Impact of Behavior-Based Safety Techniques on Commercial Motor Vehicle Drivers (2007)

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4Background Motor vehicle crashes are often predictable and prevent- able. Yet, many drivers choose to behave in ways that put themselves and others at risk for a vehicle crash and/or seri- ous injuries. At-risk driving behaviors include speed-limit violation, excessive speed/lateral acceleration on curves, un- planned lane departures, frequent hard braking, close follow- ing distances, lateral encroachment (e.g., during attempted lane changes, perhaps due to improper mirror use), failure to yield at intersections, and general disobedience of the rules of the road. At-risk non-driving behaviors include improper lift- ing techniques, improper entering/exiting the truck, and poor diet and exercise. Performing at-risk driving behaviors is likely to increase crash risk while performing at-risk non-driving behaviors is likely to increase injury and illness risk. At-Risk Behaviors One of the most significant studies on the factors that con- tribute to motor vehicle crashes was the Indiana Tri-Level Study (Treat et al., 1979). To provide insight into the factors that contribute to traffic crashes, data were collected on three levels to assess causal factors as being definite, probable, or possible. The study determined that • 90.3% involved human error, such as at-risk driving behav- ior, inadvertent errors, and impaired states; • 34.9% involved environmental factors, such as wet/slick road conditions and poor weather; and • 9.1% involved vehicle factors, such as brake failure and worn tires. Note the percentages do not total to 100% because some events were coded as involving more than a single factor. The two most frequent human behaviors found in all of the crashes investigated were “recognition failure” (i.e., driver inattention/distraction; 20.3% of the crashes) and “decision error” (i.e., excessive speed; 14.7% of the crashes). A more recent study by Hendricks, Fell, and Freedman (1999) tried to replicate the epidemiological method employed in the Indiana Tri-Level Study using the National Auto- motive Sampling System (NASS) protocol. More specifically, the researchers assessed the specific driver behaviors and un- safe driving acts that lead to crashes, and the situational, driver, and vehicle characteristics associated with these behaviors. Similar to the Indiana Tri-Level Study, Hendricks, Fell, and Freedman found human error was the most frequently cited contributing factor in these crashes (99.2%), followed by environmental (5.4%) and vehicle (0.5%) factors. Thus, it has been shown that crashes and their associated injuries and fatal- ities are likely to result from excessive unsafe driving behaviors and from deficits in safe driving behaviors. Both the Indiana Tri-Level Study (Treat et al., 1979) and the study conducted by Hendricks, Fell, and Freedman (1999) clearly indicate most vehicle crashes were the result of human error (which includes at-risk driving behaviors). How- ever, these studies were primarily focused on light vehicles. The recently completed Large Truck Crash Causation Study (LTCCS) assessed the causes of, and contributing factors to, crashes involving CMVs. While the LTCCS contains the same type of descriptive data as the primary national traffic safety databases (e.g., FMCSA’s Motor Carrier Management Infor- mation System and NHTSA’s General Estimates System), it also focused on pre-crash factors such as driver fatigue and distraction, vehicle condition, weather, and roadway prob- lems. This made the LTCCS the only national examination of all factors related to causation in large truck crashes. The LTCCS was conducted at 24 data collection sites in 17 states by researchers from NHTSA’s NASS and state truck inspec- tors. Crash data were coded in two NASS Zone Centers and reviewed by FMCSA and NHTSA personnel and national truck crash experts. The LTCCS found 87.3% of the critical reasons for crashes assigned to the large-truck driver were I N T R O D U C T I O N

5driver errors: 38% were decision errors (e.g., driver drove too fast for conditions), 28.4% recognition errors (e.g., driver did not recognize the situation by not paying proper attention), 11.6% non-performance errors (e.g., driver fell asleep), and 9.2% performance errors (driver exercised poor directional control; U.S.DOT, 2006). For all large-truck crashes (includ- ing both single-vehicle and multi-vehicle crashes), the criti- cal reason for the crash was assigned to the large truck 54.6% of the time and, as noted, driver errors predominated over vehicle and environmental factors. When crashes involving only one truck and one passenger vehicle were considered, 43.9% had a critical reason assigned to the truck and, of these, 86.2% involved driver errors. American Transportation Research Institute’s (ATRI) (2005) study, Predicting Truck Crash Involvement: Developing a Commercial Driver Behavior-Based Model and Recommended Countermeasures, analyzed data