Accident Injury Severity Research Articles

Occupant Injury Severity in General Aviation Accidents Involving Excessive Landing Airspeed.

BACKGROUND: Of all phases of flight operations, accidents during landings are the most frequent. Of these, poor speed management during landing has ramifications for injury severity since: 1) impact forces increase as a square of forward velocity; and 2) an aerodynamic stall, associated with inadequate landing speed, imparts high vertical G forces. Herein, the proportion of landing accidents involving deficient airspeed control and occupant injury severity was determined.METHODS: General aviation landing accidents (1997-2016) were identified from the NTSB database. An accident involving high-airspeed (high-energy) was one for which the NTSB cited airplane porpoising, multiple bounces, or floating, whereas an inadequate airspeed related (low energy) mishap was one citing this term or in which an aerodynamic stall occurred. An anonymous online survey of certificated pilots was used to inform landing technique. Statistical analyses used Poisson distribution and Chi-squared tests.RESULTS: Relative to the earliest period (1997-2001), the landing accident rate was undiminished for more recent years (2007-2016). Of 235 accidents, 38% involved high-energy, whereas 4% were inadequate airspeed-related. For the former, 17% resulted in occupants with fatal-serious injuries, twofold higher than for mishaps with no evidence of mis-speed. Of 1392 survey respondents, 73% selected a landing airspeed higher than required for an under-maximum weight airplane.CONCLUSION: For landing accidents involving airspeed mismanagement, those related to excessive energy predominate and are associated with more severe injuries. Two mitigating strategies are advanced: 1) pilot training should discuss landing airspeed adjustment for aircraft weight; and 2) installation of inflatable restraints for reducing injury severity should be encouraged.Boyd DD. Occupant injury severity in general aviation accidents involving excessive landing airspeed. Aerosp Med Hum Perform. 2019; 90(4):355-361.

Evaluation of the effect of airbag and seat belt use on the severity of injury in traffic accidents

Evaluation of the effect of airbag and seat belt use on the severity of injury in traffic accidents

Relationship between injury patterns and severity of motor vehicle accidents with meteorological patterns on a Korean island

Jeju Island is the largest island in South Korea, with a population of about 700,000. It has a humid subtropical climate, with a cool and dry fall and winter. Because the island is approximately 60...

Non-decreasing threshold variances in mixed generalized ordered response models: A negative correlations approach to variance reduction

Mixed Generalized Ordered Response (MGOR) models, that allow random heterogeneity in thresholds, are widely used to model ordered outcomes such as accident injury severity. This study highlights a potential limitation of these models, as applied in most empirical research, that the variances of the random thresholds are implicitly assumed to be in a non-decreasing order. This restriction is unnecessary and can lead to difficulty in estimation of random parameters in higher order thresholds. In this study, we investigate the use of negative correlations between random parameters as a variance reduction technique to relax the property of non-decreasing variances of thresholds in MGOR models. To this end, a simulation-based approach was used (where multiple datasets were simulated assuming a known negative correlation structure between the true parameters), and two models were estimated on each dataset – one allowing correlations between random parameters and the other not allowing such correlations. Allowing negative correlations helped relax the non-decreasing variance property of MGOR models. However, maximum simulated likelihood estimation of parameters on data with correlations occasionally encountered model convergence and parameter identification issues. Comparison of the models that did converge suggests that ignoring correlations leads to an estimation of fewer random parameters in the higher order thresholds and results in bias and/or loss of precision for a few parameter estimates. However, ignoring correlations leads to an adjustment of other parameter estimates such that overall likelihood values, predicted percentage shares, and the marginal effects are similar to those from the models with correlations.

