An on-road study to investigate fatigue in local/short haul trucking
ABSTRACT As a precursor to the present research, Hanowski et al. [FHWA Report no. FHWA-MC-98-029. Office of Motor Carriers, Federal Highway Administration, Washington, DC, 1998] conducted a series of focus groups in which local/short haul (L/SH) drivers provided their perspective on safety issues, including fatigue, in their industry. As a follow-up to the Hanowski et al. work, the effort presented here consisted of an on-road field study where in-service L/SH trucks were instrumented with data collection equipment. Two L/SH trucking companies and 42 L/SH drivers participated in this research. The analyses focused on determining if fatigue is an issue in L/SH operations. Of primary interest were critical incidents (near-crashes) where L/SH drivers were judged to be at fault. The results of the analyses indicated that fatigue was present immediately prior to driver involvement in at-fault critical incidents. Though it is difficult to determine with certainty why fatigue was present, the results suggest that drivers' off-hours behavior likely played a significant role in the fatigue experienced on the job. Another key finding of this research is that a small percentage of drivers were responsible for a majority of the critical incidents. This finding suggests that driver selection and monitoring could potentially improve safety in L/SH operations.
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- "Driver individuality has a unique impact on driving skills in which health and lifestyle issues such as fitness, poor diet, poor sleep habits and disorders have a strong positive effect on the correlation between performance and fatigue (Mabbott and Lloyd, 2005). According to Hanowski et al. (2003), the worst drivers (up to 25 per cent) are responsible for over 85 percent of haul road accidents. Compounding this problem is the fact that many mines have annual turnover rates of 40% necessitating expensive training programs and leading to poor-quality drivers during the training period -quality being measured both in terms of production and safety. "
ABSTRACT: Driverless haulage trucks have recently been developed for open pit mines. To predict the benefits of an Autonomous Haulage System (AHS), a deterministic/stochastic model has been created to compare AHS to a manual system by estimating benchmarked Key Performance Indicators (KPIs) such as productivity, safety, breakdown frequencies, maintenance and labor costs, fuel consumption, tire wear, and cycle times. The goal of this paper is to describe the driver/autonomous sub-models that function within a virtual 24/7 open pit mine operating with 9 trucks and 2 shovels to move ore to a crusher and waste rock to a dump.Procedia Computer Science 01/2011; 6:118-123. DOI:10.1016/j.procs.2011.08.023
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- "Sleep-related problems and similar variables have also been found to be associated with traffic incidents (Dalziel and Job, 1997; Maycock, 1997; Hanowski et al., 2003; Taylor and Dorn, 2005; see McDonald, 1989 for a review) and the link reported here between Fatigue Proneness and bus crashes can be explained with reference to this research. However, Fatigue "
ABSTRACT: There are likely to be individual differences in bus driver behaviour when adhering to strict schedules under time pressure. A reliable and valid assessment of these individual differences would be useful for bus companies keen to mitigate risk of crash involvement. This paper reports on three studies to develop and validate a self-report measure of bus driver behaviour. For study 1, two principal components analyses of a pilot questionnaire revealed six components describing bus driver behaviour and four bus driver coping components. In study 2, test-retest reliability of the components were tested in a sub-sample and found to be adequate. Further, the 10 components were used to predict bus crash involvement at three levels of culpability with consistently significant associations found for two components. For study 3, avoidance coping was consistently associated with celeration variables in a bus simulator, especially for a time-pressured drive. STATEMENT OF RELEVANCE: The instrument can be used by bus companies for driver stress and fatigue management training to identify at-risk bus driver behaviour. Training to reduce the tendency to engage in avoidance coping strategies, improve evaluative coping strategies and hazard monitoring when under stress may improve bus driver safety.Ergonomics 12/2010; 53(12):1420-33. DOI:10.1080/00140139.2010.532882 · 1.61 Impact Factor
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- "Links between truckers' shift work and sleep patterns and resulting adverse effects have attracted significant research attention (Freund, 1999; Hanowski, Wierwille, & Dingus, 2003). When the circadian rhythm, a 24-hour cycle in the physiological processes of living beings originating from a clock circuit in the hypothalamus set by information from the optic nerve, is altered due to rotating schedules, various gastrointestinal, cardiovascular, reproductive, and other dysfunctions are reported and conditions such as diabetes, epilepsy, and psychiatric disorders can be severely exacerbated (Barton & Folkard, 1993; Scott, 2000). "
ABSTRACT: A critical review was conducted of social, psychological, and health science literature on the array of health risks and morbidities of truckers. Multilevel worksite-induced strains (e.g., long work hours and fatigue, shift work and sleep deprivation, postural fatigue and exposure to noise and vibration, sedentary lifestyle and unhealthy diet, exposure to diesel exhaust fumes, and other occupational stressors) were categorized into six primary morbidities for truckers: (1) psychological and psychiatric disorders; (2) detriments resulting from disrupted biological cycles; (3) musculoskeletal disorders; (4) cancer and respiratory morbidities; (5) cardiovascular disease; and (6) risk-laden substance use and sexual practices. Elevated morbidity risks suggest the need for the design and implementation of systematic epidemiological research and environmental interventions in the transport sector.AAOHN Journal 07/2010; 58(7):285-96. DOI:10.3928/08910162-20100625-01 · 0.61 Impact Factor