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CHAPTER 2
FINDINGS
This chapter presents the most significant findings of the study and identifies the appendices in which
more detail may be found. The findings relate primarily to the development and evaluation of models
for estimating the signal timing plan and the delay at trafficactuated intersections. The signal timing
plan estimation and delay estimation results wait be addressed separately.
SIGNAL TIMING PLAN ESTIMATION
The signal timing estimation procedure included In Appendix II to HCM Chapter 9 is the logical place
to begin this discussion, because it represents the status quo. Appendix 9H recognizes that indivi
dual phase times are variable under trafficactuated control and suggests that the average cycle length
and phase times may be approximated by assuming that the controller is effective in its objective of
keeping the critical approaches nearly saturated. Mathematically, this relationship may be stated as:
CaV L/( ~ Y/XT)
Where:
(1)
Cav = The average cycle length
Lo = The total lost time per cycle, i.e., the sum of the lost times associated with the
starting and stopping of each critical movement in the phase sequence.
Y = The critical flow ratio, determined as the sum of the flow ratios for the
individual movements that are critical in each phase. The flow ratio for each
movement is defined as the ratio of the traffic volume to the saturation flow
rate. The value of Y indicates the proportion ofthe total time that is required
to accommodate all of the cntical movements, exclusive of the lost time.
XT = The target degree of saturation (volume/capacity ratio). A value of 0.95 is
suggested in Appendix 9~l for trafficactuated control.
After the average cycle length has been computed, the average green times may be determined by
dividing the total cycle time among the critical movements in proportion to their individual flow
ratios. This is a common traffic engineering concept, and it will not be belabored here.
There are four major problems with the Appendix 9~l methodology that have drawn intense criti
cism:
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l. The fixed threshold of 95 percent saturation has not been well accepted. Several studies have
indicated that a somewhat lower degree of saturation open results t3,43.
2. The method is not sensitive to the design parameters oftrafficactuated control. The effects
of detector configuration and controller settings are not reflected in Equation I.
3. The simplistic nature of this mode} does not provide for realworId complications such as
minimum and maximum green times, sharedlane permitted leg turns, left turns that are
allowed to proceed on both permitted and protected phases, phase skipping due to lack of
demand, constraints imposed by coordination, etc.
4. There is no basis in the mode} to distinguish between pretimed and tra~cactuated control.
A procedure that addresses all of these problems has been developed as a major product of this
research. The analytical aspects of the procedure are described in detail in Appendix C. A stepby
step computational process using worksheets is presented in Appendix E. The software that imple
ments the computational procedure is described in Appendix G.
Green Time Determination for TrafflcActuated Controllers
The detenn~nation of required Been time is a relatively straightforward process when the cycle length
is given; however, trafficactuated controllers do not recognize specified cycle lengths. Instead, they
determine, by a mechanical analogy, the required green time given the length of the previous red
interval and the arrival rate. They do this by holding the r~ghtofway until the accumulated queue
has been serviced.
The basic principle underlying all signal timing analysis is the queue accumulation polygon (QAP),
which plots the number of vehicles queued at the stop line over the cycle. The QAP for a simple
protected movement is illustrated in Figure 2. The queue accumulation and discharge is represented
in this very simple case as a triangle. The accumulation takes place on the leD side of the triangle
(i.e., effective red) and the discharge takes place on the right side of the triangle (i.e. effective green).
More complex polygons are generated when permitted movements occur and when a movement
proceeds on more than one phase. Chapter 9 of the HCM includes an extensive discussion on this
subject.
Two methods of determining the required green time, given the length of the previous red, are
illustrated in Figure 2. The first employs the "Target v/c" approach, which is the basis for the current
Appendix ~ method, and for the planning method described in HCM Chapter 9. Under this approach,
the green time requirement is determined by the slope of the line representing the specific target v/c
ratio. If the phase ends when the queue has dissipated under these conditions then the target v/c will
be achieved.
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!
8
:D
~6

._
an _
a)
._
~4
>
o
Proposed Analytical Model
Green time based on
phase extension time
l
As
HCM Appendix II
Green time based on
target v/c ratio
_
/ Red Time
At\
Time (seconds)
Queue
Senriec
T,mc
Phase
Extension
Talc
Figure 2. Queue accumulation polygon illustrating two methods
of green time computation
The second method recognizes the way a trafficactuated controller really works. It does not deal
explicitly with v/c ratios; in fact it has no way of determining the v/c ratio. Instead, it terminates each
phase when a gap of a particular length is encountered at the detector. Good practice dictates that
the gap threshold must be longer than the gap that would be encountered while the queue is being
serviced. Gaps large enough to terminate the phase cannot occur until the queue service time (based
on v/c = 1.0) has elapsed. Therefore, the average green time may be estimated as the sum of the
queue service time and the phase extension time as shown on Figure 2. Each of these components
is derived separately in Appendix C.
