Problem 3: Analysis of oversaturated
conditions 
U.S. 95/StynerLauder Avenue Intersection
Printable Version
In the two previous problems, we considered traffic volumes
only for the afternoon peak period. This allowed us to focus on several
important issues relating to the application of the signalized and unsignalized
intersection procedures of the Highway Capacity Manual. In problem 3, we
recognize that there are other time periods and traffic conditions that also
need to be considered. Just as we learned more about the signalization decision
when we considered the U.S. 95 corridor and the related system issues beyond the single intersection of U.S. 95/StynerLauder Avenue, we will see that there is more to learn about the
variation in traffic volumes, providing us with an even better perspective on the signalization decision.
The University of Idaho has a number of special events during
the year that attract large crowds to its sporting arenas and performing arts
venues. During these periods, demand often exceeds capacity along the U.S.
95 corridor in ways that do not happen during normal weekday periods. What tools are needed to assess the operation of the
U.S. 95 corridor in general
and the intersection of U.S. 95/StynerLauder Avenue in particular?
And what analytical issues must be faced when demand exceeds capacity at a
traffic facility? In subproblem 3a, we will use the
HCM methodologies to determine the level of service of the intersection under
these high volume conditions. In subproblem 3b, we will use a microscopic simulation model to assess these conditions.
Finally, in subproblem 3c, we will use
another method, critical movement analysis, to determine whether the
intersection can accommodate the demand during these high volume events. These
subproblems will address the following issues:

Using the analytical tools of
the Highway Capacity Manual, what would be the level of service at the intersection of
U.S. 95/StynerLauder Avenue during the high traffic volumes experienced during
University of Idaho football games if the intersection were signalized? 

Using a microscopic simulation
model, what would be the level of service at the
intersection of the U.S. 95/StynerLauder Avenue during the high traffic volumes experienced during
University of Idaho football games if the intersection were signalized? 
 Using the critical movement analysis technique, what
would be our answer to the question of whether there is sufficient capacity at the
intersection of the U.S. 95/StynerLauder Avenue during the high traffic volumes
described above if the intersection were signalized? 
Take a few minutes to think about each of these questions
before proceeding. Click on continue when you are ready.
[ Back
] to Problem 2 [ Continue ] to SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated
Intersection Analysis
Step 1. Setup
The University of Idaho has periodic special events that
attract large crowds to sporting arenas and performing arts
venues. During these time periods, demand often exceeds capacity
along the U.S. 95 corridor. What tools are needed to assess the
operation of the U.S. 95 corridor in general and the intersection of U.S. 95/StynerLauder Avenue in particular? And, what analytical issues must
be faced when demand exceeds capacity at a traffic facility?
In subproblem 3a, we will consider the conditions that occur
when traffic is leaving a football game at the University of Idaho. Demand
is high for about an hour after the conclusion of the game, and the U.S. 95
corridor experiences a high level of congestion during this period.
Consider these questions:

What is the difference between volume and demand, and
why is it important to distinguish these two terms? 

Can the intersection operate at level of service F
even when demand is less than
capacity? 

