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 Protec tion Division (EPD) worked with the Georgia Department of Transportation (GDOT) and the Floyd-Rome Urban Transportation Study (FRUTS), the Metropolitan Planning Or ganization (MPO) for Ro me, to develop mobile 1 source emissions inventories for the purpose of establishing mo tor vehicle emissions budgets (MVEB) for the 2023 maintenance year for the Rome PM2.5 Main tenance State Implementation Plan (SIP) revision, hereinafter called th e Rome PM2.5 Maintenance Pla n. 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 Ar eas 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 c onformity 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 availabl e, 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 c onformity determinations. The alignment of methodologies for MVEB and transportation c onformity 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. Environmenta l 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 Subp art 1 of the Clean Air Act., the more general nonattainment area planning and c ontrol 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 at tainment 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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## 1.2 Emissions Analysis – Models and Assumptions

In accordance with Section 93.105(b) of the Tr ansportation Conformity Rule and Sections 106(g) and 106(h) of Georgia's tr ansportation conformity SIP, a ll of which require interagency consultation for SIP development, a detailed listi ng of the procedures an d planning assumptions used for the regional emissions analysis supporting development of the MV EB was distributed to the interagency consultation committee for review on Septem ber 15, 2011. The assumptions used to develop Rome's conforming Long Range Transportation Plan and Transportation Improvement Program were also used to deve lop the network and emissions for the Rome PM2.5 Maintenance Plan MVEB, which requ ired emissions for the year 2023.

The MOVES input files reflect all federal and st ate motor vehicle emissi on control programs. In addition, the input files were customized to refl ect 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 a nd application of the travel demand models for the urban areas outside of Atlanta. This s ection 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 a lternatives and land use scenarios 3. Modeled volumes validated against observed counts 4. Reasonable agreement between travel time s used for trip-distribution and trip assignment 5. Reasonable sensitivity to time, cost and other factors affecti ng travel choices 6. Capacity-sensitive traffic assignment methodology

Model Attributes 1 & 2 (Socio-Economic Data ) The primary data inputs to tr avel demand models are socio-ec onomic data, such as population and employment, and transportation networks. Mode ling 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 know ledge). Other reasonableness and logi c 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 a nd 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 th at socio-economic data reflect the transportati on alternatives4 being considered. This relates to the fact that improved transportation accessibility can alter land use patterns. However, it is generally accepted th at significant improvements in transportation accessibility are necessary to bring about relative ly small changes in land use. Due to their complexity, land use models are generally utiliz ed in only a few large metropolitan areas in the United States. Georgia’s MPOs, with the excep tion 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 tran sportation improvements. Future forecasts are generally made by first developing regional contro l totals for expected growth. A llocation of expected growth is then done using known development patterns and proposals as the basis, taking into consideration planned infrastructure improvement s (new highways, sewer extensions, etc.). If unanticipated major projects are ev aluated 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 th e 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 th e validation of travel demand m odels against observed traffic counts. Model validation is the process of insuri ng travel models produce results that reasonably replicate observed travel patterns. Properly validated models not only replicate observed conditions, but they also use accu rate 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 accur acy and reasonableness during each step of the process. These inputs and outputs include transportation networ k attributes, trip generation parameters and results, trip distribution parame ters and average trip le ngths by purpose, auto occupancy rates, and speed-volume relationships.

