1 

Mobile Source Emissions Modeling 

for Floyd County 

PM2.5 Maintenance Plan 

Motor Vehicle Emissions Budget 

(updated February 27, 2012) 

1.0 Overview 

The Georgia Environmental Protection Division (EPD) worked with the Georgia Department of 

Transportation (GDOT) and the Floyd-Rome Urban Transportation Study (FRUTS), the 

Metropolitan Planning Organization (MPO) for Rome, to develop mobile1

 source emissions 

inventories for the purpose of establishing motor vehicle emissions budgets (MVEB) for the 

2023 maintenance year for the Rome PM2.5 Maintenance State Implementation Plan (SIP) 

revision, hereinafter called the Rome PM2.5 Maintenance Plan. The 2023 mobile emissions 

were developed consistent with the single-run, annual-average-conditions approach described in 

EPA's August 9, 2005, Guidance for Creating Annual On-Road Mobile Source Emission 

Inventories for PM2.5 Nonattainment Areas for Use in SIPs and Conformity.

2

 Consistent with 

this guidance, once this budget is found adequate or approved by EPA, subsequent emissions 

analyses for transportation conformity will also use the same annual-average-conditions 

approach used to establish the MVEB. These inventories reflect the most recent planning 

assumptions and emission factor model available, and the use of an updated travel demand 

model. The GDOT travel demand and emissions estimation modeling process was employed to 

estimate mobile source emission inventories to establish the MVEB for the Rome PM2.5 

Maintenance Plan in a manner consistent with federal regulations for performing regional 

emissions analyses used in transportation conformity determinations. The alignment of 

methodologies for MVEB and transportation conformity emissions analyses reduces the 

possibility of spurious differences between motor vehicle emission budgets and transportation 

conformity analyses that must conform to those budgets. 

Effective April 5, 2005, the U.S. Environmental Protection Agency (EPA) designated Floyd 

County as nonattainment for the annual fine particulate (PM2.5) National Ambient Air Quality 

Standard. The PM2.5 standard is subject to Subpart 1 of the Clean Air Act., the more general 

nonattainment area planning and control requirements of the Act. The designation also defined 

the year 2010 as the deadline for the Rome area to attain the PM2.5 standard. Based on qualityassured and certified monitoring data for the 2007–2009 monitoring period, EPA determined that 

the Rome Area attained the 1997 annual PM2.5 NAAQS by the applicable attainment date of 

April 5, 2010 on April 5, 2011. 

 

1

 The term "mobile" is used to describe emissions from on-road motor vehicles. 

2

 http://epa.gov/otaq/stateresources/transconf/policy/420b05008.pdf 
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3 

1.2 Emissions Analysis – Models and Assumptions 

In accordance with Section 93.105(b) of the Transportation Conformity Rule and Sections 

106(g) and 106(h) of Georgia's transportation conformity SIP, all of which require interagency 

consultation for SIP development, a detailed listing of the procedures and planning assumptions 

used for the regional emissions analysis supporting development of the MVEB was distributed to 

the interagency consultation committee for review on September 15, 2011. The assumptions 

used to develop Rome's conforming Long Range Transportation Plan and Transportation 

Improvement Program were also used to develop the network and emissions for the Rome 

PM2.5 Maintenance Plan MVEB, which required emissions for the year 2023. 

The MOVES input files reflect all federal and state motor vehicle emission control programs. In 

addition, the input files were customized to reflect the specific weather conditions and vehicle 

registration data for the Rome nonattainment area. 

2.0 Travel Demand Modeling Procedures 

Georgia DOT is responsible for the development and application of the travel demand models for 

the urban areas outside of Atlanta. This section summarizes the Rome model’s key travel 

demand modeling attributes, listed below, as they relate to the most important factors in 

estimating emissions. 

1. Socio-economic data based on best available information 

2. Consistency between transportation alternatives and land use scenarios 

3. Modeled volumes validated against observed counts 

4. Reasonable agreement between travel times used for trip-distribution and trip 

assignment 

5. Reasonable sensitivity to time, cost and other factors affecting travel choices 

6. Capacity-sensitive traffic assignment methodology 

Model Attributes 1 & 2 (Socio-Economic Data) 

The primary data inputs to travel demand models are socio-economic data, such as population 

and employment, and transportation networks. Modeling attributes one and two deal specifically 

with the socio-economic data inputs to the travel demand modeling process. 

The first modeling attribute is that the socio-economic data be based on the best available 

information. In Georgia, each MPO has the responsibility of preparing socio-economic data. 

Georgia DOT reviews the socio-economic data for reasonableness and accuracy. The data 

development process and accuracy checks rely on the best available information, such as US 

Census data, aerial photography, land use maps, knowledge of proposed new developments and 

site visits (local knowledge). Other reasonableness and logic checks are made for data at the 

traffic zone level, such as calculating statistics including population per household, population 

density and employment density. The MPOs and GDOT work cooperatively, using the best 

available data, to insure that the data inputs to travel demand models are accurate and reasonable. 

The second modeling attribute is that socio-economic data reflect the transportation alternatives 
4 

being considered. This relates to the fact that improved transportation accessibility can alter land 

use patterns. However, it is generally accepted that significant improvements in transportation 

accessibility are necessary to bring about relatively small changes in land use. Due to their 

complexity, land use models are generally utilized in only a few large metropolitan areas in the 

United States. Georgia’s MPOs, with the exception of Atlanta, do not use land use models. 

Instead, usually a single forecast for future socio-economic data is made that takes into 

consideration planned major transportation improvements. Future forecasts are generally made 

by first developing regional control totals for expected growth. Allocation of expected growth is 

then done using known development patterns and proposals as the basis, taking into 

consideration planned infrastructure improvements (new highways, sewer extensions, etc.). If 

unanticipated major projects are evaluated during the plan update process, a revised forecast may 

be developed with guidance from the MPO’s Technical Coordinating Committee. The 

population and employment forecasts for the FRUTS area are listed in Table 2.0-1. 

Table 2.0-1 

Population and Employment Forecasts for FRUTS Area 

2007 and 2023 

2007 2023 

Total Population 94,743 104,224 

Number of Households 35,939 38,352 

Employment 41,774 47,188 

Model Attribute 3 (Model Validation) 

The next attribute involves the validation of travel demand models against observed traffic 

counts. Model validation is the process of insuring travel models produce results that reasonably 

replicate observed travel patterns. Properly validated models not only replicate observed 

conditions, but they also use accurate inputs and apply reasonable calculations to do so. 

Georgia DOT applied multiple validation checks to each of the major steps in the Rome travel 

demand modeling process. In addition to socio-economic data checks, both the inputs to and 

outputs from the models were checked for accuracy and reasonableness during each step of the 

process. These inputs and outputs include transportation network attributes, trip generation 

parameters and results, trip distribution parameters and average trip lengths by purpose, auto 

occupancy rates, and speed-volume relationships. 

Highway Networks – Air Quality Attributes 

Georgia DOT develops and maintains highway networks with FRUTS review and assistance. 

Highway network attributes are reviewed for accuracy using the state roadway characteristics 

database, aerial photography and site visits / local knowledge. Network link attributes include the 

HPMS functional classification, so that modeled and observed Vehicle Miles Traveled (VMT) 

can be compared by county. Networks also include GDOT traffic count station numbers, so 

counts for the base year model can be included in output networks for validation purposes. 
5 

Highway Networks - Speed 

Since speeds are important for mobile emissions estimation, GDOT uses reasonable inputs and 

validates each of the factors that influence speed estimation; particularly the following: 

 Roadway capacities 

 Free-flow speeds 

 Modeled volumes 

 Speed-volume relationships 

Link Capacities 

Georgia DOT’s link capacities were developed using the latest Highway Capacity Manual 

Software with typical parameters for various roadway classes and area types. The density of 

population and employment is used to classify the intensity of development patterns throughout 

the study area. The Rome model uses the following seven area types to classify land use. 

 (1) - Central Business District (CBD) / High Density Urban 

 (2) - Urban Commercial 

 (3) - Urban Residential 

 (4) - Suburban Commercial 

 (5) - Suburban Residential 

 (6) - Exurban 

 (7) - Rural 

Table 2.0-2 displays the hourly capacities per lane utilized in the Rome travel demand model. 

Table 2.0-2 

Rome Model Hourly Per Lane Capacity Matrix 

Facility Type 

Area Type 

1 2 3 4 5 6 7 

Interstate 1900 1950 2000 2050 2100 2060 2020

Freeway 1600 1660 1730 1790 1850 1820 1780

Expressway 1300 1380 1450 1530 1600 1570 1540

Parkway 1170 1240 1310 1370 1440 1410 1380

Freeway to Freeway Ramp 1400 1530 1650 1780 1900 1860 1820

Freeway Entrance Ramp 900 1030 1150 1280 1400 1370 1340

Freeway Exit Ramp 800 810 810 820 820 810 790

Principal Arterial – Class I 1000 1030 1050 1080 1100 1080 1060

Principal Arterial – Class II 900 900 900 900 900 880 860

Minor Arterial – Class I 800 810 810 820 820 810 790

Minor Arterial – Class II 630 630 640 640 640 630 610

One Way Arterial 760 760 770 770 770 760 740

Major Collector 520 530 540 550 560 550 540

Minor Collector 380 390 390 400 400 390 380

One Way Collector 460 470 470 480 480 470 460

Local Road 340 350 360 370 380 370 360

Centroid Connector 0 0 0 0 0 0 0

Free-flow Speeds 

Assumed free-flow speeds are approximately 5 mph faster than typical speed limits for the 

various roadway classes and area types, taking into consideration control for delay (i.e., traffic 

signals) if applicable. Peak and off-peak free-flow speeds were evaluated using observed speeds 
6 

obtained from a travel time study conducted in the Augusta area. An analysis of the Augusta data 

indicated that Augusta’s characteristics and data results are appropriate for use as a base in the 

Rome model since the travel dynamics for these urban areas are very similar. Through the 

process of model calibration and validation, the speeds were revised slightly for a couple of 

facilities to more accurately represent travel conditions in the Rome area. Table 2.0-3 displays 

the free-flow speeds utilized in the Rome travel demand model. 

Table 2.0-3 

Rome Model Free-flow Speed Matrix 

Facility Type 

Area Type 

1 2 3 4 5 6 7 

Interstate 55 60 60 60 60 70 70 

Freeway 50 55 55 55 55 60 60 

Expressway 50 50 50 50 55 55 55 

Parkway 45 50 50 50 50 55 55 

Freeway to Freeway Ramp 55 55 55 55 55 55 55 

Freeway Entrance Ramp 45 50 50 50 50 55 55 

Freeway Exit Ramp 22 23 30 31 34 40 48 

Principal Arterial – Class I 25 28 33 34 37 47 52 

Principal Arterial – Class II 23 26 31 32 35 45 49 

Minor Arterial – Class I 22 23 30 31 34 40 47 

Minor Arterial – Class II 21 22 27 30 32 38 45 

One Way Arterial 23 26 30 32 35 42 48 

Major Collector 17 18 21 27 29 34 42 

Minor Collector 14 15 18 24 26 30 40 

One Way Collector 17 18 21 27 29 34 42 

Local Road 14 14 17 18 22 28 35 

Centroid Connector 14 14 17 18 22 28 35 

Modeled Volumes 

Output modeled volumes are validated against traffic counts at several levels – regional, 

corridors and link-by-link. Regional evaluations include VMT, Root Mean Squared Error and RSquared calculations. Corridor evaluations are primarily screenline and cutline comparisons. 

Nationally recognized maximum desirable deviation standards are applied to analyze model 

performance at the link level. 

Base year external station volumes are based directly on observed traffic counts at each location. 

