A total of 523 accident cases during 2018–2021 in China were studied in terms of accident year, region, road section, and time to reduce the probability of expressways mobile hazardous source ...transportation accidents. The characteristics and causes of accidents of moving hazardous sources on expressways are analyzed, and preventive measures are put forward. The results indicated that the number of expressways mobile hazardous source transportation accidents fluctuated less in the past four years. Provinces with a higher degree of industrialization are more prone to accidents, with 80.02% of accidents occurring on normal road sections. Summer is the high accident season and accidents are prone to occur at 7:00 and 8:00 every day. External factors cause 47.99% of accidents, liquid class mobile hazards quickly cause accidents, leakage accidents account for the heaviest proportion, and explosive accidents have the highest chance of causing secondary accidents. In order to reduce the probability of accidents, a comprehensive management framework suitable for the mobile hazard sources of expressways is proposed.
Background. The World Health Organisation estimates that 1.35 million people die as a result of road traffic crashes. Motorcycles as a means of transport are increasingly becoming the preferred and ...easiest means of transportation for most people in developing countries despite the associated risk. This study determined the prevalence and pattern of motorcycle crashes in Adidome among commercial motorcyclists. Methods. A descriptive, cross-sectional study design was used as 114 commercial motorcyclists were recruited to respond to a pretested research questionnaire in the Adidome district of the Volta Region. Data were analyzed using SPSS, version 22.0. Data were presented as simple descriptive statistics. A chi-square relationship was determined using the demographic variables, and the history of accident at a 95% confidence interval with 0.05 was considered as statistically significant. Results. The prevalence of road traffic crashes at Adidome was 64.0%. Motorcyclists (74.0%) were reported to have been involved in crashes in the past one year prior to the study. Motorcyclists attributed the last accident to excessive speeding (31.5%) and bad roads (23.3%), this accident as a result of colliding with another motorcycle (50.7%), and slippery surfaces (24.7%). The majority (63.0%) of the respondents had an accident once. The consumption of alcohol was associated with the occurrence of an accident as 34.2% occurred among cyclists who drank alcohol, compared with 29.8% who did not (p<0.05). Conclusion. There should be strict implementation of current road traffic regulations of Ghana by the MTTD of the Ghana Police Service, and penalties should be awarded against anybody caught riding a motorcycle under the influence of alcohol. Helmet and other protective devices must be made compulsory for motorcycle riders to prevent injuries, especially head injuries, if an accident occurs.
The purpose of this study is to minimize the negative influences of the severe traffic accidents in China by profoundly analyzing the complex coupling relations among accident factors contributing to ...the single-vehicle and multivehicle traffic accidents with the Bayesian network (BN) crash severity model. The BN model was established by taking the critical factors identified with the improved grey correlation analysis method as node variables. The severe traffic accident data collected from accident reports published in China were used to validate this model. The model’s efficiency was validated objectively by comparing the conditional probability obtained by this model with the actual value. The result shows that the BN model can reflect the real relations among factors and can be seen as the target network for the severe traffic accidents in China. Besides, based on BN’s junction tree engine, five-factor combination sequences for the number of deaths and three-factor combination sequences for the number of injuries were ranked according to the severity degree to reveal the critical reasons and reduce the massive traffic accidents damage.
The proportion of neck injuries due to traffic accidents is increasing. Little is known about high-cost patients with acute whiplash-associated disorder (WAD). The present study aimed to investigate ...whether time to first visit for conventional medicine, multiple doctor visits, or alternative medicine could predict high-cost patients with acute WAD in Japan.
Data from a compulsory, no-fault, government automobile liability insurance agency in Japan between 2014 and 2019 were used. The primary economic outcome was the total cost of healthcare per person. Treatment-related variables were assessed based on the time to first visit for conventional and alternative medicine, multiple doctor visits, and visits for alternative medicine. Patients were categorized according to total healthcare cost (low, medium, and high cost). The variables were subjected to univariate and multivariate analysis to compare high-cost and low-cost patients.
A total of 104,911 participants with a median age of 42 years were analyzed. The median total healthcare cost per person was 67,366 yen. The cost for consecutive medicine, for consecutive and alternative medicine, and total healthcare costs were significantly associated with all clinical outcomes. Female sex, being a homemaker, a history of WAD claim, residential area, patient responsibility in a traffic accident, multiple doctor visits, and visits for alternative medicine were identified as independent predictive factors for a high cost in multivariate analysis. Multiple doctor visits and visits for alternative medicine showed large differences between groups (odds ratios 2673 and 694, respectively). Patients with multiple doctor visits and visits for alternative medicine showed a significantly high total healthcare cost per person (292,346 yen) compared to those without (53,587 yen).
A high total healthcare cost is strongly associated with multiple doctor visits and visits for alternative medicine in patients with acute WAD in Japan.
The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic ...accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.