Highway Networks – Air Quality Attributes Georgia DOT develops and maintains highway ne tworks with FRUTS re view and assistance. Highway network attributes are reviewed for accu racy using the state roadway characteristics database, aerial photography and site visits / loca l knowledge. Network link attributes include the HPMS functional classification, so that modele d and observed Vehicle Miles Traveled (VMT) can be compared by county. Networks also incl ude GDOT traffic count station numbers, so counts for the base year model can be includ ed in output networks for validation purposes.5 Highway Networks - Speed Since speeds are important for mobile emissi ons 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 ro adway classes and area t ypes. The density of population and employment is used to classify the intensity of development patterns throughout the study area. The Rome model uses the follo wing 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 cap acities 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 mp h 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-fl ow speeds were evaluated using observed speeds6 obtained from a travel time study conducted in the A ugusta 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 thes e urban areas are very similar. Through the process of model calibration and validation, the speeds were revi sed slightly for a couple of facilities to more accurately represent travel co nditions 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 severa l levels – regional, corridors and link-by-link. Regiona l evaluations include VMT, R oot Mean Squared Error and RSquared calculations. Corridor evaluations are primarily screenline and cutline comparisons. Nationally recognized maximum de sirable deviation standards ar e 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 exte rnal station volumes are refine d 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 calib rated to accurately reflect observed traffic volumes, while retaining sensible shapes to in sure reasonable congested speeds. Peak-period7 speed data obtained from the GDOT travel tim e study was used as a r easonableness check in calibrating GDOT speed-volume curves.

Trip Generation The GDOT trip generation process primarily us es 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 ch ecks 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 assignm ent are used as input to trip distribution (i.e., feedback), which strengthen s the validity of the modeled trip lengths.

Model Attribute 4 (Feedba ck 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 assi gnment 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 “rea sonable 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 be fore the region’s person trips are converted to vehicle trips. This trip-e nd model estimates transit patronage based on socio-economic characteristics such as income or auto-own ership, 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 assignmen t algorithm. The traffic assignment algorithm is iterative, running through successive applica tions 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 assign ment is an iterative process that reflects travel demand assigned to minimum time paths as we ll as the effects of congestion. In each assignment iteration, traffic volumes are loaded on to network links and travel times are adjusted in response to the volume to capacity relatio nships. Final assigned volumes are derived by summing a percentage of the loadings from each iteration. The percentages reflect congested8 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 valid ated against traffic c ounts at several levels – regional, corridors (s creenlines) and link- by-link. Regional evaluations include VMT, Root Mean Squared Error and R-S quared calculations for volume-count matching. Corridor evaluations are primarily screenline comparis ons. 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 volum es. 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 tr avel 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 attri butes 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 (inc luding 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 pr ocedure was used that multiplied the number of intra-zonal vehicle trips from the vehicle trip ta ble by the zone centroid di stance 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 mo del was adjusted based on the VMT estimates that GDOT develops for the Highway Performan ce 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 proce ss should be made consistent in the aggregate with HPMS." Consistent with this long-st anding 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 Sour ces, 1992, http://www.epa.gov/otaq/invntory/r92009.pdf.9 traveled (VMT) shall be considered th e primary measure of VMT within the portion of the nonattainment or maintenan ce area and for the functional classes of roadways included in HPMS.... For area s 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 vali dation 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 Nonattainme nt Areas for Use in SIPs and Conformity

, identified several approaches for preparing PM2.5 emi ssions. The guidance also specified that the interagency consultation process should be used to determine which approach is most appropriate for the area. The Rome inte ragency 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 base d on the average annual daily HPMS VMT for the model calibration year. In the case of Rome there are two model calibration years, 2006 4 and 2009 5 . The current Rome model that was used to perform the last conformity determination was calibrated and validated for the year 2006. A ne w Rome model has been developed that was calibrated and validated for the year 2009. Th e 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 f unctional class level. The aggregate functional classification level was used since FHWA eliminat ed the urban/rural area type distinction from HPMS functional classifications beginni ng with the 2009 data, reported in 2010. ( Guidance for the Functional Classification of Highways (upd ated), Federal Highway Administration, October 14, 2008.)

