The large truck crash causation study

Notes: November 2002.Notes: Includes bibliographical references (p. 19)Notes: Special reportFederal Motor Carrier Safety Administration, Washington, D.C.http://deepblue.lib.umich.edu/bitstream/2027.42/1541/2/97642.0001.001.pd

Three Large Truck Crash Categories: What They Tell Us About Crash Causation

Large Truck Crash Causation Study (LTCCS) data is used to compare three categories of crash involvements: truck single-vehicle (SV) involvements, multi-vehicle (MV) involvements in which the truck has been assigned the critical reason (CR), and MV involvements in which the other vehicle (OV) has been assigned the CR. These three categories represent distinctly different causal contributions by truck drivers to the crash, with SV involvements having the greatest truck driver impairment and misbehavior. Surprisingly, paired comparisons of the three categories indicate that truck SV and truck-CR MV crash involvements were the most dissimilar in their causal profiles. Factors associated with truck SV crash involvements include non-use of safety belts, driver unfamiliarity with roadways, vehicle failures, lack of prior sleep, 16+ hours awake, and early morning driving. Dense traffic situations (e.g., rush hours) make trucks more likely to be at-fault in MV crashes. Many other factors were not associated with differences among the categories, suggesting no differential effect on truck driver safety performance, even though they might affect risk generally. Among fatigue-related factors, those related to sleep and alertness physiology were linked to SV crashes, while those related only to Hours-of-Service (HOS) work rules were not

Severity Analysis of Large Truck Crashes- Comparision Between the Regression Modeling Methods with Machine Learning Methods.

According to the Texas Department of Transportation’s Texas Motor Vehicle Crash Statistics, Texas has had the highest number of severe crashes involving large trucks in the US. As defined by the US Department of Transportation, a large truck is any vehicle with a gross vehicle weight rating greater than 10,000 pounds. Generally, it requires more time and much more space for large trucks to accelerating, slowing down, and stopping. Also, there will be large blind spots when large trucks make wide turns. Therefore, if an unexpected traffic situation comes upon, It would be more difficult for large trucks to take evasive actions than regular vehicles to avoid a collision. Due to their large size and heavy weight, large truck crashes often result in huge economic and social costs. Predicting the severity level of a reported large truck crash with unknown severity or of the severity of crashes that may be expected to occur sometime in the future is useful. It can help to prevent the crash from happening or help rescue teams and hospitals provide proper medical care as fast as possible. To identify the appropriate modeling approaches for predicting the severity of large truck crash, in this research, four representative classification tree-based ML models (e.g., Extreme Gradient Boosting tree (XGBoost), Adaptive Boosting tree(AdaBoost), Random Forest (RF), Gradient Boost Decision Tree (GBDT)), two non-tree-based ML models (e.g., the Support Vector Machines (SVM), k-Nearest Neighbors (kNN)), and LR model were selected. The results indicate that the GBDT model performs best among all of seven models

Truck mechanical condition and crashes in the large truck crash causation study

Special reportThis study examines the relationship of heavy truck mechanical condition and crash risk. The LTCCS presents an opportunity to examine in more detail than previously possible the relationship of vehicle condition to crash risk. The report includes a review of existing literature, a full analysis of the results of the post-crash truck inspections, and a series of logistic regression models to test the association of vehicle condition and crash role. Two specific hypotheses are tested: The first hypothesis is that trucks with defects and out of service conditions are statistically more likely to be in the role of precipitating a crash than trucks with no defects or out of service conditions. The second hypothesis is that defects in specific systems, such as the brake system, are associated with crash roles in which those systems are primary in crash avoidance, and that there is a physical mechanism that links the vehicle defect with the crash role. Post crash inspections showed that the condition of the trucks in the LTCCS is poor. Almost 55 percent of vehicles had one or more mechanical violations. Almost 30 percent had at least one out of service condition. Among mechanical systems, violations in the brake (36 percent of all) and lighting system (19 percent) were the most frequent. A brake OOS condition increased the odds of the truck assigned the critical reason (identifying the precipitating vehicle) by 1.8 times. Both HOS violations and log OOS increased by a larger amount—2.0 and 2.2 times respectively. In rear-end and crossing paths crashes, brake violations, especially related to adjustment, increased the odds of the truck being the striking vehicle by 1.8 times.Federal Motor Carrier Safety Administration, Washington, D.C.http://deepblue.lib.umich.edu/bitstream/2027.42/64999/1/102509.pd

Modeling Frequency of Truck Crashes on Limited-Access Highways

Freight can be efficiently transported between most locations in the U.S. using large trucks. Involvement of large trucks in crashes can cause much damage and serious injuries, due to their large sizes and heavy weights. The purpose of this study was to identify the relationships between large truck crashes and traffic and geometric characteristics on limited access highways. Crash and traffic and geometric-related data for Kansas were utilized to develop a Poisson regression model and a negative binomial regression model for understanding the relationships. Based on model-fitting statistics, the negative binomial model was found to be the better model, which was used to identify the important characteristics. By addressing identified factors, safety could be promoted through introduction of appropriate engineering improvements

