TRAFFIC PERFORMANCE ANALYSIS OF DYNAMIC MERGE CONTROL USING MICRO-SIMULATION

Jiang, Bared, Maness, Hale 1

TRAFFIC PERFORMANCE ANALYSIS OF DYNAMIC MERGE CONTROL USING 1

MICRO-SIMULATION 2

Ximiao Jiang1, Joe Bared2, Michael Maness3, David Hale4 3

4

(1) FHWA Traffic Operation R&D 5

6300 Georgetown Pike, McLean, VA, 22101 6

Phone: (202) 493-3132 7

[email protected] 8

9 (3)

FHWA Traffic Operation R&D 10

6300 Georgetown Pike, McLean, VA, 22101 11

Phone: (202) 493-3314 12

[email protected] 13

14 (3)

University of Maryland, Civil Engineering Department 15

1179 Martin Hall, College Park, MD, 20742 16

Phone: (202) 443-4580 17


19 (4)

Leidos, Inc. 20

11251 Roger Bacon Drive, Reston, VA, 20190 21

Phone: (202) 493-3296 22


24

25

November 2014 26

27

28

Word Counts: 29

30

Abstract and Manuscript Text: 4,000 31

Number of Tables and Figures: 9 (= 2,250 words) 32

33

Total: 6,250 34

35

TRB 2015 Annual Meeting Paper revised from original submittal.


ABSTRACT 1

2

Dynamic merge control (DMC) can be used in freeway merge areas to dynamically change lane 3

allocation at interchanges. It generally prioritizes the facility having higher volume, and closes a 4

lane on the lesser-volume roadway. DMC has been implemented in the Netherlands and 5

Germany, where it was reported that the application of DMC significantly improved traffic 6

operations. However the DMC strategy has rarely been studied, and has not been implemented in 7

the US. This research employed micro-simulation studies using VISSIM, to investigate 8

efficiency of the DMC strategy. Optimum traffic demand thresholds were specifically sought for 9

the geometric case where a 2-lane freeway merges with a 3-lane freeway, and tapers into 4-lanes. 10

Major-road traffic demands between 2500 and 4600 vehicles per hour (vph) were compared 11

against minor-road demands between 3000 and 4600 vph. The DMC strategy was applied by 12

closing the right lane of the major road, ahead of the merging gore area. The results indicate that: 13

1) the DMC strategy is beneficial for all abovementioned traffic demand combinations, in terms 14

of average vehicle delay and average vehicle speed; 2) when traffic demand on the minor road 15

exceeds 1900 vehicles per hour per lane (vphpl), these benefits become statistically and 16

practically significant; and 3) DMC can greatly alleviate the capacity reductions caused by lane 17

changing in the merge area. 18

19



INTRODUCTION 1

2

In response to the growing traffic congestion issue, an increasing number of active traffic 3

management (ATM) strategies have been developed and implemented internationally. Among 4

these strategies are hard shoulder running, variable speed limits (VSL), queue warning, dynamic 5

truck restrictions, managed lanes, and dynamic rerouting & traveler information. These ATM 6

strategies are capable of monitoring traffic flow; and can dynamically control speeds, reduce 7

capacity drops, and inform road users of network conditions, to optimize traffic and safety 8

performance. 9

10

Of these strategies, dynamic junction control (DJC) represents a component of the ATM system. 11

It can be used at freeway off-ramps and on-ramps, to dynamically change lane allocation for 12

interchanges. The rationale for use is that, in some traffic conditions or during certain times of 13

day, it may be more effective to use existing downstream or upstream lanes for one type of 14

movement or for traffic coming from the main lanes while at other times of day it may be more 15

effective to use the through lanes for the ramp movement [1] 16

17

One major component of DJC strategies is dynamic merge control (DMC). When ramp volumes 18

are relatively light, or when mainline volumes are heavy, it may be most effective to have the 19

ramp traffic merge into the rightmost lane. However, there may be situations in which ramp 20

volumes are heavy, while mainline volumes are light. In this case, traffic merging from the on-21

ramp will have to find gaps in the mainline traffic, despite the mainline traffic being relatively 22

light. The delay caused by hesitation and time required to find a gap may be disruptive to ramp 23

capacities and flows, thus creating a situation with higher rear-end collision potential on the 24

ramp. The DMC strategy is currently implemented in Germany and the Netherlands; where lane 25

control signs are installed over both approaches upstream of the merge, and priority is given to 26

the facility with higher volume. This strategy produces a more uniform traffic flow, with fewer 27

conflicts and safer maneuvers [2]. 28

29

Despite the advantages of DMC there is no current implementation in the US, and knowledge of 30

