National Academies Press: OpenBook

Individual Differences and the "High-Risk" Commercial Driver (2004)

Chapter: Chapter 3 - Concepts of Crash Risk

« Previous: Chapter 2 - Survey Method and Results
Page 14
Suggested Citation:"Chapter 3 - Concepts of Crash Risk." National Academies of Sciences, Engineering, and Medicine. 2004. Individual Differences and the "High-Risk" Commercial Driver. Washington, DC: The National Academies Press. doi: 10.17226/13770.
×
Page 14
Page 15
Suggested Citation:"Chapter 3 - Concepts of Crash Risk." National Academies of Sciences, Engineering, and Medicine. 2004. Individual Differences and the "High-Risk" Commercial Driver. Washington, DC: The National Academies Press. doi: 10.17226/13770.
×
Page 15
Page 16
Suggested Citation:"Chapter 3 - Concepts of Crash Risk." National Academies of Sciences, Engineering, and Medicine. 2004. Individual Differences and the "High-Risk" Commercial Driver. Washington, DC: The National Academies Press. doi: 10.17226/13770.
×
Page 16

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

CHAPTER 3 CONCEPTS OF CRASH RISK Many interacting factors affect commercial driver crash involvement. Figure 5 is a conceptualization of some major interacting factors. The focus of this synthesis is on personal “constitutional” risk factors, that is, relatively enduring char- acteristics such as health, physical skills, and some personality traits. At any given moment, however, a number of other fac- tors and influences are operative. Drivers are influenced by fatigue-related situational factors such as amount of prior sleep, time-of-day, and hours driving (time on task), but also by non- fatigue situational stressors such as pressure to deliver on time or recent events causing anger or anxiety (e.g., argument with boss or spouse). The driver may also be operating a vehicle on a roadway in bad weather. Each of these can become major crash factors, although crash investigations have not found vehicle, roadway, or environmental factors to be frequent prin- cipal causes of crashes for either large trucks or other vehicles (Craft et al. 2004, Treat et al. 1979). Other drivers and traffic are significant sources of large truck crash involvement. Indeed, it appears that the majority of large truck crashes and fatal crashes are precipitated by the actions and errors of other involved motorists (Craft et al. 2004, FMCSA 2003, Blower 1998). Commercial driver behavior and responses may be con- sidered a product of all these interacting factors. The principal interest here is personal “constitutional” risk factors. The remainder of this chapter reviews the original concept of accident proneness and describes several models or concepts of crash risk and the driver errors associated with them. 3.1 THE CONCEPT OF ACCIDENT PRONENESS Accident proneness as an industrial safety human factors concept was first proposed by Greenwood and Woods (1919). The idea spawned much research, and many studies have been conducted on the subject since Greenwood and Woods’s study. Greenwood and Woods analyzed the accident records of sim- ilarly exposed and experienced munitions workers in Britain and found that a small percentage of the workers accounted for the majority of accidents. Accident proneness was conceived as a unitary, innate trait that resulted in stable differential risk across time; in other words, the same workers would continue over time to have the greatest risk (Hansen 1988). While early studies found support for the accident proneness concept, more recent studies on the topic have not supported 14 the view that it is a unitary personality trait (Hansen 1988). Studies have found a number of different personal traits that correlate with crash and other accident risk (reviewed in Chap- ter 4), but many of these traits may be largely independent of each other. Moreover, there is an appreciation of the fact that chance (i.e., factors outside the control of drivers) plays a sig- nificant role in crash involvement and that variations in crash involvement within groups of drivers are partly the result of chance (Dewer and Olson 2002). While the concept of accident proneness as a unitary trait has been discredited, researchers have discovered that certain personal traits are related to the occurrence of a vehicle crash. Rather than using the discredited term “accident proneness,” a more appropriate term that reflects the empirical evidence may be “differential crash risk.” To the extent that this dif- ferential risk is enduring, it probably reflects constitutional or other long-term personal traits. To the extent that it varies across time, it may reflect chance variation or changeable traits such as age, maturation, or learning by experience. Chapter 4 of this synthesis presents numerous examples of personal traits that correlate with crash involvement. Most of these studies were conducted over a short time period, so the question of whether or not they document enduring influences on crash risk is unanswered. 3.2 MODELS OF DRIVER ERROR AND RISK Various models or concepts of driver errors and crash involvement have been developed (Dewer and Olson 2002, Rimmo 2002). The Indiana Tri-Level Study (Treat et al. 1979, Treat 1980) posited three major causal categories and clas- sified 420 in-depth light vehicle crash investigation cases accordingly: human (93%), environmental (34%), and vehic- ular (13%). Of course, more than one category can be opera- tive in a crash, and so these percentages total more than 100%. Within the human category, four subcategories included recog- nition errors (56% of in-depth cases), decision errors (52%), performance errors (11%), and “critical non-performance” (e.g., blackout, dozing; 2%). Again, these categories are not mutually exclusive and thus total more than 93%. Recognition errors include distraction (which may be from inside or out- side the vehicle), general inattention (e.g., daydreaming), and “improper lookout” (looked but did not see). Recognition

