Ramp Meter and Terminal Signal Integration Through Sharing of Unprocessed Loop Data

Ramp Meter and Terminal Signal Integration through sharing of unprocessed loop data

Matt Beaulieu

A research paper submitted in partial fulfillment of the Requirements for the degree of Master of Science in Civil Engineering University of Washington 2007

Program Authorized to Offer Degree: Department of Civil and Environmental Engineering University of Washington

Abstract

Ramp Meter and Terminal Signal Integration through sharing of unprocessed loop data

Matt Beaulieu

Chair of the Supervisory Committee: Assistant Professor Anne V. Goodchild Civil and Environmental Engineering

While the benefits of coordination between ramp meters and upstream traffic signals have been well documented, many agencies have yet to implement coordination. One barrier to this for agencies with extensive existing systems is the cost associated with modifying or replacing existing software and hardware. This research applies the lessons of existing coordination research to a less intrusive coordination method, modeling the benefits of sharing loop data between the ramp meter and the upstream signal. Recommendations for implementation site criteria and controller behaviors are presented in the research. 1 TABLE OF CONTENTS

Page TABLE OF CONTENTS...... 1 LIST OF Tables and Figures...... 3 1 – Introduction...... 4 2 – Background...... 4 2.1 Overview of General Ramp meter control theory...... 4 2.2 Benefits of Ramp Meter-Upstream Signal Coordination...... 2 2.3 WSDOT’s Fuzzy meter algorithm...... 2 2.4 Upstream Signal Control...... 4 2.5 Vissim...... 4 3 – Literature Review...... 4 4 – Evaluation criteria...... 7 5 – Model development...... 8 Baseline model development...... 8 Coordinated model development...... 12 6 – Data...... 13 7 – Results and Discussion...... 14 8 – Future Research and Conclusions...... 16 Works Cited...... 18 Appendix A – Output data from evenly distributed demand scenario...... 1 Appendix B – Output data from heavy left turn demand scenario...... 2 2 LIST OF Tables and Figures

Table / Figure Number Page Table 1; Fuzzylogic rule description 3 Figure 1, Vissim volume inputs for the evenly distributed scenario 13 Table 2: Average count of blocking vehicles 14 Table 3:, Meter performance 15 Table 4 :Mainline and Arterial performance 15

: 3 1 – Introduction This project will research the potential benefits of sharing induction loop data between a ramp meter controller and the adjacent ramp terminal signals. While the individual benefits of both ramp metering and of signal coordination have been well documented, and existing publications document the benefit of integrating ramp metering with arterial signal control, many agencies have not implemented the change. One reason is the cost of upgrading existing equipment, and the other is the political challenge of integrating two systems that have historically been separate. Even when both systems are operated by the same agency, different groups within the agency typically run them. This provides political challenges for coordination that add to the technical and financial hurdles and leads to the premise that near-term success will require simple detector data sharing rather than higher level coordination. Although the results of this research have relevance beyond WSDOT’s jurisdiction, mimicking WSDOT’s hardware and software in the model provides a realistic case study. By adding shared detector data to a model of existing controller behavior in Vissim (a leading micro simulation program) this research evaluated the benefits of coordination at the loop data level with WSDOT’s fuzzy meter algorithm.

2 – Background

2.1 Overview of General Ramp meter control theory Ramp meters, typically located on urban onramps, provide benefits to mainline freeway traffic by decreasing the impact of merging vehicles. One way that this is accomplished is by storing vehicles on the ramps, helping to maintain mainline volumes in the upper portion of the speed-flow curve, but constraints on ramp storage severely limit both the duration of time when the meter’s release rate is less than the arrival rate and the magnitude of the difference. The second benefit of ramp meters comes from smoothing the flow of vehicles attempting to merge with the mainline. In particular, ramp meters break up the platoons of vehicles released from traffic signals, increasing the chance that each vehicle can find a gap in mainline traffic and merge smoothly. In order to 4 accomplish this without impacting arterial performance, sufficient unused storage must be available on the ramp to accommodate all the vehicles from the intersection.

2.2 Benefits of Ramp Meter-Upstream Signal Coordination The benefits of coordinating ramp meters and the upstream traffic signals come from the ability to optimize the combined performance of the two systems, eliminating conflicts between them. Without coordination, large platoons generated by the upstream signal can overwhelm the available storage of the ramp meter, resulting in a queue that extends through the intersection onto the arterial. At best, this means that the signal’s green time is inefficiently utilized and when combined with aggressive or inattentive drivers, exceeding demand for ramp storage can result in vehicles blocking the intersection after their phase ends. With a coordinated system, this situation can be predicted and prevented with a combination of two basic approaches. When a coordinated system detects that ramp meter storage is nearing capacity and that an upcoming signal phase is likely to release more vehicles than the storage can accommodate, the meter and the signal can respond individually or together. The ramp meter can increase its release rate to clear storage space on the ramp, the traffic signal can adjust timing to store vehicles before they reach the ramp or both systems can respond.