Future year external station volumes are estimated from historical trends in traffic counts at each 

location. Extrapolated future external station volumes are refined to insure use of reasonable 

annual compounded growth rates. 

Speed-Volume Relationships 

Georgia DOT uses speed-volume relationships that are different for various roadway types and 

area types. The speed-volume curves are calibrated to accurately reflect observed traffic 

volumes, while retaining sensible shapes to insure reasonable congested speeds. Peak-period 
7 

speed data obtained from the GDOT travel time study was used as a reasonableness check in 

calibrating GDOT speed-volume curves. 

Trip Generation 

The GDOT trip generation process primarily uses parameters from the Augusta household 

survey, the Quick Response Freight Manual and US Census data. Minor adjustments are made 

to GDOT standard procedures to reflect unique characteristics in each area being modeled (e.g., 

port, military bases, etc.). Various validation checks are made to insure that trip generation 

results are reasonable. National data sources are used as reasonableness checks for trip 

generation results. 

Trip Distribution 

Trip distribution parameters are calibrated to produce reasonable average trip lengths. Expected 

average trip lengths are estimated from Census Journey-to-Work data and the population and 

geographic size of the modeled area. Travel times from trip assignment are used as input to trip 

distribution (i.e., feedback), which strengthens the validity of the modeled trip lengths. 

Model Attribute 4 (Feedback of Travel Times) 

The Rome model insures that there is reasonable agreement between travel times used for trip 

distribution and trip assignment by implementing a feedback loop. Within the feedback loop, all 

model steps from trip distribution to trip assignment are repeated until trip tables and link 

volumes change very little from one loop to the next. The Rome model includes a closure 

criterion for determining whether there is “reasonable agreement” in travel times for trip 

distribution and trip assignment. Closure is obtained if the following criterion is met: 

 Maximum link volume change =< 500 

The Method of Successive Averages is used to insure that the model reaches stable conditions. 

Model Attribute 5 (Mode Choice) 

The fifth modeling attribute calls for mode choice models to be reasonably sensitive to changes 

in travel times and costs. The Rome travel demand model utilizes a trip-end based procedure 

that determines transit-oriented person trips before the region’s person trips are converted to 

vehicle trips. This trip-end model estimates transit patronage based on socio-economic 

characteristics such as income or auto-ownership, rather than transportation system 

characteristics. 

Model Attribute 6 (Traffic Assignment) 

The sixth modeling attribute calls for the use of capacity sensitive assignment procedures. The 

Rome model uses a 24-hour equilibrium assignment algorithm. The traffic assignment algorithm 

is iterative, running through successive applications until equilibrium occurs. Equilibrium 

occurs when no trip can be made by an alternate path without increasing the total travel time of 

all trips in the network. The equilibrium assignment is an iterative process that reflects travel 

demand assigned to minimum time paths as well as the effects of congestion. In each 

assignment iteration, traffic volumes are loaded onto network links and travel times are adjusted 

in response to the volume to capacity relationships. Final assigned volumes are derived by 

summing a percentage of the loadings from each iteration. The percentages reflect congested 
8 

conditions that usually influence motorists' path selection for a portion of the day, not the entire 

day. 

Georgia DOT requires multiple validation checks to each of the major steps in the travel demand 

modeling process. Output modeled volumes are validated against traffic counts at several levels 

– regional, corridors (screenlines) and link-by-link. Regional evaluations include VMT, Root 

Mean Squared Error and R-Squared calculations for volume-count matching. Corridor 

evaluations are primarily screenline comparisons. Nationally recognized maximum desirable 

deviation standards are applied to analyze model performance at the link level. These include 

FHWA’s “Calibration & Adjustment of System Planning Models”, 1990, and the NCHRP Report 

365: “Travel Estimation Techniques for Urban Planning”, 1998. The Rome model was also 

validated using 24-hour counts and modeled volumes. Documentation on the development of the 

Rome Model is in Appendix A. 

2.1 Travel Demand Modeling Post-Processing Procedures 

The Rome regional travel demand model produces daily estimates of travel and vehicle hours 

traveled (VHT) and a peak hour speed for each link in the highway network. The links from the 

daily highway assignment contain a variety of attributes such as the number of distance, lanes, 

speed, capacities and daily volumes. The daily VMT is determined by multiplying the daily 

volume by the distance for each link. In order to account for travel conditions throughout the 

day, VMT estimates, times and speeds by hour were produced. Other refinements to the network 

link data, discussed below, were performed to produce the files needed for MOVES. The 

procedures used in estimating emissions for the Rome model area are consistent with the 

procedures used for emissions modeling (including conformity analyses) in the other 

nonattainment areas in Georgia. 

HPMS Adjustment of VMT 

In order to develop the information necessary to perform emissions modeling, post-processing of 

the output from the travel demand model was required. First, intra-zonal VMT is normally not 

reflected in the daily network assignment. A procedure was used that multiplied the number of 

intra-zonal vehicle trips from the vehicle trip table by the zone centroid distance to calculate the 

intra-zonal VMT. This VMT was then added to the network in a new link and summarized in 

the model VMT summaries. 

Next, the daily VMT from the travel demand model was adjusted based on the VMT estimates 

that GDOT develops for the Highway Performance Monitoring System (HPMS). According to 

Section 3.4.2.4 of EPA's "Volume IV" guidance,3 "[T]he detailed VMT estimates produced by 

the transportation planning process should be made consistent in the aggregate with HPMS." 

Consistent with this long-standing SIP guidance, Section 93.122(b)(3) of the Transportation 

Conformity Rule, Procedures for Determining Regional Transportation Related Emissions, says: 

"Highway Performance Monitoring System (HPMS) estimates of vehicle miles 

 

3 Procedures for Emission Inventory Preparation, Volume IV: Mobile Sources, EPA-420-R-92-009, US EPA, 

Office of Air and Radiation, Office of Mobile Sources, 1992, http://www.epa.gov/otaq/invntory/r92009.pdf. 
9 

traveled (VMT) shall be considered the primary measure of VMT within the 

portion of the nonattainment or maintenance area and for the functional classes 

of roadways included in HPMS.... For areas with network-based travel models, a 

factor (or factors) may be developed to reconcile and calibrate the network-based 

travel model estimates of VMT in the base year of its validation to the HPMS 

estimates for the same period. These factors may then be applied to model 

estimates of future VMT." 

The EPA guidance issued in August 2005, Guidance for Creating Annual On-Road Mobile 

Source Emission Inventories for PM2.5 Nonattainment Areas for Use in SIPs and Conformity, 

identified several approaches for preparing PM2.5 emissions. The guidance also specified that 

the interagency consultation process should be used to determine which approach is most 

appropriate for the area. The Rome interagency consultation group agreed to the Single-Run 

Approach recommended by EPD and GDOT for establishing the MVEB and performing 

subsequent conformity analyses. This methodology involves a single set of modeling runs using 

MOVES for each scenario year and annual average VMT. 

HPMS adjustment factors were developed based on the average annual daily HPMS VMT for the 

model calibration year. In the case of Rome there are two model calibration years, 20064

 and 

20095

. The current Rome model that was used to perform the last conformity determination was 

calibrated and validated for the year 2006. A new Rome model has been developed that was 

calibrated and validated for the year 2009. The 2009 model will be used to perform future 

conformity determinations. 

The HPMS adjustment reconciles the travel demand model link-based VMT to the average 

annual daily travel conditions at the aggregate functional class level. The aggregate functional 

classification level was used since FHWA eliminated the urban/rural area type distinction from 

HPMS functional classifications beginning with the 2009 data, reported in 2010. (Guidance for 

the Functional Classification of Highways (updated), Federal Highway Administration, October 

14, 2008.) 

To determine the “2006 HPMS VMT” adjustment factors, the average annual daily Floyd 

County VMT for the year 2006 was summarized by the HPMS functional classifications from 

the Georgia Department of Transportation’s Office of Transportation Data “445 Report.” The 

data was summarized for the Rome MPO area which consists of all of Floyd County. The 445 

Report summarizes the mileage and VMT by function classification by county. 

The following equation was used to calculate the 2006 HPMS adjustment factors: 

HPMS Adjustment Factori =(2006 HPMS VMTi /2006 Model VMTi) 

where i=HPMS functional class) 

The 2006 factors were applied to the VMT on each link in the highway network based on the 

 

4

 (the base year of validation for the current version of the regional travel model) 

5

 (the base year of validation for the updated version of the regional travel model) 
10 

aggregate functional classification for the year 2006. These factors were applied to the model 

application for 2007. A separate set of HPMS adjustment factors were developed for 2009 using 

the same method. The 2009 factors were applied to the model application for the year 2023 and 

will be applied for future conformity determinations. Table 2.1-1 lists the adjustment factors 

based on the comparison between the HPMS VMT and the VMT from the regional travel 

demand model for both years 2006 and 2009. 

Table 2.1-1 

HPMS VMT Adjustment Factors for Rome MPO Area 

 (from the FRUTS travel demand model) 

Functional 

Class Name 

Functional 

Class No. 

2006 

HPMS 

VMT 

2006 

Model 

VMT 

2006 

2009 

HPMS 

VMT 

2009 

Model 

VMT 

2009 

Adjustment 

Factors 

Adjustment 

Factors 

Interstate 1,11,12 27,444 31,841 0.86 30,000 26,804 1.12

Principal 

Arterials 2,14 1,152,782 1,114,318 1.03 1,117,000 1,005,323 1.11

Minor 

Arterials 6,16 647,805 700,058 0.93 598,000 630,480 0.95

Collectors 7,8,17 443,918 442,352 1.00 398,000 397,978 1.00

Local 9,19 726,665 351,531 2.07 816,000 374,461 2.18

Total 2,998,614 2,640,100 1.14 2,959,000 2,425,846 1.22

Table 2.1-2 shows the adjusted average annual daily modeled VMT for Floyd County used in the 

emissions modeling procedures for the years 2007 and 2023. 

Table 2.1-2 

Average Annual Daily Modeled VMT for Floyd County 

(As adjusted per Table 2.1-1) 

Year VMT 

2007 3,045,053 

2023 3,518,199 

VMT Estimation by Hour 

 Factors derived using the methodology described in the report Speed and Delay Prediction 

Models for Planning Applications were used to develop VMT estimates by hour from the daily 

estimates. The methodology is a simplified queuing-based model (QSIM) which incorporates 

several key features such as the use of temporal distribution as a basis for developing hourly 

traffic estimates and the estimation of “peak spreading” for both arterials and freeways. Because 

most analytical methods consider only the effects of peak hour congestion (such as V/C ratio), a 

new measurement of daily congestion was used: the Average Annual Daily Traffic-to-Capacity 

(AADT/C) ratio, where capacity is the two-way capacity. Hourly factors were developed based 

on the AADT/C ratio and are listed in Table 2.1-3. These factors were applied to the daily traffic 
11 

assignment to develop hourly volumes and VMT by link. Conical volume-delay curves were 

then used to develop hourly times and speeds by link. 

Roadtype Classification 

The network link data was also classified by MOVES roadtype based on functional 

classification. The mapping of FHWA highway functional system classifications to the 

appropriate MOVES roadtypes used for this modeling is listed in Table 2.1-4. Interstate and 

freeway ramps are functionally classified as local facilities in Georgia. Since these facilities 

operate with restricted access, the facility type definition variable (a unique variable in the 

highway network that defines the highway facilities based on their operation) was used to 

classify ramps as either rural or urban restricted facilities. Off-network activity is calculated 

within the MOVES process based on the source type (vehicle) population and is not an input 

from the travel demand model data. 

