is efficient and aligned with all three dimensions of sustainable development. The goal is to select the most suitable electromobility alternative for delivery implementation based on the characteristics of the requirements and the current circumstances. The proposed methodology involves the creation of a mechanism consisting of a series of fuzzy logic systems that will model expert opinions and produce a preference value as the output, defining the suitability of applying a particular LMD model. A specific methodological contribution is the creation of harmonized membership functions for fuzzy variables as a result of comparing symmetric and asymmetric membership functions aimed at achieving the most valid results. The results guide the delivery organizer in making the best decision when choosing from the analyzed models. The applicability and adequacy of the methodology are demonstrated through the results and analysis of a case study focused on the evaluation of electromobility alternatives in last-mile delivery in a part of the city of Belgrade. The obtained preference values, which range from 0 to 1 for all tested variants, are as follows within the interval: [0.481, 0.776] for e-motorcycles, [0.376, 0.564] for e-cargo bikes, and [0.5, 0.624] for e-scooters. The specific values of these indicators aim to support decision-makers in selecting a delivery model for a defined task based on the given constraints. Full article

This study provides an analysis of micro-mobility use in Jeju City, focusing on how individual choices between e-bikes, e-scooters, and e-motorcycles are influenced by a combination of personal, environmental, and contextual factors. Drawing on data from a local micro-mobility provider from April to December 2022, the research employs multinomial logistic regression to examine the impact of various determinants on the selection of micro-mobility options. The results reveal unique findings, with significant variations in mode choice correlating with gender, age, and region. Also, usage time of micro-mobility emerged as a crucial determinant, suggesting a relationship between trip length and selected mobility. Additionally, environmental variables, particularly temperature, were found to substantially impact users’ choices, reflecting the sensitivity of micro-mobility demand to weather fluctuations. Insights from this study suggest the importance of integrating responsive service design for micro-mobility that addresses the multifaceted needs of users in tourist cities, emphasizing environmental considerations. Full article

The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models—multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents’ awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade. Full article

This study explores the potential of online traffic safety education for adolescent motorcyclists in Pakistan. An e-learning platform, “Route 2 School” (R2S), was developed focusing on traffic knowledge, situation awareness, risk detection, and risk management. Male students (14–18 years) who commute to school by motorcycle were divided into an experimental group (EG) and a control group (CG), both completing pre- and post-measurement questionnaires. The EG showed significant improvement in knowledge, risk detection, and risk management compared to the CG, but not in situation awareness. Participants reported increased traffic safety awareness and suggested adding more interactive elements. The R2S platform’s scores revealed better performance in risk detection and risk management modules than situation awareness. Time spent on modules varied, with situation awareness requiring the most time. Adolescents expressed satisfaction with the platform, acknowledging its role in increasing traffic awareness. This study provides initial insights into the effectiveness of online traffic safety education in Pakistan, highlighting the potential to address the lack of comprehensive traffic safety education in schools. Further research and stakeholder engagement are recommended to integrate such platforms into formal education, potentially reducing traffic-related injuries among adolescent motorcyclists in developing countries. Full article

Autonomous outdoor moving objects like cars, motorcycles, bicycles, and pedestrians present different risks to the safety of Visually Impaired People (VIPs). Consequently, many camera-based VIP mobility assistive solutions have resulted. However, they fail to guarantee VIP safety in practice, i.e., they cannot effectively prevent collisions with more dangerous threats moving at higher speeds, namely, Critical Moving Objects (CMOs). This paper presents the first practical camera-based VIP mobility assistant scheme, ARAware, that effectively identifies CMOs in real-time to give the VIP more time to avoid danger through simultaneously addressing CMO identification, CMO risk level evaluation and classification, and prioritised CMO warning notification. Experimental results based on our real-world prototype demonstrate that ARAware accurately identifies CMOs (with 97.26% mAR and 88.20% mAP) in real-time (with a 32 fps processing speed for 30 fps incoming video). It precisely classifies CMOs according to their risk levels (with 100% mAR and 91.69% mAP), and warns in a timely manner about high-risk CMOs while effectively reducing false alarms by postponing the warning of low-risk CMOs. Compared to the closest state-of-the-art approach, DEEP-SEE, ARAware achieves significantly higher CMO identification accuracy (by 42.62% in mAR and 10.88% in mAP), with a 93% faster end-to-end processing speed. Full article

