APPENDIX E

HOUSTON-GALVESTON-BRAZORIA 1997 EIGHT-HOUR OZONE 

NONATTAINMENT AREA VEHICLE MILES TRAVELED OFFSET ON-ROAD 

MOBILE SOURCE EMISSIONS INVENTORIES ANALYSIS

2012-002-SIP-NR

Adoption

April 23, 2013

HOUSTON-GALVESTON-BRAZORIA 1997 EIGHT-HOUR OZONE 

NONATTAINMENT AREA VEHICLE MILES TRAVELED OFFSET ON-ROAD 

MOBILE SOURCE EMISSIONS INVENTORIES ANALYSIS

Appendix E documents the development of the vehicle miles traveled (VMT) offset analysis for 

on-road mobile emissions inventories (EI) to support updates to the Houston-GalvestonBrazoria (HGB) 1997 Eight-Hour Ozone Standard Nonattainment Area Motor Vehicle Emissions 

Budgets (MVEB) Update State Implementation Plan (SIP) Revision. 

The development of the VMT offset EIs was prepared by the Texas Transportation Institute 

(TTI) at the request and under the direction of the Texas Commission on Environmental Quality 

(TCEQ). The VMT offset on-road mobile source EIs reflect the most recent planning 

assumptions for the HGB transportation network. Complete documentation of the development 

and resulting VMT offset EIs is provided in the attached document, VMT-Offset Emissions 

Inventories for the HGB Eight-Hour Ozone Nonattainment Counties. The final emissions 

estimates are summarized in Table 2: HGB Emissions Inventories for the VMT Offset 

Demonstration (Tons/Day) of the Executive Summary on page 3 of the report. The supporting 

electronic documents for the development of the EIs, including Motor Vehicle Emission 

Simulator (MOVES) input and output files and the post processing spreadsheets, are available 

upon request in electronic format. Please contact the TCEQ, Air Quality Division, Mobile Source 

Programs Team if a copy of the electronic information is needed. 

Since the adoption of the 2010 HGB SIP Revision1 by the TCEQ, the United States Ninth Circuit 

Court of Appeals decision on January 27, 2012 addressed requirements for transportation 

control measures to offset VMT growth in SIP revisions for areas classified as severe and above. 

The United States Environmental Protection Agency (EPA) has prepared new guidance on what 

information must be included in a SIP revision in order to demonstrate that VMT offsets are not 

needed. Based upon the EPA guidance, Implementing Clean Air Act Section 182(d)(1)(A): 

Transportation Control Measures and Transportation Control Strategies to Offset Growth in 

Emissions Due to Growth in Vehicle Miles Travelled2 (August 2012), the inventory development 

for the HGB VMT offset analysis in this SIP revision includes the following scenarios:

• 2002 base year emissions;

• attainment year emissions assuming no new post-2002 measures with VMT growth from the 

base year to the attainment year;

• attainment year emissions assuming no new post-2002 measures and no VMT growth (2002 

VMT); and

• actual attainment year emissions with attainment year emission controls and VMT growth 

between the base year and the attainment year. 

 1 2010 HGB RFP SIP Revision (TCEQ Project No. 2009-018-SIP-NR, adopted March 10, 2010) HoustonGalveston-Brazoria Reasonable Further Progress State Implementation Plan Revision for the 1997 EightHour Ozone Standard 

(http://www.tceq.state.tx.us/assets/public/implementation/air/sip/hgb/hgb_sip_2009/09018SIP_ado.

pdf)

2 EPA, 2012. "Implementing Clean Air Act Section 182(d)(1)(A): Transportation Control Measures and 

Transportation Control Strategies to Offset Growth in Emissions Due to Growth in Vehicle Miles 

Travelled." Office of Transportation and Air Quality, U.S. Environmental Protection Agency; EPA-420-B12-053, August 2012
The four inventories were developed maintaining consistency with the 2012 HGB Reasonable 

Further Progress (RFP) inventories and in accordance with the EPA 2012 VMT offset guidance 

using a special version of the EPA’s mobile source emissions estimation model,

MOVES2010bROP. The inventory methodology, activity inputs, and MOVES model inputs used 

were consistent with the proposed 2012 HGB RFP 2002 base year and 2018 attainment year 

control strategy analysis inventories. 
January 2013

Prepared by the

VMT-Offset Emissions

Inventories for the HGB

Eight-Hour Ozone

Nonattainment Counties

Transportation

Institute

Texas A&M

DEVELOPMENT OF ON-ROAD MOBILE SOURCE, VMT-OFFSET 

EMISSIONS INVENTORIES FOR THE HGB EIGHT-HOUR OZONE 

NONATTAINMENT COUNTIES

TECHNICAL REPORT

FINAL

Prepared for the

Texas Commission on Environmental Quality

Air Quality Planning and Implementation Division

Prepared by the

Transportation Modeling Program

Texas A&M Transportation Institute

TTI Study No.: 601391-0001

Grant Activities: 582-11-11226 – FY13-08

January 2013

TEXAS A&M TRANSPORTATION INSTITUTE

The Texas A&M University System

College Station, Texas 77843-3135

iii

TABLE OF CONTENTS

List of Tables ...................................................................................................................................v

Executive Summary.........................................................................................................................1

Purpose.............................................................................................................................................3

Background......................................................................................................................................3

Development of On-Road Mobile Source, VMT Offset Emissions Inventories for the HGB 

Eight-Hour Ozone Nonattainment Counties..............................................................................4

Acknowledgments .......................................................................................................................7

Summary of Results.........................................................................................................................7

Overview of Methodology...............................................................................................................8

Major Components ....................................................................................................................11

VMT Mix...................................................................................................................................11

On-Road Fleet Link-VMT and Speeds......................................................................................11

Vehicle Population and Off-Network Vehicle Activity Estimates............................................12

MOVES Emissions Factors.......................................................................................................12

Emissions Calculations..............................................................................................................13

Development of SUT/Fuel Type VMT Mix ..................................................................................14

Estimation of VMT........................................................................................................................16

Data Sources..............................................................................................................................16

VMT Adjustments .....................................................................................................................17

2002 Historical Year Analysis – VMT Control Totals and VMT Adjustments....................17

2018 Future Year Analysis – HPMS Adjustment Factor.......................................................18

2018 Future Year Analysis – Seasonal Adjustment Factors..................................................19

2018 Future Year Analysis – VMT Summary.......................................................................19

Hourly Travel Factors................................................................................................................20

Estimation of Link Speeds.............................................................................................................22

Estimation of Off-Network Activity..............................................................................................23

Estimation of Vehicle Population..............................................................................................23

Historical Vehicle Population Estimates ...............................................................................24

Future Vehicle Population Estimates.....................................................................................25

Estimation of SHP .....................................................................................................................26

Total Available Hours by SUT/Fuel Type.............................................................................26

SHO by SUT/Fuel Type.........................................................................................................26

Estimation of Starts....................................................................................................................26

Estimation of SHI ......................................................................................................................27

SHI Scaling Factors ...............................................................................................................27

SHI Hourly Factors................................................................................................................28

County-Level CLhT_Diesel SHI by Hour Estimation...........................................................28

Estimation of Emissions Factors....................................................................................................28

MOVES Inputs, Outputs and Post-Processing ..........................................................................30

Summary of Control Programs Modeled...................................................................................31

MOVES Emissions Factor Aggregation Levels........................................................................32

MOVES Run Specifications......................................................................................................32

Scale, Time Spans, and Geographic Bounds.........................................................................34

On-Road Vehicle Equipment and Road Type .......................................................................34

Pollutants and Processes........................................................................................................34
iv

Manage Input Data Sets and Strategies .................................................................................34

Output ....................................................................................................................................35

MOVES County Input Databases..............................................................................................35

User Inputs to MOVES via CDB...............................................................................................37

Year, State, and County Inputs to MOVES ...........................................................................37

Roadtype Table Inputs to MOVES........................................................................................37

Default Activity and Population CDB Inputs to MOVES.....................................................37

Local Fleet Age Distributions and Fuel Fractions Inputs to MOVES...................................37

Local Meteorological (County and Zonemonthhour Table) Inputs to MOVES....................39

Fuels Inputs to MOVES.........................................................................................................41

Local I/M Inputs to MOVES .................................................................................................43

Model Year Cut Points...........................................................................................................46

Checks and Runs........................................................................................................................48

Post-Processing Runs.................................................................................................................48

Emissions Calculations..................................................................................................................49

Hourly Link-Based Emissions Calculations..............................................................................50

Quality Assurance..........................................................................................................................52

A. Project Management .............................................................................................................53

B. Data Generation and Acquisition..........................................................................................53

C. Assessment and Oversight ....................................................................................................55

D. Data Validation and Usability...............................................................................................55

References......................................................................................................................................58

Appendix A: HGB VMT Offset On-Road Inventories Electronic Data Submittal .......................61

Appendix B: Emissions Estimation Utilities for MOVES-Based Emissions Inventories.............67

TTI Emissions Estimation Utilities for MOVES-Based Emissions Inventories ...........................69

TRANSVMT .............................................................................................................................69

VirtualLinkVMT........................................................................................................................69

MOVESactivityInputBuild ........................................................................................................70

MOVESpopulationBuild ...........................................................................................................72

MOVESfleetInputBuild.............................................................................................................75

MOVESratesCalc.......................................................................................................................78

MOVESratesAdj........................................................................................................................79

ShpExtIdleStartActBld ..............................................................................................................80

ExtIdleHrsCalc...........................................................................................................................80

MOVESemscalc.........................................................................................................................81

Emissions Factor Interpolation Methodology........................................................................82

MOVEStabcomb........................................................................................................................83

Appendix C: TxDOT District Weekday VMT Mix by MOVES Road Type ................................85

Appendix D: TxDOT District Aggregate Weekday VMT Mix.....................................................91

Appendix E: Capacity Factors, Speed Factors, and Speed Reduction Factors..............................97

Appendix F: Vehicle Population Estimates and 24-Hour SHP, Starts, and SHI Summaries......107

Appendix G: Source Type Age and Fuel/Engine Fractions Inputs to MOVES ..........................117

Appendix H: MOVES Run Summaries.......................................................................................139
v

LIST OF TABLES

Table 1. HGB VMT Offset Demonstration Emissions Inventory Scenarios...................... 1

Table 2. HGB Emissions Inventories for the VMT Offset Demonstration. (Tons/Day).... 3

Table 3. HGB VMT Offset Demonstration Emissions Inventory Scenarios...................... 6

Table 4. HGB VMT Offset Ozone Season Weekday On-Road Mobile Source 

MOVES2010bROP-Based Emissions Inventories (Tons)............................................ 7

Table 5. MOVES Source Use Type/Fuel Types................................................................. 9

Table 6. MOVES Model Emissions Processes................................................................. 10

Table 7. Emissions Rates by Process and Activity Factor................................................ 10

Table 8. VMT Mix Year/Analysis Year Correlations. ..................................................... 16

Table 9. HGB Weekday Factors for Control Total Development. ................................... 17

Table 10. HGB 2002 Weekday VMT Control Totals and VMT Adjustment Factors...... 18

Table 11. HGB Weekday Seasonal Adjustment Factors for Future Year Analysis. ........ 19

Table 12. HGB 2018 VMT Summary............................................................................... 20

Table 13. Weekday Hourly Travel Factors....................................................................... 21

Table 14. Registration Data Categories. ........................................................................... 24

Table 15. TxDOT Vehicle Registration Aggregations and Associated SUT/Fuel Types for 

Estimating SUT/Fuel Type Populations. .................................................................... 25

Table 16. Emissions Rates by Process and Activity Factor.............................................. 30

Table 17. Control Measure Modeling by Calendar Year Control Scenario. .................... 31

Table 18. RunSpec Selections by MOVES2010bROP GUI Navigation Panel................ 33

Table 19. MOVES2010bROP County Database (CDB) Inputs. ...................................... 36

Table 20. Data Sources and Aggregations for SUT Age Distributions and Fuel/Engine 

Fractions...................................................................................................................... 39

