1. Problem Identification
Identify current and potential road safety problems using suitable scientific methods.
Road safety management is often viewed as an area in which the problems are obvious and the number of actions that we can take is numerous. This is not really the case. In other areas of public infrastructure (e.g. bridges, dams, buildings, etc.) a “failure” is obvious (a collapse or other such event) and, to an extent, infrequent. Road safety “failure” is more difficult to define. We consistently kill more than 40,000 people per year. Is every one of these fatalities a “failure”? Should we consider the vehicle involved or the road location where the crash occurred as “failed”? How do we deal with the concept of “failure”? What about “cause”? Who or what “caused’ the crash? How do these thoughts lead us to a plan for action?
When one is concerned about reducing injuries and fatalities in crashes, the first question often asked is: “What can we do?” Unfortunately, this is the wrong question. Focusing on actions before “the problem” has been identified is one of the sources of wasted safety investments. We run the risk of correcting sites that don’t need correction, spending money trying to retrain drivers who may not be a problem, or seeking recalls or engineering design changes for vehicles that may not be hazardous.
NCHRP 17-40, June 2010

2. Major Topics High Risk Sites Comparable Sites
Sites with Promise (SWiPs)
Crash Types
Motives for Action
Identifying problem entities must be conducted carefully as all the subsequent steps in analysis of a safety problem are based on this first step. This module seeks to help the student understand the importance of using scientific procedures to identify road safety problem locations, drivers, and vehicle types. Road safety investments are always budget-constrained; thus, it is important to find the “best” sites (or groups of drivers or vehicles) that represent the best opportunity for crash and injury reduction. The major topics include:
“High Risk” Sites
Comparable Sites
Sites with promise (SWiPs)
Crash Types
Motives for Action
NCHRP 17-40, June 2010 1

3. Identifying High Risk Sites
Priority/Emphasis Areas
Political Will
Funding Sources
Data-Driven
“High Risk” Sites
Overall priority, or emphasis areas drive the general focus of safety investments. These areas may be determined by political will, directed from top officials, which may be influenced by a variety of factors not necessarily based on crash data. Funding sources often tie investments to specific focus areas or activities. Preferably priority areas are determined by careful analysis of the crash data to identify areas requiring attention. Looking across the different statewide Strategic Highway Safety Plans (SHSPs), some commonly identified problems include run-off-the-road crashes, intersection-related crashes, impaired driving, occupant protection, and pedestrian safety.
These priority areas offer direction to practitioners in departments of transportation (DOTs), state highway safety offices (SHSOs), local planning agencies and others, on general problems that need attention. Within this context however, in the detailed planning of safety programs, it is necessary to step back and begin by looking not at what the problems are but where the problems are.
This is a common practice in engineering applications but is just as important for behavioral programs. Just as a DOT is not going to install a cable median barrier on every road in the state, targeting anti-impaired driving messaging and enforcement campaigns to specific problem sites is more efficient and effective than randomly “shotgunning” such efforts across a state or jurisdiction. Problem site identification or vehicle types is also critical because many different types of vehicles use the roadways and problems can vary significantly by facility (i.e., trucks on interstates vs local roads).
By starting with “high risk” site identification and then determining the specific crash factors at those locations, safety investments stand a far better chance of affecting the root safety problems and reducing fatalities and serious injuries.
NCHRP 17-40, June 2010 2