DMC is still experimental in nature. A study conducted by Parsons Brinckerhoff (PB) and the 31

Texas Transportation Institute (TTI) [3] suggested certain conditions should be met before 32

applying DMC. These conditions include: (1) Level of service (LOS) F on entrance ramps for 2 33

consecutive peak hours in the peak direction, during AM and PM peak periods; (2) Over 1,200 34

vehicles per hour (vph) on a single‐lane on-ramp, or over 2,000 vph on a two‐lane on-ramp; (3) 35

LOS D or better on the upstream mainline lanes following implementation of DMC. Research 36

conducted by TTI [4] suggested it is preferable to have at least 900 vph on the single-lane on-37

ramp, and LOS E or better on the mainline lanes following DMC implementation. 38

39



The abovementioned DMC is used to “close” the right lane of the mainline upstream of the on-1

ramp to give ramp traffic free-flow conditions onto the mainline. This strategy can be 2

generalized to prioritize high-volume roadways by dropping a lane on the lower-volume 3

roadway, especially in merge areas involving a downstream lane drop. Lane drops in the merge 4

area can easily cause a bottleneck, leading to upstream congestion on both roads. In this 5

situation, closing a lane on the lower-volume road would reduce or eliminate the friction caused 6

by “weaving” (gap acceptance) movements in the merge area. As such, the DMC strategy is 7

hypothesized to reduce traffic congestion at the merge of two freeways. 8

9

The objective of this paper is to investigate DMC efficiency where 3-lane and 2-lane freeways 10

merge into 4 lanes, and explore optimum thresholds for activating and deactivating DMC. The 11

remainder of this paper is organized as follows: (1) Describe the methodology of this study, (2) 12

explore optimum operation thresholds for DMC (2-lane and 3-lane freeways merging into 4 13

lanes) using micro-simulation, (3) examine the sensitivity of simulation results to the desired 14

safety distance parameter, and (4) discuss research results and conclusions drawn. 15

16

METHODOLOGY 17

18

Data Collection 19

20

To explore DMC efficiency for the general case of freeway-to-freeway merging, a series of 21

simulation studies were conducted on a hypothetical freeway facility. Figure 1 shows a sketch of 22

the hypothetical road geometry and lane closure strategy. The boxes on the right lane of Route A 23

indicate the location to close and open the lane for mainline traffic. 24

25

26 27

Figure 1: Sketch of the hypothetical road geometry and the lane closure strategy. 28

29

1

2

3

4

5



To explore optimum DMC operation thresholds, traffic demands on Route A were varied from 1

2500 to 4600 vehicles per hour (vph), in increments of 300 vph. Simultaneously, demands on 2

Route B were varied from 3000 to 4600 vph in increments of 200 vph. Thus with 8 demand 3

levels on Route A and 9 levels on Route B, a total of 8*9=72 demand scenarios were analyzed. 4

5

When closing a mainline lane, drivers were assumed notified of that lane closure 2500 ft 6

upstream of the closing point. In the merge area, vehicles originating from the mainline were not 7

allowed to make lane changes from lane 4 to lane 3, ensuring that ramp vehicles could freely 8

merge onto the mainline. 9

10

Evaluation Criteria 11

12

Measures of effectiveness (MOEs) employed in this study include total network throughput, 13

average vehicle delay (AVD), and average network speed (ANS). Total throughput denotes the 14

total number of completed vehicle trips during the simulation period. AVD is the difference 15

