Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations

Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes.     

Costs of large truck-involved crashes in the United States

This study provides the estimates of the costs of highway crashes involving large trucks by type of truck involved. These costs represent the present value of all costs over the victims' expected life span that result from a crash. They include medically related costs, emergency services costs, property damage costs, lost productivity, and the monetized value of the pain, suffering, and lost quality of life that a family experiences because of death or injury. Based on the latest data available, the estimated cost of police-reported crashes involving trucks with a gross weight rating of more than 10,000 pounds averaged US$ 59,153 (in 2000 dollars). Multiple combination trucks had the highest cost per crash (US$ 88,483). The crash costs per 1000 truck miles however, were US$ 157 for single unit trucks, US$ 131 for single combination trucks, and US$ 63 for multiple combinations.     

Relative risk of injury and death in ambulances and other emergency vehicles

This study addresses of the impacts of emergency vehicle (ambulances, police cars and fire trucks) occupant seating position, restraint use and vehicle response status on injuries and fatalities. Multi-way frequency and ordinal logistic regression analyses were performed on two large national databases, the National Highway Traffic Safety Administration’s Fatality Analysis Reporting System (FARS) and the General Estimates System (GES). One model estimated the relative risk ratios for different levels of injury severity to occupants traveling in ambulances. Restrained ambulance occupants involved in a crash were significantly less likely to be killed or seriously injured than unrestrained occupants. Ambulance rear occupants were significantly more likely to be killed than front-seat occupants. Ambulance occupants traveling non-emergency were more likely than occupants traveling emergency to be killed or severely injured. Unrestrained ambulance occupants, occupants riding in the patient compartment and especially unrestrained occupants riding in the patient compartment were at substantially increased risk of injury and death when involved in a crash. A second model incorporated police cars and fire trucks. In the combined ambulance–fire truck–police car model, the likelihood of an occupant fatality for those involved in a crash was higher for routine responses. Relative to police cars and fire trucks, ambulances experienced the highest percentage of fatal crashes where occupants are killed and the highest percentage of crashes where occupants are injured. Lack of restraint use and/or responding with ‘lights and siren’ characterized the vast majority of fatalities among fire truck occupants. A third model incorporated non-special use van and passenger car occupants, which otherwise replicated the second model. Our findings suggest that ambulance crewmembers riding in the back and firefighters in any seating position, should be restrained whenever feasible. Family members accompanying ambulance patients should ride in the front-seat of the ambulance.     

Analysis of crash parameters and driver characteristics associated with lower limb injury

This study aims to investigate changes in frequency, risk, and patterns of lower limb injuries due to vehicle and occupant parameters as a function of vehicle model year. From the National Automotive Sampling System-Crashworthiness Data System, 10,988 observations were sampled and analyzed, representing 4.7 million belted drivers involved in frontal crashes for the years 1998–2010.A logistic regression model was developed to understand the association of sustaining knee and below knee lower limb injuries of moderate or greater severity with motor vehicle crash characteristics such as vehicle type and model years, toepan and instrument panel intrusions in addition to the occupant’s age, gender, height and weight. Toepan intrusion greater than 2 cm was significantly associated with an increased likelihood of injury (odds ratio: 9.10, 95% confidence interval 1.82–45.42). Females sustained a higher likelihood of distal lower limb injuries (OR: 6.83, 1.56–29.93) as compared to males. Increased mass of the driver was also found to have a positive association with injury (OR: 1.04, 1.02–1.06), while age and height were not associated with injury likelihood. Relative to passenger cars, vans exhibited a protective effect against sustaining lower limb injury (OR: 0.24, 0.07–0.78), whereas no association was shown for light trucks (OR: 1.31, 0.69–2.49) or SUVs (OR: 0.76, 0.28–2.02).To examine whether current crash testing results are representative of real-world NASS-CDS findings, data from frontal offset crash tests performed by the Insurance Institute for Highway Safety (IIHS) were examined. IIHS data indicated a decreasing trend in vehicle foot well and toepan intrusion, foot accelerations, tibia axial forces and tibia index in relation to increasing vehicle model year between the year 1995 and 2013. Over 90% of vehicles received the highest IIHS rating, with steady improvement from the upper and lower tibia index, tibia axial force and the resultant foot acceleration considering both left and right extremities. Passenger cars received the highest rating followed by SUVs and light trucks, while vans attained the lowest rating.These results demonstrate that while there has been steady improvement in vehicle crash test performance, below-knee lower extremity injuries remain the most common AIS 2+ injury in real-world frontal crashes.     

