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stream. During the data collection for this project, driveway-related conflicts were identified in the field. Right-turning vehicles from a shared ATL create a similar hazard. · Sight distance. Sites with an adequate view of the downstream ATL from the stop bar experienced more ATL use, presumably because drivers feel more comfortable using an ATL when they can see the entire downstream merge area. In addition, with an adequate view of the end of the ATL, drivers in the ATL can plan for their merge back into the CTL more carefully. · Queuing downstream of the ATL merge. Traffic spilling back into the ATL taper from a downstream bottleneck could create a safety issue. Analysts should pay particular attention to potential spillback into an upstream ATL that could occur from a downstream bottleneck. · Taper design. The length and rate of the ATL taper should conform to AASHTO (1) and MUTCD policy (3). · Signing, marking, and lighting. An ATL should be clearly signed as a through-movement lane so that drivers are not discouraged from using it. Lighting may also promote better nighttime operations. OBSERVED SAFETY PERFORMANCE The 16 ATL study sites produced a combined average of 4.5 related (sideswipe plus rear-end) crashes per year on the ATL, indicating that these sites were likely not unsafe as designed. Although this research could not do so, it might be possible in the future to develop a crash modification factor (CMF) to convert a conventional intersection approach to one with an ATL. It might also be possible to use crash prediction models from the Highway Safety Manual (4), calibrated for a particular state, to estimate the number of crashes that would have occurred at a particular site if the ATL had not been installed . Until the data are available to estimate a CMF or calibrate a crash prediction model, the best interpretation of the available evidence is that ATLs at the studied sites did not seem to add many crashes. Page 30
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Proportion of ATL Crashes Although a crash reconstruction analysis was not within the scope of this research, it is generally true that the rear-end and sideswipe crashes that are the types most likely to be related to ATLs are not typically as severe as other crash types such as angle, head-on, and run-off-road crashes. Exhibit 4-1 displays a breakdown of the field crash data obtained for all 16 sites by crash type. Exhibit 4-1 Breakdown of ATL Crash Types 1% Rear End 4% 9% Sideswipe Turning 11% 52% Angle 13% Fixed Object 10% Backing Other The total number of crashes reported at all 16 sites was 1,050--this amounts to approximately eight crashes per site per year, including both related and non- related ATL crashes. Although the majority of crashes (52 percent) were rear-end crashes, only 10 percent were sideswipe crashes, which might be expected to be higher in ATLs. Exhibit 4-2 displays a summary of the crash data collected from each site. Page 31
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Exhibit 4-2 Number Rear Summary of Crash Data of Years End Sideswipe Total Approach Analyzed Crashes Crashes Crashes SB MD-2 at Arnold Rd * 9 57 13 112 NB MD-2 at Arnold Rd * 9 45 6 99 SB La Canada Dr at Magee Rd 9 42 5 54 EB NC-54 at Fayetteville Rd 6 41 12 207 WB Walker Rd at Murray Blvd 6 34 2 45 NB La Canada Dr at Orange Grove Rd 9 33 6 44 WB Magee Rd at La Canada Dr 9 29 5 48 EB Walker Rd at 185th St 9 28 1 63 WB Walker Rd at 185th St 9 27 2 58 EB Magee Rd at La Canada Dr 9 27 3 35 SB La Canada Dr at Orange Grove Rd 9 24 2 32 EB Walker Rd at Murray Blvd 6 23 4 34 NB Garrett Rd at Old Chapel Hill Rd 6 20 12 115 SB Sunset Lake Dr at Holly Springs Rd 6 17 12 33 NB La Canada Dr at Magee Rd 9 15 0 22 SB Garrett Rd at Old Chapel Hill Rd 6 12 4 49 Total 126 474 89 1050 * Denotes 2-CTL approach Although, as noted previously, calibrated crash prediction models from the HSM were not available for the four states analyzed in this effort, the researchers employed uncalibrated models to make comparisons on the proportions of crash types observed. Exhibit 4-3 shows the proportion of sideswipe crashes among all related crashes (sideswipe plus rear-end) for uncalibrated HSM crash models and the 16 ATL sites based on a summary of crash records. The exhibit shows that the proportions generally matched well. Z-tests for proportions revealed that only the proportion from the North Carolina data had a significant difference from the HSM prediction at a 95 percent confidence level . For all other states, individually and combined, the difference between the HSM prediction and the project data was not statistically significant. Exhibit 4-3 lends support to the idea that the crash types experienced at the ATL sites studied were not much different from crash types experienced at comparable conventional intersections. Exhibit 4-3 Proportion of Sideswipe among All Related Crashes Comparison of Sideswipe State HSM ATL Data Crash Data Arizona 0.15 0.12 Maryland 0.11 0.16 North Carolina 0.14 0.28 Oregon 0.13 0.06 Combined 0.13 0.15 Distribution of Crashes Relative to Location in ATL The rear-end and sideswipe crash data were aggregated by relative location within the ATL, as shown in Exhibit 4-4. The line for total crashes is simply the sum of rear-end and sideswipe crashes. Note that the distribution of sideswipe crashes is spread more evenly over the length of a typical ATL than the distribution of rear-end crashes. This suggests that, while rear-end crashes usually occur in the queuing areas near the intersection, sideswipe crashes are Page 32
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more likely to occur in other areas of the ATL. Also note that almost exactly half of these crashes were upstream of the intersection and half were downstream. Exhibit 4-4 1 Field Crash Data Distribution versus Relative ATL Position 0.9 0.8 Cumulative Probability 0.7 0.6 Total 0.5 Rear End 0.4 Sideswipe 0.3 0.2 0.1 0 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Relative Position within Upstream / Downstream ATL Relationship Between Crashes and Congestion Exhibit 4-5 plots the number of rear-end crashes from 2006 to 2008 against the maximum XT obtained from field data collected in 2009 and 2010 . XT indicates the level of congestion in the through-movement lanes assuming no ATL is present. The line in Exhibit 4-5 is the best-fit linear relationship between the maximum XT observed and rear-end crash frequency for each of the 16 sites. Only the most recent 3 years of crash data were used in order to shorten the time period between safety and operational data collection, considering that all of the operational data were obtained in 2009 and 2010. Page 33
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Exhibit 4-5 20062008 Crash Data 30 Trends versus Maximum XT Observed from Data 25 Rear-End Collisions 20 15 10 y = 11.423x0.6118 2 Rē R = 0.129 0.1292 5 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Max XT Observed in Field (15-min Interval) As shown in Exhibit 4-5, the relationship between rear-end crashes and congestion, as represented by XT , is very weakly correlated, with little observable trend above XT = 0.8. Not surprisingly, the number of rear-end crashes appears to be less frequent when congestion levels are very low compared to the remaining data set. Exhibit 4-6 displays the trend between rear-end crashes and average ATL flow observed in the field for each of the 16 sites. This exhibit does not indicate that more crashes occur at ATLs with higher flow rates--consequently, it does not provide evidence that a well-utilized ATL is less safe than a poorly utilized ATL. The two influential points in the far right portion of the exhibit with very high ATL flow are the sites with two CTLs. Exhibit 4-6 20062008 Rear-End Crashes versus Average ATL Hourly Flow Observed from 30 Data 25 Rear-End Collisions 20 15 10 5 0 0 50 100 150 200 250 300 350 400 ATL Hourly Flow (vph) Page 34