2.3 WSDOT’s Fuzzy meter algorithm WSDOT’s fuzzy meter algorithm uses a system of heuristic evaluations and weighted rules to balance between the conflicting goals of ramp metering. As local and downstream mainline conditions deteriorate, rules evaluating them push the metering rate down, storing vehicles on the ramp and as a ramp’s storage is filled by its queue, other rules push the metering rate up, releasing vehicles with decreasing headways. 1 Originally implemented in OpenVMS for the VAX operating system the fuzzy logic ramp controller was duplicated in VAP for use in the microscopic modeling program VISSIM in 2005 with further refinements in 2007. Use of a modified version of this

1 Taylor, et al, 2000, A programmer’s guide to the Fuzzy Logic ramp metering algorithm: software design, integration, testing and evaluation Washington State Transportation Center (TRAC). 5 code within a VISSIM model allows for a reasonable approximation of WSDOT’s ramp meters in the field2. The twelve rules evaluated by the fuzzy logic controller are listed in table 1, below. Adding an additional rule to the algorithm would be a preferred component of coordination, but including data from the upstream signal as part of the Advanced Queue Occupancy data in the evaluation of rule 12 serves a similar purpose without requiring changes to the non-operator tunable sections of the VAX fuzzy logic controller. In most cases, WSDOT’s meters include detection in dedicated turn bays that feed them, inherently providing a limited level of coordination. RULE RULE DESCRIPTION 1 IF Local Occupancy is VB then meter rate is VS 2 IF Local Occupancy is B then meter rate is S 3 IF Local Occupancy is M then meter rate is M 4 IF Local Occupancy is S then meter rate is B 5 IF Local Occupancy is VS then meter rate is VB 6 IF Local Speed is VS AND Local Occupancy is VB then meter rate is VS 7 IF Local Speed is S then meter rate is S 8 IF Local Speed is B then meter rate is B 9 IF Local Speed is VB AND Local Occupancy is VS then meter rate is VB 10 IF Downstream Speed is VS AND Downstream Occ is VB then meter rate is VS 11 IF Queue Occupancy is VB then meter rate is VB 12 IF Advanced Queue Occupancy is VB then meter rate is VB Table 1; Fuzzylogic rule description3

2.4 Upstream Signal Control Ramp terminal signals are those traffic signals at the intersection of an arterial and a freeway’s on-ramp and/or off-ramp. WSDOT typically retains operational control of ramp terminal signals, although in the case of coordinated arterials run by local jurisdictions, exceptions are made. Although WSDOT’s ramp terminal signals include a mix of actuated, semi-actuated, fixed time, coordinated actuated, and coordinated fixed time signals, the operation during peak hours can be reasonably approximated 2 Robinson-McCutchen, 2007 Optimizing the use of Fuzzymeter and ramp meters using Vissim CEE 600 research report, University of Washington, 2007 3 Wilson, 2006, Understanding the Fuzzy Logic ramp metering algorithm: following input data to the implemented meter rates, WSDOT 6 using fixed cycle length and detection for protected left turns. A simple controller was developed in VAP to allow the timing plan to be changed based on loop data from the ramp. This feature is not natively supported by the controller models provided with VISSIM, but that is achievable in the field via a transit priority controller module.

2.5 Vissim

Vissim is a commonly utilized microscopic traffic simulation tool developed by PTV Vision. It models individual driver behavior and vehicle performance, providing for realistic representations of traffic flow, car following, merges and lane changes. The program allows for significant adjustment to car following and lane change or merge behaviors as part of the model calibration process. In particular, the constants associated with the Vissim implementation of the Wiedemann 99 model affect roadway capacity and headways, which in turn impact lane change and merge maneuvers along with a number of lane change aggressiveness parameters. To date, there has been discussion but unfortunately little research into standardizing these critical parameters at a regional or state level.

3 – Literature Review

A literature review was conducted to locate existing records of research on the topic of coordination between ramp meters and upstream traffic signals.

Han and Reiss 4 develop a two-stage variable metering rate to accommodate the variation in demand as the upstream traffic signal changes and determine the optimal time to switch between the two rates in order to minimize delay.

4 Han and Reiss (1994) Coordinating ramp meter operation with upstream intersection traffic signal Transportation Research Record #1446 7 Tian, et al 5 developed a generalized system for coordination between ramp meters and upstream signal controllers and applyed the system to a case study using a modeled Diamond Interchange. They utilized the ALINEA traffic responsive ramp-metering algorithm as developed by Papageorgiou et al in 1991 and use VISSIM as a modeling platform. Their research did not address using data from the traffic signal to influence metering rates.

Further research by Tien et. al 6 continues to expand on this work, addressing the benefits with regard to ramp meter queue flushing.

Tian’s work provides some of the most detailed analysis available for coordination between ramp meters and the traffic signals of a diamond interchange. This primarily focuses on fixed rate ramp meters or on ramp meters using a mainline responsive algorithm (ALINEA) with only a binary response to ramp queue; automatically disabling the ramp meter and flushing the queue when storage is exceeded. Tian’s dissertation7 also includes a brief analysis of mainline benefits from the responsive system without queue flush, stating:

“Analysis results indicated that ramp-metering operations with queue flush increased the likelihood of freeway breakdown, and the freeway never recovered once breakdown occurred for the case demonstrated. Traffic-responsive ramp metering provided the flexibility of adjusting ramp metering rate and showed more effective in preventing freeway breakdown, compared to fixed-metering operation. Therefore, a conclusion might be reached based on the analyzed case that keeping ramp metering in operation for as long as possible is a preferred operating strategy over metering with queue flush.