Rollover risk of cars and light trucks after accounting for driver and environmental factors

Characteristics of the driver, roadway environment, and vehicle were associated with the likelihood of rollover occurrence in more than 14000 single-vehicle fatal and 78000 single-vehicle injury crashes during 1995-98. Rollovers were more likely in crashes involving young drivers or occurring on rural curves. After accounting for the effects of driver age and gender, roadway alignment and surface condition, and whether or not the crash occurred in a rural area, light trucks were still twice as likely as cars to experience rollovers. Some light truck models were much more likely than others to experience rollovers. However, while physical differences (e.g. center of gravity height) could explain some of this variability, other factors affecting vehicle stability may be evident only after dynamic testing.     

Do laboratory frontal crash test programs predict driver fatality risk? Evidence from within vehicle line variation in test ratings

A number of studies have examined whether the National Highway Traffic Safety Administration's (NHTSA) frontal crash test results reliably indicate the risk of fatality or injury in serious crashes. The conclusions of these studies are mixed. Generally, studies that examine crashes in the circumstances as close as possible to those of the laboratory test find that crash test results do predict real-world risk, but studies of crashes outside those specific circumstances find either no support for the predictive validity of crash test results or limited support with important inconsistencies. We provide a new test of the predictive validity of the crash test results using information from multiple crash tests within vehicle lines, thus controlling for systematic differences in driver behavior across vehicle lines. Among drivers of passenger cars, we find large, statistically significant differences in fatality risk for vehicles with one- to four-star NHTSA ratings versus a five-star rating. We also examine the Insurance Institute for Highway Safety's frontal offset crash test, though our sample of vehicle lines tested twice or more is considerably smaller than for NHTSA ratings. Our results also support the predictive validity of the frontal offset crash test results for passenger cars, but not for trucks.     

Modeling rear-end collisions including the role of driver's visibility and light truck vehicles using a nested logit structure

This paper presents an analysis of the effect of the geometric incompatibility of light truck vehicles (LTV)--light-duty trucks, vans, and sport utility vehicles--on drivers' visibility of other passenger cars involved in rear-end collisions. The geometric incompatibility arises from the fact that most LTVs ride higher and are wider than regular passenger cars. The objective of this paper is to explore the effect of the lead vehicle's size on the rear-end crash configuration. Four rear-end crash configurations are defined based on the type of the two involved vehicles (lead and following vehicles). Nested logit models were calibrated to estimate the probabilities of the four rear-end crash configurations as a function of driver's age, gender, vehicle type, vehicle maneuver, light conditions, driver's visibility and speed. Results showed that driver's visibility and inattention in the following (striker) vehicle have the largest effect on being involved in a rear-end collision of configuration CarTrk (a regular passenger car striking an LTV). Possibly, indicating a sight distance problem. A driver of a smaller car following an LTV, have a problem seeing the roadway beyond the LTV, and therefore would not be able to adjust his/her speed accordingly, increasing the probability of a rear-end collision. Also, the probability of a CarTrk rear-end crash increases in the case that the lead vehicle stops suddenly.     

