Appendix C: Travel Demand Model Validation and …...Sources: American Community Survey (ACS)...

Core of Rosslyn Transportation StudyExisting Conditions Report - Appendix

Appendix C: Travel Demand Model Validationand Data Collection Summary


Appendix C-1: Travel Demand Model ValidationSummary

Core of Rosslyn Transportation StudyTravel Demand Model Validation Summary 05.08.2018

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Metropolitan Washington Council of Governments (MWCOG) TravelDemand Model and VISUM Subarea Model ValidationThis memorandum summarizes the methodology for travel demand forecasting and model validationrelated to the Rosslyn Street Reconfiguration project. Per the approved Analysis Framework Document,a subarea modeling approach was used to develop traffic assignment and future traffic volume forecastsin the study influence area. This approach allowed for the consideration of existing and future modesplit (i.e. auto trip reduction and increased use of transit, bicycle, and pedestrian modes in the studyarea) based on the regional travel demand model outputs before conducting vehicular trafficassignment in a more refined subarea model. This approach also allowed for a more detailed modelingof travel patterns in the study area as compared to the regional model. The base year of the traveldemand model is 2016 and the future year is 2030. The base year of the VISUM subarea model is 2017,which corresponds to the existing conditions.

The process of applying the regional travel demand model, subarea models, and traffic analysis tools toaddress various questions was depicted in the Analysis Framework Document. The following sections ofthis memorandum specifically describe the validation of the regional travel demand model and VISUMsubarea model, the calibration of vehicular trip tables, and traffic routing for the subarea model.

The following summarizes validation results from the modeling process:

· The regional Metropolitan Washington Council of Governments (MWCOG) Travel DemandModel (MWCOG model) produced reasonable mode split data as compared to multiple sourcesof survey data for base year (2016), although the MWCOG model estimated a higher percentageof transit trips and lower percentage of single-occupancy auto trips for the home-based-work(HBW, or commute-to-work) purpose than the survey data.

· The MWCOG model replicated the proportion of external-to-internal, internal-to-external, andexternal-to-external (through) trips as compared to probe-vehicle data and traffic countscollected.

· Multiple cutlines were analyzed in the MWCOG model near the study area in comparison with24-hour volume estimates from VDOT and DDOT; the total cutline volumes were found toreasonably replicate estimated counts.

· The MWCOG model was less effective in terms of replicating the origin-destination patterns forsome external-to-external (through) trip pairs as compared to probe-vehicle data. As such, theseeding matrix for vehicular trips in the VISUM subarea model was based on probe vehicle dataand the collected traffic counts.

· The traffic assignment in the VISUM subarea model was adequately validated against fieldcounts for the AM and PM peak period, meeting the validation criteria and thresholds which aredetailed in this report.

Model Validation CriteriaThe validation approach and criteria used for both the MWCOG model and the VISUM subarea modelare described below.


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Validation of mode split and trip patterns were conducted qualitatively. Validation of cutline volumes1

were conducted quantitatively following the guidance provided in the Federal Highway Administration(FHWA) Travel Model Validation and Reasonableness Checking Manual (2nd edition) and VirginiaDepartment of Transportation (VDOT) Travel Demand Modeling Policies and Procedures (Version 2.00).The proposed validation criteria for each model are outlined in Table 1.

Table 1. Proposed Model Validation Criteria

Validation Check Model Validation Threshold Notes

% Difference in TotalVolume for Cutlines MWCOG

CutlineVolume

FHWAThreshold

VDOTThreshold

Must beunderFHWA

threshold

50,000 35% 10%100,000 25% 10%150,000 20% 10%200,000 18% 8%250,000 15% 8%

R-squared betweenmodeled volumes and

counts on linksVISUMsubarea 0.9

Use for alllinks with

comparablecounts in

study area% Difference in TotalVolume for Freeways

(subarea) modelVISUM

Subarea 7%

% Difference in TotalVolume for Arterials

VISUMSubarea 15%

% of Individual FreewayLinks within X%

VISUMSubarea 75% within 20% / 50% within 10%

% of Individual ArterialLinks

within X%VISUM

Subarea 75% within 30% / 50% within 15%

MWCOG Regional Travel Demand Model ValidationThe MWCOG Version 2.3.70 model was used in this study. At the inception of the study, this was themost up to date version of the MWCOG model and it incorporated Round 9.0 cooperative forecasts.Subsequently, in December 2017, Arlington County staff provided Round 9.1 cooperative forecasts,which reflect the County’s most recent land use projections. Accordingly, Version 2.3.70 MWCOG modelwith Round 9.1 cooperative forecasts for Arlington County was identified for use in this study andvalidated for the base year (2016). The validation of the base year MWCOG model was focused on thefollowing elements:

· Reasonable mode split for the study area compared to available trip survey data· Vehicular trip patterns that align both with traffic counts and with probe-vehicle origin-

destination data (i.e., StreetLight Data) at a macro scale (i.e. external to internal flows intoRosslyn and external to external flows through Rosslyn)

· Traffic volumes measured at cutlines1 along major transportation facilities near the study area

1 Cutline traffic volumes are the cumulative amount of traffic that passes through a defined imaginary line for inthe study area.


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Mode SplitBase Year Mode SplitTo establish a mode split for the base year, mode split data from the MWCOG model was comparedwith available data from two survey sources: the American Community Survey (ACS) (2011-2015) for thetwo census tracts in the Rosslyn core area and an employee survey conducted as part of the ArlingtonCounty Office Building Study (2015-2016). The Office Building Study included a survey of 15 buildings inthe Rosslyn-Ballston corridor, four of which were in the Rosslyn core. These surveys focus on the natureof commuter trips by travel modes. Figure 1 illustrates the geographic boundaries/locations of thesedata (ACS is based on Census tract).

Figure 1. Geographic Boundaries/Locations for Mode Split Data

Mode split data for work commute trips originating from Rosslyn were derived from ACS. Mode splitdata for work commute trips destined for Rosslyn were derived from the building study. This data wascompared to the home-based-work (HBW) trip mode split data included in the base year (2016)MWCOG model. It is noted that the survey data from each source included travel using non-motorizedmodes, such as biking, walking, and telecommunicating, while the MWCOG model assigns onlymotorized person trips, such as driving alone, carpooling, and transit, to the network. As such, contained


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within this memorandum is a discussion of the direct comparison of mode split between motorized trips(i.e., drive alone, carpool, and transit).

Table 2 shows the comparison for the motorized person trips for HBW trips. In general, the MWCOGmodel mode split estimates align reasonably well with the mode split derived from survey data for tripsdestined for Rosslyn. For trips originating from Rosslyn, the MWCOG travel model estimates a higherpercentage of transit trips compared to ACS data.

Table 2. Base Year (2016) Mode Split by SOV/Carpool/Transit (Home-Based Work Purpose)

DailyMotorized

Mode Share

FROM Rosslyn TO Rosslyn

ACS(HBW)

MWCOGModel (HBW)

Arlington CountyEmployee Survey

(HBW)

MWCOGModel (HBW)

Drive Alone 41% ± 3%(2,132)

25%(1,363)

48%(20,160)

39%(17,127)

Carpool 5% ± 0.2%(246)

6%(303)

7%(2,940)

12%(5,583)

Transit 54% ± 5%(2,812)

69%(3,785)

45%(18,900)

49%(21,708)

Total 100%(5,190)

100%(5,451)

100%(42,000)

100%(44,418)

Percent (Estimated number of Trips)Sources: American Community Survey (ACS) (2011-2015), MWCOG Version 2.3.70 Travel Demand Model with Round9.1 cooperative forecasts for Arlington County, and Arlington County Office Building Study (2015-2016)Note: Mode shares from the Arlington County Office Building Study were multiplied by the ACS-estimated number ofemployees in Rosslyn (42,000) to estimate the number of trips traveling to Rosslyn.

ACS data estimates that approximately 54 percent of motorized trips from study area residents occur viapublic transportation. For comparison, the MWCOG model estimates that 69 percent of commute tripsoriginating in Rosslyn occur via public transportation. It is noted that HBW trips from the MWCOG modelprimarily occur in the AM peak period due to model assumptions2. In addition, the MWCOG model issensitive to walk-to-transit access in urban core. These factors may have contributed to the differencesin mode split data between ACS and MWCOG.

The Arlington County Office Building Study (2015-2016) estimates that 45 percent of AM motorizedcommute trips for workers in Rosslyn occur via public transportation; for comparison, the MWCOGmodel estimates that 49 percent of commute trips destined for Rosslyn occur via public transportation.Despite of the fact the survey represents a fraction of the study area employees, it is indicative of themode split patterns in the study area and demonstrates that the MWCOG model mode split resultsgenerally align with surveyed data from actual employers and employees in the study area. It is notedthat both MWCOG HBW trips and Arlington County employee survey data focused on AM peak periodmode split.