5 Tian, Z (2005) An integrated control system for coordinating ramp metering and surface street signal operations, world congress on intelligent transport systems (12th: 2005: San Francisco, Ca) 6 Tian, Z (2005) Integration of diamond interchange and ramp metering operations, Transportation Research Record #1925 7 Tian (2004) Development and evaluation of operational strategies for providing an integrated diamond interchange ramp metering control system, Texas A&M P.133 8 Further more, traffic-responsive ramp metering, when properly designed, is more effective than fixed metering in preventing freeway breakdown.”

Tian’s dissertation also addresses the issue of calibration of driver behavior to develop realistic merging behavior, suggesting the use of the Wiedemann 74 driver behavior model rather than the Wiedemann 99 car following model utilized in this research effort.

Tuning and calibration of the Wiedemann 99 car following model is discussed in Woody’s 2006 research report8, indicating that the parameters most affecting merge behavior are CC1 and acceptable deceleration rates. Additionally, CC1 is also mentioned as being the primary determinant of roadway capacity at high speeds.

8 Woody, Tony (2006) Calibrating freeway simulation models in Vissim CEE600 final research report, University of Washington Spring 2006. 9 4 – Evaluation criteria

Because WSDOT uses an algorithm that is responsive to ramp queues in addition to both local and downstream mainline conditions, additional analysis is warranted before the conclusions drawn from existing research can be applied to the existing systems in Washington State. Washington’s system allows for queue dumps only when initiated by an operator, an option utilized so rarely as to be reasonably discounted. Tian’s research indicates that much of the benefit of coordination comes from decreasing the frequency of these queue dumps, but provides reason to believe that heavily oscillating meter rates also have a disproportionate impact. Because each merging vehicle requires a gap in mainline traffic, as the arrival rate increases the likelihood of sufficient gaps decreases. When a suitable gap is unavailable, a vehicle must either abort the merge process or utilize a less desirable gap, causing conflict within the mainline traffic stream.

Tian’s operating objectives, as listed in his dissertation and provided below, provide a useful starting point for evaluation criteria:

1. Maximize freeway mainline operations at free-flow conditions and minimize freeway breakdown; 2. Minimize ramp queue flushes and maximize normal ramp-metering operation; 3. Minimize ramp queue spillback into the diamond interchange signals; 4. Control vehicle entries to the ramp meters through proactive signal control at the diamond interchange; 5. Store excessive demands and queues in the most advantageous locations so that all the queue storage spaces can be efficiently used without interfering with freeway mainline and adjacent arterial signal operations.

Evaluation criteria based on mainline performance can be carried over, but a direct comparison is not possible because the magnitude of changes is dependent on link 10 length. The difference in metering algorithms eliminated the use of queue dumps as a measure of performance. The following performance metrics were used.

 Mainline Freeway Throughput & Delay  Arterial Throughput & Delay  Ramp Throughput & Delay  Number of left turning vehicles stopping within the intersection  Number of cycles in which left turning vehicles stopped within the intersection

The mainline and arterial performance metrics provided the initial basis of comparison, with ramp throughput included as a check to insure that the net metering rate was consistent between models. The number of left turning vehicles stopping within the intersection and the number of cycles in which left turning vehicles stopped within the intersection measured the direct impact of the change coordination and provided a second pair of key performance metrics.

5 – Model development

Baseline model development A section from an existing freeway performance model was modified to include the upstream ramp terminal signal for the meter to be evaluated. This section of roadway includes a fairly short auxiliary lane for weaving, but in the interest of creating a more general model, the exit ramp was eliminated. This resulted in a merge section 900 ft long, well beyond the minimum typically used in Vissim Models to allow for merging, before returning to a 4 lane cross section. In order to prevent vehicles from changing lanes from the mainline into the merge lane, the lane was coded as closed to all vehicles. This closure required vehicles forced into the lane by a connector (the ramp) to start looking for a gap to move left immediately and prevented any voluntary lane changes into the lane by vehicles on the mainline. Although basing the model geometries on a real world location assisted with the qualitative analysis of flow 11 characteristics, the model is intended to serve as a generalized application. With this in mind, the HOV bypass on the ramp was also eliminated as it introduced an unnecessary variable into the signal and ramp behavior.

Travel time segments were set up on the mainline and cross streets and a detector was placed on the ramp to monitor passage from the ramp meter. Vehicles stopping in the intersection during a left turn were counted using an additional travel time segment, as Vissim provides a count of vehicle stops and stop delay as part of the travel time reporting. These were recorded at a level of aggregation equal to the cycle time of the arterial signal to provide the ability to associate each stop with the appropriate cycle of the traffic signal. Detection for the ramp meter was placed on the ramp and mainline, as well as in the left turn bay on the arterial. The ramp queue loop was placed at the point where ¾ of the ramp storage would be used and the advance queue loop was placed at to detect left turn queues when they exceeded 2/3 of the turn bay capacity.

Demand volumes were selected to ensure that the ramp merge would serve as a primary bottleneck if unmetered and were constant over the 30-minute evaluation period with a thirty-minute seedtime. Arterial and ramp demand were held constant during the seed period, but mainline volumes were decreased slightly to provide realistic meter queue development without risk of mainline breakdown.

