Search Results (61)

Background: New York State, like many other states, experienced a significant increase in road traffic deaths during the COVID-19 pandemic that is not fully understood. Our earlier work using the Safe System framework suggests a shift in the distribution of vehicle types that may have contributed to this phenomenon. Methods: To further investigate this, variables from the Fatality Analysis Reporting System (FARS) were mapped onto the Safe System framework and used to examine factors associated with motorized two- and three-wheeled vehicle deaths. Two time periods were examined: pre-pandemic (1 April 2017–31 December 2019, n = 428) and the COVID-19 pandemic era (1 April 2020–31 December 2022, n = 600). A buffer pandemic transition period (1 January 2020–31 March 2020) was excluded. Percent difference, chi-square tests, and multivariable logistic regression (OR, 95% CI) were used. Results: Compared to pre-COVID-19, pandemic-period motorized two-wheeled deaths were 40.2% higher, helmet wearing lower (80.2% vs. 90.6%, p < 0.0001), urban roadway deaths higher (76.7% vs. 64.0%, p < 0.0001), and fully licensed drivers lower (78.4% vs. 89.9%, p < 0.0001), with unlicensed drivers doubling between the two periods (8.7% to 17.6%, p < 0.0001). Deaths associated with mopeds/motor scooters/minibikes increased 361.5% between study periods, from 3% to 10% of motorized two-wheeled deaths. Adjusted multivariable risk factors for pandemic-period death were age 30–39 years (1.601, 1.155–2.311), being unhelmeted (3.191, 2.109–4.968), being in an urban area (1.898, 1.425–2.533), being unlicensed (1.968, 1.228–33.216) and riding an off-road motorcycle (3.753. 1.391–13.063), moped or motor scooter/minibike (3.540, 1.971–6.842). Conclusions: Total mortality was higher in the COVID-19–era timeframe, with the increase differing significantly by vehicle type, helmet use, licensure status, and urbanization. Due to the increase in motorized two-wheeled vehicles, they should be incorporated into surveillance systems and injury prevention strategies. Full article

The effectiveness of transit-oriented development (TOD) in achieving emission reductions and energy savings is highly influenced by policy frameworks, the accessibility of sustainable transport systems, and the degree of land use integration. This study investigated the implementation of TOD in Dukuh Atas along the Sudirman–Thamrin corridor in Jakarta to assess its role in promoting energy efficiency and lowering emissions. The analysis incorporated carbon emission calculations, annualized traffic volumes, and emissions data, alongside land use metrics such as the floor area ratio (FAR), job-to-housing ratio, and point-of-interest (POI) density. The findings indicate that while TOD implementation in the corridor is still evolving, there were positive outcomes in several key areas. Energy efficiency measures have been partially realized through the operation of electric buses in the bus rapid transit (BRT) system, electrified rail modes, such as commuter lines, mass rapid transit (MRT), and light rail transit (LRT), and improved pedestrian infrastructure, as reflected in a favorable Pedestrian Environmental Quality Index (PEQI). Public transport ridership has significantly increased, contributing to a measurable reduction in emissions from private vehicle use. The land use analysis showed that medium- to high-density housing dominated (78.94% FAR), with a job-to-housing ratio of approximately 1:2. This study also found that the emission estimates were moderately sensitive to changes in both emission factors (EFs) and vehicle kilometers traveled (VKT). Overall, the results suggest that TOD can effectively contribute to energy savings and emission reductions by enhancing public transport usage and reducing dependence on motorcycles. Moreover, the efficacy of modal shifting in the Global South is significantly influenced by population mobility characteristics, which are intricately linked to socio-cultural factors, alongside government initiatives to improve the quality of mass public transportation systems (e.g., integration, availability, service coverage, affordable fares, and inclusive design). Full article

