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48 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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49 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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50 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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51 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