Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

scholarly journals Injury Severity and Contributing Driver Actions in Passenger Vehicle–Truck Collisions

2019 ◽  
Vol 16 (19) ◽  
pp. 3542 ◽  
Author(s):  
Jingjing Xu ◽  
Behram Wali ◽  
Xiaobing Li ◽  
Jiaqi Yang
Keyword(s):  
Large Scale ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Passenger Vehicle ◽  
Severe Injuries ◽  
Safe Driving ◽  
Explanatory Factors ◽  
Random Parameter Model ◽  
Minor Injuries

Large-scale truck-involved crashes attract great attention due to their increasingly severe injuries. The majority of those crashes are passenger vehicle–truck collisions. This study intends to investigate the critical relationship between truck/passenger vehicle driver’s intentional or unintentional actions and the associated injury severity in passenger vehicle–truck crashes. A random-parameter model was developed to estimate the complicated associations between the risk factors and injury severity by using a comprehensive Virginia crash dataset. The model explored the unobserved heterogeneity while controlling for the driver, vehicle, and roadway factors. Compared with truck passengers, occupants in passenger vehicles are six times and ten times more likely to suffer minor injuries and serious/fatal injuries, respectively. Importantly, regardless of whether passenger vehicle drivers undertook intentional or unintentional actions, the crashes are more likely to associate with more severe injury outcomes. In addition, crashes occurring late at night and in early mornings are often correlated with more severe injuries. Such associations between explanatory factors and injury severity are found to vary across the passenger vehicle–truck crashes, and such significant variations of estimated parameters further confirmed the validity of applying the random-parameter model. More implications based on the results and suggestions in terms of safe driving are discussed.

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 Exploring Unobserved Heterogeneity in Cyclists’ Occupying Motorized Vehicle Lane Behaviors at Different Bike Facility Configurations

2022 ◽  
Vol 19 (2) ◽  
pp. 792
Author(s):  
Lei Zhang ◽  
Shengrui Zhang ◽  
Bei Zhou ◽  
Yan Huang ◽  
Dan Zhao ◽  
...  
Keyword(s):  
Road Traffic ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Contributing Factors ◽  
Time Intervals ◽  
Recording Time ◽  
Heterogeneous Effects ◽  
Lane Width ◽  
Random Parameter Logit ◽  
Working Day

Cyclists occupying motorized vehicle lanes disrupt road traffic order and increase collisions. Exploring the contributing factors could help develop countermeasures to regulate such behaviors. The purpose of this study is to explore the intrinsic features influencing the behavior of cyclists in occupying motorized vehicle lanes at different bicycle facilities. We investigated a total of 34,631 cycling behavior samples in the urban area of Pingdingshan, China. A Bayesian random parameter logit model was used to account for the unobserved heterogeneous effects. The experimental results of all bike facilities demonstrate that the bike type, dividing strip type, bike lane width, temporary on-street parking, and whether it is a working day significantly affect cyclists’ occupying motorized vehicle lane behaviors. Factors associated with unobserved heterogeneity are age, barriers dividing strip, vehicle lane numbers, bike volume, vehicle volume, and daily recording time intervals. Comparing the estimated model of five type bike lane facilities across different dividing strips, we find that cyclists have a significantly different occupying probability and the heterogeneity factors of the various bike facilities also have their focus. When the non-motorized road conditions become more open, the cyclist behavior becomes more random and the heterogeneity factors become broader.

scholarly journals Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit

2020 ◽  
Vol 47 (11) ◽  
pp. 1249-1257 ◽  
Author(s):  
Sina Darban Khales ◽  
Mehmet Metin Kunt ◽  
Branislav Dimitrijevic
Keyword(s):  
Injury Severity ◽  
Weather Condition ◽  
Random Parameter ◽  
Older Drivers ◽  
Ordered Probit ◽  
Driver Model ◽  
Age Group ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Older Driver

The study analyzed injury severity of teenage and older drivers using 2015–2016 crash data from New Mexico. The fitness of the random-parameter ordered probit models developed for each age group was tested using likelihood ratio, comparing them to a unified model that combines both age groups, as well as comparing the random-parameter to fixed-parameter ordered probit for each age group. In both cases separate random-parameter ordered probit provided better results. It was found that vehicle type and age, lighting condition, alcohol or drug use, speeding, and seatbelt use were significant both for the teenage and older driver injury severity. The weather condition and gender were significant only in the teenage driver model, while driver inattention was significant for older drivers. The impacts of crash factors on injury severity was analyzed using marginal effects. The results indicate notable differences in the effects of contributing factors on driver injury severity between teenage and older drivers, including the sensitivity to changes in the mutual predictor parameter values.

A Nationwide Study of Intimate Partner Violence

2020 ◽  
Vol 86 (10) ◽  
pp. 1230-1237
Author(s):  
Jose Alfaro Quezada ◽  
Zahid Mustafa ◽  
Xiaofei Zhang ◽  
Bishoy Zakhary ◽  
Matthew Firek ◽  
...  
Keyword(s):  
Intimate Partner Violence ◽  
Partner Violence ◽  
Injury Severity ◽  
Unintentional Injury ◽  
Intimate Partner ◽  
Psychological Aggression ◽  
Severe Injuries ◽  
Data Set ◽  
Injury Mechanisms ◽  
Related Mortality