철도 유형별 사고 심각도 영향 요인 분석

철도는 도로 교통에 비해 사고율이 상대적으로 낮지만, 사고 심각도가 높아 사고 발생 시 큰 피해를 야기할 수 있다. 이에 본 연구에서는 2006~2017년까지 발생한 철도 사고 이력 자료를 기반으로 철도 유형별 사고 심각도 모형을 구축하고, 사고 심각도에 영향을 미치는 요인을 분석하였다. 철도 유형은 운영 특성을 고려하여 도시철도와 지역 간 철도(일반철도 및 고속철도)로 구분하였으며, 각 유형별 사고 심각도 분석을 위해 순서형 프로빗 모형을 활용하였다. 모형 추정 결과, 사고 시간, 장소, 원인, 열차사고여부 등의 요인이 철도 유형별 공통적으로 사고 심각도에 영향을 미치는 것으로 나타났으며, 지역간 철도의 경우 날씨 변수가 추가적으로 사고 심각도에 유의한 영향을 미치는 것으로 분석되었다. 본 연구 결과는 철도 유형별 사고 심각도 감소 대책 마련 및 철도 안전도 향상에 기여할 수 있을 것이다.

Risk Factors Affecting the Injury Severity of Rental Car Accidents in South Korea : an Application of Ordered Probit Model

Risk Factors Affecting the Injury Severity of Rental Car Accidents in South Korea : an Application of Ordered Probit Model

Road casualties in work-related and private contexts: occupational medical impact. Results from the ESPARR cohort.

Road accidents may impact victims' physical and/or mental health and socio-occupational life, notably including return to work. To assess whether the occupational medical consequences sustained by subjects injured in road accidents occurring in a work-related context differ from those associated with private accidents. 778 adults who were in work or occupational training at the time of their accident were included. Two groups were distinguished: 354 (45.5%) injured in road accidents occurring in a work-related context (commuting or on duty) and 424 (54.5%) injured in a private accident. The groups were compared on medical and occupational factors assessed on prospective follow-up at 6 months and 1 and 3 years. Multivariate analysis explored for factors associated at 6 months and 1 year with sick leave following the accident and duration of sick leave. There were no significant differences between groups for demographic data apart from a slightly higher injury severity in private accidents (32.5% of private accidents with MAIS3+(Maximum Abbreviated Injury Scale greater or equal to 3) vs. 23.7% for work-related accidents, p = 0.007). Victims of work-related accidents were more often on sick leave (OR = 1.8; 95% CI, 1.1-2.9). Although the length of sick leave is higher for work-related accidents that for private accidents, multivariate analysis showed that the injury severity and the post-traumatic stress disorder (PTSD) are significant factors to explain the time to return to work. There were no significant differences according to occupational impact during follow-up, notably including sick-leave duration, number of victims returning to work within 3 years and number of victims out of work due to incapacity. In the ESPARR (follow-up study of a road-accident population in the Rhône administrative county: Etude de Suivi d'une Population d'Accidentés de la Route dans le Rhône) cohort, the fact that a road accident occurred in a work-related context did not affect the occupational consequences.

Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates

This paper employs a correlated random parameters ordered probit modeling framework to explore time-variant and time-invariant factors affecting injury-severity outcomes in single-vehicle accidents. The proposed approach extends traditional random parameters modeling, by accounting for possible correlations among the random parameters. On the basis of an unrestricted covariance matrix for the random parameters, the proposed framework can capture the combined effect of the unobserved factors – which are captured by the random parameters – on the injury-severity mechanism. The empirical analysis is based on traditional roadway-, traffic- and crash-specific information, and detailed weather and pavement surface disaggregate data, collected in the State of Washington, between 2011 and 2013. The results show that accident injury-severity outcomes are affected by a number of time-variant (ice thickness or water depth on pavement surface, sub-surface temperature) and time-invariant (roadway geometrics, and vehicle-, driver-, and collision-specific characteristics) factors, several of which result in statistically significant parameters – thus they have mixed effects on the injury-severity generation mechanism. The findings also present statistically significant correlation effects among the random parameters, which substantiates the appropriateness of the approach. The comparative assessment between the employed approach and its lower-order counterparts (i.e., fixed parameters, and uncorrelated random parameters ordered probit modeling approaches) shows that accounting for the unobserved heterogeneity interactions results not only in superior statistical performance (in terms of model’s fit, and explanatory and prediction performance) but also in less biased and more consistent parameter estimates.