Table 21. Meteorological Inputs to MOVES.................................................................... 40

Table 22. MOVES Gasoline Inputs – HGB Summer Emissions Rates Analysis............. 42

Table 23. MOVES Diesel Inputs – HGB RFP Summer Emissions Rates Analysis......... 43

Table 24. MOVES IMCoverage Table Input Descriptions for HGB I/M Counties. ........ 46

Table 25. Explanation of Special VMT Offset Version of MOVES (Provided by EPA 

with the MOVES2010bROP Model, August 2012) ................................................... 47

Table 26. 2018 Analysis Year TxLED Adjustment Factor Summary. ............................. 49

Table 27. H-GAC TDM Road Type/Area Type to MOVES Road Type Designations. .. 51

Table 28. HGB VMT Offset Demonstration Emissions Inventory Scenarios.................. 52

Table 29. HGB VMT Offset Demonstration Emissions Inventory Scenarios.................. 63

Table 30. Registration Categories..................................................................................... 74

Table 31. SUT/Registration Category Correlation. .......................................................... 74

Table 32. SUTs/Registration Categories Correlation for SUT Age Distribution. ............ 76

Table 33. SUTs/Registration Categories Correlation for Fuel/Engine Fractions. ............ 77

Table 34. MOVES2010a Emissions Process and Corresponding Activity for Grams per 

Activity Emissions Rates. ........................................................................................... 78
vi
1

EXECUTIVE SUMMARY

The Texas Commission on Environmental Quality (TCEQ) recently approved the HoustonGalveston-Brazoria (HGB) 1997 Eight-Hour Ozone Standard Nonattainment Area Motor 

Vehicle Emissions Budgets (MVEB) Update State Implementation Plan (SIP) Revision proposal

(Proposal October 17, 2012). This MVEB Update SIP revision replaces the HGB area’s 

MOBILE model-based on-road mobile inventories and MVEBs of volatile organic compounds 

(VOC) and oxides of nitrogen (NOx) in the current SIP with inventories and MVEBs based on 

MOVES, the U.S. Environmental Protection Agency’s (EPA) latest on-road emissions model. 

EPA requires additional emissions analysis for the vehicle miles traveled (VMT) offset 

demonstration portion. TCEQ has requested that the Texas A&M Transportation Institute (TTI) 

develop the additional inventories needed for the VMT offset analysis.

This updated VMT offset analysis requirement was EPA’s response to satisfy a recent U.S. 

circuit court of appeals decision that addressed the requirements for transportation control 

measures in SIPs for severe nonattainment areas (essentially that the VMT increase does not 

trigger additional emissions controls). The EPA released its new VMT offset demonstration 

analysis guidance (August 2012) and a version of MOVES (MOVES2010bROP) modified 

specifically for performing VMT offset calculations. In consultation with TCEQ, following the 

EPA guidance and using MOVES2010bROP, TTI produced four HGB emissions inventories 

needed for the VMT offset demonstration analysis. As Table 1 shows, these four inventories 

include a base year and three attainment year scenarios, and are comprised of two activity input 

data sets and three emissions factor input data sets.

Table 1. HGB VMT Offset Demonstration Emissions Inventory Scenarios.

Inventory Activity 

Input Year Emissions Factor Input1

2002 Base Year 2002 2002 Base Year

2018 Fleet and Pre-2003 

Controls with Growth 2018 2018 Fleet and Pre-2003 

Controls1 2018 Fleet and Pre-2003 

Controls without Growth 2002

2018 Attainment Year Control 

Strategy 2018 2018 Fleet and Control Strategy 1 MOVES2010bROP provides the capability to model this emissions rate scenario required for the 

VMT offset calculations under the EPA’s 2012 VMT offset guidance in which fleet turnover is 

allowed to occur through 2018, while “freezing” emissions rates at a 2002 model year baseline 

for later model year vehicles.

The first and fourth inventories listed in Table 1, the 2002 base year and the 2018 attainment 

year control strategy inventories, were also previously developed using MOVES2010a. They are 

part of the set of reasonable further progress (RFP) inventories (HGB MOVES-Based RFP OnRoad Inventories and Control Strategy Reductions, TTI, March 2012) developed for the HGB 

RFP SIP analyses, and contained in the TCEQ’s proposed HGB MVEB Update SIP revision (as 

Appendix D). The base and attainment year 2012 HGB RFP SIP inventories (MOVES2010a-
2

based) provided the basis (methodology and input data) for development of the four 

MOVES2010bROP-based inventories produced for the VMT offset analysis.

The two “2018 fleet and pre-2003 controls” scenarios (second and third inventories in Table 

1) provided estimates of the hypothetical emissions in 2018 assuming no changes in emissions 

factors for vehicle model years after 2002, with one inventory including estimated growth (2018 

VMT) and the other inventory excluding growth (using 2002 VMT). The difference between 

these two inventories is the estimate of growth in emissions due solely to the growth in vehicle 

activity from the base year to the attainment year. The third inventory in Table 1 (2018 fleet 

with base year controls and activity) also establishes attainment year emissions “ceilings.” Any 

increased VOC and NOx emissions due to increased VMT have been adequately offset if the 

attainment year control strategy emissions estimates are not greater than their ceilings.

TTI developed the four inventories maintaining consistency with the 2012 HGB RFP 

inventories and in accordance with the EPA 2012 VMT offset guidance and MOVES2010bROP 

requirements. The inventory methodology, activity inputs, and MOVES model inputs used were 

consistent with, and mostly directly acquired from, the proposed 2012 HGB RFP 2002 base year 

and 2018 attainment year control strategy inventories analysis.

According to the EPA, the net impact of model changes at the county scale is very small 

(“EPA Releases MOVES2010b Mobile Source Emissions Model Update: Questions and 

Answers”, EPA, April 2012). A comparison of the 2012 HGB RFP and the VMT offset analysis 

inventories developed using the MOVES2010a and the MOVES2010bROP models, respectively, 

provided a check on the magnitude of the emissions difference between the 2010a and 2010b 

versions. The differences for HGB base and attainment year aggregate VOC, NOx, carbon 

monoxide (CO), and carbon dioxide (CO2) estimates were very small, within the realm of 

rounding error.

The inventory method used was the detailed, hourly, MOVES rates-per-activity, traffic 

demand model (TDM) link-based method. Network link level (roadway segment) and offnetwork (e.g., parked vehicle) emissions estimates were calculated by hour, external to the 

MOVES model, as the product of activity estimates and MOVES emissions rates. The activity 

estimates were link-based VMT and operational speed estimates, and off-network source-hoursparked (SHP), starts, and source hours extended idling (SHI) for diesel long-haul combination 

trucks only. Post-processing MOVES emissions and activity output produced all rates in 

pollutant mass per activity unit (e.g., grams/mile, grams/shp, grams/start, grams/shi) as needed in 

the emissions calculations. (See Update of On-Road Inventory Development Methodologies for 

MOVES Model Compatibility. TTI, July 2011.)

The inventories were produced using utilities developed by TTI to process on-road vehicle 

activity (travel model link-based or Highway Performance Monitoring System [HPMS] 

roadway-based), off-network vehicle activity, and emissions rate data into spatially and 

temporally detailed emissions estimates. EPA’s Technical Guidance (Using MOVES to Prepare 

Emission Inventories in State Implementation Plans and Transportation Conformity: Technical 

Guidance for MOVES2010, 2010a and 2010b. EPA, April 2012) and Explanation of Special 

VMT Offset Version of MOVES (technical sheet provided with MOVES2010bROP model, 

September 2012) were the primary technical references used for guidance on appropriate inputs 

to MOVES.
3

Table 2 shows the resulting HGB VMT offset inventories of VOC and NOx.

Table 2. HGB Emissions Inventories for the VMT Offset Demonstration (Tons/Day).

Inventory1 VMT Speed VOC NOx

2002 Base Year 128,145,285 36.40 124.47 371.89

2018 with Pre-2003 Controls 

with Growth 184,065,162 35.87 87.32 414.80

2018 with Pre-2003 Controls 

without Growth 128,145,285 36.40 58.15 282.75

2018 Attainment Year 

Control Strategy 184,065,162 35.87 51.84 109.98

1 Emissions estimates are based on EPA’s MOVES2010bROP model (September 2012).

A comparison of the inventories in Table 2 shows that the estimated 2018 attainment year 

control strategy scenario emissions (last inventory in Table 2) are below the emissions “ceilings” 

estimated as the 2018 with pre-2003 controls and without growth scenario (third inventory in 

Table 2). According to the 2012 EPA guidance, this demonstrates that the increased emissions 

due to increased VMT are adequately offset.

These MOVES2010bROP-based VMT offset demonstration analysis SIP inventories 

developed by TTI for TCEQ in support of the proposed HGB SIP Update are summarized in 

more detail in the following sections, along with the descriptions of methodologies and details of 

input data sources, development and usage.

PURPOSE

The purpose of this work was to develop on-road mobile source emissions inventories to support 

VMT offset analysis for the eight HGB ozone nonattainment area counties, required for the 

development of the HGB SIP revision.

BACKGROUND

TCEQ works with local planning districts, the Texas Department of Transportation (TxDOT), 

and TTI to provide on-road mobile source emissions inventories of air quality pollutants. 

TxDOT typically funds transportation conformity determinations required under 40 Code of 

Federal Regulations (CFR) Part 93. TCEQ funds mobile source inventory work in support of the 

Federal Clean Air Act (CAA) requirements, such as attainment of the National Ambient Air 

Quality Standards (NAAQS), and the study and control of hazardous air pollutants, including 

those from motor vehicles and/or motor vehicle fuels (as mandated under CAA sections 202 and 

211).

TCEQ prepared a revision to the HGB SIP, which was proposed in October 2012. This 

proposed SIP revision was to update the current 2010 HGB SIP revision (with the main purpose 

of replacing current MOBILE6-based inventories and MVEBs with MOVES-based 
4

counterparts). Since the 2010 HGB SIP revision, a January 27, 2012, U.S. Ninth Circuit Court of 

Appeals decision addressed requirements for transportation control measures (TCM) to offset 

VMT growth in SIP revisions for areas classified as serious and above. Although previous HGB 

SIP revisions have included VMT offset analyses that have been approved by the EPA, in 

response to the appeals court decision the EPA has prepared new guidance (Implementing Clean 

Air Act Section 182(d)(1)(A): Transportation Control Measures and Transportation Control

Strategies to Offset Growth in Emissions Due to Growth in Vehicle Miles Traveled, EPA, 

August 2012) on what information must be included in a SIP revision to adequately demonstrate 

that VMT offsets are not needed. The EPA’s new VMT-offset demonstration guidance specifies 

that four on-road inventory scenarios are needed for the demonstration. The EPA has also 

produced a special version of the MOVES on-road inventory tool, MOVES2010bROP (EPA, 

September 2012), that must be used in the VMT offset calculations.

The four emissions inventories required in the VMT offset demonstration consist of one 2002 

base year, and three 2018 attainment year scenarios. The base year inventory is based on VMT 

in that year and all controls in effect in that year. For the attainment year, two of the inventories 

are hypothetical emissions scenarios that provide the basis to identify the “growth in emissions” 

due solely to growth in VMT; and the third represents projected actual motor vehicle emissions 

after fully accounting for projected VMT growth and emissions reductions from all creditable 

controls to be in effect in the attainment year (i.e., the attainment year control strategy scenario).

The two hypothetical attainment year emissions inventories consist of one set of emissions 

factors that reflect the 2018 attainment year fleet with pre-2003 controls (i.e., 2002 base year 

controls including no new control measures), and two VMT scenarios, i.e., 2002 base year VMT 

representing the no growth scenario, and 2018 attainment year VMT representing the growth 

scenario. The emissions difference in the attainment year pre-2003 controls with growth and 

without growth scenarios yields the estimated increase in emissions due to VMT growth; and the 

pre-2003 controls no growth scenario emissions estimate also establishes the “ceiling” on 

attainment year emissions. If the attainment year control strategy emissions levels are greater 

than this emissions ceiling, additional controls would be needed to offset the emissions growth 

due to VMT, otherwise any increased emissions due to VMT have been adequately offset and no 

additional controls would be needed.

