Effects of Upstream Signalized Intersections on Two-Lane Highway Operations
A Paper Submitted for Presentation at the January 2004 Annual Meeting of the
Transportation Research Board
Prepared by
Michael P. Dixon
Assistant Professor
Civil Engineering
University of Idaho
P.O. Box 441022
Moscow, ID 83844-1022
Phone: 208-885-4338
E-mail: mdixon@uidaho.edu
Michael Kyte
Professor
Civil Engineering
University of Idaho
PO Box 440901
Moscow, ID 83844-0901
208-885-6002 (voice)
208-885-2877 (fax)
mkyte@uidaho.edu
And
Satya Sai Kumar Sarepalli
Graduate Research Assistant
Department of Civil Engineering
University of Idaho
Moscow, ID 83844-0901
Submission date: 2/16/2016
Word count: 5716 + 2 Tables and 5 Figures = 7466
Dixon, Kyte, and Sarepalli
2
ABSTRACT
Signalized intersections can significantly affect traffic operations on two-lane
highways, such as increasing percent time-spent-following (PTSF). To date no
deterministic or micro-simulation methods exist that allow this effect to be considered
when evaluating two-lane highway sections. The primary purpose of this paper is to
introduce the formulation of a methodology to assess the effects of signalized
intersections on two-lane highways. In this paper, it is shown that the effect of signalized
intersections on two-lane highway operations can be significant. Then the methodology
formulation is introduced and is comprised of three steps for estimating the PTSF, one of
the primary performance measures used in the current Highway Capacity Manual for
assessing the quality of service on two-lane highways. The methodology is then applied
to two hypothetical cases.
Dixon, Kyte, and Sarepalli
3
INTRODUCTION
Because of increased development in rural areas, more signals are being installed
on two-lane highways. The current edition of the Highway Capacity Manual, HCM
2000, provides formal deterministic procedures for the analysis of two-lane highways,
signalized intersections, and signalized arterials [1]. Stochastic simulation models can
also be used to analyze these facilities. TWOPAS and TRARR, stochastic microsimulation models, have been used to model two-lane highways. Other micro-simulation
models can be used to model signalized intersections and arterials. However, no
procedure currently exists, deterministic or otherwise, that accounts for the effects of
upstream signalized intersections on two-lane highway operations, such as increased
percent time-spent-following (PTSF).
The purpose of this paper is to develop and present the formulation of a
methodology by which the effects of a simple isolated signalized intersection on a
downstream two-lane highway section can be estimated in terms of PTSF, one of the two
measures of effectiveness for Class I two-lane highways required to determine the Level
of Service. The proposed methodology could be applied to the other measure, Average
Travel Speed (ATS), as well but it is not addressed here. This paper does not address the
effect of the signal on intersection traffic operations, for this the reader should refer the
HCM signalized intersection chapter. For this paper, the intersection was assumed to be
one with one through lane, where the primary demand volume at the intersection is
carried on the through movement feeding the subject two-lane highway section. This
assumption is reasonable if the left and right turn movements feeding the two-lane
highway sections have low demand volumes, however the methodology presented can be
expanded to include situations with significant turn movements.
A background is presented, discussing the relevant works and illustrating the
problem in greater detail. Then the methodology for estimating the effects of upstream
signals on two-lane highway operations is described. The authors’ conclusions and
recommendations for future research are then offered.
BACKGROUND
Previous traffic operations research involving two-lane highways investigated the
effects of traffic control techniques such as passing lanes, turnouts, and passing zones [14]. However, no research has been done to determine the downstream effects of
signalized intersections on PTSF.
TWOPAS and HCM 2000
It is important to understand the extent to which TWOPAS, a two-lane highway
micro-simulation model, was implemented to develop the HCM 2000 two-lane highway
procedure. It is difficult to collect field data over an entire highway section in order to
quantify two-lane highway traffic operations. As a result, deterministic two-lane
highway procedures rely heavily upon TWOPAS to quantify the relationship between
two-lane highway characteristics and operations.
After some calibration of TWOPAS to observed field conditions, TWOPAS was
run many times for different combinations of a wide range of highway conditions. The
output of these simulation runs was used as the data to develop the parameters used in the
Dixon, Kyte, and Sarepalli
4
HCM 2000 two-lane highway procedure, and this procedure predicts the level of service
(LOS) of the facility through estimates of PTSF and ATS.