Analysis of vehicle accident-injury severities: A comparison of segment- versus accident-based latent class ordered probit models with class-probability functions

Using information from 1990 single-vehicle accidents that occurred between 2011 and 2013 in the state of Washington, the injury severity level of the most severely injured vehicle occupant is studied using two latent class modeling approaches: segment-based and accident-based latent class ordered probit model with class-probability functions. The segment-based latent class ordered probit framework allows explanatory parameters to vary across unobserved groups (classes) of the highway segment population, while the modeling structure treats all segment-specific injury observations homogeneously (grouped). The accident-based latent class ordered probit framework allows for the explanatory parameters to vary across unobserved groups of the accident population, and the modeling structure treats all accident injury-severity observations individually (ungrouped). To further address heterogeneity arising from the probabilistic assignment of the highway segments or accident observations in the latent classes, the class probabilities are allowed to vary as a function of explanatory parameters, respectively. For both modeling approaches, the proposed latent class approach with class-probability functions is compared to its latent class counterpart with fixed class probabilities, and the results support the statistical superiority of the former, in terms of statistical fit and explanatory power. The empirical findings show the potential of both modeling approaches to unmask driver-, vehicle-, collision- and weather-specific sources of heterogeneity and, specifically, the capability of the segment-based approach to account for segment-specific heterogeneity. The comparative evaluation between the two modeling approaches shows that the segment-based approach provides better overall statistical fit. Furthermore, the forecasting accuracy of both approaches is explored through probability- and error-based measures demonstrating the forecasting accuracy benefits of the segment-based approach.

Predicting the Severity of Motor Vehicle Accident Injuries in Adana-Turkey Using Machine Learning Methods and Detailed Meteorological Data

Traffic accidents are among the most important issues facing every nation in the world as they cause many deaths and injuries as well as economic losses every year. In this study, the traffic accidents that took place in Adana, have been classifiedaccording to injury severity (i.e. fatal or non-fatal) and the factors affecting the accident outcome are investigated. The study included the traffic accident reports kept by Regional Traffic Division and the weather data provided by the Regional Directorate of Meteorology during 2005-2015. Five major machine learning methods (i.e. k-Nearest Neighbor, Naive Bayes, Multilayer Perceptron, Decision Tree, Support Vector Machine) and one statistical method, Logistic Regression, were employed for prediction models and performances of the models as well as the effective parameters were compared. The main objective of the study is to determine how important weather and other phenomena are for the occurrence of traffic accidents. Decision Tree, k-Nearest Neighbor and Multilayer Perceptron based models yielded higher accuracy in classification of accidents compared to other models. Furthermore, in Area Under Curve based analysis of factor importance, it was determined that Mean Cloudiness, Existence of Traffic Control and Ground Surface Temperature had higher positive effects, while Maximum Temperature and Weather (kept by traffic officers) parameters decreased the accuracy of models.

Simultaneous Equation Penalized Likelihood Estimation of Vehicle Accident Injury Severity

SummaryA bivariate system of equations is developed to model ordinal polychotomous dependent variables within a simultaneous additive regression framework. The functional form of the covariate effects is assumed fairly flexible with appropriate smoothers used to account for non-linearities and spatial variability in the data. Non-Gaussian error dependence structures are dealt with by means of copulas whose association parameter is also specified in terms of a generic additive predictor. The framework is employed to study the effects of several risk factors on the levels of injury sustained by individuals in two-vehicle accidents in France. The use of the methodology proposed is motivated by the presence of common unobservables that may affect the interrelationships between the parties involved in the same crash and by the possible heterogeneity in individuals’ characteristics and accident dynamics. Better calibrated estimates are obtained and misspecification reduced via an enhanced model specification.