In order to provide for an adjustable signal-timing plan within Vissim, a simple pretimed signal controller was developed in VAP. Although most ramp terminal signals in areas with ramp metering are actuated or actuated and coordinated depending on the time of day, use of a pretimed signal was a reasonable approximation of the constrained cycle times and behavior seen during heavy peak traffic. This allowed for a uniform phase time adjustment in the modified version. Initial phase times were set to allocate signal greens proportional to demand. Use of a VAP controller with a user editable switch to enable or disable the alternate timing plan allowed the same code to control signals in both the base model and the modified version. Because a variety of 12 cycle lengths were evaluated for the traffic signal, the cycle length used for the measurement period to trigger the alternate cycle timing in the coordinated model varied as well. .

The ramp-metering algorithm implemented in the base model was a lightly modified version of the one ported to VAP by Robinson-McCutchen as part of her 2007 research project with only a few significant edits beyond tuning of those parameters typically considered user adjustable. Provision was made for use of data from a second downstream location, fuzzy meter rate centroid calculation was automated, support for Vissim’s signal interstages was added and an enable/disable parameter was included to help with calibration.

Two adjustments to the code significantly deviated from the original VAP implementation: Use of Vissim interstages with a yellow-red time and implantation of the MaxMeterRate and MinMeterRate parameters used in the VAX based system outside the fuzzy meter module. Although most American drivers are unfamiliar with the yellow-red indicator, which precedes green in some European countries, inclusion of this option provided more realistic driver behavior in the model. The brief cycle time on ramp meters allows drivers to anticipate signal changes and respond more quickly than would be expected at a traditional signal. WSDOT studies9 have indicated that although the metering algorithm can be set to provide a metering rate in excess of 18 vehicles per minute, driver and vehicle characteristics in the field limit the functional capacity to roughly 900 vehicles per hour per lane at one car per green. Vissim allows for adjustment of driver behaviors but does not allow a specific subset of signals to have different driver behavior, making this workaround useful. Implementation of the MaxMeterRate and MinMeterRate parameters in the VAP code more accurately modeled the current performance of WSDOT’s meters, as although the fuzzy meter module calculates metering rates outside these parameters, the outgoing rate is adjusted

9 Neel, Paul, WSDOT. 2007 Personal discussion regarding historical meter performance at Front Street. 13 by the central VAX computer to fit within the boundaries before being sent to the ramp meters.

The Wiedemann 99 driver behavior model was used to model driver behavior, as it provided increased capability for calibration when compared to the Wiedemann 74 model. Initial efforts to calibrate the model were based on a section of freeway without merge or diverge segments and significant alterations to the two primary parameters of the car following model were made. However, when a merge was added these driver behavior changes resulted in unrealistic merge behavior, with a significant number of vehicles failing to merge successfully during the majority of model runs. Vehicles attempting to merge were unable to locate suitable gaps in mainline traffic, and due to Vissim’s handling of vehicle routes, would come to a stop at the end of the merge section, sitting for 180 seconds before being removed from the simulation run. A number of calibration adjustments were implemented individually with limited success. The merge problem was partially alleviated increasing the aggressiveness of necessary lane changes, setting the accepted deceleration of both the merging car and the car it merges in front of to 16.1 ft/sec2 at the final emergency stop point. This brought the merge behavior more closely in line with Vissim’s collision avoidance behavior. Even with this change, many vehicles were unable to merge and limited observations indicate that model decelerations rates rarely exceed 6 ft/sec2. The merge problem was partially addressed by returning the general car following driver parameters to the PTV-Vision defaults. Based on Vissim documentation, three additional parameters were adjusted: Following Variation (CC2) and the positive and negative following thresholds (CC4, CC5). This increased the variability in headways while in car following mode and provided a minor improvement.

An additional work-around was implemented to adjust for the difference between the Wiedemann lane change modeling and real world driver behavior. Although merging drivers have the ability to accelerate or decelerate to move into an available gap, Vissim’s lane changing model allows only deceleration. Placing a “reduced speed 14 zone” with a higher desired speed range in the merge lane resulted in merging vehicles both accelerating past vehicles in the adjacent lane and decelerating to merge behind them. In concert with the driver behavior changes, this resulted in successful merge behavior for nearly all vehicles in each simulation, eliminating the need for vehicle removal in the majority of runs and limiting it to a small number of vehicles in those model runs where it did occur.

Coordinated model development

Because this research was intended to focus on a low-level integration methodology using existing loops, controllers and controller hardware, the changes between the baseline model and coordinated model were kept to a minimum. The VAP code running the arterial signal included monitoring of occupancy of a loop adjacent to the queue loop on the ramp, and in the coordinated model, evaluated the average occupancy on this loop at a frequency equal to the standard cycle length of the arterial signal.. If the average occupancy was greater than 35%, indicating a moving or standing queue, the signal would switch to a second timing plan. The second timing plan directed the signal to operate at a cycle length of one half, decreasing signal efficiency but decreasing the size of platoons entering the ramp.

6 – Data The test section used for this evaluation had ramp demand near the maximum functional flow rate that a single meter can process and mainline demand slightly below capacity. 15

Figure 1, Vissim volume inputs for the evenly distributed scenario

The resulting volume on the downstream four-lane freeway section was close to the maximum capacity for the driver behavior parameters used. This was intended to provide conditions where freeway breakdown would be dependent on merge related conflicts. Although this model is a generalized representation, through and turning movements on the arterial were allocated proportional to those recorded at a ramp terminal signal in Seattle during the AM peak for the balanced model runs and were skewed in the direction of a higher left turn volume for the uneven demand runs.