Defective equipment and tractor-trailer crash involvement

The role of defective equipment in large truck crashes on interstate highways in Washington State was investigated using a case-control study design. For each large truck involved in a crash, three trucks were randomly selected from the traffic stream at the same time and place as the crash, but one week later both crash and comparison trucks were inspected by Commercial Vehicle Enforcement officers of the Washington State Patrol. The effects of truck equipment condition, truck operating characteristics (carrier type, carrier operation, and truck load), and driver characteristics (driver age, hours of driving) on crash involvement were analyzed by comparing their relative frequency among crash-involved and comparison sample tractor-trailers. A logistic regression model was used to estimate the adjusted odds ratio for each factor. Overall, 77% of tractor-trailers in crashes and 66% of those not involved in crashes had defective equipment warranting a citation. Forty-one percent in crashes had defective equipment warranting taking the truck out of service, and 31% not in crashes had these defects. Brake defects were the most common type and were found in 56% of tractor-trailers in crashes; steering equipment defects were found in 21%. The relative risk of crash involvement for trucks with brake defects was about one and one-half times that for trucks without brake defects. For trucks with steering defects, the relative risk of crash involvement was at least twice that for trucks with steering defects, the relative risk of crash involvement was at least twice that for trucks without defects, and the risk increased substantially for trucks with out-of-service steering defects.     

Pedestrian injuries and vehicle type in Maryland, 1995-1999

Pedestrian deaths constitute the second largest category of motor vehicle deaths in the US. The present study examined how pedestrian injury is associated with vehicle type, while controlling for vehicle weight and speed.Police, trauma registry, and autopsy data were linked for injured pedestrians. Logistic regression analyses were performed to control for vehicle weight and speed. Outcomes included pedestrian mortality, injury severity score, and injuries to specific body regions.Compared to conventional cars, pedestrians hit by sport utility vehicles and pick-up trucks were more likely to have higher injury severity scores (odds ratio=1.48; 95% confidence interval: 1.18-1.87) and to die (odds ratio=1.72; 95% confidence interval: 1.31-2.28). These relationships diminished when vehicle weight and speed were controlled for. At lower speeds, pedestrians struck by sport utility vehicles, pick-up trucks, and vans were approximately two times as likely to have traumatic brain, thoracic, and abdominal injuries; at higher speeds, there was no such association.The overall increased danger sport utility vehicles and pick-up trucks present to pedestrians may be explained by larger vehicle masses and faster speeds. At slower speeds being hit by sport utility vehicles, and pick-up trucks, and vans resulted in specific injuries, indicating that vehicle design may contribute to different injury patterns.     

A comprehensive analysis of factors influencing the injury severity of large-truck crashes

Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data – these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined.     

Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes

A multivariate logistic regression model, based upon National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data for calendar years 1999–2008, was developed to predict the probability that a crash-involved vehicle will contain one or more occupants with serious or incapacitating injuries. These vehicles were defined as containing at least one occupant coded with an Injury Severity Score (ISS) of greater than or equal to 15, in planar, non-rollover crash events involving Model Year 2000 and newer cars, light trucks, and vans. The target injury outcome measure was developed by the Centers for Disease Control and Prevention (CDC)-led National Expert Panel on Field Triage in their recent revision of the Field Triage Decision Scheme (American College of Surgeons, 2006). The parameters to be used for crash injury prediction were subsequently specified by the National Expert Panel. Model input parameters included: crash direction (front, left, right, and rear), change in velocity (delta-V), multiple vs. single impacts, belt use, presence of at least one older occupant (≥55 years old), presence of at least one female in the vehicle, and vehicle type (car, pickup truck, van, and sport utility). The model was developed using predictor variables that may be readily available, post-crash, from OnStar^®-like telematics systems. Model sensitivity and specificity were 40% and 98%, respectively, using a probability cutpoint of 0.20. The area under the receiver operator characteristic (ROC) curve for the final model was 0.84. Delta-V (mph), seat belt use and crash direction were the most important predictors of serious injury. Due to the complexity of factors associated with rollover-related injuries, a separate screening algorithm is needed to model injuries associated with this crash mode.     

Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents.

