|
Items in cart [1] |
Questions? Call 888-624-8373 |
| Copyright © 2009. National Academy of Sciences. All rights reserved. Terms of Use and Privacy Statement |
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 7
CHAPTER 2
FINDINGS
2. 1 SUMMARY OF EXISTING CONDITIONS
A comprehensive evaluation of the state-of-the-artin areas related to interchange design and
traffic operations was conducted as part of this research. This evaluation consisted of a survey of
practitioners and a review of existing traffic models. The focus of this evaluation was on issues
underlying the design and operation of interchanges in urban or suburban areas. More specifically,
the focus was on issues related to the signal-controlled ramp terminals and traffic flow along the
cross street through these terminals. Consideration was also given to the relationship between the
interchange ramp terminals and any adjacent, closely-spaced signalized intersections.
2.1.! Survey of Current Practice
The intent of this survey was to gain insight into the current practices arid concerns of
engineers who are responsible for interchange traffic operations. The survey was conducted in two
stages. The first-stage survey was intended to obtain basic types of interchange-related infonnation
such as common interchange types, traffic flow problems, and operational analysis techniques.
The second-stage survey was designed to obtain more detailed information about interchange
operations. This survey asked the respondent to select one interchange that they were familiar with
and then respond to detailed questions about its operation and any steps taken to alleviate flow
problems at this interchange. The respondent was also asked to describe the analysis techniques (or
computer models) Hat they had successfi~ly used to evaluate interchange operations. The findings
from these two surveys are summanzed in this section. A more detailed discussion of He survey
findings is provided in Appendix A.
Distribution. The first-stage survey was sent to more than 2,400 transportation engineers
in the U.S. and abroad. The members of the American Association of State Highway and
Transportation Officials' (AASHTO) subcommittees on traffic engineering, on design, and on
transportation systems operation were specifically targeted. A large number of the Institute of
Transportation Engineers ' (ITE) Urban Traffic Engineers Council and its Consultants Council were
also included in the survey. In addition, several hundred surveys were sent to over selected
members of ITE.
After a review of each returned questionnaire, a total of 350 first-stage questionnaires were
deemed completely responsive and valid for furler processing. Overall, there were 146 responses
from the public sector which included state, city, and county highway agencies. Seventeen responses
were received from outside of the United States. Responses were also received from ~ 87 consultants
in 23 states.
2
OCR for page 8
The second-stage survey was sent to 1 90 individuals who responded to the first survey. A
total of 3 1 completed surveys were returned, representing a 16 percent response rate. Of these
surveys, 29 were deter~ninedto be valid responsesin the context that they addressed the interchange
types and issues described in the survey. Overall, 21 states are represented among the 29 valid
returned surveys.
Findings. The first-stage questionnaire consisted of six questions that were primarily of the
multiple-choice type. The second-stage questionnaire consisted of eleven questions, several of
which had follow-up questions. In general, these questions inquired about the kinds of interchanges
being used or constructed, the type of signal control used, the types of operational problems found
at existing interchanges, and the methods used to evaluate and mitigate these problems. The
responses to the questions on both questionnaires are summarized in the following paragraphs.
The diamond interchange was found to be the most commonly used interchange
configuration. This trendis likely due to the reduced right-of-way end construction costs associated
with diamond interchanges relative to other configurations (e.g., partial cloverleaf). The distance
between the diamond interchange ramp terminals can vary from 60 meters in densely-developed
urban areas to 240 meters in suburban areas. In contrast, the distance between ramp terminals
associated with a partial cloverleaf interchange generally range from 180 to 280 meters.
Regardless of configuration, the interchanges that tend to experience operational problems
are those with relatively short distances between the ramp terminals or between one terminal and an
adjacent signalized intersection. These close spacings often lead to problems such as queue
spillback, flow turbulence due to weaving, and left-turn bay overflow. Queue spillback represents
the blockage of an upstream intersection by a traffic queue from a downstream intersection. The
interchanges described by the survey respondents as having operational problems had ramp terminal
distancesin the range of61 to 410 meters. The distance to the adjacent intersection for these same
interchanges was in the range of 46 to 436 meters.
The survey indicated that most interchanges have two semiactuated signal controllers, one
controller for each ramp terminal. The two controllers are typically coordinated to facilitate
progressed traffic flow along the arterial and minimum queuing on the street segment between the
two terminals. Some interchanges have pretimed control with either one or two controllers. The few
diamond interchanges that were pretimed and had one controller used four-phase-with-overlap
phasing. Only a few interchanges had fi~ll-actuated, uncoordinated control.
The distribution of operational problems found in interchange areas is shown in Figure 2.
As this figure indicates, the operational problem that occurred most frequently is queue spillback at
some junction on the cross street. This problem was generally related to the spilling back of a queue
from a downstream ramp terminal or intersection into an upstream terminal or intersection. This
spillback tended to significantly reduce the car acitY of the upstream junction. Also included in this
~Or i- - -a
category is spillback stemming from a left-turn bay overflow.
2 - 2
OCR for page 9
Percent of Responses
Spillback on Cross Street
Other Spillback
Poor Signal Timing
Weaving-related
Unbalanced Lane Volumes
0 10 20 30 40 50
Figure 2. Distribution of operational pro bZems in urban interchange areas.