To determine the “2006 HPMS VMT” adjustme nt 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 Of fice of Transportation Data “445 Report.” The data was summarized for the Rome MPO area whic h 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 calcula te the 2006 HPMS adjustment factors:

HPMS Adjustment Factor i =(2006 HPMS VMT i /2006 Model VMT i )

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 factor s were developed for 2009 using the same method. The 2009 factors were applied to the model applicati on for the year 2023 and will be applied for future conformity determina tions. Table 2.1-1 lists the adjustment factors based on the comparison between the HPMS VM T 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 2009Adjustment 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 VM T 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 me thodology 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 queu ing-based model (QSIM) which incorporates several key features such as the use of tempor al distribution as a basis for developing hourly traffic estimates and the estimation of “peak spread ing” for both arterials and freeways. Because most analytical methods consider only the effects of p eak hour congestion (s uch as V/C ratio), a new measurement of daily conge stion was used: the Average A nnual Daily Traffic-to-Capacity (AADT/C) ratio, where capacity is the two-way cap acity. 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 traffic11 assignment to develop hourly volumes and VMT by link. Conical volume-delay curves were then used to develop hourl y 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 mode ling is listed in Table 2.1-4. Interstate and freeway ramps are functionally cla ssified as local facilities in Ge orgia. Since these facilities operate with restricted access, the facility type definition variable (a unique variable in the highway network that defines the highway faci lities based on their operation) was used to classify ramps as either rural or urban restrict ed facilities. Off-network activity is calculated within the MOVES process based on the source t ype (vehicle) population and is not an input from the travel demand model data.

Table 2.1-4 Listing of FHWA Highway Func tional 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 Ru ral unrestricted access 3 Rural major collector Rural unrestricted access 3 Rural minor collector Rura l unrestricted access 3 Rural local Rural unrestricted access 3 Urban interstate Urba n 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 512Table 2.1-3Hourly Distribution of Daily Vehicle Miles Travelled (VMT) Source: Speed and Delay Prediction Models for Planning ApplicationsAADT/CRatio123456789101112131415161718192021222324<= 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 4.18 3.32 3.03 2.44 1.778 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 4.21 3.33 3.10 2.53 1.849 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 4.22 3.33 3.13 2.58 1.8810 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 4.31 3.38 3.18 2.63 1.9111 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 4.39 3.43 3.23 2.68 1.9312 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 4.48 3.48 3.28 2.73 1.9613 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 4.45 3.54 3.35 2.85 2.1414 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 4.43 3.60 3.43 2.97 2.3315 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 4.40 3.65 3.50 3.09 2.5116 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 4.38 3.71 3.58 3.21 2.7017 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 4.35 3.77 3.65 3.33 2.8818 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 4.32 3.82 3.72 3.45 3.0619 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 4.30 3.88 3.80 3.57 3.2520 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 4.27 3.94 3.87 3.69 3.4321 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 4.25 4.00 3.95 3.81 3.6222 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 4.22 4.05 4.02 3.93 3.8023 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 4.19 4.11 4.09 4.05 3.9824 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 4.17 4.17 4.17 4.17 4.17Hour of Day13 Speed Bin Classification The network link hourly data was al so stratified by speed bin. As previously mentioned, conical volume-delay curves were used to develop hou rly times and speed by link. MOVES defines 16 "speed bins" which describe the average drivin g 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 sta ndards 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 tr ansportation conformity analysis must be consistent. Interagency consulta tion helps to assure consistenc y between the two procedures. The MOVES input parameters for the Rome PM 2.5 Maintenance Plan mobile source emissions modeling were established through interage ncy consultation and are listed below:

1) Emission Factor Model: MOVES 2010a - Database: MOVES2010083014 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 I nventory 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. re gistration data for Floyd county ii. Default for HDDV Class 8B

e. Regional Vehicle Population i. Started with 2002 R.L. Polk & Co. re gistration data for Floyd county, as well as the Georgia Dept. of Reve nue’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 eith er Census person population estimates or Georgia 2007 registration data. Methodology deve loped by EPD for inputs to the SMOKE-MOVE S Integration Tool. iii. 2023 data grown from 2007 based on estimated MPO population growth iv. Vehicle population for MOVES sour ce type 62 revised using MOVES default VMT/VPOP ratios and VMT for HPMS type 60 data