2 The MWCOG model assigns all HBW transit trips to the AM peak period during its transit assignment process


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Future Year Mode SplitBase and future year daily trips as reported by the MWCOG model were compared. These tripsoriginate from and are destined to the Rosslyn study area by auto, transit, and non-motorized modes.Non-motorized trips are available from the MWCOG model only at the daily level. Table 3 shows themode split and the average annual linear growth for use of each mode from 2016 to 2030. As indicatedby the MWCOG model, from 2016 to 2030 there is a notable shift from auto to non-motorized modesfor trips, both for trips originating from and destined to the Rosslyn study area. Although transit modeshare percentages remain consistent from 2016 to 2030, the overall number of transit and non-motorized trips are anticipated to increase at a more rapid rate than auto trips.

Table 3. MWCOG Model Base and Future Year Mode Split by Auto/Transit/Non-motorized (All Trip Purposes)

Daily TripsMode Share

From Rosslyn To Rosslyn

2016 2030 AnnualGrowth 2016 2030 Annual

GrowthAuto 52% 45% 1% 51% 46% 1%

Transit 18% 19% 2% 26% 26% 2%Non-motorized 29% 36% 4% 23% 28% 4%

Source: MWCOG Version 2.3.7 Travel Demand Model with Round 9.1 cooperative forecasts for Arlington County

Trip PatternsBase year travel patterns for auto trips between internal and external zones as well as through trips(external zones to external zones) were analyzed using both the MWCOG model and probe-vehicle datafrom StreetLight. For comparison purposes, internal zones were aggregated to represent the Rosslynstudy area and some of the external zones, particularly arterials, were also aggregated to represent theArlington core west of Rosslyn. Figure 2 shows the locations of external zones, the boundary of thestudy area, and internal subzones. The methodology for developing the internal subzone system wasprovided in the Data Collection Summary Memorandum. Attachment A summarizes the Round 9.1 landuse data for the MWCOG TAZs and VISUM subarea TAZs, which were used to disaggregate the triptables to represent the subzone system.

StreetLight Data is an online data metrics tool that enables planners, engineers, and modelers to analyzetransportation data collected from mobile devices. Trip totals reported from Streetlight are expressed asStreetlight Trip Index Values. These are not trip counts, but rather normalized values on an index thatallows the user to compare relative volumes of travel at the origin-destination level. StreetLightestimates that the sample size of the data is usually less than 5 percent of trips, although this would varyby region and time.

Origin-destination information was developed based on geographies that consist of internal trafficanalysis zones (TAZs) and external zones represented by gateways into and out of the study area (i.e., I-66/US 29, Arlington Boulevard, Route 110, Key Bridge, George Washington Memorial Parkway, and theTheodore Roosevelt Bridge). A year of weekday data, between July 2016 and August 2017, was analyzedand used to develop study area trip patterns. The trip pattern validation was oriented to the followingthree aspects:

1. Relative proportion of total subarea trips originating from or destined to each zone


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2. Origin-destination travel patterns of internal-external trips and external-external (through) trips3. Relative proportions of internal-external trips vs. external-external (through) trips

Figure 2. External Gateways and Internal Zones for Rosslyn Study Area

Productions and AttractionsThis section mainly focuses on the magnitude of trips at each gateway facility in relation to othergateways and the Rosslyn study area (see Figure 2). Table 4 and Table 5 compare the proportion ofoverall subarea auto trips originating from or destined to each aggregated origin and destination zone,respectively, using StreetLight Data, MWCOG, and traffic counts. The time periods during which data foreach source was collected generally aligned, with the exception of a few traffic counts collected in 2014.The zonal proportions of trips are similar across the three data sources. This demonstrates that theMWCOG model produces a reasonable estimate of traffic flow on each facility relative to the otherfacilities as well as the trips generated by Rosslyn. This is true for both trips originating from anddestined to the Rosslyn study area. However, this information should not be used to interpret orvalidate origin-destination travel patterns, which is discussed in the next section.


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Table 4. Comparison of Origin Zone Trips

Zone

Origin Zone Trips as a % of Overall Subarea Network TripsAM Peak Period PM Peak Period

Street-Light MWCOG Traffic

CountsStreet-Light MWCOG Traffic

Counts

Arlington Core, west of Rosslyn 4% 6%(4,124)

4%(2,774) 3% 5%

(5,138)3%

(3,198)

I-66/US 29, west of Rosslyn 19% 17%(11,364)

18%(12,378) 12% 12%

(12,373)14%

(13,939)George Washington MemorialParkway, north of Rosslyn 21% 19%

(12,757)21%

(14,360) 13% 14%(14,208)

16%(15,783)

Key Bridge (US 29) 9% 5%(3,126)

6%(4,347) 10% 7%

(6,881)9%

(8,677)Theodore Roosevelt Bridge(I-66, US 50) 7% 10%

(6,764)11%

(7,918) 24% 18%(18,434)

19%(19,061)

George Washington MemorialParkway, south of Rosslyn 10% 14%

(9,782)12%

(8,112) 11% 14%(14,230)

12%(11,611)

Route 110 9% 11%(7,330)

9%(5,962) 7% 10%

(10,491)6%

(5,621)Arlington Boulevard (US 50),west of Rosslyn 11% 11%

(7,220)12%

(8,679) 5% 5%(5,181)

8%(8,309)

Rosslyn Study Area1 10% 7%(4,562)

7%(4,782) 15% 15%

(15,276)13%

(13,534)

Overall 100% 100%(67,029)

100%(69,312) 100% 100%

(102,212)100%

(99,733)Percent (Number of Trips)Traffic Data Source: June 2014 – Nov 2017; Streetlight Data: July 2016 – Aug 2017; MWCOG Model Estimates: 20161Traffic counts of trips originating in the Rosslyn Study Area were estimated as the total traffic volume exiting garages asdetermined during network volume balancing.1Traffic counts of trips originating in the Rosslyn Study Area were estimated as the total traffic volume exiting garages asdetermined during network volume balancing.


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Table 5. Comparison of Destination Zone Trips

Zone

Destination Zone Trips as a % of Overall Subarea Network TripsAM Peak Period PM Peak Period

Street-Light MWCOG Traffic

CountsStreet-Light MWCOG Traffic

Counts

Arlington Core, west of Rosslyn 2% 5%(3,530)

3%(1,784) 3% 6%

(6,228)4%

(3,872)

I-66/US 29, west of Rosslyn 9% 13%(8,453)

11%(7,698) 13% 16%

(16,806)13%

(13,327)George Washington MemorialParkway, north of Rosslyn 13% 8%

(5,692)14%

(9,683) 23% 17%(17,502)

16%(16,197)

Key Bridge (US 29) 13% 10%(6,674)

13%(8,893) 10% 6%

(6,098)9%

(8,459)Theodore Roosevelt Bridge (I-66, US 50) 26% 19%

(12,442)18%

(12,618) 13% 10%(10,487)

15%(15,076)

George Washington MemorialParkway, south of Rosslyn 13% 14%

(9,467)16%

(11,201) 10% 15%(14,772)

12%(11,754)

Route 110 5% 10%(6,701)

5%(3,589) 9% 12%

(12,290)10%

(10,284)Arlington Boulevard (US 50),west of Rosslyn 4% 5%

(3,480)7%

(5,131) 10% 9%(9,380)

13%(12,676)

Rosslyn Study Area1 15% 16%(10,591)

13%(9,144) 9% 9%

(8,650)8%

(7,616)

Overall 100% 100%(67,029)

100%(69,741) 100% 100%

(102,212)100%

(99,261)Percent (Number of Trips)Traffic Data Source: June 2014 – Nov 2017; Streetlight Data: July 2016 – Aug 2017; MWCOG Model Estimates: 20161Traffic counts of trips originating in the Rosslyn Study Area were estimated as the total traffic volume exiting garages asdetermined during network volume balancing.

Origin-Destination (O-D) PatternsThe O-D data from the MWCOG model was further reviewed in comparison to StreetLight Data O-D trippatterns for AM and PM peak periods. Notable differences between the two data sources aresummarized as follows:

AM Peak Period· The MWCOG model assigned more trips from the Rosslyn study area, Arlington core west of

Rosslyn, and I-66 west of the study area to D.C. via Route 110 (and subsequently the 14th StreetBridge) as opposed to via Key Bridge. This is likely attributable to the capacity constraint onroadways in Georgetown during peak periods and the lack of a grid network in the MWCOGmodel in the Rosslyn study area.

· The MWCOG model assigned more trips from Arlington Boulevard west of the study area toGeorge Washington Memorial Parkway south than to Theodore Roosevelt Bridge (I-66).

