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.
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.
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.
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.
Objective: The purpose of this study was to identify and better understand the features of fatal injuries in cyclists aged 75 years and over involved in collisions with either hood- or van-type ...vehicles.
Methods: This study investigated the fatal injuries of cyclists aged 75 years old and over by analyzing accident data. We focused on the body regions to which the fatal injury occurred using vehicle-bicycle accident data from the Institute for Traffic Accident Research and Data Analysis (ITARDA) in Japan. Using data from 2009 to 2013, we examined the frequency of fatally injured body region by gender, age, and actual vehicle travel speed. We investigated any significant differences in distributions of fatal injuries by body region for cyclists aged 75 years and over using chi-square tests to compare with cyclists in other age groups. We also investigated the cause of fatal head injuries, such as impact with a road surface or vehicle.
Results: The results indicated that head injuries were the most common cause of fatalities among the study group. At low vehicle travel speeds for both hood- and van-type vehicles, fatalities were most likely to be the result of head impacts against the road surface.
The percentage of fatalities following hip injuries was significantly higher for cyclists aged 75 years and over than for those aged 65-74 or 13-59 in impacts with hood-type vehicles. It was also higher for women than men in the over-75 age group in impacts with these vehicles.
Conclusions: For cyclists aged 75 years and over, wearing a helmet may be helpful to prevent head injuries in vehicle-to-cyclist accidents. It may also be helpful to introduce some safety measures to prevent hip injuries, given the higher level of fatalities following hip injury among all cyclists aged 75 and over, particularly women.
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.
Power-Two-Wheelers (PTWs) constitute a vulnerable class of road users with increased frequency and severity of accidents. The present paper focuses of the PTW accident risk factors and reviews ...existing literature with regard to the PTW drivers’ interactions with the automobile drivers, as well as interactions with infrastructure elements and weather conditions. Several critical risk factors are revealed with different levels of influence to PTW accident likelihood and severity. A broad classification based on the magnitude and the need for further research for each risk factor is proposed. The paper concludes by discussing the importance of dealing with accident configurations, the data quality and availability, methods implemented to model risk and exposure and risk identification which are critical for a thorough understanding of the determinants of PTW safety.
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