PTSF Measurement in TWOPAS
TWOPAS can be used to estimate the performance measures for a two-lane
highway section and one of these measures is “Overall % time not in state 1.” This
performance measure is taken over the space of the facility and is the TWOPAS measure
of PTSF, which was used to develop the HCM 2000 two-lane highway procedure.
TWOPAS does offer a more disaggregate measure of PTSF, percent impeded
(PI). The difference between PI and PTSF is that PTSF is a measure of the proportion of
time that vehicles are in the state of following as they progress down the highway section
[5]. In contrast, PI is a measure of the proportion of vehicles that are in the state of
following for smaller subsections, where a PI measure is given for each subsection. A
detailed picture of how PTSF varies along the length of the facility can be obtained by
specifying many subsections. During the course of this research it was found that the
average of the TWOPAS PI measurements, taken over the length of the facility, is within
3% of the PTSF value output by TWOPAS. The average of the PI measurements was
calculated as a weighted average, consistent with Equation 20-23 in the HCM 2000 for
aggregating PTSF across multiple highway sections [1].
Given the proximity of the aggregated PI over the highway section and the PTSF
of the highway section, the measurement PI can be used as a reliable measure of how
PTSF varies over the highway section. This is significant because the approximate
downstream distance at which traffic operations return to normal, as measured by PTSF,
is needed and this can be done using PI.
TWOPAS Sensitivity to Upstream Effects
To illustrate the potential effects of a signalized intersection on two-lane highway
operations, two types of TWOPAS runs were made with four replicate runs for each.
This sample size, or number of replicate runs, was selected because it results in a mean
PTSF with a 95% confidence interval of +/-10% of the mean. The condition with no
signalized intersection is represented by assuming randomly distributed headways for
entering traffic. The distribution of headways is defined in TWOPAS through the
parameter, entering percent following (EPF). When assuming random headways, this
parameter is calculated using a cumulative negative exponential distribution of headways
less than 3.0 seconds, shown in Equation 1, consistent with the HCM 2000 definition of
vehicles following [1]. A value of EPF = 40% was used for the condition with no
signalized intersection, assuming a volume of 600 vph. A higher value for EPF = 60%,
was used to represent the situation where a signalized intersection is present and
modifying the headway distribution of the traffic stream entering the two-lane highway.
This value for EPF was decided upon based on field data and research determining
feasible values for EPF. The method for determining EPF is discussed later in the
methodology section, where discussion is given regarding the validity of the EPF values.
A 47 km section of highway was simulated using TWOPAS with the following
conditions:
 Directional volume = 600 vph
 No heavy vehicles
Dixon, Kyte, and Sarepalli




5
0% no-passing zones
level terrain (i.e., no vertical or horizontal curves)
50/50 directional split
desired speed and speed standard deviation were set equal to those used to
develop the HCM 2000 procedures and can be found in the NCHRP 3-55(3) Task
6 report [5].
t
q


3600

% platooned  1001  e



(1)
Where
q
hourly flow rate of traffic entering the two-lane highway (veh/hr) and
t
headway criteria used to define when vehicles are following (3.0 sec).
Figure 1 shows the PI as it varies along the facility. To maintain consistency with
the HCM 2000, the y-axis on Figure 1 was labeled PTSF instead of PI [1]. This
convention is maintained throughout this paper for all figures illustrating how PTSF
varies along the length of a highway and for all references to these figures. However, the
actual values shown in the figure were those output by TWOPAS as PI, which are a
consistent measure of PTSF variation along a highway section [5].
In this paper, it is assumed that it is appropriate to represent the effects of a
signalized intersection through the EPF parameter, as long as the percent following
immediately downstream of a signalized intersection can be determined. Note that the
first PTSF values on Figure 1 are different than what was entered for EPF = 60. This was
investigated further by reducing the size of the subsections from 0.5 km to 30 meters and
reducing the highway length to 3000 meters. TWOPAS output of PI and percent
following (PF) were compared, where a measurement of PF is the percentage of vehicles
following at headways of 3.0 seconds or less at specific points on the highway, similar to
entering percent following, EPF. It was found that the PF value at the first observation
interval was 59, given a value of 60 for entering percent following. This suggests that the
entering percent following is being modeled correctly. It was also found that the
difference in PI and EPF persisted. This can be explained by realizing that the
measurement of PI is not the same as PF, as can be seen from their descriptions above.