between actual vehicle-seconds travelled (AVT), and theoretical vehicle-seconds travelled (TVT) 16

under free-flow conditions. Two types of AVD were investigated in this research. AVD1 is 17

computed by: 18

19

AVD2 further includes “latent delay”, caused by vehicles unable to enter the network at their 20

scheduled time: 21

22

23

ANS is a measure of highway system efficiency. It is computed by summing up total vehicle-24

miles traveled (VMT), and then dividing by the sum of vehicles hour traveled (VHT, not 25

including latent delays). One of the key objectives of active traffic management (ATM) is to 26

maximize system productivity, i.e. serving the greatest VMT at the least cost to travelers (VHT). 27

Thus a change in ANS would be a good criterion for evaluating ATM strategy success. 28

29

Model Calibration 30

31

The micro-simulation software VISSIM was used to simulate performance of the freeway facility 32

under various scenarios. Fifteen simulations with random seeds were processed for each 33

scenario, to determine typical average results for each scenario. Baseline driver and vehicle 34

settings for this research were carried over from prior research [5], where the I-66 merge with 35

VA-267 was calibrated against field-measured data. Specifically, traffic flows at on-ramps, off-36

ramps and six critical locations along both roads, and traffic speeds at three critical locations 37



(one in VA-267 and two in I-66) were employed as major performance measures for the 1

calibration purpose. The I-66 VA-267 merge contains the same geometry of interest to this 2

research, and suffers severe congestion during workday peak hours. Table 1 describes driver and 3

vehicle settings for the baseline conditions (without DMC treatment). 4

5

Table 1: Parameter Settings for the VISSIM Model 6

7

Parameters Freeway Sections Merge area

Maximum Look Ahead Distance (ft) 692.65 1169.26

Number of Observed Preceding Vehicles 2 2

CC0 - Average standstill distance (ft) 4.30 9.19

CC1 - headway at a certain speed (s) 0.90 1.23

CC2 - longitudinal oscillation (ft) 11.12 7.12

CC3 - start of the deceleration process 8.00 8.00

CC9 - acceleration behavior at v~ 80 km/h (ft/s2) 4.92 4.92

Maximum Deceleration for Leading vehicle (ft/s2) 6.04 6.59

Reduction Rate for leading Vehicle (ft) 62.53 120.40

Accepted Deceleration for Leading Vehicle (ft/s2) 1.48 3.22

Maximum Deceleration for Following Vehicle (ft/s2) 5.09 3.54

Reduction Rate for Following Vehicle (ft) 16.07 27.33

Accepted Deceleration for Following Vehicle (ft/s) 4.49 0.95

Minimum Headway (ft) 3.94 1.84

Safety Distance Reduction Factor 0.14 0.23

Max. Deceleration for Cooperative Braking (ft/s) 11.35 13.75

Max. Speed Difference for Cooperative Braking (ft/s) 15.99 10.04

Max. Collision Time for Cooperative Braking (s) 7.22 9.25

8

Despite the availability of calibrated settings for the baseline scenario without DMC, adjustments 9

to these settings were desired for scenarios with DMC. Calibration under DMC conditions 10

presented a challenge, because DMC field tests are not practical at this point in time. Under 11

DMC conditions, the upstream freeway sections and merge area must all be calibrated. 12

Regarding calibration of the upstream freeway sections, drivers were assumed to have 2500 ft to 13

change lanes before the lane close location. When basic freeway segment volumes (upstream of 14

the merge) are under-saturated, as they always were in this study, this reaction distance would 15

ensure ample opportunity for lane changes. Therefore, driver behaviors in the upstream freeway 16

sections were assumed unchanged on both Routes A and B. Regarding merge area calibration, 17

the significant reduction in lane changes caused by DMC lane closure were believed to affect 18

driver behavior, thus warranting further calibration for the DMC scenario. 19

20

VISSIM employs the Wiedemann 99 model for driver behaviors. In the Wiedemann 99 model, 21

the time (in seconds) in which a driver wants to maintain a certain speed (also known as “CC1”) 22

has a significant effect on capacity, especially in high-volume situations. The higher the value, 23

the more cautious the driver is. CC1 can be derived by: 24

25



1

where stands for the safety distance (the minimum distance in meters a driver will 2

maintain while following another vehicle), indicates the average desired standstill distance 3

