International Journal of Injury Control and Safety Promotion最新文献

Previous research solely employed a single type of conflict extremes for crash estimation, without considering the joint impact of multiple types of conflict extremes on crash risk. Therefore, two analysis frameworks based on conflict extremes were proposed: separate modeling and cooperative modeling. Based on the trajectories from five diverging areas, longitudinal and lateral conflicts were extracted. Then, a Bayesian hierarchical model for joint multi-location conflict extremes was constructed. Next, the threshold for conflict extremes was determined using automatic mean residual life plots, and a link function was established between the logarithmic scale parameter and dynamic and static variables. Finally, model parameters were estimated using the Markov Chain Monte Carlo simulation method, and a comparative analysis of crash probabilities and overall risks for diverging areas in the two frameworks was conducted by the fitted distributions. The results show that density differences, speed differences, and the ratio of large vehicles are important covariates explaining the non-stationarity of conflict extremes. In terms of crash probability, significant covariates exhibit stronger explanatory power for longitudinal conflicts compared to lateral conflicts. At the overall risk level, the accuracy of the separate modeling is higher compared to the cooperative modeling.

Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fatalities were collected from forensic and hospital records. K-means clustering grouped death locations and identified cluster centres. The Ball Tree algorithm and Google Directions API were used to find the nearest trauma centre hospital from the injury locations. Statistical tests, including chi-square and Kruskal-Wallis, examined relationships between clusters and demographic variables. The analysis identified 181 cases, mostly males (83.43%), with a median age of 37 years. Clustering the death locations into four high-risk areas resulted in a Silhouette Score of 0.94, indicating suitable EMS locations. While no significant correlation was found with demographic variables, distinct patterns were observed in road user types. Testing the prediction performance for the nearest hospital using forty new locations yielded an accuracy, precision, recall, and F1 score of 0.90. These findings emphasize the importance of targeted interventions and resource allocation in traffic injury prevention and emergency response planning, showcasing the potential of ML and geographic analysis in enhancing traffic injury management and emergency response systems.

Evaluating the effectiveness of road humps is very essential in traffic safety and transportation planning. In Uganda, no study has assessed the effectiveness of road humps. This study evaluated the effectiveness of the addition of road humps as a safety intervention in Uganda. Before and after data of the injuries and death that occurred along Kansanga-Gabba and Mukwano road were obtained from Uganda Police Forces (UPF) and used during the analysis. Scikit-Learn library in python 3.7 was used to calculate descriptive statistics and Empirical Bayes (EB) method was used to estimate the effectiveness of the addition of road humps on the road. The results show that the addition of road humps led to a reduction of the road crash death by 38%, 63%, 21%, 31% and 93% for pedestrians, bicyclists, motorcyclists, Light-Duty Vehicles (LDVs), and Heavy-Duty Vehicles (HDVs) respectively. In addition, road crash injuries decreased by 56%, 17%, 13%, 32%, and 74% for pedestrians, bicyclists, motorcyclists, LDVs and HDVs respectively. The inferences from these results will be useful to reduce the continued road crash injuries and death on the road in Uganda.

The study of road traffic injuries (RTIs) is crucial for understanding the unique challenges faced by West Asia and North Africa (WANA) states. This research evaluates road safety practices in the WANA region, comparing them to global standards, and employs secondary data analysis from sources such as the Global Road Safety Status Report, Global Road Safety Facility, and the World Health Organization. The analysis examines epidemiological data, preventive measures like seatbelt and child-restraint use, and policy development, including national action plans, to estimate road traffic death rates per 10,000 vehicles and per 100,000 population. Data from 23 countries are analyzed, focusing on road traffic injury rates by user type, road safety laws, and global safety targets. Overall, WANA states account for 10.5% of global RTI fatalities, exceeding both world and European averages. Most pedestrian fatalities occur in Ethiopia (40.0%) and Afghanistan (34.0%). This indicates that low enforcement scores (averaging 5 out of 10) in most WANA countries contribute to the insufficient effectiveness of road safety laws in reducing injuries and deaths. Achieving the Sustainable Development Goal (SDG) to reduce global road traffic deaths by 50% by 2030 requires commitment and cooperation from governments, communities, and stakeholders in the WANA region.