Factors associated with urban non-fatal road-accident severity

ABSTRACTThis paper reports on the factors associated with non-fatal urban-road accident severity. Data on accidents were gathered from the local traffic police in the City of Palermo, one of the six most populated cities in Italy.Findings from a mixed-effects logistic-regression model suggest that accident severity increases when two young drivers are involved, road traffic conditions are light/normal and when vehicles crash on a two-way road or carriageway. Speeding is more likely to cause slight or serious injury even when compared to a vehicle moving towards the opposite direction of traffic. An accident during the summer is more likely to result in a slight or serious injury than an accident during the winter, which is in line with evidence from Southern Europe and the Middle East.Finally, the severity of non-fatal accident injuries in an urban area of Southern Europe was significantly associated with speeding, the age of the driver and seasonality.

Effects of foggy conditions on driver injury levels in U.S. highway-rail grade crossing accidents

Foggy conditions are considered to be potentially dangerous for drivers and have been paid considerable attention recently. However, few studies have addressed the influence of fog-related conditions on injury severity in accidents, particularly at highway-rail grade crossings. This study estimates the effect of foggy conditions on driver injury severity at highway-rail grade crossings. Compared to accidents occurring during clear conditions, fog-related accidents tend to result in more severe injuries. Older drivers are more likely to suffer severe injuries in foggy than in clear conditions as a result of their poorer vision and reaction times. In foggy conditions, drivers were found to be willing to drive at speeds that were too high for them to be able to stop to avoid obstacles that they may be able to identify and react to. In addition, drivers in foggy conditions are more likely to be severely injured in accidents occurring in open space areas with passive control device due to low traffic volumes and higher expected vehicle speeds. Drivers are more likely to experience high level injury in accidents during early morning in winter. Finally, drivers tend to suffer more severe injuries in accidents during peak-hours as a result of the fatigue that they experience at these times.

A review of methods for highway-railway crossings safety management process

This paper reviews the literature concerning the risks associated with Highway- Railway Crossings (HRC). The aim of this study is to evaluate and validate previous investigations conducted in the United States and Canada. The main issues addressed in this paper are: a) Identify HRC with potential the average collision frequency or collision severity as black spots, tests that are used to identifying hazardous location and compare the methods, b)Identify the contributing factors to safety problems at an identified HRC location, c)Identification of countermeasures to address the contributing factors. This research has thrown up many questions in need of further investigations on risks associated with HRC to mitigate the accident frequency and injury severity.

Severity Prediction of Traffic Accidents with Recurrent Neural Networks

In this paper, a deep learning model using a Recurrent Neural Network (RNN) was developed and employed to predict the injury severity of traffic accidents based on 1130 accident records that have occurred on the North-South Expressway (NSE), Malaysia over a six-year period from 2009 to 2015. Compared to traditional Neural Networks (NNs), the RNN method is more effective for sequential data, and is expected to capture temporal correlations among the traffic accident records. Several network architectures and configurations were tested through a systematic grid search to determine an optimal network for predicting the injury severity of traffic accidents. The selected network architecture comprised of a Long-Short Term Memory (LSTM) layer, two fully-connected (dense) layers and a Softmax layer. Next, to avoid over-fitting, the dropout technique with a probability of 0.3 was applied. Further, the network was trained with a Stochastic Gradient Descent (SGD) algorithm (learning rate = 0.01) in the Tensorflow framework. A sensitivity analysis of the RNN model was further conducted to determine these factors’ impact on injury severity outcomes. Also, the proposed RNN model was compared with Multilayer Perceptron (MLP) and Bayesian Logistic Regression (BLR) models to understand its advantages and limitations. The results of the comparative analyses showed that the RNN model outperformed the MLP and BLR models. The validation accuracy of the RNN model was 71.77%, whereas the MLP and BLR models achieved 65.48% and 58.30% respectively. The findings of this study indicate that the RNN model, in deep learning frameworks, can be a promising tool for predicting the injury severity of traffic accidents.