(meters/miles) between two vehicles and is the speed (meters per second/miles per hour). 4

5

Given the significant impact of CC1, this input parameter was analyzed thoroughly at I-495 in 6

Landover, MD. Minimum time gap was used as a surrogate measure of CC1. This section of I-7

495 is in a 3-2 configuration with 3 “express” travel lanes on the left-side of the roadway and 2 8

“local” travel lanes on the right-side. The express and local lanes are separated by double white 9

lane markers and a rumble strip. The two local lanes reduce to one lane upstream of the gore, 10

which combines the local and express lanes into a 4-lane section. This site was also relevant to 11

the research because speeds were similar on the two roadways, and no obstructions or geometric 12

features limited drivers’ ability to merge or adjust lane positions. 13

14

Cameras were installed on a bridge near the gore location; with one camera pointed upstream 15

towards the local lane reduction, and another pointed downstream at the combined four-lane 16

section. The observation period was late morning / early afternoon on a clear and sunny spring 17

day, with dry roadway conditions. Footage from the cameras was analyzed by commercial image 18

processing software. At both locations, time gaps were automatically measured between rear 19

bumper of the lead vehicle, and front bumper of the following vehicle. A single individual 20

analyzed both videos to limit measurement error. To measure minimum time gaps, the individual 21

was given instructions to only record time gaps for platoons of two or more vehicles (i.e., 22

situations where the lead vehicle limited longitudinal movement of the following vehicle). As a 23

result, there are 151, 288, 105 and 204 valid observations for downstream center lane, upstream 24

center lane, downstream right lane and upstream right lane, respectively. These time gaps are 25

illustrated in Figure 2. 26

27



1 2

Figure 2: Time gaps between vehicles on I-495 (local lane and rightmost express lane). 3

4

With this data, unequal variance t-tests were conducted, to determine whether drivers behave 5

differently before and within the merge area. The null hypothesis specified no significant change 6

in car-following time gap before and after the merge area. Welch’s two sample t-tests supported 7

this hypothesis for both local lane (p=0.841) and express lane (p=0.156). 8

9

Despite this evidence that CC1 did not significantly change while traversing a real-world merge 10

area without a lane reduction, a “conservative” value of CC1 (i.e., 1.1 seconds) was used for 11

most DMC-scenario simulations in this research. This value produces lower capacities than the 12

value associated with, and calibrated for, standard freeway segments (i.e., 0.90). Therefore 13

benefits of DMC detailed later in this paper might in fact be understated, according to the I-495 14

analysis of CC1. And although 1.1 seconds was used for CC1 in most DMC simulations, a 15

number of extra simulations were performed with a wider range of CC1 values, to confirm the 16

DMC benefits. 17

18

MICRO-SIMULATION ANALYSIS 19

20

Simulation Results 21

22

The simulation period for each scenario was 8400 seconds, including 1200 seconds of 23

initialization “warm-up” time. In each scenario, traffic demands on both roads were assumed 24

fixed over the entire simulation period. Each scenario was simulated 15 times with random 25

seeds. Preliminary testing showed that simulation results would converge after approximately 9-26

10 runs, in most scenarios. 27

28



Tables 2-4 present the network-wide average vehicle delay savings (AVD1 for baseline minus 1

DMC conditions), average network speed increase (ANS for DMC minus baseline), and 2

throughput increase, respectively. The “network” extended 2 miles upstream and 1 mile 3

downstream of the merge point, illustrated earlier in Figure 1. Tables 2-4 shows that under high 4

demand combinations, DMC can reduce delay by more than 90%, increase speed by more than 5