TTI developed all four of the inventories needed for the HGB VMT offset demonstration 

analysis, consistent with the 2012 EPA VMT offset guidance, and using the MOVES2010bROP 

model. TTI determined and agreed upon the methods, scenarios, and input data for the inventory 

development in consultation with the TCEQ project manager. This work was conducted by and 

completed consistent with the list of references in the References section.

DEVELOPMENT OF ON-ROAD MOBILE SOURCE, VMT OFFSET EMISSIONS 

INVENTORIES FOR THE HGB EIGHT-HOUR OZONE NONATTAINMENT 

COUNTIES

TTI developed emissions estimates for the eight HGB ozone nonattainment counties for the 

analysis years consistent with the EPA’s 2012 VMT offset demonstration guidance, including 

each control scenario required for the VMT offset demonstration. Following the EPA’s 2012 

guidance, TTI produced four HGB area inventories:

• 2002 base year;
5

• 2018 attainment year with pre-2003 controls with growth (2018 activity estimates);

• 2018 attainment year with pre-2003 controls without growth (2002 activity estimates);

and

• 2018 control strategy (i.e., all controls to be in effect in 2018 and with growth).

TTI developed the VMT offset inventories for the HGB area consistent with inventory 

development for other SIP analyses. Since the FY 2012 HGB RFP SIP and attainment

demonstration (AD) SIP inventories had already been completed, the activity information based 

upon travel demand modeling completed at the time of the RFP and AD inventory development 

were used. Other modeling inputs used were also consistent with the FY 2012 HGB RFP SIP 

inventories.

In conjunction with the VMT offset guidance, the EPA released a special version of the onroad mobile source emissions model, MOVES2010b, designed to develop emissions factors and 

inventories consistent with the VMT offset guidance. This special version of MOVES2010b is 

referred to as MOVES2010bROP. MOVES2010bROP was used to complete this work. The

VMT offset inventories were produced based on methods agreed upon in consultation with the 

TCEQ project manager. The methods were consistent with the EPA’s 2012 VMT offset

guidance. Development is complete for the HGB RFP and AD SIP on-road inventories 

(produced with MOVES2010a) that are being used in the proposed HGB SIP revision. Although 

it is general practice to use the most recently available data for on-road SIP inventory 

development, for the VMT offset inventories to be as consistent as possible with the other HGB 

SIP inventories, some MOVES2010bROP inputs were the same as the recently developed RFP 

inventories as indicated in the following. The MOVES inputs for the FY 2012 HGB RFP onroad mobile inventories are documented in the project report HGB MOVES-Based RFP On-Road 

Inventories and Control Strategy Reductions, TTI, March 2012. 

TTI adhered to the following:

• The emissions factor model used in developing inventories for this task was the special

version of MOVES, named MOVES2010bROP (this model was said to be named 

MOVES2010b-VMTOffset, but instead was released by EPA in September 2012 with the 

name of MOVES2010bROP).

• The pollutants included are VOC, carbon monoxide CO, nitrogen oxides NOx, and 

carbon dioxide CO2.

• The day type is consistent with the HGB RFP inventory development from March 2012. 

The day type for all the inventories is ozone-season, daily. Activity levels were adjusted 

for summer season and for average weekday, Monday through Friday.

• The temperatures are consistent with the HGB RFP inventory development from March 

2012. The temperatures are consistent with the eight-hour, ozone season temperatures as 

determined using the EPA guidance.

• The humidity input is consistent with the HGB RFP inventory development from March 

2012. The humidity input was developed using the same methodology as the ozone 

season temperatures.
6

• The VMT mixes are consistent with the HGB RFP inventory development from March 

2012. The VMT mixes are consistent with the EPA MOVES source use types.

• The locality-specific age distributions are consistent with the HGB RFP inventory 

development from March 2012. The age distributions used were the age distributions for 

the RFP analysis year that corresponds to the VMT offset analysis years.

• A link-based, time-of-day emissions analysis methodology was used for all of the 

referenced counties (the VMT Offset demonstration guidance did not state an alternative 

preferred methodology).

• Control program parameters, including Reid Vapor Pressure (RVP) and fuel settings,

were determined based upon the VMT offset control scenarios documented in the VMT 

Offset demonstration guidance. TTI used fuel parameter inputs agreed upon in 

consultation with TCEQ.

• The Texas Low Emissions Diesel (TxLED) factors used were consistent with the HGB 

RFP inventory development from March 2012. The year-specific TxLED adjustment 

factors were developed using the benefit information described in the EPA Memorandum

on Texas Low Emission Diesel Fuel Benefits.

Table 3 lists the VMT offset analysis inventory scenarios produced, along with the activity 

and emissions rate components comprising each inventory.

Table 3. HGB VMT Offset Demonstration Emissions Inventory Scenarios.

Inventory Activity 

Input Year1 Emissions Factor Input2

2002 Base Year 2002 2002 Base Year

2018 Fleet and Pre-2003 

Controls with Growth 2018 2018 Fleet and Pre-2003 

2018 Fleet and Pre-2003 Controls

Controls without Growth 2002

2018 Attainment Year Control 

Strategy 2018 2018 Fleet and Control Strategy

1 Activity inputs (VMT mix; link VMT/speeds; and off-network activity: SHP, starts, SHI) were 

consistent with the FY 2012 HGB RFP SIP inventories. 2 Emissions factors were based on the EPA’s special model “MOVES2010bROP” provided to 

TCEQ for use in the VMT offset calculations. “Pre-2003 Controls” means attainment year 

emissions factors were modeled to reflect no new controls after the base year beyond those 

already credited.

The following activities were completed.

• Prepared county-level hourly and 24-hour day tables that provide roadway and source use 

type summaries of VMT, vehicle hours traveled (VHT), average speed, SHP, vehicle 

starts, SHI, and totals for the pollutants VOC, CO, NOx, and CO2 by associated emissions 

processes. These files are tab-delimited for ease of loading into spreadsheet software 

such as Microsoft® Excel.
7

• Prepared documentation, complete and self contained, including electronic data files.

TTI will maintain a record of all electronic files developed or used in conjunction with the 

completion of this project. All pertinent data relating to project activities were submitted to 

TCEQ in the specified electronic format, in conjunction with supporting electronic document 

files, and copies of the this report. The electronic file submission is described in Appendix A –

Electronic Data Submittal.

Acknowledgments

Dennis Perkinson, Ph.D., L.D. White, Stacey Schrank, and Martin Boardman, all of TTI, 

contributed to the development of the MOVES link-based emissions estimates. Dr. Perkinson 

produced the VMT mixes used to divide fleet VMT activity into MOVES SUT by fuel type 

categories, county VMT control totals, and hourly VMT factors. Houston-Galveston Area 

Council (H-GAC) staff provided the HGB regional travel model data sets. White processed 

roadway based activity (VMT and speeds) and off-network vehicle activity estimates (and 

vehicle population estimates) needed for the emissions calculations. Boardman produced 

MOVES model and MOVES output post-processor set-ups, and the MOVES-based emissions 

factors with adjustments for TxLED fuel, where needed. Schrank produced the emissions run 

set-ups and performed the emissions runs. Gary Lobaugh, of TTI, was responsible for editing, 

design, and production of this Technical Report. Each member of the assigned TTI staff 

contributed to the quality assurance of the inventory elements. Dr. Perkinson was the principle 

investigator for this project. This work was performed by TTI under contract to TCEQ. Mary 

McGarry-Barber was the TCEQ project technical manager.

The discussion is organized in the following sections: Summary of Results, Overview of 

Methodology, Development of VMT Mix, Estimation of VMT, Estimation of Link Speeds, 

Estimation of Off-Network Activity, Estimation of Emissions Factors, Emissions Calculations,

Quality Assurance, and References.

SUMMARY OF RESULTS

Table 4 summarizes the MOVES2010bROP-based VMT offset inventories (VMT and speed, and 

VOC, CO, NOx, and CO2 emissions) for the HGB region.

Table 4. HGB VMT Offset Ozone Season Weekday On-Road Mobile Source 

MOVES2010bROP-Based Emissions Inventories (Tons).

Inventory VMT Speed1 VOC CO NOx CO2

2002 Base Year 128,145,285 36.40 124.47 1,414.97 371.89 72,346.11

2018 Pre-2003 Controls

with Growth 184,065,162 35.87 87.32 1,261.23 414.80 106,368.48

2018 Pre-2003 Controls

without Growth 128,145,285 36.40 58.15 858.28 282.75 73,305.65

2018 Attainment Year

Control Strategy 184,065,162 35.87 51.84 663.10 109.98 97,995.85

1 Average speed in miles-per-hour.
8

VMT offset inventory estimates with more detail (e.g., by county, SUT/fuel type, road type) 

may be found in the electronic data submittal (see description in Appendix A).

OVERVIEW OF METHODOLOGY

During FY 2012, TTI used MOVES2010a to develop the HGB RFP SIP emissions inventories

(March 2012), which included the RFP 2002 base year and RFP 2018 attainment year control 

strategy inventories. To model the four inventories for the VMT offset analysis, TTI used the 

MOVES2010bROP model (provided by EPA specifically for VMT offset calculations), and the 

inventory methodology, activity inputs, and MOVES model inputs consistent with (e.g., mostly 

directly from) the FY 2012 HGB RFP 2002 base year and RFP 2018 attainment year control 

strategy inventories. Adjustments to inventory inputs were made to convert MOVES2010abased inputs to MOVES2010bROP model input specifications, where needed, and as required 

specifically for VMT offset calculations.

The inventory method is the detailed, hourly, MOVES rates-per-activity-based, TDM linkbased method. This method produces the hourly emissions estimates by MOVES SUT/fuel type 

(Table 5), pollutant, and emission process (Table 6) for each county inventory scenario (i.e., 

inventory analysis year, seasonal day-type and control scenario).

This new method is an adaptation of the previous TDM link-based emissions inventory

method used with MOBILE6, which applied emissions rates for all emissions processes in terms 

of miles-traveled activity (e.g., grams/mile). This MOVES “rates-per-activity” method was first 

implemented during FY 2011 by TTI to produce the HGB base case and baseline emissions 

inventories (documented in Development and Production of 2006 Base Case and 2008 Baseline 

On-road Mobile Source Emissions Inventories for the HGB Nonattainment Area, TTI, July 

2011).

In addition to the VMT-based calculations of roadway-based emissions estimates, the TTI 

MOVES emissions inventory process now uses off-network activity measures (i.e., starts, SHP, 

SHI) for off-network-based emissions estimates. “Rates-per-activity” is emphasized in the new 

process, because all emissions rates used are based in activity units, which is different than the 

standard MOVES look-up table output that provides mass-per-mile for roadway-based processes, 

and (only) mass-per-vehicle for off-network-based emissions processes. The TTI method used 

requires post-processing of MOVES output into rate tables with the off-network process 

emissions rates based in units of activity, rather than in units of vehicles.
9

Table 5. MOVES Source Use Type/Fuel Types.

Source Use Type ID Source Use Type Description Source Use Type 

Abbreviation1

11 Motorcycle MC

21 Passenger Car PC

31 Passenger Truck PT

32 Light Commercial Truck LCT

41 Intercity Bus IBus

42 Transit Bus TBus

43 School Bus SBus

51 Refuse Truck RT

52 Single Unit Short-Haul Truck SUShT

53 Single Unit Long-Haul Truck SULhT

54 Motor Home MH

61 Combination Short-Haul Truck CShT

62 Combination Long-Haul Truck CLhT

1 The SUT/fuel type labels are the combined SUT abbreviation and fuel type names separated by an underscore 

(e.g., MC_Gas, RT_Diesel, and SBus_Gas are motorcycles, diesel-powered refuse trucks, and gasoline-powered 

school buses).
10

Table 6. MOVES Model Emissions Processes.

