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If you could predict severe crashes and design a roadway to help prevent them, would you?
In fact you can, by using the Highway Safety Manual Part C Predictive Method.
The predictive method can help you estimate the long-term average crash frequency of a
particular location, and quantify the safety performance of existing and proposed designs,
and if you have this information, you can consider it with other factors like operational performance
and environmental impacts.
The predictive method helps users make more informed decisions throughout the project development process,
including planning, design, operations, environmental, and maintenance.
In this video you will learn the process for applying the Part C Predictive Method.
Specifically, how to: define the study location and study period,
identify data needs,
select and apply an appropriate Safety Performance Function, or SPF, to estimate predicted crashes
for base conditions,
apply adjustment and calibration factors to predicted crashes for base conditions to
reflect site-specific and local conditions,
and, when appropriate, employ the Empirical Bayes, or EB method,
to estimate long-term average expected crashes.
The first step of the Predictive Method is to define your study location and study period.
The Study Location is broken down to roadway segments and/or intersections.
These are the most basic unit of analysis for the Predictive Method.
You combine the safety performance results of the segments and/or intersections to estimate
the safety performance of the project.
The Predictive Method study period is used to produce annual estimates
of long-term average crash frequency.
The Study Period is defined in terms of years, and may include a single year or multiple years.
As you define the study location and period, you also need to determine the scenario of
interest for which you want to estimate the long-term average safety performance.
This will impact data requirements and the extent of your analysis.
For example, your objective may be to estimate the long-term average safety performance for
existing roadway conditions, also known as base conditions, combined with the current
and/or future traffic conditions for comparison with an alternative that incorporates some
changes to the roadway geometrics and/or operations.
Depending on your needs and the application of the predictive method, you will need to
decide if it is appropriate to produce results in terms of either predicted or expected crashes.
Predicted crashes are based on the average annual safety performance of other sites with
similar characteristics to the site of interest.
Expected crashes are based on a weighted average of the predicted crashes and the observed crash history.
It is appropriate to use expected crashes to estimate the long-term safety performance
of a site when reliable observed crash data is available.
And if the site characteristics remain generally unchanged from the existing conditions to
the scenario of interest, then there is likely value in incorporating the site-specific crash history.
For example, if you are estimating the safety performance of an existing roadway under current
or future traffic volumes, and the number of lanes and median type remain the same,
then it is appropriate to estimate the expected crashes.
If you plan to apply a crash modification factor, or CMF, to estimate the expected change
in safety performance, then it is also appropriate to estimate the expected crashes for the base
condition before applying the CMF.
Let's look at an example where you want to consider the installation of left-turn
lanes on both major road approaches at an existing four-legged, two-way stop-controlled intersection.
You can use the observed crash history as well as the predicted crashes from the SPF
to compare the long-term safety performance of the intersection with and without left-turn lanes.
You would first estimate the expected crashes under the base condition, which is the existing intersection
without left-turn lanes, and then apply the CMF to estimate the expected crashes under
the condition with left-turn lanes.
When there are significant differences between the existing and proposed conditions,
the observed crashes are no longer relevant.
It is appropriate not to use the observed crash data because the scenario of interest
is greatly different from the current conditions or the observed crash data is not applicable
to future conditions.
Let's say for example you would like to consider converting a two-lane road
to a four-lane, divided road.
In this situation, the historical crashes under the two-lane, undivided condition are
not relevant to the future safety performance under the four-lane, divided condition.
In this case, you would appropriately rely on the predicted crashes to estimate the safety performance.
Now let's discuss Step 2: Identifying data needs.
In general, you need three categories of data to apply the predictive method: crash data,
traffic volume data, and roadway characteristics data.
To estimate predicted crashes, you only need the traffic volume for existing or future
conditions and the roadway characteristics associated with the facility.
If you decide it is appropriate to estimate the expected crashes, then you will also need
the site-specific crash history.
Regarding crash data, you will need to identify several data elements for each crash during
the study period.
These include: the crash date including at least a year, crash location, crash type,
severity level in relation to the intersection, such as at intersection, intersection related,
or not intersection related.
For traffic volume, you need the annual average daily traffic, or AADT.
For roadway segments, this is simply the two-way volume.
For intersections, this includes the volume on the major and minor roads.
The roadway characteristics data requirements depend on the facility type of interest.
You can find the roadway data requirements for all facility types presented in the first
edition of the HSM.
The third step is to select and apply the appropriate SPFs.
The HSM includes SPFs for various facility types which you apply to the most basic unit
of analysis whether it's for roadway segments and/or intersections.
It is important that you understand the applicability of the SPFs, to select those that are most
appropriate to your scenario of interest.