The acceptance of e-motorcycles among German motorcyclists is the focus of this quantitative longitudinal study. By comparing survey results from 2017 and 2022, questions about changes in perception of e-motorcycles over time as well as possible stimulating factors are analyzed. The research design is built upon literature research, a secondary literature analysis, and a survey of motorcyclists. Statistical procedures are used for data analysis and interpretation. The literature analysis enables the present study to be integrated into the current state of research. The findings show that the willingness to consider an e-motorcycle as the next purchase was low in 2017 and dropped from 20% to 5% in 2022, which contrasts with the rising sales figures of e-motorcycles in the German market. Based on these findings, conclusions are drawn about the market potential of e-motorcycles in Germany and an overview of the general assessments and concerns of motorcyclists is provided. Full article

This study discusses the resonance characteristics of the LLC resonant half-bridge converter and smart battery charging for the fast charging of personal mobility devices (e.g., electric kickboards, segways, hoverboards, electric bicycles, and electric motorcycles), whose use is rapidly increasing. Through the analysis of resonance characteristics, this study aims to validate that f_S > f₀ is the most appropriate correlation in the relationship between resonant frequency (f₀) and switching frequency (f_S) that is suitable for the rapid charging of batteries of personal mobility devices. Additionally, the proposed half-bridge converter does not charge the battery from discharge or misconnection based on the detection of the battery voltage for the batteries of personal mobility devices. Therefore, the proposed converter suggests a charging system based on battery state detection that stably performs rapid charging of the batteries of personal mobility devices by equalizing the battery voltage and the charger voltage through a pre-charge operation. Full article

One of the key parameters in the analysis of some motorcycle accident dynamics is the motorcycle’s deceleration during engine braking without applying the brakes. Since this issue lies far beyond what is usually of most interest—the critical states of movement—it is only sporadically addressed in the literature; however, these rare cases can be of fundamental importance. In our research, the results of engine-braking deceleration are presented for 26 motorcycles that were in gear with the throttle back. The tests were carried out at an initial speed of 140 km/h (if this was not possible, then from the maximum speed for a given gear) to the speed at which the motorcycle reached a constant speed or when engine operation became unstable. For all motorcycles and all gears, deceleration vs. speed and speed vs. time were plotted. Regression lines were determined, and their equations are provided, along with ±σ and ±2σ limit lines. Engine-braking deceleration was shown to be inversely proportional to both motorcycle speed (higher speed—lower deceleration) and gear number (higher gear—lower deceleration). Moreover, engine-braking deceleration at the top gear of the various motorcycles tested (i.e., 5th or 6th) was found to be close to each other. The data provided are of crucial importance from the motorcycle longitudinal dynamics and vehicle accident analysis standpoints. Full article