on 540,750 drivers gathered over a 3-year time frame to determine future crash predict- ability. Their data showed reckless driving and improper turn violations as the two violations associated with the highest increased likelihood of a future crash. The four convictions with the highest associations with future crash involvement were (1) improper or erratic lane change, (2) failure to yield right of way, (3) improper turn, and (4) failure to maintain a proper lane. When a driver receives a conviction for one of these behaviors, the likelihood of a future crash increases to between 91 to 100%. In a summary of all crash data analyzed, reckless driving violations prompted the highest likelihood of a future crash (32.5%). Knipling, Hickman, and Bergoffen (2003) surveyed motor carrier safety managers regarding major safety-management problems and solutions. Among 20 different safety- management problem areas rated by respondents, “at-risk driving behaviors” (e.g., speeding, tailgating) were rated as the single most important safety-management problem. These studies, in combination, indicate that driving behaviors are a significant contributing factor of large-truck crashes, and inter- ventions aimed at increasing safe driving behaviors and reduc- ing at-risk driving behaviors will prevent many vehicle crashes. The primary focus of fleet safety managers is to reduce at- risk driving behaviors, thereby reducing crashes. However, at-risk non-driving behaviors are also a significant safety con- cern. In 2001, there were 32 million musculoskeletal injuries. Back problems remain one of the most frequent and expen- sive on-the-job injuries. Nearly 2% of all workers have a work- related back injury (U.S. Department of Labor, 2003). Truck drivers ranked second out of 127 jobs (accounting for 8% of the total frequency of musculoskeletal injuries) in the fre- quency of musculoskeletal injuries (Bureau of Labor Statis- tics, 2004). Heavy lifting and stepping in and out of the truck cab are likely to be daily behaviors performed by CMV drivers. Workload is a key factor in terms of risk (e.g., heavy lifting increases the risk of low back injury by 6 to 8 times). Moreover, whole-body vibration for extended periods is likely to pre- dispose CMV drivers to back injuries (Massaccesi et al., 2003). Truck drivers are also more likely than the general population to engage in unhealthy lifestyle behaviors, such as smoking, poor diet, and physical inactivity (Roberts and York, 2000). Behavior-Based Safety Behavior-based safety (BBS) provides robust positive results when applied in organizations seeking to reduce employee injuries due to at-risk behaviors. Primary techniques in BBS include peer observation and feedback, training and educa- tion sessions, behavior-based incentives, prompts, and goal setting (Geller, 2001; Krause, Robin, and Knipling, 1999). Almost all prior BBS research has been applied in work set- tings where employees can systematically observe the safe ver- sus at-risk behavior of their co-workers. In contrast, truck and bus drivers work alone in relative isolation and thus require alternative BBS processes such as OBSM or self-management. BBS programs are advantageous because they are easy to implement, are easy to teach, and can be implemented in the setting where the problem occurs (Daniels, 1999; Geller, 2001). BBS programs have been successfully used to increase safety- related work behaviors in a variety of organizational settings, including pizza stores (Ludwig and Geller, 1991, 1997), a paper mill (Fellner and Sulzer-Azaroff, 1984), the mining industry (Fox, Hopkins, and Anger, 1987; Hickman and Geller, 2003a), the railroad industry (Peterson,1984), a gas pipeline company (McSween, 1995), manufacturing plants (Reber and Wallin, 1984), a chemical research laboratory (Sulzer-Azaroff, 1978), a food manufacturing plant (Komaki, Barwick, and Scott, 1978), an infirmary at a residential center for mentally dis- abled individuals (Alavosius and Sulzer-Azaroff, 1986), building construction (Mattila and Hyödynmaa, 1988), a telecommunication parts manufacturing plant (Sulzer- Azaroff et al., 1990), a shipyard (Saarela, 1990), and a utility company (Loafman, 1998). In a review of 53 occupational safety and health studies covering various safety approaches, Guastello (1993) found BBS had the highest average reduction of injury rate (59.6%). Sulzer-Azaroff and Austin’s (2000) review of published BBS studies found that 96.9% of the studies they reviewed showed significant reductions in work-related injuries after the imple- mentation of BBS techniques. BBS programs have also been shown to reduce workers’ compensation claims. Behavioral Science Technology, Inc. (BST) found a 70% reduction in workers’ compensation claims in Year 3 after the introduc- tion of a BBS program (BST, 1998), and Hantula et al. (2001) showed reductions in workers’ compensation claims after the introduction of a BBS intervention. Clearly, BBS programs can be effective in reducing injuries and their associated costs.