Cycle Length Determination
This green time estimation procedure is easy to implement, but it does not lead directly to the
determination of an average cycle length or green times, because the green time required for each
phase is dependent on the green time required by the other phases. Thus, a circular dependency is
established requiring an iterative solution. with each iteration, the green time required by each phase,
given the green times required by all the other phases, may be determined.
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The logical starting point for the iterative process is the minimum times specified for each phase. If
these times turn out to be adequate for all phases, the cycle length will simply be the sum of the
minimum phase times for the critical phases. If a particular phase demands more than its minimum
time, then more time must be given to that phase. Thus, a longer red time must be imposed on all the
other phases. This, in turn, increases the green time required for the subject phase. This circular
dependency converges quite reliably through a series of repeated iterations.
Minimum Phase Times
The whole question of minimum phase time requires more attention. The specified minimum green
time constraints are valid only for pretimed phases and phases that are set to recall to the minimum
time regardless of demand. The real significance of the minimum phase time for an actuated phase
is that the phase must be displayed for its specified minimum time unless it is skipped due to lack of
demand. This situation may be addressed analytically by determining the probability of zero arrivals
on the previous cycle, PoV Assuming that the phase will be displayed for the minimum time except
when no vehicles have arrived the adjusted minimum phase time may be computed by multiplying the
specified phase time by the quantity (!  PO`,).
This relationship also has circular dependencies because, as the acljusted minimums become shorter,
the probability of zero arrivals also becomes higher, which further reduces the adjusted minimums.
Fortunately, the solution fits well into the iterative scheme that was just described. The use of
adjusted ~r~iimum green times offers a practical method for dealing with phases that are not displayed
on each cycle. The concept applies equally to pedestrian minimum times.
MultiPhase Operation
Two extensions to the methodology presented to this point are required to deal with more complex
situations. The first is the extension of the QAP from its simple triangular shape to a more complex
shape that represents different arrival and departure times at different points in the cycle. The second
is a procedure to synthesize a complete single ring equivalent sequence by combining cntical phases
in the dual ring operation. The details of both procedures are described finely in Appendix C.
Coordinated Semiactuated Operation
The nonactuated phases under se~actuated control may be coordinated with similar phases at
neighboring intersections to promote progression oftraflic on an arterial street. In the most common
coordination scheme, a background cycle length is imposed. The actuated phases receive their
allotment of green time in the usual manner, except that their maximum green times are controlled
externally to ensure conformance to the specified cycle length. If the actuated phases require all of
their nominal green time allotment, the interaction operates In a more or less pretimed manner. If not,
the unused time is reassigned to the coordinated phase.
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The computational structure developed under this project is able to approximate this operation quite
effectively. The analysis of coordinated operation requires another iterative loop which executes the
procedure described in Appendix C, adding more green time incrementally to the coordinated phases
until the background cycle length has been reached. The result is a timing plan that approximates the
operation of the controller in the field.
VolumeDensity Operation
The main differences between volumedensity control and conventional actuated control are the
rn~n~mum green settings (variable irutial), the detector configuration, the gap reduction feature and
the passage time setting for the last vehicle actuation. The refinement of the proposed analytical
mode! for volumedensity operation focused on these areas.
The "Free Queue" Parameter
The free queue parameter indicates the number of led turning vehicles that may be stored in a shared
lane awaiting gaps in the opposing traffic without blocking the passage of through vehicles. The
current HCM procedure assumes that the first waiting left turn will block all of the following vehicles
In the shared lane. This assumption produces pessimistic results in some cases. Both the through
vehicle equivalence of a led turn (EL] ~ and the lane group saturation flow rate are affected.
At this point, only the SIDRA [5] model considers the free queue explicitly. Because of its impor
tance to tra~cactuated control, it is essential that the proposed analytical model recognize this
phenomenon. The analytical basis for the model is described In Appendix C. A set of curves is devel
oped to illustrate the effect of the free queue on the estimated phase time as a Unction of the
approach volume.