· The MWCOG model assigned more trips from George Washington Memorial Parkway north intoDC via Key Bridge than via I-66/14th Street.


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· Overall, the George Washington Memorial Parkway was favored in the AM peak period by theMWCOG model.

PM Peak Period· Like the AM peak period, the MWCOG model assigned more trips from Arlington Boulevard west

of the study area to George Washington Memorial Parkway south than to Theodore RooseveltBridge (I-66).

· The MWCOG model assigned more trips from the Rosslyn study area, to D.C. (via Route 110 andsubsequently the 14th Street Bridge) as opposed to via Key Bridge.

· The MWCOG model assigned more trips from Route 110 to the Arlington core west of Rosslynand fewer trips to George Washington Memorial Parkway north.

· The MWCOG model assigned more trips from George Washington Memorial Parkway south toArlington Boulevard west and fewer trips to George Washington Memorial Parkway north.

· The MWCOG model assigned more through trips on I-66 from DC to Virginia than the number oftrips the StreetLight data suggests (whereas some of the trips would use Arlington Boulevardand other parallel routes).

· The MWCOG model assigned more trips from the Arlington core west of Rosslyn to GeorgeWashington Memorial Parkway south/Route 110 and less trips to the Rosslyn study area.

Based on these observations, it was determined that StreetLight Data trip patterns would be a moreaccurate source of base year O-D travel patterns. Accordingly, the seeding trip tables based onStreetLight O-D data and traffic counts were used in replacement of the MWCOG model seeding triptables for the AM and PM peak periods. The seeding trip tables were incorporated into the VISUMsubarea model for further reassignment to match balanced link and intersection volumes. The detailedinformation on traffic volume development and balancing were provided in the Existing Conditionsreport. The VISUM subarea model validation is discussed later in this document.

External vs. Internal TripsThe proportions of subarea trips traveling through (external to external), into (external to internal), outof (internal to external), or within (internal to internal) the Rosslyn study area were compared betweenStreetLight Data and MWCOG as an additional evaluation of travel patterns. Table 6 and Table 7 showcomparisons of external and internal zonal based trips for AM and PM peak period, respectively. Thisdemonstrates that MWCOG model and StreetLight Data produced a similar magnitude and proportionof trips that are passing through, destined for, originated from, and traveling within the Rosslyn studyarea.

Table 6. AM Peak Period Trip Pattern Validation

Seeding Trip Table* StreetLight MWCOGTrip Type Trips % % Trips %

External to External 54,828 79.4% 76.1% 51,138 76.8%External to Internal 9,535 13.8% 14.3% 10,388 15.6%Internal to External 4,374 6.3% 9.1% 4,501 6.8%Internal to Internal 309 0.4% 0.5% 524 0.8%Total 69,046 100.0% 100.0% 65,799 100.0%

*Seeding trip table was developed based on StreetLight Data and counts


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Table 7. PM Peak Period Trip Pattern Validation

Seeding Trip Table* StreetLight MWCOGTrip Type Trips % % Trips %

External to External 76,854 77.0% 75.9% 76,716 76.0%External to Internal 9,503 9.5% 9.0% 8,359 8.3%Internal to External 13,020 13.0% 14.7% 14,912 14.8%Internal to Internal 412 0.4% 0.4% 1,011 1.0%Total 99,789 100.0% 100.0% 100,997 100.0%

*Seeding trip table was developed based on StreetLight Data and counts

Cutline ComparisonCutlines were developed across major commuting corridors surrounding the study area as well as majorarterials internal to the study area. The cutline validation for the MWCOG model was focused on dailyvolumes on regional facilities. Table 8 shows the comparison between daily volumes from the MWCOGmodel and VDOT’s and DDOT’s annual average daily traffic (AADT) and annual average weekday traffic(AADWT) count databases. AADWT counts were used wherever possible and DDOT 2015 counts weregrown to 2016 based on historical AADTs. All cutline results are within the FHWA model validationthreshold. Although VDOT model validation threshold is not met, more detailed calibration is conductedin the subarea model to achieve the required level of confidence for the traffic forecast. The MWCOGmodel assigns more trips to George Washington Memorial Parkway than what counts show. It is notedthat the daily volume patterns for travel between Virginia and DC across different facilities, such as KeyBridge, Theodore Roosevelt Bridge (I-66), and Arlington Memorial Bridge, are different from thepatterns identified by StreetLight Data during AM peak period. It is likely that Key Bridge becomes amore attractive crossing alternative during the off-peak period in the MWCOG model.

Overall, this demonstrates that the MWCOG model produced reasonable magnitude of vehicular tripsrelative to the Rosslyn study area when compared to the traffic counts. It is appropriate to use theMWCOG model for future traffic growth projection.


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Table 8. Annual Average Weekday Traffic Volume Cutline Validation of MWCOG 2016 Daily Volumes

Cutline Roadway LocationAAWDTCounts(2016)

2016 MWCOGModel 24-Hour

Volume% Difference Cutline %

Difference

West ofRosslynStudyArea

GeorgeWashington MemorialParkway

Between Spout RunParkway and FairfaxCounty Line

75,000* 91,786 -22%

-4%

Spout RunParkway

Between US 29 (LeeHwy) and GeorgeWashingtonMemorial Parkway

18,000 15,352 15%

I-66Between Exit 72 andExit 73 (LeeHwy/Rosslyn)

97,000 99,042 -2%

US 29(Lee Hwy)

Between Kirkwood Rdand 21st St 21,000 19,938 5%

WilsonBlvd

Between WashingtonBlvd and Nash St 10,000 10,610 -6%

ClarendonBlvd

Between WashingtonBlvd and CourthouseRd

11,000 9,830 11%

US 50(ArlingtonBlvd)

Between WashingtonBlvd and Washington,DC Line

57,000 53,014 7%

Southand

East ofRosslynStudyArea

Route 110Between ArlingtonRidge Rd andWashington Blvd

68,000 71,793 -6%

-18%GeorgeWashington MemorialParkway

Between NCLAlexandria and SpoutRun Parkway

66,000* 82,032 -24%

DCRiver

Crossings**

I-66 TR Bridge 94,300* 98,827 -15%

-7%Key Bridge 47,000* 53,834 -15%

Arlington Memorial Bridge 54,900* 58,307 -6%

TOTALCordonZones

ALL Gateways Combined 619,200 664,365 -7%

* Factor of AAWDT/AADT for adjacent facilities was used to estimate 2016 AAWDT from AADT** DDOT 2010-2015 AADT and factor of AAWDT/AADT for adjacent facilities was used to estimate 2016 AAWDT for DC rivercrossings from previous year AADT volumes


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VISUM Subarea ModelA VISUM subarea model of Rosslyn was developed and validated against existing trip patterns and trafficvolumes. The model contained external zones at subarea network entry and exit points and internalzones that were subdivided from MWCOG TAZs to the block or block group level (see Figure 2). Figure 3shows the subarea model network. The VISUM model was coded with attributes typically entered into aregional demand model, such as link speeds and capacities. The subarea zones and associated centroidconnectors represented major garage accesses to which the trips are loaded. Using a refined gridnetwork and a more accurate representation of trip loading, origins and destinations were developed tomore closely match traffic counts than the MWCOG model.

Initial peak period trip tables, or seeding tables, were developed using the StreetLight origin-destinationpercentages and traffic counts at origin zone locations. An origin-destination matrix estimation (ODME)tool within VISUM was used to adjust the seeding tables so that the resulting traffic assignment bettermatched peak period traffic counts for links and turning movements. Validation checks based on theaforementioned FHWA and VDOT guidance were completed to evaluate the effectiveness of the VISUMsubarea model. While the MWCOG model validation was focused on daily volumes, the peak periodvolumes were validated in the VISUM subarea model. This was done to validate the models’ use inanalyzing peak period traffic impacts that will result from alternative street reconfiguration concepts.

AM Validation ResultsTable 9 and Table 10 show link validation checks and Table 11 shows cutline validation checks for theAM peak period. All validation criteria and thresholds were met. The small difference between modeledvolumes and counts on links or through cutlines shows the model accurately replicates link-levelvolumes during the AM peak period. Although there are some variations between the assignment andtraffic counts for intersection turning movements, additional adjustments to the O-D routes will beimplemented in the VISSIM simulation models to meet VISSIM calibration requirements.

PM Validation ResultsTable 12 and Table 13 show link validation checks and Table 14 shows cutline validation for the PM peakperiod. All validation criteria and thresholds were met. Links volumes and cutline volumes werevalidated to a similar level of accuracy as the AM peak period.