What is the appropriate value of the duration of
analysis parameter when demand exceeds capacity? 
 When should multiple time periods be considered in a
capacity and level of service analysis? 
Discussion:
Take a few minutes to consider
these questions. When you are ready, continue to the next page.
[ Back ]
[ Continue ] with SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated
Intersection Analysis
Now let's review each of these questions, and how they are
important to this analysis.
What is the difference between volume and demand, and
why is it important to distinguish these two terms? Most field studies at
intersections include the measurement of volume. Volume, or sometimes service volume, is the
traffic flow at the point where vehicles are entering the intersection just
past the stopline. Demand (or sometimes demand volume), by contrast, is
the traffic flow desiring to enter the intersection. When we measure
demand in the field, we must be at a point upstream of any queues that form at
the intersection. When we conduct analyses using the Highway Capacity
Manual, we must always use demand volumes and not service volumes. If
demand is less than capacity, then demand volume equals service volume, and we
can collect stopline counts for use with the HCM. However, if demand
exceeds capacity (as evidenced by continuing queues that don't dissipate at the
end of each cycle), then we must collect traffic flow data upstream of the
intersection to account for all vehicles desiring to use the intersection
during a given time period. Special care must be taken when collecting turning
movements when queues extend upstream of the intersection as it is sometimes
difficult to see the final direction that a given vehicle follows when the
observation is conducted upstream. Videotaping might be considered as an
aid in the data collection process when these conditions occur.
Can the intersection operate at level of
service F when demand is less than capacity? Level of service for a signalized intersection is defined by average control delay; and
while it is somewhat dependent on capacity, it is often more dependent on other
factors such as
arrival type. So it is possible for an intersection to
operate at level of service F (when delay exceeds 80 seconds per vehicle) while
demand is less than capacity. The
reverse is also true: lane groups, approaches, and intersections can sometimes
be found to operate at levelsofservice better than F, even while the computed
v/c ratio is greater than 1.0, especially in situations where the cycle
length is short and/or progression is very good. The point to remember from
all this is that LOS is not, by itself, a
sufficient measure of the operating status of the lane group, approach, or
intersection: other factors like v/c ratio, queue length, and cycle length must also be
considered when forming an overall judgment.
[ Back ] [ Continue
] with SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated Intersection Analysis
What is the appropriate value of the duration of
analysis when demand exceeds capacity? The default value of the duration
of an operations analysis is 15 minutes, or 0.25 hours. This value should be
used for most analyses. However, when demand exceeds capacity for a
15minute period, it may be necessary to expand the analysis period to
ensure that all demand can be accommodated. Another alternative to be
explored in this subproblem is to conduct a multiple time period analysis.
When should multiple time periods be considered?
If demand exceeds capacity for a given 15minute period, the excess
demand cannot be served during this period. In reality, this demand is
shifted to the next 15minute period. The analyst has a choice of
considering a longer duration of analysis (see above), or conducting a
multiple time period analysis. In this latter case, we would need to shift
the excess (or unserved) demand from the first time period (when demand
exceeds capacity) to the next 15minute period. In addition to considering
this demand shift, we must also take account of the initial queue to make
sure that our estimates of control delay are realistic.
We'll now consider how to setup this problem.
[ Back ] [
Continue ] with SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated
Intersection Analysis
Data were collected for a typical postfootball game
period at the U.S. 95/Styner AvenueLauder Avenue intersection. Exhibit
127 shows the demand (flow rates in veh/hr) for the
four 15minute periods immediately following the football game:
Exhibit
127. U.S.95/StynerLauder
Avenue Intersection Demand Volumes 
15min time period
beginning 
Eastbound 
Westbound 
Northbound 
Southbound 
LT 
TH 
RT 
LT 
TH 
RT 
LT 
TH 
RT 
LT 
TH 
RT 
4:00 pm 
40 
55 
175 
50 
50 
75 
100 
815 
45 
40 
700 
55 
4:15 pm 
50 
75 
375 
55 
80 
125 
215 
1,025 
50 
59 
1,975 
165 
4:30 pm 
30 
75 
125 
45 
75 
115 
20 
975 
35 
55 
1,200 
145 
4:45 pm 
45 
60 
175 
55 
85 
150 
145 
1,015 
45 
50 
1,350 
130 
How will the intersection perform, under both signal
control and stop sign control, for these demand conditions? How should
we proceed with this analysis?
We will first consider the operational performance of the
intersection operating under today's control conditions (twoway
stopcontrol) to confirm what we suspect: the high volumes on the main
street (U.S. 95) will cause long delays on the side streets (Styner and Lauder
Avenues). If this is found to be the case, it may further support a
recommendation that signalization is a valid
option to handle current and future traffic volumes. Even so, it is
important to note that eventrelated conditions would not, by themselves,
be sufficient justification for installing a traffic signal at this
intersection.
We will then consider the operation of the intersection
under signal control. Here, we will break down the analysis into four
steps, one for each of the four time periods, in sequence, starting with the
4:00 p.m. to 4:15 p.m. time period.
[ Back ] [ Continue
] with SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated
Intersection Analysis
Step 2. Results
Exhibit 128 shows the results of evaluating the
performance of the existing U.S. 95/StynerLauder Avenue intersection when
the traffic volumes are at the peak level after a football game. As we
would expect, the delays and level of service estimated by the TWSC intersection
capacity model are high, almost unreasonably high. The heavy volumes on
the main street (U.S. 95) make it extremely difficult for any minor street vehicle
to enter the intersection. Capacities are estimated to be at or near zero. We should not be too surprised to see that the model is unable to estimate the
delays. For example, for the through and rightturn movements on both
minor street approaches, the volume/capacity ratios are 68.33 (westbound) and
75.00 (eastbound). Clearly the intersection can be expected to perform quite poorly during
these high volume periods.
Exhibit 128. Delay,
Queue Length, and Level of Service at StynerLauder 
Unsignalized Control (Dataset14) 
Approach 
NB 
SB 
Westbound 
Eastbound 
Lane configuration 
L 
L 
L 
TR 
L 
TR 
v (vph) 
215 
59 
55 
205 
50 
450 
c (vph) 
256 
656 
0 
3 
0 
6 
v/c 
0.84 
0.09 