The impact that large trucks have on accident severity has long been a concern in the accident analysis literature. One important measure of accident severity is the most severely injured occupant in the vehicle. Such data are routinely collected in state accident data files in the U.S. Among the many risk factors that determine the most severe level of injury sustained by vehicle occupants, the number of occupants in the vehicle is an important factor. These effects can be significant because vehicles with higher occupancies have an increased likelihood of having someone seriously injured. This paper studies the occupancy/injury severity relationship using Washington State accident data. The effects of large trucks, which are shown to have a significant impact on the most severely injured vehicle occupant, are accounted for by separately estimating nested logit models for truck-involved accidents and for non-truck-involved accidents. The estimation results uncover important relationships between various risk factors and occupant injury. In addition, by comparing the accident characteristics between truck-involved accidents and non-truck-involved accidents, the risk factors unique to large trucks are identified along with the relative importance of such factors. The findings of this study demonstrate that nested logit modeling, which is able to take into account vehicle occupancy effects and identify a broad range of factors that influence occupant injury, is a promising methodological approach.     

Passenger car collision fatalities--with special emphasis on collisions with heavy vehicles

Between 1995 and 2004, 293 passenger car occupants died in collisions with other vehicles in northern Sweden (annual incidence: 3.3 per 100,000 inhabitants, 6.9 per 100,000 cars, or 4.8 per 10(9)km driven); half of these deaths involved heavy vehicles. The annual number of passenger car occupant deaths per 100,000 cars in car-truck/bus collisions has remained unchanged since the 1980s, but in car-car collisions it has decreased to one third of its former level. As crash objects, trucks and buses killed five times as many car occupants per truck/bus kilometer driven as did cars. The collisions were characterized by crashes in the oncoming vehicle's lane, under icy, snowy, or wet conditions; crashes into heavy vehicles generally occurred in daylight, on workdays, in winter, and on 90 and 70 km/h two-lane roads. Head and chest injuries accounted for most of the fatal injuries. Multiple fatal injuries and critical and deadly head injuries characterized the deaths in collisions with heavy vehicles. An indication of suicide was present in 4% of the deaths; for those who crashed into trucks, this percentage was doubled. Among the driver victims, 4% had blood alcohol levels above the legal limit of 0.2g/L. Frontal collision risks might be reduced by a mid-barrier, by building less injurious fronts on trucks and buses, by efficient skid prevention, and by use of flexible speed limits varying with road and light conditions.     

Occupant deaths in large truck crashes in the United States: 25 years of experience

There is public concern about the magnitude of the problem of large truck crashes in the US. Fatalities in large truck crashes have not declined much; however, more large trucks are driving more miles than ever before while fatalities per mile driven have dropped substantially. This study examined how the public health burden of large truck crashes versus the risk per unit of travel has changed over 25 years.The present study focused on the US vehicle occupants in fatal crashes involving a large truck during 1975-1999. Occupant fatalities per 100000 population, per 10000 licensed drivers, per 10000 registered trucks and per 100 million vehicle-miles of travel (VMT) were calculated to determine trends in occupant deaths in large truck crashes.In 1999, large truck crashes resulted in 3916 occupant deaths in passenger vehicles and 747 in large trucks. Passenger vehicle occupant deaths in large truck crashes per 100000 population have increased somewhat since 1975 (1.28 in 1975 and 1.44 in 1999). There have been appreciable declines in occupant deaths per truck VMT since 1975, but the percentage reduction has been greater for occupants of large trucks (67%) than for passenger vehicle occupants (43%). However, truck drivers are at elevated risk of dying relative to their numbers in the workforce. Overall large truck involvements in fatal crashes per truck VMT decreased more than passenger vehicle involvements per passenger VMT (PVMT; 68% versus 33% decreases for single-vehicle crashes and 43% versus 23% for multiple-vehicle crashes). Large truck involvement in fatal crashes has dropped substantially when measured per unit of travel, but the public health burden of large truck crashes, as measured by deaths per 100000 population, has not improved over time because of the large increase in truck mileage. Research is needed on measures to better protect both occupants of large trucks and passenger vehicle occupants colliding with them.     

Are SUVs dangerous vehicles?

