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

Chapter: Chapter 4 - Factors Related to Driver Risk

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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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Suggested Citation:"Chapter 4 - Factors Related to Driver 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.
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17 CHAPTER 4 FACTORS RELATED TO DRIVER RISK This chapter reviews personal factors relevant to com- mercial driver crash risk, including driver age and gender, driving history, non-driving history, medical conditions and health, fatigue susceptibility, personality traits, sensory- motor performance capabilities, and several other personal crash risk factors. 4.1 DRIVER AGE AND GENDER 4.1.1 Age For the overall driver population, age is one of the strongest personal factors affecting crash involvement (NHTSA 2000). Teenaged drivers, especially males, have crash involvement rates per mile traveled that are several times higher than those of the adult population. Driver errors seen in teenaged drivers include both risk-taking behaviors and misjudgments (Mayhew and Simpson 2003). Even drivers in their twenties—especially early twenties—have high crash rates. Across the spectrum of driver ages, crash rates reach their lowest levels for drivers in their 40s and 50s, remain relatively low for drivers in their 60s, and begin to rise for drivers in their 70s. Drivers aged 85 and older have crash rates (per mile traveled) that rival those of new teenaged drivers. Inexperience is probably not the main factor elevating the crash risk of teenaged drivers. Pierce (1977) studied the crash rates of 16- to 19-year-olds and found evidence contrary to the hypothesis that experience is the primary factor. He found that 14.2% of drivers licensed at 16 years old had vehicle crashes, compared with 13.4% in their second year and 11.5% in their third year (at the age of 18). Drivers who waited until age 18 to receive their license had a comparable crash rate with those who had been driving for 3 years (i.e., 11.9%). Nineteen-year- olds who were licensed since the age of 16 had a crash involvement percentage of 10.2% while the same age group who were newly licensed had an involvement percentage of 10.5%. It appears that other characteristics such as immaturity or risk-taking play stronger roles in the high crash rates of young drivers than do lack of driving experience per se or any lack of physical ability. Ulleberg (2002) found that young, high-risk drivers demonstrated a risk-taking attitude that resulted in a propensity to engage in risky driving behaviors. The above studies and statistics related to all drivers— commercial plus non-commercial. Regarding commercial drivers, in 2001, those aged 25 or younger constituted 6.9% of the large truck drivers involved in fatal crashes, 11.3% of those involved in injury crashes, and 13.8% of those involved in property damage only (PDO) crashes (FMCSA 2003). These statistics are hard to evaluate, because the relative mileage exposure of young commercial drivers is not known. In addition, younger truck drivers tend to be hired by smaller, shorter-haul companies and are more likely to drive large single-unit (straight) trucks than their older peers (Blower 1996, Corsi and Barnard 2003). So, even if rates and risk metrics could be generated, direct comparisons of them would not necessarily be valid. A detailed statistical analysis of young truck driver involve- ment in crashes was conducted by Blower (1996) using Michi- gan and North Carolina crash and violation data. Young truck drivers (defined in this study as 18 to 21 years old) had mov- ing violation rates that were almost twice those of the middle- aged drivers (30 to 49 years old) in the study. Speeding above the speed limit and unsafe speeds for conditions were the two top violations cited. The study did not report crash involve- ment rates, again due to lack of reliable mileage exposure data. However, the study did report that young commercial drivers were about 50% more likely than middle-aged drivers to be charged with a violation in a crash. In two-vehicle crashes with light vehicles, the young truck driver was twice as likely as the other driver to be charged with a hazardous action or traffic violation, which is opposite the trend for large truck/light vehicle crashes in general (FMCSA 2003). Major crash scenarios for these young truck drivers were loss of control/struck fixed object, backing into another vehicle, turning-related involvements, and rear-end (truck-striking) crash involvement. In the local/short-haul study described earlier in the syn- thesis, Hanowski et al. (2000) analyzed factors (including both personal driver factors and situational factors) predicting truck driver involvement in critical incidents (caused by the truck driver). They evaluated driver age, ambient illumina- tion, prior night’s sleep, current drowsiness rating, physical work requirements for the day, and several other factors. They found that driver age was the strongest predictor of critical incident involvement. In a study of driver attributes and rear-end crash involve- ment among the general driving population, Singh (2003) found that young drivers (younger than 25 years old) were

over-involved in rear-end crashes in both striking and struck vehicle roles. The age effect was more pronounced for the striking vehicle (at fault) role than for the struck vehicle. One possible solution to the younger commercial driver crash problem is graduated licensing, a technique that has been used successfully for novice non-commercial drivers in a number of countries and states (Mayhew and Simpson 2003). Of course, the graduated progression of driving priv- ileges for commercial drivers would be quite different than that applied to teenaged novice drivers. Corsi and Barnard (2003) found that 59% of high-safety fleets, and nearly two-thirds of large fleets, considered age 25+ to be an “important” or “very important” selection factor in driver employee hiring decisions. The percentage was even higher—69%—for hiring owner-operators. In this research project survey, carrier safety managers rated the factor “young driver (e.g., younger than 25 years old)” as having the 6th strongest association with crash risk of the 16 factors in the sur- vey. Other experts rated it as having the 5th strongest associa- tion. In contrast, both groups rated “older driver (e.g., 60 years old or older)” as having the 12th strongest association with crash risk among the 16 factors. There appears to be no major safety problem relating to older truck drivers. In 2001, those 56 years old or older con- stituted 14.3% of the large truck drivers involved in fatal crashes, 13.2% of those involved in injury crashes, and 11.2% of those involved in PDO crashes (FMCSA 2003). Again, mileage exposure levels are not known, so compara- tive crash rates cannot be generated. The Trucking Research Institute and InterScience America (1998) published an FHWA-sponsored study of the effects of increasing age on commercial driver performance and safety. The study reviewed the scientific literature on the subject and measured the performance of different age groups of drivers on a variety of performance tests. The study found that • Age alone is not a reliable predictor of job performance. • Age is not a good predictor of sensory-motor abilities. While many perceptual, sensory-motor, and cognitive abilities do generally decline with advancing age, there are huge individual differences within age groups. • Drivers past the age of 50 do begin to have slower reac- tion times, stiffer joints, and other physical signs of age. Nevertheless, these drivers are often among the safest and most reliable commercial drivers. Among non-commercial drivers, older drivers have been found to purposely limit their exposure to driving situations they regard as more difficult (Ball et al. 1998). Those drivers with objectively determined difficulties in vision and attention would avoid situations that would increase these demands. In other words, they had self-regulating behavior. In commercial driving, it’s likely that many older drivers resign from the pro- fession if they find it too demanding or if they feel that their declining psychomotor skills are making driving dangerous. 18 The findings summarized here should reassure the indus- try that hiring older drivers, especially the “young old,” does not generally create crash risks. Indeed, these drivers are often among the industry’s finest. Survey findings echoed this view; both safety managers and other experts rated “Older Driver (e.g., 60 years old or older)” as among the factors with the least driver crash risk. 4.1.2 Gender Among the general population of drivers, males have some- what higher per-mile crash rates than females in most age brackets. Their per-driver crash risks (e.g., driver involvement rates per 100,000 population) are considerably higher than females’, and their per-driver fatal crash risks are more than twice those of females (NHTSA 2000). However, per-driver crash risk statistics may be misleading because males drive sig- nificantly more miles and thus have far greater exposure risk than females. Per-mile crash rate statistics, when available, are more telling. Women constitute a small percentage of commercial driv- ers. In 2001, females were 2.6% of the large truck drivers involved in fatal crashes, 4.5% of those involved in injury crashes, and 6.0% of those involved in PDO crashes (FMCSA 2003). Once again, mileage exposure levels are not known, so comparative crash rates cannot be generated. These data do show, however, that female commercial drivers are relatively less likely than males to be involved in more serious crashes. In the Singh (2003) study of driver attributes and rear-end crash involvement mentioned above, both young males and young females 18 to 24 years old were more likely than older drivers to be involved in rear-end crashes. Comparing gen- ders, young males and young females had about the same risk of involvement in the struck vehicle role. For the striking vehicle role, however, young males had about a 50% higher risk of involvement than did young females. 4.2 DRIVING HISTORY 4.2.1 Commercial Driving Experience Experience driving a large truck or bus is clearly a factor in driver safety. In the research project survey, “inexperienced (new to commercial driving)” received identical high ratings for association with crash risk from the two respondent groups (i.e., an average of 3.2 on a 0–4 scale). This was the 4th highest-rated factor of the 16 presented. Not surprisingly, most motor carriers, particularly large carriers, require prior commercial driving experience for applicants to be consid- ered for hiring (Stock 2001). Corsi and Barnard (2003) found that 85% of carrier safety managers consider driving experi- ence with other carriers to be an important or very important hiring criterion. Similarly, Knipling, Hickman, and Bergoffen (2003) reported that 86% of their carrier safety manager