Background Intimate partner violence (IPV) refers to physical or sexual violence, stalking, and psychological aggression by an intimate partner. The present study aims to examine the incidence, injury patterns, and outcomes using a representative nationwide data set. Study Design The Nationwide Emergency Department Sample database was queried from 2010 to 2014 to identify IPV in adult patients by injury code E967.3. Demographics, diagnoses, and injury mechanisms were captured. Primary outcome was mortality, and logistic regression analyses were used to compare the baselines and outcomes. Results 132 806 IPV emergency visits were identified, with 5.1% of patients requiring hospitalization. Most patients were female (92.6%). The most common injury mechanisms were unintentional injury (36%) and striking (22.0%). Contusions of face/scalp/neck (13.2%) and unspecified head injury (6.9%) were the most common diagnoses. Males were significantly older [median and interquartile range of 39 (30, 50)] than females [33 (26, 43)], and were more frequently hospitalized (6.7% vs. 5.0%, P = .002) with more injuries with injury severity score ≥ 15 (.7% vs. .4%, P = .004) than females. Overall, IPV-related mortality was .06%, .26% in males and .05% in females ( P = .003). Older age (odds ratio (OR) = 1.053) and male gender (OR = 3.102) were significantly associated with mortality. The annual incidence rate decreased from 9.7 in 2010 to 8.2/100 000 US population in 2014 ( R2 = .659). Conclusions Young women are more likely to be victims of IPV, whereas men are more likely to be older and hospitalized with more severe injuries and worse outcomes.

scholarly journals Random-Parameter Multivariate Negative Binomial Regression for Modeling Impacts of Contributing Factors on the Crash Frequency by Crash Types

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Chenzhu Wang ◽  
Fei Chen ◽  
Jianchuan Cheng ◽  
Wu Bo ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Traffic Volume ◽  
Crash Risk ◽  
Operating Speed ◽  
Contributing Factors ◽  
Road Geometry ◽  
Elasticity Effects

Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers’ sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models.

scholarly journals Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel

2020 ◽  
Vol 17 (9) ◽  
pp. 3155 ◽  
Author(s):  
Shengdi Chen ◽  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu
Keyword(s):  
Environmental Factor ◽  
High Speed ◽  
Injury Severity ◽  
Control Measures ◽  
Crash Severity ◽  
Contributing Factors ◽  
Safety Control ◽  
Late Night ◽  
Single Vehicle ◽  
The Impact

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.

scholarly journals Exploring Risk Factors Contributing to the Severity of Hazardous Material Transportation Accidents in China

2020 ◽  
Vol 17 (4) ◽  
pp. 1344 ◽  
Author(s):  
Yingying Xing ◽  
Shengdi Chen ◽  
Shengxue Zhu ◽  
Yi Zhang ◽  
Jian Lu
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Practical Importance ◽  
Road Surface ◽  
Road Transportation ◽  
Contributing Factors ◽  
Hazardous Material ◽  
Compressed Gas ◽  
Hazmat Transportation

With the increasing demand of hazardous material (Hazmat), traffic accidents occurred frequently during Hazmat transportation, which had caused widespread concern in communities. Therefore, a good understanding of Hazmat transportation accident characteristics and contributing factors is of practical importance. In this study, 1721 Hazmat accidents that have occurred during road transportation for the period 2014–2017 in China were examined, and a random-parameters ordered probit model was established to explore the influence of contributing factors on the severity of accidents by accounting for unobserved heterogeneity in the data. Both the injuries and the number of people evacuated were considered as the indicator of accident severity and investigated, respectively. Results show that higher injury severity is likely to be associated with type of Hazmat (compressed gas, explosive, and poison), misoperation, driver fatigue, speeding, tunnel, slope, county road, dry road surface, winter, dark, more than two vehicles, rear end crash, and explosion. As for the correlation between risk factors and the severity of evacuation, type of Hazmat (compressed gas, explosive, and poison), quantity of Hazmat (10–39 t), misoperation, county road, dry road surface, weekdays, dusk, explosion significantly contribute to increasing the severity of evacuation of Hazmat accidents.

scholarly journals Determinants of Bicyclist Injury Severity Resulting from Crashes at Roundabouts, Crossroads, and T-Junctions

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinxing Shen ◽  
Tiantong Wang ◽  
Changjiang Zheng ◽  
Miao Yu
Keyword(s):  
Injury Severity ◽  
Explanatory Variable ◽  
Decision Makers ◽  
Contributing Factors ◽  
Crash Data ◽  
Wide Range ◽  
Generalized Ordered Logit ◽  
Severity Levels ◽  
Serious Injury ◽  
The Uk

This study explores the contributing factors that influence bicyclist injury severity at three types of intersection: roundabouts, crossroads, and T-junctions. Using bicycle-involved crash data in the UK over nine years (from 2009 to 2017), the bicyclist injury severity (with three severity levels: fatal injury, serious injury, and slight injury) was estimated using the generalized ordered logit (GOL) model and partial proportional odds (PPO) model. The marginal effects of each explanatory variable were computed to investigate the impacts on bicyclist injury severity occurring probabilities. A wide range of variables potentially affecting injury severity was considered, including bicyclist characteristics, intersection characteristics, environmental conditions, bicyclist movement and location preceding the crash, and types of collisions. Our findings show that the PPO model outperforms the GOL model for analyzing the factors that affect the bicyclist injury severity at intersections. The factors that affect cycling safety at various intersections show enormous differences. Specifically, nine variables have significant impacts on bicyclist injury severity at those three types of intersections. And there are only two variables, four variables, and eleven variables that have significant impact on bicyclist injury severity at roundabouts, crossroads, and T-junctions, respectively. The findings of this study can help decision makers better understand the spatial heterogeneity of the factors that influence the bicyclist injury severity at various intersections.

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