Table 2.1-4 

Listing of FHWA Highway Functional Classifications 

Mapped to MOVES Road Types 

FHWA Highway Functional System MOVES Road Type MOVES Value 

Rural interstate Rural restricted access 2 

Rural other principal arterial Rural restricted access 2 

Rural minor arterial Rural unrestricted access 3 

Rural major collector Rural unrestricted access 3 

Rural minor collector Rural unrestricted access 3 

Rural local Rural unrestricted access 3 

Urban interstate Urban restricted access 4 

Urban other freeways Urban restricted access 4 

Urban other principal arterial Urban unrestricted access 5 

Urban minor arterial Urban unrestricted access 5 

Urban collector Urban unrestricted access 5 

Urban local Urban unrestricted access 5 
12 

Table 2.1-3 

Hourly Distribution of Daily Vehicle Miles Travelled (VMT) 

 Source: Speed and Delay Prediction Models for Planning Applications

AADT/C 

Ratio 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2

<= 7 1.00 0.60 0.48 0.45 0.67 1.85 5.01 7.73 6.13 4.82 4.79 5.12 5.36 5.47 6.05 7.27 8.28 8.27 5.89 4.

8 1.01 0.61 0.48 0.43 0.64 1.82 5.04 7.67 6.42 4.97 4.82 5.19 5.41 5.53 6.07 7.14 7.97 7.90 5.87 4.9 1.01 0.61 0.48 0.42 0.63 1.81 5.06 7.64 6.56 5.05 4.84 5.22 5.43 5.56 6.08 7.08 7.81 7.71 5.86 4.

10 1.01 0.60 0.47 0.40 0.61 1.80 5.05 7.49 6.61 5.19 4.95 5.29 5.46 5.60 6.09 6.99 7.58 7.50 5.92 4.11 1.01 0.60 0.45 0.38 0.58 1.79 5.05 7.33 6.65 5.33 5.06 5.35 5.50 5.64 6.11 6.90 7.34 7.28 5.98 4.12 1.01 0.59 0.44 0.36 0.56 1.78 5.04 7.17 6.70 5.47 5.17 5.42 5.53 5.68 6.12 6.81 7.10 7.06 6.04 4.13 1.27 0.89 0.75 0.68 0.86 1.98 4.97 6.92 6.49 5.36 5.09 5.32 5.42 5.55 5.96 6.59 6.86 6.82 5.88 4.14 1.54 1.19 1.06 0.99 1.16 2.18 4.90 6.67 6.28 5.25 5.00 5.21 5.30 5.43 5.80 6.37 6.61 6.58 5.73 4.15 1.80 1.48 1.37 1.31 1.46 2.38 4.82 6.42 6.07 5.14 4.92 5.11 5.19 5.30 5.63 6.15 6.37 6.34 5.57 4.16 2.06 1.78 1.68 1.63 1.76 2.58 4.75 6.17 5.86 5.04 4.84 5.00 5.08 5.18 5.47 5.93 6.12 6.10 5.42 4.17 2.33 2.08 1.99 1.95 2.06 2.77 4.68 5.92 5.65 4.93 4.75 4.90 4.96 5.05 5.31 5.71 5.88 5.86 5.26 4.18 2.59 2.38 2.30 2.26 2.36 2.97 4.60 5.67 5.43 4.82 4.67 4.79 4.85 4.92 5.14 5.49 5.63 5.61 5.10 4.19 2.85 2.68 2.61 2.58 2.66 3.17 4.53 5.42 5.22 4.71 4.59 4.69 4.74 4.80 4.98 5.27 5.39 5.37 4.95 4.20 3.11 2.97 2.92 2.90 2.96 3.37 4.46 5.17 5.01 4.60 4.50 4.58 4.62 4.67 4.82 5.05 5.14 5.13 4.79 4.21 3.38 3.27 3.24 3.22 3.27 3.57 4.39 4.92 4.80 4.49 4.42 4.48 4.51 4.55 4.66 4.83 4.90 4.89 4.64 4.22 3.64 3.57 3.55 3.53 3.57 3.77 4.31 4.67 4.59 4.38 4.33 4.38 4.39 4.42 4.49 4.61 4.66 4.65 4.48 4.23 3.90 3.87 3.86 3.85 3.87 3.97 4.24 4.42 4.38 4.28 4.25 4.27 4.28 4.29 4.33 4.39 4.41 4.41 4.32 4.24 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.17 4.

Hour of Day
13 

Speed Bin Classification 

The network link hourly data was also stratified by speed bin. As previously mentioned, conical 

volume-delay curves were used to develop hourly times and speed by link. MOVES defines 16 

"speed bins" which describe the average driving speed on a roadtype or highway network link. 

Table 2.1-5 lists the speed bins and ranges that were assigned to the network link data by hour. 

Table 2.1-5 

Listing of MOVES Speed Bins 

Speed Bin Lower Range Upper Range 

1 <= 2.4 

2 2.5 7.4 

3 7.5 12.4 

4 12.5 17.4 

5 17.5 22.4 

6 22.5 27.4 

7 27.5 32.4 

8 32.5 37.4 

9 37.5 42.4 

10 42.5 47.4 

11 47.5 52.4 

12 52.5 57.4 

13 57.5 62.4 

14 62.5 67.4 

15 67.5 72.4 

16 => 72.5 

2.2 Development of MOVES Input Files 

The mobile source emissions used for the Rome analysis reflect all federal and state mobile 

source control rules, including federal tailpipe standards and gasoline sulfur and volatility limits. 

Interagency Consultation 

To prevent transportation conformity problems, the emissions used in preparation of emission 

inventories for SIP MVEB development and transportation conformity analysis must be 

consistent. Interagency consultation helps to assure consistency between the two procedures. 

The MOVES input parameters for the Rome PM2.5 Maintenance Plan mobile source emissions 

modeling were established through interagency consultation and are listed below: 

1) Emission Factor Model: MOVES2010a - Database: MOVES20100830 
14 

a. Emission Process - using MOVES in Inventory mode for a July day, which was 

annualized for 2007 and 2023 Maintenance Plan Inventories, and as the basis for 

2023 motor vehicle emissions budgets 

2) MOVES Inputs – 

a. Temperature and relative humidity 

i. 2007 data from National Mobile Inventory Model’s (NMIM) default 

database (NCD20090531) 

b. Fuel 

i. Floyd County MOVES defaults for July 

c. Use MOVES defaults for a July weekday for budgets 

d. 2002 Regional Fleet Age Distribution 

i. Derived from R.L. Polk & Co. registration data for Floyd county 

ii. Default for HDDV Class 8B 

e. Regional Vehicle Population 

i. Started with 2002 R.L. Polk & Co. registration data for Floyd county, as 

well as the Georgia Dept. of Revenue’s registration data for 2003 and 

2007 

ii. Vehicles by type were grown from 2002 to 2007 using different growth 

factors by vehicle type based on either Census person population estimates 

or Georgia 2007 registration data. Methodology developed by EPD for 

inputs to the SMOKE-MOVES Integration Tool. 

iii. 2023 data grown from 2007 based on estimated MPO population growth 

iv. Vehicle population for MOVES source type 62 revised using MOVES 

default VMT/VPOP ratios and VMT for HPMS type 60 data 

f. MOVES Default VMT fractions by source type, adjusted using GDOT count data 

MOVES 

MOVES20100830 is the latest database version for EPA’s motor vehicle emissions model. 

MOVES requires a variety of input files. The MOVES input data files associated with travel 

behavior have been developed using the Rome travel demand model data, Georgia vehicle 

classification counts, and MOVES national defaults. Other data sources were used to develop 

the source type population data, meteorology data, and fuel specifications. These data files were 

developed through Interagency Consultation. 

MOVES Parameters 

MOVES was run for 2007 and 2023 using the following parameters listed in Table 2.2-1. The 

RunSpecs are shown in Exhibit 1 and 3.
15 

Table 2.2-1 

MOVES Input Parameters 

Parameters Input Values 

Scale Domain: County Calculation Type: Inventory Mode Time Spans Time Aggregation: Weekday – 24 hours Month: July Year: 2007 or 2023 Geographical Boundary Floyd County Vehicles Equipment 

Fuels: 

 Diesel Fuel  Gasoline 

 Source Use Types: 

 Combination Long-haul Truck  Combination Short-haul Truck  Intercity Bus  Light Commercial Truck  Motor Home  Motorcycle  Passenger Car  Passenger Truck  Refuse Truck  School Bus  Single Unit Long-haul Truck  Single Unit Short-haul Truck  Transit Bus 

Sele

 Diesel Fuel - Combi Diesel Fuel - Combi Diesel Fuel - Interci Diesel Fuel - Light C Diesel Fuel - Motor  Diesel Fuel - Passen Diesel Fuel - Passen Diesel Fuel - Refuse  Diesel Fuel - Schoo Diesel Fuel - Single  Diesel Fuel - Single  Diesel Fuel - Transi Gasoline - Combina Gasoline - Light Co Gasoline - Motor Ho Gasoline - Motorcyc Gasoline - Passenge Gasoline - Passenge Gasoline - Refuse T Gasoline - School B Gasoline - Single U Gasoline - Single U Gasoline - Transit B
16 

Parameters Input Values 

RoadType Types: 1-5 

 Off-Network  Rural Restricted Access  Rural Unrestricted Access  Urban Restricted Access  Urban Unrestricted Access 

Pollutants and Processes PM2.5 Primary Exhaust PM2.5 – Total 

 Running Exhaust 

 Start Exhaust  Crankcase Running  Crankcase Start Exh Crankcase Extended  Extended Idle Exhau

Primary PM2.5 – Organic Carbon 

 Running Exhaust 

 Start Exhaust  Crankcase Running  Crankcase Start Exh Crankcase Extended  Extended Idle Exhau

Primary PM2.5 – Elemental 

Carbon 

 Running Exhaust 

 Start Exhaust  Crankcase Running  Crankcase Start Exh Crankcase Extended  Extended Idle Exhau

Primary PM 2.5 Sulfate 

Particulate 

 Running Exhaust 

 Start Exhaust  Crankcase Running  Crankcase Start Exh Crankcase Extended  Extended Idle Exhau

Primary PM2.5 – Brakewear  Breakwear 
17 

Parameters Input Values 

Particulate 

Primary PM2.5 – Tirewear 

Particulate 

 Tirewear 

Total Energy Consumption  Running Exhaust  Start Exhaust 

NOx Oxides of Nitrogen (NOx) 

 Running Exhaust 

 Start Exhaust  Crankcase Running  Crankcase Start Exh Crankcase Extended 

Extended Idle Exhau

Output General Output 

Units: 

 Units: Grams  Energy Units: Joules  Distance Units: Miles 

Ac

 Distance Traveled  Population 

Methodology to Develop MOVES Input Data The data files to run MOVES are entered via the County Data Importer. The methodologies to prepare the data 

Table 2.2-2 with their associated Excel Workbook and Worksheet names. The input data are shown in Exhibits 

and 2023. 
18 

Table 2.2-2 

Methodology to Prepare MOVES Input Data Files 

For the County Data Importer 

Input Data from Travel Demand Model

County Data 

Manager Inputs Worksheet Methodology 

AverageSpeed Distribution avgSpeedDistribution 

The weekday link hourly vehicle hours travelled (VHT) 

type and speed bin. The MOVES defaults for the 13 so

used to allocate to vehicle type. The fraction of time in e

hour based on vehicle type, road type, and average spe

the fractions sum to one for each combination of vehicle 

hour.

Ramp Fraction RoadType 

The weekday link VMT is summarized for interstafacilities by urban versus rural area type classificationsVMT of the total interstate/freeway and ramp VMT is ca

RoadTypeDistribution roadTypeDistribution 

The weekday link hourly VMT is summarized by ro

defaults for the 13 source types by year are used to alThe fraction of VMT by road type and vehicle type ifractions sum to one for each vehicle type. 