6While BBS focuses on workers’ safety-related behaviors, this focus does not imply a one-dimensional (i.e., behavioral) view towards safety. In fact, Geller (2001) and Krause (1997) believe safety interventions that focus exclusively on reducing at-risk safety-related work behaviors without acknowledging the system in which they occur will have modest long-term success. Comprehensive BBS programs focus on three inter- dependent factors (person, behavior, and environment) in an organization’s safety system, called the “Safety Triad” (Geller, 2001). Changes in one factor are likely to impact the other two. While specific BBS techniques have been implemented with CMV drivers, this systemic approach to BBS has not yet been adopted in CMV safety-management approaches. Scope The studies presented above introduce and demonstrate that at-risk driving and non-driving behaviors contribute to CMV crashes, injuries, and illnesses. CMV drivers generally drive responsibly and exhibit lower rates of most types of inci- dent and crash involvement than drivers in general (FMCSA, 2003; Wang, Knipling, and Blincoe, 1999). BBS approaches to injury and illness reduction have been effective in reduc- ing at-risk behaviors in industrial settings thereby reducing injuries and illnesses. This synthesis reviewed the evidence for various behavioral strategies to increase the safety-related driving and non-driving behaviors of CMV employees. More specifically, this synthesis • Summarized available information on BBS techniques with CMV drivers, • Examined the effectiveness of various BBS techniques to increase safety-related driving and non-driving behaviors, • Identified observation and BBS techniques currently used by CMV carriers, and • Examined barriers to implementing BBS techniques in CMV carriers. This synthesis focuses primarily on CMV drivers; however, the topics and results presented are applicable to bus drivers and other transportation operators. Most of the BBS tech- niques discussed in the synthesis are intended for fleet safety managers or other safety professionals working in CMV operations. The Statement of Work for the synthesis can be found in Appendix A. Approach Information on observation and BBS techniques was obtained through several means. The primary method for obtaining information was project surveys. A fleet safety manager survey was administered through various methods: (1) a secure Internet survey form, (2) a survey form com- pleted on the computer and returned via email, and (3) a tra- ditional paper-and-pencil survey form returned via facsimile or mail. Appendix B shows the computer and paper-and- pencil survey forms. Safety managers were asked if they cur- rently used the observation or BBS technique and then, if “yes,” asked to rate the effectiveness of the BBS technique. Thus, these questions yielded data on the prevalence of industry use of the observation and BBS techniques as well as subjective evaluations of BBS techniques. Two focus groups were conducted with fleet safety man- agers. These focus groups were critical in informing the ques- tions and terminology employed in the survey distributed to fleet safety managers. Further, an extensive literature review was conducted. This literature review focused on relevant observation and BBS techniques used in CMV operations or other relevant industries. The literature cited in this syn- thesis was obtained through the Transportation Research Information System (TRIS), other reference systems, FMCSA research publications, research journals on traffic safety, industrial safety, and behavioral publications. The last section of this report will describe the survey methodology and results, conclusions, and research and development needs.