1 

Mobile Source Emissions Modeling 

for Macon Nonattainment Area 

PM2.5 Maintenance Plan 

Motor Vehicle Emissions Budget 

1.0 Overview 

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

Transportation (GDOT) and the Macon-Bibb County Planning and Zoning Commission, the 

Metropolitan Planning Organization (MPO) for Macon, to develop mobile1 source emissions 

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

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

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

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

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

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

2

 Consistent with 

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

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

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

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

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

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

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

emissions analyses used in transportation conformity determinations. The alignment of 

methodologies for MVEB and transportation conformity emissions analyses reduces the 

possibility of spurious differences between motor vehicle emission budgets and transportation 

conformity analyses that must conform to those budgets. 

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

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

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

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

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

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

April 5, 2010, on June 2, 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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Based on consultation between representatives from Monroe County and GDOT, it was 

determined that GDOT would also represent Monroe County on the interagency committee. 

Monroe County signed a letter authorizing GDOT to represent their transportation interests 

throughout the conformity process under both ozone and PM2.5 standards. A copy of the signed 

letter is in Exhibit 1. 

1.2 Emissions Analysis – Models and Assumptions 

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

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

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

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

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

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

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

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

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

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

registration data for the Macon nonattainment area. 

2.0 Travel Demand Modeling Procedures 

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

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

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

estimating emissions. 

1. Socio-economic data based on best available information 

2. Consistency between transportation alternatives and land use scenarios 

3. Modeled volumes validated against observed counts 

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

assignment 

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

6. Capacity-sensitive traffic assignment methodology 

Model Attributes 1 & 2 (Socio-Economic Data) 

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

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

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

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

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

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

development process and accuracy checks rely on the best available information, such as US 
5

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

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

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

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

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

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

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

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

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

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

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

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

consideration planned major transportation improvements. Future forecasts are generally made 

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

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

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

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

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

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

Table 2.0-1 

Population and Employment Forecasts for MATS Area 

2007 and 2023 

2007 2023 

Total Population 168,747 174,291 

Number of Households 67,805 70,196 

Employment 107,423 123,249 

Model Attribute 3 (Model Validation) 

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

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

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

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

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

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

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

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

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

occupancy rates, and speed-volume relationships. 
6

Highway Networks – Air Quality Attributes 

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

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

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

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

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

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

Highway Networks - Speed 

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

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

 Roadway capacities 

 Free-flow speeds 

 Modeled volumes 

 Speed-volume relationships 

Link Capacities 

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

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

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

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

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

 (2) - Urban Commercial 

 (3) - Urban Residential 

 (4) - Suburban Commercial 

 (5) - Suburban Residential 

 (6) - Exurban 

 (7) - Rural 

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

Table 2.0-2 

Macon 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
7

Facility Type 

Area Type 

1 2 3 4 5 6 7 

One Way Arterial 760 760 770 770 770 760 740

Major Collector 520 530 540 550 560 550 540

Minor Collector 380 390 390 400 400 390 380

One Way Collector 460 470 470 480 480 470 460

Local Road 340 350 360 370 380 370 360

Centroid Connector 0 0 0 0 0 0 0

Free-flow Speeds 

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

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

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

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

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

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

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

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

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

Table 2.0-3 

Macon 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 22 28 33 34 37 47 52 

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

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

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

One Way Arterial 23 26 30 32 35 42 48 

Major Collector 17 18 21 27 29 34 42 

Minor Collector 14 15 18 24 26 30 40 

One Way Collector 17 18 21 27 29 34 42 

Local Road 14 14 17 18 22 28 35 

Centroid Connector 14 14 17 18 22 28 35 

Modeled Volumes 

Output modeled volumes are validated against traffic counts at several levels – regional, 
8

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

Nationally recognized maximum desirable deviation standards are applied to analyze model 

performance at the link level. 

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

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

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

annual compounded growth rates. 

Speed-Volume Relationships 

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

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

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

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

calibrating GDOT speed-volume curves. 

Trip Generation 

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

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

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

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

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

generation results. 

Trip Distribution 

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

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

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

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

Model Attribute 4 (Feedback of Travel Times) 

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

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

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

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

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

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

 Maximum link volume change =< 1,000 

The Method of Successive Averages is used to insure that the model reaches stable conditions. 
9

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 Macon travel demand model utilizes a trip-end based procedure 

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

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

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

characteristics. 

Model Attribute 6 (Traffic Assignment) 

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

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

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

Equilibrium occurs when no trip can be made by an alternate path without increasing the total 

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

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

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

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

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

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

day. 

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

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

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

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

evaluations are primarily screenline comparisons. Nationally recognized maximum desirable 

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

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

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

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

Macon Model is in Appendix A. 

2.1 Travel Demand Modeling Post-Processing Procedures 

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

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

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

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

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

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

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

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

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

nonattainment areas in Georgia. 
10

HPMS Adjustment of VMT 

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

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

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

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

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

the model VMT summaries. 

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

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

Section 3.4.2.4 of EPA's "Volume IV" guidance,4

 "[T]he detailed VMT estimates produced by 

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

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

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

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

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

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

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

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

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

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

estimates of future VMT." 

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

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

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

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

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

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

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

MOVES for each scenario year and annual average VMT. 

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

model calibration year of 2006. The HPMS adjustment reconciles the travel demand model linkbased VMT to the average annual daily travel conditions at the aggregate functional class level. 

The aggregate functional classification level was used since FHWA eliminated the urban/rural 

area type distinction from HPMS functional classifications beginning with the 2009 data, 

reported in 2010. (Guidance for the Functional Classification of Highways (updated), Federal 

Highway Administration, October 14, 2008.) 

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

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

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

 

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

Office of Air and Radiation, Office of Mobile Sources, 1992, http://www.epa.gov/otaq/invntory/r92009.pdf. 
11

data was summarized for the Macon MPO area which consists of all of Bibb County and a 

portion of Jones County. The 445 report summarizes the mileage and VMT by function 

classification by county. Since only a portion of Jones County was included in the Macon 

regional travel demand model, adjustments to the HPMS VMT summaries for Jones County had 

to be performed. The highway mileage for Jones County by functional classification was 

summarized for the area within the Macon model and compared to the county summary. A 

factor was developed based on the percent of the highway mileage within the model compared to 

the total mileage for the county by functional classification. This factor was then applied to the 

average daily HPMS VMT by functional classification to determine the average annual daily 

VMT that will be used in the HPMS VMT adjustment process. Table 2.1-1 lists the highway 

mileage and average annual daily VMT for the entire Jones County and for the portion of the 

county that is included in the regional travel demand model. 

Table 2.1-1 

Highway Mileage and Average Annual Daily VMT for Jones County 

 Highway Mileage 

Average Annual Weekday 

Adjusted VMT 

Functional 

Class Name 

Functional 

Class No. 

Macon 

2006 

Model 

GDOT 

HPMS 

Whole 

County 

Modeled 

Portion 

Rural Interstate 1 0.00 0.00 0 0 

Rural Principal 

Arterial 2 2.22 2.39 14,644 13,603 

Rural Minor 

Arterial 6 3.87 57.54 420,328 28,270 

Rural Major 

Collector 7 35.10 68.06 132,779 68,477 

Rural Minor 

Collector 8 20.60 46.32 85,156 37,872 

Rural Local 9 77.10 328.39 113,495 26,647 

Urbanized 

Interstate 11 0.00 0.00 0 0 

Urban Freeway 12 0.00 0.00 0 0 

Urbanized 

Principal 

Arterial 14 13.50 3.00 50,912 229,104 

Urbanized 

Minor Arterial 16 4.40 2.56 11,343 19,496 

Urbanized 

Collector 17 10.50 4.05 8,141 21,106 

Urbanized Local 19 18.90 32.46 39,795 23,171 

 186.19 544.77 876,593 467,745 
12

Table 2.1-2 lists the daily VMT for the Macon MPO area from the travel demand model by 

county. 

Table 2.1-2 

Summary of 2006 Daily VMT for Macon MPO Area 

(from the MATS travel demand model) 

Functional Class 

Name 

Functional 

Class No. 

Bibb 

County

Jones 

County Total 

Interstates 1,11,12 2,314,061 0 2,314,061 

Principal Arterials 2,14 840,422 140,967 981,389 

Minor Arterials 6,16 1,258,455 30,931 1,289,386 

Collectors 7,8,17 413,748 53,205 466,953 

Local 9,19 691,847 98,336 790,183 

Total 5,518,532 323,440 5,841,972 

Table 2.1-3 lists the adjustment factors based on the comparison between the HPMS average 

annual daily VMT and the VMT from the regional travel demand model. These factors were 

applied to the VMT on each link in the highway network based on the functional classification 

for the years 2007 and 2023. 

Table 2.1-3 

HPMS VMT Average Annual Daily Adjustment Factors for Macon 

MPO Area 

Functional Class 

Name 

Functional 

Class No. 

2006 

HPMS 

VMT

2006 Model 

VMT

Adjustment 

Factor 

Interstates 1,11,12 2,381,607 2,314,061 1.03 

Principal Arterials 2,14 1,012,878 981,389 1.03 

Minor Arterials 6,16 1,382,813 1,289,386 1.07 

Collectors 7,8,17 565,481 466,953 1.21 

Local 9,19 941,331 790,183 1.19 

Total 6,284,109 5,841,972 1.08 

The HPMS adjustment factors were developed for the entire Macon MPO which includes a 

portion of Jones County to reflect the travel activity for the entire area. However, because Jones 

County is not part of the nonattainment area, only the travel within Bibb County was used in the 

preparations of emissions for conformity determination. The following equation was used to 

calculate the 2006 HPMS adjustment factors: 

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

where i=HPMS functional class) 
13

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

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

application for 2007 and 2023. Table 2.1-4 lists the adjusted modeled VMT by year for Bibb 

County which was used in the emissions modeling procedures. 

