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Table 78. Descriptive statistics for impact severity.
90th 95th
Road Class N Mean Median Std. Dev. Minimum Maximum Percentile Percentile
All classes combined 868 38.91 18.46 57.34 0.00 584.50 98.54 143.31
Interstate 194 51.06 32.13 66.70 0.70 584.50 123.15 159.89
US Route 157 32.63 14.52 43.60 0.20 234.45 80.41 121.74
State Route 159 43.66 19.92 61.53 0.90 392.15 113.65 180.34
County Road 273 28.68 14.78 42.52 0.00 343.14 69.45 100.90
City Street 42 23.15 15.83 24.70 0.40 127.47 48.95 62.04
could be identified that pass goodness-of-fit testing for com- is 156 kJ. As shown in Table 78, the 95th percentile IS value
bined angle and speed distributions. This analysis clearly from Interstate highways was found to be 160 kJ, very near
demonstrated that, for impact conditions, roadway classifi- the target value for Test Level 3. Thus, the IS values recom-
cation provided a better discriminator for impact speed and mended by MASH for longitudinal barrier testing appear
angle than did speed limit range. Mak et al. obtained simi- to be appropriate.
lar results when he studied data collected under the national
speed limit law. Recall that departure data was found to be
4.4.4 Vehicle Orientation at Impact
more sensitive to speed limit range than highway class. These
findings may be a reflection on the effects of clear zones on a Vehicle orientation at impact has been linked to crash
driver's ability to slow down before striking a hazard. severity (15). Further, this parameter is also used to estimate
crash costs in benefit/cost models RSAP (21). Basic descrip-
tive statistics for vehicle orientation are shown in Table 79.
4.4.3 Impact Severity
Figure 13 presents a plot of vehicle orientation distribution
Impact severity has been found to be strongly correlated from the ran-off-road crash database. Note that less than
with the magnitude of loading during longitudinal barrier 40% of crashes were found to have heading angles between
impacts. IS is defined as shown below: 20 and +20 degrees.
1
IS = m ( v sin )
2
2 4.5 Encroachment Length
The distance that a vehicle travels along the roadside is
where: an important input to the design of guardrail installations.
IS = Impact severity For the last 30 years or more, guardrail designs were based
m = Vehicle mass upon findings from a study of roadside encroachments by
v = Impact velocity Hutchinson and Kennedy (H&K) (7). More recently, data from
= Impact angle an encroachment study by Cooper (33) have shown longitudi-
nal travel distances to be much shorter than those measured by
Table 78 presents descriptive statistics for impact sever- H&K. This discrepancy has been attributed to two fundamen-
ity from the first harmful event. IS has been accepted as the tal differences between the two studies (34, 35). The Cooper
primary measure of the magnitude of a barrier crash and it study involved highways with speed limits of 5962 mph
is used in MASH to set limits for minimum crash condi- (95100 km/h), while the H&K study involved speed limits
tions. The target IS value found in MASH for Test Level 3 of 70 mph. The other explanation for differences in longi-
Table 79. Descriptive statistics for vehicle orientation.
90th
Road Class N Mean Median Std. Dev. Minimum Maximum Percentile
All Classes 842 3.3 4 69.52 -168 180 102
Interstate 188 7.29 9 80.42 -159 171 129
US Route 163 -4.12 0 70.73 -165 180 96.8
State Route 165 4.44 1 67.6 -168 180 105
County Road 275 3.96 4 64.72 -164 180 90
City Street 44 13.2 11.5 47.44 -149 115 75
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Figure 13. Vehicle orientation angle distribution.
tudinal travel distances is the overrepresentation of low- 4.5.1 Raw Data
angle encroachments in the H&K data. Recall that as shown
in Figure 6, the angle of departure data from the current study The first step in the process of evaluating longitudinal travel
was found to be quite similar to that from Cooper and the distances from the current study was to compare encroach-
Pole Study, while departure angles from H&K were found ment length data from Cooper and H&K to longitudinal
to be much lower. When H&K data are adjusted to eliminate travel distances from the current study as shown in Figure 14.
the bias toward low-angle encroachments, the differences For this figure, the data from the current study was limited to
between the Cooper and H&K longitudinal travel distances access-controlled freeways with speed limits of 7075 mph.
were reduced to a level that could easily be explained by dif- The Cooper data were restricted to divided highways with
ferences in speed limit.
The database described herein should provide some clarifi-
cation of which of the two encroachment studies is most
appropriate for use in determining guardrail length. Note that
the 17-22 database has been constructed from reported acci-
dents, many of which involved impacts with roadside objects.
It is reasonable to conclude that many of these vehicles would
have traveled farther if the obstacle had not been impacted.
However, as described above, the crashes included in this study
are strongly biased toward serious injury and fatal crashes. In
effect, the data included herein was taken from the very types
of roadside crashes guardrail is intended to prevent. Thus,
designing guardrail configurations to withstand these crashes
is more appropriate than relying on roadside encroachment
data that includes very few reported crashes and undoubtedly
includes many controlled encroachments that would never
produce a crash. Figure 14. Encroachment lengths for different studies.
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Figure 15. Cooper and 17-22 (55-65 mph) encroachment
data comparison.