The phase time analysis with a free queue is best illustrated with a simple example. Consider a trivial
intersection with four singlelane approaches. All approaches are configured identically and carry
the same traffic volume. On each approach, the proportions of left sums, through vehicles and right
turns are 0.2, 0.7 and 0. i, respectively. This is an example of a simple twophase hilly actuated
operation. The rniriimum phase time for each approach is 15 seconds and the maximum phase time
is 80 seconds. The detector is 30 feet long and placed at the stop line. The allowable gap is 3
seconds. Each phase is assigned 4 seconds yellow plus allred and 3 seconds of lost time.
Each approach volume varies from 100 vph to 800 vph, while the range of free queue is set from 0
to 2. The value ofthe free queue is not necessarily an integer. Based on the proposed method, the
phase time estimation with free queues is shown in Figure 3. In this figure, the x axis represents the
approach volume, y axis shows free queue values, and the vertical axis is the estimated phase times
by the proposed method. The effect of the Bee queue value may be easily observed from this three
dimensional surface plot. Note that the curve shown at a free queue value of zero represents the
status quo.
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to
·
I to
~ en
~om
Figure 3. Effect of the free queue on phase times for the example
problem
The computational structure of the proposed analytical mode! described in Appendix E has been
designed to incorporate the Dee queue concept. While it would be difficult to verify the mode! satis
factorily, either by simulation, or in the field, it is suggested that the analysis is robust and the HCM
Chapter 9 procedure wall be enhanced if the free queue is included.
EVALUATION OF THE SIGNAL TIMING ESTIMATION MODEL
Phase Time Comparison between NETSIM and the Proposed AnalYtical Model
The proposed signal timing estimation mode} was evaluated using simulation data Tom nine inter
sections and field data Tom one intersection. The sample data sets used in the simulation study are
summarized in Appendm F. These data sets represent a wide variety of conditions. The results are
summarized graphically in Figure 4. Each point on this figure represents a single comparison between
simulated phase length obtained from NET SIM and corresponding estimates produced by the pro
posed analytical model. Note that the two methods compare very favorably. The correlation (R
squared) for this comparison was 0.90, and the slope of the regression line was close to Ill. This
suggests that the proposed analytical mode! was able to achieve substantially the same results as
NETSIM.
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80 
70
~ ~
c,
~ ~
._
ca 40
._
~ 20
lo
10
~ /
,,,,,,, ,,,,,,, ,,,,,, ,,,,,,, ,,,,,, ,,,,,,, ,,,,,, ,,,,,,, ,,,,,, ,,,,,,, ,,,,,, ,,,,w,,,,,, ,,,,,7 c.....
R2 =o.go /
............................................................................... ....... ,'
a7~.
. ~. ad.
. . , . it & 
,,~_
.,¢ .........
0 10 20 ~ 40 = ~70 80
Simulated Phase Tirne (~c)
Figure 4. Comparison of phase time estimates by NETSIM
and the proposed analytical mode!
Phase Time Comparison between the Analvtical Model and Field Data
Figure 5 shows a phase time comparison between the estimates from the analytical model and field
data from a multiphase intersection in Gainesville, Florida. All led turns had protected plus per
mitted phasing. This comparison is based on 256 observations. Two groups of data points are shown
in the figure because the through traffic phases tended to be substantially longer than the left terra
phases. Pedestrian recall was set on each approach. Therefore, the pedestrian phase time of 22 +
5 = 27 seconds becomes the lower bound for the through phase shown in Figure 5. The maximum
green time of ~ 5 seconds for lest turns set the upper bound for the left turn phases.
Although the phase time estimates from the model are slightly higher chart the measured field data for
left turn phases (low volume), the regression line is close to I: ~ slope. The dispersion of the data
poirts is small, indicating that the phase time estimates from the analytical model are close to the field
data. This is confirmed by a very high R squared value of 0.95.
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60
~ 50
m
_
oo
40
30
o

~ 20
._
in
~ 10
R 2 = o.g 5 (2 5 6 observations)
,, ~ Through Phases
Leftturn Phases
ELF
O
.