Table 2.1-4 

Average Annual Daily Modeled VMT for Bibb County 

(As adjusted per Table 2.1-1) 

Year VMT 

2007 5,865,058 

2023 7,244,222 

VMT Estimation by Hour 

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

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

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

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

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

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

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

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

on the AADT/C ratio and are listed in Table 2.1-5. These factors were applied to the daily traffic 

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

then used to develop hourly times and speeds by link. 

Roadtype Classification 

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

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

appropriate MOVES roadtypes used for this modeling is listed in Table 2.1-6. Interstate and 

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

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

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

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

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

from the travel demand model data. 

Table 2.1-6 

Listing of FHWA Highway Functional Classifications 

Mapped to MOVES Road Types 

FHWA Highway Functional System MOVES Road Type MOVES Value 

Rural interstate Rural restricted access 2 

Rural other principal arterial Rural restricted access 2 

Rural minor arterial Rural unrestricted access 3 
14

FHWA Highway Functional System MOVES Road Type MOVES Value 

Rural major collector Rural unrestricted access 3 

Rural minor collector Rural unrestricted access 3 

Rural local Rural unrestricted access 3 

Urban interstate Urban restricted access 4 

Urban other freeways Urban restricted access 4 

Urban other principal arterial Urban unrestricted access 5 

Urban minor arterial Urban unrestricted access 5 

Urban collector Urban unrestricted access 5 

Urban local Urban unrestricted access 5 
15 

Table 2.1-5 

Hourly Distribution of Daily Vehicle Miles Travelled (VMT) 

 Source: Speed and Delay Prediction Models for Planning Applications

AADT/C 

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

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

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

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

Hour of Day
16 

Speed Bin Classification 

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

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

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

Table 2.1-7 lists the speed bins and ranges that were assigned to the network link data by hour. 

Table 2.1-7 

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 Macon analysis reflect all federal and state mobile 

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

Interagency Consultation 

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

inventories for SIP MVEB development and transportation conformity analysis must be 

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

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

modeling were established through interagency consultation and are listed below: 

1) Emission Factor Model: MOVES2010a - Database: MOVES20100830 
17

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

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

2023 motor vehicle emissions budgets 

2) MOVES Inputs – 

a. Temperature and relative humidity 

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

database (NCD20090531) 

b. Fuel 

i. Bibb County - MOVES defaults for July 

ii. Monroe County - MOVES defaults for July 

c. 2002 Regional Fleet Age Distribution 

i. Derived from R.L. Polk & Co. registration data for the five counties in the 

Macon metropolitan statistical area: Bibb, Crawford, Jones, Monroe, and 

Twiggs 

ii. Default for HDDV Class 8B 

d. Regional Vehicle Population 

i. Started with 2002 R.L. Polk & Co. registration data for for the five 

counties in the Macon MSA,, as well as the Georgia Dept. of Revenue’s 

registration data for 2003 and 2007 

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

factors by vehicle type based on either Census person population estimates 

or Georgia 2007 registration data. Methodology developed by EPD for 

inputs to the SMOKE-MOVES Integration Tool. 

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

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

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

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

MOVES 

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

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

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

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

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

developed through Interagency Consultation. 

MOVES Parameters 

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

RunSpecs are shown in Exhibit 2 and 3.
18 

Table 2.2-1 

MOVES Input Parameters 

Parameters Input Values 

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

Fuels: 

 Diesel Fuel  Gasoline 

 Source Use Types: 

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

Sele

 Diesel Fuel - Combi Diesel Fuel - Combi Diesel Fuel - Interci Diesel Fuel - Light C Diesel Fuel - Motor  Diesel Fuel - Passen Diesel Fuel - Passen Diesel Fuel - Refuse  Diesel Fuel - Schoo Diesel Fuel - Single  Diesel Fuel - Single  Diesel Fuel - Transi Gasoline - Combina Gasoline - Light Co Gasoline - Motor Ho Gasoline - Motorcyc Gasoline - Passenge Gasoline - Passenge Gasoline - Refuse T Gasoline - School B Gasoline - Single U Gasoline - Single U Gasoline - Transit B
19

Parameters Input Values 

RoadType Types: 1-5 

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

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

 Running Exhaust 

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

Primary PM2.5 – Organic Carbon 

 Running Exhaust 

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

Primary PM2.5 – Elemental 

Carbon 

 Running Exhaust 

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

Primary PM 2.5 Sulfate 

Particulate 

 Running Exhaust 

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

Primary PM2.5 – Brakewear  Breakwear 
20

Parameters Input Values 

Particulate 

Primary PM2.5 – Tirewear 

Particulate 

 Tirewear 

Total Energy Consumption  Running Exhaust  Start Exhaust 

NOx Oxides of Nitrogen (NOx) 

 Running Exhaust 

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

Extended Idle Exhau

Output General Output 

Units: 

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

Ac

 Distance Traveled  Population 

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

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

and 2023. 
21 

Table 2.2-2 

Methodology to Prepare MOVES Input Data Files 

For the County Data Importer for Bibb County 

Input Data from Travel Demand Model

County Data 

Manager Inputs Worksheet Methodology 

AverageSpeed Distribution avgSpeedDistribution 

The weekday link hourly vehicle hours travelled (VHT) 

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

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

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

the fractions sum to one for each combination of vehicle 

hour.

Ramp Fraction RoadType 

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

RoadTypeDistribution roadTypeDistribution 

The weekday link hourly VMT is summarized by ro

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

Vehicle Type VMT 

HPMSVtypeYear 

The weekday VMT is summarized by MOVES roadtype the vehicle classification counts5 for the Georgia area oucounty area by the 6 HPMS vehicle types. The fractioand 30 are then re-distributed based on the MOVES southe year. This is because the vehicle classification coucounters which count vehicles by the number of axlecounts do not accurately reflect the difference betweeSUVs. The daily VMT is annualized using the EPA Excel workbook.

MonthVMTFraction MOVES National Defaults DayVMTFraction MOVES National Defaults 

5 (A summary of the vehicle classification counts and description of the methodology used is in Exhibit 5) 
22

County Data 

Manager Inputs Worksheet Methodology 

HourVMTFraction 

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

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

Input Data from Other Sources 

County Data 

Manager Inputs Worksheet Methodology 

Source Type Population sourceTypeYear 

Vehicle population by source types was developed using 

registration data. The 2002 data were grown to 2007 usin

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

corresponding VMT for HPMS type 60 data from 

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

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

Age distributions in MOBILE6 format were derived fr

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

The Mon

Macon M

13.5 squ

modeled,

Transpor

nonattain

transport

The meth

uses reas

A combin

Monroe 

running 

emission

segments

As Figur

historical

in Table 

shown in

outlier. 

ocedures

nroe County

MPO’s study

are miles an

, emissions 

rtation Conf

nment or m

tation planni

hodology to 

sonable meth

nation of tec

County. G

emissions w

ns. Figure 2

s that are inc

re 2.3-1 indic

l traffic coun

2.3-2, and w

n Table 2.3-

If the 199

s for Prod

y portion of

y area nor in 

nd per the 2

estimates w

formity Rule

maintenance

ing area, an

produce the

hods. 

chniques wa

Georgia DOT

while the p

.3-1, display

cluded in the

cates, VMT 

nts for GDO

were based o

2. The 1997

7 count w

ducing E

f the Macon 

the travel d

2010 Census

were produc

e 93.122(a)(

e area VM

nd on roadw

e mobile emi

s used to pro

T historical 

percent of t

ys the releva

e off-model e

Fi

Traffic 

forecasts for

OT count sta

on a linear e

7 count for s

were include

23 

Emissions

PM2.5 Non

demand mod

s contains a 

ced using of

(7), reasona

MT on offways outside 

issions for M

oduce the em

traffic coun

the populati

ant traffic co

estimation pr

gure 2.3-1

Count Stati

r the segmen

ation 297. T

extrapolation

station 297 w

ed, the line

s Using O

nattainment 

del. The area

population 

ff-model tec

able method

-network ro

the urban t

Monroe Coun

missions for 

nts were us

ion was us

ount stations

rocess. 

ions 

nt of State R

Traffic volum

n of the hist

was omitted

ear extrapol

Off Mode

Area is not

a encompass

of 98. Sinc

chniques. A

ds shall be 

oadways w

transportatio

nty, describe

the nonattai

sed as the b

ed to estim

s and the app

Route 18 wer

mes projectio

torical count

d because it 

lation woul

el Techni

t included i

es approxim

ce the area i

According t

used to esti

within the u

on planning 

ed in this sec

inment porti

basis to esti

mate non-run

plicable roa

re produced u

ons are disp

ts, which are

appears to b

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in the 

mately 

is not 

o the 

imate 

urban 

area. 

ction, 

ion of 

imate 

nning 

dway 

using 

layed 

e also 

be an 

ining. 
24 

Table 2.3-2 

Historical Traffic Counts 

Macon, Georgia - Off Model Emission Estimation Monroe County Portion for PM Relevant GDOT Count Stations 

