By ignoring such dynamics, one can be misled about whether some initiative was or was not helpful in reducing crashes. For instance, a harsher-than-usual winter might raise the frequency of crashes, everything else remaining constant. Given that crashes can have many causes, increases and decreases in crash frequency over time can be due to changes in the frequency of any one of these causes. Therefore, understanding the role of an individual factor, such as fatigue, in causing a crash can be a challenge. These models acknowledge that a traffic crash has a multitude of possible causes that may not function independently, resulting in a fairly complex causal structure.
A constructed matrix permits evaluation of the relative importance of different factors at different points in the crash sequence. Similarly, the so-called Haddon Matrix ( Runyan, 1998) looks at factors related to human, vehicle, and environmental attributes before, during, and after a crash. The so-called Swiss cheese model of crash causation ( Reason, 1990) posits that failures occur because of a combination of events at different layers of the phenomenon.
#Causality meaning driver
Crashes can be due to factors associated with the driver (e.g., drowsiness, distractedness, anger) the vehicle (e.g., depth of tire tread, quality of brakes) the driving situation (e.g., high traffic density, presence of road obstructions, icy road surfaces, low visibility, narrow lanes) and the policies of the carrier, including its approach to compensation and to scheduling. Second, in addition to low-quality information, the fact that crashes often are the result of the joint effects of a number of factors makes it difficult to determine whether fatigue contributed to a crash. Neither approach is entirely satisfactory: in the LTCCS approach, the concept of a “critical reason” is not well defined since many factors can combine to cause a crash, with no individual factor being solely responsible, while in the other approach, the attributed percentages can sum to more than 100 percent. This approach is fundamentally different from that of calculating the percentage of crashes attributable to different causes. To this end, they tried to provide a relatively complete description of the conditions surrounding each crash. Police assessments, augmented by more intense interviewing and other investigations, were used to determine factors contributing to crashes in such studies as the Large Truck Crash Causation Study (LTCCS) (see Chapter 5), in which the researchers attempted to determine the critical event (the event that immediately precipitated the crash) and the critical reason for that event (the immediate reason for the critical event) for each crash. Standable that police underestimate the degree of fatigued driving and its impact on crashes. They must make this determination to the best of their abilities with limited information. In most cases, the police at the scene are charged with determining whether a chargeable offense was committed whether a traffic violation occurred and whether specific conditions, such as driver fatigue, were or were not present.
#Causality meaning drivers
If drivers survive a crash and are asked whether they were drowsy, they may not know how drowsy they were, and even if they do know, they have an incentive to minimize the extent of their drowsiness. Biomarkers for fatigue that can provide an objective measurement after the fact are not available. However, assessment of whether fatigue is a causal factor in a crash is extremely difficult and likely to suffer from substantial error for two reasons.įirst, the information collected can be of low quality. Efforts have been made to assess the percentage of crashes, or fatal crashes, for which fatigue played a key role. The theme of this chapter is that methods from the relatively new subdiscipline of causal inference encompass several design and analysis techniques that are helpful in separating out the impact of fatigue and other causal factors on crash risk and thereby determining the extent to which fatigue is causal.Ī primary question is the degree to which fatigue is a risk factor for highway crashes. One of the panel’s primary tasks was to provide information to the Federal Motor Carrier Safety Administration (FMCSA) on how the most up-to-date statistical methods could assist in the agency’s work. Research Methodology and Principles: Assessing Causality
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