respondents required a minimum number of years of com- mercial driving experience and that managers rated this age hiring criterion as the 4th most effective safety management technique of the 28 presented. A contrasting view is that experience leads to complacency and unsafe practices for some drivers. One respondent claimed that “CMV drivers appear to become complacent after about 4 or 5 years on the job (regardless of age) and are less safe.” While this may be true for some drivers, no evidence was found indicating that this is a general trend. 4.2.2 Longevity with Company In the research project survey, “new to company” was not rated by either safety managers or other experts as a strong cor- relate of crash risk. Safety managers rated “new to company” as 13th of 16 factors, and other experts rated it 11th of 16. Stud- ies have shown a relationship between employment longevity with a company and crash involvement. One safety manager respondent commented that, “The problem drivers are usually the bottom 10–20% who are a constant turnover in the indus- try. Often other employers do not give all information [about them]. . . .” In a study of CMV driver retention and safety, Staplin et al. (2002) found that drivers with frequent job changes (three or more different carriers per year for 2 years or more) were more than twice as likely to be involved in a crash as the at- fault driver than drivers with less frequent job changes. One may ask whether job changes increase a driver’s risk, or whether poor driving results in dismissal or other management actions resulting in job changes. 4.2.3 Crashes, Violations, and Incidents A driver’s history of involvement in crashes, violations, and other incidents is known to be a strong predictor of future crash involvement and the driver’s role (e.g., at-fault vs. not-at-fault). For example, using a sample of more than 200,000 (mostly non-commercial) drivers in Kentucky, Chandraratna and Stamatiadis (2004) were able to use past data on driver crash involvement and violations to predict future crash involve- ment as the at-fault driver with 88% accuracy using a logis- tic regression model. One input to the predictive model was the time gap between successive crashes. If a driver was recently involved at-fault in a crash, he or she is more likely to be at-fault in a second crash event. Miller and Schuster (1983) followed 2,283 drivers in Cali- fornia and Iowa for 10 to 18 years. They found that traffic vio- lations were a better predictor (than were crashes) of future crashes. Their results indicate that violation behavior is more stable over time than crashes, thereby making them a better predictor of future crashes than crashes themselves. This find- ing may reflect that crashes are relatively rare and are the result of both at-risk driving behavior and chance. In contrast, viola- 19 tions and other unsafe driving acts are direct reflections of driving behavior. Past crash involvement was not included in the list of the 16 driver risk factors in the research project survey, but the management practices sections did ask safety managers if they checked the MVR of crashes and violations for driver appli- cants. All 178 safety manager respondents (100%) indicated that they check the MVR as part of their hiring procedures, and both safety managers and other experts rated this driver hiring practice as No.1 in effectiveness among the eight listed. Similarly, 99% of the safety manager respondents reported that they continuously track their fleet drivers’ involvement in crashes, violations, and incidents, and this performance eval- uation practice was rated by both respondent groups as the No.1 evaluation practice of the four presented. Example of Commercial Driver Risk Prediction Using Violation and Incident Data A major insurance carrier for the CMV industry has devel- oped a powerful methodology for relating past driver viola- tions and incidents to current crash risk. This insurer has made significant efforts to assist their clients in identifying high-risk drivers. These efforts involved a three-part approach, which built a crash predictive index, assembled a comprehen- sive safety history, and included a ranking system comparing drivers’ safety history with that of their peers. Predictive Index. First, the insurer teamed with several insured clients to determine the ability of various safety infractions to predict crash and incident risk. For example, the insurer partnered with a major national motor carrier of bulk liquids and chemicals to monitor the roadside inspec- tion results and crash/incident history of 1,682 drivers who were continuously employed over a 37-month period. For these 1,682 individuals, the insurer tracked the number and type of “risk-indicative” driver violations. Example viola- tion types included the following: • Traffic Enforcement: Speeding, following too close, and improper lane change. • Log Out-of-Service: Exceeding hours-of-service limits and log falsification. • General Log: Log not current, failure to retain previous 7 days’ logs. • Illegal Use of Radar Detector. • Driving While Disqualified: Cited for driving with sus- pended or revoked license. • Driver Violation Rate: Total number of risk-indicative violations per inspection. The violation data were gleaned from FMCSA carrier profiles. Carrier profile reports, which can be obtained from FMCSA (www.fmcsa.dot.gov), contain tabulations of all

state-reported crashes and roadside inspections. For each roadside inspection, these reports provide identifying infor- mation (e.g., time, date, and driver name) as well as a tabula- tion of cited violations. Next, the insurer tracked these drivers’ involvement in such events as crashes, spills, and mis-deliveries. These data were extracted from both the carrier’s internal accident/incident reg- ister and the insurer’s claim management system. The com- bined files yielded information regarding driver name, crash/incident type (e.g., preventable rear-end collision, side- swipe crashes, and chemical spills), and incident cost. During the 37-month tracking period, the 1,682 drivers were involved in 4,662 roadside inspections, 1,102 risk- indicative violations and 836 claim incidents. Drivers were involved in an average of 0.50 crashes/incidents each. How- ever, involvement rates ranged from 0 to 13 incidents per driver. Violation involvement rates ranged from 0 to 7 per driver. Interestingly, 20% of the drivers were involved in 79% of all crashes/incidents and 76% of all violations. The insured carrier next examined the statistical relationship between violations and incident involvement. This deter- mines the influence of the various factors (e.g., number of traf- fic enforcement actions, log out-of-service, general log, radar detector, and total driver violations) in the number of incidents. The findings indicated that the total number of driver violations was a very strong predictor of crash/incident involvement. For example, drivers with no violations averaged 0.41 crash/ incident events each. In contrast, drivers with two violations 20 averaged 0.64 crashes/incidents and drivers with four violations averaged 0.93 crashes/incidents. The predictive ability of violation types was also calcu- lated. Results indicated that “Driving While Disqualified” had nearly twice the predictive ability of the other violations. This was followed by “Traffic Enforcement” and “General Log” violations. A similar process was used across a range of other safety violations, such as motorist complaints (e.g., “1-800-Hows- My-Driving”) and MVR convictions (e.g., speeding, failure to yield right-of-way, and failure to obey traffic warning device). Comprehensive History. Next, the insurer assisted its clients in developing a comprehensive safety history. Typi- cally, this history comes from multiple files, similar to those shown in Figure 7. The files are often stored in vastly different data formats. Driver rosters and accident registers, for example, are often maintained in a spreadsheet (e.g., Microsoft® Excel) format. MVR reports and motorist complaints are frequently in “hard copy” format and are kept within a driver’s qualification file. Other information, such as customer complaints or observed incidents, is frequently maintained within operations or driver dispatching software programs. Peer Ranking System. With the comprehensive safety history assembled, the insured assisted their clients in devel- oping a total “Risk Index” score for each driver. This was Comprehensive Safety File MOTORIST COMPLAINTS ACCIDENT REGISTER CARRIER PROFILE MVR REPORTS OTHER COMPANY DATA Source: Internal company file Source: Federal Roadside Inspections Data Source: State MVR Reports Source: Various internal or external files Source: “How’s My Driving” Reports DRIVER ROSTER Source: Internal company roster Figure 7. Assembling comprehensive safety history.