Rosslyn Street Reconfiguration Implementation StudyTravel Demand Model Memorandum Draft 02.16.2018

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Figure 3. VISUM Subarea Network


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Table 9. AM Peak Period Link Validation

AM Peak Period Arterial FreewayTarget % Difference in

Total Volume 15% 7%

Total Links 274 66R-Squared 0.998 1.000

% RMSE 5.4% 1.5%Total Counts 270,332 292,210

Total Modeled 270,176 290,593% Difference -0.1% -0.6%

AM Peak Period Arterial Counts[Number, Percent of Links]

Freeway Counts[Number, Percent of Links]

Target % model volumecompared to count

75% within 30%50% within 15%


Within 30% 263 (96%) 66 (100%)Within 20% 249 (91%) 66 (100%)Within 15% 236 (86%) 66 (100%)Within 10% 201 (73%) 66 (100%)Within 5% 140 (51%) 63 (95%)

Table 10. AM Peak Period Critical Link Volume Calibration

# Roadway Counts Modeled Difference1 SB Fort Myer Dr 2,566 2,561 0%2 NB N Lynn St 4,223 4,283 1%3 EB Clarendon Blvd 1,883 1,818 -3%4 WB Wilson Blvd 1,442 1,456 1%5 EB Lee Hwy 4,843 4,755 -2%6 WB Lee Hwy 714 640 -10%7 EB I-66 9,958 9,810 -1%8 WB I-66 7,650 7,575 -1%9 EB 19th St N 946 957 1%

10 WB 19th St N 700 618 -12%

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Table 11. AM Peak Period Cutline Validation

Counts Modeled Difference(1) East/West CutlineEastbound 23,692 23,450 -1.0%Westbound 14,004 13,868 -1.0%(2) North/South CutlineNorthbound 21,992 21,780 -1.0%Southbound 27,408 27,298 -0.4%(3) North/South Cutline (Internal)Northbound 6,456 6,130 -5.0%Southbound 3,319 3,223 -2.9%

Table 12. PM Peak Period Link Validation

PM Peak Period Arterial FreewayTarget % Difference in

Total Volume 15% 7%

Total Links 274 66R-Squared 0.998 1.000

% RMSE 6.0% 0.8%Total Counts 401,199 449,694

Total Modeled 397,880 448,904% Difference -0.8% -0.2%

AM Peak Period Arterial Counts[Number, Percent of Links]

Freeway Counts[Number, Percent of Links]

Target % model volumecompared to count


Within 30% 255 (93%) 66 (100%)Within 20% 235 (86%) 66 (100%)Within 15% 217 (79%) 65 (100%)Within 10% 184 (67%) 63 (95%)Within 5% 124 (45%) 62 (94%)

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(2)

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Table 13. PM Peak Period Critical Link Volume Calibration

# Roadway Counts Modeled Difference1 SB Fort Myer Dr 4,598 4,353 -5%2 NB N Lynn St 5,788 5,551 -4%3 EB Clarendon Blvd 2,553 2,668 5%4 WB Wilson Blvd 2,166 2,014 -7%5 EB Lee Highway 5,639 5,709 1%6 WB Lee Highway 2436 2398 -2%7 EB I-66 14,006 13,906 -1%8 WB I-66 12,030 12,036 0%9 EB 19th St N 822 854 4%

10 WB 19th St N 957 825 -14%

Table 14. PM Peak Period Cutline Validation

Counts Modeled Difference(1) East/West CutlineEastbound 25,333 25,080 -1.0%Westbound 29,368 28,983 -1.3%(2) North/South CutlineNorthbound 36,293 36,204 -0.2%Southbound 37,114 37,083 -0.1%(3) North/South Cutline (Internal)Northbound 7,095 6,666 -6.0%Southbound 6,510 6,018 -7.6%

ConclusionsBased on the findings contained herein, the MWCOG model is considered validated and suitable for usein this study to project traffic growth because the model reasonably replicates regional trip patterns andmagnitude of volumes relative to the Rosslyn study area. Similarly, the VISUM subarea model isconsidered validated and suitable for developing O-D routes for VISSIM simulation model for AM andPM peak periods.

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Appendix C-2: Data Collection Summary

Rosslyn Street Reconfiguration Implementation Study Draft Data Collection Summary

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Data Collection The data collected as part of the Rosslyn Street Reconfiguration Implementation Study consists of traffic,

travel pattern, mode share, socioeconomic, and development data. The traffic related data accounts for

all modes of transportation, including vehicles, bicycles, pedestrians, transit, and parking. Field travel

time runs and field reviews were conducted to identify traffic operational issues and bottlenecks. The

following sections briefly describe the data collected for travel forecasting and traffic analysis.

Traffic Data

Traffic Counts and Volumes The following traffic data was acquired for the purposes of this study:

• Intersection turning movement count (TMC) data, including passenger vehicle, heavy vehicle, pedestrian, and bicycle counts

• Multi-use trail bicycle and pedestrian count data

• Volume and classification data for freeway/arterial mainlines and ramps

Intersection Counts and Volumes

There are 55 intersections within the study area (29 signalized, 26 unsignalized). Available TMC data for

40 of these intersections (collected in the last three years) were obtained from Arlington County. New

TMC data for the remaining intersections were collected for the purposes of this study on Wednesday,

November 15, 2017 between 6:00 AM and 10:00 AM as well as 3:00 PM and 7:00 PM for pedestrian,

bicycle, passenger vehicle, and heavy vehicle information in 15 minute intervals. All traffic count

locations are shown in Figure 1 and traffic count data is included in Appendix A.

https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwia1ICj1PfWAhWD4CYKHagvBjMQjRwIBw&url=https://www.arlingtonva.us/&psig=AOvVaw0BbDcjDlOpklTc4VsVnqJd&ust=1508329726052222


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Figure 1. Study Area Intersections

Traffic counts at all the signalized intersections in the study area were provided by Arlington County.

Other counts obtained from the County consist of those previously collected from other studies,

including I-66 Inside the Beltway Improvement and traffic impact studies (TIA) performed for projects in

Rosslyn. TMC data provided by the County was reviewed and supplemental data was collected by

National Data and Surveying Service (NDS) prior to the start of tolling on I-66 inside the Beltway in

December 2017. The traffic volume and congestion patterns were observed to be stable in the study

area over the last three years; therefore, the available intersection counts, collected within the last

three years, was considered applicable for this study. Table 1 summarizes the sources and date of

counts and the peak hour for each intersection.



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Table 1. Intersection Count Summary