It could also be expected that the measurements in Figure 1 for EPF-60 would
first decrease because of passing and platoon dispersion and then increase because of
natural bunching that occurs on two-lane highways. This decrease would only occur if
passing maneuvers and platoon dispersion occurred more quickly than vehicles catching
up to the platoon. Conditions such as these are not likely to exist because vehicles are
less likely to pass when in a platoon and because of higher volume conditions that would
typically exist where a traffic signal is operating.
For the simulation conditions described above for the TWOPAS runs shown in
Figure 1, the PTSF values reported by TWOPAS for the highway section with and
without a signal were 67 and 60, respectively. Figure 1 also shows this difference
between the two-lane highway operations with differing EPF values. This difference in
traffic operations indicates that there is an interaction between two-lane highway PTSF
Dixon, Kyte, and Sarepalli
6
and entering traffic conditions resulting with and without a signalized intersection. While
the difference in the PTSF values is not very remarkable, it is important to note that the
relative difference would increase as the highway section length decreased. Note that if
the highway were 10 km in length, the proportional difference in area under the two
curves relative to the area under the low curve would be larger. Given the potential
impacts of signalized intersections on two-lane highway operations, it is important to
investigate these impacts further and to develop a possible methodology whereby these
impacts can be assessed.
METHODOLOGY
The analysis of two-lane highway sections affected by signalized intersection
operations can be broken down into three steps. First, determine the percentage of
vehicles following, EPFa, where ‘a’ denotes a location immediately downstream of the
signalized intersection. Second, estimate the PTSF for the downstream highway section
with the upstream signalized intersection, PTSFsig, using TWOPAS. Third, estimate the
level-of-service based on the criteria suggested in the HCM 2000 two-lane highway
analysis procedure. The first two steps are discussed in detail in this paper. For the third
step, refer to the HCM 2000 [1].
The mathematical relationships shown in this paper are limited to the conditions
below.
 No pedestrian traffic
 Uniform arrivals
 No shared lanes
 Intergreen time is assumed equal to the lost time
 Two phases
 No passing lanes
 No lane drops
 Saturation flow rate of 1800 pcphpl
 50/50 directional split
Additional conditions such as three phase signal timing or shared lanes can also be
included using the same mathematical relationships, though with some modifications.
Step 1: Determining Entering Percent Following (EPFa) Downstream of Signal
Estimation of the percentage of traffic following, EPFa, is based on a flow profile
immediately downstream of the signalized intersection at location A shown in Figure 2.
Because of the immediate downstream location of A, no platoon dispersion model is
needed to augment estimation of EPFa. As shown in the figure, there are three
movements that contribute to the flow profile. Movement 1 is the primary contributing
movement, while the others are secondary. Also shown in Figure 2 is the flow profile
representing three basic flow states at A and they are defined as follows:
 First state: discharged from the through movement queue during the first phase;
 Second state: discharged from the through movement without a queue plus any
right-turns-on-red executed during the first phase; and
Dixon, Kyte, and Sarepalli

7
Third state: discharged from the right and left turn movements during the second
phase.
Third state traffic flow is a simplistic representation of how traffic is actually
discharged from the right and left turn movements. This was justified by the assumption
that these flows would be minor compared to the through movement.
Entering percent following at location A shown in Figure 2 (EPFa) can be
estimated using Equation 2:
VFa
Va
EPFa 
(2)
Where
VFa  VFi
i
VFa
VFi
Va
total number of vehicles following per cycle at location A (veh),
total number of vehicles following per cycle from movement i (veh), and
total number of vehicles per cycle at location A (veh).
Because Va is the summation of the cycle-by-cycle volumes from movements 1,
2, and 3, it can be determined if volumes for movements 1, 2 and 3 and the cycle length
are known. This leaves the estimation of VFa, the number of vehicles following at
location A, which can be estimated by movement.
