Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach

2021 ◽  
Vol 162 ◽  
pp. 106392
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Injury Severity ◽  
Truck Driver ◽  
Hierarchical Bayesian ◽  
Random Intercept

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

2021 ◽  
pp. 036119812110098
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Injury Severity ◽  
Binary Logistic Regression ◽  
Time Of Day ◽  
Truck Drivers ◽  
Truck Driver ◽  
Lighting Condition ◽  
Crash Data ◽  
Random Intercept ◽  
Gross Vehicle Weight ◽  
And Gender

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

Recursive Bivariate Probit Analysis of Injury Severity and Non-Truck Improper Actions in Large Truck-Related Crashes on Florida Suburban Roads

2021 ◽  
pp. 036119812199714
Author(s):  
Zhenyu Wang ◽  
Abhijit Vasili ◽  
Runan Yang ◽  
Pei-Sung Lin
Keyword(s):  
Injury Severity ◽  
Probit Model ◽  
Probit Analysis ◽  
Truck Driver ◽  
Bivariate Probit ◽  
Contributing Factors ◽  
Recursive Bivariate Probit ◽  
Annual Average Daily Traffic ◽  
Study Results ◽  
Two Factors

This study investigated the hierarchical connection among injury severity, non-truck improper actions, and contributing factors in large-truck-involved crashes. Data for 4 years (2011–2014) of crashes that involved a large truck (≥ 10,000 lb) and a non-truck vehicle were collected from suburban roads in Florida, U.S. A recursive bivariate probit model was fitted with collected data to identify the cause-effect chain, including contributing factors influenced by improper actions, the effects of improper actions on injury severity, and contributing factors indirectly affecting injury severity in large-truck-related crashes. Study results indicate that non-truck vehicle improper actions, such as excessive speed, careless driving, failure to yield right-of-way, and others, significantly increase the likelihood of fatal and severe injury in large-truck crashes, and factors such as crash month, darkness, intersection-related, surface and shoulder width, truck parking, truck driver age, non-truck driver age, and non-truck alcohol/drug impaired indirectly influence injury severity through their impacts on non-truck improper actions. Two factors—truck right-turn and non-truck driver physical defects—affect injury severity and non-truck improper actions simultaneously. Other factors, including crash year, annual average daily traffic, speed limit, crash type, truck type, truck speed, truck alcohol/drug-impaired, and motorcycle involvement, directly contribute to injury severity in large-truck crashes and have no influence on non-truck improper actions.

From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Bjarne Schmalbach ◽  
Markus Zenger ◽  
Michalis P. Michaelides ◽  
Karin Schermelleh-Engel ◽  
Andreas Hinz ◽  
...  
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Self Regulation ◽  
Model Fit ◽  
Self Report ◽  
External Criterion ◽  
Common Factor Model ◽  
Random Intercept ◽  
Using Data ◽  
Core Self

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

The lonely road: Does introversion predict truck driver performance?

2009 ◽  
Author(s):  
Mei-Chuan Kung ◽  
Joshua S. Quist ◽  
Jaclyn P. Pittman ◽  
Ted B. Kinney
Keyword(s):  
Truck Driver ◽  
Driver Performance
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