ID TMC Source

Location Type Date Collected

Int Peak Hours

AM PM

1 AC SB Key Bridge and George

Washington Parkway Signalized 11/15/2017

7:45 AM

5:15 PM

2 AC Fort Myer Dr & WB Lee Hwy Signalized 6/7/2017 8:15 AM

5:15 PM

3 AC Fort Myer Dr & EB Lee Hwy Signalized 11/15/2017 7:30 AM

5:15 PM

4 AC Fort Myer Dr & 19th St N Signalized 6/15/2017 8:15 AM

5:45 PM

5 AC Fort Myer Dr & Wilson Blvd Signalized 6/15/2017 8:00 AM

5:15 PM

6 - N Nash St & 17th St N Unsignalized - - -

7 AC Fort Myer Dr & Fairfax Dr Signalized 6/15/2017 7:45 AM

4:00 PM

8 - Fort Myer Dr/Meade St & Arlington

Blvd Unsignalized - - -

9 NDS N Moore St & EB Lee Hwy Unsignalized 11/15/2017 7:30 AM

4:30 PM

10 AC N Moore St & 19th St N Signalized 6/7/2017 8:30 AM

5:00 PM

11 TIA N Moore St & Wilson Blvd Signalized 6/3/2014 8:00 AM

5:00 PM

12 AC N Lynn St & WB Lee Hwy Signalized 6/7/2017 7:45 AM

5:00 PM

13 AC N Lynn St & EB Lee Hwy Signalized 6/7/2017 7:45 AM

4:45 PM

14 AC N Lynn St & 19th St N Signalized 6/7/2017 8:15 AM

4:30 PM

15 AC N Lynn St & Wilson Blvd Signalized 6/15/2017 8:00 AM

5:15 PM

16 - N Lynn St & 17th St N Signalized - - -

17 AC N Lynn St & Fairfax Dr Signalized 6/15/2017 7:45 AM

4:45 PM

18 AC N Nash St & WB Lee Hwy Signalized 6/21/2017 7:45 AM

6:00 PM

19 AC N Nash St & EB Lee Hwy Signalized 6/21/2017 7:30 AM

4:45 PM

20 TIA N Nash St & Key Blvd East Unsignalized 2/23/2016 8:15 AM

5:00 PM

21 AC N Nash St & Wilson Blvd Signalized 6/20/2017 8:00 AM

5:00 PM

22 NDS N Nash St & Arlington Blvd Unsignalized 11/15/2017 8:15 AM

5:15 PM

23 TIA N Meade St & Arlington Blvd Unsignalized 9/17/2015 7:30 AM

5:00 PM

24 AC N Kent St & 19th St N Signalized 6/7/2017 8:30 AM

5:00 PM



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ID TMC Source


Int Peak Hours

AM PM

25 TIA N Kent St & Wilson Blvd Signalized 6/5/2014 8:00 AM

5:00 PM

26 TIA N Arlington Ridge Rd & Wilson Blvd Unsignalized 6/5/2014 8:00 AM

5:00 PM

27 AC N Oak St & Wilson Blvd Signalized 6/20/2017 8:00 AM

5:30 PM

28 AC N Pierce St & Wilson Blvd Signalized 6/20/2017 8:30 AM

5:15 PM

29 AC N Quinn St & Wilson Blvd Signalized 6/21/2017 8:15 AM

5:30 PM

30 AC N Rhodes St & Wilson Blvd Signalized 6/20/2017 8:15 AM

5:15 PM

31 AC N Rhodes St & Clarendon Blvd Signalized 6/20/2017 8:15 AM

5:00 PM

32 NDS N Quinn St & Clarendon Blvd Unsignalized 11/15/2017 8:15 AM

5:00 PM

33 NDS N Queen St & Clarendon Blvd Unsignalized 11/15/2017 8:15 AM

5:00 PM

34 AC N Pierce St & Clarendon Blvd Signalized 6/20/2017 8:00 AM

5:00 PM

35 NDS N Ode St & Clarendon Blvd Unsignalized 11/15/2017 8:15 AM

5:00 PM

36 TIA N Oak St & Clarendon Blvd/Wilson

Blvd Signalized 4/10/2016

8:00 AM

5:00 PM

37 TIA N Oak St & 17th St N Unsignalized 5/21/2014 8:00 AM

5:00 PM

38 NDS N Rhodes St & 16 St N Unsignalized 11/15/2017 8:15 AM

5:15 PM

39 NDS N Rhodes St & 14 St N Unsignalized 11/15/2017 8:00 AM

5:15 PM

40 NDS N Rhodes St & Arlington Blvd Unsignalized 11/15/2017 8:15 AM

5:15 PM

41 NDS Fort Myer Dr & Arlington Blvd Unsignalized 11/15/2017 8:15 AM

5:15 PM

42 NDS N Quinn St & 14th St N Unsignalized 11/15/2017 8:00 AM

5:00 PM

43 TIA N Queens St & Fairfax Dr (include US

50 ramps) Unsignalized 11/17/2015

8:15 AM

5:00 PM

44 NDS N Pierce St & Farifax Dr Unsignalized 11/15/2017 8:00 AM

4:30 PM

45 NDS N Rhodes St & Lee Hwy Unsignalized 11/15/2017 7:30 AM

5:45 PM

46 NDS N Rhodes St & Key Blvd Unsignalized 11/15/2017 8:00 AM

5:45 PM

47 AC N Scott St & Lee Hwy Signalized 6/21/2017 8:00 AM

6:00 PM

48 AC N Scott St & N Quinn St Signalized 6/21/2017 8:15 AM

5:30 PM



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ID TMC Source


Int Peak Hours

AM PM

49 TIA N Ode St & Key Blvd Unsignalized 9/23/2014 7:45 AM

5:15 PM

50 TIA N Oak St & Key Blvd Unsignalized 2/23/2016 8:15 AM

5:00 PM

51 TIA N Nash St & Key Blvd West Unsignalized 4/14/2016 8:15 AM

5:00 PM

52 TIA N Oak St & 18th St N Unsignalized 2/23/2016 8:15 AM

5:00 PM

53 NDS N Quinn St & WB Lee Hwy Unsignalized 11/15/2017 8:00 AM

5:45 PM

54 NDS N Quinn St & EB Lee Hwy Unsignalized 11/15/2017 7:30 AM

5:30 PM

55 AC N Oak St and Lee Hwy Signalized 6/21/2017 7:45 AM

6:00 PM

Based on the traffic data, the network peak hours of traffic were identified as 8:00 AM to 9:00 AM for

the AM peak and 5:00 PM to 6:00 PM for the PM peak. The network peak hours were determined by

evaluating the traffic volumes for all intersections excluding freeway mainline and ramps. Figure 2

shows the distribution of the total 15-minute volumes for the intersections during AM and PM peak.

The network peak hours (hereinafter referred to as peak hours) selected also represent those with the

heaviest observed traffic conditions on the N Lynn Street and N Fort Myer Drive corridors based on field

observations. Both AM and PM peak periods extend beyond the peak hours, with steady volume growth

leading up to the peak hour followed by a relatively steady decrease in traffic volumes. The total

network volumes for arterial intersections in the AM peak hour is higher than that in the PM peak hour

by about 4,000 vehicles. This is consistent with field observations, where heavier congestion was

observed in the AM peak than in PM peak. Field observations are summarized in a later section of this

memo.



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Figure 2. 15-Minute Intersection Volume

Traffic balancing was conducted for both peak hour and peak period counts and considered sources of

midblock traffic that could have resulted in volume imbalances between adjacent intersections such as

garage entrances and on-street parking. The balanced peak hour counts serve as the basis for

developing traffic routing in the VISSIM model area. The balanced peak period counts will be used as

target volumes for subarea travel demand model validation and O-D adjustment. The process for

volume balancing was as follows:

• Start from the Rosslyn core area and balance toward the freeway ramps at the edge of the network

• Keep recently collected counts constant whenever possible

• Use garage ingress and egress access points for balancing based on estimated garage capacity/occupancy

Figure 3 (a, b, c) shows the balanced intersection peak hour volumes.

Parking garages in the Rosslyn area, including those that were considered during the balancing process

are described in the parking section of this memorandum.

0

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00

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Existing AM and PM Peak 15-Minute Total Intersection Volumes

Netowrk peak hour 8 - 9 AM5 - 6 PM



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Figure 3a. Existing Intersection Peak Hour Volumes



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Figure 3b. Existing Intersection Peak Hour Volumes



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Figure 3c. Existing Intersection Peak Hour Volumes



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Freeway Mainline and Ramp Volumes Volume and classification data on I-66 mainline directly east of the US 29/Spout Run Parkway

interchange (Exit 72) were obtained from VDOT. The data consist of counts in 5-minute intervals from

October 1, 2017 to October 31, 2017. Only weekday counts were used to develop the peak hour

volumes. Peak hour volumes for other freeway mainlines and ramps were developed based on 2016

Annual Average Daily Traffic (AADT) counts as reported by VDOT, and modified using a “K factor” (peak-

to-daily traffic ratio). The distribution of volumes derived from the I-66 counts was applied uniformly to

all the freeway mainlines and ramps in the study area assuming the peaking patterns are consistent. As

previously described, the freeway peak hour volumes were adjusted to match the balanced arterial

intersection volumes given that the arterials in the Rosslyn core are the focus of this study and the

intersection volumes were based on turning movement counts. Figure 4 shows the existing freeway

peak hour volumes.



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Figure 4. Existing Freeway Peak Hour Volumes



Page 12

Pedestrian and Bicycle Data The pedestrian and bicycle volumes at the VISSIM study intersections were summarized in this section.

Most of the pedestrian and bicycle counts at intersections were collected in 2017 as part of the

intersection counts, although some date back to 2014. In addition, bicycle counts from two permanent

count stations (Key Bridge East and West Cyclists stations) were used to develop the seasonal

adjustment factors for bicycle volumes. Seasonal adjustment factors were not applied to pedestrian

volumes.

There are 28 intersections in the VISSIM analysis area that will have bicycle and pedestrian volume

inputs in the VISSIM model. Pedestrian and bicycle delay will be analyzed and reported in VISSIM for the

following thirteen locations selected by the project team based on pedestrian/bike volumes at each

intersection and proximity of the intersection to the Rosslyn core.

• #2 - Fort Myer Drive and westbound Lee Highway

• #3 - N Fort Myer Drive and eastbound Lee Highway

• #4 - Fort Myer Drive and 19th Street N

• #5 - Fort Myer Drive and Wilson Boulevard

• #10 - N Moore Street and 19th Street N

• #12 - N Lynn Street and westbound Lee Highway

• #13 - N Lynn Street and eastbound Lee Highway

• #14 - N Lynn Street and 19th Street N

• #15 - N Lynn Street and Wilson Boulevard

• #21 - N Nash Street and Wilson Boulevard

• #27- N Oak Street and Wilson Boulevard

• #36 - N Oak Street and Clarendon Boulevard

• Mid-block crossings over Fort Myer Drive and N Lynn Street

Table 2 summarizes the pedestrian volumes for VISSIM intersections in the AM and PM peak hour. It

should be noted that there were no recorded pedestrian volumes at the intersections of Lee Highway

and N Moore Street and 17th Street and N Lynn Street.