The reported flow problems related to queue spillback between the ramp terminals were
generally associated with tight or compressed diamond interchanges. Flow problems related to
queue spillback between a ramp terminal and adjacentintersection were more commonly associated
with conventional (wide) diamond interchanges and partial cloverleaf interchanges. The wide
spacing between ramp terminals for these interchanges tends to be associated with shorter distances
between these terminals and the adjacent intersections. By design, the single point diamond
configuration does not experience spillback between its terminals; however, it can experience
spillback between it and the adjacent intersection during high-volume conditions.
Other frequently cited problems at interchanges include unbalanced large volumes on the
ramp terminal approaches, flow turbulence due to weaving, and a lack of effective signal
coordination between the ramp terminals. The unbalancedlane volume problem stems from frequent
driver propositioning for downstream turns in interchange areas. Drivers desiring to turn left (right)
at a downstream intersection tend to move into the inside (outside) lane of a multilane lane group
at the upstream intersection. This propositioning effectively reduces the capacity of the lane group
by leaving some traffic lanes underutilized, even during high volume conditions.
The weaving maneuver that is predominate in interchange areas is the off-ramp right-turn
movement that weaves across the arterial to make a left-turn at the next downstream signalized
intersection. This maneuver typically has a high volume associated with it such that considerable
turbulence is created on the cross street. This turbulence results in significant speed reductions to
the nonweaving traffic movements.
2 - 3
OCR for page 10
The lack of effective signal coordination along the cross street in interchange areas occurs
for a variety of reasons. These reasons generally include incompatibility ofinterchar~ge phasing with
the cross street system coordination plan and institutional barriers (i.e., the state operates He
interchange and the city operates the adjacent intersection). The lack of efficient signal coordination
can lead to increased delays and stops and precipitate the occurrence of spillback when the ramp
terminals or intersections are closely spaced.
A wide range of methods were described by the respondents for alleviating the
aforementioned operationalproblems. Geometric improvements were most commonly cited. These
improvements included adding a second left-turn lane or an additional through lane to the cross
street. Many respondentsindicated that improved or updated signal timing and coordination helped
mitigate some operationalproblems. These latterimprovements were often obtained through the use
of existing software-based traffic analysis models.
In general, software programs are more frequently used than manual methods for evaluating
interchange traffic operations. The most commonly used software program is the signalized
intersection analysts procedure included in the Highway Capacity Software (HCS). In general, this
procedure was used to evaluate the individual ramp tenninals after appropriate calibration of the
progression adjustment factors to account for nearby intersections. The popularity ofthis program
may be due to its widespread acceptance by transportation engineers, its consistency with the
methods described in Chapter 9 ofthe Highway Capacity Manual (HCM) (3j, and the relative ease
with which it can be used. The most frequently cited strength of this program is that it is easier to
use than multiple-intersectionsoftware programs (e.g., PASSER II, TRANSYT-7F,NETSIM, etch.
Ofthe various software programs available, TRANSYT-7F was cited by nearly half of all
the respondents as being used! for analyzing interchange operations. This finding may be due to
the fact that TRANSYT-7F is sensitive to the proximity of adjacent ramp terminals or signalized
intersections in its signal timing optimization routine. Another software model, PASSER- was
also cited by many of the respondents as being a usefi~] too] to analyze arterial traffic flow through
interchange ramp terminals. In the case of this latter model, the large response may be due to the
fact that PASSERS optimizes signal phasing based on progression analysis. NETSIM was used
by some of the respondents. This program was noted to be the only one that modeled queue
spilIback and congested flow conditions.
The respondents also noted that the existing software programs had some weaknesses that
limited their ability to accurately mode} interchange traffic operations. The weaknesses cited for the
HCS program (i.e., the HCM Chapter 9 procedure) were that it did not accurately model the effect
of closely-spaced upstream intersections and that it did not yield queue length estimates. The
weaknesses cited for PASSER Il were Hat it did not provide progression solutions for left-turn
movements, did not consider queues when determining progression, did not allow the user to enter
some types of interchange phasing, and did not fillly consider right-turn demand. NETSIM was
noted to be very time consuming to use due to its microscopic simulation formulation. A couple of
respondents nosed that none ofthe programs dealt explicitly with the coordination of a downstream
2 - 4
OCR for page 11
ramp meter with the ramp terminal. Further evaluations of these computer-based models, primarily
for research applications in this project, watt be presented later in this chapter
The survey found that the most commonly selected measure of effectiveness (MOE) for
evaluating interchange traffic operations was traffic signal delay, followed by spilIback frequency,
and volume-to-capacityratio. Delay was likely chosen by the practitioners because it represents the
most tar~gible measure of effectiveness that is also comprehensible by the motoring public. After
delay, queue spilIback frequency was the next most frequently cited MOE by the respondents.
2.~.2 Field Survey of Interchange Operations
The research team studied over a dozen service interchanges dunug the field studies and
spent marry hours observing traffic operations at the sites. Comparisons could be rapidly made
among interchange types, types of operational problems observed, and the hypothesized probable
cause of these problems. Our summary of these field sites having congested operations are noted
below:
Designlife of interchange probably exceeded, overall traffic demand exceeded interchange
capacity during rush hours.
Due to growth in suburban areas, older four-lane crossing arterials now need to be six lanes.
The average daily traffic on many of We four-lane crossing arterials exceeded 30,000 ADT.
Many "next" downstream signalized intersections along Me crossing arterial experience high
access demands toffrom the Leeway (interchange) and are routinely too closely spaced to
provide good operating conditions. Better access management, intersection spacing
and design policies are needed.
Traffic management of quelling and spilIback is difficult at interchanges due to high
volumes and high percentages of turning traffic having typical lane distribution problems.
