Large Truck Crash Causation Study Research Articles

Work-stress factors associated with truck crashes: An exploratory analysis

AbstractResearchers have studied truck crashes extensively using methods appropriate for behavior, technology, and regulatory enforcement. Few safety studies associate crashes with economic pressure, a pervasive latent influence. This study uses data from the US Large Truck Crash Causation Study to predict truck crashes based on work pressure factors that have their origins in market pressures on motor carriers and truck drivers. Logistic regression shows that factors associated with the work process, including an index of work-pressure attributes, predict the likelihood that crash analysts consider the truck driver to be the person whose last action could have prevented the crash. While not proving causation, the data suggest that economic factors affecting drivers contribute significantly to truck crashes.

A synthetic approach to compare the large truck crash causation study and naturalistic driving data

Truck crashes represent a significant problem on our nation’s highways. There is a great opportunity to learn about crash causation by analyzing and comparing the Large Truck Crash Causation Study (LTCCS) and naturalistic driving (ND) data. These data sets provide in-depth information, but have contrasting strengths and weaknesses. The LTCCS contains information on high-severity crashes (crashes and fatal crashes), but relied on data collected during crash investigations. The LTCCS identified principal driver errors in the crash, such as the Critical Reason, but not detailed behaviors or scenario sequences. The ND data sets relate primarily to non-crashes that are detectable from dynamic vehicle events, such as hard braking, swerve, etc., provide direct video observations of the driver and the surrounding driving scene and precise information on driver inputs (kinematics) and captured events, and provide certain types of exposure data that cannot easily be obtained using crash reconstruction data. The ND data are collected continuously, thereby capturing both safety-critical events and normative driving (i.e., baseline). The current project evaluated large-truck crash data from the LTCCS and two large-truck ND data sets, the Naturalistic Truck Driving Study and the Drowsy Driver Warning System Field Operational Test. A synthetic risk ratio analysis on the associated factor, Following Too Closely, indicated that truck drivers in the LTCCS were 1.34 times more likely to be involved in a crash, than an ND crash-relevant conflict, if they were following too closely (i.e., tailgating). Given several caveats noted in the paper, this study suggests it’s possible to use the ND data set to calculate the exposure of a given behavior and use the LTCCS data set to calculate the crash exposure to the same behavior.

Potential Benefits of Underride Guards in Large Truck Side Crashes

Objective: To evaluate the maximum potential for side underride guards (SUGs) to reduce passenger vehicle occupant fatalities and injuries in crashes with large trucks in the United States. Methods: Examination of the Large Truck Crash Causation Study (LTCCS) identified 206 crash events involving a passenger vehicle impact with the side of a large truck. Each case was evaluated to determine whether the most severe injury sustained by a passenger vehicle occupant was a result of the impact with the side of the truck and whether an SUG could have reduced the injury severity. Data from the 2006–2008 Fatality Analysis Reporting System (FARS) and Trucks Involved in Fatal Accidents (TIFA) survey were used to compare the types of trucks involved in all fatal side impacts with passenger vehicles with the truck types in the LTCCS cases that were studied. FARS and TIFA data also were used to estimate the total annual number of passenger vehicle occupants killed in truck side impacts. Results: In 143 of the 206 cases, the truck side impact produced the most severe injury sustained by a passenger vehicle occupant. In the other cases, no passenger vehicle occupant was injured or the most severe injury was due to an event preceding or following the truck side impact. Forty-nine of these occupants sustained injuries coded as level 3 or higher on the abbreviated injury scale (AIS) or were killed. SUGs could have reduced injury severity in 76 of the 143 cases, including 38 of the 49 cases with an AIS ≥ 3 coded injury or fatality. Semi-trailers were the most common type of impacted truck unit, both overall and when considering only cases where an SUG could have mitigated injury severity. Crashes where the front of the passenger vehicle struck the side of the semi-trailer perpendicularly or obliquely from the oncoming direction were less common overall than side-to-side and oblique/same direction crashes but more often produced an AIS ≥ 3 injury or fatality. The distribution of truck types in the LTCCS sample was similar to that in the FARS and TIFA data. Overall, around 1600 passenger vehicle occupants were killed in 2-vehicle truck side impact crashes during 2006–2008, or 22 percent of all passenger vehicle occupants who died in 2-vehicle crashes with large trucks. Conclusions: Structural incompatibility was a common factor in LTCCS crashes between passenger vehicles and the sides of large trucks. SUGs could have reduced injury risk in around three fourths of the crashes that produced an AIS ≥ 3 injury or fatality. Most of these crashes involved semi-trailers. However, the necessary strength and location of these SUGs present technical challenges that need to be addressed.

