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

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

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

Evidence and Dimensions of Commercial Driver Differential Crash Risk

This paper highlights evidence from several instrumented vehiclestudies that crash risk varies significantly among commercial truck drivers, andalso cites findings from surveys of fleet safety managers and other experts on thetopic of individual differences in commercial driver crash risk. Within varioussubject groups, 10-15% of the drivers typically account for 30-50% of the crashrisk. This pattern is seen in measures of driver errors associated with crashes andalso in measures of driver drowsiness. The evidence also suggests, but does notyet prove, that these individual differences are long-term. To the extent that theseindividual differences are long-term, they may be considered personal traits. Thispaper conceptualizes driver risk factors, provides illustrative examples ofdifferential individual risk within groups of drivers, identifies driver factorsthought to be most associated with crash risk, and considers the opportunities forimproved commercial driving safety presented by differential crash risk

Threats to Scientific Validity in Truck Driver Hours-of-Service Studies

Commercial truck driver Hours-of-Service (HOS) rules are periodically revised to reduce driver fatigue and improve driver health in costefficient ways. HOS research must demonstrate causal relationships between HOS parameters and important safety outcomes. Thus, two scientific requirements are internal validity (demonstration of true cause-effect relationships) and external validity (generalizability to important real-world consequences). HOS rules ostensibly act by mitigating driver fatigue; thus, dependent measures in most HOS studies must verifiably capture and measure alertness/fatigue. That is, dependent measures must have construct validity. This paper examines these basic scientific validity requirements and finds significant threats to them within the designs of major U.S. HOS studies. Lessons learned apply to many other areas of behavioral research. Improved designs and compensatory methods are suggested for addressing validity threats and thereby increasing internal, external, and construct validity. Improving scientific validity would in turn raise the likelihood that HOS changes based on research would be safety-effective in the real world of truck transport on our nation’s highways

Lane change/merge crashes: problem size assessment and statistical description.

Mode of access: Internet.Author corporate affiliation: Information Management Consultants, Inc., McLean, Va.Author corporate affiliation: National Highway Traffic Safety Administration, Washington, D.C.Subject code: DECSubject code: DEFSubject code: EASubject code: GHGSubject code: GHLSubject code: JA*JDSubject code: JISubject code: JJSSubject code: JLKSubject code: NRQMSubject code: XLMSubject code: XLM

Commercial vehicle safety -- technology and practice in Europe.

Federal Highway Administration, Office of Policy Planning, Office of International Program, Washington, D.C.Mode of access: Internet.Author corporate affiliation: American Trade Initiatives, Alexandria, Va.Subject code: DEFGSubject code: IJESubject code: KNSubject code: NMCSubject code: QG*EOSubject code: RCGESubject code: RCGKSubject code: RCGLSubject code: W