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

MOVES MOVES20100830 is the latest data base 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, me teorology data, and fuel specifica tions. 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 3.15Table 2.2-1MOVES Input ParametersParametersInput ValuesScaleDomain: County Calculation Type: Inventory ModeTime SpansTime Aggregation: Weekday –24 hours Month: July Year: 2007 or 2023GeographicalBoundary Floyd CountyVehiclesEquipmentFuels:Diesel FuelGasolineSource 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 BusSelections:Diesel Fuel - Combination Long-haul TruckDiesel Fuel - Combination Short-haul TruckDiesel Fuel - Intercity BusDiesel Fuel - Light Commercial TruckDiesel Fuel - Motor HomeDiesel Fuel - Passenger CarDiesel Fuel - Passenger TruckDiesel Fuel - Refuse TruckDiesel Fuel - School BusDiesel Fuel - Single Unit Long-haul TruckDiesel Fuel - Single Unit Short-haul TruckDiesel Fuel - Transit BusGasoline - Combination Short-haul TruckGasoline - Light Commercial TruckGasoline - Motor HomeGasoline - MotorcycleGasoline - Passenger CarGasoline - Passenger TruckGasoline - Refuse TruckGasoline - School BusGasoline - Single Unit Long-haul TruckGasoline - Single Unit Short-haul TruckGasoline - Transit Bus16ParametersInput ValuesRoadType Types: 1-5Off-NetworkRural Restricted AccessRural Unrestricted AccessUrban Restricted AccessUrban Unrestricted AccessPollutants andProcessesPM2.5Primary Exhaust PM2.5 – TotalRunning ExhaustStart ExhaustCrankcase Running ExhaustCrankcase Start ExhaustCrankcase Extended Idle ExhaustExtended Idle ExhaustPrimary PM2.5 – Organic CarbonRunning ExhaustStart ExhaustCrankcase Running ExhaustCrankcase Start ExhaustCrankcase Extended Idle ExhaustExtended Idle ExhaustPrimary PM2.5 – ElementalCarbonRunning ExhaustStart ExhaustCrankcase Running ExhaustCrankcase Start ExhaustCrankcase Extended Idle ExhaustExtended Idle ExhaustPrimary PM 2.5 SulfateParticulateRunning ExhaustStart ExhaustCrankcase Running ExhaustCrankcase Start ExhaustCrankcase Extended Idle ExhaustExtended Idle ExhaustPrimary PM2.5 – BrakewearBreakwear17ParametersInput ValuesParticulatePrimary PM2.5 – TirewearParticulateTirewearTotal Energy ConsumptionRunning ExhaustStart ExhaustNOxOxides of Nitrogen (NOx)Running ExhaustStart ExhaustCrankcase Running ExhaustCrankcase Start ExhaustCrankcase Extended Idle ExhaustExtended Idle ExhaustOutput General OutputUnits:Units: GramsEnergy Units: JoulesDistance Units: MilesActivity:Distance TraveledPopulationMethodology to Develop MOVES Input DataThe data files to run MOVES are entered via the County Data Importer. The methodologiesto prepare the data files are described inTable 2.2-2 with their associatedExcel Workbook and Worksheet names. The input data are shown in Exhibits 1-4 for the years 2007and 2023.18Table 2.2-2Methodology to Prepare MOVES Input Data FilesFor the County Data ImporterInput Data from Travel Demand ModelCounty DataManager InputsWorksheetMethodologyAverageSpeedDistributionavgSpeedDistributionThe weekday link hourly vehicle hours travelled (VHT) is summarized by roadtype and speed bin. The MOVES defaultsfor the 13 source types by year areused to allocate to vehicle type. Thefraction of time in each speed bin for eachhour based on vehicle type,road type, and average speed is calculated wherethe fractions sum to one for each combination of vehicle type and road type byhour.Ramp Fraction RoadTypeThe weekday link VMT is summarized for interstate/freeway and rampfacilities by urban versus rural area typeclassifications. The percent of rampVMT of the total interstate/freeway and ramp VMT is calculated by area type.RoadTypeDistribution roadTypeDistributionThe weekday link hourly VMT is summarized by roadtype. The MOVESdefaults for the 13 source types by yearare used to allocate to vehicle type.The fraction of VMT by road type andvehicle type is calculated where thefractions sum to one for each vehicle type.Vehicle Type VMT HPMSVtypeYearThe weekday VMT is summarized by MOVES roadtype and then weighted bythe vehicle classification counts6for the Georgia area outside of the Atlanta 20county area by the 6 HPMS vehicle types.The fractions for vehicle type 20and 30 are then re-distributed based onthe MOVES source vehicle defaults forthe year. This is because the vehicleclassification counts are collected usingcounters which count vehicles by the number of axles and as a result, thecounts do not accurately reflect the difference between passenger cars andSUVs. The daily VMT is annualizedusing the EPA AADVMT CalculatorExcel workbook.6(A summary of the vehicle classification counts and description of the methodology used is in Exhibit 5)19County DataManager InputsWorksheetMethodologyMonthVMTFractionMOVES National DefaultsDayVMTFractionMOVES National DefaultsHourVMTFractionThe weekday link hourly VMT is summarized by roadtype and hour. TheMOVES defaults for the 13 source types by year are used to allocate to vehicletype. The fraction of VMT by road typeand vehicle type is calculated wherethe fractions sum to one foreach vehicle type by roadtype.ThehourVMTFraction must sum to 1 for each source type-road type-type of daycombination.Input Data from Other SourcesCounty DataManager InputsWorksheetMethodologySource TypePopulationsourceTypeYearVehicle population by source types wasdeveloped using 2002 R.L. Polk & Co.registration data. The 2002 data were grown to 2007 using either census personpopulation estimates or Georgia annualvehicle registration data. Vehiclepopulation for source type 62 (long-haultrucks) was recalculated usingcorresponding VMTfor HPMS type 60 data from theHPMSVtypeYearworksheet and national default ratios ofVMT and vehicle population in orderto account for activity from trucksnot registered but run locally. MPOpopulation projection was used tocalculate 2023 vehicle population.I/M Programs IMCoverageNo I/M ProgramFuel SupplyFuel SupplyMOVES Defaults for Floyd CountyFuel FormulationMOVES Defaults for Floyd CountyMeteorology Data ZoneMonthHour NCD20090531-2007 data for Floyd CountyAge Distribution sourceTypeAgeDistributionAge distributions in MOBILE6 formatwere derived from2002 R.L. Polk &Co. registration data for all vehicle types, except for HDDV Class 8B whereMOBILE6 defaults were used. They wereconverted into MOVES format usingEPA converter.20 