In order to provide a reasonable sample size, thirty separate pairs of Vissim runs were completed, each using a different seed for Vissim’s randomizing function. Because Vissim assigns vehicles to driver and performance distributions, routes and arrival patterns based on the seed values upon vehicle creation, use of identical seed values ensures that the baseline and modified models differ only in aspects resulting from the change in signal timing. 7 – Results and Discussion

The direct results expected from the cycle change alterations were a decrease in left turn vehicles stopping in the intersection and a decrease in the variability of the metering 16 rate. As can be seen in the tables below, this is reflected in both the number of vehicles stopping and the number of affected cycles.

Vehicles blocking intersection Heavy left turn volume Even demand left stops affected cycles left stops affected cycles Baseline avg 7.33 3.30 2.44 1.13 Modified avg 5.19 2.67 3.26 1.53 Difference -2.140 -0.630 0.83 0.40 Confidence 0.97 0.90 0.61 0.75 Table 2: Average count of blocking vehicles and cycles affected in 30 runs of 30 minutes each

For the model with heavy left turning volume, an analysis of statistical significance shows a 97% confidence level that the average number of stopping left turners decreases with the change, and a weaker 90% confidence that the number of affected cycles has changed. Using a count of vehicles that came to a complete stop provides only a rough metric of the impact of queue spill-over, but this makes a strong argument that the change would result in a decreased number of vehicles facing a green indicator while queued. This contrasts with the model with evenly distributed volumes which showed slight, although non significant increases.

There was no significant change in the one-minute metering release rate or the 20- second variability and the confidence levels in the table below are low enough to make it unlikely that a significant change could be measured even with a larger sample size. A minor impact on the metering rate can be seen for the model with evenly distributed demand, as right turning vehicles queued in a shared right/through lane have no direct impact on the metering rate. Table 3:, Meter performance averages for 30 runs of 30 minutes each Meter performance Heavy left turn volume Even demand Average rate 20 sec Var Average rate 20 sec Var Baseline avg 14.71 0.46 14.69 0.58 Modified avg 14.70 0.47 14.54 0.51 Difference -0.003 0.009 -0.15 -0.07 Confidence 0.07 0.11 0.88 0.62 17 A similar difference in impacts can be seen when comparing mainline, ramp and arterial performance changes between the heavy left scenario and the evenly distributed traffic scenario. Mainline and Arterial performance Heavy Left Volumes Mainline throughput Mainline Delay Arterial Throughput CrossStreet Delay Baseline avg 7487.67 0.91 535.73 20.17 Modified avg 7489.20 0.82 536.53 19.78 Difference 0.77 -0.09 0.80 -0.39 Confidence 0.34 0.97 0.66 0.45 Even volumes Mainline throughput Mainline Delay Arterial Throughput CrossStreet Delay Baseline avg 7484.47 4.90 536.73 17.24 Modified avg 7489.20 5.36 531.27 24.01 Difference 2.37 0.46 -5.47 6.77 Confidence 0.53 0.90 0.94 1.00 *Delay in seconds per vehicle and throughput rates in veh/hour Table 4 :Mainline and Arterial performance for 30 runs of 30 minutes each

In the scenario with heavy lefts, although the mainline showed a statistically significant improvement in delay, it is so small as to be discounted as within the expected error in the model and the arterial was not significantly affected. In contrast, with even demand from different directions, the half cycle operation resulted in very large increases in travel time for the cross street. This is because the right turning vehicles on the arterial had fewer opportunities for right on red movements, backing up vehicles along the length of the segment. Although these numbers are dramatic and this behavior is a concern, in practice motorists continuing through the intersection would merge left, creating a de facto right turn lane.

As a whole, the results of the analysis were mixed, with operational benefits for the case with heavily skewed demand and negative impacts for the scenario with evenly distributed volumes. This is likely related to the base timing plan selected for each scenario, as the double-cycled signals were over saturated in many model runs and arterial performance was significantly impacted by the frequency of right turn on red movements. This study reasonably supports implementation of this level of 18 coordination for sites with a heavy left turn volume to the onramp and observed cases of vehicles unable to progress through the intersection on a green indicator but additional analysis would be needed before implementing coordination on anything but a case by case basis. 8 – Future Research and Conclusions This research illustrates the need for additional work to develop a standardized set of parameters for driver behavior in Vissim Modeling. Because these parameters control both freeway capacity and the impact of merge and weave conflicts, differences in calibration technique make comparison between different models difficult and make the extrapolation of model results to real world applications suspect The majority of the time spent on the baseline model was intended to obtain parameter values that recreated both freeway capacity and appropriate merge behavior and observation of individual vehicle behavior during the runs shows that significant room for improvement still exists.

The use of a shortened cycle when the ramp storage was heavily utilized did decrease the frequency of vehicles caught in the intersection, a situation that is mirrored in the real world when drivers facing a green arrow are unable to proceed because there is not room on the ramp to accept their vehicles. Reducing the frequency of this occurrence would provide benefits to arterial performance and decreases in driver frustration, but this study did not show significant benefits to freeway or ramp meter performance from this level of coordination and showed the potential for significant impacts if applied without analysis at each specific location. In this case, running the signal at half-cycle resulted in a unreasonably short cycle time of 30 seconds –creating an over saturated signal which was unable to handle the arterial demand.