Process ID Process Name

1 Running Exhaust

2 Start Exhaust

9 Brake Wear

10 Tire Wear

11 Evaporative Permeation

12 Evaporative Fuel Vapor Venting

13 Evaporative Fuel Leaks

15 Crankcase Running Exhaust

16 Crankcase Start Exhaust

17 Crankcase Extended Idle Exhaust

181 Refueling Displacement Vapor Loss

191 Refueling Spillage Loss

90 Extended Idle Exhaust

991 Well-to-Pump

1 Not subject to on-road mobile emissions analysis.

Table 7 shows the emissions rate units with associated processes and activity factors used in 

this MOVES analysis.

Table 7. Emissions Rates by Process and Activity Factor.

Emission Processes Activity1 Emissions Factor Units

Running Exhaust

Crankcase Running Exhaust VMT grams/mile (g/mi)

Evaporative Permeation

Evaporative Fuel Vapor Venting

Evaporative Fuel Leaks

VMT g/mi

SHP g/shp

Start Exhaust

Crankcase Start Exhaust starts g/start

Extended Idle Exhaust 

Crankcase Extended Idle Exhaust SHI g/shi

1 The amount of travel on roads (VMT), SHP, vehicle starts, and SHI are the basic activity factors. SHI 

is for Combination Long-Haul Trucks only. Evaporative permeation, fuel vapor venting, and fuel 

leaks occur both during operation and while parked.
11

Major Components

The county emissions inventory estimation process requires development of the following major 

inventory components. All are inputs to the emissions calculations, except for vehicle 

populations, which are an intermediate input needed for calculating estimates of SHP and vehicle 

starts.

• Area source use type (SUT)/fuel type VMT mix;

• County, hourly link fleet VMT and average speeds;

• County SUT/fuel type vehicle populations;

• County, hourly SUT/fuel type SHP;

• County, hourly SUT/fuel type starts;

• County, hourly SUT/fuel type SHI; and

• County, hourly SUT/fuel type emissions rates: g/mile, g/SHP, g/start, and g/SHI.

VMT Mix

The VMT mix designates the SUT/fuel types included in the analysis, and specifies the fraction 

of on-road fleet VMT attributable to each SUT/fuel type by MOVES road type.

The SUT/fuel type VMT mixes were estimated using TTI’s SUT/fuel type mix method

(Methodologies for Conversion of Data Sets for MOVES Model Compatibility, TTI, August 

2009). The VMT mix method sets Texas vehicle registration category aggregations for MOVES 

SUT categories to be used in developing the VMT mix estimates, as well as for developing other 

fleet parameter inputs needed in the process (e.g., SUT age distributions). The current VMT mix 

method produced a set of 24-hour average SUT/fuel type VMT allocations by MOVES road 

type, estimated for each TxDOT district associated with the eight-county HGB area (i.e., 

Houston and Beaumont districts). The data sources used were recent, multi-year TxDOT vehicle 

classification counts, year-end TxDOT/TxDMV registration data, along with MOVES default 

data where needed.

On-Road Fleet Link-VMT and Speeds

The TDM link-based method was used to estimate fleet VMT and speed inputs to the roadwaybased emissions calculations (product of “mass per mile” emissions factors and VMT).

TTI post-processed directional, four-period time-of-day, TDM network traffic assignments 

and trip matrix data provided by H-GAC to produce the hourly, directional, link VMT 

(consistent with HPMS VMT) and associated average fleet speed estimates, of the ozone season 

weekday (i.e., average Monday through Friday during the June through August period). The 

seasonal period, day type, and hourly distributions used were based on factors developed with 

TxDOT Automatic Traffic Recorder (ATR) data from the Houston area. The hourly average

operational fleet speeds were estimated corresponding to the link VMT estimates using the 

Houston speed model, which estimates operational speeds based on a link’s estimated free-flow 

speed and congestion-related speed reduction.
12

Vehicle Population and Off-Network Vehicle Activity Estimates

The non-roadway travel-related emissions estimates (e.g., from vehicle starts, parked vehicle 

evaporative permeation and tank vapor venting, and SHI) were calculated as the product of the 

amount of associated activity and the pollutant mass per unit of activity (emission factor units as 

shown in Table 7). To estimate the SHP and vehicle starts activity, SUT/fuel type category 

population estimates were needed, whereas SHI was based on HGB county-specific actual 

estimates (Heavy-Duty Vehicle Idle Activity and Emissions Characterization Study, ERG, August 

2004).

SUT/Fuel Type Populations: TTI based vehicle population estimates on registration data, 

SUT/fuel type VMT mix-based vehicle population factors, and VMT-based growth estimates 

(for future years). For historical years, the SUT/fuel type vehicle population estimates are based 

solely on mid-year TxDOT (or Texas Department of Motor Vehicles [TxDMV]) county 

registrations data and regional, all roads-weekday SUT/fuel type VMT mix-based population 

factors for the analysis year. For the 2018 future year, SUT/fuel type populations were estimated 

as a function of base (e.g., latest available, mid-year) registrations, grown to the 2018 future 

value (growth as a function of base and future VMT), and all roads-weekday SUT/fuel type

VMT mix-based population factors applicable to the analysis year.

SHP: The SHP was estimated as a function of total hours (hours a vehicle exists) minus its 

source hours of operation (SHO) on roads (which is the same as VHT). The SUT/fuel type SHP 

estimates were based on VMT mix, link VMT and speeds, and the vehicle population estimates. 

The VMT mix was applied to the link VMT to produce SUT/fuel type-specific VMT estimates. 

Link VMT was divided by the associated speed to produce SHO estimates, which were

subtracted from source hours resulting in SHP estimates. This was performed for each analysis 

year by county and hour.

Starts: Engine starts were based on the MOVES national default starts per vehicle, and the 

local, county SUT/fuel type vehicle population estimates. MOVES default weekday starts per 

vehicle were used. The starts were calculated as the product of starts/vehicle from MOVES, and 

the county SUT/fuel type population estimates. This was performed for each analysis year by 

county and hour.

SHI: The SHI (for Combination Long-Haul Trucks) was estimated based on information 

from a TCEQ extended idling study, and additional scaling factors developed by TTI. The 2004 

idle activity study produced 2004 summer weekday SHI estimates by Texas county. TTI used

the HGB county summer weekday 24-hour 2004 base year SHI estimates from this study in 

combination with 2004 base year and analysis year link VMT and SUT/fuel type VMT mixes 

(for producing the SHI scaling factors) to estimate county, hourly SHI activity for each of the 

analysis years. SHI hourly factors (estimated as the inverse of the hourly VMT factors) were 

used to allocate the 24-hour SHI to each hour of the day.

MOVES Emissions Factors

TTI post-processed the MOVES2010bROP (emissions and activity) output to calculate the 

emissions rates in the needed “per activity” units (as summarized in Table 7). A fundamental 

aspect of the emissions rates modeling method is that the activity inputs used in MOVES are 

essentially MOVES defaults, which are later divided out (to unity) in the production of emissions 
13

rates, via post-processing of the MOVES activity and emissions output (i.e., emissions divided 

by activity). The actual local, scenario-specific activity estimates for each county are then used 

in the emissions calculations outside of MOVES.

Look-up tables of MOVES emissions factors were developed (post-processed from 

“emissions rate calculation type” MOVES model output) by pollutant, process, speed (for 

roadway-based processes), hour, road type (including a category for off-network emissions 

processes), and average SUT/fuel type. MOVES outputs were post-processed in two ways: 1) to 

calculate the emissions rates from emissions and activity output, and 2) to extract the rates for 

only those pollutants needed in the emissions calculations, and to apply TxLED adjustments to 

each county’s diesel vehicle NOx emissions rates (depending on analysis scenario) as well as to 

combine the effects of two inspection and maintenance (I/M) test-types where the test-type 

change occurred in the preceding 12-month I/M cycle (i.e., only for the Harris County 2002 base 

year).

County-level emissions factors were developed for the MOVES weekday day type for each 

of the three scenarios: 2002 base year, 2018 with pre-2003 controls, and 2018 control strategy. 

Local emissions factor modeling input parameters were developed and used to produce 

emissions factors reflective of the local scenario conditions (e.g., weather and fleet 

characteristics, fuel properties, and I/M program).

Emissions Calculations

Emissions were calculated for each county and analysis control scenario using the major inputs 

as described previously, and summarized here: TxDOT district-level 24-hour SUT/fuel type

VMT mix by MOVES road type; county, hourly on-road fleet link VMT and speed estimates; 

county hourly off-network activity estimates by SUT/fuel type of SHP, starts, and SHI; and the 

county-level look-up tables of activity-based hourly emissions rates by SUT/fuel type and 

emissions process.

For the VMT-based calculations, a MOVES road type to TDM network road type/area type 

designation was used to match the appropriate VMT mixes and link VMT. The VMT mixes by 

MOVES road type were multiplied by the link fleet VMT to distribute each link’s VMT to the 23 

different SUT/fuel type categories. Emissions rates for each link’s average speed were 

interpolated (see procedure in Appendix B) from the appropriate set of look-up table emissions 

factors and corresponding index speeds (i.e., the average bin speeds of 2.5, 5.0, 10.0, 15.0, ... 

75.0 mph), bounding the link’s average speed. For link speeds below or above the minimum and 

maximum average bin speeds of 2.5 and 75 mph, the rates for those bounding speeds were used. 

The estimated SUT/fuel type and MOVES road type combination link speed-specific emissions

factors for each pollutant process were then multiplied by the associated VMT to produce the 

link-based emissions estimates. This process was executed for each hour, analysis year and 

control scenario.

For the off-network emissions calculations, which are county level, the emissions factors by 

SUT/fuel type were multiplied by the appropriate county total activity estimate, as determined by 

the pollutant process. This process was executed for each hour, analysis year and control 

scenario.
14

The emissions estimates are organized in a tab-delimited output file for each county by 

pollutant/process, roadway type, and SUT/fuel type combination for each hour, and for the 24-

hour period. This tab-delimited file also includes hourly and 24-hour summaries of the offnetwork activity, VMT, VHT, and speed by roadway. Appendix A contains more detailed output 

definitions and specifications.

TTI developed and maintains a series of computer utilities to calculate and summarize 

detailed on-road mobile source emissions inventories in various formats, such as those used in 

this analysis. Appendix B describes these applications.

DEVELOPMENT OF SUT/FUEL TYPE VMT MIX

The estimated SUT/fuel type VMT mix inputs to the emissions inventory calculations for the 

VMT offset demonstration analysis, developed as described again here, were acquired from the 

FY 2012 HGB RFP SIP inventories project.

The SUT/fuel type VMT mix is a major input to the MOVES link-based emissions 

estimation process. It is an estimate of the fraction of on-road fleet VMT attributable to each 

SUT by fuel type, and is used to subdivide the total VMT estimates on each link into VMT by 

SUT/fuel type. These hourly VMT estimates by SUT/fuel type are combined with the 

appropriate emissions factors in the link-emissions calculations.

TxDOT district-level, 24-hour average, weekday SUT/fuel type VMT mixes (for gasolinepowered and diesel-powered vehicles) were estimated by the four MOVES road-type categories 

following the methodology detailed in the report entitled, Methodologies for Conversion of Data 

Sets for MOVES Model Compatibility, TTI, August 2009. This methodology characterizes VMT 

by SUT/fuel type for a region (or district) as follows.

• TxDOT Classification Counts by County and TxDOT District — This is the standard 

TxDOT classification data assembled and used for determining the in-use road fleet mix 

(e.g., VMT mix under MOBILE).

• Redefine Roadway Functional Classifications from Federal Highway Administration 

(FHWA)/TxDOT to MOVES types — A straightforward transposition of FHWA/TxDOT 

roadway functional classifications in the classification count data into the five MOVES 

road types.

• Define MOVES vehicle categories. For example, PV21 – Passenger vehicles equivalent 

to FHWA C minus .001 for MCs.

• Define MOVES vehicle categories - Passenger and Light Commercial Trucks —

Separates FHWA light-truck category (P) into passenger trucks and light commercial 

vehicles using approximate (rounded) MOVES default values. Note this disaggregation 

is similar to the MOBILE6 distinction between the two primary LDT categories (LDT12 

and LDT34).