68.33 

75.00 
95% queue length 
6.8 
0.3 

28.0 

58.4 
Control delay 
64.3 
11.0 




LOS 
F 
B 
F 
F 
F 
F 
Approach delay 
 
 


Approach LOS 
 
 


How will the intersection perform under signal control?
[ Back ] [ Continue
] with SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated Intersection Analysis
We now consider the performance of the intersection for each
of the four 15minute time periods with the proposed signal control. The
results for the first 15minute time period (4:00 pm to 4:15 pm) are shown in
Exhibit 129:
Exhibit 129. Lane Group Capacity, Control Delay, and
LOS Determination at StynerLauder  Signal Control (4:004:15 pm) (Dataset15) 
Approach 
EB 
WB 
NB 
SB 
Movement 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
Adj flow rate 
40 
230 
50 
125 
100 
860 
40 
755 
Lane group capacity 
322 
421 
246 
432 
405 
2,089 
330 
2,083 
v/c ratio 
0.12 
0.55 
0.20 
0.29 
0.25 
0.41 
0.12 
0.36 
Green ratio 
0.25 
0.25 
0.25 
0.25 
0.58 
0.58 
0.58 
0.58 
Control delay 
18.2 
24.6 
19.6 
19.9 
7.5 
7.5 
4.2 
4.5 
Lane group LOS 
B 
C 
B 
B 
A 
A 
A 
A 
Approach delay 
23.6 
19.8 
7.5 
4.5 
Approach LOS 
C 
B 
A 
A 
Intersection delay 
9.4 
Intersection LOS 
A 
For this first time period, while there is some delay on
the side streets, the intersection operates at acceptable levels of service. Continue to the next page to see the results of the analysis for the second time period.
[ Back ] [ Continue
] with SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated
Intersection Analysis
The results for the second 15minute time period (4:15 pm to
4:30 pm) are shown in Exhibit 130:
Exhibit 130. Lane Group Capacity, Control Delay, and
LOS Determination at StynerLauder  Signal Control (4:154:30 pm) (Dataset16) 
Approach 
EB 
WB 
NB 
SB 
Movement 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
Adj flow rate 
50 
450 
55 
205 
215 
1,075 
59 
2,140 
Lane group capacity 
283 
416 
127 
432 
127 
2,091 
240 
2,081 
v/c ratio 
0.18 
1.08 
0.43 
0.47 
1.69 
0.51 
0.25 
1.03 
Green ratio 
0.25 
0.25 
0.25 
0.25 
0.58 
0.58 
0.58 
0.58 
Control delay 
19.0 
90.3 
29.3 
22.9 
355.8 
8.3 
6.2 
35.0 
Lane group LOS 
B 
F 
C 
C 
F 
A 
A 
D 
Approach delay 
83.2 
24.2 
66.2 
34.3 
Approach LOS 
F 
C 
E 
C 
Intersection delay 
49.1 
Intersection LOS 
D 
The increase in traffic flow rates during this second time
period results in a much poorer performance at the intersection. The major
thing to point out from Exhibit 130 is that the volume/capacity ratio for
three lane groups exceeds 1.0.
What is the practical implication of this result? The
excess demand for each of these lane groups (that is, the difference between
the lane group capacity and the adjusted flow rate) is transferred to the
next time period (4:30 pm to 4:45 pm). The excess demand is shown in
Exhibit 131. Note that since the demand is shown first in
vehicles/hour that we also show the actual number of vehicles that would be
transferred to the next time period.
Exhibit 131. Excess Demand at StynerLauder
under Signal Control (4:154:30 pm) 
Approach 
EB 
WB 
NB 
SB 
Movement 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
Adj flow rate 
50 
450 
55 
205 
215 
1,075 
59 
2,140 
Lane group capacity 
283 
416 
127 
432 
127 
2,091 
240 
2,081 
Excess demand (veh/hr) 