Roadway classifications and the accident injury severities of heavy-vehicle drivers

Previous heavy-vehicle (a truck with a gross vehicle weight rating greater than 10,000 pounds) injury severity studies have disaggregated data by factors such as urban/rural and time-of-day, yet a focus on contributing factors by roadway classification is lacking. Taking this into consideration, the current study aims to extend traditional heavy-vehicle driver injury severity analyses, through the application of a mixed logit modeling framework, by determining statistically significant injury severity contributing factors by roadway classification. In the course of identifying statistically significant injury severity factors, a parameter transferability test is conducted to determine if roadway classifications need to be considered separately for safety analyses. Empirical results show that roadway classifications need be modeled separately with a high level of confidence, as the estimated parameters are statistically different by classification based on corresponding chi-square statistics and degrees of freedom. The majority of significant contributing factors are exclusive to a specific road classification, however, two factors were found to impact injury severity regardless of classification while some factors were significant for two classifications. Findings from this study can prompt future work to focus on injury severity, as well as other safety measures, by roadway classification and/or other subpopulations within crash datasets.

A random thresholds random parameters hierarchical ordered probit analysis of highway accident injury-severities

This study uses highway accident data collected in the State of Washington, between 2011 and 2013, to study the factors that affect accident injury-severities. To account for the fixed thresholds limitation of the traditional ordered probability models – which typically leads to incorrect estimation of outcome probabilities for the intermediate categories – and for the possibility of unobserved factors systematically varying across the observations, a random thresholds hierarchical ordered probit model with random parameters is estimated. This approach simultaneously allows the explanatory parameters to vary across roadway segments, and the thresholds to vary both as a function of explanatory parameters and across the observations, thus accounting for unobserved and threshold heterogeneity, respectively. Using goodness-of-fit measures, likelihood ratio tests and forecasting accuracy measures, the model estimation results are compared with the hierarchical and fixed thresholds ordered probit model counterparts, with fixed and random parameters. The comparative assessment among the ordered probit modeling approaches reveals the relative benefits and the overall statistical superiority of the random thresholds random parameters hierarchical ordered probit model.

Decision Tree Model for Non-Fatal Road Accident Injury

Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type.

Multinomial Logit Framework to Evaluate the Impact of Seating Position on Senior Occupant Injury Severity in Traffic Accidents

Multinomial Logit Framework to Evaluate the Impact of Seating Position on Senior Occupant Injury Severity in Traffic Accidents

Driver injury severity study for truck involved accidents at highway-rail grade crossings in the United States

Although trucks only account for approximately 4% of all the vehicles based on the Federal Railway Administration (FRA) database, about 25% involved truck accidents happen at highway-rail grade crossings. This study applied an ordered probit model to explore the determinants of injury severity of truck drivers at highway-rail grade crossing in the United States. Given the importance of trucking to the economics of a country and the safety concerns posed by the trucks (as a result of their large size and weight making them difficult to control, maneuver, and stop), a comprehensive research on truck accidents is critical. Based on data analysis results, the strong effects of driver-, environmental-, weather- characteristics on the injury severities in truck accidents happened at highway-rail grade crossings are found. The findings reveal that better speed control for trucks will significantly reduce driver injury severity in accidents occurring at highway-rail grade crossings. In addition, several truck driver behavior characteristics (such as driving under influence of fatigue during peak hour) were found to be statistically significant predictors of high-level injury severity. Thus, education and enforcement targeted to truck drivers could facilitate safety improvements. Moreover, environmental factor (such as area type and roadway pavement) is found to be statistically significant. Truck drivers are more likely to have severe injury in open space area with low traffic volume compared with other areas. The bad weather and visibility condition is found to increase the probability of truck drivers’ high level injury severity.