80%, and increase throughput by more than 10%. However when demand on Route B gets too 6

low, DMC may reduce delay by less than 10%, with a negligible impact on speed and 7

throughput. 8

9

Table 2: Network-Wide Average Vehicle Delay (AVD1, seconds) Savings under DMC 10

11

Route B Route A

2500 2800 3100 3400 3700 4000 4300 4600

3000 0.48 0.49 0.49 0.53 0.51 0.57 0.58 0.53 9.0% 8.9% 8.5% 8.6% 7.9% 8.2% 7.7% 6.4%

3200 0.78 0.82 0.87 0.9 1.01 1.14 1.29 2.06 13.2% 13.4% 13.5% 13.4% 14.0% 14.8% 15.3% 20.6%

3400 1.22 1.23 1.28 1.39 1.59 1.87 2.42 4.6 18.4% 18.1% 18.0% 18.6% 19.9% 21.6% 24.8% 36.2%

3600 1.71 1.88 1.91 2.72 2.43 2.98 5.49 21.38 23.1% 24.3% 23.9% 30.0% 26.8% 29.8% 42.1% 72.0%

3800 2.74 5.88 7 8.65 6.88 26.48 42.69 88.95 31.4% 48.9% 52.4% 56.6% 49.9% 78.5% 84.6% 91.2%

4000 24.76 44.54 35.04 59.16 69.06 83.5 99.48 111.18 79.6% 87.3% 83.9% 89.5% 90.5% 91.7% 92.5% 92.7%

4200 83.46 91.21 89.06 102.04 101.81 107.54 112.65 119.1 92.5% 93.0% 92.6% 93.3% 93.1% 93.1% 93.1% 92.9%

4400 119.45 115.46 114.49 111.48 114.15 113.93 115.11 121.88 94.2% 94.0% 93.8% 93.5% 93.4% 93.2% 92.9% 92.8%

4600 124.42 122.58 119.66 117.89 115.97 116.14 119.01 122.02 93.9% 93.7% 93.5% 93.3% 93.0% 92.8% 92.6% 92.3%

12

13

14

15

16

17

18

19

20



Table 3: Network-Wide Average Speed Increase (mph) under DMC 1

2

Route B Route A

2500 2800 3100 3400 3700 4000 4300 4600

3000 0.17 0.17 0.17 0.18 0.17 0.19 0.2 0.17 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.4% 0.3%

3200 0.28 0.3 0.31 0.31 0.36 0.4 0.45 0.7 0.5% 0.5% 0.5% 0.5% 0.6% 0.7% 0.8% 1.2%

3400 0.44 0.43 0.45 0.49 0.56 0.66 0.84 1.57 0.8% 0.7% 0.8% 0.9% 1.0% 1.2% 1.5% 2.8%

3600 0.61 0.67 0.68 0.93 0.85 1.04 1.88 6.3 1.1% 1.2% 1.2% 1.6% 1.5% 1.9% 3.4% 12.5%

3800 0.97 1.91 2.25 2.61 2.2 7.38 11.37 20.92 1.7% 3.4% 4.1% 4.8% 4.0% 14.8% 24.9% 58.5%

4000 6.26 11.19 8.96 14.57 16.86 19.87 22.96 24.81 12.2% 24.2% 18.5% 34.1% 41.8% 53.5% 67.7% 78.1%

4200 19.33 20.92 20.42 23.46 23.35 24.53 25.31 26.12 50.8% 57.5% 55.5% 69.7% 69.3% 75.7% 80.5% 86.0%

4400 26.22 25.81 25.66 25.17 25.65 25.55 25.67 26.45 84.7% 82.4% 81.7% 79.2% 82.1% 81.9% 83.0% 88.5%

4600 27.07 26.83 26.36 26.12 25.81 25.81 26.09 26.36 90.8% 89.4% 86.6% 85.4% 83.7% 84.0% 86.2% 88.6%