The lack of mainline and meter improvements is most likely partially due to the ramp meter’s need to release vehicles at nearly the arrival rate once available storage is utilized, making the benefits of decreased variation in metering rate relatively small. Because the benefits are relatively small and depend so critically on driver behavior 19 characteristics, further research on properly tuning these parameters is necessary if this type of benefit is to be reliably quantified. 20 Works Cited

Taylor, et al, 2000, A programmer’s guide to the Fuzzy Logic ramp metering algorithm: software design, integration, testing and evaluation Washington State Transportation Center (TRAC).

2 Robinson-McCutchen, 2007 Optimizing the use of Fuzzymeter and ramp meters using Vissim CEE 600 research report,

University of Washington, 2007

3 Wilson, 2006, Understanding the Fuzzy Logic ramp metering algorithm: following input data to the implemented meter rates,

4 Han and Reiss (1994) Coordinating ramp meter operation with upstream intersection traffic signal Transportation Research

Record #1446

5 Tian, Z (2005) An integrated control system for coordinating ramp metering and surface street signal operations, world congress on intelligent transport systems (12th: 2005: San Francisco, Ca)

6 Tian, Z (2005) Integration of diamond interchange and ramp metering operations, Transportation Research Record #1925

7 Tian (2004) Development and evaluation of operational strategies for providing an integrated diamond interchange ramp metering control system, Texas A&M P.133

8 Woody, Tony (2006) Calibrating freeway simulation models in Vissim CEE600 final research report, University of Washington

Spring 2006.

9 Neel, Paul, WSDOT. (2007) Personal discussion regarding historical meter performance at Front Street 1 Appendix A – Output data from evenly distributed demand scenario

Evenly Distributed Demand scenario Vissim Model output Baseline (no coordination) Ramp meter cycles Ramp ramp Mainline Mainline Arterial CrossStreet Average Vissim Model output throughput delay throughput Delay Throughput Delay left stops L.S Cycles rate 20 sec Var Modified (coordination) Ramp meter cycles 1 436 28.1417 3777 3.88 567 13.3166667 0 0 14.56667 0.213358 Ramp Mainline Mainline CrossStreet CrossStreet Average 2 445 43.3483 3715 7.24833333 476 26.775 8.01 4 14.83333 0.089888 throughput ramp delay throughput Delay throughput Delay left stops L.S Cycles rate 20 sec Var 3 441 28.5767 3707 4.071667 554 15.5283333 0 0 14.73333 0.190886 1 432 35.0183333 3776 6.05666667 561 14.095 0 0 14.43333 0.258302 4 445 30.79 3715 4.33333333 536 16.1566667 2 1 14.83333 0.269663 2 449 41.7733333 3764 5.915 478 34.365 3.97 3 14.96667 0.202247 5 444 33.1033 3753 4.34833333 551 13.7416667 0 0 14.8 0.314607 3 442 29.6266667 3706 4.343333 549 16.2666667 0 0 14.73333 0.595381 6 433 30.0967 3761 4.30833333 534 14.2183333 0 0 14.43333 0.539326 4 433 34.685 3714 6.63166667 526 18.2466667 9.01 5 14.43333 0.516854 7 440 26.7233 3758 3.62666667 532 13.555 0 0 14.66667 0.550437 5 374 62.9016667 3751 8.66666667 479 53.4666667 15.98 6 12.46667 1.101124 8 449 33.898 3742 4.398333 550 14.59833 0 0 14.96667 0.449438 6 436 27.38 3770 3.905 533 14.675 0 0 14.53333 0.786517 9 449 37.005 3757 4.651667 534 14.78167 1.02 1 14.96667 0.157303 7 440 26.4966667 3757 3.77833333 539 13.4366667 0 0 14.66667 0.47191 10 437 29.412 3744 3.776667 531 12.805 0 0 14.6 0.449438 8 447 37.99167 3746 5.313333 549 17.07333 0 0 14.9 0.100999 11 450 