Table 2. Existing Intersection Pedestrian Volume (VISSIM Model)

AM Peak Hour PM Peak Hour

Intersection N S E W Total N S E W Total

1 0 0 0 184 184 2 0 3 251 256

2 316 21 26 103 466 270 26 218 175 689

3 83 46 26 128 283 66 71 206 156 499

4 444 136 385 178 1143 404 158 454 137 1153

5 946 483 443 302 2174 965 359 619 263 2206

6 278 4 82 15 379 209 6 49 31 295

7 77 16 94 34 221 50 29 110 46 235

8 20 69 9 108 206 14 30 5 64 113

9 - - - - 0 - - - - 0

10 328 205 152 283 968 345 270 496 367 1478



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11 763 60 11 60 894 888 74 1 80 1043

12 327 22 239 28 616 246 51 339 35 671

13 0 44 13 26 83 0 46 57 28 131

14 621 617 644 284 2166 534 731 814 194 2273

15 388 479 641 350 1858 254 406 721 228 1609

16 - - - - 0 - - - - 0

17 82 22 49 10 163 68 16 73 12 169

18 466 1 135 2 604 399 0 83 3 485

19 3 12 23 3 41 13 20 70 12 115

20 8 44 40 140 232 4 68 128 164 364

21 537 290 181 99 1107 541 250 245 69 1105

22 0 22 0 0 22 0 35 0 0 35

23 0 0 28 102 130 0 0 125 91 216

24 67 62 38 67 234 74 65 100 65 304

25 76 14 63 4 157 29 32 41 1 103

26 6 35 0 3 44 0 16 3 3 22

27 509 186 210 164 1069 737 209 453 254 1653

36 0 320 124 104 548 0 76 76 64 216

* N, S, E, W represent the north leg, south leg, east leg and west leg of an intersection

Bicycle counts in Rosslyn vary significantly throughout the different seasons; therefore, a seasonal factor

was applied to the available bicycle counts. Arlington County provided bicycle counts for the past three

years (November 3, 2014 to October 30, 2017) from several different count stations near the study area.

Seasonal factors were calculated by averaging the volumes for each season, e.g. summer, fall etc., and

dividing them by the average daily bicycle volumes over the three years. Table 3 shows the average

bicycle volumes for each season, and the calculated seasonal factors.

Table 3. Seasonal Factor for Bicycle Volumes

Average Volumes Dates Seasonal Factor

Winter 2740 Dec 21 to March 20 0.5388

Spring 6063 March 21 to June 20 1.1921

Summer 6710 June 21 to Sept 21 1.3195

Fall 4718 Sept 22 to Dec 20 0.9277 * Average daily bicycle volume over the three-year period is 5086.

By applying these seasonal factors, the intersection bicycle volumes for the VISSIM intersections in the

AM and PM peak hour were estimated. It is noted that bicycle counts were not available at the following

intersections and bicycle volume were estimated based on counts at adjacent intersections.

• N Lynn Street & eastbound Lee Highway (#13)

• N Lynn Street & 17th Street N (#16)

• N Nash Street & Key Boulevard (#20)



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• N Oak Street & Clarendon Boulevard (#36)

Table 4. Existing Intersection Bicycle Volume (VISSIM Model)



1 25 46 0 0 71 16 90 0 0 106

2 3 29 191 21 244 2 41 40 134 217

3 0 12 9 0 21 0 8 0 0 8

4 0 19 2 2 24 0 18 2 6 26

5 0 7 18 2 26 3 4 15 1 23

6 0 2 1 0 3 0 0 0 2 2

7 0 2 13 2 17 0 0 1 1 2

8 8 4 0 0 12 3 1 0 0 4

9 1 0 4 0 5 0 0 3 1 4

10 0 2 4 3 9 0 1 8 6 15

11 0 2 13 0 16 0 2 19 1 23

12 23 30 188 20 261 31 34 29 114 209

13 30 30 4 0 64 30 34 3 0 67

14 30 1 2 0 32 30 1 6 2 39

15 27 0 6 3 37 9 0 12 1 22

16 - - - - 0 - - - - 0

17 9 0 14 1 23 5 0 2 1 7

18 3 1 170 4 179 5 0 50 152 207

19 2 5 2 0 9 4 4 1 4 12

20 - - - - 0 - - - - 0

21 1 2 14 0 16 0 0 16 2 17

22 2 0 41 7 50 0 0 14 38 52

23 4 0 27 0 31 8 1 5 0 14

24 1 0 10 0 11 4 0 3 5 12

25 0 0 2 0 2 0 2 1 2 5

26 0 0 0 0 0 0 0 0 1 1

27 3 0 1 2 6 2 0 2 9 12

36 3 0 13 0 16 2 0 14 0 16

Figure 5 and Figure 6 show the peak hour intersection pedestrian and bicycle volumes, respectively. All

the critical intersections identified have significant pedestrian volumes, particularly those critical

intersections immediately surrounding the Rosslyn Metrorail station. Significant bicycle activities were

observed at Lee Highway and N Lynn Street and Lee Highways at N Fort Myer Drive. The intersections

surrounding the Rosslyn Metrorail station have moderate bicycle volumes.



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Figure 5. Existing Intersection Peak Hour Pedestrian Volumes



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Figure 6. Existing Intersection Peak Hour Bicycle Volumes



Page 17

Travel Time and Speed Data Travel time data was collected using the following two methods:

• Floating car method to collect travel time and stop delay

• Google Real-Time Data Application Programming Interface (API) to collect GPS data

Travel time along Eastbound Clarendon Boulevard, westbound Wilson Boulevard, northbound N Lynn

Street, and Fort Myer Drive were collected by Arlington County using the floating car method. The

Google Maps API was used to collect the four arterial routes mentioned above as well as the freeway

routes. The travel time routes are shown in Figure 7.

Figure 7: Travel Time Data Collection Routes

The County collected the reported travel time using the floating car method on Tuesday, November 14,

and Wednesday, November 15, 2017. AM travel time runs were performed between 7:30 and 9:30 AM.

PM travel time runs were performed between 4:30 and 5:30 PM. The field data contains 6 to 8 runs of

data for each segment and time period. The average travel time and stop delay is summarized in Table 5

below and in Appendix B.



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Table 5. Travel Time Summary

Field Travel Time Runs Google API Travel Time

AM PM AM PM

Route Distance Time [sec]

St. Dev [sec]

Delay [sec]

Stops Speed [mph]

Time [sec]

St. Dev [sec]

Delay [sec]

Stops Speed [mph]

Time [sec]

St. Dev [sec]

Speed [mph]

Time [sec]

St. Dev [sec]

Speed [mph]

Art

eria

l Ro

ute

s WB Wilson Boulevard 3690 296 39 166 4 9 288 79 130 5 9 382 41 7 366 31 7

EB Clarendon Boulevard 3560 277 63 100 3 9 192 17 68 2 13 267 71 9 242 30 10

NB N Lynn Street 3300 331 70 186 5 7 172 19 63 1 13 347 68 6 251 68 9

SB Fort Myer Drive* 3350 133 8 37 2 17 127 10 26 1 18 166 26 14 159 26 14

Free

way

Ro

ute

s

EB I-66 7392 204 123 25 226 121 22

WB I-66 7920 101 3 53 103 4 52

EB Arlington Boulevard 5808 493 212 8 169 94 23

WB Arlington Boulevard 5280 81 1 44 91 9 40

NB GW Parkway 5808 140 77 28 104 28 38

SB GW Parkway 5280 195 74 18 123 34 29

NB Route 110 2640 50 3 36 49 3 37

SB Route 110 2112 32 1 45 43 23 33

*Excludes travel time under Wilson Boulevard tunnel

Field Travel Time Runs = 11/14, 11/15; 7:30-9:30 AM and 4:30-5:30 PM

Google API Travel Time = 11/28-11/30; 12/5-12/7; 12/12-12/14; 7:30-9:30 AM and 4:30-6:30 PM



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Travel time data derived from GPS position data was collected via the Google API for the routes

illustrated in Figure 7. The travel time data was processed for every five minutes on each day recorded.

The average of data collected will be used to supplement field travel time data for calibrating travel time

results from existing VISSIM models.

Stop delay was reported from the field travel time runs and average travel speeds were calculated from

both field runs and Google API data. The follow is a brief summary of travel time and speed results.

• It was noted that the both arterial and freeway routes heading to Washington DC experienced

significant reduction in travel speeds as compared to free-flow speeds during AM and PM peak

periods.