3

4

Table 4: Network-Wide Total Throughput Increase (number of vehicles) under DMC 5

6

Route B Route A

2500 2800 3100 3400 3700 4000 4300 4600

3000 1 0 -4 -3 -11 -4 1 -3 0.0% 0.0% 0.0% 0.0% -0.1% 0.0% 0.0% 0.0%

3200 -1 -1 -6 -5 -12 -6 0 -6 0.0% 0.0% 0.0% 0.0% -0.1% 0.0% 0.0% 0.0%

3400 -2 -3 -9 -7 -15 -7 -4 -7 0.0% 0.0% -0.1% -0.1% -0.1% 0.0% 0.0% 0.0%

3600 -5 -5 -9 -10 -16 -11 -1 95 0.0% 0.0% -0.1% -0.1% -0.1% -0.1% 0.0% 0.6%

3800 -15 0 2 16 1 113 220 497 -0.1% 0.0% 0.0% 0.1% 0.0% 0.7% 1.4% 3.1%

4000 94 210 199 328 400 512 689 894 0.7% 1.6% 1.4% 2.3% 2.7% 3.3% 4.3% 5.5%

4200 568 622 669 763 847 955 1101 1315 4.4% 4.7% 4.8% 5.3% 5.7% 6.2% 6.9% 8.1%

4400 1086 1057 1157 1171 1264 1360 1465 1725 8.5% 7.9% 8.4% 8.1% 8.5% 8.8% 9.2% 10.6%

4600 1470 1552 1582 1606 1653 1752 1932 2097 11.5% 11.7% 11.4% 11.2% 11.1% 11.3% 12.2% 12.9%

7



Paired t-tests were conducted to determine whether average delays significantly differed under 1

baseline and DMC conditions. These t-tests were based on the average results of 15 simulation 2

runs (having different random number seeds) for both baseline and DMC conditions. The t-tests 3

showed that all values in Table 2 are statistically significant, which means DMC would improve 4

network performance for all traffic demand combinations in Table 2. 5

6

Figure 3 illustrates the network-wide savings of average vehicle delay (AVD2) when accounting 7

for latent delay. After implementing DMC, average delay per vehicle was reduced by up to 540 8

seconds (around 98%). Although the volumes (4600 vph and lower) used in data collection 9

would not cause oversaturation on all 2-lane basic freeway segments, oversaturation in the 10

baseline scenario was nonetheless ensured by friction between weaving vehicles. Thus the fact 11

that AVD2 savings (Figure 1, Figure 3) exceeded AVD1 savings (Table 2) implies that DMC 12

would reduce and/or eliminate the oversaturation that causes latent delay. 13

14

15

Figure 3: Network-wide reduction of average vehicle delay (AVD2). 16

17

Figure 4 compares traffic density in the merge area between baseline and DMC conditions. 18

Densities under baseline conditions were averaged across lanes 2, 3 and 4 (rightmost and 19

leftmost lanes excluded), between 0 ft and 300 ft downstream of the merge gore. This area 20

contained the highest densities during simulation. Unlike base conditions, densities of the DMC 21

conditions were measured between 0 ft and 300 ft downstream of the lane drop point. This is the 22

section where the highest densities were observed. This is sensible because mainline traffic was 23

not allowed to use the center lane, which prevented equal utilization of all lanes. Lane utilization 24

starts to balance at the end of the lane-closing section (the end of the lane drop point); which 25

requires lane changes, and thus relatively higher density. Under low demand combinations, 26

Figure 4 shows DMC control modestly increasing merge-area density (removing one lane from a 27



low-volume freeway). However under high demand combinations, Figure 4 shows DMC control 1

substantially reducing traffic densities. 2

3

Figure 4: Traffic density analysis in the merge area. 4

5

To better understand the impact of lane changes on capacity, flow rates within 300 ft after the 6

lane drop were investigated. Flow rates under free-flow conditions were calculated by dividing 7

combined demand from Routes A and B by the total number of lanes (4 in the measured section). 8

Figure 5a presents lane-drop-area flow rates for 4600 vehicles per hour (vph) on Route A, and 9

various demands on Route B. Figure 5a indicates that Route B flow rates were very similar under 10

DMC and free-flow conditions, for all considered traffic demands. However under baseline 11

conditions, when demands on Route B exceed 3600 vph, flow rates remain capped at around 12

2000 vphpl. This implies that without DMC control, merge-area capacity is constrained by lane 13

changing disturbances. Figure 5b presents lane-drop-area flow rates for 4600 vph in Route B, 14

and various demands on Route A. Figure 5b shows nearly identical Route A flow rates between 15