Vehicles following in movement one
Movement one is discharged during flow states one and two. Because vehicles
discharged during state one are in a platoon, it can be assumed that all of the queued
vehicles are in a platoon at location A and are following except the platoon leader, as
represented in Equation 3. As tQ1 increases, the number of vehicles following per cycle
for movement 1 increases. Furthermore, tQ1 increases as the difference between C and g1,
the red time for movement 1, increases. As a result, as the g/C ratio for movement 1
decreases, the number of vehicles following, VF1, increases resulting in an increase in
EPFa. Note that Equation 3 is based on a deterministic queue length model, consistent
with queue length equations used in the HCM 2000 signalized intersection analysis
procedure. Stochastic queue length models could be used instead to provide more
realistic representations of the effects of random vehicle arrivals on queue length.
 tQ1 
  1
VFQ1  s1 
 3600 
(3)
v1
(C  g1 )
s1  v1
(4)
tQ1 
Where
Dixon, Kyte, and Sarepalli
8
VFQ1 number of vehicles following in movement one that are discharging from a queue
(veh),
s1
saturation flow rate for movement one (veh/hr),
tQ1
time for queue at movement one to dissipate (sec),
v1
movement one demand flow rate (veh/hr),
C
cycle length (sec), and
g1
phase one effective green time.
Vehicles discharging from movement one when no queue is present may or may
not be following at location A. If it is assumed that they are following if their headway is
less than or equal to 3.0 seconds, then the number of vehicles following at location A can
be estimated using the negative exponential distribution as shown in Equation 5. Note
that the through movement volume, v1, is used in Equation 5 because it is estimating the
number of vehicles following in movement 1. The volume, vNQa, 2, shown in Equation 6,
is used in Equation 5 because it is the volume occurring during flow state 2 at location A.
Also note that Equation 6 is comprised of the movement 1 flow rate (the first element)
and the right-turn-on-red flow rate (the second element), which are the movements
arriving at location A during flow state 2.
The time during the green time when no queue is present for movement 1 is g1 –
tQ1. The proportion of these non-queued vehicles that are following, given a headway
criterion of t, is estimated by the value in the brackets containing the negative exponential
in Equation 5.
t
a,2
 v NQ
( g1  tQ1 )
3600
VFNQ1  v1
(1  e
)
3600
a ,2
vNQ
 v1  v2 PRTOR
C
g1  tQ1
(5)
(6)
Where
VFNQ1 number of vehicles following in movement one that are discharging when no
queue is present (veh),
a,2
vNQ
flow rate at location A during flow state 2, when no queue is present (veh/hr),
v2
movement two demand flow rate (veh/hr), and
PRTOR portion of right turn vehicles turning on red.
Finally, the total number of through vehicles that are following at location A can
be estimated as shown in Equation 7. Note that an alternative to the estimation of tQ1, as
given by Equation 4, is available as part of the signalized intersections procedure in the
HCM 2000 [1] and should be considered as part of a formal procedure implementing the
methodology proposed in this paper.
VF1  VFQ1  VFNQ1
(7)
Dixon, Kyte, and Sarepalli
9
Vehicles following in movements two and three
It is assumed that the secondary nature of these movements does not justify
determining how these movements interact with other movements during the second
phase. Instead, during the second phase, or flow state three, these movements are
aggregated and analyzed together. The number of vehicles following from movements
two and three are estimated using Equations 8 and 9, respectively, assuming a negative
exponential distribution of headways during the second phase and an average arrival rate
at location A. However, if these movements contribute substantially to the traffic volume
at location A, then they should be considered at the same level of detail as movement 1.
In Equation 8, the vehicles following from the right-turn movement are
determined for flow state 2 and 3. The right-turn vehicles following during flow state 2
are turning on red and are represented by the second component in the brackets, while the
vehicles following during flow state 3 are represented by the first component in the
brackets. On the other hand, vehicles following from the left-turn movement are
determined for flow state 3 only (see Equation 9). Note that the flow state 3 volume used
in Equations 8 and 9 is comprised of left-turn vehicles and right-turn-on-green vehicles,
as shown in Equation 10.
t
t
a,2
v a ,3
 v NQ

C 
3600
3600
VF2  v2
)  PRTOR (1  e
)
(1  PRTOR )(1  e
3600 

(8)
t
VF3  v3
v a ,3 
v a , 3
C
(1  e 3600 )
3600
C
v3  v2 (1  PRTOR )
C  g1
(9)
(10)
Where
va,3
flow rate at location A during flow state 3, (veh/hr) and
v3
movement 3 demand flow rate, (veh/hr).
Equation 2 was validated using a CORSIM model of an isolated fixed-time
signalized intersection. Conditions of the simulation were as follows:
 Cycle lengths of 45, 60, and 90 seconds
 50/50 green split
 2 phase signal
 500 vph for the through movements
 100 vph for the right turn movement
 100 vph for the left turn movement
 one lane approaches
 left-turn and right-turn only lanes for all approaches
 5 replicate runs of 60 minutes each for each cycle length
Dixon, Kyte, and Sarepalli
10
The simulated EPFa values were compared to those estimated using Equation 2.