34 


88 


59 
Excess demand (vehicles) 

9 


22 


15 
[ Back ] [ Continue
] with SubProblem 3a 
Page Break
Subproblem 3a: Oversaturated
Intersection Analysis
Exhibit 132 shows both the initial and revised demands for
time period 3 (4:30  4:45 pm):
Exhibit
132. Service
Volumes vs. Demand Volumes at StynerLauder under Signal Control (4:30 
4:45 pm) 
Time
Period 
Eastbound 
Westbound 
Northbound 
Southbound 
LT 
TH 
RT 
LT 
TH 
RT 
LT 
TH 
RT 
LT 
TH 
RT 
Initial demand (veh/hr), time period 3 
45 
60 
175 
55 
85 
150 
20 
1,015 
45 
50 
1,350 
130 
Excess demand from time period 2 

6 
28 



88 



54 
5 
Final demand (veh/hr), time period 3 
45 
66 
203 
55 
85 
150 
108 
1,015 
45 
50 
1,405 
135 
For this analysis, we will use the initial demand. In
addition, we will use the excess demand as the initial queue, or unmet
demand, that is one of the required parameters in the computation of control delay in the HCM Chapter
16 procedure. The results of this analysis, for time period 3 (4:30 
4:45 pm), are
shown in Exhibit 133:
Exhibit 133. Lane Group Capacity, Control Delay, and
LOS Determination at StynerLauder under Signal Control (4:30  4:45 pm) (Dataset17) 
Approach 
EB 
WB 
NB 
SB 
Movement 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
Adj flow rate 
45 
235 
55 
235 
20 
1,060 
50 
1,480 
Lane group capacity 
257 
422 
257 
430 
149 
2,097 
247 
2,082 
v/c ratio 
0.18 
0.56 
0.21 
0.55 
0.13 
0.51 
0.20 
0.71 
Green ratio 
0.25 
0.25 
0.25 
0.25 
0.58 
0.58 
0.58 
0.58 
Control delay 
19.1 
31.9 
19.7 
24.5 
193.5 
8.3 
5.5 
9.5 
Lane group LOS 
B 
C 
B 
C 
F 
A 
A 
A 
Approach delay 
29.9 
23.6 
11.7 
9.4 
Approach LOS 
C 
C 
B 
A 
Intersection delay 
13.3 
Intersection LOS 
B 
The unserved demand that was present at the end of time
period 2 (4:15  4:30 pm) was successfully served during time period 3 (4:30
 4:45 pm). The delay for
the NB LT movement is still quite high, but all volume/capacity ratios are less
than 1.0. Therefore, the analysis of the effects of oversaturation can be
considered to be complete. It is worth noting that
the control delay reported in Exhibit 133 includes the additional delay
experienced by vehicles in the residual queue that was transferred to time
period 3 from time period 2. The HCM procedure accounts for the carryover of
excess demand through the third (d3) term of the delay equation.
We will still want to analyze the last 15minute time period in order
to determine the average control delay for the hour, and this is done on the
next page.
[ Back ] [ Continue ] with
SubProblem 3a 