## 2.4 Nonattainment Area Emissions Analysis Summary

A factor was developed to annualize the weekda y emissions to include weekend activity. The factor was based on the MOVES defaults for DayV MTFractions 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 "Impor t 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 regiona l 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 performe d for the future years 2023, 2035 and 2040. These tests assumed different growth scenarios in the nonattainment ar ea and were used to develop safety margins. The last major Regional Tr ansportation 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 trans portation improvements.21Exhibit 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.dat) fileRun#5 - Used revised HDveh Pop 62 - revised src popRun 6 - Revised vmtvehicletype using MOVES lookups for 2007 for veh type 20 & 30-Revised avg spd dstrb file to use moves lookups for 2007-Revised weather to averagefor year - 082611Run#7 Used Revised Cube script for roadtypedistributionRun#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="DieselFuel" sourcetypeid="62" sourcetypename="Combination Long-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="DieselFuel" sourcetypeid="61" sourcetypename="Combination Short-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="41" sourcetypename="Intercity Bus"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="32" sourcetypename="Light Commercial Truck"/><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-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="52" sourcetypename="Single Unit Short-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="42"sourcetypename="Transit Bus"/><onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline"sourcetypeid="61" sourcetypename="Combination Short-haul Truck"/><onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline"sourcetypeid="32" sourcetypename="Light Commercial Truck"/><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-haul Truck"/><onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline"sourcetypeid="52" sourcetypename="Single Unit Short-haul Truck"/><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="Running Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="15" processname="CrankcaseRunning Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="16" processname="CrankcaseStart Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="17" processname="CrankcaseExtended IdleExhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="90" processname="ExtendedIdle Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="15" processname="Crankcase RunningExhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="16" processname="Crankcase Start Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="17" processname="Crankcase Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="116" pollutantname="Primary PM2.5 - Brakewear Particulate" processkey="9" processname="Brakewear"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" processname="Crankcase RunningExhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" processname="Crankcase StartExhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" processname="Crankcase Extended IdleExhaust"/>23<pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" processname="Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="15" processname="Crankcase RunningExhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="16" processname="Crankcase StartExhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="17" processname="Crankcase Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="15" processname="Crankcase RunningExhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="16" processname="Crankcase StartExhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="17" processname="Crankcase ExtendedIdle Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="90" processname="Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="117" pollutantname="Primary PM2.5 - Tirewear Particulate" processkey="10" processname="Tirewear"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="15" processname="Crankcase Running Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="16" processname="Crankcase Start Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="17" processname="Crankcase Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="90" processname="ExtendedIdle Exhaust"/></pollutantprocessassociations><databaseselections><databaseselection servername="" databasename="floyd_2007_pm25_inventory_in8" description=""/></databaseselections><internalcontrolstrategies><internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgressStrategy"><![CDATA[useParameters 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 0.2669527 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 0.34802828 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 0.34802829 sourceTypeid roadTypeID RoadTypeVMTFraction 54 4 0.014664