DMC and free-flow conditions, with baseline flow rates consistently 150-200 vphpl lower. 16

17 (a) Fixed demand on Route A at 4600 vph (b) Fixed demand on Route B at 4600 vph 18

Figure 5: Flow rate analysis in the merge area. 19



Due to the improvement in merge area operations, DMC can provide significant benefits 1

upstream of the merge area, on Route B. Figure 6 illustrates the upstream change in traffic 2

densities. Traffic densities were measured on Route A from the lane closure point to 2100 ft 3

upstream, and on Route B from the merge gore to 2100 ft upstream. Figure 6a reveals that Route 4

B traffic densities significantly decreased when demands exceeded 3600 vph. When demands on 5

Route B exceeded 3800 vph, traffic densities reached the critical line of 45 vpl (Level of Service, 6

LOS F) where queues tend to form. However, LOS remained at E even when demand on Route 7

B reached 4600 vph (2300 vphpl). Moreover, the Route B mobility improvement did not 8

significantly compromise mobility on Route A, as shown in Figure 6b. 9

10

11 (a) Traffic density on Route B 12

13

14 (b) Traffic density on Route A 15

16

Figure 6 Density analysis in the upstream area. 17



Sensitivity Analysis 1

2

As mentioned in the Methodology section, desired following distance has a significant 3

freeway capacity. Even though the authors selected a relatively conservative value of 4

1.1 seconds) in this study, it was desirable to further assess the impact of this important 5

parameter. For the scenario where traffic demands on Routes A and B are both 4000 vph, 6

values of 0.9, 1.0, 1.1, and 1.2 seconds were simulated in the merge area. Other 7

settings were kept constant as described in Table 1. Preliminary tests showed that 10 8

were sufficient to derive stable performance measures. Hence, each scenario was 9

10 runs with random seeds. 10

Table 5 presents the mean and standard deviation of traffic delay, speed, and throughput under 11

four levels of CC1. Operational performance deteriorated across the board (i.e. for all 3 MOEs, 12

and for both baseline and DMC scenarios) as CC1 increased. The rate of operational degradation 13

was most pronounced between CC1 values of 1.1 and 1.2. This is likely due to the typical 14

relationship between operational performance and degree of saturation, as discussed in the 15

Highway Capacity Manual. At low degrees of saturation, traffic delays tend to increase at a 16

constant, linear rate. At high degrees of saturation, traffic delays tend to increase at an 17

accelerated, exponential rate [6]. With the given traffic volume, the saturation rate under the base 18

condition is high thus operations become very sensitive to the headways induced by CC1. 19

However, under the DMC condition, the capacity in the merge area was greatly improved. Thus, 20

the change of traffic delay along with the increase of CC1 is relatively moderate. 21

22

Table 5: Network Performance under Four Levels of CC1 23

(Note: values in brackets indicate standard deviation) 24

CC1 value CC1=0.9 CC1=1.0 CC1=1.1 CC1=1.2

Operation type BASE DMC BASE DMC BASE DMC BASE DMC

Average delay (seconds)

31.8

(28.3)

7.6

(0.1)

35.9

(31.1)

7.8

(0.2)

48.0

(31.9)

8.7

(0.4)

70.8

(21.8)

19.2

(9.5)

24.2 (76.1%) 28.0 (78.1%) 39.3 (82.0%) 51.6 (73.0%)

Average speed

(mph)

50.6

(6.8)

57.0

(0.1)

49.6

(7.4)

56.9

(0.1)

46.7

(7.6)

56.7

(0.2)

41.1

(4.7)

53.3

(2.3)

6.4 (12.7%) 7.4 (14.9%) 10.0 (21.4%) 12.2 (29.6%)

Throughput

(number of vehicles)

15821

(164)

15988

(126)

15794

(131)

15989

(127)

15735

(117)

15991

(127)

15607

(135)

15957

(141)

167 (1.1%) 195 (1.2%) 256 (1.6%) 350 (2.2%)