Using the methodology developed by the National Highway Traffic Safety Administration (NHTSA) for motorcyclists, this paper estimates bicycle helmet effectiveness factors (HEFs), defined as the percentage greater chance that a helmeted bicyclist will avoid a fatality or serious injury relative to a non-wearer. We analyse reported motor vehicle-bicycle collisions in Colorado between 2006 and 2014. We conclude that NHTSA's motorcycle HEF methodology did not provide reasonable results given underreporting of low-severity collisions of helmeted bicyclists. The HEF methodology may be applied to bicycles in future research if more complete bicyclist collision reporting can be obtained. To account for underreporting, we calibrated our bicycle HEFs to past research and found that approximately one of every two bicyclists killed may have survived (HEF = 0.50) and one of every three seriously injured bicyclists may have been less seriously injured (HEF = 0.33) if wearing a helmet at the time of the collision.

This study analysed motorcyclist violation (MV) crashes on suburban roads of China to investigate how determinants affect MV crash injury severity and explore the temporal stability of determinants. Crash data from Xi'an, China (2015-2018) were utilized to investigate three MV crash injury categories: no injury, minor injury and severe injury. Motorcyclist-related, crash-related, roadway-related, environment-related and time-related characteristics were analysed utilizing a group of random parameters multinomial logit models with heterogeneity in means and variances. The temporal instability was measured by performing likelihood ratio tests. Marginal effects were calculated to further illustrate the temporal variations of these factors. The study found an overall temporal instability, with some violations like alcohol-impaired riding, speeding, and unlicensed riding having significant effects on MV crash injury severity. Additionally, the study revealed a significant risk compensation mechanism of riders under adverse riding conditions. The findings provided insights and recommendations for suburban motorcycle crash prevention strategies.

The analysis of injury severity in accidents allows traffic management agencies to assess crash risk more effectively and develop cost-effective interventions. The aim of this research is to present a two-layer stacking model as a means of forecasting accident severity. In the initial layer, the system incorporates benefits derived from many base classification algorithms through a three-stage process to evaluate the outcomes of each model configuration. These base algorithms include Random Forests, Decision Tree, K Nearest Neighborhood and Support Vector Machine; in the second layer, Logistic Regression and Random Forest algorithms are used to classify crash injury severity. In total, 24,141 traffic accidents were recorded on 135 two-way, two-lane roads. The process of model calibration entails the optimization of several parameters, such as the number of trees in three fundamental methods of classification, the learning rate and the regularization coefficient which is achieved by the utilization of a systematic grid search strategy. To validate the model, the Stacking model's performance is assessed in comparison to other conventional models. The results indicate that the Stacking model has greater performance. Consequently, each component included in the prediction of severity is categorized into distinct groups according to its impact on results.

This study examines the interdependent relationship between fault status and injury severity in motorcycle rear-end crashes in Thailand using data from 1,549 crashes (2011-2015) integrated from the Department of Highway's Accident Information Management System and Traffic Information Movement System. This article employs a bivariate probit model alongside various boosting techniques for simultaneous estimation of injury severity and at-fault status. Among the tested models (AdaBoost, CatBoost and LightGBM), both the bivariate probit and XGBoost-Endogenous models demonstrate superior performance in accuracy and F1-score. The bivariate probit model reveals that injury severity is significantly influenced by rider characteristics (age, gender), road features, and traffic conditions. Riders under 55 years old, female riders and those on roads with depressed medians or higher traffic volume show lower injury severity risk. Conversely, drunk riding, nighttime crashes on unlit roads, and higher truck traffic percentages increase severe injury likelihood. The XGBoost model corroborates these findings, identifying traffic volume, truck percentage and nighttime conditions on unlit roads as the most crucial predictors of injury severity. Regarding fault status, younger riders and those using safety equipment show a higher probability of being at-fault. This novel analytical approach provides valuable insights for motorcycle safety policy development and future research directions.

India does not have a national crash-level surveillance system. Instead, police stations report crashes in standardized tables that are summarized at the state level. Since tabulations provide limited insights into crash patterns, we developed a crash database from police First Information Reports (FIRs) on all (n = 11,175) fatalities in Chhattisgarh during 2017-2019. The data show that not only were motorcycle riders the most common victims (59% of fatalities), but they also posed a substantial threat to other road users. Motorcycle impacts caused 16% of all fatalities (37% of pedestrians). Although truck occupants comprised only 5% of fatalities, trucks were the most common striking vehicle. Remarkably, 94% of tractor occupants were killed in single-vehicle crashes, and more than were rollovers. The FIR database provides a richer description of crashes than tabulations and an important information source for safety management. India and other LMICs will benefit substantially by investing in crash surveillance systems.