Memphis and Shelby County

Metropolitan Planning Organization (MPO)

Travel Demand Model

Tennessee-Kentucky Model User Group Meeting

October 26, 2006

Model Development Team

• Kimley-Horn and Associates, Inc.• Cambridge Systematics• HNTB• NuStats

Model History in Memphis

• Originally Developed in Late 60s• Last Major Model Update was in 1995• Existing Model Parameters

– Model Boundary– Demographic Variables– Submodels

• 1998 Household Travel Survey

Review Process

• Client Review• Steering Committee• Peer Review Committee

Documentation and Meetings

• 12 Documents• 2 Face-to-Face Peer Review Meetings• 2 Peer Review Conference Calls• 3 Steering Committee Meetings• 2 Expert Panel Meetings for Land Use and

Demographics• Update Meetings with Engineering and

Technical Committee monthly

New Travel Demand Model

• Decision to Change Platform– GIS Based Program (TransCad)– Flexibility in Model Applications– Ease of Database Manipulation– Reporting Features and Tools– Consistency with State and other MPOs

Land Use

• Coverage• Traffic Analysis Zones

Development of TAZ Structure

• Expansion of Prior Zonal Coverage– In the North (Tipton County)– In the East (Fayette County)– In the South (DeSoto and Marshall

Counties)• Census TIGER Line Files• Geographic Features• Transportation Facilities

Model Area Boundary

Traffic Analysis Zones

Development of TAZ Structure

• Special Generators• Census Boundaries

– Tracts (Suburban/Rural)– Block Groups (Urban/Suburban)– Blocks (Urban)

• Centroid Connectors and the Network• TAZs in Previous Model: 800 (app)• TAZs in Current Model: 1,237

Development of Baseline Data

• Population and Household Variables– Used Data from Census 2000 (SF1,

SF3)– Matched Census Geography to TAZs

• Employment Variables– Used 2000 At-Place Employment Data– Reconciled using BLS– Grouped into Generalized Industry

Categories (NAICS)

Development of Baseline Data

• 2004 Estimation– Consultation with Planning Staff– Comparison with Available Data (E.G.,

Building Permits)

Typical Model Processes (How the model works)

• 4-Step Travel Demand Modeling Process– Trip Generation (How Many Trips?)– Trip Distribution (Where Do You Want to Go?)– Mode Choice (How Do You Want to Get

There?)– Trip Assignment (Which Route?)

• Data Requirements• Base, Future, and Interim Year Models

Household Survey — Key Features• MPO Region

– Shelby County and Part of Fayette County, TN– Part of DeSoto County, MS

• Memphis MSA (Census 2000 Figures)– Ranked 44 Out of All the Other MSAs in Country by

Population– Population = 1,135,614 :: Households = 424,202

• Type of Survey– Travel Diaries with Detailed Activities Description

• One Day, 24-Hour Travel Record for Every HH Member

– Computer-Aided Interviewing (CATI) Procedures• Conducted in 1998 (September-November)

Survey Sample

• # Households = 2,526• # Persons = 6,438• # Trips = 198,519

Trip Generation

• Trip Generation Submodels– Internal Person Trip Productions and

Attractions (P’s and A’s)– External/Internal Vehicle Trips– Special Generators– Vehicle Availability Model

Trip Generation – Trip Purposes

• 9 Internal Trip Purposes– Journey to Work– Home Based School– Home Based University– Home Based Shopping– Home Based Social-Recreational– Home Based Pick-up and Drop-off– Home Based Other– Non-Home Based Work– Non-Home Based Non-Work

Vehicle Availability Models• Models

– Multinomial Logit (MNL) Model– Ordered Response Logit Model (ORL)

• Model Inputs Tested– HH Characteristics – Accessibility, Socio-

Demographics (# Persons, # Workers, Income Level Dummies)

• Validation– Validated Census Data– Selected Model with Best Performance

Multinomial Logit (MNL) Model

0 Vehicle HHs

1 Vehicle HHs

2 Vehicle HHs

3+ Vehicle HHs

All Households (HHs)

• Probability– Prob(nth alt) =

• Utility Equations:

∑=

max

0

n

i

U

U

i

n

e

e

∑=

+=nv

jnjnjnn XbbU

10

Vehicle Availability ModelCoefficients of ordered response logit model

Variable 0/1+ 1/2+ 2/3+

Constant 1.12 -2.41 -1.62

2 Person HH 2.20

3+ Person HH 2.25 0.59

1 Worker 0.90

2+ Workers 1.48 1.09

3+ Workers 1.76

LMed Income 1.42 1.19

HMed Income 1.86 2.40 0.58

High Income 3.10 2.88 0.98

% Emp w/in 15 min -0.05 -0.05 -0.04

Trip Generation Application Results

Trip Purpose Productions Attractions % DiffJTW 783,436 706,159 -10%HBSc 343,361 372,993 9%HBU 56,147 46,202 -18%HBSh 223,496 232,395 4%HBSR 238,801 254,492 7%HBPD 207,017 201,188 -3%HBO 612,326 608,710 -1%NHBW 138,182 142,692 3%NHBNW 512,547 573,675 12%Total 3,115,313 3,138,506 -1%

External Trip Generation

• External-External Trips from Statewide Models

• External-Internal Trip Generation:Ej = ATj Dj B

where:Ej = Number of EI Trips Generated in Internal Zone jTj = Total Internal Trip Attractions Generated in Internal Zone jDj = Distance from Zone j to the Nearest External StationA, B = Model Parameters

External Station Classification

• Expressway• Arterial Near Expressway• Arterial Not Near Expressway• Collector/Local

Trip Generation – Special Generators

• Memphis International Airport• Graceland• Federal Express (Airport Hub)

Time of Day Model

Figure 1. Percent of Trips by Time and Purpose

0.0

5.0

10.0

15.0

20.0

25.00-

11-

22-

33-

44-

55-

66-

77-

88-

99-

1010

-11

11-1

212

-13

13-1

414

-15

15-1

616

-17

17-1

818

-19

19-2

020

-21

21-2

222

-23

23-2

4

Time Period

Perc

ent o

f Trip

s

Journey-to-WorkHome-Based School/UniversityHome-Based OtherNon Home-BasedAll Trip Purposes

Time of Day Model Percent of Trips by Purpose

Time Period Journey-to-

Work HBSchool/

HBUniversity

Other Home-Based

Purposes Non-Home-

Based All Purposes 0:00-1:00 0.8 0.0 0.4 0.1 0.40 1:00-2:00 0.2 0.0 0.1 0.1 0.15 2:00-3:00 0.3 0.0 0.2 0.1 0.15 3:00-4:00 0.4 0.0 0.1 0.0 0.17 4:00-5:00 0.7 0.0 0.2 0.0 0.30 5:00-6:00 2.9 0.2 0.5 0.2 1.16 6:00-7:00 9.3 7.8 2.5 0.8 5.46 7:00-8:00 16.7 23.6 7.0 3.8 12.52 8:00-9:00 7.8 11.7 5.8 3.4 6.79 9:00-10:00 3.1 3.1 5.1 3.8 3.90

10:00-11:00 1.3 2.6 4.4 5.4 3.27 11:00-12:00 1.8 3.3 4.7 13.2 4.42 12:00-13:00 2.2 3.7 4.8 19.1 5.17 13:00-14:00 2.4 2.1 4.7 12.2 4.41 14:00-15:00 4.0 13.8 7.0 11.4 8.54 15:00-16:00 7.1 12.3 8.4 9.0 9.40 16:00-17:00 10.1 3.6 7.3 5.2 7.39 17:00-18:00 12.3 4.4 8.6 3.7 8.56 18:00-19:00 6.4 1.9 8.9 3.1 6.22 19:00-20:00 3.1 1.6 7.4 2.3 4.20 20:00-21:00 2.0 2.3 5.1 1.4 2.95 21:00-22:00 1.9 0.9 3.7 1.0 2.24 22:00-23:00 1.7 1.0 2.1 0.4 1.32 23:00-24:00 1.4 0.2 1.2 0.2 0.90

Total 100.0% 100.0% 100.0% 100.0% 100.0%

Time of Day Model

• Time of Day Directional Trip Factors (Post-Mode Choice)