Vehicle Type VMT – HPMSVtypeYear Worksheet

HPMSVtypeID yearID HPMSBa seYearVMT 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 0.0048030 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 42.266731 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 0.01166632 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 request33Exhibit 3: 2023 RunSpecrunspec><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 road type -Run11 - 091411 - Use new model set with Cube scripts for 2023Run 12 - 091911 - Ramp FractionsRun 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="DieselFuel" sourcetypeid="62" sourcetypename="Combination Long-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="DieselFuel" sourcetypeid="61" sourcetypename="Combination Short-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="41" sourcetypename="Intercity Bus"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="32" sourcetypename="Light Commercial Truck"/><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-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="52" sourcetypename="Single Unit Short-haul Truck"/><onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="42"sourcetypename="Transit Bus"/><onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline"sourcetypeid="61" sourcetypename="Combination Short-haul Truck"/><onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline"sourcetypeid="32" sourcetypename="Light Commercial Truck"/><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-haul Truck"/><onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline"sourcetypeid="52" sourcetypename="Single Unit Short-haul Truck"/><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="Running Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="15" processname="CrankcaseRunning Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="16" processname="CrankcaseStart Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="17" processname="CrankcaseExtended Idle Exhaust"/><pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="90" processname="ExtendedIdle Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="15" processname="Crankcase Running Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="16" processname="Crankcase Start Exhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="17" processname="Crankcase Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="110" pollutantname="Primary Exhaust PM2.5 - Total" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="116" pollutantname="Primary PM2.5 - Brakewear Particulate" processkey="9" processname="Brakewear"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" processname="Crankcase Running Exhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" processname="Crankcase Start Exhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" processname="Crankcase Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="2" processname="Start Exhaust"/>35<pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="15" processname="Crankcase Running Exhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="16" processname="Crankcase Start Exhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="17" processname="Crankcase Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="15" processname="Crankcase Running Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="16" processname="Crankcase Start Exhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="17" processname="Crankcase Extended IdleExhaust"/><pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="117" pollutantname="Primary PM2.5 - Tirewear Particulate" processkey="10" processname="Tirewear"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="15" processname="Crankcase Running Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="16" processname="Crankcase Start Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="17" processname="Crankcase Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="31" pollutantname="Sulfur Dioxide (SO2)" processkey="90" processname="Extended Idle Exhaust"/><pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="1" processname="Running Exhaust"/><pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="2" processname="Start Exhaust"/><pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="90" processname="ExtendedIdle Exhaust"/></pollutantprocessassociations><databaseselections><databaseselection servername="" databasename="floyd_2023_pm25_inventory_in13" description=""/></databaseselections><internalcontrolstrategies><internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgressStrategy"><![CDATA[useParameters 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 0.2589838 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 0.6429739 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 0.6429740 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 HPMSBa seYearVMT 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 0.0825941 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 78.050042 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 0.02440543 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 request44 