20 30 40
Phase Time from field Data (see)
7:00 AM  8:00 PM
0 10
50 60
Figure 5. Phase time comparison between the proposed
analytical mode! and field data
ESTIMATION OF DELAY AT TRA~ICACTUATED INTERSECTIONS
The traditional delay formulation used by virtually all analytical models is based on two terms which
are added together to produce the total delay (D) in seconds per vehicle. For nonplatooned arrivals,
D is the sum of the first delay term (D1) and the second delayterm (I)2). The total delay per vehicle
for a given lane group can be expressed as:
D =D1 +D2
(2)
1. The first (uniform) delay term, D1, which is mainly based on the uniform delay (Du) as
sumes that ad vehicles arrive In a completely uniform manner. The uniform delay is com
puted as the area contained within the queue accumulation polygon (QAP).
2. The second (incremental) delay term, D2, adds a correction factor to compensate for
randomness in the arrival patterns and occasional oversaturation.
Three delay models will be discussed in this chapter. The first is the existing HCM delay model as
it appears in the current version of Chapter 9. The other two were developed by members of the
NCHRP 348 project team. These will be referred to as Mode! ~ and Model II, respectively.
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The BCM DelaY Model
In the current HCM delay model, a delay adjustment factor, DF, is applied to Do to account for the
impact of control type and signal progression on delay. These two effects are mutually exclusive.
Table 913 In the HCM indicates the appropriate values of DF for all of the possible control modes.
For isolated trafficactuated operation (no progression effect), the DF factor is fixed at 0.85 for all
lane groups. Therefore, the HCM delay mode! for isolated traff~cactuated operation becomes
D = 0.85 D~+D2
(3)
Since the QAPs must be developed in detail by the proposed analytical mode! to determine the phase
times, the value ofthe uniform delay, Du (i.e., the area contained within the QAP), may be computed
by a simple extension to the existing phase time prediction model. The detailed development of the
unifonn delay equations for computing the QAP areas of possible phasing alternatives is descnbed
in Appendix D. For simple protected movements with triangular QAPs, the HCM expresses the
uniform delay as:
D,
The incremental delay is expressed as:
D2 = 225 x2
where
g =
C =
0.5 C jl ~ ( C)
1 _ ( g ) [Min (x, 1.0)] u
(x  1) + ~
(x1)2+ 16X
c
(5)
Effective green time of the lane group;
Cycle length;
x = Volume/capacity ratio ofthe lane group. For the led turn movement in com
pound led turn protection, x is the ratio of total leD turn volume to total led
turn capacity;
Hourly capacity (vehicles/hour) of the lane group. It is the maximum arrival
flow that can be served under prevailing flow conditions: c = S */C where S
is the adjusted saturation flow (vehicles/hour). Note that for the left turn
movement in the compound led turn protection, c is the total capacity for the
led turn movement.
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Note that the HCM signalized intersection analysis method deals in stopped delay as opposed to total
delay. Therefore, the values shown in the HCM Chapter 9 delay equations are divided by I.3.
Delay Model I
Delay model I refers to the delay model developed by Ak~elik and Chung [6]. The total delay (D)
is equal to the sum of the uniform delay IDS) and the incremental delay (D:) as shown in Equation
1:
D=D1 +D2
The D, valueis the product ofthe uniform delay (Du) based on the QAP and a uniform delay adjust
ment parameter (fat). The uniform delay adjustment parameter proposed by Ak~elik and Chung [4]
IS:
D1 =
fdlDu forx< 1.0 (6)
D1 = fd~(X=l) Du for x > 1.0
where
fd1 = Uniform term parameter' which can be computed by the following formula.
Note that fdl(X=l) is the value of the uniform term parameter at the degree of
saturation (x) equal to 1.0.
fat = [1 + 0.40 (S g / 3600) (v / S) ~ (7)
where
g = Effective green time (seconds) of the lane group;
S = Adjusted saturation flow (vehicles/hour) of the lane group;
and
v = Traffic volume (vehicles/hour) of the lane group.
For compound left turn protection, fat is computed individually for the protected phase and permitted
phase. Thus, in the computation of the firsttemn parameter for each phase, it is necessary to use the
(S go and (v / S) ~ values for the relevant green periods (l = I, 2 denotes the green periods) to com
pute (fat) ~ individually.