Trip Purpose Direction AM Peak Midday Peak PM Peak Off-Peak

1 JTW % From Home 95.59 64.42 10.81 26.93 % To Home 4.41 35.58 89.19 73.07

2 HBSchool % From Home 99.69 42.46 1.45 13.49 % To Home 0.31 57.54 98.55 86.51

3 HBUniv % From Home 96.60 40.13 25.78 9.19 % To Home 3.40 59.87 74.22 90.81

4 HBShop % From Home 63.65 53.43 38.01 37.57 % To Home 36.35 46.57 61.99 62.43

5 HBPUDO % From Home 64.42 63.00 43.36 38.75 % To Home 35.58 37.00 56.64 61.25

6 HBSR % From Home 85.22 58.93 58.41 38.58 % To Home 14.78 41.07 41.59 61.42

7 HBO % From Home 88.58 54.90 35.46 37.65 % To Home 11.42 45.10 64.54 62.35

8 NHBW N/A 50.0 50.0 50.0 50.0 9 NHBNW N/A 50.0 50.0 50.0 50.0

Time of Day Model

• Time of Day External Trip Factors

Facility Type Direction AM Peak Midday Peak PM Peak Off-Peak

% of Daily 16.4 30.3 24.3 29.0 % Inbound 70 51 39 43 1 Interstate

% Outbound 30 49 61 57 % of Daily 16.9 30.7 28.7 23.7 % Inbound 62 51 48 42 2

Other Principal Arterial % Outbound 38 49 52 58

% of Daily 19.6 26.6 29.0 25.1 % Inbound 63 49 49 40 6 Minor

Arterial % Outbound 37 51 51 60 % of Daily 18.0 27.5 29.2 25.3 % Inbound 63 49 49 40 7/8/9 Collector/

Local % Outbound 37 51 51 60

Trip Distribution

• Trip Distribution Model Components– Intrazonal Travel Times– Terminal Times– Primary Destination Choice– Intermediate Travel Times

Trip Distribution – Primary Destination Choice

• Gravity Model

• Destination Choice Model

∑ =×

××= n

j ijj

ijj

iij FAFA

PT1

)(

∑=

jj

i

i UUP

)exp()exp(

Logit Destination Choice Model

Utility of Choosing Destination Zone j = B1 (impedanceij )+ B2 ln (size variable)+ B3 (prod or attr zone dummy variable 1)+ …+ Bn (prod or attr zone dummy variable n-2)

Destination Choice Model for JTW TripsVariable Parameter Estimate

Mode Choice Logsum 0.057

Production-Attraction DummiesProduction and Attraction Ends in CBDProduction End is Urban Zone and Attraction End is in CBDProduction End is Suburban/Rural and Attraction End is in CBD

1.5840.2800.579

Attraction End DummiesAttraction End is an Urban ZoneAttraction End is a Suburban ZoneAttraction End is a Rural Zone

0.1060.1500.00 (Base)

Production-Attraction Highway Distance Power SeriesDistanceSquare of DistanceCube of Distance

-0.2610.009-0.00018

Multiplier for Size Variables 0.723

Size Variables (Coefficients Shown Are Exponents of Estimates)ServiceRetailIndustrial/ManufacturingWholesaleOfficeGovernment

1.000 (Base)0.4380.5330.5970.3940.386

Attraction Zone Area in Square Miles 0.0487

Destination Choice Model for NHBW Trips

Variable Parameter Estimate

Mode Choice Logsum 0.32

Attraction End DummiesAttraction End is a CBD/Urban ZoneAttraction End is a Suburban Zone

-0.45-0.35

Production-Attraction Highway Distance Power SeriesDistanceSquare of DistanceCube of Distance

-0.620.03-0.0004

Natural Log (Non-Home Based Work Modeled Attractions) 0.71

Journey to Work Stops Model

Number of Stops

Variable 0 1 2+

Constant -1.55 -2.73

Home-to-work chain -0.31 -0.75

1-vehicle household 0.58 1.24

2-vehicle household 0.58 1.21

3+ vehicle household 0.56 0.92

Presence of kids in household 0.76 0.98

Avg. Travel Time ComparisonPurpose Model (min) Observed (min)JTW 17.07 17.08HBO 12.22 12.21HBPUDO 10.90 11.34HBSc 9.61 9.63HBSh 11.26 11.18HBSR 12.71 12.70HBU 19.25 16.31NHBW 12.05 11.99NHBNW 12.41 12.22