AADT Projected Traffic 

Station # 1997 1998 1999 2000 2001 2002 2003 2005 2006 2007 2008 2010 2015 2

208 5128 5467 5836 6660 5701 6560 6930 5400 6150 5990 6310 7084 7697 8210 3800 3828 4086 4769 4325 5443 5300 5010 4870 5320 5300 6221 7135 8212 2893 3005 3208 3737 3325 4269 4080 4330 4200 4200 3890 4945 5692 6295 2021 1901 1942 1960 2103 2383 2410 2450 2510 2460 2340 2724 3039 3297 2503 1411 1373 1400 1500 1514 1470 1250 1400 1500 1400 1492 1530 1

Average 

208 + 210 6,002 6,115 5,205 5,510 5,655 5,805 6,653 7,416 8

Difference 1,117 1,630 390 1,280 670 1,010 863 562 

27% of 

volume 1,620 1,651 1,405 1,488 1,527 1,567 1,796 2,002 2

MOVES was run twice for Monroe County for each year. First MOVES was run in Emission Rate mode to roadtype and speed. The output from the Rate per Vehicle and Rate per Distance were used to estimate the runMOVES was run in Inventory mode. The percentage of the county population within the nonattainment area wthe non-running emissions. Table 2.3-3 lists the input files to the County Data Importer. The input data are showthe years 2007 and 2023. 
25

Table 2.3-3 

Methodology to Prepare MOVES Input Data Files 

For the County Data Importer for Monroe County 

Input Data from Travel Demand Model

County Data 

Manager Inputs Worksheet Methodology 

AverageSpeed Distribution avgSpeedDistribution MOVES National Defaults Ramp Fraction RoadType MOVES National Defaults RoadTypeDistribution roadTypeDistribution MOVES National Defaults Vehicle Type VMT 

HPMSVtypeYear 

GDOT 445 Report was used to determine annual average National Default vehicle type fractions were used to alloc

The daily VMT was annualized using the EPA AADVMworkbook. (Future year DVMT was grown using past V

MonthVMTFraction MOVES National Defaults DayVMTFraction MOVES National Defaults HourVMTFraction MOVES National Defaults 

Input Data from Other Sources 

County Data 

Manager Inputs Worksheet Methodology 

Source Type Population sourceTypeYear 

Vehicle population by source types was developed using registration data. The 2002 data were grown to 2007 usin

population estimates or Georgia annual vehicle registrati

population projection was used to calculate 2023 vehicle 

I/M Programs IMCoverage No I/M Program Fuel Supply Fuel Supply MOVES Defaults for Monroe County 
26

County Data 

Manager Inputs Worksheet Methodology 

Fuel Formulation MOVES Defaults for Monroe County 

Meteorology Data ZoneMonthHour NCD20090531- 2007 data for Monroe County Age Distribution sourceTypeAgeDistribution

Age distributions in MOBILE6 format were derived fr

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

Tables 2.3-4 and 2.3-5 list the summary of the emission calculations for the nonattainment area of Monroe Coun

The spreadsheets are available upon request. 
27

Table 2.3-3 

Emissions for Nonattainment portion of Monroe County for 2007 

Year

HPMS 

Code

MOVES 

Road 

Type Volume Mileage VMT No Adj

Final 

VMT Speed

MOVES 

Speed Bin

Running 

Emission 

Factor

Running 

Emissions (in 

grams)

Run

Emi

Fa

2007 9 3 1,527 0.25 381.7 1.0000 381.7 10 3 10.3917 3,966.6607 1Year Station

HPMS 

Code Volume Mileage VMT No Adj

Final 

VMT Speed

MOVES 

Speed Bin

Running 

Emission 

Factor

Running 

Emissions (in 

grams)

Run

Emi

Fa

2007 297 7 3 1,500 0.25 375.0 1.0000 375.0 35 8 5.7922 2,172.0656 1

Total Running Emissions 6,138.7263 

Year

Total 

Vehicle 

Population

Partial 

County 

Factor*

Total County 

Level NonRunning 

Emissions (in 

grams)**

Non-Running 

Emissions prorated by partial 

county factor(in 

grams)

Total 

Leve

Run

Emiss

gram

2007 24,426 0.004 26,933.9456 107.7358 1,547,3Year

Total Emissions 

(in grams)

2007 6,246.4621 Year

Total Daily 

Emissions (in 

tons)

2007 0.0069 Year

Total Daily 

Emissions (in 

tons)

2007 2.3549 

Note:

 *Partial county factor is based on percent of the population within the non-attainment area - using 2010 Census Total Annual Emissions in Tons PM2.5 Running Emissions

PM2.5 

Non-Running Emissions PM2.5 Entry/Exit RoadsBased on Stations 208 & 210SR 18 FacilityPro-rated to one stationTotal Daily Emissions in GramsTotalDaily Emissions in Tons PM2.5 

PM2.5 
28

Table 2.3-4 

Emissions for Nonattainment portion of Monroe County for 2023 

Year

HPMS 

Code

MOVES 

Road Type Volume Mileage VMT No Adj

Final 

VMT Speed

MOVES 

Speed Bin

Running 

Emission 

Factor

Running 

Emissions (in 

grams)

Runni

Emissi

Facto

2023 9 3 2,332 0.25 583.1 1.0000 583.1 10 3 1.3428 782.9165 17Year Station

HPMS 

Code Volume Mileage VMT No Adj

Final 

VMT Speed

MOVES 

Speed Bin

Running 

Emission 

Factor

Running 

Emissions (in 

grams)

Runni

Emissi

Facto

2023 297 7 3 1,591 0.25 397.8 1.0000 397.8 35 8 0.5214 207.3872 10

Total Running Emissions 990.3037 

Year

Total 

Vehicle 

Population

Partial 

County 

Factor*

Total County 

Level NonRunning 

Emissions (in 

grams)

Non-Running 

Emissions prorated by partial 

county factor(in 

grams)

Total Co

Level N

Runni

Emission

gram

2023 34,441 0.004 3,054.5299 12.2181 368,397Year

Total Emissions 

(in grams)

2023 1,002.5219 Year

Total Daily 

Emissions (in 

tons)

2023 0.0011 Year

Total Daily 

Emissions (in 

tons)

2023 0.3779 

Note: *Partial county factor is based on percent of the population within the non-attainment area - using 2010 Census 

Total Annual Emissions in Tons PM2.5 Total Daily Emissions in GramsTotal Daily Emissions in Tons PM2.5 

PM2.5 

Non-Running Emissions PM2.5 

Entry/Exit RoadsBased on Stations 208 & 210SR 18 FacilityPro-rated to one station

Running Emissions

PM2.5 
29 

2.4 Nonattainment Area Emissions Analysis Summary 

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

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

the MOVES inputs. The following formula was used: 

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

Weekend Adjustment Factor(.7793) = 

341.9809 (rounded to 342) 

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

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

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

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

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

emissions in annual tons. 

Table 2.4-1 

Summary of Mobile Source Emissions for Macon Nonattainment Area 

(Daily Grams) 

 PM2.5 Nox 

Year County Running 

Nonrunning Total RunningNonrunning Total

2007 

Bibb 661,868.7737 38,192.5713 700,061.3450 17,386,111.1673 2,498,616.5412 19,884,727.7085

Monroe 6,138.7263 107.7358 6,246.4621 108,255.8569 6,189.5293 114,445.3862

Total 668,007.5000 38,300.3071 706,307.8071 17,494,367.0242 2,504,806.0705 1,999,173.0947

 

 PM2.5 Nox 

Year County Running 

Nonrunning Total RunningNonrunning Total

2023 

Bibb 172,857.1837 12,328.0220 185,185.2057 3,743,833.9585 1,522,244.4094 1,534,572.4314

Monroe 990.3037 12.2181 1,002.5218 14,222.9109 1,473.5917 15,696.5026

Total 173,847.4874 12,340.2401 186,187.7275 3,758,056.8694 1,523,718.0011 1,550,268.9340

Table 2.4-2 

Summary of Mobile Source Emissions for Macon Nonattainment Area 

(Annual Tons) 

 PM2.5 Nox 

Year County Running 

Nonrunning Total RunningNonrunning Total

2007 Bibb 249.5183 14.3983 263.9166 6,554.3982 941.9546 7,496.3528
30

 PM2.5 Nox 

Year County Running 

Nonrunning Total RunningNonrunning Total

Monroe 2.3142 0.0406 2.3548 40.8114 2.3334 43.1448

Total 251.8325 14.4389 266.2714 6,595.2096 944.2880 7,539.4976

 

 PM2.5 Nox 

Year County Running 

Nonrunning Total RunningNonrunning Total

2023 

Bibb 65.1655 4.6476 69.8131 1,411.3897 573.8717 1,985.2614

Monroe 0.3733 0.0046 0.3779 5.3619 0.5555 5.9174

Total 65.5388 4.6522 70.1910 1,416.7516 574.4272 1,991.1788

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

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

safety margins. The last major Regional Transportation Plan was developed in 2009. The 

update of the Regional Transportation Plan for 2040 is underway and is due in 2013. Model runs 

were performed for 2040 with various growth assumptions. The estimated growth rate between 

2023 and 2040 is less than 10%. An alternative scenario was tested where the growth increased 

by 30% for the years 2023 and 2040 to account for uncertainty in the future of growth 

projections and implementation of transportation improvements. The test for 2023 was to 

account for the potential impact the Transportation Investment Act may have on travel patterns 

and growth for 2023. 