This study was a population cohort study of all licensed passenger vehicles in New Zealand in the years 2005–2006. The objective of the study was to evaluate the effect on road safety of sports utility vehicles (SUVs) compared to other passenger vehicle types. Statistical models were fitted to the population of 2,996,000 vehicles of which 17,245 were involved in an injury crash. Controlling for distance driven, vehicle and owner characteristics, SUVs were found to be relatively safe vehicles in terms of injury crash involvement and in terms of the injury rate of their own occupants or other road users into which they crashed. Current research on SUV safety clearly shows them to be a road safety concern, but only once a collision occurs. The present study shows that SUVs in New Zealand have relatively few collisions compared to other passenger vehicle types, allowing for factors such as distance driven, some allowance for the type of driving exposure (via the owners’ addresses) and for owner age and gender. Overall, the vehicle type implicated most frequently in injury crashes and involving the highest rate of road injuries was sports cars, causing clearly the most harm per licensed vehicle. Instead of concerning themselves primarily with SUVs, the focus of road safety agencies should be on the relatively high crash risk of sports cars, which are clearly a road safety concern. Their high crash involvement rate and injury rate is likely to be largely due to the way they are driven rather than to inherent characteristics of the vehicles themselves.     

Determining the effective location of a portable changeable message sign on reducing the risk of truck-related crashes in work zones

Truck-related crashes contribute to a significant percentage of vehicle crashes in the United States, which often result in injuries and fatalities. The amount of truck miles traveled has increased dramatically with the growing rate of freight movement. Regarding truck crashes in the highway work zones, many studies indicated that there was a significant increase in crash severity when a truck crash occurred in work zones. To mitigate the risk of truck crashes in work zones, a portable changeable message sign (PCMS) was frequently utilized in addition to standard temporary traffic control signs and devices required by the Manual on Uniform Traffic Control Devices. To justify the use of a PCMS in work zones, there is a need to study the effective location of a PCMS deployed in a work zone by measuring the changes of truck and passenger car speed profiles. The difference of speed changes between trucks and passenger cars was considered as one of the major reasons which caused truck-related crashes in work zones. Therefore, reducing the difference of speed changes between trucks and passenger cars could potentially improve safety in work zones. The outcomes of this study will provide required knowledge for traffic engineers to effectively utilize the PCMS in work zones with the purpose of reducing truck-related crashes. In addition, the success of this study will provide a roadmap to investigate the effective deployment of other temporary traffic control devices on mitigating the risk of truck-related crashes in work zones.     

The effects of age,gender, and crash types on drivers’ injury-related health care costs

There are many studies that evaluate the effects of age, gender, and crash types on crash related injury severity. However, few studies investigate the effects of those crash factors on the crash related health care costs for drivers that are transported to hospital. The purpose of this study is to examine the relationships between drivers’ age, gender, and the crash types, as well as other crash characteristics (e.g., not wearing a seatbelt, weather condition, and fatigued driving), on the crash related health care costs. The South Carolina Crash Outcome Data Evaluation System (SC CODES) from 2005 to 2007 was used to construct six separate hierarchical linear regression models based on drivers’ age and gender. The results suggest that older drivers have higher health care costs than younger drivers and male drivers tend to have higher health care costs than female drivers in the same age group. Overall, single vehicle crashes had the highest health care costs for all drivers. For males older than 64-years old sideswipe crashes are as costly as single vehicle crashes. In general, not wearing a seatbelt, airbag deployment, and speeding were found to be associated with higher health care costs. Distraction-related crashes are more likely to be associated with lower health care costs in most cases. Furthermore this study highlights the value of considering drivers in subgroups, as some factors have different effects on health care costs in different driver groups. Developing an understanding of longer term outcomes of crashes and their characteristics can lead to improvements in vehicle technology, educational materials, and interventions to reduce crash-related health care costs.     