accomplished by calculating the point value for each recorded incident and then totaling driver-specific safety score sum- maries by driver name. As shown in Figure 8, the insured then used this informa- tion to identify high-risk drivers. Once the pool (e.g., say, worst 25%) of high-risk drivers was identified, the client’s managers then focused their intervention and training efforts on these individuals. This approach toward identifying and managing at-risk behavior is new to the motor carrier industry, and the insurer reported that many clients had been using the system for less than 1 year. As a result, a validated sampling of reduced crash involvement was not available at the time this synthesis was prepared. Informal interviews of clients who used such an approach, however, reported such benefits as • It allowed them to focus their “scare” safety intervention resources (e.g., limited field safety staff on those individ- uals who are most at risk for future crash involvement). • It provided the opportunity for “pre-crash” intervention and management, which in many cases salvaged the careers of individuals who would not “float to the top” until their records would require immediate termination. 4.2.4 Defensive Driving This synthesis focuses on commercial driver risk factors, but most analyses of driver-related factors in crashes between large trucks and passenger vehicles have indicated that passenger vehicle driver errors or other driver factors are cited much 21 more often than truck driver factors. Most studies show that the ratio of passenger vehicle driver errors to truck driver errors in crashes, including fatal crashes, is at least 2:1 (Craft 2004, FHWA 1999, Blower 1998). An obviously important element of safe commercial driving is defensive driving (i.e., avoiding crashes that might be caused by other drivers). Are there significant individual differences in commercial drivers’ defensive driving skills? Data from the local/short-haul truck instrumented vehicle study (Hanowski et al. 2000) de- scribed earlier imply that there are such variations in truck drivers’ abilities to avoid the mistakes of other drivers. In the study, 42 truck drivers were involved in 137 CIs caused primarily by other drivers during 1,376 hours of driving, yielding an average rate of 0.12 such CIs per hour. Figure 9 shows the frequency distribution of rates of involvement in other driver CIs. Of the 42 truck drivers, 10 had other driver CI/hour rates greater than 0.20. These 10 drivers drove 16% of the total driving hours of the study but were associated with 45% of all the other driver CIs (61 of 137). The correlation between truck driver involvement rate in other driver CIs and involve- ment rate in truck driver CIs was +0.24, suggesting some association between involvement risk for the two types of traffic incidents. 4.3 NON-DRIVING CRIMINAL HISTORY Having a criminal record is likely to make it difficult for individuals to find work as a commercial driver. Of course, a driver’s criminal record is relevant to other carrier concerns Figure 8. Comparative safety scores illustrating variation in driver risk prediction.

22 0 5 10 15 20 .0 .01-.10 .11-.20 .21-.30 >.30 Critical Incident/Hour N um be r o f D riv er s (N = 42 ) Figure 9. Frequency distribution of other driver critical incident rates among 42 local/short-haul truck drivers (adapted from Hanowski et al. 2000). besides driving safety; most notably, it is relevant to cargo and other operational security. Sixty-one percent of responding safety managers check a driver applicant’s criminal record, although this was not rated as among the most effective safety management practices. Checking a driver’s credit history has been suggested as a way to assess dependability and stability, but only 21% of safety manager survey respondents reported doing this, and it was rated as the least effective practice of the eight presented. 4.4 MEDICAL CONDITIONS AND HEALTH Driving is a task requiring various physical abilities for sat- isfactory safety performance. Medical conditions and health status can have a significant impact on drivers’ performance if their cognitive, perceptual, and psychomotor skills are affected. Drivers must pass a medical examination to qualify for a commercial driver’s license (CDL), and specific dis- qualifying medical conditions (per 49 CFR 391.41) include vision and hearing impairment, diabetes, and epilepsy. 4.4.1 Sleep Apnea In the past decade, sleep apnea has been one of the most studied medical conditions in terms of its relationship to crash risk. Sleep apnea is a breathing disorder characterized by brief interruptions of breathing during sleep. Among the general population of drivers, sleep apnea has been shown to increase the likelihood of being involved in vehicular crashes anywhere from two- to sixfold (see Table 10). That is, drivers with sleep apnea are two to four times more likely to have a motor vehicle accident. Further, Young et al. (1993) estimated that approximately 4% of middle-aged male drivers, the predomi- nant population of truck drivers, may have some form of sleep Study Sleep-Related Medical Condition Odds Ratio: Crash Risk Teran-Santos et al. (1999) Sleep apnea (AHI ≥ 10) 6.3 Wu and Yan-Go (1996) Sleep apnea 3.0 Young et al. (1997) Sleep apnea: (AHI 5-15) 4.2 Young et al. (1997) Sleep apnea: (AHI >15) 3.4 Connor et al. (2002) Acute sleepiness 11 Masa et al. (2000) Habitually sleepy drivers 13.3 Cummings et al. (2001) Drowsiness 14.2 Note: All studies involved non-commercial drivers. AHI = Apnea/Hypopnea Index (a measure of sleep apnea severity). TABLE 10 Elevated crash risks (odds ratios) associated with various medical conditions per various studies

apnea. A subsequent study by Young, Blustein, Finn, and Palta (1997) determined that the relative risk of being involved in a motor vehicle crash if one has sleep apnea is approxi- mately four times normal risk. Findley et al. (1989) conducted a driving simulator experi- ment in patients with and without sleep apnea. The patients with sleep apnea hit a greater number of obstacles during their 30-min simulated drive than did the control group (drivers with no known sleep apnea). However, studies have also shown that drivers who are successfully treated for sleep apnea reduced their crash risk significantly (George et al. 1996). Young et al. (1993) have also suggested that increased preva- lence of sleep apnea occurs due to many factors that drivers could detect themselves, such as age, gender, weight, and snoring. However, commercial drivers may be reluctant to be screened or treated for sleep apnea due to cost and concerns of job security. Further, the method for treatment (e.g., con- tinuous positive airway pressure [CPAP], surgery) can be con- sidered costly, too obtrusive, or too burdensome if treatment is needed nightly (Flemons 2002). Long-term health conse- quences of sleep apnea and the long-term relationship of this disorder to crash risks are major concerns. Pack et al. (2002) estimated the prevalence of sleep apnea among commercial drivers and quantitatively assessed the effects of sleep apnea on driving-related performance. The study found that mild sleep apnea occurs in 17.6% of those holding CDLs, moderate sleep apnea in 5.8%, and severe sleep apnea in 4.7%. Deficits in vigilance and various sensory-motor tasks were associated with the increasing severity of sleep apnea. Objective measures associated with sleep apnea severity included sleep latency (a standard mea- sure of drowsiness), Psychomotor Vigilance Test (PVT) reaction time and lapses, and tracking errors on a divided attention task. Sleep apnea was associated with shorter sleep durations, but neither sleep apnea severity nor sleep duration was associated with subjective self-reports of sleepiness by driver subjects. Thus, driver self-monitoring of their levels of alertness and drowsiness is not likely to provide valid assessments. Obesity is a prime risk factor for sleep apnea, and the inci- dence of obesity among CMV drivers is approximately twice that of the general population (Roberts and York 2000). Other health conditions and behaviors for which commercial drivers compare unfavorably with the general population include diet, exercise, hypertension, stress, and smoking. 4.4.2 Narcolepsy Narcolepsy is principally characterized by a permanent and overwhelming feeling of sleepiness and fatigue. Other symp- toms involve abnormalities of dreaming sleep, such as dream- like hallucinations and finding oneself physically weak or paralyzed for a few seconds. It impacts 1 in every 2,000 Americans (http://www-med.stanford.edu/school/Psychiatry/ 23 narcolepsy). While sleep apnea is more prevalent in drivers who are male, middle-aged, and slightly overweight, nar- colepsy affects both men and women, can run in families, and can appear at an earlier age. Excessive daytime sleepiness occurs every day, regardless of the amount of sleep obtained at night and can therefore put drivers at an increased risk for crash involvement. Findley et al. (1995) conducted a study on vigilance and automobile accidents in patients with narcolepsy as well as those with sleep apnea. A computer program simulating a long and monotonous highway drive was presented to drivers for 30 min. The patients with untreated narcolepsy hit a higher percentage of obstacles while performing on the simulator, indicating a greatly increased likelihood of crash involvement. George, Boudreau, and Smiley (1996) also conducted a laboratory-based divided attention driving test (DADT) in a simulated environment on patients with narcolepsy and patients with sleep apnea. Their study showed that narcolepsy patients were sleepier than patients with obstructive sleep apnea, and tracking error was much worse in both sets of patients when compared with a control group. Aldrich (1989) also conducted a study of automobile crashes in patients with sleep disorders, primarily narcolepsy and sleep apnea. Aldrich found the highest sleep-related crash rates in narcoleptics. Aldrich (1989) and Findley et al.’s findings sug- gest that both sleep apnea and narcolepsy are sleep disorders that need to be investigated further. 4.4.3 Diabetes Diabetes, a common chronic disease, is of concern to licens- ing agencies because individuals with diabetes may experience periods of hypoglycemia when treated with insulin. Hypo- glycemia can alter judgment and perception and can even lead to a loss of consciousness while driving. Laberge-Nadeau (2000) estimated the impact of diabetes on crash risk in a study consisting of commercial drivers and truck-permit holders. Data on permits, medical conditions, and crashes of 13,453 per- mit holders from 1987 to 1990 were used in the analysis as well as a telephone survey conducted from 1990 to 1991 that collected information on driving patterns and exposure. The findings showed that there was an increased crash risk for the permit holders and for the professional drivers with the same type of permit and with uncomplicated diabetes not treated with insulin. Permit holders for single-unit trucks who are diabetic without complications and not using insulin had an increased crash risk of 1.68 when compared with healthy per- mit holders of the same permit class. Commercial drivers with a single-unit truck permit and the same diabetic condition had an increased risk of 1.76. Surprisingly, the findings also showed that insulin use was not identified with higher crash risk. However, this could be due to the limited number of com- mercial drivers with severe diabetes. Many fleets screen their drivers and applicants for insulin-based diabetes.