 
Exhibit 1: Monroe C

31 

ounty/GDOT Agreement 
32

Exhibit 2: 2007 RunSpec for Bibb County 

<runspec>  <description><![CDATA[Bibb County 2007 PM2.5 maintenance plan Inventory Readjust HPMS factors based 2006 model Weight vmt by type by the model vmt by road type 071411 - Run #1 091211 - Run #5 -Used Cube roadtype dstrb]]></description>  <modelscale value="Inv"/>  <modeldomain value="SINGLE"/>  <geographicselections>  <geographicselection type="COUNTY" key="13021" description="GEORGIA - Bibb County"/>  </geographicselections>  <timespan>  <year key="2007"/>  <month id="7"/>  <day id="5"/>  <beginhour id="1"/>  <endhour id="24"/>  <aggregateBy key="Hour"/>  </timespan>  <onroadvehicleselections>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="62" sourcetypename="Combination Long- <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="61" sourcetypename="Combination Short-

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

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

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="11" sourcetypename="Motorcycle"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="21" sourcetypename="Passenger Car"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="31" sourcetypename="Passenger Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="51" sourcetypename="Refuse Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="43" sourcetypename="School Bus"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="53" sourcetypename="Single Unit Long-hau
33

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="52" sourcetypename="Single Unit Short-hau

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

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

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

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

 <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" procExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processn <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="2" processn
34

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

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

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

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

 </pollutantprocessassociations>  <databaseselections>  <databaseselection servername="" databasename="bibb_2007_pm_inventory_input5" description=""/>  </databaseselections>  <internalcontrolstrategies> <internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgreuseParameters No ]]></internalcontrolstrategy>  </internalcontrolstrategies>  <inputdatabase servername="" databasename="" description=""/>  <uncertaintyparameters uncertaintymodeenabled="false" numberofrunspersimulation="0" numberofsimulations="0"/>  <geographicoutputdetail description="COUNTY"/>  <outputemissionsbreakdownselection>  <modelyear selected="false"/>  <fueltype selected="false"/> 
35

 <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="bibb_2007_pm_SO2_output5" 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="bibb_2007_pm_inventory_input5" 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="bibb_2007" truncateoutput="false" truncateactivity="false"/> </runspec> 
36

Exhibit 2: MOVES Input Data for Bibb County Emissions 

for PM2.5 SIP Budget – 2007 

Ramp Fraction 

roadTypeID RampFraction 

2 0 

4 0.0400 

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 0.20783 

21 3 115 9 0.20783 

31 3 115 9 0.20783 

32 3 115 9 0.20783 

41 3 115 9 0.20783 

42 3 115 9 0.20783 

43 3 115 9 0.20783 

51 3 115 9 0.20783 

52 3 115 9 0.20783 

53 3 115 9 0.20783 

54 3 115 9 0.20783 

61 3 115 9 0.20783 

62 3 115 9 0.20783 

11 3 115 10 0.26139 

21 3 115 10 0.26139 

31 3 115 10 0.26139 
37

sourceTypeid roadTypeID hourDayID AvgSpeedBinID AvgSpeedFraction 

32 3 115 10 0.26139 

41 3 115 10 0.26139 

42 3 115 10 0.26139 

43 3 115 10 0.26139 

51 3 115 10 0.26139 

52 3 115 10 0.26139 

53 3 115 10 0.26139 

54 3 115 10 0.26139 

61 3 115 10 0.26139 

62 3 115 10 0.26139 

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

Road Type Distribution 

sourceTypeid roadTypeID RoadTypeVMTFraction

11 1 0.00000

11 2 0.00192

11 3 0.08081

11 4 0.42909

11 5 0.48818

21 1 0.00000

21 2 0.00192

21 3 0.08081

21 4 0.42909

21 5 0.48818

31 1 0.00000

31 2 0.00192

31 3 0.08081

31 4 0.42909

31 5 0.48818

32 1 0.00000

32 2 0.00192

32 3 0.08081

32 4 0.42909

32 5 0.48818

41 1 0.00000

41 2 0.00192

41 3 0.08081

41 4 0.42909

41 5 0.48818

42 1 0.00000
38

sourceTypeid roadTypeID RoadTypeVMTFraction

42 2 0.00192

42 3 0.08081

42 4 0.42909

42 5 0.48818

43 1 0.00000

43 2 0.00192

43 3 0.08081

43 4 0.42909

43 5 0.48818

51 1 0.00000

51 2 0.00192

51 3 0.08081

51 4 0.42909

51 5 0.48818

52 1 0.00000

52 2 0.00192

52 3 0.08081

52 4 0.42909

52 5 0.48818

53 1 0.00000

53 2 0.00192

53 3 0.08081

53 4 0.42909

53 5 0.48818

54 1 0.00000

54 2 0.00192

54 3 0.08081

54 4 0.42909

Source Type Population

yearID sourceTypeID sourceTypePopulation

2007 11 2256

2007 21 74020

2007 31 39305

2007 32 12921

2007 41 46

2007 42 28

2007 43 455

2007 51 65
39

yearID sourceTypeID sourceTypePopulation

2007 52 2434

2007 53 174

2007 54 269

2007 61 1031

2007 62 1142

Vehicle Type VMT – HPMSVtypeYear Worksheet 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT 

