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APPENDIX A
A SIMULATION-BASED APPROACH TO ATL EVALUATION
These guidelines are primarily focused on deterministic approaches for
predicting operations of an auxiliary through lane (ATL) adjacent to one or two
continuous through lanes (CTL). The ATL volume prediction models described
in Chapter 3 are based on a deterministic analysis framework and are directly
compatible with the Highway Capacity Manual (HCM) procedures.
The HCM recognizes that the use of alternative analysis tools, and
specifically microsimulation approaches, has merit in a number of applications.
In an effort to study the utility of microsimulation tools for ATL applications, this
appendix describes guiding principles and considerations for applying
simulation to ATL evaluation. The discussion covers both the operational
evaluation of ATLs (delays, queue lengths, etc.), as well as approaches for
estimating safety performance measures (conflicts) from simulation using the
SSAM post-processing tool developed by FHWA (1).
The lessons learned described in this appendix are closely related to the
experience of the research team for NCHRP Project 3-98, which involved a
significant microsimulation modeling and calibration effort. The project used the
VISSIM simulation package (2), but this appendix describes the analysis
principles in generic terms to the extent possible.
Principles of Lane Change Algorithms
Microsimulation tools explicitly model the movement of individual vehicles
using a series of behavioral rules known as algorithms. Among these, lane
changing algorithms are most critical for accurately describing ATL behavior.
Most simulation models distinguish between "voluntary" and "mandatory" lane
changes. Voluntary lane changes apply when a driver has multiple lanes available
on the desired route, and switches lanes to--for example--pass a slower vehicle.
The key point here is that the subject vehicle would have arrived at its desired
destination regardless of whether it changed lanes. Mandatory lane changes on the
other hand, are those that are necessary for performing a turning maneuver or
for prepositioning in anticipation of a downstream lane drop. In other words, a
mandatory lane change has to take place if a vehicle is to continue on its desired
path. In the application to ATLs, the driver's decision to enter the ATL is
generally a consequence of a voluntary lane change (e.g., to pass a queue of
vehicles in the CTL). To be precise, the desire for a voluntary lane change is
initially triggered by the car-following algorithm if the target vehicle's desired
speed exceeds that of a vehicle ahead of it in the same lane. The voluntary lane
change then describes the process of searching for suitable gaps in the adjacent
lane (in this case, the ATL), and then ultimately switching lanes. On the other
hand returning from the ATL to the CTL represents a mandatory lane change.
Most simulation tools have different parameter sets in their voluntary and
mandatory lane change algorithms and the analyst needs to understand the
associated settings to accurately model the lane-changing behavior.
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In the case of mandatory lane changes, a common parameter used in the
algorithm is the upstream decision distance. This distance is typically measured
relative to the ATL lane drop and refers to the point at which drivers begin to be
concerned with the lane drop. Exhibit A-1 illustrates this concept.
Exhibit A-1
Illustration of Upstream
Decision Distance in
Simulation
Direction of Flow
Upstream Decision Distance
The upstream decision distance describes the point at which the mandatory
lane change algorithm becomes active. In most simulation tools, drivers will
begin trying to merge at this decision point if gaps are available and will become
increasingly aggressive about their lane-changing behavior as the distance to the
downstream drop decreases. Further, in most cases, the mandatory lane change
algorithm will override any voluntary lane changes. As a result, no voluntary
lane changes will take place past the upstream ATL decision point, and
consequently no CTL-to-ATL maneuvers will take place past that point. In this
context, it is important to emphasize that a coded upstream decision distance
that is greater than the total ATL length will prevent any voluntary lane changes into
the ATL and will therefore result in zero through flow on the ATL.
In NCHRP Project 3-98, this upstream decision distance (described in
VISSIM as the lane change distance, LCD), proved to be the single best predictor
of ATL lane utilization and a critical calibration factor to replicate field-observed
ATL utilization in VISSIM.
Calibration of Simulation Models
Consistent with any simulation analysis, the parameter set used in the
simulation model needs to be calibrated to match field conditio ns or known
relationships in traffic flow theory. Calibration can include various changes to
built-in simulation algorithms, including speed distributions, car-following logic,
or lane-changing parameters. Significant research is available on the topic of
simulation calibration, including material compiled by FHWA in the Traffic
Analysis Toolbox. For this discussion, the topic of calibration is condensed to the
specific application to ATLs.
