Capacity Analysis of Interchange Ramp Terminals Final Report (1997) / Chapter Skim
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CHAPTER 2 Findings
CHAPTER 2 Findings
Pages 7-76
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From page 7... ...
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.
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From page 8... ...
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.
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From page 9... ...
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.
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From page 10... ...
too] to analyze arterial traffic flow through interchange ramp terminals.
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From page 11... ...
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.
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From page 12... ...
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.
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From page 13... ...
. 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
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From page 14... ...
-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 .
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From page 15... ...
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 | ?
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From page 16... ...
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.
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From page 17... ...
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.
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From page 18... ...
One important factor associated with urban freeway ramp terminals that cannot be simulated by FREFLO is ramp metering. Also, the model deals with operations on freeways, whereas ramp terminals/arterial systems are important in this project.
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From page 19... ...
2.3 FIELD STUDIES Several analytic models were developed during this study to facilitate Me evaluation of interchange ramp terminal capacity and level of service. This section provides a description of the traffic flow problems for which models were developed, a descnption of the field study sites, and some summary statistics from the field study database.
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From page 20... ...
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 downstream signalized intersection. This maneuver has been observed to cause significant turbulence in the arterial traffic flow resulting in significant increases in travel time and, in some cases, lengthy queues on the off-ramp.
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From page 21... ...
Vanations of these two interchange forms stem Tom variations in the distance between the ramp terminals and from the routing of the tmff~c movements malting the equivalent of a left or right-turn movement at the interchange. An a assessment of the correlation between interchange type, the extent of its operational problems, and its frequency of application in urban areas led to the following six interchange types being identified as the most appropriate candidates for the field studies: 2- 15
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From page 22... ...
Of these sites is provided in Appendix B 2.3.3 Data Collection Details of the traffic signalization at each The data collection activities focused on the collection ofthe basic characteristics describing traffic flow at and between signalized ramp terminals and adjacent intersections.
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From page 23... ...
to SR- 1 | Arizona l l l l 1 23 Notes: 1 - Distance measured from stop line to stop line in the same direction, except at SPUI's. At SP13I's, the "same direction" concept is also applied but the opposing direction Trough stop line is used as the reference point at the second ramp terminal (since the through stop line at the second ramp terminal does not exist at the SPUI)
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From page 24... ...
-_ ~ Boundary of / | Study Zone LEGEND Video Camera I "< end field of view ~ Tape Switch Sensor Camera 2 Boundary of / Study Zone ~ Photocell Sensor `p Tape Switch Speed Trap Figure 5. Capacity study data collection setup for a diamond interchange.
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From page 25... ...
As the columns in this table indicate, the saturation flow rate and start-up lost time date were segregated into "with" arid "without" spillback categories. The "with" spillback category relates to the vehicles observed during signal phases that experienced queue spillback from the downstream intersection.
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From page 26... ...
On the other hand, the data in Table 6 indicate that the left-turn movements may be discharging more efficiently than the through movements at the study sites, however, the difference is relatively small. As with Me saturation flow rates, the start-up lost times in Table 6 varied among the "with" and "without" spillback categories.
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From page 27... ...
The lane utilization factors computed for the through movement lane groups at the twelve study sites are shown in Table 7. The factors recommended in the ~ 994 HEM 63, Chapter 99 for application at isolated intersections are also shown in this table.
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From page 28... ...
This date was obtained at eight of the twelve study sites. Studies were not conducted at four sites because of the lack of an adequate view of the traffic queue.
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From page 29... ...
The weaving maneuver that was examined in this study was the offramp right-turn movement that weaves across the arterial to make a left-turn at We downstream signalized intersection. The two camera recordings were synchronized in time and played back simultaneously to obtain the travel time and stopping location of weaving and non-weaving vehicles.
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From page 30... ...
2.4 CAPACITY CHARACTERISTICS _ This section summarizes He models that can collectively be used to predict the capacity of traffic movements at signalizedinterchange ramp terminals and other closely-spaced intersections. Specifically, these models predict three important capacity characteristics,they are: saturation flow rate, start-up lost time, and clearance lost time.
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From page 31... ...
us where: He = through movement minimum discharge headway, sec/veh; us = speed at saturation flow, m/s; and vl' = demand flow rate per lane (i.e., traffic pressure) , vpcpl.