10 2007 6089363.468 0 

20 2007 1131933964 0 

30 2007 671602403.6 0 

40 2007 10356630.66 0 

50 2007 62908806.1 0 

60 2007 122846433.9 0 

Vehicle Type VMT – Sample HourVMTFraction 

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 2 5 1 0.01000 

11 2 5 2 0.00600 

11 2 5 3 0.00480 

11 2 5 4 0.00450 

11 2 5 5 0.00670 

11 2 5 6 0.01850 

11 2 5 7 0.05011 

11 2 5 8 0.07732 

11 2 5 9 0.06131 

11 2 5 10 0.04821 

11 2 5 11 0.04791 

11 2 5 12 0.05121 

11 2 5 13 0.05361 

11 2 5 14 0.05471 

11 2 5 15 0.06051 

11 2 5 16 0.07271 

11 2 5 17 0.08282 

11 2 5 18 0.08272 

11 2 5 19 0.05891 

11 2 5 20 0.04181 

11 2 5 21 0.03321 

11 2 5 22 0.03031 

11 2 5 23 0.02440 
40

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 2 5 24 0.01771 

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 0.07730 

11 3 5 9 0.06136 

11 3 5 10 0.04824 

11 3 5 11 0.04791 

11 3 5 12 0.05122 

11 3 5 13 0.05362 

11 3 5 14 0.05472 

11 3 5 15 0.06052 

11 3 5 16 0.07269 

11 3 5 17 0.08276 

11 3 5 18 0.08265 

11 3 5 19 0.05891 

11 3 5 20 0.04181 

11 3 5 21 0.03321 

11 3 5 22 0.03032 

11 3 5 23 0.02442 

11 3 5 24 0.01773 

11 4 5 1 0.01009 

11 4 5 2 0.00609 

11 4 5 3 0.00487 

11 4 5 4 0.00453 

11 4 5 5 0.00671 

11 4 5 6 0.01849 

11 4 5 7 0.05014 

11 4 5 8 0.07698 

11 4 5 9 0.06182 

11 4 5 10 0.04859 

11 4 5 11 0.04805 

11 4 5 12 0.05137 

11 4 5 13 0.05370 

11 4 5 14 0.05482 

11 4 5 15 0.06052 
41

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 4 5 16 0.07234 

11 4 5 17 0.08196 

11 4 5 18 0.08177 

11 4 5 19 0.05889 

11 4 5 20 0.04194 

11 4 5 21 0.03329 

11 4 5 22 0.03050 

11 4 5 23 0.02465 

11 4 5 24 0.01789 

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

Meteorology Data 

monthID zoneID HourID temperature relHumidity 

7 130210 1 57.417 76.100

7 130210 2 56.317 78.308

7 130210 3 55.308 79.892

7 130210 4 54.308 81.383

7 130210 5 53.467 82.650

7 130210 6 52.692 83.733

7 130210 7 52.117 84.350

7 130210 8 52.833 83.592

7 130210 9 55.942 79.233

7 130210 10 60.933 70.883

7 130210 11 65.750 61.967

7 130210 12 69.800 54.733

7 130210 13 72.967 49.625

7 130210 14 75.108 46.183

7 130210 15 76.467 44.058

7 130210 16 77.117 42.958

7 130210 17 77.017 42.900

7 130210 18 75.733 44.617

7 130210 19 72.508 49.658

7 130210 20 68.200 57.167

7 130210 21 64.433 64.017

7 130210 22 61.942 68.467

7 130210 23 60.175 71.458

7 130210 24 58.633 74.117
42

Age Distribution - Sample 

sourceTypeID yearID ageID ageFraction

21 2007 0 0.01120

21 2007 1 0.04500

21 2007 2 0.04950

21 2007 3 0.06050

21 2007 4 0.05960

21 2007 5 0.05660

21 2007 6 0.06200

21 2007 7 0.06170

21 2007 8 0.07150

21 2007 9 0.06340

21 2007 10 0.05760

21 2007 11 0.05230

21 2007 12 0.04650

21 2007 13 0.04440

21 2007 14 0.04280

21 2007 15 0.03620

21 2007 16 0.03170

21 2007 17 0.02650

21 2007 18 0.02350

21 2007 19 0.01910

21 2007 20 0.01140

21 2007 21 0.00690

21 2007 22 0.00670

21 2007 23 0.00530

21 2007 24 0.00419

21 2007 25 0.00332

21 2007 26 0.00262

21 2007 27 0.00208

21 2007 28 0.00164

21 2007 29 0.00130

21 2007 30 0.03295

31 2007 0 0.01547

31 2007 1 0.03961

31 2007 2 0.04477

31 2007 3 0.04793

31 2007 4 0.04774

31 2007 5 0.05304

31 2007 6 0.06438

31 2007 7 0.02946

31 2007 8 0.03986

31 2007 9 0.04598

31 2007 10 0.04906
43

sourceTypeID yearID ageID ageFraction

31 2007 11 0.04226

31 2007 12 0.03997

31 2007 13 0.05252

31 2007 14 0.05539

31 2007 15 0.05388

31 2007 16 0.04229

31 2007 17 0.05027

31 2007 18 0.04733

31 2007 19 0.04471

31 2007 20 0.03223

31 2007 21 0.02617

31 2007 22 0.02961

31 2007 23 0.00046

31 2007 24 0.00039

31 2007 25 0.00036

31 2007 26 0.00032

31 2007 27 0.00028

31 2007 28 0.00025

31 2007 29 0.00023

31 2007 30 0.00378

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

Exhibit 3: 2023 RunSpec 

<runspec>  <description><![CDATA[Bibb County 2023 PM2.5 maintenance plan Inventory Readjust HPMS factors based 2006 model Rerun using scripts similar to ARC's Weight vmt by type by the model vmt by road type Run#5 - Used Cube scripts - 091211 ]]></description>  <modelscale value="Inv"/>  <modeldomain value="SINGLE"/>  <geographicselections>  <geographicselection type="COUNTY" key="13021" description="GEORGIA - Bibb County"/>  </geographicselections>  <timespan>  <year key="2023"/>  <month id="7"/>  <day id="5"/>  <beginhour id="1"/>  <endhour id="24"/>  <aggregateBy key="Hour"/>  </timespan>  <onroadvehicleselections>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="62" sourcetypename="Combination Long- <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="61" sourcetypename="Combination Short-

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

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

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="11" sourcetypename="Motorcycle"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="21" sourcetypename="Passenger Car"/>  <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"/> 
45

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="53" sourcetypename="Single Unit Long-hau

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

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

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

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

 <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" procExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processn
46

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

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

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

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

 </pollutantprocessassociations>  <databaseselections>  <databaseselection servername="" databasename="bibb_2023_pm_inventory_input5" description=""/>  </databaseselections>  <internalcontrolstrategies> <internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgreuseParameters No ]]></internalcontrolstrategy>  </internalcontrolstrategies>  <inputdatabase servername="" databasename="" description=""/>  <uncertaintyparameters uncertaintymodeenabled="false" numberofrunspersimulation="0" numberofsimulations="0"/>  <geographicoutputdetail description="NATION"/>  <outputemissionsbreakdownselection>  <modelyear selected="false"/> 
47

 <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="bibb_2023_pm_inventory_output5" 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="bibb_2023_pm_inventory_input5" 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="bibb_2023" truncateoutput="false" truncateactivity="false"/> </runspec>
48

Exhibit 4: MOVES Input Data for Bibb County Emissions 

for PM2.5 SIP Budget – 2023 

Ramp Fraction 

roadTypeID RampFraction 

2 0 

4 0.0800 

Sample Average Speed Distribution 

sourceTypeid roadTypeID hourDayID AvgSpeedBinID AvgSpeedFraction 

11 3 15 8 0.23614 

21 3 15 8 0.23614 

31 3 15 8 0.23614 

32 3 15 8 0.23614 

41 3 15 8 0.23614 

42 3 15 8 0.23614 

43 3 15 8 0.23614 

51 3 15 8 0.23614 

52 3 15 8 0.23614 

53 3 15 8 0.23614 

54 3 15 8 0.23614 

61 3 15 8 0.23614 

62 3 15 8 0.23614 

11 3 15 9 0.30415 

21 3 15 9 0.30415 

31 3 15 9 0.30415 

32 3 15 9 0.30415 

41 3 15 9 0.30415 

42 3 15 9 0.30415 

43 3 15 9 0.30415 

51 3 15 9 0.30415 

52 3 15 9 0.30415 

53 3 15 9 0.30415 

54 3 15 9 0.30415 

61 3 15 9 0.30415 

62 3 15 9 0.30415 

11 3 15 10 0.14167 

21 3 15 10 0.14167 

31 3 15 10 0.14167 

32 3 15 10 0.14167 

41 3 15 10 0.14167 
49

sourceTypeid roadTypeID hourDayID AvgSpeedBinID AvgSpeedFraction 

42 3 15 10 0.14167 

43 3 15 10 0.14167 

51 3 15 10 0.14167 

52 3 15 10 0.14167 

53 3 15 10 0.14167 

54 3 15 10 0.14167 

61 3 15 10 0.14167 

62 3 15 10 0.14167 

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

Road Type Distribution 

sourceTypeid roadTypeID RoadTypeVMTFraction

11 1 0.00000

11 2 0.00216

11 3 0.08445

11 4 0.47367

11 5 0.43971

21 1 0.00000

21 2 0.00216

21 3 0.08445

21 4 0.47367

21 5 0.43971

31 1 0.00000

31 2 0.00216

31 3 0.08445

31 4 0.47367

31 5 0.43971

32 1 0.00000

32 2 0.00216

32 3 0.08445

32 4 0.47367

32 5 0.43971

41 1 0.00000

41 2 0.00216

41 3 0.08445

41 4 0.47367

41 5 0.43971

42 1 0.00000

42 2 0.00216

42 3 0.08445

42 4 0.47367
50

sourceTypeid roadTypeID RoadTypeVMTFraction

42 5 0.43971

43 1 0.00000

43 2 0.00216

43 3 0.08445

43 4 0.47367

43 5 0.43971

51 1 0.00000

51 2 0.00216

51 3 0.08445

51 4 0.47367

51 5 0.43971

52 1 0.00000

52 2 0.00216

52 3 0.08445

52 4 0.47367

52 5 0.43971

53 1 0.00000

53 2 0.00216

53 3 0.08445

53 4 0.47367

53 5 0.43971

54 1 0.00000

54 2 0.00216

54 3 0.08445

54 4 0.47367

54 5 0.43971

61 1 0.00000

61 2 0.00216

61 3 0.08445

61 4 0.47367

61 5 0.43971

62 1 0.00000

62 2 0.00216

62 3 0.08445

62 4 0.47367

62 5 0.43971
51

Source Type Population

yearID sourceTypeID sourceTypePopulation

2023 11 2347

2023 21 76988

2023 31 40881

2023 32 13439

2023 41 48

2023 42 29

2023 43 474

2023 51 68

2023 52 2531

2023 53 181

2023 54 279

2023 61 1073

2023 62 1454

Vehicle Type VMT – HPMSVtypeYear Worksheet 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT 