• Define MOVES vehicle categories – Single-Unit Trucks RTF51 — These are refuse 

trucks. These are currently assigned a nominal default value (.001) taken from the 

combined FHWA single-unit truck category total (SU2, SU3, and SU4). To be modified 

as improved or locally-specific data become available.
15

• Define MOVES vehicle categories – Single-Unit Trucks Short-Haul versus Long-Haul 

(SUSH52 and SULH53) per SUT_SSHX — Separates single-unit trucks into short-haul 

and long-haul based on local (TxDOT district) registrations versus observed vehicles 

from the classification counts. District allocations verified against statewide allocation.

• Define MOVES vehicle categories – Single-Unit Trucks MH54 — These are motor 

homes/recreational vehicles. These are currently assigned a nominal default value (.001) 

taken from the combined FHWA single-unit truck category total (SU2, SU3, and SU4). 

To be modified as improved or locally-specific data become available.

• Define MOVES vehicle categories - Buses (approximate MOBILE6 defaults) — To be 

modified as improved or locally-specific data become available.

• Define MOVES vehicle categories - Combination Trucks Short-Haul versus Long-Haul 

(CSH61 and CLH62) per SUT_HDX9 and SUT_CSHX — Separates combination trucks 

into short-haul and long-haul based on local (TxDOT district) registrations versus 

observed vehicles from the classification counts. District allocations verified against 

statewide allocation.

• Define MOVES vehicle categories - MCs — Nominal default value taken from passenger 

cars (FHWA C). To be modified as improved or locally-specific data become available.

• Fuel Type Allocation - PV and LDT fuel type allocation per TxDOT registration data and 

MOVES defaults (21, 31, and 32) per AgeReg9X and MF_Fuel — Other fuel types 

currently treated as de minimus. Additional fuel types can be incorporated as local or 

regional data become available, or from the MOVES national default database (though 

this latter option is not recommended). Note allocation of fuel type varies with analysis 

year.

• Fuel Type Allocation - Single Unit and Combination Trucks per TxDOT registration data 

per SUT_HDV9 — As with PV and LDT, other fuel types currently treated as de 

minimus.

• Aggregate and Calculate MOVES SUTs and apply day-of-week factors from urban area 

classification count data (Friday, Saturday, and Sunday).

TxDOT district-level weekday SUT/fuel type VMT mixes by MOVES road-type category 

(included as Appendix C) were produced based on recent multi-year vehicle classification counts 

and appropriate end-of-year TxDOT vehicle registrations data. Using the same data sets and a 

similar procedure, aggregate (i.e., all road-type categories) TxDOT district-level weekday 

SUT/fuel type VMT mixes (used in the vehicle population estimation) were also produced and 

included as Appendix D. To ensure general applicability and consistency across all study areas, 

all VMT mixes were developed in five-year increments beginning with the year 2000 and 

applied to the analysis years based on Table 8.
16

Table 8. VMT Mix Year/Analysis Year Correlations.

VMT Mix Year Analysis Years

2000 1998 through 2002

2005 2003 through 2007

2010 2008 through 2012

2015 2013 through 2017

2020 2018 through 2022

ESTIMATION OF VMT

The estimated link VMT inputs to the emissions inventory calculations for the VMT offset 

demonstration analysis, developed as described again here, were acquired directly from the FY 

2012 HGB RFP SIP inventories project.

The detailed, hourly, link-based emissions process requires VMT estimates by hour and 

direction for each link in the TDMs. This analysis also required that VMT be adjusted for 

HPMS consistency and to reflect estimated levels characteristic of a typical summer ozone 

season (June through August) weekday (Monday through Friday). The TRANSVMT utility (see 

Appendix B for a description of the utility), the latest available HGB 2002 and 2018 TDMs, and 

post-processing factors developed from several other data sources, were used to produce this 

hourly VMT by direction. The hourly and 24-hour VMT and VHT summaries by county and 

road type were provided electronically to TCEQ (see Appendix A for electronic data 

descriptions).

Data Sources

The latest available HGB 2002 (dated July 3, 2007) and 2018 (dated October 18, 2011) TDMs 

were used to estimate the directional link VMT and speeds by hour. Since intrazonal VMT are 

not accounted for in the TDMs, the intrazonal VMT was estimated using the TDM’s trip matrix 

and zonal radii.

Several other data sources were used to adjust the VMT for HPMS consistency and to 

estimate the seasonal sub-period day types. The first data source is HPMS VMT estimates, 

which are based on traffic count data collected according to a statistical sampling procedure 

specified by the FHWA designed to estimate VMT. The county total HPMS Annual Average 

Daily Traffic (AADT) VMT was used to ensure the travel model VMT was consistent with the 

HPMS VMT estimates. (EPA and FHWA have endorsed HPMS as the appropriate source of 

VMT and require that VMT used to construct on-road mobile source emissions estimates be 

consistent with that reported through HPMS.)

The second data source is ATR vehicle counts, which are collected by TxDOT at selected 

locations throughout Texas on a continuous basis. These vehicle counts are collected on a 
17

continuous basis and are available by season, month, and weekday, as well as on an annual 

average daily basis (i.e., AADT). The counts are very well suited for making seasonal, day-ofweek, and time-of-day comparisons (e.g., seasonal adjustment and hourly allocation factors), 

even though there may be relatively few ATR data collection locations in any given area.

Multiple years (2000 through 2010) of data from the ATR stations were grouped for this 

analysis at different aggregation levels, depending upon the purpose. This data source was used 

to produce the day-type-specific adjustment factor, in which the data from the ATR stations 

within the Beaumont TxDOT District were combined for use with Chambers and Liberty 

counties and the ATR data was combined from those stations with in the Houston TxDOT 

District for use with Harris, Galveston, Fort Bend, Brazoria, Montgomery, and Waller counties. 

This data source was also used to product the time-of-day (hourly) allocation factors, in which 

the data from the ATR stations with in the eight-county region was combined.

VMT Adjustments

For each analysis year, the TDM VMT was adjusted for HPMS consistency and for seasonality 

(i.e., ozone season summer weekday). For 2002, which by definition is a historical year (i.e., 

HPMS VMT data exists for 2002), county-level VMT control totals were used. For the 2018

analysis year, which is considered a future year (i.e., HPMS VMT data does not exist), a regional 

HPMS factor and a seasonal weekday factor were used. Hourly travel factors were also applied 

to distribute this adjusted VMT over each hour of the day.

2002 Historical Year Analysis – VMT Control Totals and VMT Adjustments

To estimate the HPMS-consistent ozone season summer weekday for the 2002 historical year, 

county-level VMT control totals were used to develop county-level VMT adjustment factors. 

The VMT control totals are comprised of two key components: the county-level HPMS AADT 

VMT for 2002 and an ozone season summer day-of-week (i.e., weekday) adjustment factor.

The ozone season summer weekday adjustment factors were developed using aggregated 

ATR data for the years 2000 through 2010. Since the HGB area spans two TxDOT districts, two 

ozone season summer weekday adjustment factors were developed. One factor was developed 

for Liberty and Chambers counties (which are located in the Beaumont TxDOT District) and one 

factor was developed for Harris, Galveston, Fort Bend, Brazoria, Montgomery, and Waller 

counties (which are located in the Houston TxDOT District). These regional factors were 

calculated by dividing the average day-of-week count by the AADT traffic count. The same 

weekday adjustment factors were used for each historical year analysis. Table 9 shows the HGB 

weekday factors used in developing the VMT control totals.

Table 9. HGB Weekday Factors for Control Total Development.

TxDOT District Weekday Adjustment Factor

Beaumont

1 1.08998

Houston2 1.04160

1 Only used for Liberty and Chambers counties.

2 Only used for Harris, Galveston, Fort Bend, Brazoria, Montgomery, and Waller counties.
18

The VMT control totals were then developed by multiplying the analysis year HPMS AADT 

VMT for each county by the appropriate ozone season summer weekday adjustment factors to 

produce eight VMT control totals (one for each county) for the 2002 historical analysis year. To 

develop the county-level VMT adjustment factors, each county’s respective control total was 

divided by the total VMT (TDM assignment VMT plus intrazonal VMT estimate) from the 

analysis year TDM to produce eight county-level VMT adjustment factors. For each link in the 

TDM, the volume was multiplied by the corresponding VMT adjustment factor (based on the 

county where the link is located). The adjusted link volumes were then multiplied by the 

associated link lengths to produce the link-level HPMS consistent, period day-type-specific 

VMT estimates. Table 10 shows the weekday VMT control totals, the total TDM VMT, and the 

VMT adjustment factors for 2002.

Table 10. HGB 2002 Weekday VMT Control Totals and VMT Adjustment Factors.

County VMT Control Total TDM VMT1 VMT Adjustment Factor

Harris 93,380,035 94,641,477.08 0.986671361

Brazoria 5,564,463 5,871,848.97 0.947650906

Fort Bend 7,738,690 7,968,385.94 0.971174095

Waller 1,865,629 1,753,131.92 1.064169204

Montgomery 9,293,357 9,167,873.35 1.013687323

Liberty 2,301,454 2,228,487.13 1.032742781

Chambers 2,268,351 2,410,984.11 0.940840294

Galveston 5,733,306 4,820,989.76 1.189238370

1 2002 TDM, including intrazonal VMT.

2018 Future Year Analysis – HPMS Adjustment Factor

For the 2018 future year analysis, an HPMS adjustment factor was used to adjust the total VMT 

(TDM assignment VMT plus intrazonal VMT estimate) for HPMS consistency for the TDM. 

This factor was developed using the total TDM VMT from the 2005 travel model validation

(dated November 16, 2011), the 2005 HGB HPMS VMT reported by TxDOT, and aggregated 

2006 ATR data (to produce the annual non-summer weekday traffic [ANSWT] adjustment factor 

in the following equation). The 2006 ATR data was used instead of the 2005 ATR data because 

of data irregularities due to the possible effects of a hurricane on the traffic counters. The 

formula for the HPMS factor calculation is:

HPMS VMT (AADT) × ANSWT Adjustment Factor = HPMS VMT (ANSWT)

HPMS VMT (ANSWT) / Model VMT (ANSWT) = HPMS Factor
19

Applying the ANSWT adjustment to the HPMS AADT VMT (i.e., conversion from AADT 

to ANSWT) produces seasonal, day-of-week consistency between the TDM VMT and HPMS 

VMT components of the HPMS factor. The actual values for the HPMS factor are:

132,093,142 × 1.059088 = 139,898,261.5 (HPMS ANSWT VMT)

139,898,261.5 / 138,790,409.7 = 1.007982193 (HPMS Factor)

2018 Future Year Analysis – Seasonal Adjustment Factors

For the 2018 future year analysis, seasonal adjustment factors were used to adjust the TDM 

and estimated intrazonal VMT to ozone season summer weekday VMT. The seasonal 

adjustment factors were developed using aggregated ATR data for the years 2000 – 2010. Since 

the HGB area spans two TxDOT districts, two ozone season summer weekday adjustment factors 

were developed. One factor was developed for Liberty and Chambers counties (which are 

located in the Beaumont TxDOT District) and one factor was developed for Harris, Galveston, 

Fort Bend, Brazoria, Waller, and Montgomery counties (which are located in the Houston 

TxDOT District). These factors were calculated by dividing the average day-of-week (weekday) 

count by the ANSWT traffic count. Table 11 shows the seasonal adjustment factors by TxDOT 

district.

Table 11. HGB Weekday Seasonal Adjustment Factors for Future Year Analysis.

TxDOT District Weekday Seasonal Adjustment Factor

Beaumont1 1.06428

Houston2 0.96769

1 Only used for Liberty and Chambers counties.

2 Only used for Harris, Galveston, Fort Bend, Brazoria, Montgomery, and Waller counties.

2018 Future Year Analysis – VMT Summary

The 2018 future year final HPMS-consistent, day-type specific VMT is comprised of two parts –

the link-level VMT and the estimated intrazonal VMT. The volume for each link was multiplied 

by the HPMS factor, the seasonal adjustment factor, and the link’s respective length to estimate 

the link-level VMT (hourly factors were applied to distribute the resulting VMT over each hour 

of the day, discussed in a later section). The HPMS and seasonal adjustment factors (as well as 

the hourly factors mentioned previously) were also applied to the estimated intrazonal VMT.