Page Break
Subproblem 3a: Oversaturated
Intersection Analysis
Exhibit 134 shows the results of the analysis for time period
4 (4:45  5:00 pm):
Exhibit 134. Lane Group Capacity, Control Delay, and
LOS Determination at StynerLauder under Signal Control (4:45  5:00 pm) (Dataset17) 
Approach 
EB 
WB 
NB 
SB 
Movement 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
LT 
TH/RT 
Adj flow rate 
45 
235 
55 
235 
145 
1,060 
50 
1,480 
Lane group capacity 
257 
422 
257 
430 
148 
2,092 
246 
2,078 
v/c ratio 
0.18 
0.56 
0.21 
0.55 
0.98 
0.51 
0.20 
0.71 
Green ratio 
0.25 
0.25 
0.25 
0.25 
0.58 
0.58 
0.58 
0.58 
Control delay 
19.1 
31.9 
19.7 
24.5 
607.3 
8.3 
5.5 
9.5 
Lane group LOS 
B 
C 
B 
C 
F 
A 
A 
A 
Approach delay 
29.9 
23.6 
80.4 
9.4 
Approach LOS 
C 
C 
F 
A 
Intersection delay 
38.3 
Intersection LOS 
D 
The results for the fourth time period are much the same
as for the third time period. All volume/capacity ratios are less than 1.0,
so all demand is served during this 15minute period. As before, delays are
high for the NB LT movement.
[ Back ] [
Continue ]
to SubProblem 3b 
Page Break
SubProblem 3b: Using a
Microscopic
Simulation Model
While the HCM model
for signalized intersections can be used to evaluate the performance of the
U.S. 95/Styner AvenueLauder Avenue as we showed in subproblem 3a, we
should also consider the use of a microscopic simulation model. Why? The HCM model is only an approximate solution for conditions when demand
exceeds capacity. Since it considers only macroscopic conditions, the HCM cannot provide the same level of accuracy as
can a microscopic simulation
model for high demand periods or when the flows from one intersection
interact with the flows from an adjacent intersection.
There are several microscopic simulation models that can
be used for this problem. The application of microscopic simulation is
covered in Chapter 34 of the HCM 2000. You are encouraged to review
this chapter to learn more about some of the advantages and disadvantages of
using microscopic simulation models. Here, we will use one such model,
the CORSIM model developed by the Federal Highway Administration. It should
be emphasized that the CORSIM model is being used here for illustrative
purposes, and other microscopic simulation models are available that would
work equally well.
Microscopic simulation models consider much more detail
than macroscopic models such as the HCM. Individual vehicle
interactions and the detailed operation of traffic controllers are two of
the more important features of these microscopic models. This more
detailed treatment of traffic flow and controller operations allows the
models to consider more directly the oversaturated conditions that we found
in subproblem 3a. In addition, we can input the traffic flow data for
all four time periods for this problem at once and not have to run several
analyses as we did in subproblem 3a. The models are also stochastic in
nature, which means they explicitly account for the probabilistic nature of
traffic flow and driver behavior. But there is a cost for this
additional detail: more input data, more time required to ensure
that the model is calibrated for local conditions, and multiple simulations
in order to assure that statistically valid results are obtained.
[ Back ] [ Continue
] with SubProblem 3b 
Page Break
SubProblem 3b: Using a
Microscopic
Simulation Model
CORSIM produces a
very detailed output, covering several measures of delay and travel time, as
well as other measures such as vehicle emissions and fuel consumption. We will consider the average control delay measure, similar to the measure
produced by the HCM signal model.
The results from the CORSIM simulation model are shown in
Exhibit 135. We've also included the results from the HCM analysis
for easy comparison between these two models.
Exhibit 135.
Average control delay per vehicle (sec/veh) at StynerLauder  Signal
Control
(Multiple
Datasets) 
Time period 
Eastbound 
Westbound 
Northbound 
Southbound 
HCM 
CORSIM 
HCM 
CORSIM 
HCM 
CORSIM 
HCM 
CORSIM 
4:00 pm to 4:15 pm 
23.6 
13.2 
19.7 
11.2 
7.5 
7.0 
4.5 
7.1 
4:15 pm to 4:30 pm 
83.2 
27.7 
24.2 
13.5 
66.2 
42.4 
34.3 
8.6 
4:30 pm to 4:45 pm 
33.6 
16.0 
23.7 
8.6 
60.4 
28.5 
10.1 
8.2 
Exhibit 135 shows that there are some significant
differences between the estimates of average control delay produced by the
two models, HCM and CORSIM. While we have no way of verifying the quality of
either estimate, we can say that CORSIM and other similar microscopic
simulation models have the potential to
produce more realistic results. Why? Because the microscopic
simulation models are better at accounting for the microscopic interactions between vehicles,
they are more
likely to better represent conditions of oversaturation than
the macroscopic approach taken by the HCM. But this is true only if the
model is correctly coded, calibrated, and applied (including multiple runs
to account for the stochastic nature of the simulation). Our major advice is this: if
you are dealing with conditions in which demand exceeds capacity, queues
from one intersection interacting with an adjacent intersection,
intersections that are closely spaced, or multiple
time periods, you should consider the use of a microscopic simulation model
to produce estimates of control delay. Just like the HCM procedures, use of
any microscopic model requires special care and expertise to assure that the
accuracy of the results is commensurate with user expectations.
[ Back ] [ Continue
] to SubProblem 3c 
Page Break
Subproblem 3c: Critical Movement Analysis
In subproblem 3b, we saw how we can use a microscopic simulation model to
address conditions in which demand exceeds capacity. The model produces
detailed statistics on the performance of the U.S. 95/StynerLauder Avenue
intersection under these conditions. We'll now see in this subproblem
that we can use a much simpler approach, critical movement analysis, to
determine whether the demand will exceed capacity for this intersection during
the high volume conditions present after football games at the University of
Idaho.