10 2023 7,569,538.22 0.00 

20 2023 1,442,340,512.78 0.00 

30 2023 774,433,109.86 0.00 

40 2023 13,120,109.04 0.00 

50 2023 78,086,337.55 0.00 

60 2023 161,836,266.26 0.00 

Vehicle Type VMT – Sample HourVMTFraction 

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 2 5 1 0.01004 

11 2 5 2 0.00603 

11 2 5 3 0.00479 

11 2 5 4 0.00437 

11 2 5 5 0.00653 

11 2 5 6 0.01834 

11 2 5 7 0.05030 

11 2 5 8 0.07686 

11 2 5 9 0.06306 

11 2 5 10 0.04921 

11 2 5 11 0.04817 

11 2 5 12 0.05165 

11 2 5 13 0.05390 
52

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 2 5 14 0.05509 

11 2 5 15 0.06063 

11 2 5 16 0.07190 

11 2 5 17 0.08080 

11 2 5 18 0.08035 

11 2 5 19 0.05882 

11 2 5 20 0.04202 

11 2 5 21 0.03327 

11 2 5 22 0.03073 

11 2 5 23 0.02499 

11 2 5 24 0.01815 

11 3 5 1 0.01002 

11 3 5 2 0.00601 

11 3 5 3 0.00481 

11 3 5 4 0.00449 

11 3 5 5 0.00668 

11 3 5 6 0.01848 

11 3 5 7 0.05013 

11 3 5 8 0.07720 

11 3 5 9 0.06154 

11 3 5 10 0.04837 

11 3 5 11 0.04797 

11 3 5 12 0.05128 

11 3 5 13 0.05365 

11 3 5 14 0.05477 

11 3 5 15 0.06053 

11 3 5 16 0.07258 

11 3 5 17 0.08249 

11 3 5 18 0.08235 

11 3 5 19 0.05891 

11 3 5 20 0.04186 

11 3 5 21 0.03323 

11 3 5 22 0.03038 

11 3 5 23 0.02450 

11 3 5 24 0.01777 

11 4 5 1 0.01005 

11 4 5 2 0.00604 

11 4 5 3 0.00482 

11 4 5 4 0.00447 

11 4 5 5 0.00665 
53

sourceTypeid roadTypeID dayID hourID HourVMTFraction 

11 4 5 6 0.01845 

11 4 5 7 0.05016 

11 4 5 8 0.07702 

11 4 5 9 0.06196 

11 4 5 10 0.04865 

11 4 5 11 0.04806 

11 4 5 12 0.05141 

11 4 5 13 0.05373 

11 4 5 14 0.05486 

11 4 5 15 0.06055 

11 4 5 16 0.07233 

11 4 5 17 0.08190 

11 4 5 18 0.08169 

11 4 5 19 0.05890 

11 4 5 20 0.04194 

11 4 5 21 0.03327 

11 4 5 22 0.03050 

11 4 5 23 0.02466 

11 4 5 24 0.01793 

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

Meteorology Data 

monthID zoneID HourID temperature relHumidity 

7 130210 1 57.417 76.100

7 130210 2 56.317 78.308

7 130210 3 55.308 79.892

7 130210 4 54.308 81.383

7 130210 5 53.467 82.650

7 130210 6 52.692 83.733

7 130210 7 52.117 84.350

7 130210 8 52.833 83.592

7 130210 9 55.942 79.233

7 130210 10 60.933 70.883

7 130210 11 65.750 61.967

7 130210 12 69.800 54.733

7 130210 13 72.967 49.625

7 130210 14 75.108 46.183

7 130210 15 76.467 44.058

7 130210 16 77.117 42.958

7 130210 17 77.017 42.900

7 130210 18 75.733 44.617

7 130210 19 72.508 49.658
54

monthID zoneID HourID temperature relHumidity 

7 130210 20 68.200 57.167

7 130210 21 64.433 64.017

7 130210 22 61.942 68.467

7 130210 23 60.175 71.458

7 130210 24 58.633 74.117

Age Distribution - Sample 

sourceTypeID yearID ageID ageFraction

21 2023 0 0.01120

21 2023 1 0.04500

21 2023 2 0.04950

21 2023 3 0.06050

21 2023 4 0.05960

21 2023 5 0.05660

21 2023 6 0.06200

21 2023 7 0.06170

21 2023 8 0.07150

21 2023 9 0.06340

21 2023 10 0.05760

21 2023 11 0.05230

21 2023 12 0.04650

21 2023 13 0.04440

21 2023 14 0.04280

21 2023 15 0.03620

21 2023 16 0.03170

21 2023 17 0.02650

21 2023 18 0.02350

21 2023 19 0.01910

21 2023 20 0.01140

21 2023 21 0.00690

21 2023 22 0.00670

21 2023 23 0.00530

21 2023 24 0.00419

21 2023 25 0.00332

21 2023 26 0.00262

21 2023 27 0.00208

21 2023 28 0.00164

21 2023 29 0.00130

21 2023 30 0.03295

31 2023 0 0.01547

31 2023 1 0.03961

31 2023 2 0.04477

31 2023 3 0.04793
55

sourceTypeID yearID ageID ageFraction

31 2023 4 0.04774

31 2023 5 0.05304

31 2023 6 0.06438

31 2023 7 0.02946

31 2023 8 0.03986

31 2023 9 0.04598

31 2023 10 0.04906

31 2023 11 0.04226

31 2023 12 0.03997

31 2023 13 0.05252

31 2023 14 0.05539

31 2023 15 0.05388

31 2023 16 0.04229

31 2023 17 0.05027

31 2023 18 0.04733

31 2023 19 0.04471

31 2023 20 0.03223

31 2023 21 0.02617

31 2023 22 0.02961

31 2023 23 0.00046

31 2023 24 0.00039

31 2023 25 0.00036

31 2023 26 0.00032

31 2023 27 0.00028

31 2023 28 0.00025

31 2023 29 0.00023

31 2023 30 0.00378

Note: File contains 373 records and is available on request 

 
56

Exhibit 5: Vehicle Classification Counts 

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

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

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

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

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

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

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

the travel demand model by road type by vehicle type for input into AADVMT worksheet..
57

Table 1 

GDOT Vehicle Classification Counts 

2008 Statewide minus ARC 13 County MPO Area

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

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

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

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

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

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

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

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

2009 Statewide minus ARC 13 County MPO Area

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

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

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

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

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

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

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

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

Code HPMS Vehicle Type

FHWAVeh Class

Weekday Counts Percent by 

Weekday Counts Percent by 

Code HPMS Vehicle Type

FHWAVeh Class
58

Table 1 

GDOT Vehicle Classification Counts (continued) 

2010 Statewide minus ARC 13 County MPO Area

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

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

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

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

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

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

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

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

2008‐2010 Statewide minus ARC 13 County MPO Area Summary

RuralRestrictedAccess (FC=1)

RuralUnrestrictedAccess(FC=2‐7)

UrbanRestrictedAccess(FC=11‐12)

UrbanUnrestrictedAccess(FC=14‐19)

RuralRestrictedAccess(FC=1)

RuralUnrestrictedAccess(FC=2‐7)

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

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

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

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

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

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

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

Code HPMS Vehicle Type

FHWAVeh Class

Weekday Counts Percent by 

Code HPMS Vehicle Type

FHWA

Veh Class

Weekday Counts Percent by 
59 

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 

Unrestricte

d Access 

Factor

10 Motorcycles Class 1 0.002909 0.003512 0.003196 0.002817

20 Passenger Cars Class 2 0.568990 0.646239 0.666475 0.737280

30 

Other 2 axle-4 tire 

vehicles Class 3 0.165098 0.223307 0.187197 0.207472

40 Buses Class 4 0.008700 0.005776 0.006583 0.003800

50 Single Unit Trucks 

Class 5-

7 0.034469 0.040796 0.031867 0.029349

60 Combination Trucks 

Class 8-

13 0.219834 0.080369 0.104681 0.019282

 Total 1.000000 1.000000 1.000000 1.000000

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

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

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

Table 3 

MOVES Defaults 

Percent VMT by Vehicle Type 

Vehicle Type 

Year 10 20 30 40 50 60

1999 0.00390 0.58310 0.33480 0.00280 0.02610 0.04920

2000 0.00380 0.58250 0.33600 0.00280 0.02570 0.04920

2001 0.00340 0.58240 0.33740 0.00250 0.02590 0.04850

2002 0.00330 0.58110 0.33850 0.00240 0.02660 0.04820

2003 0.00330 0.57880 0.34060 0.00230 0.02690 0.04810

2004 0.00340 0.57260 0.34600 0.00220 0.02730 0.04850

2005 0.00360 0.57060 0.34770 0.00220 0.02710 0.04870

2006 0.00400 0.55990 0.35850 0.00220 0.02750 0.04780

2007 0.00450 0.55070 0.36630 0.00220 0.02790 0.04840

2008 0.00510 0.54610 0.36980 0.00230 0.02870 0.04800

2009 0.00530 0.54760 0.37480 0.00210 0.02660 0.04370

2010 0.00530 0.54360 0.37770 0.00220 0.02770 0.04350

2011 0.00530 0.53940 0.37830 0.00240 0.02970 0.04500

2012 0.00530 0.53700 0.37740 0.00250 0.03140 0.04640
60

Vehicle Type 

Year 10 20 30 40 50 60

2013 0.00520 0.53640 0.37610 0.00260 0.03240 0.04730

2014 0.00520 0.53780 0.37370 0.00260 0.03310 0.04760

2015 0.00511 0.54185 0.36926 0.00266 0.03358 0.04754

2016 0.00505 0.54655 0.36414 0.00270 0.03413 0.04743

2017 0.00500 0.55148 0.35868 0.00274 0.03465 0.04744

2018 0.00495 0.55719 0.35240 0.00278 0.03509 0.04759

2019 0.00490 0.56346 0.34559 0.00281 0.03548 0.04777

2020 0.00484 0.57033 0.33840 0.00283 0.03581 0.04779

2021 0.00479 0.57743 0.33138 0.00285 0.03599 0.04755

2022 0.00475 0.58459 0.32444 0.00286 0.03613 0.04723

2023 0.00471 0.59142 0.31755 0.00288 0.03639 0.04705

2024 0.00466 0.59782 0.31089 0.00291 0.03674 0.04698

2025 0.00462 0.60374 0.30470 0.00294 0.03709 0.04692

2026 0.00458 0.60921 0.29896 0.00296 0.03745 0.04684

2027 0.00455 0.61410 0.29377 0.00300 0.03784 0.04674

2028 0.00452 0.61852 0.28903 0.00303 0.03828 0.04662

2029 0.00450 0.62265 0.28449 0.00307 0.03876 0.04652

2030 0.00448 0.62625 0.28038 0.00311 0.03930 0.04648

2031 0.00444 0.62984 0.27688 0.00313 0.03959 0.04611

2032 0.00440 0.63303 0.27380 0.00316 0.03987 0.04574

2033 0.00436 0.63573 0.27104 0.00319 0.04023 0.04545

2034 0.00432 0.63812 0.26857 0.00321 0.04058 0.04519

2035 0.00429 0.64015 0.26636 0.00324 0.04096 0.04501

2036 0.00425 0.64184 0.26447 0.00327 0.04134 0.04483

2037 0.00421 0.64323 0.26287 0.00330 0.04173 0.04465

2038 0.00418 0.64417 0.26173 0.00333 0.04211 0.04447

2039 0.00414 0.64476 0.26096 0.00336 0.04249 0.04428

2040 0.00411 0.64532 0.26017 0.00340 0.04289 0.04411

2041 0.00407 0.64586 0.25937 0.00343 0.04331 0.04396

2042 0.00404 0.64630 0.25864 0.00346 0.04374 0.04381

2043 0.00401 0.64666 0.25799 0.00350 0.04418 0.04366

2044 0.00398 0.64693 0.25741 0.00353 0.04462 0.04352

2045 0.00395 0.64711 0.25692 0.00357 0.04507 0.04338

2046 0.00392 0.64719 0.25653 0.00360 0.04552 0.04324

2047 0.00389 0.64720 0.25620 0.00364 0.04598 0.04310

2048 0.00386 0.64715 0.25593 0.00368 0.04643 0.04295

2049 0.00383 0.64704 0.25572 0.00371 0.04690 0.04281

2050 0.00380 0.64689 0.25554 0.00375 0.04736 0.04266
61

Exhibit 5: Vehicle Registration Data 

Georgia's Revised Registration Distribution by Age 

Overview

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

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

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

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

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

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

type, gross vehicle weight rating (GVWR)6 class, model year, and registration 

geography (i.e., county). 