The mean percent error and corresponding standard deviation were 3.7% and 2.8%,
respectively. Based on these results, Equation 2 does predict EPFa with an adequate level
of accuracy. However, more sophisticated models of EPFa may improve the accuracy of
the prediction and are necessary for more complex representation of signal operations.
Figure 3 illustrates the results of step one for a range of through movement v/c
ratios and cycle lengths, given a 50/50 green split. It is interesting to note that for a given
v/c ratio, EPFa increases as the cycle length increases. This occurs because the platoon
leader is not following, so as the cycle lengths increase there are fewer platoon leaders
relative to the number of vehicles in platoons and hence a larger portion of vehicles
following. The expected trend of increasing EPFa as v/c increases for a given cycle
length is also reflected in the results shown in the figure.
From the plots in Figure 3, it can be seen that EPFa is sensitive to cycle length.
However, the sensitivity is such that if cycle length were estimated to within twenty five
percent of the true cycle length then a reasonable estimate of EPFa will result. This can
be verified by noting that for a given v/c (i.e., 0.80) the estimated EPFa varies within a
range of approximately 4 for cycle lengths between 45 and 90 seconds.
Actuated control is most likely for the situations in which the proposed
methodology would be used. However, the proposed methodology for estimating EPFa
requires values for the cycle length and green time. In the case of actuated intersections,
these values can be estimated using a procedure defined in the HCM 2000 [1].
Step 2: Determining PTSF with signalized intersection, PTSFsig
This step can be completed in two ways. One method is using TWOPAS and
another method is using the HCM 2000 two-lane highway directional analysis procedures
and deterministic adjustment factors based on a series of runs made in TWOPAS. The
following discussion describes the two methods. The series of runs used to illustrate the
TWOPAS method of determining PTSFsig were also used later to determine the
adjustment factors and parameters needed for the other method.
To model the effects of a signalized intersection on two-lane highway operations,
the EPFa for the direction corresponding to that of the traffic leaving the signalized
intersection was set equal to the EPFa estimated in step one. To illustrate the relationship
between EPFa and PTSFsig, four replicate runs were made for a range of volumes and
EPFa. Figure 4 shows the relationship between EPFa and PTSFsig by directional volume.
A linear regression line was created for each volume input into TWOPAS (i.e., 400 vph,
500 vph, 600 vph, 700 vph, and 800 vph) providing a statistical relationship between
PTSFsig and EPFa. The t-statistics for each of the parameters for these regression
equations were all greater than 21.8. Notice that the regression line slopes are similar,
tending to increase with volume. Most importantly, Figure 4 shows that for a given
volume, PTSFsig increases as the EPFa increases, lending statistical support to the
statement that signalized intersections can significantly affect two-lane highway
operations.
Sensitivity of PTSFsig to EPFa can be ascertained by referring to Figure 4. Note
that for a range of 10 EPFa the corresponding range in PTSFsig would be approximately 4.
Dixon, Kyte, and Sarepalli
11
This suggests that if reasonable estimates for EPFa are input then a reasonable estimate of
PTSFsig should result.
CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH
Signalized intersections can have a large effect on two-lane highway operations,
as was illustrated in the introduction and shown through statistical analysis. The methods
presented in this paper give HCM users the tools needed to analyze situations that are
commonly encountered in the field, but not possible to analyze with the current HCM
procedures. The effects of signalized intersections can be modeled using the
methodology presented in this paper. The proposed deterministic procedure is similar in
nature to those used in the HCM 2000 to adjust for the effects of passing lanes and so
should be familiar to transportation engineers.
It is recommended that future research be conducted to determine the level of
detail necessary to accurately model EPFa resulting from signalized intersection
operations. These models could include features such as the effects of opposing queue
discharge on left turn discharge and the effects of signal coordination on the number of
vehicles following from the through movement. Protected left turn phasing and
downstream lane drops may also prove significant when estimating EPFa.
The methodology presented here assumes that no other signalized intersections
affect traffic flow on the two-lane highway being analyzed. However, traffic operations
in one direction are affected by the degree of platooning in the opposing traffic stream so
the traffic stream in the opposing direction may actually have benefited from the
increased platooning due to the traffic signal. If this is the case then it is possible that one
signalized intersection that is, say 10 km from another one, may actually offset some of
the increase in platooning caused by the other signalized intersection.