4. Ranking Using Crash Frequency1 2
Site
Observed Crash
Frequency F0
20.5
16.3 3
15.5 4
9.7 5
7.5 6
5.5 7
4.7 8
1.3 9
1.0 10
0.5
So, how do we identify high risk sites?
In many ways, the most intuitive measure to use for high risk sites is the number of crashes per year averaged over several years. The measure has appeal because we ultimately seek to reduce injuries and fatalities in crashes and the more crashes at a location, the generally higher level of injuries and fatalities expected. A variation on this theme would be to conduct the analysis using only mean numbers of injury and fatal crashes per year, but this does not fundamentally change the argument.
This table shows 10 hypothetical sites, ranked initially by the observed crash frequency per year (F0) measured over two to three years. Note that the sites have a substantial range of crash frequencies. Using F0 as the measure of risk, sites 1, 2, and 3 are identified for action.
A limitation of using crash frequency is the high expected number of crashes may be a primary manifestation of high traffic volumes, which may not be easily changed. Are such sites really those with the greatest promise of serious crash reduction?
NCHRP 17-40, June 2010 3

5. Ranking Using Crash Rates1 2 3
Site
Observed Crash
Frequency
Observed Crash Rate F0 R0
20.5
3.9
16.3
5.1
15.5
7.4 4
9.7
8.8 5
7.5
5.0 6
5.5
2.1 7
4.7
2.3 8
1.3 9
1.0
10.0 10
0.5
0.7
Looking at those same 10 sites, column 3 shows the crash rate per year (number of crashes divided by an exposure measure such as million vehicle miles for road sections or millions of entering vehicles for intersections or other junctions). Using this measure, sites 4, 8, and 9 are selected for treatment. Notice that sites 8 and 9 have few crashes per year. Their rate is high because of low exposure. Are these two sites worthy of consideration for treatment?
Some, having reflected upon this situation, have decided that a combination of column 2 and 3 should be used. This is referred to as the “number-rate” method. First a minimal number of annual crashes are selected to initially screen sites to a smaller number, and then the rate is used to generate another ranking. The difficulty is that the selection of the cut-off number for inclusion is arbitrary and there is still no assurance that the sites identified will have promise for improvement.
NCHRP 17-40, June 2010 4

6. Considering What is Normal for Comparable Sites1 2 3 4 5 6 7 8 9 10 11
Site
Observed Crash
Frequency
Observed Crash Rate
Normal Crash Frequency
Standard Deviation
Normal Crash Rate
Rate Standard
Deviation
Difference in Frequencies
Scaled Diff.
Diff. in Rates
Scaled Rate Diff. F0 R0 FN σF RN σR ΔF
ΔF/σF ΔR
ΔR/σR
20.5
3.9
26.5
9.4
5.1
1.8
-6.0
-0.6
-1.2
-0.7
16.3
4.8
1.5
+0.0
15.5
7.4
9.0
2.5
4.3
1.2
+6.5
+2.6
+3.1
9.7
8.8
7.6
2.1
6.9
1.9
+2.1
+1.0
+1.9
7.5
5.0
3.8
1.4
0.9
+3.7
+2.5
+2.8
5.5
12.0
4.6
-6.5
-2.5
+1.2
4.7
2.3
3.0
0.6
0.3
+1.7
+0.8
+2.7
1.3
0.2
3.1
+0.7
+3.5
+4.4
+4.9
1.0
10.0
0.5
+0.5
+5.0
+2.4
0.7
1.7
2.4
-3.3
-1.9
-4.8
-2.0
One way to think of this promise is to consider the performance of the site in comparison to what is expected or normal at similar sites. Remember our SPF? This is the notion of deviation from the norm; the difference between what is expected at similar sites and what is experienced at a specific site.
Here, we have added to the table the normal expected frequency and rate in columns 4 and 6 and the differences between observed and normal are shown in columns 8 and 10. Note that, yet again, the ranking of sites would change if these differences from the norm are used to identify sites; now site 1 is well below the mean for comparable sites for frequency while sites 3, 4, and 5 appear above the norm. Similarly, use of the difference in rates results in sites 3, 8, and 9 rising to the top.
Here again we see the importance of the concept of considering and using information about comparable sites. Sites that seemed to hold promise using the crash frequency are now viewed as actually performing better than average using that same measure!
Note: Column 4 is the SPF.
NCHRP 17-40, June 2010 5