Some approaches along the crossing arsenal and within the interchange can have almost
constant demand within Me cycle, so queuing can not be mitigated using traditional signal
coordination techniques.
Four-quad parclos would seem to be more susceptible to constant demand conditions within
the interchange because oftheir free flowing loop ramps. All parclo interchanges,including
the four-quad AB that exits both left and right turns from the same side of a cross arterial
approach, may experience high lane imbalances ofarrival flow on that side ofthe street, even
at intersections along the crossing arterial upstream of the interchange.
Many ofthe congested interchanges noted above had a predominant number of single-lane
left turn bays within the interchange and/or have single lanes assigned on approach ramps
2 - 5
OCR for page 12
at ramp terminals to serve left and/or right turning movements. Many approach ramps were
single lane with only a modest flare to a two-lane approach at the ramp terminal.
6.
e
Traffic actuated operations on high-volume, single-lane movements appear to result in
excessively long cycles that reduce the overall input capacity of the interchange. Protected-
permissive left-turn operations, while reducing delays during moderate traffic, loses capacity
during rush-hour conditions and, consequently, cannot be depended upon to provide
significant capacity increases during these critical times.
7. Most traffic control strategies employed appear to be based on undersaturated flow
conditions and may lose efficiency when oversaturated conditions arise. Management of
queue spillback to mitigate the onset of congestion is needed together with the need to
transition to downstream bottleneck control strategies once oversaturation has occurred.
2.2 SURVEY OF EXISTING TRAFFIC MODELS
The first-round survey inquired about the types of analysis methods used to evaluate (not
optimize timing) signalized interchange traffic operations. In general, software models were more
frequently used than manual methods. The most commonly used software method is the Highway
Capacity Software (HCS). PASSER II and TRANSYT-7F were also found to be frequently used
in practical engineering applications. However, research applications usually require more complex
computer simulation models than application-specific models like HCS and PASSER II.
Computer simulationis a viable method with which to analyze situations which may occur
at signalizedinterchanges,but for whatever reason are difficult to witness or collect date from field
studies. This investigation was primarily based on literature and manuals for each model, and
discussions withindividuals familiar with the models. Experience with each model is arguably the
most informative method of discovering what a program can and cannot do. Time constraints
always limit the depth with which each ofthese models can tee investigated. A list ofthe simulation
models investigated is included in Table l.
Simulation models can be described by their analysis approach, basis, objective and outcome.
A model's analysis approach is either macroscopic or microscopic. A macroscopic simulation model
is one in which the traffic stream is moved as one homogenous aggregate group, whereas a
microscopic simulation model is vehicle specificin which each vehicle moves as its ownidentifiable
entity. A simulationmodel's analysis basis is either empirical or analytical. The analysis basis refers
to the algorithm on which the model is based. An empirical model is based on field observations
or data and\or previous experience. Analytical models use mathematical formulas based on
theoretical relationships. The analysis objective refers to the purpose of the simulation model.
Models simulate traffic given certain geometric constraints, and/or optimize some specific traffic
parameter. Lastly, a simulation model is described by its analysis outcome, which is either
stochastic or deterministic. A stochastic model attempts to model human behavior by providing a
2 - 6
OCR for page 13
degree of randomness to its methodology. In this way the output is never the same given a set of
inputs. Given the same Inputs, a detennin~stic model would have the same output every time the
same data is input. Each model's analysis is also given in Table I.
Table I. Simulation Models Examined
Model
FREFLO
. . l
_
Analysis
i| Description ~Approach | Basis | Oboe ctive | Outcome
' 1 Freeway Macroscopic | Analytical | Sim ration
Simulation
Freeway and Microscopic Analytical Simulation or
Surtace Street Optimization
Network Model
Freeway and Macroscopic Analytical Simulation
Surface Street
Network Model
Freeway and Microscopic Analytical Simulation or
Surface Street Optimization
Network Model
Urban Street Microscopic Analytical Simulation
Network Model
Signalized Macroscopic Analytical Optimization
Diamonci
Interchanges
Signalized Multi- Macroscopic Analytical Optimization
~ Intersections
| Signalized Multi- Macroscopic Analytical Simulation or
Intersections Optimization
Isolated Microscopic Analytical Simulation
Intersection
~Analysis
r COmplete Macroscopic Empirical Simulation
Implementation of
1985 HCM
Freeway Weaving Macroscopic Empirical Simulation
Analysis
, 1
Deterministic
INTRAS
1 980
Stochastic
CORFLO
Deterministic
INTEGRATION
Version 1 5
Unknown
· 11
NETSIM
Stochastic
PASSER 111
1 990
Deterministic
PASSER 11
1 990
TRANSYT-7F
TEXAS
Version 3.11
HCS
Deterministic
Deterministic
Stochastic
Deterministic
FREWEV
Version 1.1
Deterministic
2 - 7
OCR for page 14
2.2.1 Input and Output
Obviously, each mode} has a required amount of input. Mar~y models have options that may
or may not be important to ~is project, and therefore ~e input for some data is optional. An
abbreviatedlist of model inputsis included~n Table 2. The table indicates~e inputs (bosh requ~red
and optional) by each model. The list is not all-~nclusive. Model names were abbreviated in Table
2, but they are presented in ~e same order as they are listed in Table I.