4.4.4 Other Medical Conditions Medical conditions having an impact on the crash severity of commercial motor vehicle crashes were identified by Laberge-Nadeau et al. (1996). In their study, crash severity was measured by the total number of injured victims. Their study indicated that truck drivers with binocular vision problems and bus drivers with hypertension had more severe crashes than healthy drivers. No other medical condition considered in this study—including diabetes, mellitus, and coronary heart disease—was significantly associated with crash severity. A study done with non-commercial drivers indicates that other health problems, including heart disease and stroke, were also associated with an increased likelihood of being involved in both at-fault and not-at-fault automobile crashes (McGwin et al. 2000). The study also found an increased risk of crash involvement for drivers with arthritis and diabetic neuropathy. A confounding factor in this study is that many people with these conditions are taking prescription drugs that can impact their cognitive, perceptual, and psychomotor abilities. 4.5 ALCOHOL AND DRUG ABUSE Commercial driver alcohol use while driving is infrequent, especially in comparison with non-commercial drivers. In 2001, alcohol use on the part of large truck drivers was involved in 2% of their fatal crashes, 1% of their injury crashes, and less than 0.5% of their PDO crashes. Alcohol use on the part of bus drivers represented 3% of their fatal crashes and less than 0.5% for both injury and PDO crashes. For passenger vehicles (cars and light trucks), the comparable percentages were 27% fatal, 5% injury, and 3% PDO (NHTSA 2002). Craft (2004) reported preliminary findings on 210 light-truck vehicle crashes from the FMCSA/NHTSA Large Truck Crash Causation Study. None of these 210 crashes involved alcohol or illegal drug use by truck drivers. Of the light vehicle drivers involved in these crashes, 11% were under the influence of alcohol and 9% had used illegal drugs. Federal law requires all motor carriers employing commer- cial drivers to have drug and alcohol testing programs. The random testing rates are 10% for alcohol and 50% for con- trolled substances (illegal drugs). In 1999, 0.2% of CDL hold- ers tested positive for alcohol use and 1.3% tested positive for controlled substances (FMCSA 2001). These statistics indicate that commercial driver alcohol and illegal drug use are not major factors in the crashes. Nev- ertheless, any commercial driver identified as an alcohol or drug abuser should be considered a high-risk driver. 4.6 DRIVER FATIGUE Drivers who are sleep deprived have significant deficits in vigilance and other cognitive abilities related to driving. McCartt et al. (2000) identified factors associated with why 24 long-distance truck drivers reported falling asleep at the wheel. They found six underlying, independent factors, including (1) greater daytime sleepiness, (2) more arduous schedules, with more hours of work and fewer hours off-duty, (3) older, more experienced drivers, (4) shorter, poorer sleep on the road, (5) symptoms of sleep disorder, and (6) greater ten- dency to nighttime drowsy driving. The authors also suggest that if these six factors were to be ranked, a tendency toward daytime sleepiness was most highly predictive of falling asleep at the wheel, followed by an arduous work schedule and older, long-time drivers. Hakkanen and Summala (2001) found similar findings in an analysis they conducted on 567 professional drivers that included five different commercial driver types (long-haul drivers, short-haul drivers, bus drivers, drivers transporting wood, and drivers transporting dangerous goods). They found that regardless of the commercial driver type, sleepiness-related problems was strongly related to pro- longed driving, sleep deficit, and driver’s health status. Sagberg (1999) conducted a study of crashes caused by driv- ers falling asleep. The study showed that fatigue was a strong contributing factor in nighttime accidents, run-off-road acci- dents, and accidents after driving more than 150 km on one trip. Although his study was conducted on non-commercial drivers, many findings were consistent with McCartt et al.’s study. In addition, Sagberg found that more males than females were involved in sleep-related accidents. Sagberg also suggests that drivers’ lack of awareness of important precursors of falling asleep in addition to the reluctance to discontinue driving despite feeling tired contributed to sleep-related accidents. Pack et al. (1995) investigated the characteristics associ- ated with sleep-related crashes among the general population of drivers. They found that the crashes were primarily drive- off-the road type and took place at higher speeds. The crashes occurred primarily at two times of day: during the over- night hours (midnight to 7 a.m.) and during mid-afternoon (3:00 p.m.). Young drivers were overrepresented, especially in overnight crashes. The times of occurrence of fatigue- related crashes corresponded to the known circadian variation in sleepiness. There is a major peak during the night with a secondary peak during the mid-afternoon. When older drivers were involved in these crashes, it tended to be in the after- noon rather than in the overnight hours. Lyznicki et al. (1998) present a comprehensive review of sleepiness, driving, and motor vehicle crashes in a report to the Journal of the American Medical Association. Their report indicates that drivers at high risk for fatigue or sleep-related crashes include (1) younger drivers who lack sleep due to demands from school and jobs, extracurricular activities, late-night socializing, and poor sleep habits, (2) shift work- ers, who may have reduced opportunities for sleep due to disruptions of the biological process that programs daytime wakefulness and nighttime sleepiness, (3) drivers who use alcohol and other drugs, and (4) drivers with sleep disorders. In the landmark FHWA-sponsored Driver Fatigue and Alertness Study (DFAS, Wylie et al. 1996), 80 long-haul com-

mercial drivers in the United States and Canada were moni- tored over a 4- to 5-day work week. In the study, there was continuous video monitoring of drivers’ faces, which enabled judgments of alertness based on eyelid droop, facial expres- sion, and muscle tone. Approximately 4.9% of the sampled video segments during the 4,000 hours of subject driving were scored as drowsy based on reviewers’ assessments. One of the major observations of the study was the pronounced individual differences in the incidence of drowsiness among the 80 drivers. Twenty-nine of the drivers (36%) were never judged drowsy whereas, at the other extreme, 11 of the drivers (14%) were responsible for 54% of all the drowsiness episodes observed in the study. Figure 10 shows the frequency distri- bution of drowsiness episodes among the 80 drivers, plotted with five frequency ranges. The two drivers in the far right bin had 38 and 40 drowsiness episodes, respectively. Their total of 78 was greater than the total drowsiness episodes exhibited by the best 51 of the DFAS drivers. This distribution differs very significantly from the distribution expected from chance variation alone. Personal factors possibly related to the high-drowsiness incidence for the two driver subjects were not identified in the DFAS report. Interestingly, two of the 80 drivers were diag- nosed as having sleep apnea, but they were not the two highest- drowsiness subjects. As noted, each DFAS driver drove for only 1 week, so the study did not address the question of whether individual dif- ferences in drowsiness incidence were enduring. Enduring individual differences in fatigue susceptibility would imply the existence of a fatigue susceptibility trait (i.e., a long-term characteristic), whereas the lack of such enduring differences would imply that situational or other factors lead to short-term differences in driver states. An instrumented vehicle study of long-haul drivers using sleeper berths yielded a similar positively skewed distribution of high-drowsiness episodes. Figure 11 shows the distribution of high-drowsiness episodes per hour for 27 drivers. Of the 27 truck drivers, 7 drivers had high-drowsiness episode rates of greater than 0.30/hour. These 7 drivers drove 25% of the 25 total driving hours of the study but were responsible for 226 (70%) of the 323 high-drowsiness episodes. In contrast, the 9 most alert drivers (the first bar in Figure 11) drove 24% of the driving hours but had no high-drowsiness episodes. Among the 27 drivers, there was a moderate correlation between driver high-drowsiness rates and other CI involvement rates. In the study, a situational factor contributing to the dispersion of driver drowsiness incidence was team versus solo driving. The study included both types, and the solo drivers exhibited significantly more drowsiness than the team drivers. Indeed, there are numerous situational factors that increase the probability of drowsy driving, such as night driving, irreg- ular schedules, sleeper berth use (versus sleep in a bed), length of working shift, delivery schedule pressure, and amount of sleep. “Sleep hygiene” refers to sleep and alertness-related personal habits and schedules. In a survey of 511 commercial drivers, Abrams, Schultz, and Wylie (1997) identified many sleep hygiene-related variables, including work shift length, sleeper berth use, split sleeper berth sleep, hours resting, frequency and duration of napping, drowsiness episodes in the past month, willingness to forgo sleep when behind schedule, and other health-related behaviors (i.e., diet and exercise). A wide range of responses were given on most of these topics, indicating the sleep hygiene practices of drivers vary widely. One question relating directly to fatigue risk asked how often drivers had dozed or fallen asleep at the wheel in the past month. The distribution of responses was as follows: 0 inci- dents, 72.0%; 1 to 5 incidents, 22.8%; 6 to 15 incidents, 4.0%, and >15 incidents, 1.4%. Of the seven drivers constituting the highest category, four reported 30 episodes in the past month and one reported 60. Of course, the above survey data are subject to a number of vagaries, including variations in driver memory, candidness, criteria for “dozed or fallen asleep,” and self-assessment of drowsiness level. On the latter point, the DFAS and other stud- ies (e.g., Itoi et al. 1993) have found that drivers are not very good judges of their own levels of drowsiness, in particular the probability of imminent sleep episodes. The Itoi et al. study found variations in sleepiness across subjects for the same level 0 5 10 15 20 25 30 35 0 1-10 11-20 21-30 31+ High-Drowsiness Episodes N um be r o f D riv er s (N =8 0) Figure 10. Frequency distribution of long-haul truck driver high-drowsiness episodes among 80 drivers of the DFAS.