7. SPFs for Problem Identification
1.2 X
X X
X X X C
X X D
1.0
SPF
X X
X X
Crashes per
unit time X
0.6
XXX XX
To further illustrate this concept of deviation from the norm, let’s look back at our SPF from module 4-2 and add in the averages for each of our 4 intersections across the 6 years of data. If the SPF identifies the number of crashes we should normally expect from comparable sites at the different levels of ADT, which sites appear to be “high risk”?
Site D has a high overall average number of crashes per mile per year. While it is high, it falls along the SPF, indicating that it is approximately equal to what is expected at such a site and ADT.
Site B is the lowest overall and is below the SPF, indicating a safer than expected site.
Let’s think a bit about what is represented by sites A and C. These represent locations where the mean crash frequency is greater than expected. Put another way, these represent sites that have a higher than expected crash frequency. One may speculate things are occurring at these sites that contribute to this deviation from what is expected; the larger the difference, the greater the deviance. This deviance can be thought of as representing the excess crashes at the site. The excess represents an opportunity for reduction. Which brings us to the concept of Sites With Promise (SWiPs) to indicate sites with a promise for significant crash (and injury/fatality) reduction. The scale of the distance above the SPF indicates the potential for safety improvement; exactly what we are looking for in this step of safety management. T
he basic point here is that the initial screening is computer-based. With limited resources, we need to be able to screen efficiently for sites with promise or SWiPs. X
0.4 A X B
XX XX
0.2
Annual Driving Miles
1,000
2,000
3,000
4,000
5,000
Safety 101
NCHRP 17-40, June 2010 6

8. Addressing Variability in Crashes Year-to-Year1 2 3 4 5 6 7 8 9 10 11
Site
Observed Crash
Frequency
Observed Crash Rate
Normal Crash Frequency
Standard Deviation
Normal Crash Rate
Rate Standard
Deviation
Difference in Frequencies
Scaled Diff.
Diff. in Rates
Scaled Rate Diff. F0 R0 FN σF RN σR ΔF
ΔF/σF ΔR
ΔR/σR
20.5
3.9
26.5
9.4
5.1
1.8
-6.0
-0.6
-1.2
-0.7
16.3
4.8
1.5
+0.0
15.5
7.4
9.0
2.5
4.3
1.2
+6.5
+2.6
+3.1
9.7
8.8
7.6
2.1
6.9
1.9
+2.1
+1.0
+1.9
7.5
5.0
3.8
1.4
0.9
+3.7
+2.5
+2.8
5.5
12.0
4.6
-6.5
-2.5
+1.2
4.7
2.3
3.0
0.6
0.3
+1.7
+0.8
+2.7
1.3
0.2
3.1
+0.7
+3.5
+4.4
+4.9
1.0
10.0
0.5
+0.5
+5.0
+2.4
0.7
1.7
2.4
-3.3
-1.9
-4.8
-2.0
Upon additional reflection, one would also like to consider some notion of yearly variability in the data because, as we saw in Module 4-2, variability in crash counts is very common. Our table shows the standard deviation of both the frequency (column 5) and rate (column 7). One can then construct a scaled difference in frequency and rate as shown in columns 9 and 11 respectively. These columns may be interpreted as indicating the number of standard deviations from the mean represented by the frequency and rates for each site. Yet again the rankings change; 3, 7, and 8 are ranked highest for difference in frequency, while 3, 8, and 9 have higher rates.
Another interpretation to the use of the standard deviation is illustrated with a comparison of sites 4 and 8. Site 4 would be a candidate as a SWiP because it has a difference in frequencies of 2.1 (column 8). However, inspection of column 5 indicates that deviations of 2.1 are common for this type of site, so the 2.1 cannot be considered abnormal. On the other hand, site 8 has a frequency difference of 0.7, but a similar site standard deviation of only 0.2. The interpretation of a difference of 0.7 is unusual for this group of sites (the norm is 0.2) so site 8 now appears deviant!
Several issues arise in these deliberations. We have stressed the use of the standard deviation but the concept of basic importance is the recognition of variability in crash frequency and rate. Many times during the development and evolution of these core competencies over 5 or more years, disagreement has occurred over the importance (or even the existence) of variability. This is one example where the recognition of variability in crash occurrence is important and has direct implications for effective safety management. The details and statistical nuances are not important as a foundation for all safety professionals; the recognition of variability and the need to address it are important.
NCHRP 17-40, June 2010 7