t
Table 2. Mode! Inputs
Input Model
| FRE | INT | COE. | ITG | NET | PIII | PII | T-7F | TX | HCS | WEV
lus Stop Delay
2apaciiy ~X ~ X ~X ~ X ~ X
~river & Vehicle X X
'haractenstics l l l l l l I ::
~rades ~X
Horiz. Curve Data X
Incident Data X
ntersection Spacing l ~l ~I X | X
,ink Lengths | X | X | X | X | X | X |
,oad Factors
Numb~er of X X X X
Approaches
Numb~er of I ~neS X X XX X X X X X X
)-D Travel Pauerns l l l | X | X l l
'edestrianAcolation l l l | X X
'ercentageof l ~X l l l l l X
|Vehicle Types
Ramp Metering Rate X X
Rte. Detouring Data X
Saturation Flows X X X
Signal & Sign ~| X l | X | X X ~ X X ~ X X
Control Parameters
Simulation X X X X X X X
peed l l l l l l |
average X X X X
free flow X X X X X
Through Volumes X X X X X X X X X X
r~ng percentages ~ x ~ ~ x ~ ~ ~ ~ ~ ~ F
~Turning Volumes X X X X X X X X .
Vertical Curve Data l | X l l l l l l I |
X
X
X
X
X
X
2 - 8
OCR for page 15
The ou~ut available for each model investigated is ~ncluded ~n Table 3 The table does
not include all output for every model. FREFLO, INTRAS, CORFLO, INTEGRATION,
NETSIM, and TRANSYT-7F display most of its output on a link specific basis. TEXAS Model
provides output by lane, approach, and for the ~ntersection as a whole.
Table 3. Model Outputs
OIl~Ut ~
_
Degree of Saturation | I r | | x | x | x | I I
queue X X X X X
smpped l l l l | X | l l | X
Density I X _ L x I x ~
|| uel Consumpd m | | X | l | | X | X | I I [
Graphical Simulation ll X T I x
Level of Service L _ Il l X :=
Lane Changes l l l l l l l l l I
3-D Chart T | x I 1 X I I I ! I I I 11
Optimal Ti~ng X X X
_
Person tmiPleSSes | X l | X ~1010
Queue Length l | ? I rX | X | | X | X |
Saturation Flow l l l l l l | X | | X |
ime mean X X X
space ~nean | l l I ~ ~ ~ ~ 11
rime Space Diag. I I I I I I x I x I I I I Il
rrave1 T v~eear~gveehm | X | X |~ 1 1 L 11
~s XX X X X X ~
Volume 1 l I X TX I I I I x I I I ~
2 - 9
x
x
OCR for page 16
2.2.2 Summary of Mode! Capabilities
An important decision in this project is what models should be used and how should they
be used for research purposes. All the models investigated have some link to interchange and
arterial operations. However, they may be used to develop relationships for situations where it
would be difficult to collect field data. As a result, a list of geometric and operational
characteristics, as well as other concerns, typical of interchange operations has been compiled in
Table 4. Each model was then investigated as to its capability to model the stipulated geometr.c
or operational characteristic. The results were shown in Table 4, and a brief discussion of the
results follows.
An interchange ramp terminal/frontage road operates differently from an arterial street due
to the effect of the freeway and its ramps. For this reason, a model capable of simulating traffic
on both arterial streets and freeways would be advantageous. INTRAS and CORFLO are the only
two models investigated in this initial study capable of interacting freeway vehicles and arterial
street vehicles. Because INTRAS is a microscopic model, a greater level of detail can be both
input and extrapolated from INTRAS than from CORFLO.
Weaving is another Important factor. A level of service can be assumed from FREFLO
output (and CORFLO) for weaving areas such as an entrance ramp closely followed by an exit
ramp. For INTRAS, entrance/exit ramp weaving is not specifically addressed in the manual;
however, TTI has used INTRAS for freeway weaving analysis and has found the model to operas
adequately. However, it is improbable that the logic used in FREFLO and INTRAS for a freeway
weaving analysis can be applied to a Interchange ramp terminal weaving sections.
Other weaving scenarios Involve the interaction of vehicles exiting the freeway and
requiring a right turn at the ramp terminal intersection or vehicles turning out of a driveway and
requiring a left turn at the first downstream intersection. These scenarios cannot be specifically
modeled in INTRAS; however, INTRAS output does contain O-D charts which can quantify those
maneuvers, and the output also quantifies the number of missed maneuvers. In other words, if
a vehicle was destined to exit the freeway and turn right at the next intersection on the frontage
road, but could not complete the maneuver, INTRAS includes this information in its output.
NETSIM, on the other hand, is capable of traffic assignment parameters which could require a
certain percentage of freeway exiting vehicles to turn right at the frontage road intersection. This
process is, however, very complex and careful attention must be made to keep percentages of
vehicle movements at each link equal to 100 percent. PASSERIII deals specifically with diamond
interchanges at which such a weaving maneuver would take place, however, simulationof weaving
in the vicinity of the intersection is beyond its scope.