of sleep deprivation and also variations in the ability of sleep- deprived subjects to accurately predict the imminent occur- rence of involuntary sleep. Of 31 subjects, accurate predictions of imminent involuntary sleep (i.e., “I will fall asleep in the next two minutes.”) ranged from 25% to 97%. Accurate pre- dictions of non-sleep (i.e., “I will not fall asleep in the next two minutes.”) ranged from 9% to 84%. Several of the 31 subjects performed at or near chance levels of accuracy in predicting the imminent occurrence of involuntary sleep. In a large FMCSA-sponsored study of sleep apnea, Pack et al. (2002) recorded amounts of nightly sleep for 340 com- mercial drivers, including drivers at high risk and low risk for sleep apnea. For both groups, they found wide ranges in aver- age hours of nightly sleep, from less than 6 hours for about 10% (both subsamples combined) to more than 8 hours for about 24%. The study employed four subjective measures of sleepiness (e.g., Stanford Sleepiness Scale) and four objective tests (e.g., PVT) and found that average sleep duration signif- icantly affected measures on all scales. Clearly, variations in amount of nightly sleep are a major source of variations in commercial driver alertness and performance. Are there large individual differences in alertness for indi- viduals with controlled amounts of sleep? In a major FMCSA- sponsored controlled experiment on the effects of different amounts of sleep, Balkin et al. (2000) permitted driver subjects 3, 5, 7, or 9 hours in bed nightly for 1 week. As expected, it was found that, between groups, alertness and performance var- ied directly with amount of sleep and that these differences increased over successive days. Another finding, however, was that sleepiness and alertness performance varied signif- icantly between subjects within the same rest duration group. For example, mean sleep latency, a standard measure of drowsiness, varied widely among subjects, from about 1 to 20 minutes. At the extremes, some 3- and 5-hour subjects had sleep latencies of more than 10 minutes, whereas some 7- and 9-hour subjects had sleep latencies of 1 minute. Indi- vidual sleepiness was not a direct function of the amount of sleep; marked individual differences and distribution overlaps among groups were observed. 26 The Balkin study also included a field study where 25 long- haul and 25 short-haul commercial drivers wore wrist acti- graphs for 20 days to assess their amount of sleep and factors influencing it. Not surprisingly, they found that total off-duty period had a strong effect on principal sleep duration. Some drivers had highly variable sleep durations from night to night, whereas others were very consistent in their sleep routines. Overall sleep hygiene habits may be long term and thus a source of enduring individual differences among drivers in their levels of alertness. And the Balkin et al. sleep depriva- tion study showed that the effects of sleep deprivation may vary widely among drivers during a week of partial sleep deprivation. But, over a longer period of time, would differ- ent drivers respond characteristically differently to lack of sleep? Dinges et al. (1998) deprived 14 subjects of sleep over 40 hours in a test of different physiological measures of alertness. A principal, and previously validated, perfor- mance measure of alertness in the study was the frequency of subject lapses (non-responses) on the PVT. The PVT was administered during 20 “bouts” in the 40 hours. To test the physiological measures of alertness more rigorously, the researchers created two subject subgroups post hoc: six “high lapsers” and eight “low lapsers.” The high lapsers were 42% of the subjects but accounted for 69% of the lapses observed in the study. The researchers split the 40 hours in half—2 to 22 hours and 22 to 42 hours—and observed the marked lapse-frequency differences between the subject groups during both halves of the sleep deprivation. Indeed, the high-lapser lapse incidence during the first 20 hours of sleep deprivation was almost as high as the low-lapser inci- dence during the second 20 hours. The best physiological measure of alertness in the study was found to be Percent Eyelid Closure (PERCLOS), a measure of eyelid droop associated with drowsiness. PERCLOS was almost equally accurate across both the high and low lapser groups and across both halves of the deprivation period. This implies that the same or similar physiological processes are occur- ring among all the subjects, but that the rate of alertness dete- rioration is different for different subjects. 0 5 10 15 20 0 .01-.15 .16-.30 .31-.45 >.45 High-Drowsiness Episodes/Hr Nu m be r o f D riv er s (N =2 7) Figure 11. Frequency distribution of long-haul truck driver high-drowsiness episode rates among 27 drivers of the sleeper berth study (Dingus et al. 2001).

A separate follow-up experiment in the study brought back four of the subjects (4 to 7 months later) for the same 40 hours of sleep deprivation, but this time with occasional auditory and vibrotactile alerting stimuli. These stimuli were found to have no overall effect on the time course of alertness deterioration for any of the subjects. Remarkably, each subject nearly dupli- cated their original time course of alertness deterioration as measured by PVT lapses. “There is an apparently reproducible ‘fingerprint’ quality to the overall bout-to-bout profile of PVT lapses for each of the subjects between experiments I and II” (Dinges et al. 1998, Page 91). Figure 12 shows bout-to-bout PVT lapses for a single typical subject during the first experi- ment without alerting stimuli and the second experiment with them. Although this part of the study involved only four sub- jects, the individual differences in fatigue susceptibility were significant and remarkably reliable. Important new research findings (Van Dongen et al. 2004) strongly corroborate the view that there are significant “trait- like” individual differences in susceptibility to alertness loss as a result of sleep deprivation. In the study, 21 healthy adults were sleep-deprived in a laboratory for 36 hours three different times, separated by intervals of at least 2 weeks. Every 2 hours they underwent “neurobehavioral” testing con- sisting of 13 objective and subjective measures of alertness. There were two main factors under examination in the study: inter-individual variation and variation due to prior sleep his- tory. A striking finding of the study was that there were stable 27 inter-individual differences in the response to sleep depriva- tion for all the tests employed. That is, individual subjects tended to respond similarly on all tests during their three sleep deprivation periods, but differed considerably among each other. Intraclass correlation coefficients (used to quan- tify trait-like inter-individual variance in of each of the tests) showed that, across the 13 tests, 68% to 92% of the variance in the neurobehavioral data were explained by stable individ- ual differences. The effect of the amount of sleep obtained in days before (i.e., prior sleep history) on performance during sleep deprivation was statistically significant, but modest in comparison with the observed inter-individual differences. Although each subject tended to show a stable response on specific tests, those showing the greatest deficits on one test did not necessarily show the same level of impairment on other tests. In particular, inter-individual differences in subjec- tive measures of alertness did not correspond well with inter- individual differences in objective measures of alertness. Figure 13 provides a sample of these results for one measure (PVT) and two of the sleep deprivation periods. In the figure, data for 18 subjects are plotted. The horizontal axis is the aver- age PVT lapses in the last 24 hours of the first sleep depriva- tion session, and the vertical axis is the corresponding measure for the second sleep deprivation session. The scatter plot shows huge differences (about a sixfold difference) between the best and worst performances. The scatter plot also illustrates a high correlation between the first and second sleep deprivation PVT PVT Lapses - no alerting PVT Lapses - with alerting Figure 12. Time course of vigilance deterioration for a single subject sleep deprived twice several months apart, once without alerting stimuli and once with alerting stimuli. (SOURCE: Dinges et al. 1998.)