The foremost goal in the calibration of a simulated CTL-ATL system is to
match the field-observed ATL utilization or, in the absence of field data, the ATL
volume predicted from the models presented in these guidelines. By varying the
LCD parameter in VISSIM, the research team was able to successfully calibrate 19
of the 22 studied ATL approaches. The remaining three approaches exhibited
very low utilization (less than 10 percent). These low-utilization percentages
could not be replicated without also making significant adjustments to the car-
following logic, which in turn resulted in more simulated "crashes."
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Exhibit A-2 shows the resulting relationship between field-observed and
simulated ATL percentages for the 19 approaches (black) and a best-fit line. The
three low-utilization approaches (gray) are treated as outliers.
Exhibit A-2
Calibration Result Showing Field-
50 Observed vs. Simulated ATL
Utilization
45 Calibrated Sites
R2 = 0.9499
Simulated ATL Utilization (%)
40 Low-Utilization Sites
35
30
25
20
15
10
5
0
0 5 10 15 20 25 30 35 40 45 50
Field-Observed ATL Utilization (%)
The simulated ATL utilization percentages shown in Exhibit A-2 were the
result of free lane selection by drivers on the intersection approach, subject to the
algorithms of car-following, lane changing, etc. The ATL utilizations were not
"forced" in the sense that a fixed percentage of through traffic was routed
through the ATL. In this sense, the resulting R2 of 0.95 shows a high rate of
success in calibrating ATL utilization through the LCD parameter in VISSIM.
Other calibration efforts may include accurate coding of turning-movement
flows, speed distributions, signal-timing parameters, etc. The analyst should
further validate some of the outputs from the simulation model to field data if
available. These outputs may include approach delays, total through travel time,
or vehicle queues.
ATL Utilization Prediction Model
Given the sensitivity of the LCD parameter on ATL utilization, an effort was
made to predict the correct LCD setting for (future) ATL sites, where the true
utilization is unknown. The dependent variable LCD was expressed in the
following way:
LCD % Total: the LCD expressed as a percentage of the total ATL length,
computed as
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Other forms of the dependent variable were explored, but the one quoted
above emerged as the preferred definition. Several explanatory variables were
hypothesized to affect the LCD used to calibrate VISSIM, including traffic
volumes, approach speeds, upstream and downstream length, and a distinction
between single and dual CTLs. Ultimately, the following two explanatory
variables were used in the LCD prediction model:
Volume: through traffic flow rate expressed in vehicles per hour (vph)
Upstream: the length of the ATL segment upstream of the stop bar, in feet
The resulting model predicting LCD%TOTAL as a function of these two
variables is given below:
R2 = 0.622
The R2 value suggests that 62.2 percent of the variability in the LCD variable
that provided the best match to the field data is explained by the variables in the
model for the regression data set. This suggests that the model can be used to
arrive at a reasonable initial estimate for the LCD parameter if VISSIM is used to
model the ATL. For other simulation tools, this model may similarly guide an
initial parameter estimate, but the model has not been calibrated for such
applications.
The model suggests that the LCD begins at 89.696 percent of the total ATL
length, which coincides approximately with the highest LCD value observed in
previous VISSIM calibration. That term is then discounted with increasing
upstream length and through volume. This implies that ATL utilization increases
with increasing upstream length and through volume. This relationship is
consistent with field observation. A closer exploration of the two explanatory
variables also suggests a good model fit as shown in Exhibit A-3.
The exhibit shows that LCD%Total is approximately linear with respect to
the upstream length of the ATL and the combined through volume.
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Exhibit A-3
LCD%TOTAL VS. Upstream Length LCD%TOTAL VS. Through Volume Sensitivity of LCD%TOTAL vs.