With interchanges being an integral part of freeway traffic management systems in some
states, and with ramp metering becoming more prevalent, the issue of queue length could play an
important role in freeway corridor operations. Queue length would aid in determining an adequate
distance between a ramp exit or entrance and the interchange. Therefore, it would be desirable
2- 10
OCR for page 17
Table 4. Computer Simulation Model's Capabilities
Computer Model
Model Constraints ~ FREFLO ~INTRAS ~CORF~O ~ NETSIM ~PASSER III ~ PASSER II ~ TRANSY ~ TEXAS
freeway Simulation ~Yes ~Yes ~Yes ~n/a ~n/a ~n ~n/a
Frontage Road n/a Yes Yes Yes No No No
Simulation
nterchange Simulation ~No ~Yes ~Unknown ~Yes ~Yes ~Y s ~Yes
driveways ~n/a ~Yes ~Yes ~Yes ~No ~it; ~No
Type of Traffic Control No Ramp Stop, Yield, Stop, Yield, Stop, Yield, Pretimed or Signals Pre-timed
Metering Fixed, Actuated Pretimed Signal Fixed Traffic- Signals or
Control, 3 types Control, Some Control, Responsive Unsignali
of Ramp Actuated Actuated Fixed Sequence zed
Metering, Merge Control Control Signals
and Diver e
freeway Weaving ~LOS ~Yes ~ LOS Provided ~n/a ~n/a ~n' ~n/a
Analysis _ Provided
l rterial Weaving | n/a | Yes l No | nknown | n/a | n/ | n/a
Analysis Caused by Two
Closely Spaced Ramps
arying Distance of | Yes | Yes l Yes | Yes | n/a | n/ | n/a !
I Weaving Area
| rterial Weaving | n/a | O-D quip l No | Yes | No | N | No |
Analysis Caused by
Either Vehicle Exiting
Freeway and Turning
Right at Interchange, or
Vehicle Turning From
Driveway and Turning
Left at Intersection .
Varying Distance n/a Yes Yes Yes Yes No No
Between Exit Ramp
Terminal and
Downstream Arterial
| Inter sect ion
U-Turn Area at n/a Yes Unknown Yes Yes No Unknown
| Interchanges
Exit Ramp Vehicles n/a Yes. All lanes Unknown Unknown No No No
Able to Yield to Cross yield
Arterial Traffic
l I 'rediction of Queue | n/a | Unknow | Unknown | Yes | No | Ye | Yes
Length at Intersections
. sadistic Output At or ~No ~Yes ~Unknown ~ 1; Known ~No ~No t Unknown
Near Capacity Levels
~ at V/C > 0.95
n/a
No
Yes
No
No
Control,
Stop,
Yield,
Pretimed,
Semi
actuated,
or Full
Actuated
n/a
n/a
n/a
Unknown
Yes
Yes
No
No
Unknown
2- 11
OCR for page 66
2.6.3 Sensitivity Analysis
Figure 27 illustrates the effect of arterial flow rate on weaving and arsenal maneuver speed.
This figure shows the behavior of both models when all other factors are held constant. The values
selected for these factors represent their respective average values as found in the database. The
range of flow rates over which the two models are compared is larger than the corresponding range
in the database. This extension was undertaken to show the overall behavior of each mode} when
extrapolated to extreme (but realistic) values.
The trends in Figure 27 show that both models predict an exponentially decreasing maneuver
speed wad increasing arterial flow rate. This trend is somewhat consistent with the traditional
speed-flow relationship for uninterrupted traffic streams in uncongested conditions. This figure also
shows Mat the arterial maneuver speed is always higher than the weaving maneuver speed for the
same flow rate. This trend is reasonable since the arterial vehicles enter the weaving section at speed
while the weaving vehicles often must accelerate from a stopped (or sIowed) condition when
departing the off-ramp. The trend toward convergence of the two models at higher flow rates is also
reasonable as the weaving maneuver speed should approach the arterial maneuver speed as the
capacity of the weaving section is neared.
Maneuver Speed, m/s
10
8
6
4
o
.
12 ~
~~ ~61
M/ea~jn9 ~
- _ _ _
us = 13.4 m/s
Nt -2
IL= 0
2 VW = 170 VPh
, I , i I I
O 500 1000 1500 2000 2500 3000 3500
Average Arterial Flow Rate, vph
Figure 27. Effect of arterialflow rate on weaving and arterial maneuver speeds.
2 -60
OCR for page 67
2.7 RAMP VVEAVING CAPACITY MODEL
The section of the cross arterial roadway between an interchange ramp terminal and a
closely-spaced downstream intersection generally experiences operational problems, reduced
capacity, and deteriorated Levels of Service (LOS) when the ramp-to-intersection weaving is heavy
and difficult to perform. The more difficult traffic maneuver to perform usually is the off-ramp right
turn trying to cross and then turn left at the next downstream intersection. When the downstream
intersection is signalized, additional queuing in the left turn lane shortens the elective weaving
length, resulting in increased operational problems.
An additional operational constraint is the physical capacity of the ramp-arterial crossing
maneuver. This maneuver usually operates like a freeway merge operation dunng rush-hour
conditions because even free right-turn maneuvers are usually performed from a stopped position
in queue. The Highway Capacity Manual (3) does not address arterial weaving. This section will
present a method for estimating arterial crossing capacity based on NETSIM traffic simulation
studies. Bow random and progressed flow conditions along the arsenal can be evaluated. Models
to predict operating speeds in arsenal weaving sections are presented in Appendix E.
2.7.1 Study Methodology
The expenmentaltestbed shown in Figure 28 was coded In TRAF-NETSIM to simulate Me
study conditions. An arterial free speed of 60 km/in was assumed. The distance between the ramp
terminal and the downstreamintersection was 200 meters. The ramp traffic, on yield control, made
a right turn onto the arterial and then made a left turn at the downstream intersection. The arterial
traffic went through the downstream intersection without making any turns. The strategy was to
heavily Toad the cross weave with abundar~t demand, i.e., maintain a standing off-ramp queue so that
the maximum ramp crossing volume could be observed for different operating conditions.
l
111
l
Diamond Interchanged
Figure 28. Arterial testbedfor ramp-to-arterial weaving analysis.
2 - 61
1 1
1 1
1 1
1 1
~ t
.]