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

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

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

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

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

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

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

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

Class 1 0 - 6,000 lbs. 

Class 2 6,001 - 10,000 lbs. 

Class 3 10,001 - 14,000 lbs. 

Class 4 14,001 - 16,000 lbs. 

Class 5 16,001 - 19,500 lbs. 

Class 6 19,501 - 26,000 lbs. 

Class 7 26,001 - 33,000 lbs. 

Class 8 33,001 - 150,000 lbs. 

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

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

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

light truck/van, and motorcycle aftermarket. 

 6

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

manufacturer. 
62

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

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

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

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

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

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

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

vans from GVWR class 1. 

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

examples of the types of vehicles they include. 

Number Abbreviation Description 

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

- Class 1 GVWR 

- Include: Passenger Cars 

- Fuel: All Types 

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

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

LVW7

) 

- Class 1 GVWR 

- Trucks, SUVs, & Vans 

- Exclude Full-Size Pickups & Vans 

- Fuel: All Types 

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

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

LVW) 

- Class 1 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

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

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

- Include Vehicle Types: Incomplete Pickup + Cab Chassis 

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

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

4 LDT3 LD Trucks 3 (6,001-8,500 lbs. GVWR, 0-5,750 lbs. ALVW8

) 

- Class 2 GVWR 

- Trucks, SUVs, & Vans 

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

Models 

 7

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

8

 Adjusted loaded vehicle weight, average of the gross vehicle weight and the curb weight. 
63

Number Abbreviation Description 

- 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 

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 
64

Number Abbreviation Description 

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 
65

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

GVWR) 

- Class 8 GVWR 

- Trucks, SUVs, & Vans 

- Fuel: All Types 

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

(Coach) 

- Exclude Wheels: 4x2 

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

14 HDBS School Buses

- Include Vehicle Type: School Bus 

- Fuel: All Types 

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

Number Abbreviation Description 

15 HDBT Transit & Urban Buses

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

- Fuel: All Types 

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

16 MC Motorcycles (All) 

- Fuel: All Types 

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

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

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

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

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

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

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

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

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

with EPA guidance, defaults were used for this vehicle type. 
66

Exhibit 6: Source Type Population Input Data Preparation 

1) Date sources 

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

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

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

county, and has been used to develop age distribution. 

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

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

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

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

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

Trucks include Truck Tractors, Trucks, and Wreckers. 

2) Methodology 

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

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

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

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

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

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

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

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

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

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

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

population data were used as the growth indicator. 

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

Vehicle types Growth factor 

HDBS Georgia registration data (2003 and 2007), Buses 

HDBT Georgia registration data (2003 and 2007), Buses 

HDV2B Population 2002 and 2007 

HDV3 Population 2002 and 2007 

HDV4 Population 2002 and 2007 

HDV5 Population 2002 and 2007 

HDV6 Population 2002 and 2007 

HDV7 Population 2002 and 2007 

HDV8A Population 2002 and 2007 

HDV8B Population 2002 and 2007 

LDT1 Population 2002 and 2007 

LDT2 Population 2002 and 2007 

LDT3 Population 2002 and 2007 

LDT4 Population 2002 and 2007 

LDV Population 2002 and 2007 
67

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

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

Vehicle types ATL13 ATL7 GAGAS GAOTHER Total 

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

HDBT 1,362 102 139 540 2,143

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Vehicle types ATL13 ATL7 GAGAS GAOTHER Total 

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

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

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

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

Table 4. Comparison between different growth factors 

 Ratios 

 2002 2003 2007 2007/2002 2007/2003

Motor vehicle registration 

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

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

Motor Cycle 108,724 174,617 1.606 

Bus 21,237 35,124 1.654 

Population in Georgia, U.S. Census 

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

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

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

MOVES national SALESGROWTH factor defaults 

Motorcycle 1.383 1.311 

Passenger Car 0.940 1.001 

Passenger Truck 0.972 0.948 

Light Commercial Truck 0.972 0.948 

Intercity Bus 1.353 1.268 

Transit Bus 1.353 1.268 

School Bus 1.353 1.268 

Refuse Truck 1.353 1.268 

Single Unit Short-haul Truck 1.353 1.268 

Single Unit Long-haul Truck 1.353 1.268 

Motor Home 1.353 1.268 

Combination Short-haul Truck 1.464 1.405 

Combination Long-haul Truck 1.464 1.405 
68

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

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

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

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

populations are the sum of populations of all counties sharing the same reference counties. 
69

Exhibit 7: 2007 RunSpec for Monroe County 

<runspec>  <description><![CDATA[Monroe County 2007 PM2.5 maintenance plan lookups Using HPMS vmt factored by road type Defaults for average speed distribution and vmt by hour Run #3 - Use 2007 yearly average meterology data - 090911 Run#4 - Revised vmtby type using HPMS data in spreadsheet - 090911- Lookups Run for SO2 in inventory mode]]></description>  <modelscale value="Inv"/>  <modeldomain value="SINGLE"/>  <geographicselections>  <geographicselection type="COUNTY" key="13207" description="GEORGIA - Monroe County"/>  </geographicselections>  <timespan>  <year key="2007"/>  <month id="7"/>  <day id="5"/>  <beginhour id="1"/>  <endhour id="24"/>  <aggregateBy key="Hour"/>  </timespan>  <onroadvehicleselections>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="62" sourcetypename="Combination Long- <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="61" sourcetypename="Combination Short-

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

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

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="11" sourcetypename="Motorcycle"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="21" sourcetypename="Passenger Car"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="31" sourcetypename="Passenger Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="51" sourcetypename="Refuse Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="43" sourcetypename="School Bus"/> 
70

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="53" sourcetypename="Single Unit Long-hau

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

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

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

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 <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" procExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processn
71

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

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

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

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

 </pollutantprocessassociations>  <databaseselections>  <databaseselection servername="" databasename="monroe_2007_pm25_lookups_input1" description=""/>  </databaseselections>  <internalcontrolstrategies> <internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgreuseParameters No ]]></internalcontrolstrategy>  </internalcontrolstrategies>  <inputdatabase servername="" databasename="" description=""/>  <uncertaintyparameters uncertaintymodeenabled="false" numberofrunspersimulation="0" numberofsimulations="0"/>  <geographicoutputdetail description="NATION"/>  <outputemissionsbreakdownselection>  <modelyear selected="false"/> 
72

 <fueltype selected="false"/>  <emissionprocess selected="true"/>  <onroadoffroad selected="true"/>  <roadtype selected="true"/>  <sourceusetype selected="true"/>  <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="monroe_2007_pm25_SO2_output4" 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="monroe_2007_pm25_lookups_input1" 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="Monroe_2007" truncateoutput="false" truncateactivity="false"/> </runspec>
73

Exhibit 8: MOVES Input Data for Monroe County Emissions 

for PM2.5 SIP Budget – 2007 

Source Type Population

yearID sourceTypeID sourceTypePopulation

2007 11 7002007 21 123842007 31 77172007 32 24942007 41 02007 42 02007 43 782007 51 112007 52 3452007 53 242007 54 352007 61 1722007 62 466

Vehicle Type VMT – HPMSVtypeYear Worksheet 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT 

10 2007 2,783,976.15 0.00 20 2007 433,560,565.63 0.00 30 2007 265,025,409.86 0.00 40 2007 7,214,859.49 0.00 50 2007 32,794,066.07 0.00 60 2007 166,893,725.63 0.00 

Meteorology Data 

monthID zoneID HourID temperature relHumidity 

7 132070 1 56.458 75.367
74

monthID zoneID HourID temperature relHumidity 

7 132070 2 55.400 77.3587 132070 3 54.325 79.1177 132070 4 53.358 80.6007 132070 5 52.542 81.7507 132070 6 51.775 82.8507 132070 7 51.267 83.3427 132070 8 51.958 82.6337 132070 9 55.083 78.3087 132070 10 59.967 70.3427 132070 11 64.742 61.8757 132070 12 68.775 54.9337 132070 13 71.908 49.9177 132070 14 74.092 46.5507 132070 15 75.533 44.3177 132070 16 76.242 43.1757 132070 17 76.033 43.2427 132070 18 74.758 44.8007 132070 19 71.625 49.5587 132070 20 67.333 56.7587 132070 21 63.600 63.3007 132070 22 61.025 67.7587 132070 23 59.250 70.7177 132070 24 57.742 73.283

Age Distribution - Sample 

sourceTypeID yearID ageID ageFraction

21 2007 0 0.0112021 2007 1 0.0450021 2007 2 0.0495021 2007 3 0.0605021 2007 4 0.0596021 2007 5 0.05660
75

sourceTypeID yearID ageID ageFraction

21 2007 6 0.0620021 2007 7 0.0617021 2007 8 0.0715021 2007 9 0.0634021 2007 10 0.0576021 2007 11 0.0523021 2007 12 0.0465021 2007 13 0.0444021 2007 14 0.0428021 2007 15 0.0362021 2007 16 0.0317021 2007 17 0.0265021 2007 18 0.0235021 2007 19 0.0191021 2007 20 0.0114021 2007 21 0.0069021 2007 22 0.0067021 2007 23 0.0053021 2007 24 0.0041921 2007 25 0.0033221 2007 26 0.0026221 2007 27 0.0020821 2007 28 0.0016421 2007 29 0.0013021 2007 30 0.0329531 2007 0 0.0154731 2007 1 0.0396131 2007 2 0.0447731 2007 3 0.0479331 2007 4 0.0477431 2007 5 0.0530431 2007 6 0.0643831 2007 7 0.02946
76

sourceTypeID yearID ageID ageFraction

31 2007 8 0.0398631 2007 9 0.0459831 2007 10 0.0490631 2007 11 0.0422631 2007 12 0.0399731 2007 13 0.0525231 2007 14 0.0553931 2007 15 0.0538831 2007 16 0.0422931 2007 17 0.0502731 2007 18 0.0473331 2007 19 0.0447131 2007 20 0.0322331 2007 21 0.0261731 2007 22 0.0296131 2007 23 0.0004631 2007 24 0.0003931 2007 25 0.0003631 2007 26 0.0003231 2007 27 0.0002831 2007 28 0.0002531 2007 29 0.0002331 2007 30 0.00378