Safety Benefits of Stability Control Systems for Tractor–Semitrailers Estimated with Hardware-in-the-Loop Simulation

This research estimated the safety benefits of electronic stability control systems for tractor–semitrailers by using hardware-in-the-loop (HiL) simulation. The HiL system used in this study consisted of a pneumatic tractor–semitrailer braking system and a truck simulation engine, TruckSim. Additionally, the Bendix electronic stability control system was integrated with the HiL system for the evaluation. The study evaluated the performance of the stability control system by using the HiL platform for a portion of the rollover and loss-of-control crashes selected from the database of the Large Truck Crash Causation Study. Subsequently, the evaluated performance was combined with the effectiveness rated by the expert panel at the University of Michigan Transportation Research Institute to calculate the total crash prevention ratios. Finally, this study estimated crash reductions and cost benefits. It provides lessons learned to researchers and practitioners through the course of evaluation procedures.

Real-World Performance of Longitudinal Barriers Struck by Large Trucks

Very little is known about the real-world performance of traffic barriers when subjected to impacts by large trucks. This study investigated real-world impacts of large trucks into traffic barriers to determine barrier crash involvement rates, the impact performance of barriers not specifically designed to redirect large trucks, and the real-world performance of barriers specifically designed for large trucks. Data sources included the Fatality Analysis Reporting System (2000 to 2009), the General Estimates System (2000 to 2009), and the Large Truck Crash Causation Study (155 in-depth crashes of large trucks into barriers). Impacts of large trucks into longitudinal barriers constituted 3% of all police-reported impacts into longitudinal barriers and roughly the same proportion of barrier fatalities. A logistic regression model predicting barrier penetration showed that the risk of a large truck penetrating a barrier increased by a factor of 6 for impacts with barriers designed primarily for passenger vehicles. Although barriers specifically designed for impacts by large trucks performed better than barriers not specifically designed for impacts by heavy vehicles, the penetration rate of the former was 17%. This penetration rate is of concern, because barriers used for higher test levels are designed to protect other road users, not the occupants of large trucks. Barriers not specifically designed for impacts by large trucks prevented penetration by a large truck approximately half the time. This finding suggests that adding costlier barriers that meet higher test levels may not always be warranted, especially on roadways with lower truck volumes.

Analysis of large truck crash severity using heteroskedastic ordered probit models

Long-combination vehicles (LCVs) have significant potential to increase economic productivity for shippers and carriers by decreasing the number of truck trips, thus reducing costs. However, size and weight regulations, triggered by safety concerns and, in some cases, infrastructure investment concerns, have prevented large-scale adoption of such vehicles. Information on actual crash performance is needed. To this end, this work uses standard and heteroskedastic ordered probit models, along with the United States’ Large Truck Crash Causation Study, General Estimates System, and Vehicle Inventory and Use Survey data sets, to study the impact of vehicle, occupant, driver, and environmental characteristics on injury outcomes for those involved in crashes with heavy-duty trucks. Results suggest that the likelihood of fatalities and severe injury is estimated to rise with the number of trailers, but fall with the truck length and gross vehicle weight rating (GVWR). While findings suggest that fatality likelihood for two-trailer LCVs is higher than that of single-trailer non-LCVs and other trucks, controlling for exposure risk suggest that total crash costs of LCVs are lower (per vehicle-mile traveled) than those of other trucks.

Condition of Trucks and Truck Crash Involvement

This study, using the Large Truck Crash Causation Study (LTCCS), examined the relationship between the mechanical condition of heavy trucks and crash involvement. Two specific hypotheses were tested. First, trucks with defects and out-of-service (OOS) conditions were statistically more likely to be in the role of precipitating a crash than trucks with no defects or OOS conditions. Second, defects in specific systems, such as the brake system, were associated with crash roles in which those systems were primary mechanisms for avoiding the crash and physical mechanisms that link the vehicle defect with the crash role. Postcrash inspections showed that the condition of the trucks in the LTCCS was poor. Almost 55% of the vehicles had one or more mechanical violations. Almost 30% had at least one OOS condition. Of mechanical systems, violations in the brake system (36% of all) and the lighting system (19%) were the most frequent. Both driver and vehicle factors were found to contribute to crashes. A brake OOS condition increased the odds of the truck's being assigned the critical reason (a variable identifying the precipitating vehicle) by 1.8 times. Both hours of service violations and log OOS violations increased the odds by a larger amount—2.0 and 2.2 times respectively. In rear-end and cross-paths crashes, brake violations, especially violations related to adjustment, increased the odds of the truck's being the striking vehicle by 1.8 times.

Fire in Large Truck Crashes: Comparing Results from the Large Truck Crash Causation Study with FARS and NASS/GES Data

Fire in Large Truck Crashes: Comparing Results from the Large Truck Crash Causation Study with FARS and NASS/GES Data

Study of heavy truck accidents with focus on manoeuvres causing loss of control

An analysis of heavy truck accidents based on the Large Truck Crash Causation Study database with respect to loss of control is presented. Heavy truck accidents were analysed with regard to the accident type, loss of control type, critical manoeuvre, vehicle combination type and different road characteristics. Three critical manoeuvres were identified as the most common manoeuvres causing loss of control. Based on the accident analysis results, relevant existing test manoeuvres were adapted for trucks and compared to determine a suitable test for evaluation of yaw stability of heavy trucks on a dry and level road. The comparison was conducted on a 5DOF tractor-trailer model in Matlab-Simulink.