25

Table 5 also presents the percentage difference in performance measures between baseline and 26

DMC operation, for each level of CC1. Results show that regardless of the CC1 value, average 27

delay decreased by over 75%, speed increased by 12-30%, and throughput increased by 1-2%. 28

Therefore, the DMC benefits summarized in the previous subsection appear reasonable and 29

achievable. 30



1

CONCLUSIONS 2

3

The authors investigated performance of the DMC strategy on a hypothetical road geometry; 4

involving a 2-lane freeway merging with a 3-lane freeway, and dropping into 4 lanes. Traffic 5

demands on the minor road were varied from 3000 to 4600 vph (increments of 200), and varied 6

on the major road from 2500 to 4600 vph (increments of 300), for a total of 72 demand 7

combinations. The major findings can be summarized as follows: 8

9

Implementing the DMC strategy produced benefits at almost all demand combinations, in 10

terms of vehicle delays and speeds 11

When traffic demand on the minor road reaches 1900 vphpl, the benefits of applying 12

DMC become both statistically and practically significant 13

DMC control reduces lane changes in the merge area; therefore increasing capacity, and 14

delaying the formation of bottlenecks 15

16

Note that a statistically significant improvement does not necessarily imply an improvement 17

significant enough to justify deployment. Agencies may set up their own criteria based on a 18

comprehensive evaluation of all benefits, and on benefit-to-cost ratios. 19

20

The research presented was constrained to a specific set of geometric and traffic conditions, and 21

the study was solely based on simulation work. Although DMC has not been implemented in the 22

US, field experiments are desired to validate the proposed strategy. Moreover, the current study 23

closed a lane approximately 1000 ft upstream of the merge gore, and informed drivers 2500 ft 24

upstream. The next phase of this study will investigate varying lane closing locations, and 25

varying distances to inform drivers. Furthermore, the current research assumes a 100% 26

compliance ratio when closing a lane. In the next phase, various compliance ratios shall be 27

evaluated. 28

29

ACKNOWLEDGMENTS 30

31

The authors would like to thank the National Research Council (NRC) for sponsoring this 32

research. The authors appreciate VDOT support in providing data and being open to future 33

research. The authors also are grateful for the contribution of Professor Wenlong Jin and his PhD 34

candidate Qijian Gan (University of California, Irvine), Professor Daniel Dailey (FHWA visting 35

scholar from the University of Washington), Taylor Lochrane, and Cory Krause (of FHWA 36

R&D) for their suggestions and guidance on this research. 37

REFERENCES 38

39



[1] Mirshahi, M., J. Obenberger, C. Fuhs, C. Howard, R. Krammes, B. Kuhn, R. Mayhew, M. 1

Moore, K. Sahebjam, C. Stone, and J. Yung, 2007. Active Traffic Management: The Next Step in 2

Congestion Management. Report No. FHWA-PL-07-012. Alexandria, VA: American Trade 3

Initiatives for Federal Highways Administration. 4

[2] Helleman, B., “Managed Motorways in the Netherlands” Centre for Transport and 5

Navigation. Presentation to the FGD Scan Team. June 8, 2010. 6

[3] Parsons Brincherhoff (PB), Texas Transportation Institute (TTI), 2009. Screening Criteria 7

for Managed Use Lane Projects. NYSDOT research report. 8

[4] Texas Transportation Institute (TTI), 2011. General Guidelines for Active Traffic 9

Management Deployment. UTCM Project # 10-01-54. 10

[5] Lu, X.Y., J. Lee, D. Chen, J. Bared, D.J. Dailey, S.E. Shladover. Freeway Micro-simulation 11

Calibration: Case Study Using Aimsun and VISSIM with Detailed Field Data. Transportation 12

Research Board 92nd Annual Meeting Compendium of Papers, January, 2014. 13

[6] Highway Capacity Manual 2000 (-24). Washington, DC: Transportation Research 14

Board. 15

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TRAFFIC PERFORMANCE ANALYSIS OF DYNAMIC MERGE CONTROL USING MICRO-SIMULATION

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