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From page 32... ...
discharge headway sec/veh0.61 Demand flow rate per lane (traffic pressure) vpcpl5 Distance to back of downstream queue meters35 2 - 26 Maximum 6.8 .
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From page 33... ...
If there are no vehicles on the downstream segment at the start of the phase, then the effective distance to queue would equal the distance to the through movement stop line at the downstream intersection. The calibrated mode} indicates that the minimum discharge headway decreases with increasing distance to downstream queue.
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From page 34... ...
. In this regard, the saturation flow rate would be equal to the ideal rate when all factor effects are optimum for efficient traffic flow and the corresponding adjustment factors are equal to 1.0.
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From page 35... ...
With one exception, the left-turn movements included in this study represent left-turns at interchange ramp terminals. The orate exception was a left-turn movement at an adjacent signalized intersection.
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From page 36... ...
The left-turn movements studied rarely, if ever, experienced queue spilIback during the study periods due to the nature of the signal phase coordination between the two interchange ramp terminals. Hence, in contrast to the through movements studied, the variability in left-turn headways among sites cannot be explained by differences in the distance to the downstream queue.
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From page 37... ...
To avoid confounding the effect ofthese factors with those specifically being considered in this study (e.g., turn radius) , the study sites were selected to have as near ideal conditions as possible for all non-relevar~t factors.
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From page 38... ...
In this regard, the saturation flow rate would be equal to the ideal rate when all factor effects are optimum for efficient traffic flow arid the corresponding adjustment factors are equal to I.0. Based on this definition, it was determined that an infinite radius, a traffic pressure of 10.0 vpcpI, arid a g/C ratio greater than 0.27 were representative of ideal conditions for left-turn movements.
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From page 39... ...
. The definition of ideal conditions was also used to derive the following adjustment factors: 1 ~ ~ (12)
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From page 40... ...
As a result, there is an inherent relationship between saturation flow rate and start-up lost time. This section describes the calibration of the start-up lost time model for through movements.
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This relationship is shown in Figure 8. As this figure indicates, the start-up lost times for saturation flow rates of 1,800 arid 1,900 vphgp!
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From page 42... ...
As this figure indicates, the start-up lost times for saturation flow rates of 1,800 and 1,900 pcphgpl are about 2.0 arid 2.5 seconds, respectively. These values are slightly larger than the ~ .0 to 2.0 seconds recommended in Chapter 2 of the HCM (3J.
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From page 43... ...
interval. These phases were observed at twelve interchange ramp terminals and at twelve intersection approaches.
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From page 44... ...
Model Statistics Value R2 0.1 1 Root Mean Square Error: 1.33 seconds Observations: 1044 Range of Model Variables Variable Variable NameUnits Minimum gY ~see 0.02 _ SL Approach speed limit km/in 56 Xi Volume-to-capacity ratio in lane i na 0.08 Maximum 7.3 72 1.3 The R2 statistic in Table ~ 3 indicates that the calibrated model accounts for eleven percent of the variability in the green extension data. The remaining variability is likely due to random sources; although, some of it may be due to differences among the study sites (that was not explained by speed limit and volume-to-capacityratio)
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From page 45... ...
The lane utilization model developed in this research is based on a quantitative descnption of the two problems previously described: (~) drivers not distributing themselves as evenly as possible, and (2)
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From page 46... ...
In general, the large utilization factor increases with number of lanes and decreases with increasing volume. It should be noted that the predicted lane utilization factors exceed the values recommended by the HCM (39 (i.e., ~ .05 at two through lanes, ~ .
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From page 47... ...
I-his agreement is partly due to the fact that both data bases had the majority of their observations in this higher range of flow rates. This agreement suggests Mat the calibrated lane utilization model may be applicable to all signalized intersections.
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From page 48... ...
Traffic control plans that are designed to provide priority arterial flow dunng undersaturation may not provide the same relative priority and expected performance during oversaturation. A series of microscopic traffic simulation studies are presented which illustrate the response sensitivities of traffic signal systems observed for throughtput (arterial volume)
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From page 49... ...
For traffic flow In the i-j-k direction, traffic signal j is defined as the downstream node of link ij and the upstream node of linkj-k. Thus, precedence and dependency relationships exist between links and may be operative at any time conditions warrant.
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From page 50... ...
However, this research has shown that closely spaced signalized intersections, whose traffic signals are poorly timed, can have both demand starvation on the link and still cause flow blockages on the next upstream link due to queue spilIback, even dunng nominally undersaturated conditions. Demand starvation results in wasted green at the downstream signal.