scores for the 18 subjects. Only one of the 18 subjects per- formed substantially differently across the two sessions, and many subjects performed almost identically. A factor analysis of scores on the 13 neurobiological mea- sures revealed three common personal factors underlying the score differences: self-evaluation (sleepiness and mood), cognitive processing (ability to engage in complex thinking), and vigilance (behavioral alertness). Intuitively, one would predict that hours of sleep deprivation would be a stronger predictor of subject performance than individual differences, but this was not the case. On every measure, the influence of individual differences was stronger than the influence of sleep deprivation duration. The authors summarized their study findings as follows: In this study involving repeated exposure to sleep deprivation under carefully controlled laboratory conditions, we found that neurobehavioral impairment from sleep loss was sig- nificantly different among individuals, stable within indi- viduals, and robust relative to experimental manipulation of sleep history. Thus, this study is the first to demonstrate that inter-individual differences in neurobehavioral deficits from sleep loss constitute a differential vulnerability trait (Van Dongen et al. 2004). In summary, it appears that there are significant individual differences among commercial drivers in the incidence and sus- 28 ceptibility to drowsiness. This was illustrated by the FHWA- sponsored DFAS and by a study of fatigue associated with the use of sleeper berths. Studies have shown that humans are gen- erally not very good judges of their own levels of sleepiness, but there are even large individual differences in the accuracy of self-assessment. Variations of amount of nightly sleep are one obvious source of individual differences in alertness, but sig- nificant differences are seen even when subjects receive con- trolled amounts of sleep. Moreover, a person’s ability to stay awake and perform during sleep deprivation seems to be remarkably consistent over time, even though there are large differences among different people. 4.7 PERSONALITY In the present context, “personality” refers to enduring per- sonal traits or tendencies that affect behavior. Personality is most often viewed in relation to interpersonal interaction (e.g., introversion-extroversion), but personality traits can also be associated with driving and other safety-related behaviors. In the survey, personality traits such as aggres- siveness, impulsivity, and inattentiveness were rated by both respondent groups as having the highest associations with risk of the various factors listed, which included demographic, experience, personal/family, and medical factors. Corsi and 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Average PVT Lapses in Last 24 Hours of First 36-Hour Sleep Deprivation Av e ra ge PV T La ps e s in La st 24 H o u rs o f S ec o n d 36 -H o u r Sl e e p D e pr iv a tio n Figure 13. Scatter plot showing large variations in vigilance (alertness) among 18 sleep deprivation subjects, but high similarities between individuals’ performance during the first and second sleep deprivation periods. (SOURCE: Van Dongen et al. 2004)

Barnard (2003) found that driver personality traits such as hon- esty, patience, reliability, self-discipline, and self-motivation were highly valued by fleets. The driver personality trait “socia- ble” was also valued by fleets, but to a lesser degree. The remainder of this section discusses some specific personality traits with possible relationships to driving safety. 4.7.1 Impulsivity and Risk-Taking Impulsivity is characterized by behavioral instability and an inability to control impulses, sometimes including threatening behavior and violence. Impulsivity has been suggested to be related to an increase in crash risk. Logically, it is easy to assume that if an individual reacts quickly and without ade- quate forethought, he or she will be at higher risk for errors and vehicle crashes. Schuman, Pelz, Ehrlich, and Seltzer (1967) assessed 288 unmarried male drivers and found that both a high-crash/other accident group and high-violation group scored higher on a measure of impulsivity than those with a low number of crashes/other accidents and violations. Crashes in this study were defined as vehicle incidents that caused prop- erty damage or injuries (whether or not the respondent was at fault). Traffic violations were defined by self-reported traffic violations over the prior year. Risk-taking in driving obviously creates unsafe situations and increases the probability of crash involvement. Dewer (2002) makes a distinction between “high-level” and “low- level” risk-related decisions. In driving, a “high-level” deci- sion might relate to the decision to drive or not drive under particular conditions, such as during adverse weather or after inadequate sleep. “Low-level” decisions refer to choices made while driving, such as decisions to speed or tailgate. Turns across a stream of traffic are a test of driver judgment and decision-making, and driver tendencies toward impulsivity and risk-taking obviously increase crash risk. Dewer (2002) discusses sensation-seeking as a related individual charac- teristic, and cites an analysis by Jonah (1997) documenting correlations between sensation-seeking and risky driving behaviors such as speeding, frequent lane changes, alcohol use, and failure to wear safety belts. Rimmo (2002) noted that sensation-seeking is strongly associated with misbehaviors (violations of rules) but only weakly associated with driving errors not associated with rule violations, for example, failure to see another vehicle. As many vehicle crashes are the result of at-risk behaviors, such as speeding, improper following distance, and driving fatigued, it appears that drivers’ subjective risk of their actions determine the extent to which drivers engage in at-risk behav- iors. For example, few would doubt that speeding is more risky than not doing so; yet, there are situations where the risk of crash or injury while speeding is minimal. The accumulation of these instances may confound a driver’s evaluation of crash or injury potential because he or she may be basing individ- ual conclusions on prior outcomes (e.g., no crash) rather than on objective risk (Haight 1986). 29 This explanation fits well within a behavioral framework, which surmises that people are motivated to behave based on the expected consequences of their actions. Behavior can be explained by its antecedents (events prior to behavior that direct behavior) and consequences (events after behavior that motivate behavior) of specified behavior(s). For example, being late for a delivery (antecedent) may prime a commercial driver to speed (behavior). The consequences of speeding may be positive (deliver on time) or negative (receive a speeding ticket, cause a crash). Behaviors followed by positive con- sequences are more likely to be repeated in the future and those followed by negative consequences are less likely to be repeated in the future. The immediate and reliable positive consequence of making the delivery on time may outweigh the low probability of a negative consequence of getting a ticket or being involved in a crash (Daniels 1989, Geller 2001). A behavioral approach to commercial driver safety management would emphasize the use of rewards (e.g., bonuses, positive recognition) for safe driving behaviors as a way of counteracting unsafe driving practices. 4.7.2 Social Maladjustment and Aggressive/Angry Personalities Social maladjustment is a set of behaviors and personality characteristics that have been found to be related to accident rates in a variety of settings. People are often considered socially maladapted if they have a general tendency to disregard laws and rules. Behaviors may include law breaking, disregard for other people, hostility or aggression, irresponsibility, self- centeredness, problem drinking, and authority problems. In the research project survey, the adjective “aggressive/anger” was among the factors with the highest-rated association with crash risk. Dishonesty as a personality trait was considered by respon- dents to have a relatively weak association with crash risk. When comparing 20 taxi drivers with a poor crash record (more than four crashes in the past 15 years) with 20 taxi drivers with low crash rates, Tillman and Hobbs (1949) found that drivers in the high-crash group were significantly more likely to be violent, to be delinquent, to have frequent job changes, to report themselves as being unfaithful, and to have a general lack of responsibility. In a more controlled study, McGuire (1972) matched two groups of 67 non-commercial drivers on age, driving experience, miles driven, and marital status. One group had a crash in the previous 3 months, while drivers in the other group did not have a crash in the last 3 months. Drivers in the crash group were described as less mature, holding negative views toward laws and authority, and having poor social adjustment. When studying South African bus drivers with repeated crashes, Shaw and Sichel (1961, 1971) described these individuals as being selfish, self- centered, overconfident, resentful and bitter, intolerant, and having antisocial attitudes and criminal tendencies. Sweeney (1998) correlated a number of “temperament” scales with the 3-year crash records of a group of U.S. Army soldiers and