The computation of the incremental delay term also adopts the delay model proposed by Ak~elik and
Chung [61. The incremental delay, D2, can be computed by the following formula:
NCHRP Project 348 Final Report: Page 22
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D2 = 90O Tp
(X 1)+
D2 = 0 otherwise
where
kd =
~y2 ~ kit (x  xO)
p
for x > xO (8)
Tp = Peak flow period (analysis periods in hours. The default value is 0.25;
c =
Hourly capacity (vehicles/hour) of the lane group. It is the maximum arrival
flow that can be served under prevailing flow conditions: Q = S (g / C);
Incremental term parameter, computed as
kd = 0.40 (g S /3600~° Is (v / S)i ~
(9)
For compound led turn protection, use the total (g S) for the led turn move
ment, (g S) = ~ (g Ski, use total left turn volume per hour, v = £ vi, and use
the average saturation flow for the two green periods, S = £ (g Ski / £gi = (g
_
S) / £ gi;
x0 = Critical degree of saturation ofthe lane group. Note that with compound left
turn protection, the value of xO for the left turn movement is the same as that
for the protected left turn movement. If the degree of saturation, x, is below
xO' the average incremental queue is zero (D2 = 0~ Xo iS expressed as follows:
X = 0 42 h a! ~Go.2
where
(10)
ho = Allowable gap setting as a headway value (seconds). Note
that with compound left turn protection, the value of ho
for the left turn movement is the same as the value of ho
for the protected left turn movement; and
MxG = Maximum green time setting as a controller (displayed)
value (seconds). Note that with compound led turn pro
tection, MxG for the left turn movement uses the value of
MxG for the protected led turn movement.
Note that the delay model proposed by Akcelik and Chung was derived by calibration of the general
model using simulation data.
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Delav Model ~
The structure of Delay Mode} IT has been developed more recently to achieve consistent modeling
of delay for different intersection types, and for different performance statistics for the same
intersection type. Model IT is based on the same general mode} structure as Model ~ but uses fat, =
I.0, x0 = o.o and kd as a Unction of the gap setting (given in Table I). It was developed by IN [7],
discussed in Li, Rouphai! and Ak~elik ~] and adopted by Fambro, Rouphail, Messer and Li [93.
Mode! I! uses the uniform delay mode} for the first term and assigns all additional delays due to
randomness (overflows) to the second term.
· . · .
Table I. Values of the delay mode! parameter kit in Delay Mode! ~
Gap setting headway,hO(seconds) ~ 2.5  3.5  4.0  5.0 
Delay parameter, kd (Li model)  0.084  0.1 19  0.125  0.23 1 
Comparison of Delav Estimates between Delav Models and NETSIM Simulation
For traff~cactuated operation, the accuracy of the delay model depends on the accuracy of the
estimated signal timing plan. The different analytical models were incorporated as options into the
computational structure described in Appendix E. In this study, NETSIM was used as an evaluation
too! for delay estimation.
The data sets included four HCM Chapter 9 sample calculations, five hypothetical intersections and
one field data set. Some of the traffic volumes, phase sequences and operational data were modified
slightly to increase the range of conditions included in this analysis. A complete description of the
data sets is given in Appendix F. Since the delay is not meaningful for movements with volume/
capacity ratio greater than one, only the delay estimates for movements with v/c ratio less than I.0
were used for comparison.
Four specific comparisons were performed. In each case, the delay estimate from NET SIM was
plotted against the corresponding estimate Tom an analytical procedure. The four analytical proce
dures that were compared included:
The existing HCM Chapter 9 method; i.e., timing computation by the Appendix IT
method and delay estimates by the current HCM Chapter 9 model;
I.
Delay estimates from the current HCM Chapter 9 model, based on timing computa
tions by the proposed analytical model;
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Delay estimates from Mode} I, based on timing computations by the proposed
analytical model; and
Delay estimates Dom Mode! IT, based on timing computations by the proposed
analytical model.
Each of these comparisons wait be discussed separately.
Delay Estimates Based on the Existing BCM Chapter 9 Method
This comparison was carried out to indicate the performance of the existing HEM Chapter 9 method
as a basis for evaluating the effectiveness of the proposed improvements to the methodology. Figure
6 shows the NET SIM delay estimates plotted against the corresponding estimates from the HCM
delay mode! based on the phase times predicted by the Appendix IT technique. The sample size
included 433 observations. When the value of the delay estimate increases, the dispersion ofthe data
points increases. A large dispersion occurs at delay values over 50 seconds per vehicle. The
correlation is very low (R square = 0.45~. The slope of the regression line is about 0.75. This
indicates that, when compared to NET SIM, the HCM Chapter 9 technique predicted smaller delay
values.