Table 12 shows the TDM and ozone season weekday VMT summary.
20

Table 12. HGB 2018 VMT Summary.

County

2018

TDM Weekday

Harris 132,292,219 129,039,721

Brazoria 8,841,141 8,623,775

Fort Bend 14,411,651 14,057,330

Waller 2,752,993 2,685,309

Montgomery 15,512,473 15,131,088

Liberty 3,377,161 3,622,935

Chambers 3,623,749 3,887,468

Galveston 7,194,417 7,017,537

Hourly Travel Factors

Hourly travel factors were used to distribute the TDM and intrazonal VMT to each hour of the 

day. These hourly travel factors were developed using multi-year (2000 through 2010) 

aggregated ATR station data for the eight-county HGB region. To maintain VMT proportions 

within each of the four assignment time periods (including those proportions produced 

specifically for the weekend day types as described previously), the hourly fractions were 

normalized within each time period. Each factor (i.e., 24, or one for each hour of the day) was 

then multiplied by the link volume (in addition to the other VMT adjustment factors). These 

adjusted link volumes were then multiplied by their respective link lengths to estimate the link 

level, ozone season summer weekday VMT estimates for each analysis year. These factors were 

also multiplied by the estimated intrazonal VMT to produce the final hourly-adjusted VMT. 

Table 13 shows the weekday hourly travel factors.
21

Table 13. Weekday Hourly Travel Factors.

Assignment Hour Weekday

AM Peak

6:00 a.m. 0.319975

7:00 a.m. 0.368425

8:00 a.m. 0.311600

Mid-Day

9:00 a.m. 0.160270

10:00 a.m. 0.155898

11:00 a.m. 0.162983

12:00 p.m. 0.168764

1:00 p.m. 0.171777

2:00 p.m. 0.180308

PM Peak

3:00 p.m. 0.238603

4:00 p.m. 0.259721

5:00 p.m. 0.275395

6:00 p.m. 0.226281

Overnight

7:00 p.m. 0.204604

8:00 p.m. 0.159440

9:00 p.m. 0.143563

10:00 p.m. 0.113907

11:00 p.m. 0.077553

12:00 a.m. 0.043044

1:00 a.m. 0.028404

2:00 a.m. 0.026089

3:00 a.m. 0.024941

4:00 a.m. 0.044559

5:00 a.m. 0.133896
22

ESTIMATION OF LINK SPEEDS

The estimated link speeds inputs to the emissions inventory calculations for the VMT offset 

demonstration analysis, developed as described again here, were acquired directly from the FY 

2012 HGB RFP SIP inventories project.

The operational speeds for each link, excluding centroid connectors and the special 

intrazonal links, were calculated using the Houston speed model. The Houston speed model 

calculates these speeds using the travel model speed, speed factors (consisting of a free-flow 

speed factor and level of service [LOS] E speed factor) and a volume-to-capacity (V/C) ratiobased speed reduction factor (SRF) for each link.

The speed factors were used to convert the travel model speed to a free-flow speed and an 

LOS E speed (i.e., application of these factors results in two speeds). These factors were 

grouped into seven functional groups. Appendix E shows the speed factors and the network 

functional class and functional group relationship.

The link-specific V/C ratio is calculated as the time period (hourly) volume divided by the 

time period capacity. The V/C ratio is expressed as:

v/c ratio = Vh / Ch

Where:

Vh = the hourly link volume (travel model × HPMS factor × seasonal adjustment factor 

× hourly time period factor; Weekend profile factor is included for Saturday and 

Sunday); and

Ch = the hourly link capacity (travel model capacity × hourly capacity factor). 

Appendix E shows the hourly capacity factors.

After the V/C ratio was calculated, the link-specific SRF was determined using the V/C ratio, 

the link-specific SRF area type, the link-specific SRF functional class, and the SRFs. The SRFs 

are for V/C ratios of 0 to 1 in 0.05 increments (i.e., 0, 0.05, 0.10, ... , 0.95, 1.0). Appendix E

shows these SRFs. The link-specific SRF was calculated using linear interpolation. For V/C

ratios greater than 1.0, a SRF is not required.

The speed model (for V/C ratios from 0.00 to 1.00) is expressed as:

SV/C = S0.0-SRFV/C × (S0.0 - S1.0)

Where:

SV/C = estimated directional speed for the forecast V/C ratio on the link in the given 

direction;

S0.0 = estimated free-flow speed for V/C ratio equal to 0.0;

S1.0 = estimated LOS E speed for V/C ratio equal to 1.0; and

SRFV/C= speed reduction factor for the V/C ratio on the link. The V/C ratio can be 0.0 

to 1.0.
23

For V/C ratios greater than 1.0 and less than 1.5, the following model extension was used. 

The speed model extension is:

SV/C = S1.0 × (1.15/(1.0 + (0.15 × (v/c)4)))

Where:

Sv/c = estimated directional speed for the forecast V/C ratio on the link in the given 

direction;

S1.0 = estimated LOS E speed for the V/C ratio equal to 1.0; and

v/c = the forecast V/C ratio on the link. The V/C ratio can be 1.0 to 1.5.

For V/C ratios greater than 1.5, the speed was calculated using the previous speed model 

extension, except the V/C ratio was set to 1.5.

These speed models were applied to all functional classes excluding the centroid connector 

and intrazonal functional classes. For these functional classes, capacity data were not used. The 

centroid connector travel model input speeds were used as the centroid connector operational 

speeds estimates. Operational speeds for the intrazonal functional class were estimated by zone 

as the average of the zone’s centroid connector speeds.

The hourly and 24-hour speed (VMT/VHT) summaries by county and road type were 

provided electronically to TCEQ (see Appendix A for electronic data descriptions).

ESTIMATION OF OFF-NETWORK ACTIVITY

The estimated off-network (or parked vehicle) activity inputs to the emissions inventory 

calculations for the VMT offset demonstration analysis, developed as described again here, were 

acquired directly from the FY 2012 HGB RFP SIP inventories project.

To estimate the off-network (or parked vehicle) emissions using the grams per activity 

emissions rates (i.e., grams per SHP, grams per start, and grams per SHI), county-level estimates 

of the SHP, starts, and SHI are required by hour and SUT/fuel type for each analysis year and 

day type. One of the main components of the SHP and starts off-network activity estimation is 

the county-level vehicle population for each analysis year. Summaries of the vehicle population

and 24-hour SHP, starts, and SHI off-network activity are included as Appendix F. Hourly SHP, 

starts, and SHI activity estimates are included with the detailed emissions inventory data 

provided (see inventory data file descriptions in Appendix A).

The county-level vehicle population estimates were developed using the 

MOVESpopulationBuild utility. The county-level SHP and starts by hour and SUT/fuel type 

estimates were developed using the ShpExtIdleStartActBld utility. The county-level SHI by 

hour and SUT/fuel type estimates were developed using the ExtIdleHrsCalc utility. Appendix B 

contains a description of the utilities.

Estimation of Vehicle Population

The vehicle population estimates (by SUT and fuel type) are needed to estimate the SHP and 

starts off-network activity. The vehicle population estimates (included as Appendix G) were 
24

produced for each county and analysis year. The vehicle population estimates are a function of 

vehicle registration data (TxDOT registration data sets), population scaling factors (where 

applicable), and SUT/fuel type VMT mix.

For estimating vehicle populations, a historical analysis year is defined as any year where 

actual TxDOT registration data and HPMS VMT data (used in developing population scaling 

factors) exists. Therefore, the 2002 analysis year was considered a historical year and the 

vehicle population estimates were based on the TxDOT registration data for 2002. For the 2018 

future analysis year, the vehicle population estimates were based on the most recent year (2010) 

TxDOT registration data set for which HPMS VMT data exists, and analysis year population 

scaling factors.

The VMT mix used to estimate the vehicle population is the aggregate (i.e., all road-type 

categories) TxDOT district-level weekday SUT/fuel type VMT mixes. The development of 

these VMT mixes are described in more detail in the “Development of SUT/Fuel Type VMT

Mix” section and summaries are included in Appendix D.

Historical Vehicle Population Estimates

The county-level vehicle population estimates for the 2002 historical analysis year were 

calculated using the analysis year county-level, mid-year TxDOT vehicle registrations and the 

assigned aggregate SUT/fuel type VMT mix. The vehicle estimation process assumes that all of 

the non-long-haul SUT category populations for a county are represented in the county vehicle 

registrations data. This process also estimates the long-haul category populations as an 

expansion of the county registrations. There are three main steps in the vehicle estimation 

process: registration data category aggregation, calculation of the SUT/fuel type population 

factors, and estimation of the county-level vehicle population by SUT/fuel type.

The first step in the vehicle estimation process is the registration data category aggregation. 

For each county, the analysis year vehicle registrations were aggregated into five categories. 

Table 14 shows these five categories.

Table 14. Registration Data Categories.

Registration Data Category Vehicle Registration Aggregation

1 Motorcycles

2 Passenger Cars (PC)

3 Trucks <= 8.5 K gross vehicle weight 

rating (GVWR) (pounds)

4 Trucks > 8.5 and <= 19.5 K GVWR

5 Trucks > 19.5 K GVWR

The second step is calculating the SUT/fuel type population factors. Using the assigned

aggregate SUT/fuel type VMT mix, SUT/fuel type population factors were calculated for each 

SUT/fuel type combination. For the non-long-haul SUT categories, the SUT/fuel population 
25

factors were calculated by dividing the SUT/fuel type VMT mix by the summed total of the 

SUT/fuel type VMT mix fractions in its associated vehicle registration data category. For 

example, the LCT_Diesel population factor using the VMT mix is LCT_Diesel/(PT_Gas + 

PT_Diesel + LCT_Gas + LCT_Diesel). For the long-haul SUTs, the SUT/fuel type population 

factors were calculated by taking the ratio of the long-haul and short-haul VMT mix values. For 

example, the SULhT_Gas population factor using SUT mix fractions is 

SULhT_Gas/SUShT_Gas. Table 15 shows the vehicle registration aggregations and their 

associated MOVES SUT/fuel types.

Table 15. TxDOT Vehicle Registration Aggregations and Associated SUT/Fuel Types for 

Estimating SUT/Fuel Type Populations.

Vehicle Registration1 Aggregation Associated MOVES SUT/Fuel Type2

Motorcycles MC_Gas

Passenger Cars (PC) PC_Gas; PC_Diesel

Trucks <= 8.5 K GVWR (pounds) PT_Gas; PT_Diesel;

LCT_Gas; LCT_Diesel

Trucks > 8.5 and <= 19.5 K GVWR

RT_Gas; RT_Diesel

SUShT_Gas; SUShT_Diesel

MH_Gas; MH_Diesel

IBus_Diesel

TBus_Gas; TBus_Diesel

SBus_Gas; SBus_Diesel

Trucks > 19.5 K GVWR CShT_Gas; CShT_Diesel

NA1 SULhT_Gas; SULhT_Diesel

CLhT_Gas; CLhT_Diesel 1 The four long-haul SUT/fuel type populations are estimated using a long-haul-to-short-haul weekday SUT VMT 

mix ratio applied to the short-haul SUT population estimate. 2 The mid-year TxDOT county registrations data extracts were used (i.e., the three-file data set consisting of: 1 -

light-duty cars, trucks, and motorcycles; 2 - heavy-duty diesel trucks; and 3 - heavy-duty gasoline trucks) for 

estimating the vehicle populations.

The third step is the estimation of the county-level vehicle population by SUT/fuel type. The 

non-long-haul SUT/fuel type vehicle populations were estimated by applying their SUT/fuel type

population factors to the appropriate registration data category. For the CLht_Gas type, the 

vehicle population was set to 0. For the remaining three long-haul SUT/fuel types (SULhT_Gas, 

SULhT_Diesel, and CLhT_Diesel), the vehicle populations were calculated as the product of the 

corresponding short-haul category vehicle population and the associated long-haul population 

factor (e.g., SULhT_Gas vehicle population = SUShT_Gas vehicle population x [SULhT_Gas 

SUT mix fraction/ SUShT_Gas SUT mix fraction]).