Vehicle Type VMT HPMSVtypeYear 

The weekday VMT is summarized by MOVES roadtype the vehicle classification counts6 for the Georgia area ou

county area by the 6 HPMS vehicle types. The fractioand 30 are then re-distributed based on the MOVES southe year. This is because the vehicle classification coucounters which count vehicles by the number of axlecounts do not accurately reflect the difference betweeSUVs. The daily VMT is annualized using the EPA Excel workbook.

6 (A summary of the vehicle classification counts and description of the methodology used is in Exhibit 5) 
19 

County Data 

Manager Inputs Worksheet Methodology 

MonthVMTFraction MOVES National Defaults DayVMTFraction MOVES National Defaults HourVMTFraction 

The weekday link hourly VMT is summarized by roadtypMOVES defaults for the 13 source types by year are used type. The fraction of VMT by road type and vehicle type the fractions sum to one for each vehicle type by roadtyp

hourVMTFraction must sum to 1 for each source type-road combination.

Input Data from Other Sources 

County Data 

Manager Inputs Worksheet Methodology 

Source Type Population sourceTypeYear 

Vehicle population by source types was developed using 

registration data. The 2002 data were grown to 2007 usin

population estimates or Georgia annual vehicle regipopulation for source type 62 (long-haul trucks) w

corresponding VMT for HPMS type 60 data from 

worksheet and national default ratios of VMT and vehito account for activity from trucks not registered bpopulation projection was used to calculate 2023 vehicle 

I/M Programs IMCoverage No I/M Program Fuel Supply Fuel Supply MOVES Defaults for Floyd County Fuel Formulation MOVES Defaults for Floyd County Meteorology Data ZoneMonthHour NCD20090531- 2007 data for Floyd County Age Distribution sourceTypeAgeDistribution

Age distributions in MOBILE6 format were derived fr

Co. registration data for all vehicle types, except for HMOBILE6 defaults were used. They were converted into EPA converter.
20 

2.4 Nonattainment Area Emissions Analysis Summary 

A factor was developed to annualize the weekday emissions to include weekend activity. The 

factor was based on the MOVES defaults for DayVMTFractions which is being used as part of 

the MOVES inputs. The following formula was used: 

Number of weekday equivalents in a year = 365*(5/7)+365*(2/7)*MOVES Urban 

Weekend Adjustment Factor(.7793) = 

341.9809 (rounded to 342) 

This is shown in cell D34 of tab "Import HPMS AADVMT and Factors" in the EPA 

AADVMTCalculator Excel workbook. The daily emissions are produced in grams and are 

converted to tons by dividing by 907,184.74. The daily emissions in tons are then multiplied by 

342 to get annual emissions. Table 2.4-1 lists the results from the regional emissions analysis 

produced using the travel demand model in daily grams while Table 2.4-2 lists the emissions in 

annual tons. 

Table 2.4-1 

Summary of Mobile Source Emissions for Rome Nonattainment Area 

(in Daily Grams) 

   PM2.5 Nox 

Year Running 

Nonrunning Total Running Non-running Total

2007 289,662.78 21,369.56 311,032.33 7,670,499.78 1,290,807.42 8,961,307.20

2023 76,927.03 7,531.20 84,458.23 1,596,478.25 650,232.98 2,246,711.23

Table 2.4-2 

Summary of Mobile Source Emissions for Rome Nonattainment Area 

(in Annual Tons) 

   PM2.5 Nox 

Year Running Non-running Total Running Non-running Total

2007 109.2001 8.0561 117.2562 2,891.7053 486.6221 3,378.3274

2023 29.0008 2.8392 31.8400 601.8571 245.1316 846.9887

A series of sensitivity tests were performed for the future years 2023, 2035 and 2040. These 

tests assumed different growth scenarios in the nonattainment area and were used to develop 

safety margins. The last major Regional Transportation Plan was developed in 2008. The 

update of the Regional Transportation Plan for 2040 is underway and is due April 29, 2012. The 

estimated growth rate between 2023 and 2040 is less than 10%. An alternative scenario was 

tested where the growth doubled between 2023 and 2040 to account for uncertainty in the future 

growth projections and transportation improvements. 
21 

Exhibit 1: 2007 RunSpec 

<runspec>  <description><![CDATA[Floyd County 2007 PM2.5 maintenance plan Inventory  - Revised Mode Inputs using VMT by vehicle group from GDOT HPMS data weighted by model vmt by road distribution(in vehicletype.dRun#5 - Used revised HDveh Pop 62 - revised src pop Run 6 - Revised vmtvehicletype using MOVES lookups for 2007 for veh type 20 & 30-Revised avg spd dstrb file to use moves lookups for for year - 082611 Run#7 Used Revised Cube script for roadtypedistribution Run#8 Revise Ramp Fraction]]></description>  <modelscale value="Inv"/>  <modeldomain value="SINGLE"/>  <geographicselections>  <geographicselection type="COUNTY" key="13115" description="GEORGIA - Floyd County"/>  </geographicselections>  <timespan>  <year key="2007"/>  <month id="7"/>  <day id="5"/>  <beginhour id="1"/>  <endhour id="24"/>  <aggregateBy key="Hour"/>  </timespan>  <onroadvehicleselections>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="62" sourcetypename="Combination Long- <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="61" sourcetypename="Combination Short-

 <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="41" sourcetypename="Intercity Bus"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="32" sourcetypename="Light Commercial T <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="21" sourcetypename="Passenger Car"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="31" sourcetypename="Passenger Truck"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="51" sourcetypename="Refuse Truck"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="43" sourcetypename="School Bus"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="53" sourcetypename="Single Unit Long-h <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="52" sourcetypename="Single Unit Short-h <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="42" sourcetypename="Transit Bus"/> 

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="61" sourcetypename="Combination Short-ha <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="32" sourcetypename="Light Commercial Tru

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="11" sourcetypename="Motorcycle"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="21" sourcetypename="Passenger Car"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="31" sourcetypename="Passenger Truck"/> 
22 

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="51" sourcetypename="Refuse Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="43" sourcetypename="School Bus"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="53" sourcetypename="Single Unit Long-hau

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="52" sourcetypename="Single Unit Short-hau <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="42" sourcetypename="Transit Bus"/>  </onroadvehicleselections>  <offroadvehicleselections>  </offroadvehicleselections>  <offroadvehiclesccs>  </offroadvehiclesccs>  <roadtypes>  <roadtype roadtypeid="1" roadtypename="Off-Network"/>  <roadtype roadtypeid="2" roadtypename="Rural Restricted Access"/>  <roadtype roadtypeid="3" roadtypename="Rural Unrestricted Access"/>  <roadtype roadtypeid="4" roadtypename="Urban Restricted Access"/>  <roadtype roadtypeid="5" roadtypename="Urban Unrestricted Access"/>  </roadtypes>  <pollutantprocessassociations>  <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="1" processname="Ru

 <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="2" processname="St

 <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="15" processname="C <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="16" processname="C <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="17" processname="CExhaust"/>  <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="90" processname="E <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="1" processna

 <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="2" processna <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="15" processnExhaust"/>  <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="16" processn <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="17" processnExhaust"/>  <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="90" processn <pollutantprocessassociation pollutantkey="116" pollutantname="Primary PM2.5 - Brakewear Particulate" processkey="9" pr

 <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" procExhaust"/> 
23 

 <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" procExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processn <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="2" processn <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="15" procesExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="16" procesExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="17" procesExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="90" proces <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="1" proce <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="2" proce <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="15" procExhaust"/>  <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="16" procExhaust"/>  <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="17" procIdle Exhaust"/>  <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="90" procExhaust"/>  <pollutantprocessassociation pollutantkey="117" pollutantname="Primary PM2.5 - Tirewear Particulate" processkey="10" pro <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="1" processname="Runni

 <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="2" processname="Start E

 <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="15" processname="Cran

 <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="16" processname="Cran <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="17" processname="Cran <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="90" processname="Exte <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="1" processname=" <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="2" processname=" <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="90" processname=

 </pollutantprocessassociations>  <databaseselections>  <databaseselection servername="" databasename="floyd_2007_pm25_inventory_in8" description=""/>  </databaseselections>  <internalcontrolstrategies> <internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgreuseParameters No ]]></internalcontrolstrategy>  </internalcontrolstrategies>  <inputdatabase servername="" databasename="" description=""/> 
24 

 <uncertaintyparameters uncertaintymodeenabled="false" numberofrunspersimulation="0" numberofsimulations="0"/>  <geographicoutputdetail description="NATION"/>  <outputemissionsbreakdownselection>  <modelyear selected="false"/>  <fueltype selected="false"/>  <emissionprocess selected="true"/>  <onroadoffroad selected="true"/>  <roadtype selected="true"/>  <sourceusetype selected="false"/>  <movesvehicletype selected="false"/>  <onroadscc selected="false"/>  <offroadscc selected="false"/>  <estimateuncertainty selected="false" numberOfIterations="2" keepSampledData="false" keepIterations="false"/> 

 <sector selected="false"/>  <engtechid selected="false"/>  <hpclass selected="false"/>  </outputemissionsbreakdownselection>  <outputdatabase servername="" databasename="floyd_2007_pm25__Inventory_output8" description=""/>  <outputtimestep value="Hour"/>  <outputvmtdata value="true"/>  <outputsho value="false"/>  <outputsh value="false"/>  <outputshp value="false"/>  <outputshidling value="false"/>  <outputstarts value="false"/>  <outputpopulation value="true"/>  <scaleinputdatabase servername="localhost" databasename="floyd_2007_pm25_inventory_in8" description=""/>  <pmsize value="0"/>  <outputfactors>  <timefactors selected="true" units="Hours"/>  <distancefactors selected="true" units="Miles"/>  <massfactors selected="true" units="Grams" energyunits="Joules"/>  </outputfactors>  <savedata>  </savedata>  <donotexecute>  </donotexecute>  <generatordatabase shouldsave="false" servername="" databasename="" description=""/> 
25 

 <donotperformfinalaggregation selected="false"/>  <lookuptableflags scenarioid="draft_rates" truncateoutput="false" truncateactivity="false"/> </runspec> 
26 