To understand the applicability, you must consider the facility type and the associated
roadway characteristics, such as area type, number of lanes, median type, traffic control,
and number of intersection approaches.
For example, if your project of interest is an urban four-legged signalized intersection,
then you should select an SPF that applies to that type of intersection.
After you select an appropriate SPF, you apply it to estimate the long-term average crashes
under base conditions.
For example, the SPF for a four-legged, two-way stop-controlled intersection on a rural, two-lane,
two-way road is given by this equation.
The number of predicted crashes under base conditions for a four-legged stop-controlled
intersection is equal to an exponential function where the AADT major is the annual average
daily traffic on the major road in vehicles per day, and the AADT minor is the average
annual daily traffic on the minor road in vehicles per day.
For example, assuming the major road AADT is 8,000 vehicles per day, and the minor road
AADT is 1,000 vehicles per day, you will calculate 2.9 predicted crashes per year from the SPF
under base conditions.
However, it is important to understand that the base conditions for the SPF may or may
not reflect the actual conditions for the location of interest.
In the fourth step, you apply adjustment and calibration factors to reflect site specific
and local conditions.
For example, the base conditions for a four-legged two-way stop-controlled intersection on a
rural two-lane two-way road include no intersection skew angle, no left turn lanes,
no right turn lanes, and no lighting.
If any of these features are present at the location of interest, then you need to apply
adjustment factors to reflect those conditions.
Refer to the HSM Part C or Part C spreadsheets for a complete list of applicable adjustment
factors by facility type.
For example, assume your intersection of interest has left-turn lanes on both approaches of the major road.
From the HSM, the adjustment factor for the presence of left-turn lanes on both major road approaches
is 0.52, indicating a 48-percent reduction in predicted crashes when left-turn lanes are present.
Applying this adjustment factor to the SPF under base conditions, the adjusted predicted crashes
from the previous example is 1.5 crashes per year.
SPFs represent the average safety performance of sites based on specific locations and time periods.
As such, users can improve the reliability of results by calibrating to reflect local conditions.
The calibration factor for local conditions is applied as a multiplier to the SPF.
Calibration factors greater than 1.0 indicate the SPF is underpredicting crashes for local
conditions, and there is a need to inflate the predictions.
Calibration factors less than 1.0 indicate the SPF is overpredicting crashes for local
conditions, and there's a need to reduce the predictions.
You can refer to the "User's Guide to Develop HSM SPF Calibration Factors" for further guidance
on how to develop a calibration factor for use with a Part C predictive method.
It is included on the resource web page at the end of this video.
As an example, assume that in your jurisdiction your calibration factor for local conditions
for four-legged, two-way stop-controlled intersections on rural, two-lane, two-way roads is 1.10.
This value is greater than 1.0 which indicates that the adjusted SPF is under-predicting
crashes for local conditions, even after adjusting the base predictions for site-specific geometric
and operational characteristics.
Applying this calibration factor to the previous example, the predicted crashes is 1.6 per year.
At this point, you have estimated the predicted crashes for your site based on the average
annual safety performance of other sites with similar characteristics and adjusted for site-specific
and local conditions.
However, this prediction does not consider the crash history which can help you account
for site-specific characteristics that are not reflected in the adjustment or calibration factors.
In the final step, when appropriate, you can employ the Empirical Bayes, or EB, method,
to combine the observed crash history with the predicted crashes.
The EB method produces an estimate of the expected crashes based on a weighted average
of the predicted crashes and observe crash history.
This is the EB method equation, where N expected is the number of expected crashes during the
study period.
N SPF predicted, is the number of predicted crashes from the SPF during the study period.
N observed is the number of observed crashes from the site of interest during the study period.
and W is the weight computed from the second equation.
In the second equation, K is the over dispersion parameter associated with the SPF, where smaller
values of K indicate a more reliable SPF prediction.
For example, you will find in the HSM that 0.24 is the over dispersion parameter or K,
for four-legged two-way stop-controlled intersections on rural two-lane two-way roads.
Continuing with the example, and assuming six observed crashes over a three-year study period,
the weight is 0.46 and the expected crashes is 5.5 over a three-year period
or 1.83 crashes per year.
This video demonstrated the use of the Highway Safety Manual Part C Predictive Method to
estimate the long-term average crashes for a site of interest under existing or future conditions.
You can use these estimates to consider safety impacts alongside other typical factors like
operational performance or environmental impacts.
They can also help you consider multiple design alternatives from a safety perspective,
allowing you to make confident, informed decisions and prevent certain crashes
from happening in the first place.
Refer to this resource page for additional guidance, examples, and courses on the use
of the Predictive Method.
At the Federal Highway Administration, safety is our top priority.
For more information, visit us online at safety.fhwa.dot.gov.