scholarly journals Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

2021 ◽  
Vol 18 (21) ◽  
pp. 11131
Author(s):  
Tong Zhu ◽  
Zishuo Zhu ◽  
Jie Zhang ◽  
Chenxuan Yang
Keyword(s):  
Traffic Control ◽  
Injury Severity ◽  
Safety Management ◽  
Management Strategies ◽  
Mixed Logit ◽  
Random Parameters ◽  
Severe Injuries ◽  
Crash Data ◽  
Electric Bicycle ◽  
The Impact

Accidents involving electric bicycles, a popular means of transportation in China during peak traffic periods, have increased. However, studies have seldom attempted to detect the unique crash consequences during this period. This study aims to explore the factors influencing injury severity in electric bicyclists during peak traffic periods and provide recommendations to help devise specific management strategies. The random-parameters logit or mixed logit model is used to identify the relationship between different factors and injury severity. The injury severity is divided into four categories. The analysis uses automobile and electric bicycle crash data of Xi’an, China, between 2014 and 2019. During the peak traffic periods, the impact of low visibility significantly varies with factors such as areas with traffic control or without streetlights. Furthermore, compared with traveling in a straight line, three different turnings before the crash reduce the likelihood of severe injuries. Roadside protection trees are the most crucial measure guaranteeing riders’ safety during peak traffic periods. This study reveals the direction, magnitude, and randomness of factors that contribute to electric bicycle crashes. The results can help safety authorities devise targeted transportation safety management and planning strategies for peak traffic periods.

scholarly journals Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Clustering Methods ◽  
Severe Injuries ◽  
Traffic Crash ◽  
Crash Data ◽  
Boosted Decision Trees ◽  
The Individual ◽  
Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

scholarly journals Child safety programs for primary school children decrease the injury severity of dog bites

2021 ◽  
Author(s):  
Bernhard Kienesberger ◽  
Christoph Arneitz ◽  
Vanessa Wolfschluckner ◽  
Christina Flucher ◽  
Peter Spitzer ◽  
...  
Keyword(s):  
Primary School ◽  
School Children ◽  
Prevention Program ◽  
Injury Severity ◽  
Primary School Children ◽  
Child Safety ◽  
Dog Bite ◽  
Severe Injuries ◽  
Dog Bites ◽  
The Impact

AbstractThis study focuses on the impact of a prevention program regarding dog bites in children. As a consequence of our previous investigation in 2005, we have initiated a child safety program for primary school children starting January 2008 until present to teach children how to avoid dog attacks and how to behave in case of an attack. In our retrospective study, we analyzed all patients younger than 15 years presenting with dog-related injuries between 2014 and 2018. As the main indicator for success of the prevention measures taken, we have defined the severity of injury in comparison to our previous study. Out of 296 children with dog-related injuries, 212 (71.6%) had sustained a dog bite. In the vast majority (n = 195; 92%), these patients presented with minor injuries; the extremities were most commonly affected (n = 100; 47%). Injuries to the head (n = 95; 45%) and trunk (n = 18; 8%) were less frequent. The proportion of severe injuries (8%) was significantly lower compared to our previous study, where 26% of children presented with severe injuries necessitating surgical intervention, while the number of patients requiring in-hospital treatment declined from 27.5% in the period 1994–2003 to 9.0% in the period between 2014 and 2018 (p < 0.05).Conclusion: Teaching of primary school children may effectively reduce the injury severity of dog bites. What is Known:• Dog bites are a substantial healthcare problem especially in children. What is New:• This study shows that a broad-based prevention program for primary school children can effectively decrease the severity but not the frequency of dog bite injuries in children.

Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors

2020 ◽  
Vol 2674 (5) ◽  
pp. 452-464 ◽  
Author(s):  
Mohammad Razaur Rahman Shaon ◽  
Xiao Qin
Keyword(s):  
Injury Severity ◽  
Model Performance ◽  
Severe Injuries ◽  
Crash Data ◽  
Severity Prediction ◽  
Safety Interventions ◽  
Driving Task ◽  
Driver Error ◽  
Crash Characteristics ◽  
Crash Injury Severity

Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

scholarly journals Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

2019 ◽  
Vol 11 (11) ◽  
pp. 3169 ◽  
Author(s):  
Ho-Chul Park ◽  
Yang-Jun Joo ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Byung-Jung Park
Keyword(s):  
South Korea ◽  
Injury Severity ◽  
The Elderly ◽  
Type I ◽  
Severe Injuries ◽  
Crash Data ◽  
Level Model ◽  
Multi Level ◽  
Pedestrian Crashes ◽  
Upper Level

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.