Car-Truck Crashes in the National Motor Vehicle Crash Causation Survey

The National Motor Vehicle Crash Causation Survey (NMVCCS) provided in-depth investigative data on pre-crash factors and other characteristics of 5,471 crashes involving light passenger vehicles (“cars”). Within the dataset, 199 crashes, representing 79,721 crashes nationally, were collisions between cars and large trucks. These 199 car-truck crashes constitute the second largest U.S. truck in-depth crash investigation dataset ever compiled, but its findings have not previously been published. NMVCCS is a significant source of information about the genesis of car-truck crashes. This includes variables relating to crash configurations, critical reasons, associated factors, and conditions of occurrence. Findings supplement and generally corroborate those from the Large Truck Crash Causation Study. However, NMVCCS data are more recent and represent a wider range of crash severities. Cars were more likely than trucks to be the encroaching/precipitating vehicle in car-truck collisions. Overall, 71.0% of assigned Critical Reasons (CRs) were to the car. Cars were more likely to be outof-control prior to impact and to violate rights-of-way. Associated, contributing factors relating to driver impairment or stress were noted more frequently for car drivers. Trucks were more likely to be assigned vehicle-related CRs and associated factors, however. Nationally, about 80% of truck-related fatalities occur in car-truck crashes. Understanding their genesis is essential for the development of effective countermeasures

Real-World Performance of Longitudinal Barriers Struck by Large Trucks

Outside of relatively limited crash testing with large trucks, very little is known regarding the performance of traffic barriers subjected to real-world large truck impacts. The purpose of this study was to investigate real-world large truck impacts 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 large-truck-specific barriers. Data sources included the Fatality Analysis Reporting System (2000-2009), the General Estimates System (2000-2009) and 155 in-depth large truck-to-barrier crashes from the Large Truck Crash Causation Study. Large truck impacts with a longitudinal barrier were found to comprise 3 percent of all police-reported longitudinal barrier impacts and roughly the same proportion of barrier fatalities. Based on a logistic regression model predicting barrier penetration, large truck barrier penetration risk was found to increase by a factor of 6 for impacts with barriers designed primarily for passenger vehicles. Although large-truck-specific barriers were found to perform better than non-heavy vehicle specific barriers, the penetration rate of these barriers were found to be 17 percent. This penetration rate is especially a concern because the higher test level barriers are designed to protect other road users, not the occupants of the large truck. Surprisingly, barriers not specifically designed for large truck impacts were found to prevent large truck penetration approximately half of the time. This suggests that adding costlier higher test level barriers may not always be warranted, especially on roadways with lower truck volumes

Naturalistic Driving Events: No Harm, No Foul, No Validity

This paper challenges the validity of vehicle-based Naturalistic Driving (ND) Safety Critical Events (SCEs) in relation to injury and fatal crashes. It asserts that mixed SCE datasets have no known or likely representativeness in relation to serious crashes and are likely invalid in regard to their causal factors. This argument is made in the context of ND attempts to associate truck driver Hours-of-Service parameters and safety. But the argument generally applies to other mixed SCE datasets. In part, the challenge is to a monolithic “Heinrich Triangle.” Crashes are heterogeneous, both “horizontally” within any severity strata and “vertically” across strata. Serious crashes account for the vast majority of human harm, and are very different from minor crashes. Yet all crashes have, and are defined by, tangible external consequences. In contrast, SCEs are defined by driver maneuvers. Their datasets contain almost no crashes, let alone harm. As such, they are not properly part of the “triangle.” Mixed SCE datasets are collections of multiple, disparate driver maneuvers chosen and defined by researchers. They are thus contrived, not analytically derived from the phenomenon of importance, serious crashes. No valid quantitative inferences about the genesis of crash harm can be made from such datasets. This deficiency does not invalidate all ND applications, however. And SCE and real crash datasets could be linked by systematic sampling and case weighting based on objective crash characteristics

Driver Attitudes and Crash Patterns in Western North Dakota Oil Counties: Links between Perceptions and Reality

Traffic safety conditions in the 17-county oil region of western North Dakota have changed considerably in recent years. Roads previously used for low-volume, agricultural purposes are presently utilized at high volumes to serve expanding oil interest. Traffic volume in the region has grown rapidly since the advent of hydraulic fracturing as a viable technique for extracting oil, especially with regard to the overweight and oversized vehicles needed for oil production. Three studies were conducted to understand how changing traffic conditions are perceived by local drivers. First, a survey questionnaire was sent to drivers in the region to measure perceptions of traffic safety priorities. County-level crash data were gathered for rural road crashes in North Dakota between 2004 and 2013 to examine statewide crash trends. Survey responses were linked to crash data and found that safety perceptions from drivers are valid: conditions in oil counties are actually more dangerous than elsewhere in North Dakota. Second, using Decision Theory as a theoretical lens to guide decision-making, crash data were queried to establish if driving conditions in certain parts of the oil region are more dangerous. Proximity to oil wells, city limits, and travel on major roadways were found to have an effect on overall crash severity. Third, written survey responses were qualitatively studied via emergent theme content analysis. Crash types relating to these themes were then subjected to cluster analysis using ArcGIS. Respondent zip codes were matched with crash zip codes to provide a mixed methods approach to understanding key traffic safety issues such as perceived danger, large truck danger, and law enforcement presence

Large Truck Crash Analysis for Freight Mobility and Safety Enhancement in Florida [Summary]

BDV26-977-02Trucks have been a critical link in the delivery of U.S. goods for over a hundred years. Whether carrying freight between cities or distributing freight delivered by trains, planes, or ships, trucks are moving day and night. With Florida\u2019s large population and very active ports, semis are common on Florida highways. Unfortunately, with so many vehicles on Florida\u2019s roads, large trucks are involved in collisions from time to time. Even a minor incident with a large truck can cause significant delays, with ripple effects on hundreds of homes and businesses