Objectives: To systematically review and analyze electronic systems in competitive motorcycles (2020–2025), examining their technical specifications, performance impacts, and technological evolution across MotoGP, World Superbike (WSBK), MotoE, British Superbike (BSB), and Spanish Championship (ESBK) categories. Eligibility criteria: Included studies reporting technical specifications or performance data of electronic systems in professional motorcycle racing, published between January 2020 and December 2025 in English, Spanish, Italian, or Japanese. Excluded: opinion pieces, amateur racing, and studies without quantitative data. Information sources: IEEE Xplore, SAE Technical Papers, Web of Science, Scopus, and specialized motorsport databases were searched through 15 December 2025. Risk of bias: Modified Cochrane Risk of Bias tool for experimental studies and Newcastle-Ottawa Scale for observational studies. Synthesis of results: Synthesis of results: Random-effects meta-analysis using DerSimonian-Laird method for homogeneous outcomes; narrative synthesis for heterogeneous data. Included studies: 87 studies met inclusion criteria (52 experimental, 38 simulation, 23 technical descriptions, 14 comparative analyses). Electronic systems were categorized into six domains: Engine Control Units (ECU, 28 studies, 22%), Vehicle Dynamics (23 studies, 18%), Traction Control (19 studies, 15%), Data Acquisition (21 studies, 17%), Braking Systems (18 studies, 14%), and Emerging Technologies (18 studies, 14%). Note that studies could address multiple domains. Limitations of evidence: Proprietary restrictions limited access to 31% of technical details; 43% lacked cross-category comparisons. Interpretation: Electronic systems are primary performance differentiators, with computational power following Moore’s Law. Future developments point toward distributed architectures and 5G telemetry. Full article

By applying the Non-Perturbative Approach (NPA), the corresponding linear differential equation is obtained. Aimed at organizational investigation, the resulting linear equation is used. Strong agreement between numerical calculations and the precise frequency is demonstrated, and the reliability of the results acquired is established by the correlation with the numerical solution. Additionally, this study explores a new control process to affect the stability and behavior of dynamic motorcycle systems that vibrate nonlinearly. A multiple time-scale method (MTSM) is applied to examine the analytical solution of the nonlinear differential equations describing the aforementioned system. Every instance of resonance was taken out of the second-order approximations. The simultaneous primary and 1:1 internal resonance case ($\mathsf{\Omega }\approx {\omega }_{eq},$${\omega }_2\approx {\omega }_{eq}$) is recorded as the worst resonance case caused while working on the model. We investigated stability with frequency–response equations and bifurcation. Numerical solutions for the system are covered. The effects of the majority of the system parameters were examined. In order to mitigate harmful vibrations, the controller under investigation uses (PD) proportional derivatives with (PPF) positive position feedback as a new control technique. This creates a new active control technique called PDPPF. A comparison between the PD, PPF, and PDPPF controllers demonstrates the effectiveness of the PDPPF controller in reducing amplitude and suppressing vibrations. Unwanted consequences like chaotic dynamics, limit cycles, or loss of stability can result from bifurcation, which is the abrupt qualitative change in a system’s behavior as a parameter. The outcomes showed how effective the suggested controller is at reducing vibrations. According to the findings, bifurcation analysis and a control are crucial for designing vibrating dynamic motorcycle systems for a range of engineering applications. The MATLAB software is utilized to match the analytical and numerical solutions at time–history and frequency–response curves (FRCs) to confirm their comparability. Additionally, case studies and numerical simulations are presented to show how well these strategies work to control bifurcations and guarantee the desired system behaviors. An analytical and numerical solution comparison was prepared. Full article

This study analyzes factors influencing injury severity in motorcycle accidents involving non-helmeted riders using Bayesian spatiotemporal logistic models. Five models were developed, four of which incorporated different spatiotemporal configurations, including spatial, temporal, and spatiotemporal interaction error terms. The results indicate that the optimal model integrated Leroux CAR spatial priors, temporal random walks, and interaction terms, achieving 86.74% classification accuracy, with a 3% reduction in the DIC value; obtaining the lowest numerical fit demonstrating spatiotemporal interactions is critical for capturing complex risk patterns (e.g., rain amplifying nighttime collision severity). The results highlight rain (OR = 1.53), age ≥ 50 (OR = 1.90), and bi-directional roads (OR = 1.82) as critical risk factors. Based on these findings, several sustainable traffic safety strategies are proposed. Short-term measures include IoT-based dynamic speed control on high-risk roads and app-enforced helmet checks via ride-hailing platforms. Long-term strategies integrate age-specific behavioral training focusing on hazard perception and reaction time improvement, which reduced elderly fatalities by 18% in Japan’s “Silver Rider” program by directly modifying high-risk riding habits (non-helmets). These solutions, validated by global case studies, demonstrate that helmet use could mitigate over 60% of severe head injuries in these high-risk scenarios, promoting sustainable traffic governance through spatiotemporal risk targeting and helmet enforcement. Full article

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