Artificial Neural Networks and Logit Models for Traffic Safety Analysis of Toll Plazas

2002 ◽  
Vol 1784 (1) ◽  
pp. 115-125 ◽  
Author(s):  
Hassan T. Abdelwahab ◽  
Mohamed A. Abdel-Aty
Keyword(s):  
Neural Networks ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Logit Models ◽  
Severe Injuries ◽  
Toll Plazas ◽  
Artificial Neural ◽  
Toll Plaza ◽  
The Impact

Little research has been conducted to evaluate the traffic safety of toll plazas and the impact of electronic toll collection (ETC) systems on highway safety, but analyses indicate that toll plazas do contribute to traffic accidents. Traffic safety issues related to toll plazas and ETC systems were studied using the 1999 and 2000 toll plaza traffic accident reports of the Central Florida expressway system. The analysis focused on accident location with respect to the plaza structure (before, at, after plaza) and driver injury severity (no injury, possible, evident, severe injuries). Two well-known artificial neural network (ANN) paradigms were investigated: the Multi-Layer Perceptron and Radial Basis Functions neural networks. The performance of ANN was compared with calibrated logit models. Modeling results showed that vehicles equipped with ETC devices, especially medium/heavy-duty trucks, have higher risk of being involved in accidents at the toll plaza structure. Also, main-line toll plazas have a higher percentage of accident occurrence upstream of the toll plaza. In terms of driver injury severity, ETC users have a higher chance of being injured when involved in an accident. Older drivers tend to have higher risk of experiencing more severe injuries than younger drivers. Female drivers have a higher chance of experiencing a severe injury than do male drivers.

scholarly journals The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Changxi Ma ◽  
Wei Hao ◽  
Wang Xiang ◽  
Wei Yan
Keyword(s):  
Open Space ◽  
Injury Severity ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Driving Behaviors ◽  
Grade Crossings ◽  
High Level ◽  
The Impact

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

Analyzing the Injury Severity Sustained by Non-Motorists at Mid-Blocks considering Non-Motorists’ Pre-Crash Behavior

2018 ◽  
Vol 2672 (38) ◽  
pp. 138-148 ◽  
Author(s):  
Bowen Dong ◽  
Xiaoxiang Ma ◽  
Feng Chen
Keyword(s):  
Injury Severity ◽  
Mixed Logit ◽  
Night Time ◽  
The Road ◽  
Related Data ◽  
Road Users ◽  
Five Factors ◽  
Injury Severities ◽  
Side Collision ◽  
The Impact

Non-motorized travel is considered as one of the most beneficial transportation modes. Compared with other road users, non-motorists as a whole account for about 13% of all fatal transportation-related accidents, and from 2002 to 2009 nearly 30% of those fatalities occur at mid-blocks. In addition, there are few reported studies that investigated the impact of non-motorists’ pre-crash behavior on injury severities. To examine the risk factors of non-motorist injury severity at mid-blocks, 8-year crash-related data from the General Estimates System were explored, based on the mixed logit model. The data contain various information including time characteristics, crash features, environmental conditions, roadway attributes, non-motorists’ characteristics, and their pre-crash behaviors. The results show that five factors tend to have mixed effects on injury severities, including the speed limit between 30 and 55 mph, night time indicator, right-side collision, and hit-and-run action on the incapacitating injury, as well as no action of motorists on the non-incapacitating injury. Moreover, heavy and light truck, dark not lighted indicator, and age over 65 are found to increase the likelihood of fatal injury, while age below 25 decreases the likelihood of fatality. Other indicators including roadway alignment, number of lanes, and so forth also affected injury severity. After controlling for these factors, non-motorists’ pre-crash behaviors such as darting or running into the road, activities in the roadway, and improper passing are found to have a significant impact on severity outcomes.