36.772 3726 5.153333 567 15.485 0 0 15.03333 0.629213 9 449 36.315 3756 4.77 528 18.46833 2 1 14.96667 0.696629 12 452 35.438 3728 4.606667 530 13.855 0 0 15.06667 0.516854 10 438 29.53667 3746 3.948333 531 13.06 0 0 14.6 0.359551 13 439 32.057 3731 4.91 568 16.56 2.02 2 14.63333 0.651685 11 424 45.81667 3728 9.545 549 44.13333 3.98 2 14.13333 0.629213 14 441 37.108 3772 4.823333 512 18.11333 2.01 2 14.7 1.325843 12 451 36.11667 3736 4.45 531 12.01167 0 0 15.03333 0.719101 15 431 30.105 3751 3.958333 548 13.375 0 0 14.36667 0.606742 13 433 38.97167 3725 6.74 568 40.52833 4 2 14.43333 0.797628 16 417 44.782 3697 8.716667 473 24.79167 13 6 13.9 2.11236 14 441 35.945 3760 4.991667 508 23.91167 8.02 5 14.7 1.280899 17 419 20.865 3737 3.118333 570 14.41833 0 0 13.96667 0.539326 15 432 29.54833 3755 3.925 552 14.14 0 0 14.4 0.337079 18 448 38.862 3756 5.136667 520 22.51833 5.01 2 14.93333 0.47191 16 423 43.65667 3749 6.43 462 41.99167 9 4 14.06667 0.954931 19 449 44.84 3753 5.978333 528 25.69 9.02 5 14.96667 0.539326 17 416 20.99833 3680 3.828333 571 13.41 0 0 13.86667 0.685268 20 441 34.687 3741 4.611667 547 18.23333 2.99 2 14.7 0.516854 18 448 38.615 3771 5.523333 509 28.31667 7 3 14.93333 0.674157 21 434 19.708 3740 3.171667 553 13.91833 0 0 14.46667 0.539326 19 22 422 48.377 3749 6.983333 503 31.99 18.03 5 14.06667 1.213483 451 42.245 3745 6.968333 533 49.77833 3 2 15.03333 0.044944 23 447 36.322 3747 5.898333 557 18.26333 3 1 14.93333 1.011236 20 440 34.51667 3745 4.901667 545 23.13 0 0 14.66667 0.179775 24 449 37.812 3735 5.581667 531 16.42333 0 0 14.96667 0.224719 21 434 19.92833 3741 3.128333 553 13.8 0 0 14.46667 0.651685 25 444 34.345 3731 6.045 516 21.58333 4 2 14.8 0.157303 22 436 39.24 3754 6.263333 524 41 4.03 2 14.73333 0.582272 26 435 25.112 3738 3.78 577 13.905 0 0 14.5 0.606742 23 448 35.27667 3751 4.74 555 18.12833 1 1 14.93333 0.044944 27 445 35.152 3774 4.476667 516 15.63167 3 1 14.8 0.516854 24 436 42.36167 3730 7.355 506 32.865 1.98 1 14.53333 0.247191 28 443 32.262 3756 5.143333 553 14.83167 0 0 14.76667 0.719101 25 414 41.795 3740 5.845 489 32.23333 16 4 13.8 0.921348 29 447 39.642 3741 5.253333 525 16.46333 0 0 14.86667 0.561798 26 435 23.76 3750 3.456667 576 13.94333 0 0 14.5 0.292135 30 444 36.273 3735 5.088333 543 15.53167 0 0 14.76667 0.808989 27 445 34.78167 3774 4.468333 515 15.455 3.01 1 14.83333 0.157303 average 440.5333 34.054 3742.233 4.902611 536.7333 17.23528 2.437 1.133333 14.68667 0.583134 28 440 33.26833 3748 4.275 552 13.68 1 1 14.66667 0.426966 var 81.63678 44.155 356.1851 1.491455 635.5126 22.28265 18.7115 3.016092 0.090544 0.166003 29 446 38.27167 3742 5.11 524 16.6 4.96 3 14.86667 0.089888 std dev 9.035307 6.6449 18.87287 1.221251 25.20938 4.720451 4.325679 1.73669 0.300905 0.407434 30 444 33.65 3728 5.495 543 17.97167 0 0 14.8 0.516854 average 435.9 35.68294 3744.6 5.358944 531.2667 24.00606 3.264667 1.533333 14.53667 0.51077 Ramp ramp Mainline Mainline Arterial CrossStreet Average var 229.3345 68.93643 423.9724 2.281641 802.823 156.8423 20.1222 3.291954 0.256962 0.103394 throughput delay throughput Delay Throughput Delay left stops L.S Cycles rate 20 sec Var std dev 15.14379 8.302796 20.59059 1.51051 28.33413 12.52367 4.485777 1.814374 0.506914 0.321549 Change -4.63333 1.6292 2.366667 0.456333 -5.46667 6.770778 0.827667 0.4 -0.15 -0.07236 Paired T-Test 0.100443 0.1779 0.474266 0.095643 0.063289 0.001261 0.393919 0.254952 0.117475 0.376019 2 Appendix B – Output data from heavy left turn demand scenario