Future Vehicle Population Estimates

The process for estimating the county-level vehicle population estimates for a future analysis 

year (e.g., 2018) is very similar to the historical vehicle population estimates except that instead 

of using the analysis year registration data sets, the most recent (2010) mid-year TxDOT 

registration data sets for which HPMS data exists were used. Using these registration data sets 
26

and the assigned VMT mix, the base SUT/fuel type population for 2010 was calculated. To 

estimate the 2018 future analysis year county-level vehicle populations, 2018 future year countylevel vehicle population scaling factors were applied to the base SUT/fuel type population for 

2010. These future year county-level vehicle population scaling factors were calculated as the 

ratio of the county-level weekday VMT for the 2018 analysis year to the county-level weekday 

VMT for the year of the most recent (2010) mid-year TxDOT registration data (i.e., vehicle 

population estimates increase linearly with VMT).

Estimation of SHP

The first activity measure needed to estimate the off-network emissions using the grams per 

activity emissions rates are county-level weekday estimates of SHP by hour and SUT/fuel type

for each analysis year. For each hour, the county-level SHP by SUT/fuel type was calculated by 

taking the difference between the total available hours minus the SHO by SUT/fuel type. Since 

this calculation was performed at the hourly level, the total available hours by SUT/fuel type is 

the same as the vehicle population by SUT/fuel type. The SHO was calculated using the link 

VMT and speeds and the TxDOT district-level SUT/fuel type VMT mixes by MOVES road-type 

category (see the “Development of SUT/Fuel Type VMT Mix” section for more details). 

Appendix F includes the 24-hour summaries of the county-level weekday estimates of SHP by 

hour and SUT/fuel type for each analysis year (hourly summaries were provided electronically to 

TCEQ; see Appendix A for electronic data descriptions).

Total Available Hours by SUT/Fuel Type

The total available hours by SUT/fuel type is typically calculated as the vehicle population times 

the number of hours in the time period. Since this calculation was performed at the hourly level, 

the total available hours by SUT/fuel type for each analysis year was set equal to the vehicle 

population by SUT/fuel type for the analysis year.

SHO by SUT/Fuel Type

To calculate the SHO (or VHT) for a given link, the VMT was allocated to each SUT/fuel type

using the TxDOT district-level SUT/fuel type VMT mixes by MOVES road-type category, 

which was then divided by the link speed to calculate the link SHO by SUT/fuel type. These

VMT mixes are the same VMT mixes used to estimate emissions in the emissions estimation 

process (see Appendix C). This SHO calculation was performed for each link in a given hour, 

aggregating the SHO to one value per SUT/fuel type per hour. The hourly SHO by SUT/fuel 

type was then set equal to the hourly VHT by SUT/fuel type.

Estimation of Starts

The second activity measure needed to estimate the off-network emissions using the grams per 

activity emissions rates are county-level weekday estimates of starts by hour and SUT/fuel type

for each analysis year. The hourly default starts per vehicle by SUT/fuel type were multiplied by 

the analysis year county-level vehicle population by SUT/fuel type to estimate the county-level 

starts by hour and SUT/fuel type. Appendix F includes the 24-hour summaries of the countylevel starts by hour and SUT/fuel type for each analysis year (hourly summaries were provided 

electronically to TCEQ; see Appendix A for electronic data descriptions).
27

For the hourly default starts per vehicle, the MOVES defaults were used. The MOVES 

activity output was used to estimate the hourly starts per vehicle for a MOVES weekday and 

MOVES weekend run by dividing the MOVES start output by the MOVES vehicle population 

output. These MOVES default starts per vehicle do not vary by year or geography (i.e., county), 

only by MOVES day type. Since the emissions inventories are for weekday, only the MOVES 

weekday default starts per vehicle were used.

Estimation of SHI

Unless otherwise stated, all activity factors referenced in the discussion of estimation of SHI 

pertain only to the CLhT_Diesel vehicle category.

The third activity measure needed to estimate the off-network emissions using the grams per 

activity emissions rates are county-level weekday estimate of SHI by hour and SUT/fuel type for 

each analysis year. These SHI estimates were for source type 62, fuel type 2 (CLhT_Diesel) 

only. The SHI was based on information from a TCEQ extended idling study, which produced 

2004 summer weekday SHI estimates for each Texas county. SHI scaling factors (for 2002 and 

2018) were applied to the base 2004 summer weekday SHI values from the study to estimate the 

24-hour SHI by analysis year. SHI hourly factors were then applied to allocate the 24-hour SHI 

by analysis year to each hour of the day. To ensure valid hourly SHI values were used in the 

emissions estimation, the hourly SHI was compared to the hourly CLhT_Diesel SHP (i.e., hourly 

SHI values cannot exceed the hourly SHP values). Appendix F includes the 24-hour summaries 

of the county-level estimates of SHI by hour and SUT/fuel type for each analysis year (hourly 

summaries were provided electronically to TCEQ; see Appendix A for electronic data 

descriptions).

SHI Scaling Factors

To estimate the county-level 24-hour SHI by analysis year, county-level SHI scaling factors were 

developed using county-level 2004 summer weekday link-level on-road fleet VMT and speeds, 

the TxDOT district-level base weekday SUT/fuel type VMT mix (by MOVES road type), the 

county-level analysis year weekday fleetwide link-level VMT and speeds, and the TxDOT 

district-level analysis year SUT/fuel type VMT mix (by MOVES road type). The 2004 summer 

weekday link-level on-road fleet VMT and speeds were developed using a process similar to the 

2002 historical analysis year weekday link-level fleet VMT and speeds, using the HGB 2005 

TDM (run date November 16, 2011) and a 2004 summer weekday county VMT control totals. 

The SUT/fuel type VMT mixes were the same VMT mixes used to estimate emissions in the 

emissions estimation process (see Appendix C). For the base weekday SUT/fuel type VMT mix, 

the 2005 weekday SUT/fuel type VMT mix was used.

For each link in the 2004 summer weekday link-level fleetwide VMT and speeds, the link 

VMT was allocated to CLhT_Diesel using the base weekday SUT/fuel type VMT mix. This 

VMT allocation was performed for each link and hour in the 2004 summer weekday link-level

fleet VMT and speeds, with the individual link VMT aggregated by hour to produce the hourly 

and 24-hour 2004 summer weekday CLhT_Diesel VMT estimates. Using a similar allocation 

process, the analysis year hourly and 24-hour CLhT_Diesel VMT was calculated using the 

analysis year weekday link-level on-road fleet VMT and speeds and the analysis year SUT/fuel 

type VMT mix. The county-level 24-hour SHI scaling factors for 2002 and 2018 were calculated 
28

by dividing the analysis year and day type CLhT_Diesel 24-hour VMT by the CLhT_Diesel 24-

hour 2004 summer weekday VMT.

SHI Hourly Factors

To allocate the analysis year and weekday county-level 24-hour SHI to each hour of the day, SHI 

hourly factors were used. These SHI hourly factors were calculated as the inverse of the analysis 

year weekday CLhT_Diesel hourly VMT fractions. The analysis year weekday CLhT_Diesel 

hourly VMT fractions were calculated using the analysis year weekday CLhT_Diesel hourly 

VMT. The analysis year weekday CLhT_Diesel hourly VMT were converted to hourly 

fractions, therefore creating analysis year weekday CLhT_Diesel hourly VMT fractions. The 

inverse of these hourly VMT fractions were calculated and the inverse for each hour was divided 

by the sum of the inverse hourly VMT fractions across all hours to calculate the county-level 

analysis year weekday SHI hourly factors.

County-Level CLhT_Diesel SHI by Hour Estimation

The base analysis year weekday SHI by hour was calculated by multiplying the 24-hour 2004 

summer weekday SHI by the SHI scaling factor and by the SHI hourly factors. For each hour, 

the base analysis year weekday SHI was then compared to the analysis year weekday SHP to 

estimate the final analysis year weekday SHI by hour. If the base analysis year weekday SHI 

value was greater than the analysis year weekday SHP value, then the final analysis year 

weekday SHI for that hour was set to the analysis year weekday SHP value. Otherwise, the final 

analysis year weekday SHI for that hour was set to the base analysis year weekday SHI value. 

All calculations (scaling factors, SHI hourly factors, and SHI by hour calculations) were 

performed by county and analysis year (i.e., eight SHI scaling factors were calculated per 

analysis year).

ESTIMATION OF EMISSIONS FACTORS

TTI developed the VMT offset inventory analysis MOVES emissions factors consistent in 

method and input with the FY 2012 HGB RFP SIP inventories project, adapting inputs where 

needed to account for input specification changes between MOVES model versions

(MOVES2010a used for the RFP inventories and MOVES2010bROP used for this analysis), and 

using model set-ups needed for VMT offset calculations, as specified in EPA’s 2012 VMT offset 

guidance materials. Although most of the MOVES model local inputs for this analysis were 

acquired directly from the FY 2012 HGB RFP inventory project, details on input data sources 

and development, post-processing procedure, etc., are included again here for completeness.

TTI developed the emissions factors using MOVES2010bROP, the special version of 

MOVES2010b modified by EPA especially for VMT offset calculations, and provided to TCEQ, 

September 2012. MOVES includes a feature (Strategies – Rate-of-Progress) that allows 

emission rates to be “frozen” after 1990 (i.e., disables all motor vehicle requirements of the 1990 

Clean Air Act Amendments (CAAA). In MOVES2010bROP the Strategies – Rate-of-Progress 

function was modified to allow a different baseline year to be set at which later model year 

emissions rates are “frozen” (i.e., as in this analysis, 2002).

The emissions factors were developed based on EPA’s 2012 VMT offset guidance and the 

EPA’s current MOVES guidance. The EPA’s 2012 VMT offset guidance is documented in 
29

Implementing Clean Air Act Section 182(d)(1)(A): Transportation Control Measures and 

Transportation Control Strategies to Offset Growth in Emissions Due to Growth in Vehicle Miles 

Travelled. EPA, August 2012, and in a one-page document entitled Explanation of special VMT 

offset version of MOVES (provided to TTI by TCEQ, September 2012, along with the 

MOVES2010bROP model). The current MOVES guidance is documented in Using MOVES to 

Prepare Emission Inventories in State Implementation Plans and Transportation Conformity: 

Technical Guidance for MOVES2010, 2010a, and 2010b, EPA, April 2012. This MOVES 

emissions inventory Technical Guidance and EPA’s 2012 VMT offset guidance, along with 

Motor Vehicle Emission Simulator (MOVES) User Guide for MOVES1010b, EPA, March 2012; 

and Update Of On-Road Inventory Development Methodologies For Motor Vehicle Emissions

Simulator (Moves) Model Compatibility Technical Report, TTI, July 2011 (MOVES output postprocessing utilities used are summarized in Appendix B of this Technical Report), are the main 

references where additional detail may be found, if desired.

The three sets of county-level emissions rates developed for input to the emissions 

calculations are:

• 2002 base year;

• 2018 attainment year with pre-2003 controls; and

• 2018 attainment year control strategy.

The 2002 base year and 2018 attainment year control strategy emissions rates were 

developed consistent with the FY 2012 HGB RFP inventory project emissions rate counterparts, 

and serve as a check between the MOVES model versions (as EPA has stated that 

“MOVES2010b does not significantly affect criteria pollutant emissions from MOVES2010”).

In comparing the rates between the two projects, TTI found that the VOC, CO, NOx, and CO2

emissions rates for the scenarios are essentially the same, e.g., within rounding error.