What is critical movement analysis? 

What data are needed to conduct critical movement
analysis? 

What outputs are produced by critical movement
analysis? 

Are the results from critical movement analysis any
more or less valid than the results produced by the HCM or by microscopic
simulation models? 

Why is there virtually no difference
between estimated delay on the eastbound and westbound approaches to the
intersection? 

What is the effect of grade and heavy
vehicles? 

How do changes in vehicle mix affect the intersections
when the intersection operates near or at capacity? 

What effects do heavy vehicles have on the intersection
beyond changes to saturation flow rate? 
Discussion:
Take a few minutes
to consider these issues before we proceeding with this
analysis.
[Back] [Continue]
with SubProblem 3c 
Page Break
Subproblem 3c: Critical movement analysis
What is critical movement analysis? Critical
movement analysis is a method to determine whether the projected volumes at a
signalized intersection will be under, near, or over the intersection's capacity
to accommodate them. The method is fully documented as a planninglevel
procedure in Transportation Research Circular 212. The method considers each of the
four conflicting movement pairs at the intersection (for example, the NB LT and
the SB TH movements). The critical movements for each intersection phase
(for example, the maximum of either the NB LT/SB TH movement and the SB LT/NB TH
movement) are summed. This sum is compared with the following standards:
Exhibit 136. Intersection Performance Assessment by
Critical Volume 
Sum
of Critical Volumes (v/hr) 
Intersection Performance Assessment 
01,200 
Under capacity 
1,201  1,400 
Near capacity 
1,401 and above 
Over capacity 
What data are needed to conduct critical movement
analysis? The approach volumes, the number of lanes, and the lane
configuration on each approach are the data
needed to conduct a critical movement analysis.
What outputs are produced by critical movement analysis? Critical movement analysis produces only
an assessment of the intersection's sufficiency to accommodate the projected
volumes. It does NOT provide estimates of delay, LOS, or queue lengths.
Are the results from critical movement analysis any more or
less valid than the results produced by the HCM or by microscopic simulation
models? The results of a critical movement analysis help to determine
whether the intersection will operate under, near, or over capacity. It is
more of an approximation than either the HCM or other models, but in many cases
the results from a critical movement analysis are sufficient to answer the
question at hand.
[ Back ] [ Continue ] with SubProblem 3c 
Page Break
Subproblem 3c: Critical Movement
Analysis
Why is there virtually no difference
between estimated delay on the eastbound and westbound approaches to the
intersection? The
volume to capacity ratio of the intersection with a traffic signal installed
is 0.35. Essentially, the intersection is operating well below capacity as
you would expect because a signal was installed. The addition of heavy
vehicles will reduce the saturation flow rate at the intersection. The
presence of heavy vehicles may also have an effect other factors such as
lost time (this will be discussed later on this page). Specifically for this
intersection, the impact of 25% heavy vehicles on the traffic stream is to
reduce the saturation flow rate by 20%, (the f_{HV} for 25% trucks
is 0.80). This is applied within the signalized intersection methodology and
results in a new v/c ratio at the intersection of 0.43, which is
approximately 20% higher than the existing scenario.
What is the effect of grade and heavy
vehicles? Similar to the effect heavy vehicles have on the saturation
flow rate, grades are treated by a separate factor f_{g}.
How do changes in vehicle mix affect the intersections
when the intersection operates near or at capacity? The HCM delay equation is more sensitive to changes in
saturation flow rate when an intersection is near or at capacity. For instance,
under postfootball game traffic volumes, assuming the heavy vehicle percentage
as a part of that analysis would
yield the results described on the next page.
What effects do heavy vehicles have on the intersection
beyond changes to saturation flow rate? Beyond design
issues that must be considered for accommodating the heavy vehicles at an
intersection, the presence of heavy vehicles may have an impact on the lost
time, lane utilization, and arrival type at an intersection. Heavy
vehicles will have an effect on lost time at the intersection because of the
acceleration characteristics of these vehicles. This will be exacerbated on a
uphill grade.
Heavy vehicles will also affect the arrival type for each approach to the intersection
by increasing the amount of time that a platooned flow may arrive on a standing
queue. Simulation models will explicitly model the vehicle characteristics and
performance on the approach to consider the relationships between the factors.
We will now consider how to set up this problem.
[
Back ] [ Continue
] with SubProblem 3c 
Page Break
Subproblem 3c: Critical
Movement
Analysis
Application of the critical movement analysis methodology to
the StynerLauder/U.S. 95 intersection yields the following results, in vehicles
per lane:
Exhibit 137. Critical Movement Analysis at StynerLauder/U.S.
95
Intersection for peak event (Signal Control) 
Conflicting Movements 
Conflicting Volumes 
Conflicting Movements 
Conflicting Volumes 
EB LT volume 
50 
NB LT volume 
215 
WB TH/RT volume 
205 
SB TH/RT volume 
1,070 
Total
conflicting volume 
260 
Total
conflicting volume 
1,285 
WB LT volume 
55 
SB LT volume 
59 
EB TH/RT volume 
450 
NB TH/RT volume 
522 
Total
conflicting volume 
505 
Total
conflicting volume 
581 

