Synthesis of Safety for Traffic Operations - Transports Canada

Conducting and Authoring Research
In larger jurisdictions, there is no sound reason why the practitioner should not be using
“best practices” to conduct safety research. There is certainly a need to do so to assist in
making informed technical decisions on the safety implications of day-to-day matters.
Furthermore, the data that is required to undertake the appropriate analysis is usually
contained in existing files and records. The arguments of insufficient staff time or
inadequate knowledge of the appropriate methods are weak, and would not pass the test
of due diligence.
By suggesting that road safety research be conducted using the best practices, it is
recognized that the data and the expertise to do so is not always available to practitioners
from smaller jurisdictions. This should not preclude action/evaluation. It is very likely
that research and evaluations will continue to be conducted using methods that are not
considered “best practice”. This is acceptable, as long as the limitations of the evaluation
are recognized, documented, and considered by the traffic operations professional when
applying the results in practice.
One of the most common sources of error found in road safety studies is regression-tothe-mean
(RTTM). It is prevalent in road safety studies and bears mention here to assist
practitioners in designing and conducting road safety research. RTTM potential occurs
when sites that are selected for treatment are done so because of an abnormally high
collision frequency or rate. This is usually the case, and why RTTM is a frequent issue.
RTTM is briefly explained as follows:
The long-term average crash frequency (all things remaining stable) is the true measure
of safety at a location. The annual crash count is a short-term measure that is generally
used to approximate the long-term average. However, we know that crash counts are
subject to some random variation from year-to-year. Therefore, if sites are selected for
treatment because of the abnormally high annual crash count, one has to ask – is the high
crash count representative of the long-term average, or is it a random fluctuation?
In some instances the short-term crash count is representative of the long-term average, in
other instances the short-term count is randomly high. In the case that the count is
randomly high, we would expect that the next set of crash counts would be more
representative of the long-term average. That is to say, the next set of counts would be
closer to the a long-term average (i.e., lower). Hence, if the abnormally high crash count
is used as the “before” data, and the short-term crash count is not reflective of the longterm
average, then a safety benefit would be exhibited even if no treatment was applied.
This tendency for short-term high crash frequencies to produce lower (more average)
crash counts in the subsequent observation periods is known as RTTM.
It is common practice for road safety researchers to use a three to five year crash history
to account for random variation in annual crash counts. This is an important step in
minimizing RTTM effects, but is insufficient to eliminate them.
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