Exhibit 2: MOVES Input Data for Floyd County Emissions 

for PM2.5 SIP Budget – 2007 

Source Type Population 

yearID sourceTypeID sourceTypePopulation

2007 11 2545

2007 21 40909

2007 31 27051

2007 32 8683

2007 41 10

2007 42 6

2007 43 273

2007 51 25

2007 52 963

2007 53 68

2007 54 110

2007 61 400

2007 62 404

Ramp Fraction 

roadTypeID RampFraction 

2 0 

4 0.3601 

Sample Average Speed Distribution 

sourceTypeid roadTypeID hourDayID AvgSpeedBinID AvgSpeedFraction 

11 3 115 8 0.29629 

21 3 115 8 0.29629 

31 3 115 8 0.29629 

32 3 115 8 0.29629 

41 3 115 8 0.29629 

42 3 115 8 0.29629 

43 3 115 8 0.29629 

51 3 115 8 0.29629 

52 3 115 8 0.29629 

53 3 115 8 0.29629 

54 3 115 8 0.29629 

61 3 115 8 0.29629 

62 3 115 8 0.29629 

11 3 115 9 0.26695 

21 3 115 9 0.26695 

31 3 115 9 0.26695 
27 

sourceTypeid roadTypeID hourDayID AvgSpeedBinID AvgSpeedFraction 

32 3 115 9 0.26695 

41 3 115 9 0.26695 

42 3 115 9 0.26695 

43 3 115 9 0.26695 

51 3 115 9 0.26695 

52 3 115 9 0.26695 

53 3 115 9 0.26695 

54 3 115 9 0.26695 

61 3 115 9 0.26695 

62 3 115 9 0.26695 

11 3 115 10 0.17178 

21 3 115 10 0.17178 

31 3 115 10 0.17178 

32 3 115 10 0.17178 

41 3 115 10 0.17178 

42 3 115 10 0.17178 

43 3 115 10 0.17178 

51 3 115 10 0.17178 

52 3 115 10 0.17178 

53 3 115 10 0.17178 

54 3 115 10 0.17178 

61 3 115 10 0.17178 

62 3 115 10 0.17178 

Note: File contains19,969 records and is available on request 

Road Type Distribution 

sourceTypeid roadTypeID RoadTypeVMTFraction

11 1 0

11 2 0

11 3 0.348028

11 4 0.014664

11 5 0.637308

21 1 0

21 2 0

21 3 0.348028

21 4 0.014664

21 5 0.637308

31 1 0

31 2 0

31 3 0.348028
28 

sourceTypeid roadTypeID RoadTypeVMTFraction

31 4 0.014664

31 5 0.637308

32 1 0

32 2 0

32 3 0.348028

32 4 0.014664

32 5 0.637308

41 1 0

41 2 0

41 3 0.348027

41 4 0.014663

41 5 0.637310

42 1 0

42 2 0

42 3 0.348025

42 4 0.014661

42 5 0.637314

43 1 0

43 2 0

43 3 0.348029

43 4 0.014663

43 5 0.637309

51 1 0

51 2 0

51 3 0.348028

51 4 0.014663

51 5 0.637309

52 1 0

52 2 0

52 3 0.348028

52 4 0.014664

52 5 0.637308

53 1 0

53 2 0

53 3 0.348028

53 4 0.014665

53 5 0.637308

54 1 0

54 2 0

54 3 0.348028
29 

sourceTypeid roadTypeID RoadTypeVMTFraction

54 4 0.014664

Vehicle Type VMT – HPMSVtypeYear Worksheet 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT 

10 2007 3,190,897.45 0 

20 2007 573,623,546.46 0 

30 2007 381,547,677.11 0 

40 2007 4,716,005.83 0 

50 2007 34,749,741.64 0 

60 2007 43,522,253.35 0 

Vehicle Type VMT – Sample HourVMTFraction 

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 3 5 1 0.01000 

11 3 5 2 0.00600 

11 3 5 3 0.00480 

11 3 5 4 0.00450 

11 3 5 5 0.00670 

11 3 5 6 0.01850 

11 3 5 7 0.05011 

11 3 5 8 0.07731 

11 3 5 9 0.06132 

11 3 5 10 0.04821 

11 3 5 11 0.04791 

11 3 5 12 0.05121 

11 3 5 13 0.05361 

11 3 5 14 0.05471 

11 3 5 15 0.06051 

11 3 5 16 0.07271 

11 3 5 17 0.08281 

11 3 5 18 0.08271 

11 3 5 19 0.05891 

11 3 5 20 0.04181 

11 3 5 21 0.03321 

11 3 5 22 0.03031 

11 3 5 23 0.02441 

11 3 5 24 0.01772 

11 4 5 1 0.01000 

11 4 5 2 0.00600 

11 4 5 3 0.00480 
30 

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 4 5 4 0.00450 

11 4 5 5 0.00669 

11 4 5 6 0.01849 

11 4 5 7 0.05012 

11 4 5 8 0.07730 

11 4 5 9 0.06140 

11 4 5 10 0.04825 

11 4 5 11 0.04792 

11 4 5 12 0.05123 

11 4 5 13 0.05362 

11 4 5 14 0.05473 

11 4 5 15 0.06052 

11 4 5 16 0.07268 

11 4 5 17 0.08273 

11 4 5 18 0.08261 

11 4 5 19 0.05891 

11 4 5 20 0.04182 

11 4 5 21 0.03321 

11 4 5 22 0.03033 

11 4 5 23 0.02443 

11 4 5 24 0.01771 

Note: File contains 1,561 records and is available on request 

Meteorology Data 

monthID zoneID HourID temperature relHumidity 

7 131150 1 54.5833 75.0083

7 131150 2 53.5083 76.6667

7 131150 3 52.5500 78.0500

7 131150 4 51.6250 79.4583

7 131150 5 50.8417 80.4917

7 131150 6 50.1833 81.2333

7 131150 7 49.8917 81.6917

7 131150 8 51.1583 79.8917

7 131150 9 54.5417 74.8167

7 131150 10 59.2750 67.2333

7 131150 11 63.8167 59.5167

7 131150 12 67.6750 53.2000

7 131150 13 70.6667 48.6583

7 131150 14 72.7500 45.4750

7 131150 15 74.1250 43.3917

7 131150 16 74.7250 42.2667
31 

monthID zoneID HourID temperature relHumidity 

7 131150 17 74.5500 42.1583

7 131150 18 73.1083 43.8750

7 131150 19 69.9583 48.6000

7 131150 20 65.8417 55.3000

7 131150 21 62.0500 61.9583

7 131150 22 59.5083 66.4833

7 131150 23 57.6000 69.9167

7 131150 24 55.9750 72.7167

Age Distribution - Sample 

sourceTypeID yearID ageID ageFraction

21 2007 0 0.009402

21 2007 1 0.041008

21 2007 2 0.047009

21 2007 3 0.056111

21 2007 4 0.054511

21 2007 5 0.053411

21 2007 6 0.060812

21 2007 7 0.057812

21 2007 8 0.072314

21 2007 9 0.063913

21 2007 10 0.062212

21 2007 11 0.054211

21 2007 12 0.049310

21 2007 13 0.045609

21 2007 14 0.045309

21 2007 15 0.040608

21 2007 16 0.032907

21 2007 17 0.026605

21 2007 18 0.024405

21 2007 19 0.019004

21 2007 20 0.011402

21 2007 21 0.006201

21 2007 22 0.005601

21 2007 23 0.005501

21 2007 24 0.005403

21 2007 25 0.005306

21 2007 26 0.005212

21 2007 27 0.005119

21 2007 28 0.005027

21 2007 29 0.004937

21 2007 30 0.023807

31 2007 0 0.011666
32 

sourceTypeID yearID ageID ageFraction

31 2007 1 0.037502

31 2007 2 0.048189

31 2007 3 0.043896

31 2007 4 0.045392

31 2007 5 0.053793

31 2007 6 0.072358

31 2007 7 0.026665

31 2007 8 0.038199

31 2007 9 0.044025

31 2007 10 0.044785

31 2007 11 0.039277

31 2007 12 0.033409

31 2007 13 0.049020

31 2007 14 0.058062

31 2007 15 0.054176

31 2007 16 0.038568

31 2007 17 0.054861

31 2007 18 0.056149

31 2007 19 0.050207

31 2007 20 0.031296

31 2007 21 0.025647

31 2007 22 0.035746

31 2007 23 0.000424

31 2007 24 0.000328

31 2007 25 0.000289

31 2007 26 0.000253

31 2007 27 0.000215

31 2007 28 0.000188

31 2007 29 0.000167

31 2007 30 0.005248

Note: File contains 373 records and is available on request 
33 

Exhibit 3: 2023 RunSpec 

runspec>  <description><![CDATA[Floyd County 2023 PM2.5 maintenance plan Inventory  - Revised Mode Inputs 070811 using VMT by vehicle group from GDOT HPMS data (in vehicletype.dat) file weighted by model vmt by rRun11 - 091411 - Use new model set with Cube scripts for 2023 Run 12 - 091911 - Ramp Fractions Run 13 - 101111 - Using HPMS adj factors from 2009 model ]]></description>  <modelscale value="Inv"/>  <modeldomain value="SINGLE"/>  <geographicselections>  <geographicselection type="COUNTY" key="13115" description="GEORGIA - Floyd County"/>  </geographicselections>  <timespan>  <year key="2023"/>  <month id="7"/>  <day id="5"/>  <beginhour id="1"/>  <endhour id="24"/>  <aggregateBy key="Hour"/>  </timespan>  <onroadvehicleselections>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="62" sourcetypename="Combination Long- <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="61" sourcetypename="Combination Short-

 <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="41" sourcetypename="Intercity Bus"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="32" sourcetypename="Light Commercial T <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="21" sourcetypename="Passenger Car"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="31" sourcetypename="Passenger Truck"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="51" sourcetypename="Refuse Truck"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="43" sourcetypename="School Bus"/>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="53" sourcetypename="Single Unit Long-h <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="52" sourcetypename="Single Unit Short-h <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="42" sourcetypename="Transit Bus"/> 

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="61" sourcetypename="Combination Short-ha <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="32" sourcetypename="Light Commercial Tru

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="11" sourcetypename="Motorcycle"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="21" sourcetypename="Passenger Car"/> 
34 

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="31" sourcetypename="Passenger Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="51" sourcetypename="Refuse Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="43" sourcetypename="School Bus"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="53" sourcetypename="Single Unit Long-hau

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="52" sourcetypename="Single Unit Short-hau <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="42" sourcetypename="Transit Bus"/>  </onroadvehicleselections>  <offroadvehicleselections>  </offroadvehicleselections>  <offroadvehiclesccs>  </offroadvehiclesccs>  <roadtypes>  <roadtype roadtypeid="1" roadtypename="Off-Network"/>  <roadtype roadtypeid="2" roadtypename="Rural Restricted Access"/>  <roadtype roadtypeid="3" roadtypename="Rural Unrestricted Access"/>  <roadtype roadtypeid="4" roadtypename="Urban Restricted Access"/>  <roadtype roadtypeid="5" roadtypename="Urban Unrestricted Access"/>  </roadtypes>  <pollutantprocessassociations>  <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="1" processname="Ru

 <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="2" processname="St

 <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="15" processname="C <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="16" processname="C <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="17" processname="C <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="90" processname="E <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="1" processna

 <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="2" processna <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="15" processn <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="16" processn <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="17" processnExhaust"/>  <pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="90" processn <pollutantprocessassociation pollutantkey="116" pollutantname="Primary PM2.5 - Brakewear Particulate" processkey="9" pr

 <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" proc <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" proc <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" proc <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processn <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="2" processn
35 

 <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="15" proces <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="16" proces <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="17" procesExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="90" proces <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="1" proce <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="2" proce <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="15" proc <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="16" proc <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="17" procExhaust"/>  <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="90" proc <pollutantprocessassociation pollutantkey="117" pollutantname="Primary PM2.5 - Tirewear Particulate" processkey="10" pro <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="1" processname="Runni

 <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="2" processname="Start E

 <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="15" processname="Cran

 <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="16" processname="Cran <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="17" processname="Cran <pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="90" processname="Exte <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="1" processname=" <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="2" processname=" <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="90" processname=

 </pollutantprocessassociations>  <databaseselections>  <databaseselection servername="" databasename="floyd_2023_pm25_inventory_in13" description=""/>  </databaseselections>  <internalcontrolstrategies> <internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgreuseParameters No ]]></internalcontrolstrategy>  </internalcontrolstrategies>  <inputdatabase servername="" databasename="" description=""/>  <uncertaintyparameters uncertaintymodeenabled="false" numberofrunspersimulation="0" numberofsimulations="0"/>  <geographicoutputdetail description="NATION"/>  <outputemissionsbreakdownselection>  <modelyear selected="false"/>  <fueltype selected="false"/>  <emissionprocess selected="true"/>  <onroadoffroad selected="true"/>  <roadtype selected="true"/>  <sourceusetype selected="false"/> 
36 

 <movesvehicletype selected="false"/>  <onroadscc selected="false"/>  <offroadscc selected="false"/>  <estimateuncertainty selected="false" numberOfIterations="2" keepSampledData="false" keepIterations="false"/> 

 <sector selected="false"/>  <engtechid selected="false"/>  <hpclass selected="false"/>  </outputemissionsbreakdownselection>  <outputdatabase servername="" databasename="floyd_2023_pm25_inventory_output13" description=""/> 