5962 mph (95100 km/h) speed limits and the H&K data bution of encroachment lengths from the H&K study. As
were collected on rural Interstate highways with a 70 mph shown in Table 80, the runout length associated with high-
speed limit. Notice that the 17-22 travel distances are close to volume, high-speed roadways was based upon the 85th per-
those from Cooper and that the differences can be explained centile encroachment length while lower volume roadways
by the higher speed limits associated with the current study. were assigned runout lengths based upon a lower percentile
Figure 15 illustrates the effects of speed limit by comparing encroachment length. Note that 92% of the encroachments
data from the current study collected on access-controlled collected by H&K were from highways with a 70 mph design
highways with 5565 mph speed limits to the Cooper data speed and traffic volumes less than 6000 vehicles per day. Hence
taken from divided highways with 5962 mph (95100 km/h) the traffic volume categories shown in Table 80 were based
speed limits. These two distributions are not only visually upon the source of the H&K data. The data from Table 80
similar; a two tailed T-test analysis indicated that the differ- was then extrapolated to lower design speeds. A more recent
ences are not statistically significant with a p-value of 0.966. study of guardrail length-of-need utilized this same approach
The excellent comparison between Cooper's data and the to apply Cooper's data to this problem (36). Table 81 presents
17-22 data supports the hypothesis that the long encroach- comparable results from the Cooper data. Thus, encroach-
ments observed in the H&K study are associated with the ment length distributions, presented in tabular form as shown
overrepresentation of low-angle encroachments in the study. in Tables 80 and 81 have been used to develop the recom-
Procedures contained in the 2006 AASHTO Roadside Design mended values for the guardrail runout length parameter.
Guide identify the required length of a guardrail in terms of The 17-22 longitudinal encroachment lengths will therefore
a runout length parameter, which is based upon the distri- be presented in this same format.
Table 80. RDG runout lengths for 70 mph design speed.
Traffic Volume (ADT) >6000 2000-6000 800-2000 <800
Design Runout Length, m 146.3 134.1 121.9 109.7
Enc. Length Percentile 85% 80% 75% 70%
Table 81. Encroachment length distributions.
Encroachment Length Percentile
Source
Average Speed Limit 90% 85% 80% 75% 70%
60.5 mph 96.3 78.6 69.2 57.3 52.4
Cooper
50.3 mph 54.9 46.9 42.4 38.4 34.7
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Table 82. Departure length segregated by speed limit.
Departure Length Percentile
Speed Limit No. of Cases 90% 85% 80% 75% 70% 50%
70-75 169 109.9 101.0 85.4 73.8 66.3 49.5
55-65 424 77.0 65.4 57.0 50.0 46.5 33.8
55 353 74.4 62.0 52.0 47.0 44.7 32
45-50 253 63.1 50.0 43.2 38.8 34.8 24
45 186 60.8 47.1 41.8 37.0 33.0 24
Table 83. Departure length segregated by traffic volume.
Departure Length Percentile
Volume Class No. of Cases 90% 85% 80% 75% 70% 50%
High 189 92.1 80.9 70.2 61.2 57 38
Medium 207 95.3 84 71.2 64.6 61.8 42.6
Low 388 65.2 53 47 43.6 40 26.6
Longitudinal departure length data from the 17-22 data set of access control on departure length, it is necessary to iso-
were first examined when categorized by speed limit, access late the evaluation to a constant speed limit. This type of
control, and traffic volume. Table 82 presents departure length evaluation could not be conducted on the tail of the depar-
data segregated by speed limit. Note that there were too few ture length distribution as shown in Tables 82 through 84
cases with 65 and 50 mph to reliably establish the tail of the because of the small sample sizes at any one speed limit.
distributions. These cases were lumped with the next lower Therefore, the effect of access control was evaluated at the
speed limit categories to illustrate the general trend between median for a 55 mph speed limit. The median departure
speed limit and departure length. Table 82 shows that there lengths for a 55 mph roadway were found to be 45.2 m and
is a relatively strong trend for departure length to increase 32.0 m for full and no access control, respectively. The nearly
with higher speed limits. 50% increase in median departure length demonstrates that
The effects of traffic volume and access control on depar- full access control has a significant effect beyond its correla-
ture lengths were then explored as shown in Tables 83 and tion with speed limit.
84, respectively. Notice that there is no clear trend between In light of the finding that traffic volume had no consistent
traffic volume category and departure length and there appears effect on departure length, this parameter was eliminated
to be a strong relationship between access control and depar- from further consideration. Departure length data was then
ture length. However, there is also correlation between speed segregated by access control and speed limit as shown in
limit and access control. In order to isolate the importance Table 85. Note that for the 5565 mph category, there were
Table 84. Departure length segregated by access control.
Departure Length Percentile
Access Control No. of Cases 90% 85% 80% 75% 70% 50%
Full 263 102.7 89.3 76.5 68 62.8 45.4
None 493 66.7 54 47 43.5 40 28
Table 85. Departure length segregated by speed limit
and access control.
Speed Access No. of Departure Length Percentile
Limit Control Cases 90% 85% 80% 75% 70% 50%
70-75 Full 151 109.1 101.1 88 75.1 66.7 50
55-65 Full 98 89.6 76.9 65 60.5 54 40
55-65 None 284 68.7 57.8 49.2 46 43 32
45-50 None 205 61 49 42.9 37 33 24.8