9. Crash Types Crash (contributing) Factors
Human
Roadway
Vehicle
Environmental
Collision Type (manner of collision)
Run-off Road
Rear End
Head On
Sideswipe Same Direction
We’re almost there. As we just mentioned, one may speculate things are occurring at our SWiPs that contribute to their deviation in crash frequency from what is expected. The identification of contributing factors is crucial, and it is essential that contributing factors be identified for effective countermeasure identification. In addition to the potential contributing crash factors discussed in module 4-1, crash types can also be based on the manner of collision, such as run-off-road, rear-end, head-on, or sideswipe same direction.
The typology is generally derived through road safety engineering studies and audits (as previously learned about) and/or data analysis using police crash reports or hospital-based reports of crash victims. As discussed in the data section, jurisdictions have historically evolved with particular wording to describe these crash typologies. Efforts have been underway to standardize the data elements collected and their descriptions.
Screening by crash type should identify those types of crashes over-represented with respect to the group as a whole, thus further refining our list of SWiPs for the next step of identifying appropriate countermeasures.
NCHRP 17-40, June 2010

10. Motives for Action Economic Efficiency
Professional and Institutional Responsibility (QC)
Fairness/equity
While the picture concerning SWiP identification is cleared considerably, it is important here to address other factors that may influence decisions on which SWiPs get treated. Hauer argues for the need to consider three interdependent motives for action:
Motive 1: Economic efficiency -- identify sites at which a treatment would be cost-effective. Having knowledge about the effectiveness of specific countermeasures (e.g. high visibility enforcement campaigns) one may seek to apply this knowledge to high crash frequency sites experiencing related problems. It is really a search that starts with a countermeasure and looks for suitable sites.
Motive 2: Professional and institutional responsibility -- Identify and correct sites that are deficient because of how they were built or because they have deteriorated (i.e., eroded shoulder lanes). The unusually high crash frequency relative to the norm makes these sites easy to identify.
Motive 3: Fairness -- Identify sites that pose an unacceptably high risk to a specific set of users (i.e., older drivers). These are often low exposure sites that would not otherwise appear on a “list” based on crash frequency. This is one place where crash rates are useful and potentially important. In fairness cases, it is often difficult to figure out the appropriate balance since you never have enough resources to treat all sites and other sites may have a much higher crash frequency. Our point is that analysis helps you reach the balance. 9
NCHRP 17-40, June 2010 9

11. Review High Risk Sites Comparable Sites Sites with Promise (SWiPs)
Crash Types
Motives for Action
Sites With Promise (SWiPs) are intended to express sites with a promise for significant crash (and injury/fatality) reduction.
Limitations to using crash frequency and/or crash rates only to determine high risk sites can largely be overcome by considering the performance of the site in comparison to what is normal or experienced at similar sites.
The scale of the distance above the SPF indicates the potential for safety improvement; exactly what we are looking for in this step of safety management.
Identifying overrepresented contributing factors at our SWiPs is a critical step before identifying appropriate countermeasures.
Three interdependent motives for action are economic efficiency, professional and institutional responsibility, and fairness. 10
NCHRP 17-40, June 2010 10

12. Exercise #6 High Risk Entities
This lesson showed the correct method for identifying sites with promise for improving safety. Practice your new knowledge by completing Exercise number 6. 11
NCHRP 17-40, June 2010 11