errors are often associated with rear-end crashes and some intersection crashes (Najm et al. 1995). Decision errors include conscious decisions to drive unsafely (e.g., speeding, tailgat- ing) and also gap judgment errors resulting in a crossing-path- type crash. Performance errors are motor responses improperly executed (e.g., overcorrection following a lane departure). Critical non-performance includes both medical causes and asleep-at-the-wheel. Risk-taking behavior is most likely to result in decision errors, but any of these four categories could be sources of chronic driver risk. For example, a medical con- dition could leave one vulnerable to blackout and a non- performance crash. A follow-up analysis of the Indiana data found that young, unmarried males were the highest risk group and that most of their crashes were caused by poor decision- making, including overt risk-taking behaviors like speeding. The FMCSA/NHTSA Large Truck Crash Causation Study (LTCCS) employs a causal classification similar to that of the Tri-Level Study. In 286 large truck crashes presented as preliminary LTCCS data (Craft and Blower 2003), 34% of the crashes had a “critical reason” assigned to the driver of the large truck. For the other 66%, the critical reason was assigned to another involved driver or to a vehicle or environmental fac- tor. The truck-driver–associated “critical reasons” were classi- 15 fied according to the same human factors classification as used in the Indiana Tri-Level Study and occurred as follows: • Recognition error: 14% • Decision error: 15% • Performance error: 2% • Driver non-performance: 3% Another common classification for driver errors resulting in crashes is as follows (Dewer and Olson 2002): • Rule-based (failure to obey rules or regulations) • Knowledge-based (failure to understand required safe behavior) • Skill-based (lack of proper skills to perform the task) Most fatal crashes involve misbehaviors or rule viola- tions such as alcohol use and speeding. Drivers can also make mistakes without obvious misbehaviors, such as fail- ure to see another vehicle or misjudgment of a gap in the traffic stream. Red-light running may be regarded as a rule- based misbehavior if it is intentional, a skill-based mistake if it is not. Figure 5. Major interacting factors affecting commercial driver crash involvement.

Reason (1990) proposed three error categories: violations (deliberate deviations), mistakes (intended action with unin- tended consequences), and lapses/slips (execution of un- intended action). Rimmo (2002) has expanded this by splitting the lapses/slips category into inattention errors (unintended action resulting from recognition failure) and inexperience errors (unintended action resulting from lack of knowledge or skill). Rimmo’s classification, with examples, follows: • Violations – Deciding to drive when known to be very fatigued – Deliberately exceeding speed limits – Accelerating at green-to-yellow signal change • Mistakes – Misjudging gap when crossing traffic – Misjudging speed of oncoming vehicle – Misjudging stopping distance • Inattention Errors – Failing to notice red light at intersection – Failing to see that vehicle has stopped in lane ahead – Failing to notice sign • Inexperience Errors – Having to check gear with hand – Driving in too low a gear – Switching on wrong appliance in truck Figure 6 shows a schematic of how the four error types contribute to driver risk. Rimmo’s (2002) research on these variables employed questionnaires asking driver subjects to rate the frequency of various driving behaviors and errors in these categories. Analysis of the questionnaire data demon- strated that the four-factor model was applicable across dif- 16 ferent driver age levels. Studies of the intercorrelations among the four error types have found that the correlations between violation behavior (as measured by questionnaire) and the other three factors were less than the intercorrelations within the other three factors. Different individuals may be “violation prone” or “error prone,” and both are associated with accident involvement. However, of the two, high vio- lation scores are more predictive of crash involvement than high error scores. Figure 6. Simplistic model of how different types of error contribute to risk.

Next: Chapter 4 - Factors Related to Driver Risk »
Individual Differences and the "High-Risk" Commercial Driver Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

TRB's Commercial Truck and Bus Safety Synthesis Program (CTBSSP) Synthesis 4: Individual Differences and the “High-Risk” Commercial Driver explores individual differences among commercial drivers, particularly as these differences relate to the “high-risk” commercial driver. The synthesis identifies factors relating to commercial vehicle crash risk and assesses ways that the high-risk driver can be targeted by various safety programs and practices, at both fleet- and industry-wide levels.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!