 <outputtimestep value="Hour"/>  <outputvmtdata value="true"/>  <outputsho value="false"/>  <outputsh value="false"/>  <outputshp value="false"/>  <outputshidling value="false"/>  <outputstarts value="false"/>  <outputpopulation value="true"/>  <scaleinputdatabase servername="localhost" databasename="floyd_2023_pm25_inventory_in13" description=""/>  <pmsize value="0"/>  <outputfactors>  <timefactors selected="true" units="Hours"/>  <distancefactors selected="true" units="Miles"/>  <massfactors selected="true" units="Grams" energyunits="Joules"/>  </outputfactors>  <savedata>  </savedata>  <donotexecute>  </donotexecute>  <generatordatabase shouldsave="false" servername="" databasename="" description=""/>  <donotperformfinalaggregation selected="false"/>  <lookuptableflags scenarioid="draft_rates" truncateoutput="false" truncateactivity="false"/> </runspec> 
37 

Exhibit 4: MOVES Input Data for Floyd County Emissions 

for PM2.5 SIP Budget – 2023 

Source Type Population 

yearID sourceTypeID sourceTypePopulation

2023 11 2790

2023 21 44846

2023 31 29654

2023 32 9518

2023 41 11

2023 42 7

2023 43 299

2023 51 28

2023 52 1055

2023 53 75

2023 54 120

2023 61 439

2023 62 449

Ramp Fraction 

roadTypeID RampFraction 

2 0 

4 0.4481 

Sample Average Speed Distribution 

sourceTypeid roadTypeID hourDayID AvgSpeedBinID AvgSpeedFraction 

11 3 15 8 0.33047 

21 3 15 8 0.33047 

31 3 15 8 0.33047 

32 3 15 8 0.33047 

41 3 15 8 0.33047 

42 3 15 8 0.33047 

43 3 15 8 0.33047 

51 3 15 8 0.33047 

52 3 15 8 0.33047 

53 3 15 8 0.33047 

54 3 15 8 0.33047 

61 3 15 8 0.33047 

62 3 15 8 0.33047 

11 3 15 9 0.25898 

21 3 15 9 0.25898 

31 3 15 9 0.25898 

32 3 15 9 0.25898 
38 

sourceTypeid roadTypeID hourDayID AvgSpeedBinID AvgSpeedFraction 

41 3 15 9 0.25898 

42 3 15 9 0.25898 

43 3 15 9 0.25898 

51 3 15 9 0.25898 

52 3 15 9 0.25898 

53 3 15 9 0.25898 

54 3 15 9 0.25898 

61 3 15 9 0.25898 

62 3 15 9 0.25898 

11 3 15 10 0.19630 

21 3 15 10 0.19630 

31 3 15 10 0.19630 

32 3 15 10 0.19630 

41 3 15 10 0.19630 

42 3 15 10 0.19630 

43 3 15 10 0.19630 

51 3 15 10 0.19630 

52 3 15 10 0.19630 

53 3 15 10 0.19630 

54 3 15 10 0.19630 

61 3 15 10 0.19630 

62 3 15 10 0.19630 

Note: File contains19,969 records and is available on request 

Road Type Distribution 

sourceTypeid roadTypeID RoadTypeVMTFraction

11 1 0.00000

11 2 0.00000

11 3 0.34001

11 4 0.01702

11 5 0.64297

21 1 0.00000

21 2 0.00000

21 3 0.34001

21 4 0.01702

21 5 0.64297

31 1 0.00000

31 2 0.00000

31 3 0.34001

31 4 0.01702

31 5 0.64297
39 

sourceTypeid roadTypeID RoadTypeVMTFraction

32 1 0.00000

32 2 0.00000

32 3 0.34001

32 4 0.01702

32 5 0.64297

41 1 0.00000

41 2 0.00000

41 3 0.34001

41 4 0.01703

41 5 0.64297

42 1 0.00000

42 2 0.00000

42 3 0.34001

42 4 0.01702

42 5 0.64297

43 1 0.00000

43 2 0.00000

43 3 0.34001

43 4 0.01702

43 5 0.64297

51 1 0.00000

51 2 0.00000

51 3 0.34000

51 4 0.01703

51 5 0.64297

52 1 0.00000

52 2 0.00000

52 3 0.34001

52 4 0.01702

52 5 0.64297

53 1 0.00000

53 2 0.00000

53 3 0.34001

53 4 0.01702

53 5 0.64297

54 1 0.00000

54 2 0.00000

54 3 0.34001

54 4 0.01702

54 5 0.64297
40 

sourceTypeid roadTypeID RoadTypeVMTFraction

61 1 0.00000

61 2 0.00000

61 3 0.34001

61 4 0.01702

61 5 0.64297

62 1 0.00000

62 2 0.00000

62 3 0.34001

62 4 0.01702

62 5 0.64297

Vehicle Type VMT – HPMSVtypeYear Worksheet 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT 

10 2023 3,681,075.81 0.00 

20 2023 718,351,473.19 0.00 

30 2023 385,703,070.19 0.00 

40 2023 5,437,626.61 0.00 

50 2023 40,045,911.27 0.00 

60 2023 49,937,856.49 0.00 

Vehicle Type VMT – Sample HourVMTFraction 

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 3 5 1 0.01000 

11 3 5 2 0.00600 

11 3 5 3 0.00480 

11 3 5 4 0.00449 

11 3 5 5 0.00669 

11 3 5 6 0.01849 

11 3 5 7 0.05012 

11 3 5 8 0.07730 

11 3 5 9 0.06141 

11 3 5 10 0.04826 

11 3 5 11 0.04792 

11 3 5 12 0.05123 

11 3 5 13 0.05363 

11 3 5 14 0.05473 

11 3 5 15 0.06052 

11 3 5 16 0.07267 

11 3 5 17 0.08271 

11 3 5 18 0.08259 
41 

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 3 5 19 0.05890 

11 3 5 20 0.04182 

11 3 5 21 0.03321 

11 3 5 22 0.03033 

11 3 5 23 0.02444 

11 3 5 24 0.01774 

11 4 5 1 0.01000 

11 4 5 2 0.00600 

11 4 5 3 0.00480 

11 4 5 4 0.00449 

11 4 5 5 0.00669 

11 4 5 6 0.01849 

11 4 5 7 0.05012 

11 4 5 8 0.07728 

11 4 5 9 0.06143 

11 4 5 10 0.04828 

11 4 5 11 0.04793 

11 4 5 12 0.05124 

11 4 5 13 0.05363 

11 4 5 14 0.05474 

11 4 5 15 0.06052 

11 4 5 16 0.07266 

11 4 5 17 0.08268 

11 4 5 18 0.08256 

11 4 5 19 0.05891 

11 4 5 20 0.04182 

11 4 5 21 0.03321 

11 4 5 22 0.03033 

11 4 5 23 0.02444 

11 4 5 24 0.01775 

11 5 5 1 0.01002 

11 5 5 2 0.00601 

11 5 5 3 0.00480 

11 5 5 4 0.00446 

Note: File contains 1,561 records and is available on request 

Meteorology Data 

monthID zoneID HourID temperature relHumidity 

7 131150 1 54.5833 75.0083

7 131150 2 53.5083 76.6667

7 131150 3 52.5500 78.0500
42 

monthID zoneID HourID temperature relHumidity 

7 131150 4 51.6250 79.4583

7 131150 5 50.8417 80.4917

7 131150 6 50.1833 81.2333

7 131150 7 49.8917 81.6917

7 131150 8 51.1583 79.8917

7 131150 9 54.5417 74.8167

7 131150 10 59.2750 67.2333

7 131150 11 63.8167 59.5167

7 131150 12 67.6750 53.2000

7 131150 13 70.6667 48.6583

7 131150 14 72.7500 45.4750

7 131150 15 74.1250 43.3917

7 131150 16 74.7250 42.2667

7 131150 17 74.5500 42.1583

7 131150 18 73.1083 43.8750

7 131150 19 69.9583 48.6000

7 131150 20 65.8417 55.3000

7 131150 21 62.0500 61.9583

7 131150 22 59.5083 66.4833

7 131150 23 57.6000 69.9167

7 131150 24 55.9750 72.7167

Age Distribution - Sample 

sourceTypeID yearID ageID ageFraction

21 2023 0 0.009402

21 2023 1 0.041008

21 2023 2 0.047009

21 2023 3 0.056111

21 2023 4 0.054511

21 2023 5 0.053411

21 2023 6 0.060812

21 2023 7 0.057812

21 2023 8 0.072314

21 2023 9 0.063913

21 2023 10 0.062212

21 2023 11 0.054211

21 2023 12 0.049310

21 2023 13 0.045609

21 2023 14 0.045309

21 2023 15 0.040608

21 2023 16 0.032907

21 2023 17 0.026605

21 2023 18 0.024405
43 

sourceTypeID yearID ageID ageFraction

21 2023 19 0.019004

21 2023 20 0.011402

21 2023 21 0.006201

21 2023 22 0.005601

21 2023 23 0.005501

21 2023 24 0.005403

21 2023 25 0.005306

21 2023 26 0.005212

21 2023 27 0.005119

21 2023 28 0.005027

21 2023 29 0.004937

21 2023 30 0.023807

31 2023 0 0.011666

31 2023 1 0.037502

31 2023 2 0.048189

31 2023 3 0.043896

31 2023 4 0.045392

31 2023 5 0.053793

31 2023 6 0.072358

31 2023 7 0.026665

31 2023 8 0.038199

31 2023 9 0.044025

31 2023 10 0.044785

31 2023 11 0.039277

31 2023 12 0.033409

31 2023 13 0.049020

31 2023 14 0.058062

31 2023 15 0.054176

31 2023 16 0.038568

31 2023 17 0.054861

31 2023 18 0.056149

31 2023 19 0.050207

31 2023 20 0.031296

31 2023 21 0.025647

31 2023 22 0.035746

31 2023 23 0.000424

31 2023 24 0.000328

31 2023 25 0.000289

31 2023 26 0.000253

31 2023 27 0.000215

31 2023 28 0.000188

31 2023 29 0.000167

31 2023 30 0.005248

Note: File contains 373 records and is available on request 
44 

Exhibit 5: Vehicle Classification Counts 

Weekday vehicle classification counts from GDOT were obtained for the years 2008 through 2010 for the entire 

state of Georgia. The counts for the 13 county nonattainment Atlanta MPO area were eliminated from the 

calculations. The vehicle classification counts collected were stratified based on the FHWA vehicle 

classifications. The counts were then summarized into the 6 HPMS vehicle type categories based the FHWA 

vehicle classifications. The percent by vehicle type by road type based on functional classification was calculated 

by year and then averaged for the three years. Table1 list the counts by year. Table 2 lists the final factors of 

VMT by vehicle type by road type based on the counts. These values were used to weight the VMT from 

the travel demand model by road type by vehicle type for input into AADVMT worksheet.
45 