Mode Choice

• Multinomial Logit Model (Like Destination Choice Model)

• On-Board Transit and Household Survey• Travel Modes Included…

Mode Choice

Modes included: – Transit with Auto Access (Includes Bus and Trolley)– Bus with Walk Access– Trolley with Walk Access – Non-Motorized (Including Walk/Wheelchair and

Bicycle)– Shared-Ride– Drive Alone– Spare Mode for Future Use

Survey Data Set SummaryMode JTW HBSc HBU HBSh HBPD HBSR HBO NHBW NHBNW All

Bus - Auto 225 18 63 31 - 17 88 36 47 525

Bus - Walk 1,238 121 293 158 - 89 394 107 131 2,531

Trolley - Auto

11 1 4 6 0 5 25 6 20 78

Trolley - Walk

51 0 6 17 0 6 52 38 34 204

Walk 124 469 4 94 38 137 154 52 83 1,155

Bicycle 11 20 - - - 5 5 2 4 47

School Bus 4 554 2 - 5 - 48 - 107 720

Shared Ride

793 1,010 45 599 889 603 2,068 225 1,792 8,024

Drive Alone 3,292 46 257 598 465 421 1,182 498 917 7,676

All 5,749 2,239 674 1,503 1,397 1,283 4,016 964 3,135 20,960

Mode Choice – On-Board Transit Survey Results

60%

25%

3% 1%11%

0%

20%

40%

60%

80%

100%

None One Two Three Four or m ore

• Vehicle Ownership

Mode Choice – On-Board Transit Survey Results

• Employment Status

1.9%

2.4%

3.3%

15.2%

16.2%

18.4%

42.6%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Hom em aker

Unem ployed, Not Looking

Retired

Student

Unem ployed, Looking

Em ployed, Part tim e

Em ployed, Full tim e

Mode Choice – On-Board Transit Survey Results

• Income Level

1.3%

3.2%

11.2%

31.8%

18%

34%

0.5%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

More than $90,000

$60,001 - $90,000

$42,001 - $60,000

$30,001 - $42,000

$18,001 - $30,000

$6,000 - $18,000

Less than $6,000

Mode Choice – On-Board Transit Survey Results

• Age

0.9%7.8%

19.5% 19.0%

35.6%

15.6%

1.7%0%

20%

40%

60%

80%

100%

Under 16 16-18 19-24 25-34 35-49 50-64 65 or older

Mode Choice – On-Board Transit Survey Results

• Ethnicity

1.2% 0.7% 0.6% 0.4%

88.9%

8.2%

0%

20%

40%

60%

80%

100%

Black/AmericanAmerican

White Other Native American Hispanic Asian American

Freight Model

• Trip Gen — Quick Response Freight (QRF) Manual

Commercial Vehicle Trip Destinations (or Origins) per Unit per Day

Generator (Unit)Four-Tire

TrucksSingle Unit

TrucksCombination

TrucksTotal Trucks

Agriculture, Mining, and Construction

1.110 0.289 0.174 1.573

Manufacturing, Transportation, Communications, Utilities, and Wholesale Trade

0.938 0.242 0.104 1.284

Retail Trade 0.888 0.253 0.065 1.206

Office and Services 0.437 0.068 0.009 0.514

Households 0.251 0.099 0.038 0.388

Freight Model (cont.)

•Trip Distribution - Gravity Model

•Calibrated to Classification Counts

Roadway Network Development

• Used network provided by MPO (with some cleaning)

• Developed data collection tool in TransCAD to enter in network attribute data

• Collected street data for all streets in network (through TRIMS and windshield data)

Network Collection Tool

• Allowed for data entry in the field by a two person team

• Could copy and paste data from one link to another

• Helped to minimize coding errors

TRIMS Image Data

Roadway Network Development

• Coordinated with TAZ development to ensure appropriate level of detail for both

• Developed centroid connectors in coordination with local staff

• Centroid connectors indicated auto/non-auto access

• Aerial photography and measurement data used to clean interchanges in network

Roadway Network Development

• One TransCAD file contained all years of development – baseline, existing plus committed, long range plan, etc., by year

• Network contained “link-dating” that indicated when a particular section will open (or close)