TWOPAS could also be improved. To facilitate research on the effects of
signalized intersections on two-lane highway operations, it would be very beneficial if
TWOPAS were augmented to include the modeling of signalized and unsignalized
intersections.
Finally, further study is needed to enhance the proposed methodology. Field
evaluations must be conducted to calibrate the models and validate them under a variety
of field observed conditions. Facility length also seems to affect PTSF and should be
considered as an input variable for predicting PTSF.
ACKNOWLEDGEMENTS
This project was funded through the Idaho Transportation Department.
REFERENCES
1.
2.
Highway Capacity Manual. TRB, National Research Council. Washington, D.C.
2000.
Harwood, D.W., and C.J. Hoban. Low-Cost Methods for Improving Traffic
Operations on Two-Lane Roads: Informational Guide. FHWA/IP-87/2. FHWA,
U.S. Department of Transportation. McLean, Virginia. 1987.
Dixon, Kyte, and Sarepalli
3.
4.
5.
12
Harwood, D.W., A.D. May, I.B. Anderson, L. Leiman, and A.R. Archilla.
Capacity and Quality of Service of Two-Lane Highways. Final Report of NCHRP
Project 3-55(3). TRB, National Research Council. Washington, D.C. 1999.
Messer, C.J. Two-Lane, Two-Way Rural Highway Capacity. Final Report of
NCHRP Project 3-28A. TRB, National Research Council. Washington, D.C.
1983.
Leiman, L., A.R. Archilla, and A.D. May. Capacity and Quality of Service of
Two-Lane Highways: Task 6. Interim Report of NCHRP Project 3-55(3). TRB,
National Research Council. Washington, D.C. 1998.
Dixon, Kyte, and Sarepalli
13
Tables
TABLE 1 Factors for estimating PTSFsig, showing fsig, Lde, and PTSF.
EPFa
50
Factors
fsig
Lde (km)
PTSFend
60
fsig
Lde (km)
PTSFend
70
fsig
Lde (km)
PTSFend
400
3.50
33.59
63.07
2.62
44.10
63.07
3.01
47.0
63.07
Directional Demand Volume
500
600
700
1.54
1.42
(N/A)
34.59
38.10
(N/A)
74.47
78.87
84.33
1.97
1.36
1.56
38.10
47.00
32.60
74.47
80.00
84.33
2.45
1.67
1.50
40.60
47.00
47.00
74.47
78.87
84.33
800
(N/A)
(N/A)
88.17
1.37
47.00
88.17
1.47
47.00
88.17
Dixon, Kyte, and Sarepalli
14
Figures
90.00
80.00
Difference in Areas Under Curves
70.00
PTSF
60.00
50.00
EPF-40
EPF-60
40.00
30.00
20.00
10.00
45
40
35
30
25
20
15
10
5
0
0.00
Distance (km)
FIGURE 1 Potential impacts of a signalized intersection on two-lane highway
operations.
Vehicles per Cycle at Location A
Dixon, Kyte, and Sarepalli
State one
15
State two
State three
t
Phase one
Phase two
3
Major Street
1
va
Two-lane highway
Location A
2
FIGURE 2 Two-lane highway traffic flow downstream of a signalized intersection.
Dixon, Kyte, and Sarepalli
16
100.00
EPFa
90.00
80.00
C = 45
C = 60
70.00
C = 90
60.00
50.00
0.50
0.60
0.70
0.80
0.90
1.00
v/c
FIGURE 3 EPFa verses through movement v/c by cycle length.
Dixon, Kyte, and Sarepalli
17
100.00
90.00
80.00
Volume-400
Volume-500
Volume-600
Volume-700
Volume-800
Volume-400 vph
Volume-500 vph
Volume-600 vph
Volume-700 vph
Volume-800 vph
70.00
PFa
EPFa
60.00
50.00
40.00
30.00
20.00
10.00
0.00
40.00
50.00
60.00
70.00
80.00
PTSF
FIGURE 4 EPFa verses PTSFsig by directional volume.
90.00
Dixon, Kyte, and Sarepalli
18
Lt,sim
Lt
PTSFend
PTSFLt
1
S
PTSFLde
PTSFBeg,sig
PTSFBeg,d
A1
A2
Lde
L’de
Position along Highway (km)
FIGURE 5 Model of effect of upstream traffic signal on two-lane highway
operations.