Table 1 

GDOT Vehicle Classification Counts 

2008 Statewide minus ARC 13 County MPO Area

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

10 Motorcycles Class 1 415,477 276,557 703,762 273,810 0.3% 0.4% 

20 Passenger Cars Class 2 78,146,859 45,813,738 134,370,835 70,721,022 55.0% 64.5% 

30 Other 2 axle‐4 tire vehicles Class 3 23,048,320 16,181,222 37,536,241 19,040,830 16.2% 22.8% 

40 Buses Class 4 1,348,962 412,115 1,464,298 372,563 0.9% 0.6% 

50 Single Unit Trucks Class 5‐7 5,177,758 3,054,596 6,643,091 2,735,728 3.6% 4.3% 

60 Combination Trucks Class 8‐13 33,983,937 5,320,479 24,746,197 1,724,995 23.9% 7.5% 

142,121,313 71,058,707 205,464,424 94,868,948 100% 100% 

2009 Statewide minus ARC 13 County MPO Area

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

10 Motorcycles Class 1 543,569 380,461 552,296 334,712 0.3% 0.3% 

20 Passenger Cars Class 2 99,860,922 74,033,369 123,617,071 92,974,090 57.6% 65.0% 

30 Other 2 axle‐4 tire vehicles Class 3 29,013,174 25,257,242 35,153,989 26,580,997 16.7% 22.2% 

40 Buses Class 4 1,445,231 636,498 1,260,763 480,718 0.8% 0.6% 

50 Single Unit Trucks Class 5‐7 5,931,816 4,577,695 5,977,298 3,774,967 3.4% 4.0% 

60 Combination Trucks Class 8‐13 36,572,792 9,058,107 19,363,526 2,566,080 21.1% 7.9% 

173,367,504 113,943,372 185,924,943 126,711,564 100% 100% 

Code HPMS Vehicle Type

FHWAVeh Class

Weekday Counts Percent by 

Weekday Counts Percent by 

Code HPMS Vehicle Type

FHWAVeh Class
46 

Table 1 

GDOT Vehicle Classification Counts (continued) 

2010 Statewide minus ARC 13 County MPO Area

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

10 Motorcycles Class 1 531,781 465,637 767,240 413,477 0.3% 0.3% 

20 Passenger Cars Class 2 115,785,383 90,783,273 163,511,794 103,655,999 58.1% 64.4% 

30 Other 2 axle‐4 tire vehicles Class 3 33,029,619 31,077,060 45,607,435 29,985,205 16.6% 22.1% 

40 Buses Class 4 1,648,242 837,435 1,403,597 520,635 0.8% 0.6% 

50 Single Unit Trucks Class 5‐7 6,527,272 5,527,587 7,477,099 4,162,187 3.3% 3.9% 

60 Combination Trucks Class 8‐13 41,728,831 12,222,357 21,492,738 2,746,397 20.9% 8.7% 

Total 199,251,128 140,913,349 240,259,903 141,483,900 100% 100% 

2008‐2010 Statewide minus ARC 13 County MPO Area Summary

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

10 Motorcycles Class 1 1,490,827 1,122,655 2,023,298 1,021,999 0.3% 0.3% 

20 Passenger Cars Class 2 293,793,164 210,630,380 421,499,700 267,351,111 57.7% 67.7% 

30 Other 2 axle‐4 tire vehicles Class 3 85,091,113 72,515,524 118,297,665 75,607,032 16.7% 20.7% 

40 Buses Class 4 4,442,435 1,886,048 4,128,658 1,373,916 0.9% 0.6% 

50 Single Unit Trucks Class 5‐7 17,636,846 13,159,878 20,097,488 10,672,882 3.4% 3.8% 

60 Combination Trucks Class 8‐13 112,285,560 26,600,943 65,602,461 7,037,472 21.0% 6.9% 

Total 514,739,945 325,915,428 631,649,270 363,064,412 100% 100% 

Code HPMS Vehicle Type

FHWAVeh Class

Weekday Counts Percent by 

Code HPMS Vehicle Type

FHWA

Veh Class

Weekday Counts Percent by 
47 

Table 2 

Final Factors for VMT by Vehicle Type by Road Type 

Code 

HPMS Vehicle 

Type 

FHWA 

Veh 

Class 

Rural 

Restricted 

Access 

Factor

Rural 

Unrestricted 

Access 

Factor

Urban 

Restricted 

Access 

Factor 

Urban 

Unrestricted 

Access 

Factor

10 Motorcycles Class 1 0.002909 0.003512 0.003196 0.002817

20 Passenger Cars Class 2 0.568990 0.646239 0.666475 0.737280

30 

Other 2 axle-4 tire 

vehicles Class 3 0.165098 0.223307 0.187197 0.207472

40 Buses Class 4 0.008700 0.005776 0.006583 0.003800

50 Single Unit Trucks 

Class 5-

7 0.034469 0.040796 0.031867 0.029349

60 Combination Trucks 

Class 8-

13 0.219834 0.080369 0.104681 0.019282

 Total 1.000000 1.000000 1.000000 1.000000

Since the vehicle classification counts are collected using counters that do not adequately 

distinguish between passenger cars and SUVs, the MOVES defaults for vehicle types 20 and 30 by 

road type were used to redistribute the VMT. The MOVES Defaults are listed in Table 3. 

Table 3 

MOVES Defaults 

Percent VMT by Vehicle Type 

Vehicle Type 

Year 10 20 30 40 50 60

1999 0.00390 0.58310 0.33480 0.00280 0.02610 0.04920

2000 0.00380 0.58250 0.33600 0.00280 0.02570 0.04920

2001 0.00340 0.58240 0.33740 0.00250 0.02590 0.04850

2002 0.00330 0.58110 0.33850 0.00240 0.02660 0.04820

2003 0.00330 0.57880 0.34060 0.00230 0.02690 0.04810

2004 0.00340 0.57260 0.34600 0.00220 0.02730 0.04850

2005 0.00360 0.57060 0.34770 0.00220 0.02710 0.04870

2006 0.00400 0.55990 0.35850 0.00220 0.02750 0.04780

2007 0.00450 0.55070 0.36630 0.00220 0.02790 0.04840

2008 0.00510 0.54610 0.36980 0.00230 0.02870 0.04800

2009 0.00530 0.54760 0.37480 0.00210 0.02660 0.04370

2010 0.00530 0.54360 0.37770 0.00220 0.02770 0.04350

2011 0.00530 0.53940 0.37830 0.00240 0.02970 0.04500

2012 0.00530 0.53700 0.37740 0.00250 0.03140 0.04640
48 

Vehicle Type 

Year 10 20 30 40 50 60

2013 0.00520 0.53640 0.37610 0.00260 0.03240 0.04730

2014 0.00520 0.53780 0.37370 0.00260 0.03310 0.04760

2015 0.00511 0.54185 0.36926 0.00266 0.03358 0.04754

2016 0.00505 0.54655 0.36414 0.00270 0.03413 0.04743

2017 0.00500 0.55148 0.35868 0.00274 0.03465 0.04744

2018 0.00495 0.55719 0.35240 0.00278 0.03509 0.04759

2019 0.00490 0.56346 0.34559 0.00281 0.03548 0.04777

2020 0.00484 0.57033 0.33840 0.00283 0.03581 0.04779

2021 0.00479 0.57743 0.33138 0.00285 0.03599 0.04755

2022 0.00475 0.58459 0.32444 0.00286 0.03613 0.04723

2023 0.00471 0.59142 0.31755 0.00288 0.03639 0.04705

2024 0.00466 0.59782 0.31089 0.00291 0.03674 0.04698

2025 0.00462 0.60374 0.30470 0.00294 0.03709 0.04692

2026 0.00458 0.60921 0.29896 0.00296 0.03745 0.04684

2027 0.00455 0.61410 0.29377 0.00300 0.03784 0.04674

2028 0.00452 0.61852 0.28903 0.00303 0.03828 0.04662

2029 0.00450 0.62265 0.28449 0.00307 0.03876 0.04652

2030 0.00448 0.62625 0.28038 0.00311 0.03930 0.04648

2031 0.00444 0.62984 0.27688 0.00313 0.03959 0.04611

2032 0.00440 0.63303 0.27380 0.00316 0.03987 0.04574

2033 0.00436 0.63573 0.27104 0.00319 0.04023 0.04545

2034 0.00432 0.63812 0.26857 0.00321 0.04058 0.04519

2035 0.00429 0.64015 0.26636 0.00324 0.04096 0.04501

2036 0.00425 0.64184 0.26447 0.00327 0.04134 0.04483

2037 0.00421 0.64323 0.26287 0.00330 0.04173 0.04465

2038 0.00418 0.64417 0.26173 0.00333 0.04211 0.04447

2039 0.00414 0.64476 0.26096 0.00336 0.04249 0.04428

2040 0.00411 0.64532 0.26017 0.00340 0.04289 0.04411

2041 0.00407 0.64586 0.25937 0.00343 0.04331 0.04396

2042 0.00404 0.64630 0.25864 0.00346 0.04374 0.04381

2043 0.00401 0.64666 0.25799 0.00350 0.04418 0.04366

2044 0.00398 0.64693 0.25741 0.00353 0.04462 0.04352

2045 0.00395 0.64711 0.25692 0.00357 0.04507 0.04338

2046 0.00392 0.64719 0.25653 0.00360 0.04552 0.04324

2047 0.00389 0.64720 0.25620 0.00364 0.04598 0.04310

2048 0.00386 0.64715 0.25593 0.00368 0.04643 0.04295

2049 0.00383 0.64704 0.25572 0.00371 0.04690 0.04281

2050 0.00380 0.64689 0.25554 0.00375 0.04736 0.04266
49 

Exhibit 5: Vehicle Registration Data 

Georgia's Revised Registration Distribution by Age 

Overview

R.L. Polk & Co. (Polk) maintains databases encompassing all registered vehicles 

in operation by state. Polk acquires the source registration data from the states 

and then processes and enhances the data. Key data elements Polk used for 

grouping vehicle registered in Georgia by their appropriate composite (i.e., 

gasoline and diesel) vehicle types were: vehicle make, vehicle model, engine 

make, engine model, fuel type, cab type, bed length, wheel configuration, vehicle 

type, gross vehicle weight rating (GVWR)7 class, model year, and registration 

geography (i.e., county). 

Vehicle characteristic data elements used by Polk are derived from the unique 17 

position vehicle identification number (VIN) assigned to every vehicle. Vehicle 

geography is assigned based on the registration address linked to each VIN. 

In order to assign a MOBILE6 category to all registered vehicles, Polk 

constructed a master vehicle workfile using data from Polk’s TIPNet and NVPP 

databases. This master vehicle workfile accounts for all registered vehicles, 

including: cars, vans, sport utility vehicles, trucks, buses, school buses, and 

motorcycles (GVWR classes 1-8 + motorcycle). The GVWR classes are: 

Class 1 0 - 6,000 lbs. 

Class 2 6,001 - 10,000 lbs. 

Class 3 10,001 - 14,000 lbs. 

Class 4 14,001 - 16,000 lbs. 

Class 5 16,001 - 19,500 lbs. 

Class 6 19,501 - 26,000 lbs. 

Class 7 26,001 - 33,000 lbs. 

Class 8 33,001 - 150,000 lbs. 

The TIPNet database contains vehicles from full-size pickups/vans through class 8 

(GVWR classes 1c-8), and is structured to serve the commercial vehicle market. The 

NVPP database contains vehicles GVWR classes 1-3 and is designed to serve the car, 

light truck/van, and motorcycle aftermarket. 

 7

 The GVWR is the maximum weight of the vehicle when it is fully loaded, as specified by the 

manufacturer. 
50 

Using the data elements listed above, Polk assigned one of the 16 MOBILE6 

categories to each of the vehicles in the workfile. Care was taken to assure that no 

makes and models are duplicated between the two databases. Note that the unit 

volume for same make/model vehicles can be divided among two or more MOBILE6 

categories due to varying vehicle types and GVWR classes within a specific 

make/model. TIPNet data supplies GVWR classes 1c-8 (full-size pickups/vans & 

heavier), while NVPP data provides passenger car, motorcycle, light truck, and light 

vans from GVWR class 1. 

The 16 composite MOBILE6 vehicle types are listed and defined below, with 

examples of the types of vehicles they include. 