Interest in advanced driver assistance systems (ADAS) is booming in recent years. One of the most effervescent ADAS features is blind spot detection (BSD), which uses radar sensors or cameras to detect vehicles in the blind spot area and alerts the driver to avoid a collision when changing lanes. However, this kind of BSD system fails to notify nearby vehicle drivers in this blind spot of the possible collision. The goal of this research is to design a proactive bus blind spot warning (PBSW) system that will immediately notify motorcyclists when they enter the blind spot or the area of the inner wheel difference of a target vehicle, i.e., a bus. This will increase the real-time functionality of BSD and can have a significant impact on enhancing motorcyclist safety. The proposed hardware is placed on the motorcycle and consists of a Raspberry Pi 3B+ and a dual-lens stereo camera. We use dual-lens cameras to capture and create stereoscopic images then transmit the images from the Raspberry Pi 3B+ to an Android phone via Wi-Fi and to a cloud server using a cellular network. At the cloud server, we use the YOLOv4 image recognition model to identify the position of the rear-view mirror of the bus and use the lens imaging principle to estimate the distance between the bus and the motorcyclist. Finally, the cloud server returns the estimated distance to the PBSW app on the Android phone. According to the received distance value, the app will display the visible area/blind spot, the area of the inner wheel difference of the bus, the position of the motorcyclist, and the estimated distance between the motorcycle and the bus. Hence, as soon as the motorcyclist enters the blind spot of the bus or the area of the inner wheel difference, the app will alert the motorcyclist immediately to enhance their real-time safety. We have evaluated this PBSW system implemented in real life. The results show that the average position accuracy of the rear-view mirror is 92.82%, the error rate of the estimated distance between the rear-view mirror and the dual-lens camera is lower than 0.2%, and the average round trip delay between the Android phone and the cloud server is about 0.5 s. To the best of our knowledge, this proposed system is one of few PBSW systems which can be applied in the real world to protect motorcyclists from the danger of entering the blind spot and the area of the inner wheel difference of the target vehicle in real time. Full article

(1) Background and aims: The exploration of the intention to adopt electric vehicles has been intensively studied in developed countries where passenger cars are the most common private motorized vehicle. However, the same is not true for motorcycle-dependent countries such as Vietnam. This study aimed to advance our understanding of the determinants of motorcyclists’ intention to use electric vehicles (i.e., electric passenger cars) in settings where the motorcycle is the dominant mode. (2) Data and methods: The partial least squares structural equation modeling (PLS-SEM) and the data from 330 motorcycle owners in Hanoi, Vietnam, were utilized to empirically test a research framework formulated based on the push–pull–mooring migration model. (3) Results: The results indicated that the push factor was formulated by safety concerns and environmental concerns while the pull factor was established by perceived usefulness, perceived ease of use, and financial incentive policy. Both the push factor and the pull factor significantly contributed to the adoption intention but the effect of the latter was stronger. Knowledge did not affect the intention; however, it moderated the link between the pull factor and the intention. For the respondents with a higher level of knowledge, the positive impact of the pull factor on the intention was lower (compared to those with a lower level of knowledge). (4) Conclusions: Based on the findings of the associated factors, policy implications were suggested to increase the prevalence of electric vehicles. Although the theoretical and practical contributions of this study may be significant to the literature regarding developing countries, more research is needed to validate and extend them. Full article

Faced with increasing environmental pollution due to traffic concentration in big cities, Vietnam, as well as many countries worldwide, has encouraged its people to use environmentally-friendly vehicles. Because the transport mode is dominated by two-wheelers (i.e., motorcycles and mopeds) (2Ws), electrifying 2Ws has the potential for significant air pollution reductions as an alternative to gasoline-powered vehicles in Vietnam. Therefore, there has recently been an increasing trend of shifting from traditional gasoline two-wheeler vehicles to electric two-wheelers (E2Ws). Depending on different local contexts, some countries/regions quickly adopted the policies/incentives, and new technologies for E2W usage, while others acted more slowly. In order to advance the use of E2Ws in Vietnam, assessing E2W user preferences is essential to classify and prioritize further solutions, which would be instrumental in fulfilling user expectations. However, a few academic works pay attention to this field of the Vietnamese E2W market. In response to this research gap, this paper aims to overview the current status of E2W usage, assess the market development of E2Ws, and evaluate the battery charging business models in Vietnam. The questionnaire survey was carried out to evaluate the preferences of E2W users in the Vietnamese market, while the assessment of E2W development was conducted based on the SWOT (strengths, weaknesses, opportunities, and threats) analysis. The results demonstrated that E2W deployment is still at an exploratory stage in the transportation industry and is growing significantly in Vietnam. This study also revealed significant challenges for E2W adoption, especially the E2W battery charging/swapping system. Thus, it is recommended that incentives for E2W uptake and the battery charging infrastructure system should be improved and implemented. The evaluation of E2W perceptions in the three-city context is realized as exploratory, generating the baseline for further research when the survey can engage more respondents in other places to confirm the current research findings. The study can also assist policymakers and investors in comprehensively assessing the opportunities and challenges and provide recommendations for accelerating the growth of E2Ws in Vietnam for establishing a national e-mobility roadmap and thereby promoting sustainable transport in alignment with the COP26. Full article