100 100 Upstream Length and Through
90
Volume
90
80 80
70 70
LCD%Total
LCD%Total
60 60
50 50
R2 = 0.5138
40 40
30 30
R2 = 0.5677
20 20
10 10
0 0
0 500 1000 1500 2000 0 700 1400 2100
Upstream Length (ft) Through Volume (vph)
Surrogate Safety Assessment
As part of the safety analysis, the researchers investigated a technique to use
the Surrogate Safety Assessment Methodology (SSAM) in conjunction with a
calibrated VISSIM model in order to predict safety conflicts at ATL approaches.
SSAM was developed by FHWA as a post-processing tool to estimate vehicular
conflicts from simulation trajectory files. These raw simulation output files store
the speed, position, and acceleration of every simulated vehicle during each
simulation time step among other data. From the trajectory files, SSAM defines a
conflict, for example, if two vehicles occupied the same space within a user-
defined time-to-crash (TTC) threshold. Full details on the SSAM tool and
definitions of terms can be found in the documentation for SSAM (1).
In this research, the SSAM tool was applied to all 16 of the ATL approaches
that were part of the safety evaluation in Chapter 4. This evaluation was
performed after the ATL utilization was calibrated to empirical observations. The
results of this investigative conflict study were not fully validated by the crash
data presented in Chapter 4, mostly due to the low crash sample sizes, flaws in
crash reporting, and other errors. Nonetheless, the SSAM output can still be used
to examine relationships between conflict frequency and key ATL design
elements such as downstream length.
From the calibrated simulation models, SSAM uses the trajectory (*.trj) files
generated by the simulation and can apply various filters during analysis to
define a conflict:
Conflict type. SSAM distinguishes between angle, lane change, and rear-end
conflicts by the angle at which the conflict occurs. Only lane change and rear-end
conflicts were targeted in this analysis.
Time to crash (TTC). The threshold for what defines a "conflict" is the TTC,
which can be adjusted to 0.5, 1.0, or 1.5 seconds. This analysis used a TTC equal
to 1.5 seconds to get a conservative estimate of the number of conflicts to
compare with crash data.
Link. The analyst can filter conflicts by the link ID number used in the
simulation tool. This allows the analyst to filter conflicts by the upstream or
downstream portion of the ATL (or by multiple ATL approaches within one
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simulated intersection) as long as they are numbered as separate links in the
simulation network.
Trajectory file. If multiple runs are simulated, the analyst can obtain the
conflict frequency for each trajectory file within the multi-run simulation.
Using these methods, the analyst can compare design alternatives such as
downstream length, speed, congestion, and the presence or absence of an ATL.
Exhibits A-4 and A-5 show how the number of SSAM rear-end and sideswipe
conflicts, respectively, changed for an exclusive lane with respect to downstream
ATL length and XT. Rear-end conflicts remained relatively consistent as
downstream length increased, but the number of sideswipe conflicts spiked at a
downstream length of 800 feet. This may be due to some quirk in the simulation
or SSAM logic and the low sample size of sideswipe conflicts. Conflicts tended to
increase fairly steadily with increasing XT, as might be expected.
Exhibit A-4 250
SSAM Rear-End Conflict
Comparison (No Right Turns)
200
150
Conflicts
XT = 0.75
100 XT = 1.00
XT = 1.25
50
0
0 200 400 600 800 1000 1200
Downstream Length (ft)
Exhibit A-5 16
SSAM Sideswipe Conflict
Comparison (No Right Turns) 14
12
10
Conflicts
8 XT = 0.75
6 XT = 1.00
4 XT = 1.25
2
0
0 200 400 600 800 1000 1200
Downstream Length (ft)
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350 Exhibit A-6
SSAM Rear End Conflict
300 Comparison (200 Right-Turning
Vehicles per Hour)
250
Conflicts
200
XT = 0.75
150
XT = 1.00
100 XT = 1.25
50
0
0 200 400 600 800 1000 1200
Downstream Length (ft)
Exhibits A-6 and A-7 show the same types of comparisons for a shared ATL
with 200 right turns per hour. The exhibits indicate that low-to-moderately
congested approaches had low levels of conflicts when compared to those
simulated at XT = 1.25. While rear-end conflicts remained relatively unaffected by
changes in downstream length, the number of sideswipe conflicts tended to
increase as downstream length increased. This could be explained by the
exposure, as a greater downstream length tended to generate more conflicts in
SSAM simply because the conflict area was lengthened . Note that if the number
of conflicts was normalized by downstream length, a decreasing trend would
emerge. Also note that there were many more conflicts generated by the shared
lane than by the exclusive ATL scenario.