• Changes in network carried over to all potential scenarios and years

Roadway Network Quality Control

• TransCAD tools — such as “Check Line Layer Connectivity” — were used

• Trip path tests and test loadings also were used to identify network issues

• Plots with network attributes (lanes, speeds, median type, etc.) were submitted for review

• Checks against available aerial photography

Capacity Equations• Based on HCM and TDOT Data• Doesn’t use standard lookup

tables – completely based on attributes

• “Live” – update lanes on a link, capacity updates

• Calculated hourly and daily capacity

• Calculated LOS A through LOS E

Capacity EquationsThe general form of the equation was:

SF = c * N * fw * fHV * Fp * FE * fd * FSD* FCLT * FPark * (v/c)i

Where the variables were:• SF = Maximum service flow for desired level-of-service• c = Capacity under ideal conditions (vehicles per hr per lane)• N = Number of lanes• fw = Factor due to lane and shoulder width• fHV = Factor due to percent heavy vehicles• Fp = Factor due to driver population• FE = Factor due to driving environment• fd = Factor due to directional distribution• FSD = Factor due to signal density• FCLT = Factor for continuous left-turn lane (for undivided sections)• FPark = Factor for on-street parking• (v/c)i = Rate of service flow for levels-of-service A through E

Capacity Equations

Lookup Tables• No hardcoding of values • Separates interface development

from model development• More efficient model

adjustments/calibration• Subsequent model updates don’t

necessarily need new code• Data is more transparent and

accessible

Lookup Tables

Network Development – Highway Network

Network

Network Development – Highway Network

Screenlines and Cutlines

Network Development – Highway Network

Area Types

Network Development – Highway Network

Code Facility Type Centerline-miles Daily Counts TOD CountsClass

CountsSupplementary

Counts1 Rural Interstate 51 11 7 0 22 Rural Principal Arterial 137 42 25 1 13 Rural Freeway Ramp 116 Rural Minor Arterial 78 76 42 47 Rural Major Collector 263 166 93 7 38 Rural Minor Collector 261 250 152 159 Rural Local Access 374 32 18 1 4

11 Urban Interstate 143 105 61 0 912 Urban Freeways/Expressways 59 47 31 0 113 Urban Freeway Ramp 10314 Urban Principal Arterial 276 443 289 4 616 Urban Minor Arterial 556 894 578 22 2217 Urban Collector 332 499 326 9 919 Urban Local Access 113 36 22 3 4

Total Rural Roads 1175 577 337 28 10Total Urban Roads 1582 2024 1307 38 51Total - All Roads 2757 2601 1644 66 61

Network Development – Transit Network

129 1-way routes

Network Development – Transit Network

Network Development – Transit Network

4 Park and Ride Lots

Assignment• Roadway and Transit Networks

– Level of Detail– Data Collection Effort– Quality Control

• All or Nothing Preload– Heavy Commercial Vehicles– External-External Trips

• Equilibrium Multi-Class Assignment• Pathfinder Transit Assignment

Highway Assignment Validation Targets

Table 1. Percent Difference Targets for VMT by Functional Classification

Facility Type Target

Freeways 8-12%

Principal Arterials 18-22%

Minor Arterials 27%

Collectors 33%

Table 2. Percent Difference Volume Targets by Functional Classification

Facility TypeTarget (+/-)

Freeway 7%

Major Arterial 10%

Minor Arterial 15%

Collector 25%

Local 25%

Table 3. Percent Difference Volume Targets by Daily Volume Groupings (totaled over entire group)

Volume GroupTarget (+/-)

<1,000 200%

1,000-2,500 100%

2,500-5,000 50%

5,000-10,000 25%

10,000-25,000 20%

25,000-50,000 15%

>50,000 10%

Highway Assignment Validation Targets

Table 4. Percent of Links within aSpecified Percent of Count by Facility Type

Facility Type Target within Count

Range Compared to

Counts

Freeway 75% 20%

Freeway 50% 10%

Major Arterial 75% 30%

Major Arterial 50% 15%

Minor Arterial 75% 40%

Minor Arterial 50% 20%

Note: Table 4 can be read as “75% of the freeway links need to be within 20% of counts, 50% of the freeway links need to be within 10% of counts”.