IntroductionBrazilian records show a high rate of traffic accidents involving motorcycles (30% of deaths, 55% of hospitalizations). The number of Brazilian workers who use motorcycles is increasing ...and it is necessary to understand the context of these accidents in order to develop public policies and promote education.ObjectivesTo describe the proportion of mortality by work-related injuries (PM_WRI) involving motorcycle in Brazil, 2007–2018.MethodsThe study was conducted with data from the Brazilian Mortality Information System for those aged 18–65 years, in 2007–2018. Motorcycle-related deaths correspond codes V20 to V29 (ICD-10). PM_WRI are presented by calendar year, sex, age and occupational groups.ResultsThere were 121,124 records of fatal injuries involving motorcycles, with a increasing linear trend from 7,502 in 2007 to 9,725 in 2018. Work-related data (WRD) were registered for only 48,716 (40.2%) cases, from which 3,692 were classified as occupational. From 2007 to 2010, the PM_WRI went from 7.6% to 8.9% (maximum) when it started to fall until 2015 (6.4% minimum). The average for the last three years was 7.4%. No significant difference of PM_WRI by sex was found. PM_WRI increased with age but declined in the oldest age group (50–65 age years). Occupation was registered for 33,784 cases (69% of the WRD). The highest PM_WRI was estimated among workers from service industry (14.3%) followed by administrative services (13.6%). Agriculture had the largest number of motorcycle-related deaths (35%) but only 3.1% was recognized as work-related.ConclusionThe work-related data in death certificates were poorly recorded, limiting conclusions on the contribution of labor on motorcycle associated deaths. PM_WRI estimates were presumably underestimated and findings could be biased. Motorcycle-related deaths doubled over the study time and the role of labor for this need to be better understood. Improvements in the quality and completion of WRD are urgently needed and prevention programs implemented.
Traffic accidents continue to be a significant cause of fatalities, injuries, and considerable disruptions on our highways. Understanding the underlying factors behind these incidents is crucial for ...improving safety on road networks. While recent studies have highlighted the usefulness of predictive modeling in uncovering factors leading to accidents, there remains a gap in explaining the inner workings of complex machine learning and deep learning models and how various features influence accident prediction. This lack of transparency may lead to these models being perceived as black boxes, potentially undermining trust in their findings among stakeholders. The primary aim of this research is to develop predictive models using diverse transfer learning techniques and shed light on the most influential factors using Shapley values. In predicting injury severity in accidents, we employ Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNET), EfficientNetB4, InceptionV3, Extreme Inception (Xception), Visual Geometry Group (VGG19), AlexNet, and MobileNet. Among these models, MobileNet emerges with the highest accuracy at 0.9817. Furthermore, by comprehending how different features impact accident prediction models, researchers can deepen their understanding of the factors contributing to accidents and devise more effective interventions for their prevention.
Intersections are crucial and high-risk areas in urban road networks due to dense traffic and complex scenarios. Deploying Roadside Units (RSUs) can enhance safety and efficiency by providing ...real-time traffic information. However, the impact of traffic accident risks on RSU deployment is largely ignored. This study introduces an innovative RSU deployment strategy that prioritizes the risk of traffic accidents at intersections. The approach begins with analyzing environmental conditions, traffic patterns, and historical accident data at target intersections to identify key risk dimensions: road, accident, and environmental. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are used to weigh the indicators to evaluate their importance in accident risk assessment. Then, construct an objective function based on the accident risk value of the intersection. To overcome the redundancy problem in risk assessment, this study proposes an improved 0-1 knapsack algorithm that considers the redundancy of intersection accident risk to determine the optimal deployment location of RSUs. Simulations with SUMO, TraCI, Veins, and OMNeT++ demonstrate the algorithm's superiority over traditional methods in all metrics. The results show that the vehicle coverage of this strategy is on average 2.63% and 2.86% higher than that of the IIA-ORD and UDA algorithms, respectively. It also leads by about 5.04% in traffic accident coverage and 5.72% in accident risk coverage. This intersection-focused RSU deployment method ensures timely information dissemination after incidents, providing valuable insights and practical guidelines for improving urban intersection safety and efficiency.
Ridehailing services such as Uber have been promoted as viable interventions for curbing alcohol-involved driving fatalities. However, evidence of ridehailing's impact has been mixed, with some ...studies finding no association but others finding either an increase or a decrease in fatalities. We contribute to this literature by examining more recent years of data, which capture a period during which Uber ridership has grown substantially and alcohol-involved fatalities have increased. Furthermore, we test whether the relationship between Uber availability and traffic fatalities depends on local characteristics. We employ multivariate regression models to test the association between Uber availability and total, alcohol-involved, and weekend and holiday-specific traffic fatalities in the 100 most populated metropolitan areas in the United States between 2009 and 2017. We find that Uber availability is not associated with changes in total, alcohol-involved, and weekend and holiday-specific traffic fatalities in aggregate, yet it is associated with increased traffic fatalities in urban, densely populated counties.
Background
Previous latent trajectory studies in adult bereaved people have identified individual differences in reactions postloss. However, prior findings may not reflect the complete picture of ...distress postloss, because they were focused on depression symptoms following nonviolent death. We examined trajectories of symptom‐levels of persistent complex bereavement disorder (PCBD), depression, and posttraumatic stress disorder (PTSD) in a disaster‐bereaved sample. We also investigated associations among these trajectories and background and loss‐related factors, psychological support, and previous mental health complaints.
Methods
Latent class growth modeling was used to identify distinct trajectories of PCBD, depression, and PTSD symptoms in people who lost loved ones in a plane disaster in 2014. Participants (N = 172) completed questionnaires for PCBD, depression, and PTSD at 11, 22, 31, and 42 months postdisaster. Associations among class membership and background and loss‐related variables, psychological support, and previous mental health complaints were examined using logistic regression analyses.
Results
Two PCBD classes emerged: mild (81.8%) and chronic (18.2%) PCBD. For both depression and PTSD, three classes emerged: mild (85.6% and 85.2%), recovered (8.2% and 4.4%), and chronic trajectory (6.2% and 10.3%). People assigned to the chronic PCBD, depression, or PTSD class were less highly educated than people assigned to the mild/recovered classes.
Conclusions
This is the first latent trajectory study that offers insights in individual differences in longitudinal symptom profiles of PCBD, depression, and PTSD in bereaved people. We found support for differential trajectories and predictors across the outcomes.
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