Analysis of work zone rear-end crash risk for different vehicle-following patterns

This study evaluates rear-end crash risk associated with work zone operations for four different vehicle-following patterns: car–car, car–truck, truck–car and truck–truck. The deceleration rate to avoid the crash (DRAC) is adopted to measure work zone rear-end crash risk. Results show that the car–truck following pattern has the largest rear-end crash risk, followed by truck–truck, truck–car and car–car patterns. This implies that it is more likely for a car which is following a truck to be involved in a rear-end crash accident. The statistical test results further confirm that rear-end crash risk is statistically different between any two of the four patterns. We therefore develop a rear-end crash risk model for each vehicle-following pattern in order to examine the relationship between rear-end crash risk and its influencing factors, including lane position, the heavy vehicle percentage, lane traffic flow and work intensity which can be characterized by the number of lane reductions, the number of workers and the amount of equipment at the work zone site. The model results show that, for each pattern, there will be a greater rear-end crash risk in the following situations: (i) heavy work intensity; (ii) the lane adjacent to work zone; (iii) a higher proportion of heavy vehicles and (iv) greater traffic flow. However, the effects of these factors on rear-end crash risk are found to vary according to the vehicle-following patterns. Compared with the car–car pattern, lane position has less effect on rear-end crash risk in the car–truck pattern. The effect of work intensity on rear-end crash risk is also reduced in the truck–car pattern.     

Occupant injury in rollover crashes – Contribution of planar impacts with objects and other vehicles

Planar impacts with objects and other vehicles may increase the risk and severity of injury in rollover crashes. The current study compares the frequency of injury measures (MAIS 2+, 3+, and 4+; fatal; AIS 2+ head and cervical spine; and AIS 3+ head and thorax) as well as vehicle type distribution (passenger car, SUV, van, and light truck), crash kinematics, and occupant demographics between single vehicle single event rollovers (SV Pure) and multiple event rollovers to determine which types of multiple event rollovers can be pooled with SV Pure to study rollover induced occupant injury. Four different types of multiple event rollovers were defined: single and multi-vehicle crashes for which the rollover is the most severe event (SV Prim and MV Prim) and single and multi-vehicle crashes for which the rollover is not the most severe event (SV Non-Prim and MV Non-Prim). Information from real world crashes was obtained from the National Automotive Sampling System – Crashworthiness Data System (NASS-CDS) for the period from 1995 through 2011. Belted, contained or partially ejected, adult occupants in vehicles that completed 1–16 lateral quarter turns were assigned to one of the five rollover categories. The results showed that the frequency of injury in non-primary rollovers (SV Non-Prim and MV Non-Prim) involving no more than one roof inversion is substantially greater than in SV Pure, but that this disparity diminishes for crashes involving multiple inversions. It can further be concluded that for a given number of roof inversions, the distribution of injuries and crash characteristics in SV Pure and SV Prim crashes are sufficiently similar for these categories to be considered collectively for purposes of understanding etiologies and developing strategies for prevention.     

Modeling occupant-level injury severity: An application to large-truck crashes

Most of the injury-severity analyses to date have focused primarily on modeling the most-severe injury of any crash, although a substantial fraction of crashes involve multiple vehicles and multiple persons. In this study, we present an extensive exploratory analysis that highlights that the highest injury severity is not necessarily the comprehensive indicator of the overall severity of any crash. Subsequently, we present a panel, hetroskedastic ordered-probit model to simultaneously analyze the injury severities of all persons involved in a crash. The models are estimated in the context of large-truck crashes. The results indicate strong effects of person-, driver-, vehicle-, and crash-characteristics on the injury severities of persons involved in large-truck crashes. For example, several driver behavior characteristics (such as use of illegal drugs, DUI, and inattention) were found to be statistically significant predictors of injury severity. The availability of airbags and the use of seat-belts are also found to be associated with less-severe injuries to car-drivers and car-passengers in the event of crashes with large trucks. Car drivers’ familiarity with the vehicle and the roadway are also important for both the car drivers and passengers. Finally, the models also indicate the strong presence of intra-vehicle correlations (effect of common vehicle-specific unobserved factors) among the injury propensities of all persons within a vehicle.