• Northbound N. Lynn Street, eastbound I-66, and eastbound Arlington Boulevard operated at low

speeds that correspond to jam density, which is the maximum number of vehicles that can be

accommodated along a roadway in congested conditions.

• The numbers of stops collected from the field runs indicate that traffic platoons were frequently

stopped at signals on most of the arterial routes, more often in AM than in PM peak period.

• Generally, AM travel time in the study area is greater than that of PM travel time. This is

consistent with field observations, described in a later section of this report, which note

significant areas of congestion during the AM peak period.

Freeway Mainline Speed (INRIX) INRIX data was collected from Regional Integrated Transportation Information System (RITIS) for

freeway segments on I-66 and Arlington Boulevard. Average speeds were collected in 15-minute

intervals using INRIX data for Tuesdays, Wednesdays, and Thursdays in October 2017. The data was

visualized in plots over space and time, as shown in Figure 8 and Figure 9, for I-66 and Arlington

Boulevard, respectively. This data was used to confirm bottleneck locations and duration of bottleneck

on freeways and for calibrating existing VISSIM models.

Traffic Signal Timings Existing signal timings for each of the study intersections were obtained from Arlington County in PDF

format. The County also provided Synchro files for the AM and PM peak periods for each of the study

area intersections. PDF files were compared against the Synchro models to verify proper intersection

coding and operations. The Synchro files were used to import signal timing parameters into the VISSIM

microsimulation models.



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Figure 8. Average Speed - I-66

7:30 AM

8:00 AM

8:30 AM

9:00 AM

9:30 AM

10:00 AM

10:30 AM

Eastbound I-66 Westbound I-66

D.C

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Average Speed (mph)

7050 6040302010

4:30 PM

5:00 PM

5:30 PM

6:00 PM

6:30 PM

7:00 PM

7:30 PM



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Figure 9. Average Speed - Arlington Boulevard

7:30 AM

8:00 AM

8:30 AM

9:00 AM

9:30 AM

10:00 AM

10:30 AM

Eastbound US 50 Westbound US 50

Ro

ose

velt

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Eastbound I-66 Westbound I-66

D.C.

Route 11

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N Lynn S

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Field Observations Kimley-Horn conducted field observations on Wednesday, November 29, 2017. The field observations

were focused on queue lengths and duration, traffic and bus flow, and bicycle and pedestrian activities,

as well as confirming study area intersection geometry. Kimley-Horn observed queue length data for the

critical intersections over multiple time points of the peak period. Operational issues, driver behaviors,

pedestrian activity, and abnormal activity (e.g. traffic incidents, unexpected lane closures) were

documented. Information gathered during the field observations was used to validate outputs from

traffic analysis software programs used to analyze existing conditions. During the AM peak, traffic

conditions were observed to be severely congested (i.e., the extent and duration of queues were more

significant than “typical” conditions known in the study area from local knowledge and Google Maps

typical traffic). However, there were no incidents reported by Google Maps’ live traffic conditions. The

PM peak traffic conditions were observed to be consistent with typical conditions. Other notable

observations from the site visit are summarized in the section below and illustrated graphically in Figure

10 and Figure 11.

AM Peak Period

Operational Issues

• Northbound Key Bridge entering Washington, DC, was a major bottleneck, causing queuing

along N Lynn Street northbound back to Arlington Boulevard.

• Traffic along N Lynn Street northbound and 19th Street N eastbound required multiple cycles to

clear intersections.

• The westbound I-66 off-ramp to Lee Highway (Exit 73) experienced queuing back onto the I-66

mainline at its peak due to congestion on Key Bridge.

• Eastbound Clarendon Boulevard experienced queuing and slow-moving platoons back to N

Rhodes Street primarily due to heavy eastbound left-turns from Wilson Boulevard to N Lynn

Street.

• Eastbound Arlington Boulevard queues extended past the 10th Street interchange due to

congestion on the Theodore Roosevelt Bridge.

• Eastbound Key Boulevard to N Nash Street or 19th Street N was used as an alternative route to

Lee Highway during the peak hour, causing queuing along Key Boulevard eastbound.

Driver Behavior

• Turning vehicles from side streets onto N Lynn Street used full yellow and red clearance

intervals; intersections were occasionally blocked along N Lynn Street and 19th Street N.

• The segment along Fort Myer Drive between the ramp from Wilson Boulevard and the

intersection with Fairfax Drive was a difficult weave movement; vehicles from the ramp did not

always yield to tunnel traffic. It is noted that the County implemented a tactical urbanism design

element that changes the short weave to a yield from the ramp as well as the lane configuration

at this location after the field observations were conducted.

• Illegal U-turn activity was observed along westbound Wilson Boulevard at Fort Myer Drive and

along eastbound 19th Street N at N Moore Street.

• Illegal lane utilization:



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• Eastbound through vehicles at 19th Street N & N Moore Street used eastbound left-turn

lane;

• Eastbound left-turning vehicles at Lee Highway & N Lynn Street used the through lane;

• Eastbound left-turns at 19th Street & N Lynn Street used through lane (buses).

• There was significant pick-up and drop-off activity at 1919 N Lynn Street, causing reduced

speeds and queueing in the curb lane along N Lynn Street.

Nonmotorized Activity

• Generally, pedestrians were present at most study area intersections throughout the entire AM

peak period, but the peak of pedestrian activity was from 7:45 AM to 9:15 AM; not all

pedestrians obey pedestrian signal indications. Locations with significant pedestrian volumes

included Lee Highway and N. Lynn Street, N. Lynn Street and 19th Street, N. Lynn Street and

Wilson Boulevard, N. Nash Street and Wilson Boulevard

• Pedestrian crossings caused backups at intersection approaches such as eastbound Wilson

Boulevard at Fort Myer Drive the westbound right approach to N. Lynn Street.

• Heavy bike traffic crossed the northern leg of the intersection of westbound Lee Hwy and N

Lynn Street (Custis Trail traffic).

Other Notable Activity

• Police began assisting with directing traffic at the intersection of Wilson Boulevard and N Lynn

Street between 7:30 AM and 10:00 AM. The westbound right-turn from Wilson Boulevard and

the northbound through movement from the 17th Street ramp were closed due to this police

activity.

• Police were stopped in the left-turn lane on N Lynn Street north of westbound Lee Highway (to

access the GW Parkway) for most of the peak period.

PM Peak Period

Operational Issues

• Northbound Key Bridge entering Washington, DC, was again a bottleneck, causing queuing along

N Lynn Street northbound back to 19th Street N.

• The eastbound I-66 weave between the on-ramp from eastbound Lee Highway and the off-ramp

to Route 110 caused queuing along I-66 and Lee Highway.

Driver Behavior

• The segment along Fort Myer Drive between the ramp from Wilson Boulevard and the

intersection with Fairfax Drive was a difficult weave movement; vehicles from the ramp did not

always yield to tunnel traffic, and there was a lot of late lane changing approaching Arlington

Boulevard.

• Illegal U-turn activity was again observed along westbound Wilson Boulevard at Fort Myer Drive.

• Vehicles did not yield to pedestrians crossing the westbound Arlington Boulevard on-ramp.

• There was again significant pick-up and drop-off activity at 1919 N Lynn Street blocking the right

lane and right-turns from westbound 19th Street N.



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Nonmotorized Activity

• Pedestrian activity was fairly heavy during between 4:00 PM to 6:00 PM, mainly north of Fairfax

Drive.

• Pedestrian crossings caused backups at approaches with shared through/left-turn and

through/right-turn lanes such as eastbound Wilson Boulevard at Fort Myer Drive, northbound N

Lynn Street at Wilson Boulevard, and northbound Fort Myer Drive at Wilson Boulevard

The queue observations were focused on key intersection and ramp approaches where significant

queuing impact exist. Figure 10 and Figure 11 show the extent of queues and operational issues

observed in the field during the AM and PM peak period.



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Figure 10. AM Peak Queues and Operational Issues



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Figure 11. PM Peak Queues and Operational Issues



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Travel Pattern and Mode Data

Baseline Travel Patterns and Mode Choices Travel patterns and mode split data for commuting trips originating from Rosslyn were collected from

The US Census American Community Survey (ACS) from 2011 to 2015. In addition, commuter survey

data from the transportation studies conducted for development projects was obtained from Arlington

County. To examine commuting trips destined for Rosslyn, information was gathered from a recent

Arlington County Office Building Study (2015-2016), in which 15 buildings in the Rosslyn-Ballston

corridor, including four buildings in the Rosslyn core, were surveyed. These patterns were compared to

mode split data from the base year (2016) MWCOG travel demand model run.