scholarly journals Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

2020 ◽  
Vol 17 (20) ◽  
pp. 7466
Author(s):  
Arshad Jamal ◽  
Waleed Umer
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Injury Severity ◽  
Back Propagation ◽  
Model Performance ◽  
Mitigation Strategies ◽  
Feed Forward Neural Network ◽  
Crash Data ◽  
The Impact ◽  
Crash Injury Severity

A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.

scholarly journals Linked Records of Children with Traumatic Brain Injury

2015 ◽  
Vol 54 (04) ◽  
pp. 328-337 ◽  
Author(s):  
T. D. Bennett ◽  
J. M. Dean ◽  
H. T. Keenan ◽  
M. H. McGlincy ◽  
A. M. Thomas ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Health Information ◽  
Record Linkage ◽  
Comparative Effectiveness ◽  
Injury Severity ◽  
Data Bank ◽  
Severe Injuries ◽  
Effectiveness Studies ◽  
The Impact

SummaryObjective: Record linkage may create powerful datasets with which investigators can conduct comparative effectiveness studies evaluating the impact of tests or interventions on health. All linkages of health care data files to date have used protected health information (PHI) in their linkage variables. A technique to link datasets without using PHI would be advantageous both to preserve privacy and to increase the number of potential linkages.Methods: We applied probabilistic linkage to records of injured children in the National Trauma Data Bank (NTDB, N = 156,357) and the Pediatric Health Information Systems (PHIS, N = 104,049) databases from 2007 to 2010. 49 match variables without PHI were used, many of them administrative variables and indicators for procedures recorded as International Classification of Diseases, 9th revision, Clinical Modification codes. We validated the accuracy of the linkage using identified data from a single center that submits to both databases.Results: We accurately linked the PHIS and NTDB records for 69% of children with any injury, and 88% of those with severe traumatic brain injury eligible for a study of intervention effectiveness (positive predictive value of 98%, specificity of 99.99%). Accurate linkage was associated with longer lengths of stay, more severe injuries, and multiple injuries.Conclusion: In populations with substantial illness or injury severity, accurate record linkage may be possible in the absence of PHI. This methodology may enable linkages and, in turn, comparative effectiveness studies that would be unlikely or impossible otherwise.

Cerebral Blood Flow and Metabolism Following Decompressive Craniectomy for Control of Increased Intracranial Pressure

Neurosurgery ◽  
2010 ◽  
Vol 67 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Jean F. Soustiel ◽  
Gill E. Sviri ◽  
Eugenia Mahamid ◽  
Veniamin Shik ◽  
Sergey Abeshaus ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Intracranial Pressure ◽  
Decompressive Craniectomy ◽  
Injury Severity ◽  
Normal Range ◽  
Severe Injuries ◽  
Neuronal Viability ◽  
Cellular Energy ◽  
The Impact

Abstract OBJECTIVE Decompressive craniectomy (DC) is a common practice for control of intracranial pressure (ICP) following traumatic brain injury (TBI), although the impact of this procedure on the fate of operated patients is still controversial. METHODS Cerebral blood flow (CBF) and metabolic rates were monitored prospectively and daily as a surrogate of neuronal viability in 36 TBI patients treated by DC and compared with those of 86 nonoperated patients. DC was performed either on admission (n = 29) or within 48 hours of admission (n = 7). RESULTS DC successfully controlled ICP levels and maintained CBF within a normal range although the cerebral metabolic rate of oxygen (CMRO2) was significantly lower in this group. In 7 patients, pre- and postoperative recordings showed a significant ICP decrease that correlated with CBF augmentation but not with concurrent improvement of CMRO2 that remained particularly low. Logistic regression analysis of all investigated variables showed that DC was not associated with higher mortality despite more severe injuries in this group. However, operated patients were 7-fold more likely to have poor functional outcomes than nonoperated patients. Good functional outcome was strongly associated with higher CMRO2 but not with higher CBF values. CMRO2 levels were significantly lower in the DC group, even after adjustment for injury severity, and showed a progressive and sustained trend of deterioration significantly different from that of the non-DC group. CONCLUSION These results suggest that DC may enhance survival in the presence of severe brain swelling, although it is unlikely to represent an adequate answer to mitochondrial damage responsible for cellular energy crisis and edema.

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