Exhibit A-7
SSAM Sideswipe Conflict
70
Comparison (200 Right-Turning
Vehicles per Hour)
60
50
Conflicts
40
XT = 0.75
30
XT = 1.00
20 XT = 1.25
10
0
0 200 400 600 800 1000 1200
Downstream Length (ft)
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The analysis of variance for the rear-end conflict data across all levels tested
indicated that there were statistically significant interactions between the right-
turn volume and XT and between the right-turn volume and the downstream
length. The former interaction is intuitive, as the effect of right-turning vehicles
as a detriment to safety magnifies when there is more through traffic in the
shared ATL. In terms of the sideswipe conflicts, multiple significant interactions
existed--some of these were unexpected and may be due to the low sample size
for sideswipe conflicts.
In general, it appears that the SSAM logic confirms intuition. First, it appears
that changes in downstream length are not associated with changes in the
observed number of conflicts in the ATL. Second, shared ATLs (at least those
with 200 or more right-turning vehicles per hour) tend to have more conflicts
than exclusive ATLs. Finally, the crash increase with the increase in XT discussed
in Chapter 4 was supported by the increase in conflicts with XT shown in Exhibits
A-4 through A-7, particularly as XT increased beyond 1.0.
Proposed Work Flow of ATL Simulation Study
If an analyst is studying the feasibility of an ATL intersection improvement,
the following list of steps represent a proposed analysis work flow. Please note
that additional steps may be necessary, depending on the specific location, and
the practitioner should exercise sound judgment in any simulation analysis.
Step 1: Gather input data, including existing and proposed intersection
geometry, traffic turning movements (current and forecast), approach speed
limits, and signal timing data.
Step 2: Model baseline, representing the existing intersection without ATL
approaches.
Step 3: Calibrate baseline, by comparing the modeled operations to field data or
other analysis approaches. Make any necessary adjustment to traffic volumes,
speed inputs, signal timing, or other simulation algorithms.
Step 4a: Estimate initial LCD parameter, using the predictive model in this
appendix as a function of the total (future) through traffic flow and proposed
total ATL length. An initial estimate of the ATL upstream and downstream
lengths is therefore needed for this analysis.
Step 4b: Estimate ATL predicted volume, using the models described in
Chapter 3 of these guidelines. These estimates will be used to validate that the
ATL utilization is modeled correctly.
Step 5: Model ATL geometry, using proposed geometry, signal timing, and
volumes (step 1), and the initial LCD parameter from Step 4a.
Step 6: Calibrate ATL operations, by modifying the LCD until the simulated
ATL volume matches (approximately) the predicted volume from Step 4b. As a
general guidance, a longer LCD will result in lower utilization of the ATL.
Step 7: Evaluate ATL performance, by running repeated iterations of the
baseline and ATL scenarios and comparing the average performance. A
suggested performance measure is the total through travel time, which is readily
compared to field data. Additionally, approach delay and queue lengths are
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important performance measures that are also predicted in the deterministic
HCM analysis approach.
Step 8: Evaluate safety performance, by extracting trajectory files from
simulation and analyzing for conflicts in the SSAM post-processing tool. To
estimate ATL safety performance, the SSAM evaluation should be limited to the
specific ATL link in question and should distinguish between rear-end and lane-
changing conflicts, as well as conflicts in the upstream and downstream portions
of the ATL.
Depending on the objective of the analysis, it may be useful to obtain
performance measures on a per-lane basis, to be able to isolate the performance
of the ATL. As general guidance, it is important to use the same definitions of
performance measures for baseline and any ATL scenarios to assure an even
comparison. For example, any travel time segments should be defined for a
distance long enough to contain the longest queue length in the baseline scenario
and should not be changed when moving to the ATL scenario.
References
1. Surrogate Safety Assessment Model (SSAM). Version 2.0. Federal Highway
Administration. Siemens ITS: 2004.
2. VISSIM. Version 5.30-02. PTV America: Portland, Oregon, 2010.
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