Area of Study
1 1
1 1
~1
1 1
1 1
1 1
1 1
~1
~ ~ '
OCR for page 68
Preliminary testing revealed that the weaving capacity from the ramp terminal follows the
pattern of a negative exponential function with increasing arsenal volume. Thus, negative
exponential regression analysis was performed to mode! the weaving capacity. The basic form of
the exponential regression equation for the predicting ramp capacity is shown below.
OR ~-e~PQ.
where:
OR
Q.
a
T
1 C
He
ramp crossing/weaving volume (vph);
arterial through volume (vph)'
coefficient ofthe model = Tc / 3600;
coefficient of the model = Hs / 3600,
critical gap of ramp weave, see, and
minimum follow-up headway, sec.
The coefficients of the exponential equation, a and ,B, for random flow were determined on
the basis ofthe simulations for various arsenal through volume conditions. The coefficients a and
,B were computed by inputting the simulated arsenal and the ramp crossing volumes into SAS, a
statistical software analysis package (49, and perfonning the desired regression analysis.
For the random flow conditions, the arterial traffic was vaned from 100 vph to 2000 Ash.
Weaving across one, two, and three arterial lanes was studied for the volume conditions noted. Also,
the effect of the change in decile gap acceptance distribution in NETSIM was studied.
For progressed flow conditions, the arterial traffic was vaned from a v/c of 0.2 (500 vph) to
a v/c of 0.8 (2000 vph) for a three lane arterial. A cycle length of 100 seconds and a clearance
interval offour seconds per phase were also assumed. Various PF ranging from 0.l to ~ .8 were also
simulated by varying Me percent vehicles arriving on green (PINGS at the upstream intersection.
2.7.2 Study Results
The next section consists ofthe results obtained in the various cases involving random flow
conditions along the arterial. Also, the computed coefficients for determining the ramp crossing
volumes for different arterial flow conditions are presented. Changes in the gap acceptance
distnbution were observed to affect the ramp crossing vol~ne. The second section presents the
calibration coefficients for the proposed negative exponential equation for computing the ramp
crossing volume for different arterial through volumes. The third section covers the results of
simulations involving several volume conditions and different progression factors. The effect of
progression on the ramp crossing volume is discussedin detail in this section. The development of
the final mode} fonn and the methodology used to predict the ramp capacitor across the arterial
weaving section for various progression factors are presented In Chapter 3
and Appendix E.
2 - 62
OCR for page 69
Random Flow. Initial ramp capacity studies were conducted with NETSIM assuming that
the cross arterial had no progression end random flow. Moreover, preliminary testing ofthe mode!
assessed the sensitivity of capacity to the default gap acceptance function provided in the model. The
~ 994 HCM states that the critical gap for a right turn from a Yield sign onto a major street could be
taken as 5.5 seconds (3~. TRAF-NETSIM assumes a decile distribution wherein the default median
value is taken as 6.4 seconds. In order to simulate the HCM recommended distribution, Card Type
145 in TRAF-NETSIM was coded to produce a decile distribution having a median value of 5.5
seconds. Hence the data file with the new decile distribution and an upstream link length of 365
meters was simulated for random flow.
The effect of changing the decile gap distribution for three lanes can be seen
_ . . ~. . . . . .
in Figure 29
Due to the lower (better) gap acceptance cnter~a, more ramp vehicles can make a right turn onto the
arterial. Though the trend is similar' the ramp crossing volume for the HCM decile distribution is
slightly higher thar1 the TRAF-NETSIM default decile distribution. Follow ing a review of the gap
acceptance study results shown in Figure 29, it was arbitranly decided to continue using the
NETSIM default distribution in subsequent mode} building.
1
1800 1 1
1600-
%
1400 - "a
£L 1200
_
i ~
1 ~1000
~ 800
I
1.
| - - - - - Using default NETSIM median gap value
I Using HCM median gap value
600
400
200
100 500 1000
Arterial Volume (vph)
Figure 29. Elect of NETSIMdecile gap distribution for three-lane arterial.
~ 500 2000
The effect of the number of lanes on ramp crossing volumes is illustrated in Figure 30. The
drop in the ramp crossing volume is sharper u ith an increase in the number of vehicles on the one-
lane arterial because all the vehicles have to use the single lane so the number of acceptable gaps
available is reduced. For the two and the three lane cases, the same number of vehicles are
distributed over two or three lanes, as the case may be, and there is a lesser effect on the ramp
crossing vehicles. The net increase in the vehicles per hour per lane for the one lane arterial case is
largest and hence its ramp capacity is affected the most.
2 - 63
OCR for page 70
as
E
! ~
_
O
as
. .
1 ~ Boo
1600 ~L ~. 1-lane ~2-lane 3-lane 1
~ 400 _
1200 1
,
000 'I
Boo 1
600 1
Too 1
200 +
O !
100 500 1000 1500 2000 ,
_ _ =
500 1000 1500
Arte ria I Vo lu m e (vph )
Figure 30. Elect of number of lanes on maximum ramp volume.
Observations ofthe simulation results of Figure 30 suggest~at an exponentialmode} would
reasonably fit the interchange ramp capacity results generated by NETSIM. The values of ramp
capacity were obtained by simulation of the desired conditions and the coefficients of the model
were determined using SAS, a statistical analysis software package f49. Figure 3 1 shows how well
We model fits the traffic simulation program values. The points indicate the average of ten
simulation runs while Me lines indicate Me trend using the calibrated exponential model.