Electric bicycles (e-bikes) have gained enormous popularity in recent years, and as a result, they have successively become more involved in traffic accidents. The aim of the present study was to assess differences in severity and localization of injuries to the lower extremities after accidents with e-bikes, conventional bicycles, and motorcycles. A retrospective cohort-analysis of patients who sustained traumatic accidents with two-wheeled vehicles transferred to a level 1 trauma center in Switzerland was performed. We assessed patient demographics, injury pattern, and trauma severity (ISS), with a subgroup analysis of outcomes stratified by vehicle. In total, 624 patients (71% male) with injuries to the lower extremities after bicycle (n = 279), electric bike (n = 19), and motorcycle (n = 326) accident were included. The mean age of all assessed patients was 42.4 years (SD 15.8), with a significantly higher age in the e-bike cohort (p = 0.0001). High-velocity injuries were found significantly more often in the motorcycle and e-bike group. The motorcycle group had a significantly higher mean ISS (17.6) than the other groups (p = 0.0001). E-bike accidents produce a different injury profile to the lower extremities compared to motorcycle or bicycle accidents. Higher age, higher velocity, and different protective equipment seem to have an impact on these fracture patterns. Full article

Recent years have witnessed an increasing number of electric bicycles on the roads and a rise in the number of road crashes involving e-bicyclists. There are various causes of road crashes involving e-bicyclists. Out of the total number of fatalities in traffic, bicyclists account for 9% in the Republic of Serbia. Road crashes occurring when motor vehicles overtake bicyclists make up 13% of the total number of road crashes involving bicyclists. Having in mind the above-mentioned, an experimental study has been conducted in order to analyze whether the use of helmets and reflective vests, different vehicle categories, and the speed of electric bicycles affect the lateral distance when overtaking e-bicycles. This research analyzed the lateral distance of 1228 vehicles overtaking an e-bicyclist. There are statistically significant differences in the lateral distance when overtaking an e-bicycle regarding helmet use, different vehicle categories overtaking the e-bicycle, and different speeds of the e-bicycle. The results showed that a faster speed of an e-bicycle led to a shorter lateral distance when overtaking it and that SUV drivers (along with motorcycle drivers) had the shortest lateral distance when overtaking e-bicycles. Full article

Human milk banks (HMB) are responsible for screening and recruiting milk donors with surplus milk to their own infant’s needs, followed by transporting, heat-treating (pasteurising) and microbiologically confirming the donor human milk (DHM) is safe to issue to vulnerable infants. Maintaining the safety and quality of DHM are vital requirements in HMB operations. DHM must be maintained in ideal temperature conditions throughout the whole period—from expression until delivery. In this regard, monitoring technologies (e.g., sensors, Big Data and the Internet of Things) have become a viable solution to avoid food loss, allowing prompt corrective action. Therefore, this study aimed to understand the trade-offs between optimising DHM transportation and the environmental impact of implementing such technologies. The environmental performance was carried out through an Organisational Life Cycle Assessment (O-LCA). The electricity consumed during milk storage is the main driver for the environmental impacts in this organisation, responsible for up to 82% of the impacts in ionising radiation. The transportation stage and the treatment of discarded DHM were also relevant for ozone formation and marine eutrophication, respectively. Considering the strategy to integrate monitoring technologies to control the temperature conditions during transportation and the reduction of milk discarded by 3%, an environmental impact reduction can be also observed. In some categories, such as global warming, it could avoid around 863 kg of CO₂-eq per year. The sensitivity analysis showed that the impacts of the HMB depend highly on the transport distance. In addition, changing the transportation mode from motorcycles to drones or electric vehicles can affect the environmental performance of this organisation. Therefore, human milk transport logistics must be studied in a multidisciplinary way to encompass all possible impacts of these strategies. Full article