### 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 nona ttainment 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 ba sed on the counts. These values we re used to weight the VMT from the travel demand model by road type by ve hicle type for input into AADVMT worksheet.45Table 1GDOT Vehicle Classification Counts2008StatewideminusARC13CountyMPOAreaRuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)RuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)10 Motorcycles Class1 415,477 276,557 703,762 273,810 0.3% 0.4% 0.3% 0.3%20 PassengerCars Class2 78,146,859 45,813,738 134,370,835 70,721,022 55.0% 64.5% 65.4% 74.5%30 Other2axle‐4tirevehicles Class3 23,048,320 16,181,222 37,536,241 19,040,830 16.2% 22.8% 18.3% 20.1%40 Buses Class4 1,348,962 412,115 1,464,298 372,563 0.9% 0.6% 0.7% 0.4%50 SingleUnitTrucks Class5‐7 5,177,758 3,054,596 6,643,091 2,735,728 3.6% 4.3% 3.2% 2.9%60 CombinationTrucks Class8‐13 33,983,937 5,320,479 24,746,197 1,724,995 23.9% 7.5% 12.0% 1.8%142,121,313 71,058,707 205,464,424 94,868,948 100% 100% 100% 100%2009StatewideminusARC13CountyMPOAreaRuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)RuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)10 Motorcycles Class1 543,569 380,461 552,296 334,712 0.3% 0.3% 0.3% 0.3%20 PassengerCars Class2 99,860,922 74,033,369 123,617,071 92,974,090 57.6% 65.0% 66.5% 73.4%30 Other2axle‐4tirevehicles Class3 29,013,174 25,257,242 35,153,989 26,580,997 16.7% 22.2% 18.9% 21.0%40 Buses Class4 1,445,231 636,498 1,260,763 480,718 0.8% 0.6% 0.7% 0.4%50 SingleUnitTrucks Class5‐7 5,931,816 4,577,695 5,977,298 3,774,967 3.4% 4.0% 3.2% 3.0%60 CombinationTrucks Class8‐13 36,572,792 9,058,107 19,363,526 2,566,080 21.1% 7.9% 10.4% 2.0%173,367,504 113,943,372 185,924,943 126,711,564 100% 100% 100% 100%Code HPMSVehicleTypeFHWAVehClassWeekdayCounts PercentbyRoadTypeWeekdayCounts PercentbyRoadTypeCode HPMSVehicleTypeFHWAVehClass46Table 1GDOT Vehicle Classification Counts (continued)2010StatewideminusARC13CountyMPOAreaRuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)RuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)10 Motorcycles Class1 531,781 465,637 767,240 413,477 0.3% 0.3% 0.3% 0.3%20 PassengerCars Class2 115,785,383 90,783,273 163,511,794 103,655,999 58.1% 64.4% 68.1% 73.3%30 Other2axle‐4tirevehicles Class3 33,029,619 31,077,060 45,607,435 29,985,205 16.6% 22.1% 19.0% 21.2%40 Buses Class4 1,648,242 837,435 1,403,597 520,635 0.8% 0.6% 0.6% 0.4%50 SingleUnitTrucks Class5‐7 6,527,272 5,527,587 7,477,099 4,162,187 3.3% 3.9% 3.1% 2.9%60 CombinationTrucks Class8‐13 41,728,831 12,222,357 21,492,738 2,746,397 20.9% 8.7% 8.9% 1.9%Total 199,251,128 140,913,349 240,259,903 141,483,900 100% 100% 100% 100%2008‐2010StatewideminusARC13CountyMPOAreaSummaryRuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)RuralRestrictedAccess(FC=1)RuralUnrestrictedAccess(FC=2‐7)UrbanRestrictedAccess(FC=11‐12)UrbanUnrestrictedAccess(FC=14‐19)10 Motorcycles Class1 1,490,827 1,122,655 2,023,298 1,021,999 0.3% 0.3% 0.2% 0.2%20 PassengerCars Class2 293,793,164 210,630,380 421,499,700 267,351,111 57.7% 67.7% 73.5% 76.6%30 Other2axle‐4tirevehicles Class3 85,091,113 72,515,524 118,297,665 75,607,032 16.7% 20.7% 16.5% 18.6%40 Buses Class4 4,442,435 1,886,048 4,128,658 1,373,916 0.9% 0.6% 0.7% 0.5%50 SingleUnitTrucks Class5‐7 17,636,846 13,159,878 20,097,488 10,672,882 3.4% 3.8% 3.0% 2.7%60 CombinationTrucks Class8‐13 112,285,560 26,600,943 65,602,461 7,037,472 21.0% 6.9% 6.2% 1.4%Total 514,739,945 325,915,428 631,649,270 363,064,412 100% 100% 100% 100%Code HPMSVehicleTypeFHWAVehClassWeekdayCounts PercentbyRoadTypeCode HPMSVehicleTypeFHWAVehClassWeekdayCounts PercentbyRoadType47 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 coll ected 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 0.0464048 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 0.0426649 