Vissim Model output Baseline (no coordination) Ramp meter cycles Ramp ramp Mainline Mainline CrossStreet CrossStreet L.S Average Vissim Model output throughput delay throughput Delay throughput Delay left stops Cycles rate 20 sec Var Modified Ramp meter cycles 1 439 32.57 3777 0.4383333 567 14.13 0 0 14.6333 0.258302 Ramp ramp Mainline Mainline CrossStreet CrossStreet L.S Average 2 450 43.247 3764 1.9633333 477 27.24666667 25.19 10 14.9667 0.404494 throughput delay throughput Delay throughput Delay left stops Cycles rate 20 sec Var 3 441 29.723 3705 0.625 554 17.95 4.98 2 14.7 0.718602 1 415 34.662 3777 0.6983333 565 11.14 2.99 2 14.1 0.87441 4 437 33.062 3704 1.0783333 532 16.89166667 11.04 4 14.5667 0.966292 2 449 42.1 3776 1.5416667 478 32.18166667 25.33 9 14.9667 0.13483 5 444 32.268 3752 0.4816667 547 14.47333333 0 0 14.8 0.157303 3 444 30.255 3704 0.535 554 13.98833333 0 0 14.8 0.55044 6 435 30.858 3772 0.4183333 535 16.22166667 1.08 1 14.5 1.235955 4 442 30.342 3692 0.76 534 17.13833333 11.08 5 14.7333 0.29213 7 438 29.057 3752 0.325 534 14.33333333 0 0 14.6 0.966292 5 439 31.795 3751 0.495 556 12.83166667 0 0 14.7 0.32572 8 449 38.7 3743 0.80167 553 18.38 4.07 2 15 0.685268 6 438 29.577 3772 0.4833333 536 14.255 0 0 14.6 0.69663 9 446 37.5 3754 0.865 532 18.105 1.96 2 14.9 0.921348 7 442 26.858 3757 0.3433333 532 14.24666667 0 0 14.7333 1.15718 10 442 32.3 3746 0.42 529 13.815 0 0 14.7333 0.47191 8 448 38.3 3746 0.93333 548 22.116667 6.01 4 14.9667 0.12347 11 446 37.8 3717 0.84 571 18.57 0 0 14.8667 0.11236 9 450 36.2 3754 0.70333 529 16.89 0 0 15.0333 0.75268 12 450 37.1 3737 0.57667 530 14.496667 0 0 15 0.089888 10 443 31.1 3747 0.45333 528 14.518333 0 0 14.7667 0.24719 13 440 32.9 3726 0.76833 566 26.961667 7.83 4 14.6667 0.179775 11 446 36.3 3722 0.865 566 19.023333 3.04 2 14.9 0.40449 14 413 44.7 3745 1.85833 493 20.673333 17.11 8 13.7667 0.629213 12 450 37 3736 0.795 529 12.341667 0 0 15 0.58427 15 428 30.5 3754 0.57833 549 14.871667 0 0 14.3 0.797628 13 437 33.8 3726 0.81333 567 20.115 1 1 14.5667 0.5618 16 448 38.7 3745 1.655 487 25.526667 24.31 9 14.9333 0.179775 14 438 37.8 3731 1.29833 509 26.563333 11.17 7 14.6 0.31461 17 419 22 3737 0.26 573 15.148333 0 0 13.9667 0.808989 15 429 30.6 3755 0.62833 549 13.801667 1.96 1 14.3 0.64032 18 448 40.2 3753 1.49833 509 25.571667 16.98 10 14.9333 0.292135 16 448 38.1 3751 1.22 483 30.19 11.14 6 14.9333 0.11236 19 449 44.4 3751 1.79833 529 36.82 27.23 11 14.9667 0.089888 17 418 20.8 3706 0.33667 571 15.006667 0 0 13.9333 0.67416 20 439 36.7 3739 1.125 541 22.448333 5.01 4 14.6333 0.303246 18 437 40.5 3763 1.32667 504 28.391667 15.27 7 14.5667 0.22472 21 434 24.4 3746 0.31167 547 14.895 0 0 14.4667 0.58427 22 450 40.6 3756 1.41 520 37.82 16.19 6 15 0.044944 19 448 44.2 3753 1.48667 534 44.468333 10.22 5 14.9 0.12347 23 449 36.9 3736 0.85167 555 23.725 8.01 3 14.9667 0.067416 20 443 33.1 3736 0.80833 544 19.553333 5.04 3 14.8 0.69663 24 449 38.2 3740 0.825 525 18.061667 5.93 3 14.9667 0.224719 21 426 27.2 3745 0.34333 549 13.89 0 0 14.2 0.47191 25 443 36.6 3756 1.43167 507 26.691667 15.99 7 14.7667 0.224719 22 450 40.5 3772 1.29 527 35.348333 10.08 6 15 0.38202 26 432 26.7 3738 0.30667 573 15.226667 0 0 14.4 0.876404 23 446 37.3 3750 0.86667 552 23.363333 3.91 4 14.8667 0.69663 27 444 35.7 3758 0.705 519 16.181667 2.08 1 14.8 0.089888 24 449 36.2 3738 0.85 529 17.756667 7.06 2 14.9667 0.06742 28 441 39 3752 0.935 554 18.245 3.91 2 14.7 0.23583 25 440 36.1 3763 0.935 509 27.625 6.08 4 14.7 0.33708 29 445 39.9 3729 1.17 517 22.396667 16.06 8 14.8333 0.539326 26 427 27.5 3724 0.335 572 13.028333 0 0 14.2333 0.80899 30 447 37.5 3731 1.07333 547 19.185 4.92 2 14.9 0.561798 27 445 36.4 3763 0.89167 517 14.445 7.06 3 14.8333 0.101 average 441.16667 35.3 3743.833 0.91317 535.73333 20.168778 7.329 3.3 14.7078 0.457266 28 441 33.8 3752 0.755 556 13.871667 3.99 3 14.7 0.8764 var 81.316092 31 279.1782 0.24685 626.75402 39.856588 72.76 12.976 0.08978 0.113083 29 449 38.5 3742 0.96167 523 14.056667 6.14 3 14.9333 0.42697 std dev 9.0175436 5.56 16.70863 0.49684 25.035056 6.3132074 8.53 3.6022 0.29964 0.336279 30 445 36.4 3734 0.885 546 21.346667 7.24 3 14.8 0.32572 average 440.73333 34.4 3744.6 0.82128 536.53333 19.783111 5.194 2.6667 14.7044 0.46619 Ramp ramp Mainline Mainline Arterial CrossStreet L.S Average var 86.409195 26.1 442.3862 0.11462 577.29195 64.560303 33.99 6.7126 0.08297 0.07812 throughput delay throughput Delay Throughput Delay left stops Cycles rate 20 sec Var std dev 9.2956547 5.11 21.03298 0.33855 24.026901 8.0349426 5.83 2.5909 0.28804 0.2795 Change -0.4333333 -0.9 0.766667 -0.0919 0.8 -0.385667 -2.136 -0.633 -0.0033 0.008922 Paired T-Test 0.7497429 0.02 0.657326 0.02676 0.3436642 0.5538327 0.029 0.0974 0.93405 0.889236