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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.
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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.
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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
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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.
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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)
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