200 -
1'
1800
1600: ~ S/m 2/ane
1400 i. ~I ~ Sim. 3-/ane
1200 5` '\ &\ ~- Reg. 1-/ane
1 000 _ \ . . W
Q 600 . ~ ` ` __ _ ^
400 ~-- ~ -
-_. ~
100 500 1 000 1500 2000
~ .__ ___ .___ .___ ____
Arterial Volume (vphJ 1l
Figure 3 1. Comparisons of ramp capacityfor simulation and exponential regression mode! results.
2 - 64
OCR for page 71
Applying the exponential regression analysis in SAS, the average of the observed volume
data for each case was used to estimate the Known coefficients cat and ,8 in the exponential
equation. a multiplied by 3600,denotedas Tc, is the average critical gap time of the corresponding
lane configuration, while ~ multiplied by 3600' denoted as Hi, is the minimum headway of the
e e · r_4 ' , ~ ~ _ my_ e
artery weaving section. ~ ~
i, ,
~ la '~e 11 s lows t :le coen~c~ents ax and ~ of the exponential model
computed for one, two and three lane arterials. The coefficients in the proposed exponential
equation are accurate estimations of the TRAF-NETSIM simulated operations in terms of standard
errors and their variances. Table ~ ~ illustrates the coefficients of the mode! on a per lane basis. For
the per large analysis, the results of the one, two and three lane cases were pooled and regressed. It
cart be observed that the values of TC arid Hs are close to that of the one lane case.
Table 17. Coefficients of the exponential regression mode'
I,anes ~ -lane 2-lane 3-lane
Coefficients a ~ ~a ~a ~
_
Exponential 0.00195 0.000657 0.001 I S 0.000574 0.00088 0.000565
R2 Value 0.9977 0.9995 0.9989
Conversion of ~Tc ~Tc ~ Its ~Tc ~ E
Coefficients
Values (sec.) 7.02 2.36 4.26 2.06 3.17 2.03
Table IS. Coefficients of the exponential regression mode! on a per lane basis
Coefficients Exponential R2 Value Conversion of Values
Coefficients (sec.)
a ~ 0.002091 ~ 0.9970 ~ Tc ~ 7.52
~ 0.000583 Hs 2.10
Progressed Flow. The NETSIM simulations were used to determine ramp crossing volumes
for progressed arterial flow. Different progression factors were analyzed, ranging from PFs of 0.1
to Its. A PF value of I.0 is essentially uncoordinated, uniformly distributed flow. Progression
factors from unity reflect the degree of platooning ofthe dominant flow. Volume-to-capacity ratios
of 0.2, 0.4, 0.6, 0.7 and 0.8 on the upstream feeding movements were studied for a three-lane
arterial.
operating in two chases to create two platoons flowing downstream
~ _'
In order to simulate various PF, vehicles were emitted from the upstream intersection
~ such that one platoon aIrives
, ~ . . _
on red and the other platoon arrives on green. At the merge point, the notion of red and green only
characterizes the degree of platooning in the arterial flow, as there is no signal at the merge point.
2 - 65
OCR for page 72
Figure 32 summarizes the experimental results. Polynomial regression equations for these
plots were determined using SAS. Dunug low volume conditions on the arterial, little change
occurred in ramp crossing capacity for different PFs. As the volume on the arterial increased, the
ramp crossing volume decreased significantly due to fewer acceptable gaps for the weaving
maneuver. For higher volumes, the change in the ramp crossing volumes for venous PFs becomes
more significant. A PFof ~ .0 is considered random flow and the ramp crossing volume is the least
for a PFof I.0 when compared to other PFs between O.1 to 1.~. The flow graph takes the shape of
a parabola which has its minimum at a PF of 1 .0. Figure 32 clearly indicates the trend of ramp
crossing volumes for v/c ratios of 0.2, 0.4, 0.6, 0.7 and 0.S, respectively. Me difference in ramp
crossing volume for a PFof O.] to that of a PFof I.0 Increases with an increase in arsenal volume.
In other words, We difference between the ramp crossing volumes for a PF of 0. ~ to that of a PF of
~ .0 increases with an increase in the v/c ratios on the arterial. Since the green ratio (green t~me/cycle
length) is the same along both the approaches of the arsenal, Me PEG for a PF less than I.0
corresponds to the PER for a PF greater than ~ .0 and vice-versa. Plots of ramp crossing volumes
for PF less than I.0 are a mirror image of plots for PF greater than ~ .0 about the axis of PF of ~ .0.
1200
1 ~
~ 1100
>
1
~ 1000
._
tan
en
O0 900
! ~
`~: 800
.~For\dc=04 ~For~c=0.6 ~For~c=0.7 ~For~c=0.8~
v
m-
,^~%,, a.,
~_7 ~m
Ha_
_7x
~3< X ~
INK
700 1 ~,
. , , , 1
o.o 0.2 0.4 0.6 0.8 1.0 1.2 1. ~1.6 1.8 1l
PF I
Figure 32. Elect of PF on ramp crossing volume for various v/c.
Adjustment Factors for Progression. In order to further simplify the simulation results,
the regression equations from the graphs for various v/c ratios were used to determine individual
values of ramp crossing volume. A PFof I.0, also considered es random flow, was used as the basis
for development ofthe adjustment factors. The factors for over PF were computed by determining
the ratio of the value at PF of ~ .0 to that of another PF. Because the coves were parabolic and the
values on one side of the curve were mirror images of the over, adjustment factors for PF from 0.
