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CHAPTER 3
Classifying Noncompliance
This taxonomy is intended to aid public transit agencies in identifying the reason(s) or root
cause(s) for any given instance of safety-related rule noncompliance. The taxonomy is based on
several human error/violation taxonomies that exist in other human performance domains.
Combining the results of a review of transit accidents with some of the classic taxonomies, the
authors present the following information, which is tailored for public transit agencies to use to
investigate the cause of safety-related rule noncompliance. Because this taxonomy is tailored to
the needs of public transit agencies to investigate noncompliance, the user is discouraged from
comparing other taxonomies to this one as there may be subtle but meaningful differences. The
examples provided are based on actual transit incidents and accidents but have been altered to
enhance their illustrative properties.
The taxonomy is influenced by the work of James Reason (1990), who identified that accidents
are the result of a long chain of events. While the employee's act is the last link in the chain of events
leading to an accident, there are many other contextual factors that most likely contributed to the
event. Reason suggested that creating barriers to these contextual factors may thwart the opportu-
nity for future accidents by breaking the intermediate links in the chain. Akin to the metaphor of
a chain, Reason proposed the Swiss cheese model of accident causation (Figure 3). In this model,
contextual factors are the latent failures or preconditions that allow unsafe acts, such as rule
noncompliance, to occur. Ultimately, "an accident is one incident too many" (Reinhart 1996).
Consider the head-on collision of a bus with a car. The accident report indicated that the bus
operator fell asleep at the wheel and veered into the lane of oncoming traffic. By examining the
chain of events leading to this accident, the creation of a robust explanation for the accident
is possible. The operator unintentionally fell asleep at the wheel (Level I factor). The employee did
not have adequate rest prior to his or her shift (Level II factor). The employee's supervisor sched-
uled the employee to return to duty without an adequate opportunity for rest (Level III factor). The
public transit agency recently had budget cuts which reduced the number of bus drivers (Level IV).
This made it difficult for supervisors to have an adequate number of personnel for the service
routes. If one or more of these events did not occur, the accident may have been avoided. By
addressing these factors after an incident occurs, future accidents may be prevented.
The following guidelines are recommended practices for using the taxonomy. Chapter 2
explains much of the terminology in the taxonomy. It is important to review this entire chapter
before attempting to use the taxonomy. The first step is to determine the employee's intent.
Determining whether noncompliance was intentional or due to human error is key to under-
standing why an employee failed to comply. Understanding the employee's intent will also allow
identification of the appropriate response or remedial action.
The taxonomy consists of four levels that are based on Reason's accident causation model. The
levels are designated by the Roman numerals I through IV and the subfactors for each level are
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