Critical movement
EW approaches 
Conflicting Volumes 
Critical movement
NS approaches 
Conflicting Volumes 
WB LT
EB TH/RT 
505 
NB LT
SB TH/RT 
1,285 
Sum of critical movements 
1,790 
Assessment (under, near,
above capacity) 
Above capacity 
The results shown in Exhibit 137 are consistent with those that we obtained
in earlier subproblems from both the HCM methodology and from the
application of a microscopic simulation model. It should be noted that
all volumes presented in this Exhibit are expressed in terms of vehicles per
hour per lane, or vphpl. Specifically, the sum of the critical movements (1,790) is greater than
the estimated capacity threshold of 1,400. Thus, the intersection
demand exceeds its capacity and we can conclude with a fair amount of
confidence that the intersection is above
capacity. If the sole question we were trying to answer
was whether or not the intersection has sufficient capacity in its current
configuration to accommodate the projected traffic volumes, the
critical movement analysis might have been the most appropriate analysis
tool to use, because it is able to provide the answer with much less effort
and time than would be required by the other methodologies we have explored.
Thus, the critical movement analysis methodology can be an effective and
efficient way to answer some questions, as long as its limitations and
constraints are always kept in mind.
[ Back ] [ Continue ]
to Problem 3 Analysis 
Page Break
Problem 3: Analysis
We will now consider the concepts presented in each of the three
subproblems.
In subproblem 3a,
we consider the special eventrelated traffic conditions that necessitate a
second look at the intersections during event traffic. In this analysis we
considered key concepts such as oversaturated conditions and demand volume.
Specifically, we analyzed traffic across multiple scenarios, where demand exceeds
capacity and arrivals from one scenario are served in the adjacent time
period. In this case, we are able to analyze a scenario that provides
an insight into oversaturated conditions. Where volume exceeds capacity, the
collection of demand volumes becomes critical to our analysis in order to
capture the effect of oversaturated conditions. This is a challenge that
requires significant investment in data collection.
In subproblem 3b,
we utilize a simulation model to analyze the conditions. Our analysis using
the simulation model provides us with an opportunity to test a variety of
variables to determine the effect of specific measures and changes.
Finally, in subproblem 3c,
the concept of critical movement analysis is used to exhibit another
methodology for determining whether an
intersection is at or near capacity.
Discussion:
The ability of a traffic signal
to handle fluctuations is a function of the signal timing that is in the
controller in the field. In time period 3 (4:30  4:45 pm) of our previous analysis, we changed
the green ratio slightly to serve the traffic at the postgame traffic at the
intersection. Would this green ratio be possible under the existing pretimed
control? Take a few minutes to identify other
factors that you think should be considered in this analysis, and continue to
the next page when you are ready.
[ Back ] [ Continue ]
to Problem 3 Discussion 
Page Break
Problem 3: Discussion
What is
next to consider in this problem, after looking at the effects of varying
traffic intensities presented by event traffic, is the signal
timing settings that affect the green ratio for the intersection. Note that we
have focused our analysis so far on pretimed control, but most signalized
intersections today feature detection that can respond to traffic based on
signal timing settings. In problem 4, we will consider these settings and
determine the effect they have on performance of the U.S. 95/StynerLauder Avenue
intersection. [ Back
]
[ Continue
] to Problem 4 