2 - 66
OCR for page 73
to 1.0 were computed. Figure 33 presents adjustment factors for various PFrarging from 0.1 to 1.0
for volume-to-capacity (v/c) ratios of 0.4, 0.6, 0.7 and 0.~.
1.25
1.
1=
JO 1.05
L.
~ 1.00
1.10
0.95
1 20
- 1
r
I -~-v/c= 0.4
___. vlc= 0.6
_____v /c = 0.7
vlc=0.8
1.
i_
0.90 , , , I
0.1 0.3 o.s 0.7 0.9
PF
Figure 33. Capacity adjustment factors for various progression factors.
·1
l
ll
Table 19 shows the actual (average) capacity adjustment factors simulated for various
progression factors ranging from 0.1 to 1.0. Table 20 provides related capacity adjustment factors
obtained using the exponential equation (Equation 31) shown below as developed from Table 19
average results using SAS. Note that average arterial lane volumes V are used in Equation 3 1 to
provide a more convenient data input format.
fPF = 1 + °,0l5*eLo-oo44*v-3os*pFl
where:
~_
JPF
PF
ramp weaving capacity adjustment factor;
progression factor, and
V = arsenal volume per hour per lane (vphpl).
(32)
From a comparison ofTables 19 and 20, it can be seen that the above exponential equation
follows a close fit of the actual average adjustment factors. The sum of squares error (SSE) was
determined to be 0.00217.
2 - 67
OCR for page 74
2.7.3 Determination of Ramp Capacity during Random Flow
The ramp capacity during random flow, denoted by OR can be determined by inputting the
total arterial volume in Equation 3 1. The computed coefficients a, ,8 shown in Table 1 7 have been
computed on the basis of number of lanes in the arterial section and can be used in the exponential
equation. The results of one, two and three lane arterials were pooled and regressed to compute the
common coefficients for single and multilane arterials. Depending on the decree of accuracy
required oy tne user, me a~rerent coe~nc~ents could ne used for predicting ramp capacity.
Table 19. Actual adjustment factors for PF of 0.1 to 1.0
Progression Factors, PF
v/c
0~4
0~6
0~7
0~8
0~1
1 046
1098
1150
1205
0~2 1 0~3 1 0~4 1 0~5 1 0~6 1 0~7 1 0~8
1 036 T 1~028 11.020 11~014 11 009 T 1 005 11 002
077 T 1 059 11 043 11~030 11 009 T 1 011 11~005
1.118 T 1 090 11.066 11046 11 019 T 1 017 1 1 007
1.162 T 1 124 11.091 11063 11 030 T 1 023 1 1.010
0~9 1 1~0
1 001
1.001
1 002
1.000
1.000
1.000
1.003 1 eOOO
Table 20. Computed adjustment factors for PF of 0.1 to 1.0
Progression Factors, PF
v/c
0~4
0~6
0~7
0~8
0~1
1 048
1.100
.142
1208
0~2 1 0~3 1 0~4 1 0~5 1 0~6 1 0~7 1 0~8
1 035 1 026 1 019 1 014 1~010 1 008 1 006
1 074 1 054 1 040 1029 1 021 1 016 1 012
.
1~105 1 077 1 057 1042 1.031 1 023 1 017
.
1153 1 113 1 083 1061 1 045 1 033 1 025
0~9 1 1~0
1 004
1 009
1 012
1 018
1 003
1 006
1 009
1 013
2~7~4 Adjustment for Sneakers
Comparison ofthe simulation results between random flow and progressed flow at a PF of
1.0 revealed that the ramp crossing volume for a PF of 1.0 was higher. The difference between the
ramp crossing volumes between random and progressed flow increased with an increase in arsenal
volumes. This difference in ramp crossing volumes was attributed to the sneakers crossing during
the two phase change intervals i.e., sneakers (S,9. In over words, the ramp vehicles completed the
weaving maneuver by making use of the large gap available to the ramp vehicles during the two
phase change intervals of four seconds each at the upstream intersection. The random flow
conditions had a situation wherein the upstream intersection had 100 percent green on the arsenal
movement and hence the effect of sneakers was not observed. On the average, approximately three
vehicles were completing the weaving maneuver during each phase change interval. The effect of
2 - 68
OCR for page 75
sneakers was confirmed visually by observing the animation of the simulation for the required
conditions in GTRAF. Thus, the ramp crossing capacity, adjusted for sneakers, would be
QR Qua Sm
where:
Q R
QR
sm
ramp capacity adjusted for sneakers (vph);
ramp capacity for random flow (vph); and
sneaker volume (vph).
2.7.5 Application ofAdjusiment Factors
(33)
In order to obtain the ramp capacity for different progression factors, the adjustment factors
for progression,fpF, needs to be multiplied to the ramp capacity which has been adjusted for sneakers
as follows:
=
where:
QPF
Q R
fPF
QPF QR fPF
ramp capacity adjusted for progression (vph);
ramp capacity for random flow (vph); and
adjustment factor for progression.
(34)
The application methodology of this formulation of arterial weaving wall be presented in
Chapter 3. Moreover, field studies of arterial weaving operations were conducted and are described
in detail in Appendix E (129. Several empirical models of maneuver speeds and delays as related
to local conditions are provided. These studies were extremely tedious and time consuming. Other
initial NETSIM simulation studies of arteriaVramp weaving operations were conducted end reported
(139. All of these studies showed the benefit of increased signal separation between the interchange
and the next downstream signal together with the benefit of arterial signal coordination during
undersaturated conditions.
2 - 69
OCR for page 76
Representative terms from entire chapter:
lane utilization