Travel Patterns The OnTheMap online GIS portal from US Census Longitudinal Employer-Household Dynamics (LEHD)

Origin Destination Employment Statistics (LODES) was used to understand trip flow patterns. LODES

Version 7 is a partially synthetic dataset that describes geographic patterns of jobs by their employment

locations and residential locations as well as the connections between the two locations1. Figure 12

provides a heat map of work zip codes for Rosslyn study area residents. For employed people who live in

the study area (approximately 6,000 people), popular work destinations include Tysons, McLean, and

downtown Washington, DC. Approximately 400 people live and work in the study area zip code. For

comparison, the MWCOG model estimates that more than 80 percent of Rosslyn residents work in

Washington DC, Arlington, or Fairfax County, with over 40 percent working in Washington DC on its

own.

1 Federal workers were included in the jobs via data sourced from the Office of Personnel Management (OPM). OPM excludes some agencies from the data for security purposes. As a result, jobs from these agencies are excluded from the overall job data.



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Figure 12. Work zip codes of Rosslyn study area residents (source:LEHD)

Figure 13 provides a heat map of home zip codes for Rosslyn study area workers. For people who work

in the study area (approximately 21,000 people), many live along the I-66 corridor or in south Arlington.

Employees are traveling from a wide variety of locations around the region – the 25 zip codes that

produce the most trips to the study area only comprise approximately one-third of the total work trips.

This is likely attributable to Rosslyn’s centralized location in the Washington, DC metropolitan area and a

wide variety of commuting options. For comparison, the MWCOG model estimates that 29 percent of

study area workers come from Fairfax County, with another 15 percent from Arlington County; no other

jurisdictions comprise more than 10 percent of work trips into Rosslyn.



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Figure 13. Home zip codes of Rosslyn study area employees (source: LEHD).

The number of inflow commute trips to the Rosslyn study area is approximately 3.5 times that of the

outflow commute trips. Approximately 400 people live and work in the study area based on LEHD data.

Figure 14 shows the estimated inflow/outflow job counts in 2015.



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Figure 14. Inflow/Outflow Counts in 2015 (Source: LEHD)

Origin-Destination Data Origin-Destination (O-D) data was acquired from StreetLight Data analytics, an online data metrics tool

that enables planners, engineers, and modelers to analyze transportation data collected from mobile

devices. For this study, the data was derived from navigation GPS devices. Kimley-Horn developed O-D

information based on geographies that consist of internal traffic analysis zones (TAZs) and external

zones represented by gateways into and out of the study area (i.e., I-66/US 29, Arlington Boulevard,

Route 110, Key Bridge, George Washington Memorial Parkway, and the Theodore Roosevelt Bridge).

Figure 15 illustrates the internal and external zones. Trips to Rosslyn study area were disaggregated

based on land use data contained in the subzone system. A year of weekday data, between July 2016

and August 2017, was analyzed and used to develop study area trip O-Ds for baseline conditions. The

details of the O-D trip table development and how StreetLight Data was used to validate baseline travel

demand models are further discussed in the Travel Demand Model Validation Memorandum.



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Figure 15. External Gateways and Internal Zones for Rosslyn Study Area

Mode Split Data

Commute Trips from Rosslyn Study Area

ACS Commute to Work data estimates that approximately 47 percent of all commute trips from study

area residents are via public transportation (see Figure 16).



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Figure 16. Mode Share of Workers Who Live in Rosslyn Study Area (Source ACS)

Commute Trips to Rosslyn Study Area

The Arlington County Office Building Study (2015-2016) estimated that 41 percent of all AM commute

trips for workers in the Rosslyn-Ballston corridor are via public transportation. The study surveyed 15

buildings on the Rosslyn-Clarendon-Ballston Corridor, in which 4 buildings are in Rosslyn study area. The

mode split information from the study was provided in Figure 17, extracted from the report2. The survey

provides a perspective of the mode split that is comparable to the MWCOG regional model estimate

based on the sampled number of employees (638). It is noted this is a small sample of the total

workforce (approximately 30,000 employees) in the Rosslyn study area, but this data was compared to

the MWCOG model and found to be reasonable for the purposes of this study.

2 Arlington County Office Building Study (2015-2016) Report

Drove alone36%

Carpooled4%

Public Transportation

47%

Walked8%

Bicycle2%

Taxicab, motorcycle, or other means

<1%

Worked at home

3%

Mode Share of Workers Who Live in Study Area



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Figure 17. Arlington County Employee Survey Mode Choices (Source: Arlington County)

Parking The following parking data were obtained from the County and ParkMe, a third-party real-time parking

management developed by INRIX.

• Parking garage capacity and occupancy percentage

• On-street and curbside parking

Garage capacity data obtained from the County was different from the data from the County for some

garages potentially due to public vs. private parking. The occupancy percentage data was extracted from

the ParkMe website approximately at the end of the AM peak period (between 9:30 to 9:45 AM). It is

noted that many of the parking garages in the area have significant of reserved parking, whether it be

for monthly parkers, building employees, or resident. Thus, the occupancy percentages reported herein

refer to the total available “ad-hoc” parking during the AM peak period. For the purposes of this study it

is assumed that the reserved parking is fully occupied as both on- and off-street parking demand is

significant in this part of the County. The garage access points were identified and mapped during the

field review. Garage data was used to verify midblock locations where large volume imbalances exist.

Figure 18 shows the parking data for the garages in the Rosslyn study area.



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Figure 18. Garage Parking Capacity, Occupancy, and Access



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Socioeconomic and Development Data

Existing and Future Socioeconomic Data (Round 9.1) The County provided existing and future socioeconomic and development data for the purposes of this

study—this included a recently-developed socioeconomic dataset that was submitted by Arlington

County to MWCOG for incorporation to TPB Cooperative Forecast Round 9.1 data. Round 9.1 data was

not adopted by MWCOG at the time of model development. Therefore, adopted Round 9.0 data was

used outside Arlington County for the travel demand modeling effort. The socioeconomic data was used

to assess population and employment growth in the study area, to assist subarea traffic analysis zone

(TAZ) split and to provide input to travel demand forecasting. The Round 9.1 data for the five TAZs in the

study area is summarized in Table 6.

Table 6. TPB Cooperative Forecast Round 9.1 Data - Study Area TAZs

Population 2015 2020 2025 2030 2015-2030

Housing Units 8,710 9,380 11,040 12,140 +39%

Households 8,040 8,670 10,280 11,280 +40%

Group Quarters 20 20 20 20 0%

Household Population 13,370 14,450 17,230 18,950 +42%

Total Population 13,390 14,470 17,250 18,970 +42%

Employment 2015 2020 2025 2030 2015-2030

Office 23,450 29,800 30,340 31,750 +35%

Retail 2,580 2,780 3,090 3,900 +51%

Other 2,370 2,600 2,490 2,470 +4%

Other-Self Employed 340 390 390 410 +21%

Industrial 1,030 1,080 1,120 1,270 +24%

Total 29,770 36,650 37,430 39,800 +34% Source: Arlington County, CPHD, Planning Division, Center for Urban Design and Research, December 2017. Land use data were rounded up to the nearest 10.

TAZ = 1470, 1471, 1472, 1473, 1474, 1475



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Total Population (By TAZ)

TAZ 2015 2020 2025 2030 2015-2030

1470 1,270 1,280 1,290 1,290 +1%

1471 - - - - 0%

1472 550 1,130 1,730 1,730 +215%

1473 3,320 3,420 5,600 6,640 +100%

1474 2,430 2,430 2,430 2,430 +0%

1475 5,820 6,210 6,210 6,890 +18%

Total Employment (By TAZ)

TAZ 2015 2020 2025 2030 2015-2030

1470 490 490 490 490 +0%

1471 - - - - +0%

1472 11,980 17,570 20,000 21,820 +82%

1473 4,470 5,150 3,390 4,190 -6%

1474 200 210 210 210 +5%

1475 12,620 13,230 13,350 13,090 +4%

The County provided block-level socioeconomic data for the study area. Population and employment

estimates for blocks in the study area are shown in Figure 19 and Figure 20, respectively. This data was

used to assist with subdividing the study area TAZs for travel demand forecasting. The five TAZs were

subdivided into a total of 24 zones as shown in Figure 21. The zone split was performed based on the

following steps:

• The starting TAZ boundary was based on MWCOG TAZs

• Census block group boundary was then used to further divide up the MWCOG TAZs

• Further disaggregation of zones was performed to represent concentrated population and

employment subzones based on the socioeconomic data described above, as well as roadway

network/parking accesses in the study area

As such, the disaggregated zone structure will provide better trip loading to the network and represent

access to major developments or parking garages.



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Figure 19. 2017 Population Estimate



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Figure 20. 2017 Employment Estimate



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Figure 21. TAZ Split


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