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From page 51... ...
The results from several studies of arterial throughput and delay follow. Figure 12 shows that throughput flow problems are occulting on the short downstream link j-k pnmanly due to "demand starvation." For offsets of 40 seconds or more, the throughput volume drops 39%, from a nominal two-lane flow of about 1400 vph to a flow rate of about 850 vph.
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From page 52... ...
These maximum link delays occur for Me same operating conditions as do the reductions in capacity caused by queue spilIback in Figure 14. The link has Redoubtably become oversaturated for these inefficient traffic signal offset)
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From page 54... ...
Simulation results of arsenal throughput are not illustrated,but they show a slight aIld expected moderation in the effects Of demand starvation because of Me ability to feed some traffic into Me empty arterial during these conditions. As shown in Figure 13, traffic delay experienced upon anival at a traffic signal is known to vary with arrival pattern 639.
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From page 55... ...
Selection ofprogression adjustment factors for delay estimationin HCM-level analyses should reflect this finding. No benefit ofprogression should tee assumed or expected for any signal timing plar1 developed when upstream flows are nearly constant throughout the cycle.
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From page 56... ...
2.5.2 Oversaturated Conditions Dunng oversaturated conditions, when upstream traffic demand exceeds downstream signal capacity, queue spillback along the affected links will routinely fill during the signal cycle, and link flow becomes highly output dependent, rather than upstream demand dependent. As the following NETSIM simulation experiments show, variations in flow and delay do occur during oversaturation conditions depending on the length ofthe link, upstrearntraff~c pasterns, and signal offset.
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From page 57... ...
The maximum delay per vehicle experienced while traveling on a link is primarily related to the average Ravel speed on the link according to the following delay model, which is developed in Appendix D Delays may be less that this vague during oversaturated conditions if demand starvation also occurs in addition to queue spilIback.
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From page 58... ...
Tragic delay on link with variation in offset for oversat? vrated conditions and preclominantly arterial traffic pattern.
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From page 59... ...
= no + tam g ~ `£Sm g O < n(L,g) < no where: (2~ n(L, g3 = number of vehicles operating on the link of length ~ at end of green, vehicles; number of vehicles operating on Me link at start of green vehicles; total arrival flow into link during green destined to movement m, vph; output saturation flow of movement m, subject to Sm g < capacity of link serving output movement m, vphg; and maximum number of vehicles that can store on link, vehicles, kqL with a typical storage density of 143 vpkmpI, or a storage spacing of 7.0 m/vein (23 D/veh)
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From page 60... ...
As long as Me arrival flow to the link plus the queue storage at start of downstream green exceeds the downstream phase capacity, the throughput on the link watt not change with offset, `9,j. However, the link's signal of Eset if, does control which upstream feeding movements benefit from the available, albeit insufficient, link capacity and which movements get little or no service.
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From page 61... ...
The resulting traffic delays expenencedon the three input movements to the link reflect the limited capacity available and allocated to each one by changing the downstream signal offset. Traffic delays observed on the exterior input movements to the upstream intersection follow a consistent but reciprocalpatternto observed flows in that when capacity goes down delay goes up, as Figure 26 depicts for a ~ 00 meter link.
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From page 62... ...
Figure 26. Traffic delay experienced on upstream input movements as downstream offset changes for oversaturated conditions and balanced traffic pattern.
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From page 63... ...
The weaving maneuver that is considered is the off-ramp nght-turn movement that weaves across the arterial to make a left-turn at the downstream signalized intersection. Although several other weaving maneuvers exist in interchange areas, the off-ramp weave maneuver is generally found to have the largest volume and to be the most disruptive to arterial traffic flow.
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From page 64... ...
This modeIrelates the weaving maneuver speed to the average speed of arterialvehicles entering the weaving section. This latter speed was measured as a spot speed at the point of entry to the arterial weaving section.
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From page 65... ...
In general, the models were calibrated with sites having two or Tree arsenal through lanes (in the subject direction) , closely spaced intersections, and a wide range of arterial flow rates.
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From page 66... ...
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.
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From page 67... ...
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.
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From page 68... ...
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.
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From page 69... ...
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.
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From page 70... ...
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.
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From page 71... ...
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.
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From page 72... ...
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.
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From page 73... ...
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)
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From page 74... ...
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.
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From page 75... ...
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)
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