found “dependability” to have the highest (negative) correla- tion with crash involvement. The Dula Dangerous Driving Index (DDDI, Dula and Bal- lard 2003) is a 28-item pencil/paper measure used to assess the driver’s level of aggression, hostility, and impatience when driving. The DDDI is made up of four subscales, includ- ing aggressive driving, angry driving, risky driving, and nega- tive emotions while driving. The DDDI was used to examine the relationship between dangerous driving and aggressive personality and anger among 119 male and female college students. Scores on each driving behavior subscale were significantly and positively correlated with measures of ag- gression and anger. A statistical analysis indicated that dis- positional (personality) aggression and anger, driver history variables, and gender accounted for a significant portion of the variance in all DDDI scales. Further, males displayed sig- nificantly more aggressive, risky, and angry driving than females. Males and females reported similar levels of dan- gerous driving (DDDI total scores) and negative emotions while driving. Dangerous driving was positively related to self-reported traffic citations and at-fault crashes. There appears to be strong support for the view that deviant or social maladjustment characteristics are associated with higher crash risk. As noted, survey respondents corroborated this conclusion by their high crash association ratings for the “aggressive/angry” personality trait. In addition, many had written comments about driver “attitude,” including the following: • “A driver’s attitude dictates his compliance with company policies and his behavior behind the wheel.” • “Attitude is everything!! Getting rid of the ‘King of the Road’ syndrome is key to defensive driving.” • “A driver’s attitude and his attention to detail are the main indicators of safe driving.” • “High-risk guys need to be found before employment, not after. Their behaviors are generally [caused] by personal psychology that is ingrained and difficult to change.” • “High-risk drivers . . . tend to be ‘hot tempered’ and in contest with others on the road.” • “Emotional instability for any reason equals high risk.” • “I no longer hire for experience. I hire for ‘heart.’ I find that the more ‘heart’ a driver has, the better employee he or she will make. ‘Heart’ means caring for other people.” • “Ninety percent of the time, high-risk drivers think they are very good drivers.” 4.7.3 Introversion-Extroversion Eysenck (1947) proposed the personality dimension intro- version-extroversion (I-E). This construct exists along a con- tinuum. Introversion is defined as a person’s preference to attend to his/her inner world of experience with an emphasis on reflective, introspective thinking. Conversely, extroversion is defined as a person’s preference for attending to the outer world of objective events with an emphasis on active involvement in the environment. Individuals who are associated with extrover- 30 sion are predicted to have higher accident rates because of their lower level of neural activation. Introverted individuals place more value on being in control of their actions and would there- fore tend to be more vigilant (Keehn 1961). Fine (1963) found that drivers with high extroversion scores were disproportion- ately more likely to be involved in traffic crashes. This find- ing was later supported by Smith and Kirkham (1981). They reviewed the crash and traffic violation records of 113 young male drivers. They found a significant correlation between extroversion and traffic crashes and violations. Powell, Hale, Martin, and Simon (1971) found that extroversion and accident rates were highly correlated in a variety of industrial settings. Finally, Schenk and Rausche (1979) showed that differing lev- els of extroversion predicted groups with no accidents, one acci- dent, and more than one accident. Studies of this personality dimension have not universally supported a correlation to crash involvement, however; Lancaster and Ward (2002) reviewed several reports with contradictory or inconclusive findings. In the research project survey, respondents were asked to rate the association of “introverted/unsociable” with crash risk. The rated association for both respondent groups was among the lowest of the factors rated; indeed, the work of Eysenck and others on this topic may indicate that introverted drivers are as safe as, or safer than, extroverted ones. 4.7.4 Locus of Control The locus of control personality construct was developed by Rotter (1966), whose theory differentiates individuals with an internal or external locus of control. An individual with an internal locus of control is defined as someone who believes he/she has the power to achieve mastery over life events. Con- versely, an individual with an external locus of control has the belief that his/her efforts to effect change are useless. A driver who believes he/she has little control over his/her involvement in a vehicle crash should have a higher probability of incurring a vehicle crash. This is exactly what Bridge (1971) found when assessing the rate of automobile driver crashes. Mayer and Treat (1977) found that young drivers with three or more vehicle crashes in a 3-year period were more external than drivers with no vehicle crashes. In a more controlled study, Jones and Fore- man (1984) classified bus driver applicants with two or more moving violations into a high-risk group and those with no moving violations into a low-risk group. Of those in the high- risk group, 79% scored high external locus of control, versus only 31% in the low-risk group. 4.7.5 Extreme (“Dichotomous”) Thinking “Dichotomous” thinking is the tendency to think in “all or none” terms and thus have views that might be considered extreme or strongly opinionated. Plummer and Das (1973) studied two groups of drivers. A frequent-crash group com- prised drivers who had been in two or more vehicle crashes in the preceding year, while an infrequent-crash group comprised

drivers who had not been involved in a vehicle crash in the pre- vious year. The frequent-crash group displayed more dichoto- mous thinking than the infrequent-crash group. Dichotomous thinking involves polarizing events so that more extreme choices are selected as the appropriate course of action. The dichotomous thinker is more likely to make an inappropriate choice in favor of extreme action when presented with a dan- gerous driving situation. This study lends moderate support for the view that extreme thinking is associated with crash risk. 4.8 SENSORY-MOTOR PERFORMANCE As a dynamic sensory-motor task, driving performance is obviously affected by physical abilities. Reliable percep- tion, quick response, and accurate maneuvering are essen- tial features of safe driving (Dewer 2002). However, if physical prowess were the primary factor influencing crash involvement, then teenagers and young adults would likely be the safest drivers, and individual athletic prowess would correlate with driving safety. A 1998 study by Trucking Research Institute and Inter- Science America provided an extensive and detailed list of driving-related sensory-motor abilities, as follows: • Perceptual – Static visual acuity (stationary objects) – Dynamic visual acuity (moving objects) – Contrast sensitivity – Useful field of view (area of visual field in which information is acquired) – Field independence (ability to perceive targets embed- ded in a complex scene) – Depth perception • Cognitive – Decision-making – Selective attention (ability to attend to one stimulus while filtering out “noise”) – Attention sharing – Information processing (ability to acquire information and perform mental operations on it) • Psychomotor – Reaction time – Multi-limb coordination – Control precision – Tracking (follow a path or pursue a moving target) – Range of motion This study on five different older commercial driver groups was conducted to examine the impact of increasing age on perceptual, cognitive and psychomotor abilities, and driving performance. Age, in and of itself, was not reliably predictive of driving performance. One reason was that the individual variation within age groups was much greater than the varia- tion across groups. Tests comparing driver performance on the above sensory-motor tasks with performance on an inter- active commercial truck driving simulator indicated that the most predictive abilities were depth perception, useful field- 31 of-view, field independence, attention sharing, and range of motion. Sensory-motor tests such as these may be helpful in assessing medical conditions as well as alcohol and drug impairment (Llaneras et al. 1995). 4.9 OTHER RISK FACTORS 4.9.1 Stress Stress is generally seen as a human response to an aver- sive or threatening situation, not as an enduring personal trait. However, if stressful situations are long-lasting or recur- rent, stress can become an individual characteristic. Height- ened stress has been implicated in increasing the risk of vehicle crashes. Brown and Bohnert (1968) reported that 80% of drivers involved in fatal crashes, but only 18% of controls, were under serious stress involving interpersonal, marital, vocational, or financial areas prior to the crash. Finch and Smith (1970) reported similar findings. Among the general population of drivers, the association of alcohol with crash involvement may reflect life stress as well as its direct dele- terious effects on driving. Seltzer and Vinokur (1974) admin- istered self-report questionnaires assessing stressful life events (Holmes and Rahe’s Life Events Checklist), alcohol abuse (Michigan Alcoholism Screening Tests), several per- sonality variables (aggressions, paranoid thinking, depression, and suicidal tendencies), and driving history (exposure, viola- tions, and accidents and crashes) with two groups of drivers. The general group comprised drivers renewing their drivers’ licenses or completing driver safety school. The other group, called the alcohol group, comprised drivers receiving in- patient or outpatient treatment for alcoholism. The Seltzer and Vinokur found that drivers under greater social stress (regardless of group) were correlated with more crashes and other accidents. Subjective stress to life events was more highly correlated with prior crashes and accidents than either demographic and personality variables. McMur- ray (1970) found divorce to be a significant predictor of crash risk. In his study of 410 drivers involved in divorce proceedings, drivers had twice as many crashes during the year of their divorce than during 7 previous years. This rate was even higher for the period 6 months before and after the divorce. These studies provide support for an association of life stressors and crash risk, with alcohol use as a frequent concomitant factor. In the research project survey, the stress-related driver situa- tions, “unhappy/disgruntled with job or company,” “debt or other financial problems,” and “unhappy marriage or other family problems” were generally rated near the middle of the 16 surveyed factors in terms of their association with crash risk. 4.9.2 Recent Involvement in Other Crashes One source of stress might be recent involvement in a crash. A recent crash might also be an indication that a driver is not performing safely during the time period in question. Blasco,