Number Abbreviation Description 

1 LDV Light-Duty (LD) Vehicles (Passenger Cars)

- Class 1 GVWR 

- Include: Passenger Cars 

- Fuel: All Types 

- Source: R.L. Polk NVPP as of October 2002 

2 LDT1 LD Trucks 1 (0-6,000 lbs. GVWR, 0- 3,750 lbs. 

LVW8

) 

- Class 1 GVWR 

- Trucks, SUVs, & Vans 

- Exclude Full-Size Pickups & Vans 

- Fuel: All Types 

- Source: R.L. Polk NVPP as of October 2002 

3 LDT2 LD Trucks 2 (0-6,000 lbs. GVWR, 3,751-5,750 lbs. 

LVW) 

- Class 1 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

- Include all Full-Size Pickups & Vans (e.g. 150/1500 series vehicles: F150, 

C/K 1500, E150, Ram 1500 etc.) 

- Include Vehicle Types: Incomplete Pickup + Cab Chassis 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 & NVPP as of October 2002 

4 LDT3 LD Trucks 3 (6,001-8,500 lbs. GVWR, 0-5,750 lbs. ALVW9

) 

- Class 2 GVWR 

- Trucks, SUVs, & Vans 

- GVWR: 6,001-8,000 for Ford, Chevy, Dodge, plus all Toyota Tundra 

Models 

 8

 Loaded vehicle weight, the weight of vehicle sitting empty (curb weight) plus 300 pounds. 

9

 Adjusted loaded vehicle weight, average of the gross vehicle weight and the curb weight. 
51 

- Fuel: All Types 

- Exclude: Pickups with Long Bed or Vans with Extended Length (Except 

Tundra) 

- Exclude Vehicle Types: Incomplete Pickup + Cab Chassis + Incomplete 

Vehicle + Straight Truck + School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 & NVPP as of October 2002 

Number Abbreviation Description 

5 LDT4 Light-Duty Trucks 4 (6,001-8,500 lbs. GVWR, >5,750 lbs. 

ALVW) 

- Class 2 GVWR 

- Trucks, SUVs, & Vans 

- GVWR: 6,001-8,000 for Ford, Chevy, & Dodge 

- Exclude: all Toyota Tundra Models 

- Fuel: All Types 

- Include: Pickups with Long Bed or Vans with Extended Length 

- Include Vehicle Types: Incomplete Pickup + Cab Chassis + Incomplete 

Vehicle + Straight Truck 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 

6 HDV2B Class 2b Heavy-Duty Vehicles (8,501-10,000 lbs. 

GVWR) 

- Class 2 GVWR 

- Trucks, SUVs, & Vans 

- GVWR: 8,001-10,000 for Ford, Chevy, & Dodge 

- Exclude: All Toyota Tundra Models 

- Fuel: All Types 

- Include: Pickups with Long Bed or Vans with Extended Length 

- Include Vehicle Types: Incomplete Pickup + Cab Chassis + Incomplete 

Vehicle + Straight Truck 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 

7 HDV3 Class 3 Heavy-Duty Vehicles (10,001-14,000 lbs. 

GVWR) 

- Class 3 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 

Number Abbreviation Description 
52 

8 HDV4 Class 4 Heavy-Duty Vehicles (14,001-16,000 lbs. 

GVWR) 

- Class 4 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 

9 HDV5 Class 5 Heavy-Duty Gasoline Vehicles (16,001-19,500 lbs. 

GVWR) 

- Class 5 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 

10 HDV6 Class 6 Heavy-Duty Vehicles (19,501-26,000 lbs. 

GVWR) 

- Class 6 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 

11 HDV7 Class 7 Heavy-Duty Vehicles (26,001-33,000 lbs. 

GVWR) 

- Class 7 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Source: R.L. Polk TIPNet as of March 2003 

12 HDV8A Class 8a Heavy-Duty Vehicles (33,001-60,000 lbs. 

GVWR) 

- Class 8 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

- Exclude Vehicle Types: School Bus + Bus Non-School (Coach) 

- Include Vehicle Types: 4x2 Non-Tractor Vehicles + All Tractors + Motor 

Home Chassis 

- Source: R.L. Polk TIPNet as of March 2003 

13 HDV8B Class 8b Heavy-Duty Vehicles (>60,000 lbs. 

GVWR) 

- Class 8 GVWR 

- Trucks, SUVs, & Vans 
53 

- Fuel: All Types 

- Exclude Vehicle Types: All Tractors + School Bus + Bus Non-School 

(Coach) 

- Exclude Wheels: 4x2 

- Source: R.L. Polk TIPNet as of March 2003 

14 HDBS School Buses

- Include Vehicle Type: School Bus 

- Fuel: All Types 

- Source: R.L. Polk TIPNet as of March 2003 

Number Abbreviation Description 

15 HDBT Transit & Urban Buses

- Include Vehicle Type: Bus Non-School (Coach) 

- Fuel: All Types 

- Source: R.L. Polk TIPNet as of March 2003 

16 MC Motorcycles (All) 

- Fuel: All Types 

- Source: R.L. Polk NVPP as of October 2002 

The data acquired from Polk was queried to determine the number of vehicles 

registered in the Rome-Floyd metropolitan statistical area by age and vehicle type. 

Results of this query were used to develop Registration distribution by age inputs. 

For each of the 16 composite vehicle types, the fraction of all vehicles of that type 

which are zero-to-one model year old, two model years old, three model years old, 

etc., up to the oldest category, 25-model-years-and-older, was determined. The 

resulting input data is shown on the following two pages. Note that the Polk-derived 

distribution for Class 8b vehicles (vehicle type 13) is commented out; in accordance 

with EPA guidance, defaults were used for this vehicle type. 
54

Exhibit 6: Source Type Population Input Data Preparation 

1) Date sources 

2002-2003 Polk’s data: Registration data from R. L. Polk & Co.'s National Vehicle Population 

Profile ® (current as of October 2002) and R. L. Polk & Co.'s TIPNet ® (current as of March 2003) 

are used. This database includes number of vehicles by age and 16 vehicle types in each Georgia 

county, and has been used to develop age distribution. 

Georgia registration data (2003 and 2007): These registration data were obtained from 

www.georgiastats.uga.edu. This database includes number of vehicles by passenger vehicles, trucks, 

trailers, motorcycles, buses and others in each county as explained on the Georgia Department of 

Revenue website (http://motor.etax.dor.ga.gov/stats/renewalsstats.aspx). Passenger Vehicles include 

Ambulances, Convertibles, Coupes, Hearses, Jeeps, Limousines, Mixers, Motor Homes, MultiPurpose Vehicles, Roadsters, Station Wagons, Touring Cars, Vans, 2 Doors, 3 Doors, and 4 Doors. 

Trucks include Truck Tractors, Trucks, and Wreckers. 

2) Methodology 

The Polk’s data were summarized by 16 vehicles types in each county and then grown to 2007 using 

different growth factors by vehicle types (Table 1). The number of HDBS, HDBT and MC in Polk’s 

data is comparable to Georgia registration data (Table 2 and Table 3). Therefore, the numbers of 

HDBS and HDBT were grown to 2007 by multiplying ratios of the number of buses in Georgia motor 

vehicle registration data in 2007 and 2003. The number of MC was grown to 2007 by multiplying 

ratios of the number of motor cycles in Georgia motor vehicle registration data in 2007 and 2003. 

The number of the rest of vehicle types was grown to 2007 by multiplying ratios of human population 

in 2007 and 2002. The Georgia motor vehicle registration data were not used for these vehicle types 

due to the difficulty to match the vehicle type used in Georgia motor vehicle registration data to the 

16 vehicle types as used in the Polk's data. Since the ratios of 2007 and 2003 passenger cars and 

trucks in motor vehicle registration data are comparable to the ratios of population data (Table 4), 

population data were used as the growth indicator. 

Table 1. List of different growth factors used by vehicle types 

Vehicle types Growth factor 

HDBS Georgia registration data (2003 and 2007), Buses 

HDBT Georgia registration data (2003 and 2007), Buses 

HDV2B Population 2002 and 2007 

HDV3 Population 2002 and 2007 

HDV4 Population 2002 and 2007 

HDV5 Population 2002 and 2007 

HDV6 Population 2002 and 2007 

HDV7 Population 2002 and 2007 

HDV8A Population 2002 and 2007 

HDV8B Population 2002 and 2007 

LDT1 Population 2002 and 2007 

LDT2 Population 2002 and 2007 

LDT3 Population 2002 and 2007 

LDT4 Population 2002 and 2007 

LDV Population 2002 and 2007 
55

MC Georgia registration data (2003 and 2007), Motor cycles

Table 2. Summary of 2002-2003 Polk’s data by 16 mobile vehicle types in four Georgia regions 

Vehicle types ATL13 ATL7 GAGAS GAOTHER Total 

HDBS 7,854 1,333 2,032 8,221 19,440

HDBT 1,362 102 139 540 2,143

HDV2B 56,809 11,761 15,405 49,039 133,014

HDV3 27,628 5,996 7,822 23,797 65,243

HDV4 13,623 2,262 2,850 10,351 29,086

HDV5 6,005 1,162 1,550 5,577 14,294

HDV6 19,294 4,088 5,932 21,688 51,002

HDV7 16,380 2,528 3,838 15,309 38,055

HDV8A 37,555 4,307 6,619 31,883 80,364

HDV8B 14,449 2,201 3,061 10,926 30,637

LDT1 722,044 131,873 181,393 607,189 1,642,499

LDT2 95,101 28,933 37,692 156,187 317,913

LDT3 302,139 62,530 75,409 284,851 724,929

LDT4 43,616 8,019 9,721 39,432 100,788

LDV 1,723,769 255,647 346,907 1,383,696 3,710,019

MC 50,081 10,657 13,767 41,123 115,628

Table 3. Summary of 2003 Georgia registration data by 4 mobile vehicle types 

Vehicle types ATL13 ATL7 GAGAS GAOTHER Total 

Buses 10,676 1,559 2,434 9,797 21,237 

Trucks 558,496 168,930 237,022 823,867 1,788,315 

Passenger Cars 2,259,027 339,456 449,177 1,744,474 4,792,134 

Motorcycles 46,836 10,203 13,124 38,561 108,724 

Table 4. Comparison between different growth factors 

 Ratios 

 2002 2003 2007 2007/2002 2007/2003

Motor vehicle registration 

Passenger Car 4,792,134 5,330,256 1.112 

Trucks 1,788,315 1,952,470 1.092 

Motor Cycle 108,724 174,617 1.606 

Bus 21,237 35,124 1.654 

Population in Georgia, U.S. Census 

Population 8,585,535 8,735,259 9,533,761 1.110 1.091 

Total Average Annual Daily VMT in Georgia, Georgia DOT 445 report, miles 

VMT 292,562,380 296,810,994 305,327,543 1.044 1.029 

MOVES national SALESGROWTH factor defaults 

Motorcycle 1.383 1.311 

Passenger Car 0.940 1.001 

Passenger Truck 0.972 0.948 

Light Commercial Truck 0.972 0.948 

Intercity Bus 1.353 1.268 

Transit Bus 1.353 1.268 

School Bus 1.353 1.268 

Refuse Truck 1.353 1.268 

Single Unit Short-haul Truck 1.353 1.268 

Single Unit Long-haul Truck 1.353 1.268 

Motor Home 1.353 1.268 

Combination Short-haul Truck 1.464 1.405 

Combination Long-haul Truck 1.464 1.405 
56

The projected 2007 vehicle population by 16 vehicle types in each county were then converted to 32 

vehicles types, which were matched with 28 vehicle types and 12 vehicle types (corresponding to 12 

SCC codes) as shown in the EPA MOVES converter tool. The EPA MOVES converter tool was also 

used to convert vehicle population in MOVES format by each of the four reference counties. These 

populations are the sum of populations of all counties sharing the same reference counties. 
57