Note: File contains 373 records and is available on request 
77

Exhibit 9: 2023 RunSpec for Monroe County 

<runspec>  <description><![CDATA[Monroe County - PM2.5 Lookup for Maintenance SIP - - Run #4 - Re run with new weather - 091211 - plus re-run with MOVESdefault files and revised source population]]></description>  <modelscale value="Rates"/>  <modeldomain value="SINGLE"/>  <geographicselections>  <geographicselection type="COUNTY" key="13207" description="GEORGIA - Monroe County"/>  </geographicselections>  <timespan>  <year key="2023"/>  <month id="7"/>  <day id="5"/>  <beginhour id="1"/>  <endhour id="24"/>  <aggregateBy key="Hour"/>  </timespan>  <onroadvehicleselections>  <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="62" sourcetypename="Combination Long- <onroadvehicleselection fueltypeid="2" fueltypedesc="Diesel Fuel" sourcetypeid="61" sourcetypename="Combination Short-

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

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

 <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="54" sourcetypename="Motor Home"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="11" sourcetypename="Motorcycle"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="21" sourcetypename="Passenger Car"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="31" sourcetypename="Passenger Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="51" sourcetypename="Refuse Truck"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="43" sourcetypename="School Bus"/>  <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="53" sourcetypename="Single Unit Long-hau <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="52" sourcetypename="Single Unit Short-hau <onroadvehicleselection fueltypeid="1" fueltypedesc="Gasoline" sourcetypeid="42" sourcetypename="Transit Bus"/>  </onroadvehicleselections> 
78

 <offroadvehicleselections>  </offroadvehicleselections>  <offroadvehiclesccs>  </offroadvehiclesccs>  <roadtypes>  <roadtype roadtypeid="1" roadtypename="Off-Network"/>  <roadtype roadtypeid="2" roadtypename="Rural Restricted Access"/>  <roadtype roadtypeid="3" roadtypename="Rural Unrestricted Access"/>  <roadtype roadtypeid="4" roadtypename="Urban Restricted Access"/>  <roadtype roadtypeid="5" roadtypename="Urban Unrestricted Access"/>  </roadtypes>  <pollutantprocessassociations>  <pollutantprocessassociation pollutantkey="3" pollutantname="Oxides of Nitrogen (NOx)" processkey="1" processname="Ru

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

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

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

 <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="1" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="2" proces <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="15" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="16" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="17" procExhaust"/>  <pollutantprocessassociation pollutantkey="112" pollutantname="Primary PM2.5 - Elemental Carbon" processkey="90" procExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="1" processn <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="2" processn <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="15" procesExhaust"/> 
79

 <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="16" procesExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="17" procesExhaust"/>  <pollutantprocessassociation pollutantkey="111" pollutantname="Primary PM2.5 - Organic Carbon" processkey="90" proces <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="1" proce <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="2" proce <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="15" procExhaust"/>  <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="16" procExhaust"/>  <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="17" procIdle Exhaust"/>  <pollutantprocessassociation pollutantkey="115" pollutantname="Primary PM2.5 - Sulfate Particulate" processkey="90" procExhaust"/>  <pollutantprocessassociation pollutantkey="117" pollutantname="Primary PM2.5 - Tirewear Particulate" processkey="10" pro <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="1" processname=" <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="2" processname=" <pollutantprocessassociation pollutantkey="91" pollutantname="Total Energy Consumption" processkey="90" processname=

 </pollutantprocessassociations>  <databaseselections>  <databaseselection servername="" databasename="monroe_2023_pm_input4" description=""/>  </databaseselections>  <internalcontrolstrategies> <internalcontrolstrategy classname="gov.epa.otaq.moves.master.implementation.ghg.internalcontrolstrategies.rateofprogress.RateOfProgreuseParameters No ]]></internalcontrolstrategy>  </internalcontrolstrategies>  <inputdatabase servername="" databasename="" description=""/>  <uncertaintyparameters uncertaintymodeenabled="false" numberofrunspersimulation="0" numberofsimulations="0"/>  <geographicoutputdetail description="LINK"/>  <outputemissionsbreakdownselection>  <modelyear selected="false"/>  <fueltype selected="false"/>  <emissionprocess selected="true"/>  <onroadoffroad selected="true"/>  <roadtype selected="true"/>  <sourceusetype selected="true"/>  <movesvehicletype selected="false"/>  <onroadscc selected="false"/>  <offroadscc selected="false"/>  <estimateuncertainty selected="false" numberOfIterations="2" keepSampledData="false" keepIterations="false"/> 
80

 <sector selected="false"/>  <engtechid selected="false"/>  <hpclass selected="false"/>  </outputemissionsbreakdownselection>  <outputdatabase servername="" databasename="monroe_2023_pm25_lookups_output4" 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="Monroe_2023_pm_input4" 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="monroe_2023_1" truncateoutput="false" truncateactivity="false"/> 

</runspec>
81

Exhibit 10: MOVES Input Data for Monroe County Emissions 

for PM2.5 SIP Budget – 2023 

Source Type Population

yearID sourceTypeID sourceTypePopulation

2023 11 987

2023 21 17461

2023 31 10881

2023 32 3516

2023 41 0

2023 42 0

2023 43 111

2023 51 15

2023 52 486

2023 53 34

2023 54 50

2023 61 243

2023 62 657

Vehicle Type VMT – HPMSVtypeYear Worksheet 

HPMSVtypeID yearID HPMSBaseYearVMT baseYearOffNetVMT 

10 2023 3,273,420.87 0.00 

20 2023 411,263,213.35 0.00 

30 2023 220,821,301.48 0.00 

40 2023 2,003,423.42 0.00 

50 2023 25,306,731.87 0.00 

60 2023 32,714,715.77 0.00 

Meteorology Data 

monthID zoneID HourID temperature relHumidity 

7 132070 1 56.458 75.367

7 132070 2 55.400 77.358

7 132070 3 54.325 79.117

7 132070 4 53.358 80.600

7 132070 5 52.542 81.750

7 132070 6 51.775 82.850

7 132070 7 51.267 83.342

7 132070 8 51.958 82.633

7 132070 9 55.083 78.308

7 132070 10 59.967 70.342

7 132070 11 64.742 61.875
82

monthID zoneID HourID temperature relHumidity 

7 132070 12 68.775 54.933

7 132070 13 71.908 49.917

7 132070 14 74.092 46.550

7 132070 15 75.533 44.317

7 132070 16 76.242 43.175

7 132070 17 76.033 43.242

7 132070 18 74.758 44.800

7 132070 19 71.625 49.558

7 132070 20 67.333 56.758

7 132070 21 63.600 63.300

7 132070 22 61.025 67.758

7 132070 23 59.250 70.717

7 132070 24 57.742 73.283

Age Distribution - Sample 

sourceTypeID yearID ageID ageFraction

21 2023 0 0.01120

21 2023 1 0.04500

21 2023 2 0.04950

21 2023 3 0.06050

21 2023 4 0.05960

21 2023 5 0.05660

21 2023 6 0.06200

21 2023 7 0.06170

21 2023 8 0.07150

21 2023 9 0.06340

21 2023 10 0.05760

21 2023 11 0.05230

21 2023 12 0.04650

21 2023 13 0.04440

21 2023 14 0.04280

21 2023 15 0.03620

21 2023 16 0.03170

21 2023 17 0.02650

21 2023 18 0.02350

21 2023 19 0.01910

21 2023 20 0.01140

21 2023 21 0.00690

21 2023 22 0.00670

21 2023 23 0.00530

21 2023 24 0.00419

21 2023 25 0.00332
83

sourceTypeID yearID ageID ageFraction

21 2023 26 0.00262

21 2023 27 0.00208

21 2023 28 0.00164

21 2023 29 0.00130

21 2023 30 0.03295

31 2023 0 0.01547

31 2023 1 0.03961

31 2023 2 0.04477

31 2023 3 0.04793

31 2023 4 0.04774

31 2023 5 0.05304

31 2023 6 0.06438

31 2023 7 0.02946

31 2023 8 0.03986

31 2023 9 0.04598

31 2023 10 0.04906

31 2023 11 0.04226

31 2023 12 0.03997

31 2023 13 0.05252

31 2023 14 0.05539

31 2023 15 0.05388

31 2023 16 0.04229

31 2023 17 0.05027

31 2023 18 0.04733

31 2023 19 0.04471

31 2023 20 0.03223

31 2023 21 0.02617

31 2023 22 0.02961

31 2023 23 0.00046

31 2023 24 0.00039

31 2023 25 0.00036

31 2023 26 0.00032

31 2023 27 0.00028

31 2023 28 0.00025

31 2023 29 0.00023

31 2023 30 0.00378

Note: File contains 373 records and is available on request 

 
84