### Exhibit 5: Vehicle Registration Data

Georgia's Revised Registra tion Distribution by Age

Overview

R.L. Polk & Co. (Polk) maintains databa ses encompassing all registered vehicles in operation by state. Polk acquires the source registration data from the states and then processes and enha nces 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 regist ration address linked to each VIN.

In order to assign a MOBILE6 category to all registered vehicles, Polk constructed a master vehicle workfile us ing data from Polk’s TIPNet and NVPP databases. This master vehicle workf ile accounts for all registered vehicles, including: cars, vans, sport utility ve hicles, trucks, buses, school buses, and motorcycles (GVWR classes 1-8 + moto rcycle). The GV WR 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 vehi cles GVWR classes 1-3 and is designed to serve the car, light truck/van, and motorc ycle 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 wo rkfile. 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 prov ides passenger car, motorcyc le, 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 lb s. GVWR, 0- 3,750 lbs. LVW 8 )

- 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: Inco mplete 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. ALVW 9 )

- 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 th e 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 Description52 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, & Vans53 - Fuel: All Types

- Exclude Vehicle Types: All Tracto rs + 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 deve lop Registration distribution by age inputs. For each of the 16 composite vehicle types, th e fraction of all vehicles of that type which are zero-to-one model year old, tw o 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 vehi cles 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 explai ned on the Georgia Department of Revenue website (http://motor.eta x.dor.ga.gov/stats/renewalsstats.aspx) . Passenger Vehicles include Ambulances, Convertibles, Coupes, Hearses, Jeep s, Limousines, Mixers, Motor Homes, MultiPurpose Vehicles, Roadsters, Station Wagons, Touri ng 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 t ypes in each county and then grown to 2007 using different growth factors by vehicl e types (Table 1). The number of HDBS, HDBT and MC in Polk’s data is comparable to Georgia registration data (T able 2 and Table 3). Therefore, the numbers of HDBS and HDBT were grown to 2007 by multiplying ratio s 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 Georgi a motor vehicle registrati on data in 2007 and 2003. The number of the rest of vehicle types was gr own to 2007 by multiplying ratios of human population in 2007 and 2002. The Georgia motor vehicle registrati on data were not used for these vehicle types due to the difficulty to match the vehicle type used in Georgia motor vehicle re gistration data to the 16 vehicle types as used in the Polk's data. Since the ratios of 2007 a nd 2003 passenger cars and trucks in motor vehicle registrati on data are comparable to the rati os 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 dat a (2003 and 2007), Buses HDBT Georgia registration dat a (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 200755 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 8,735,25 9 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 1.40556 The projected 2007 vehicle population by 16 vehicle types in each count y were then converted to 32 vehicles types, which were matched with 28 vehi cle types and 12 vehicle types (corresponding to 12 SCC codes) as shown in the EPA MOVES converter tool. The EPA MOVES c onverter tool was also used to convert vehicle population in MOVES form at by each of the four reference counties. These populations are the sum of popula tions of all counties sharing the same reference counties.57