90
ID 8 0
A, 70
In
_'
_ 6 0
HE 50
40
a
30
20
1 0
R2= 0.45 (433 observations)
~ '%
~_ ' ~ it_
1 _ ~ _ ~_~ _
a ~_ _
0 10
20 30 40 50 60 70 80 90
Total Delay from TRAFNETSIM (sec/veh)
Figure 6. Delay comparison between the current OCM
Chapter 9 procedure and NETSIM
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DCM Delay Model Estimates Based on Timing Estimated by the Proposed Analytical Model
Figure 7 plots the NET SIM delay est~ates against the corresponding estimates Dom the HCM delay
mode! associated with the phase times predicted by the proposed analytical model. A total of 433
observations were used in this analysis. The value of R square is equal to 0.~. The regression line
is slightly less shall I: ~ slope, indicating slightly smaller delay values predicted by the HCM. Results
Tom Figure 7 are much better than results Tom Figure 6, indicating that, based on the same HCM
delay model, a much better delay estimation can be achieved if the phase times predicted by the
developed analytical mode! are used instead of the phase times predicted by the Appendix 9~l
method.
90
so
7 0
60

50 _
6~ 40
~ 30 _
o
<, 20
a
10
O , . . . . . .
0 1 0 20 30 40 50 60 70
Total Delay from TRAFNETSIM (sec/veh)
R 2 = 0.88 (433 observations)
~ .~ :
~F
:~
80 90
Figure 7. Delay comparison between the lICM Delay Model
and NETSIM
Model ~ Delay Estimates Based on Timing Estimated by the Proposed Analytical Mode!
Figure ~ plots the NETSIM total delay estimates against the corresponding estimates Dom Delay
Mode} ~ associated with the phase times predicted by the proposed analytical model. A total of 433
observations was used in the analysis. A small dispersion of the data points is shown in this figure,
as confirmed by a high R square value of 0.92. The regression line is close to I:! slope. This
indicates that when Mode} ~ uses phase times predicted by the proposed analytical model, delay
estimates are very close to those estimated by NETSIM. A comparison of Figures 7 and ~ suggests
that, based on the same phase times, a better delay estimate can be achieved by Delay Mode} ~ over
the HCM delay model.
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So 
~ 80 ,
a'
70
 60
50
40
c, 30
c,
~ 20
o
1 0
_ / I
R2= 0.92 (433 observations)
a._',~'%_ ~
'a 
l it'
_aer
0 1 0 20 30 40 50 60 70 80 90
Total Delay from TRAFNETSIM (secJveh)
Figure S. Delay comparison between Mode! ~ and NETSIM
Mode! ~ Delay Estimates Based on Timing Estimated by the Proposed Analytical Mode!
Figure 9 plots the NET SIM total delay estimates against the corresponding estimates Dom Delay
Model II associated with the phase times predicted by the proposed analytical model. A small disper
sion of the data points is shown in this figure, as confirmed by a high R square value of 0.90. The
regression line is close to 1:1 slope. This indicates that when Delay Model ~ uses the phase times
predicted by the proposed analytical model, delay estimates are also close to those estimated by
NET SIM. A comparison of Figures 8 and 9 suggests that Model I shows a slightly better correlation
with NETSIM results. Model I appears to produce slightly lower delay estimates than NETS~, and
Model II produces slightly higher estimates.
It is clear Dom the above discussion that the analytical model proposed for signal timing estimation
offers superior performance to the existing HEM Chapter 9 Appendix II method. Implementing the
timing model alone' with no changes in the delay estimation model may be expected to improve the
credibility of the HCM procedure.
Further improvements may be made by adopting either one of the delay estimation models described
in this chapter. The differences between Mode} ~ and Model II are very small. Model ~ produces
delay values that are higher that NETSIM's estimates, and Model II produces values that are lower.
No clear superiority can be established Tom the simulation compansons. This observation was con
firrned in the limited field studies that are reported in Appendix H.
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90 T
~80
:70
6 0

50
is, 40
I_
<' 3 0
a'
=~' 20 ~ ~
~ 1 0  _ Jew
R2= o.go (433 observations)
.
:  '
e '
It i.'
O _
. . . .
0 1 0 20 30 40 50 60 70 80 90
Total Delay from TRAPNETSIM (sec/veh)
Figure 9. Delay comparison between Mode! ~ and NETSIM
There is no performance basis to recommend one mode} over the other at this time. Under these
circumstances, the most rational choice would be the one that requires the least conceptual
modification to the current HEM model. This rationale favors Delay Mode} IT.
NCHRP Project 348 Final Report: Page 28