Highway Assignment Global Results

Screenlines and Cutlines

Highway Assignment Global Results

Highway Assignment Calibration/Trouble Shooting

• Globally Low Modeled Volumes versus Observed Volumes

• Assignment Bias Toward Interstate versus Non-Interstate Facilities (Particularly Pronounced in Urban Area)

Future Year Model

• Demographics• Area Type• Signals• Highway and Transit Network

What’s New About this Forecasting Process?

• Employed a Rigorous Analytical Model• Integrated this Economic Model with the

Benefits of Local Planning Knowledge– Review by Expert Panel – Review by Local Planners

45 Sub-County Areas (SCAs)

Memphis Forecasting SequenceNATIONAL FORECASTIntegration of federal data

REGIONAL FORECASTIndustry linkages to U.S.

SCA FORECASTSAllocation of regional forecastsTo 45 sub-county areas (SCAs)

TAZ FORECASTSAllocation of SCA forecasts to

1,237 traffic analysis zones (TAZs)

Expert Panel Review/Revision

Local Planners Review/Revision

Study Area Forecast

0100000200000300000400000500000600000700000800000900000

2004 2040

HouseholdsJobs

Forecast by CountiesShelby County

0

100000

200000

300000

400000

500000

600000

700000

Jobs Households

2004

2040

2004

2040

Forecast by Counties

DeSoto County

0

20000

40000

60000

80000

100000

120000

140000

Jobs Households

2004

2040

2004

2040

Portion of Fayette County

0

5000

10000

15000

20000

25000

30000

35000

Jobs Households

2004

2040

2004

2040

Portion of Tipton County

0

5000

10000

15000

20000

25000

30000

35000

Jobs Households

2004

2040

2004

2040

Study Area Portion of Other Counties

Portion of Marshall County

0

2000

4000

6000

8000

10000

12000

14000

Jobs Households

2004

2040

2004

2040

Study Area Portion of Other Counties

Households

Households

Employment

Employment

MSA Forecast ResultsRegional Population Forecast

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

1980 1990 2000 2004 2010 2020 2030 2040

Regional Employment Forecast

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

1980 1990 2000 2004 2010 2020 2030 2040

Between 2004 and 2040

54% Gain in Population 56% Gain in Total Employment

Future Year Signal Identification

• Capacity equations used intersection penalties, signal density, and signal coordination

• If you use signals, you have to forecast them….. somehow

• Basic warrant analysis flagged potential new signals

• User then used tool to accept/reject pending flags

Future Year Signal IdentificationFlags potential signals using basic warrant analysis on AM peak (using TDOT standards)

Corridors are grouped into sections for capacity equations (Signal density and signal coordination)

Area Type Model• Forecasted by 6 Categories

– CBD, CBD Fringe, Urban, Suburban, Rural and OBD (Outlying Business District)• Forecasting Methodology

– No downgrade is possible– New CBD zone must be adjacent to existing CBD zones– New urban zone must be adjacent to the existing urban cluster– New urban cluster will be created if the area is >10 square miles– Existing or new OBD zones will become urban if they become adjacent to any

urban zone– No neighboring constraints on OBD, suburban, and rural zones

• Unique Algorithmic Features– The forecasting process is conducted inside out, similar to an urban sprawl

process, to avoid invalid neighbors– If one zone can upgrade to more types, they are evaluated in priority order, so all

possibilities will be considered– CBD,CBD-fringe and urban zones are evaluated by recursively finding the fringe

zones and finalize it step by step– All future area types are decided based on final decisions already made — e.g.,

not dependent on the particular evaluation order

Memphis Model Demo• Introduction

– Installation– Scenario Management– Model Run Control – Export Results

• Future Year Network Structure and Project Management– Query Project– Modify/Redefine Existing Project– Add/Delete project

• Future Year Signal Forecasting– Forecasting Signal Locations– Signal Density– Signal Coordination

• Reports– Highway– Transit

• Maps– Highway– Transit

Questions?

Contacts• Mark Dunzo

Email: mark.dunzo@kimley-horn.comPhone: 919.677.2075

• Kenny MonroeEmail: kenny.monroe@kimley-horn.comPhone: 901.374.9109

• Zhiyong GuoEmail: zhiyong.guo@kimley-horn.comPhone: 901.374.9109

• Pramoda GodeEmail: pramoda.gode@kimley-horn.comPhone: 919.653.2949

Presentation Copies

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