Prieto, and Cornejo (2003) have presented evidence that vehi- cle crashes tend to be clustered more than would occur by chance alone. They analyzed the crash rates of 2,319 bus drivers in a Spanish city over a period of 8 years from 1976 to 1983. No personality or other personal characteristics that might be associated with crashes were identified or studied. The authors compared their empirical evidence with models of crash rates predicted by chance and by equal probabilities. Analysis of the crashes indicated that they tended to occur closer together than can be explained by chance; the occur- rence of a crash seemed to increase the probability of a driver having another crash. 4.9.3 Safety Belt Use Section 392.16 of the Federal Motor Carrier Safety Regu- lations (FMCSRs) requires commercial drivers to wear safety belts while driving. Nevertheless, 311 of 588 fatally injured large truck drivers in 2002 were not wearing safety belts. One hundred thirty-four fatally injured drivers of large truck crashes were ejected (FMCSA web page; www.fmcsa.dot.gov/ safetybelt). Most of these ejections occur during rollover crashes, a particularly injurious type of crash for large truck occupants. In 2003, FMCSA completed a study of safety belt use by truck drivers (3,909 trucks were observed). Of nearly 4,000 commercial vehicle occupants observed, the usage rate was 48%. This compares unfavorably with a current passenger vehicle occupant usage rate of 79% (U.S. DOT 2003). Accord- ingly, FMCSA has announced a goal of increasing commercial driver safety belt use. The most obvious connection between safety belt use and injury risk is the occupant protection afforded by safety belts. However, there is also evidence among the general population of drivers that non–safety belt use is associated with various risky driver attitudes and behaviors. Lancaster and Ward (2002) reviewed studies indicating that driver non–safety belt use is associated with speeding, short headways (tailgating), alcohol use, red light running, more previous traffic viola- tions, and sensation-seeking personalities. Eby, Kostyniuk, and Vivoda (2003) observed that safety belt use among drivers using hand-held cell phones was lower in every age group studied than among comparable non–cell phone users. Thus, non–belt use by a commercial driver should probably be regarded as a safety “red flag.” 4.10 RISK FACTORS IDENTIFIED IN OTHER TRANSPORTATION MODES The research project also scanned other transportation modes to identify research on high-risk operators. This included mar- itime, rail, and air operations. The majority of research focus- ing on operator safety seems to have been focused on fatigue and generally emphasizes situational determinants of behav- ior rather than individual constitutional differences. For exam- ple, there have been many studies of night-shift transportation 32 operators and other workers and the effects of shift changes or jet lag (Comperatore, Kirby, Kingsley, and Rivera 2001a). Night-shift workers perform worse on tasks of vigilance and reaction times when compared with day workers, and aviators flying in flight simulators at night have reduced hand-eye coordination, poorer vigilance and calculation proficiency, and impaired flight performance compared with day fliers (Rothblum et al. 2002). The identification of high-risk individuals has focused on personal readiness or fitness for duty. According to Rothblum et al. (2002, p. 34): Personal readiness failures occur when individuals fail to pre- pare physically, mentally, or physiologically for duty. For instance, violations of work-rest rules, use of intoxicants and certain medications, and participating in exhausting domes- tic or recreational activities prior to reporting for duty can impair performance on the job and can be preconditions for unsafe acts. Rothblum et al. (2002) point out that screening tests have been used for many years to select suitable employees for safety-sensitive professions such as law enforcement and fire safety. However, this is a controversial area with test validity and reliability being at issue. Tests are available for deter- mining if someone is under the influence of drugs or alcohol or severely fatigued, and simulator-type tests have been used to analyze driver readiness. Further research is needed to identify valid, reliable methods across modes. 4.10.1 Maritime Operations After the March 1988 Exxon Valdez accident, Exxon assessed the performance of all its ships’ masters and mates. Most did very well, but it was determined that proficiency tests for individuals should take place early in the selection and training process rather than after they are on the job. Some ship operating companies do use screening tests to identify risk fac- tors in individuals, but more research is needed to validate them (Alex Landsburg, Maritime Administration, personal communication February 6, 2004). Crew endurance programs are also used in the maritime environment (Comperatore et al. 2001b). This type of pro- gram educates personnel on how fitness for duty can affect not only job performance but long-term health. These programs assist personnel in controlling the hazards that affect fitness for duty (Rothblum et al. 2002). Comperatore et al. (2001a) state that operators of Coast Guard systems should be moni- tored following the principles set out by crew endurance man- agement (CEM) practices, in which “endurance refers to the ability to maintain performance within safety limits.” Signs of stress include alienation, withdrawal, and lack of participa- tion. Other behaviors that should be monitored include visible daytime sleepiness and degradations in performance (i.e., low energy, lack of motivation, depression, irritability, introver- sion, reduced and unclear communication with coworkers,

problems with decision-making ability and performance of mental function requiring logical ability, apathy, reduced attention to detail, degraded endurance, and reduced safety). Further, according to the International Maritime Organiza- tion (IMO) (2001), some of the more recognizable symptoms of fatigue found in pilots (operators) are stress, mood swings, headaches and gastro-intestinal problems. Fatigue can affect pilot performance by impacting pilots’ ability to think clearly or make decisions, to concentrate, to focus attention appro- priately, to assess risky situations, or to act as quickly as nec- essary. Other impairment signs include poor memory (failure to remember task sequences), slow response (to emergencies), loss of bodily control (slurred speech), and mood or attitude changes (irritable or a “don’t care” attitude). One of the most alarming consequences of fatigue is uncontrollable micro- sleeps that may last a few seconds to a couple of minutes, and of which drivers may be unaware. Micro-sleep lapses have been well documented as causing a number of maritime and other transportation incidents (IMO 2001). As discussed earlier, there is evidence of large individual differences in susceptibility to micro-sleeps and other manifestations of fatigue. 4.10.2 Rail In 2002, 14,404 railroad accidents/incidents occurred in which there were 11,103 nonfatal injuries and 951 fatalities (Federal Railroad Administration 2003). Of the 2,944 train accidents for which a major cause was identified, “human factors” was assigned as the principal causal category for 1,050 (36%). In terms of operator violations, stop signal vio- lations were the most common, followed by speeding viola- tions (Coplen, 2004). The process of becoming a certified locomotive engineer (operator) is outlined in CFR 240. Railroad employee records and driving records (within 36 months concerning alcohol/ controlled substances) are reviewed (CFR 240 115 b1). Vision and hearing acuity are evaluated by a medical examiner. Spe- cific training, knowledge (of the railroad’s rules and practices for the safe operation of trains), and skills (operating, equip- ment inspection, and train handling practices, and compliance with federal safety rules) are also tested before certification. No research focusing on individual differences and high- risk rail operators could be identified. Perhaps most relevant is research planned by England’s Rail Safety and Standards Board of signals passed after danger (RSSB 2002). This proj- ect will establish a method of assessing existing driver work- load, including risk associated with overload/underload with a goal of establishing control measures to reduce driver work- load (RSSB 2002). The program will also develop methods of quality assurance for (1) staff skills, training, and management and (2) incident investigation and analysis methods to iden- 33 tify contributing situational factors and factors associated with particular drivers. 4.10.3 Aviation In 2002, there were approximately 4,000 airspace incidents, the majority of which were categorized as operational errors, pilot deviations, or surface incidents. Each year approximately 1,000 aviation-related fatalities occur (FAA 2003). Operator performance is a prominent issue in aviation incidents, but a sin- gle “operator profile” is difficult to ascertain. Airplane and heli- copter pilots have a variety of training backgrounds (e.g., high-performance military aircraft combat or training or more conservative modes of air transport). As with rail, airline pilots are trained in accordance with federal regulations. Violations involving drugs or alcohol are a basis for imme- diate grounding and dismissal. A pilot who reports to fly with a blood-alcohol content (BAC) > 0.06, or whose unannounced urine test displays traces of prohibited drugs, can be relieved of duty, grounded, or fired, with little or no recourse. Similarly, a pilot who receives a driving under the influence (DUI) citation while driving a vehicle may be grounded, punished, or fired (Phil Olsen, personal communication, January 30, 2004). The primary infractions that result in license suspension or revocation generally fall into a few broad categories: • Runway incursion (caused by disorientation, distraction, or inattention) • Failure to follow company or aircraft operating proce- dures • Failure to follow air traffic control guidance (altitude, heading, airspeed, taxi instruction) • Violation of airspace restrictions In a large fraction of the cases, it is pilot distraction or inattention that starts the chain of events that leads to an infraction (Jim Chadwick, personal communication, Febru- ary 2, 2004). Adams, Koonce, and Hwoschinsky (2002) surveyed 4,000 pilots in an attempt to characterize the decision-making styles of accident-free and accident-prone pilots. They reported that accident-prone pilots were more likely to expose themselves to unsafe flying experiences, feel time pressure when making decisions, have a false sense of their ability to handle a situ- ation, and not review alternative options or solutions. The concept of crew resource management (CRM) is seen in aviation as well as maritime transport. In aviation, the dic- tatorial pilot command concept has been replaced by CRM, which trains aircrews to encourage discussion and evaluation by the entire cockpit crew in emergency avoidance and man- agement prior to an ultimate decision by the pilot (Phil Olsen, personal communication, January 30, 2004).

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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.

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