The detailed link-based emissions inventory method of analysis requires emissions rates by 

speed in look-up table form; the MOVES Emission Rate calculation type was therefore selected 

to direct MOVES to output emissions rates, emissions, and activity data by MOVES speed bin 

average speed (2.5, 5, 10..... 75 mph). The emissions inventory method required that all rates be 

in terms of mass/activity (as opposed to the off-network rates in terms of mass/vehicle as output 

by MOVES) for the external emissions calculations. TTI post processed the MOVES emissions 

and activity output to produce all emissions rates in mass/activity terms (see Table 16, which was 

included in a previous section, but is provided again here for convenience).
30

Table 16. Emissions Rates by Process and Activity Factor.

Emission Processes Activity1 Emissions Factor Units

Running Exhaust

Crankcase Running Exhaust VMT grams/mile (g/mi)

Evaporative Permeation

Evaporative Fuel Vapor Venting

Evaporative Fuel Leaks

VMT g/mi

SHP g/shp

Start Exhaust

Crankcase Start Exhaust starts g/start

Extended Idle Exhaust 

Crankcase Extended Idle Exhaust SHI g/shi

1 The amount of travel on roads (VMT), SHP, vehicle starts, and SHI are the basic activity factors. 

SHI is for Combination Long-Haul Trucks only. Evaporative permeation, fuel vapor venting, and 

fuel leaks occur both during operation and while parked.

The MOVES model is equipped with default modeling values for the range of conditions that 

affect emissions factors. MOVES defaults may be replaced by alternate input data sets that 

better reflect local scenario conditions. Where local data were available, MOVES defaults were 

replaced by local input values, via the MOVES Run Specifications file (RunSpec or MOVES 

Run Specifications [MRS]) and MOVES CDB (county input database). (The MOVES 

RunSpecs, CDBs, and MOVES default database provide the data for each local scenario model 

run.) Local inputs were developed and used to produce emissions factors characteristic of the 

June through August period peak ozone season average weather conditions, summer fuel 

properties, vehicle fleet characteristics, and emissions control programs (depending on the local

control scenario). In the case of the activity input data to MOVES, the MOVES defaults were in

general used, which is basic to the emissions rates method (default activity is divided out to unity 

in the rates calculation, and actual local activity estimates are applied later in the external 

emissions calculations).

MOVES Inputs, Outputs and Post-Processing

There is one RunSpec required per county and calendar year control scenario, and a 

corresponding number of county data bases (CDBs), and output databases (i.e., one output 

database per run). Therefore, for eight counties and three calendar year control scenarios (plus 

the additional I/M scenario for Harris 2002 needed for the I/M test-type switch modeling 

procedure) there are 25 each RunSpecs, CDBs, MOVES output databases, and 

MOVESRatesCalc runs; and 24 MOVESRatesAdj runs that produce the final emissions rates, by 

performing NOx TxLED effect adjustments for attainment year control strategy scenario, and for 

the Harris 2002 I/M test-type switch effects combines two sets of rates into one, and extracts and 

stores the rates for the inventoried pollutants in a separate, smaller database for input to the 

emissions runs.
31

The utilities used to calculate the emissions rates from the MOVES emissions and activity 

output and to adjust the emissions rates are MOVESratescalc and MOVESratesadj, respectively 

(see descriptions in Appendix B).

Summary of Control Programs Modeled

Table 17 shows the control measures modeled (marked by a “√”) in each of the calendar year

control scenarios.

Table 17. Control Measure Modeling by Calendar Year Control Scenario.

Individual Control Measures1 Method

Calendar Year Control Scenario

2002 Base 

Year

2018 Pre-2003 

Controls

2018 Control 

Strategy

Pre-2003 FMVCP

Tier 0

Tier 1

National Low Emission 

Vehicle Program

MOVES √ √ √

Post-2002 FMVCP1

Tier 2

Heavy-Duty

2004 Diesel

2005 Gasoline

2007 Gasoline and Diesel

Highway Motorcycle 2006

MOVES N/A N/A √

RFG1 MOVES √ (2002 fuel 

formulation)

√ (2002 fuel 

formulation)

√ (2011 fuel 

formulation)

Federal low-sulfur highway 

diesel (1993 rule 500 ppm 

maximum)

MOVES √ (2002 fuel 

formulation)

√ (2002 fuel 

formulation) N/A

Federal low-sulfur highway 

diesel (2006 rule 15 ppm 

maximum, with provisions)

MOVES N/A N/A √ (2011 fuel 

formulation)

I/M Program1 MOVES √ (2002 

program)

√ (2002 

program)

√ (latest 

program)

TxLED Fuel1

Post-process 

diesel vehicle 

NOx rates

N/A N/A √

1 RFG input parameters were based on Houston fuel survey data (summer 2002 and latest available summer 2011). 

Diesel sulfur values were based on historical fuel survey data (Houston 2003 survey for 2002 base year and pre2003 scenario, and MOVES default for 2018 control strategy). The only I/M county in 2002 was Harris County,

which was modeled for the 2002 base year and for the 2018 pre-2003 controls scenario; for the 2018 control 

strategy scenario, I/M was modeled for the five HGB counties under the current I/M program. TxLED effects 

were modeled as a post-processing procedure adjustment to diesel vehicle NOx emissions for all counties, for the 

2018 control strategy scenario only.
32

MOVES Emissions Factor Aggregation Levels

The summer weekday emissions factor look-up tables produced provide the MOVES emissions

rates by:

• Up to 13 source types (i.e., vehicle types);

• Up to 4 fuel types;

• Up to 5 road types (four actual MOVES road categories and “off-network”);

• Each of the 24 hours in a day;

• 16 speed bins (i.e., 2.5, 5, 10, 15,.... 75 mph) (only included in miles-based rate tables);

• Up to 38 pollutants; and

• Up to 13 emissions processes.

The vehicle fleet was assumed to be powered only by the predominant on-road fuels of 

gasoline or diesel. The five road type categories in MOVES are Off-Network (not actually a 

road type), Rural Restricted Access, Rural Unrestricted Access, Urban Restricted Access, and 

Urban Unrestricted Access. Of the two rate tables produced (by post-processing) for input to the 

emissions calculations, one rate table contains off-network rates, and the other includes rates for 

each of the actual four MOVES road types, indexed by 16 speeds. The speeds index corresponds 

to the 16 MOVES speed bin average speeds: 2.5, 5, 10, 15,.... 75 mph.

MOVES Run Specifications

The MOVES Run Specifications (MRS) (XML file) defines place, time, vehicle, road, fuel, 

emissions producing process, and pollutant parameters for the modeling scenario. TTI 

developed the analysis MRS files by first creating an MRS template using the MOVES 

Graphical User Interface (GUI), then looping through the template with a file-building utility to 

create one MRS for each county, year, and control scenario. This process was used to produce 

the 25 MRSs for the analysis (three calendar year control scenarios x eight counties + one extra 

for the Harris County 2002 base year scenario needed for the I/M test-type switch effects 

modeling procedure).

Table 18 describes the MRS selections TTI used, with further details on the selections 

following the table.
33

Table 18. RunSpec Selections by MOVES2010bROP GUI Navigation Panel.

Navigation Panel Detail Panel Selection

Scale Domain/Scale;

Calculation Type

County;

Emissions Rates

Time Spans1 Time Aggregation Level;

Years – Months – Days – Hours

Hour;

<YEAR>1 - July - Weekday - All

Geographic 

Bounds2

Region;

Selections;

Domain Input Database

Zone and Link;

<COUNTY>2;

<COUNTY INPUT DATABASE (CDB) NAME>2

On-Road Vehicle 

Equipment SUT/fuel combinations

SUT Gasoline Diesel

Motorcycle X -

Passenger Car X X

Passenger Truck X X

Light Commercial Truck X X

Intercity Bus - X

Transit Bus - X

School Bus X X

Refuse Truck X X

Single Unit Short-Haul Truck X X

Single Unit Long-Haul Truck X X

Motor Home X X

Combination Short-Haul Truck X X

Combination Long-Haul Truck - X

Road Type Selected Road Types

Off-Network –

Rural Restricted Access – Rural Unrestricted Access – Urban 

Restricted Access – Urban Unrestricted Access

Pollutants and 

Processes3

VOC; CO; NOx;

Atmospheric CO2

Depending on pollutant, processes may include:

Running Exhaust, Start Exhaust, Extended Idle Exhaust, 

Crankcase Running Exhaust, Crankcase Start Exhaust, Crankcase 

Extended Idle Exhaust, Evap Permeation, Fuel Vapor Venting, or 

Fuel Leaks

Manage Input 

Data Sets

Additional input database 

selections None

Strategies4 Rate-of-Progress “No Clean Air Act Amendments” box was checked only for the 

2018 calendar year pre-2003 controls runs.4

General Output

Output Database;

Units;

Activity

<MOVES OUTPUT DATABASE NAME>;

Pounds, KiloJoules, Miles;

Distance Traveled, Source Hours, Source Hours Idling, Source 

Hours Operating, Source Hours Parked, Population, Starts

Output Emissions

Detail

Always;

For All Vehicles/Equipment;

On Road

Time: Hour – Location: Link – Pollutant;

Fuel Type, Emissions Process;

Source Use Type

Advanced 

Performance 

Measures

Aggregation and Data Handling All check boxes are to be “un-checked”

1 County scale allows one year and county per run. The years are 2002 (base year) and 2018 (for the attainment year 

pre-2003 controls and control strategy runs). 2 The counties are Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, and Waller. 3 Chained pollutants require other pollutants (not listed in the table) to be selected (e.g., VOC requires Total Gaseous 

Hydrocarbons and Non-Methane Hydrocarbons: CO2 requires Total Energy Consumption). 4 For 2018 pre-2003 controls, in addition to checking the Strategies Rate-of-Progress box, the modelyearcutpoints table 

was updated for a 2002 baseline, in accordance with EPA’s instructions provided with MOVES2010bROP. 
34

Scale, Time Spans, and Geographic Bounds

The MOVES Domain/Scale “County” was selected as is required for SIP inventory analyses. 

The MOVES Calculation Type “Emissions Rates” was selected for MOVES to produce the 

activity and emissions output needed for input to the post-processing algorithms that calculate 

the emissions rate look-up tables with speed bin indexing, as needed for the detailed link-based 

emissions estimation process (see MOVESratescalc utility description in Appendix B).

The Time Spans parameters were specified to provide the most detail available, which is the 

hourly aggregation level, for all hours of the day, for the selected year, month and day type. One 

analysis year per run was selected, as MOVES allows only one “Years” selection for the County 

Domain Scale. For TTI’s MOVES-based link emissions estimation process, which is for a single 

day, one “Months” (July) and one “Days” (Weekdays) selection was made.

Under Geographic Bounds for the County Domain Scale, only one county may be selected. 

The user-produced CDB containing the calendar year scenario-specific input data for the county 

was specified as the County Domain Input Database, and under Region, “Zone & Link” was 

selected as required for the emissions rates calculation type. With these required set-ups, one 

county, one year, one day type, and 16 (speed bin) average speeds were modeled per run.

On-Road Vehicle Equipment and Road Type

All of the SUTs associated with gasoline and diesel fuels were specified. Note that for this 

analysis, the MOVES default fuel/engine fractions were replaced with local inputs, which 

showed no compressed natural gas (CNG) vehicles in the fleet (the MOVES default database 

includes some CNG Transit Buses), and no gasoline Transit Buses. (The local SUT/fuel type 

VMT mixes developed for the study define the SUT/fuel type combinations included in the 

MOVES runs.) For emissions rate calculations, all five MOVES road type categories were 

selected.

Pollutants and Processes

In MOVES, VOC is a “chained” pollutant, meaning that VOC emissions are calculated based on 

one or more other pollutants whose emissions must also be calculated. In addition to the 

pollutants called for in this analysis, the following pollutants were additionally required for 

MOVES to produce the VOC emissions rates: Total Hydrocarbons (THC), Non-Methane 

Hydrocarbons (NMHC) and Methane. Additionally, Total Energy Consumption (TEC) was 

required for MOVES to calculate CO2 rates. All of the associated processes available by the 

selected pollutants were included, except for the two refueling emissions processes.

Manage Input Data Sets and Strategies

The Manage Input Datasets feature allows alternate inputs other than those included in the CDB. 

No additional inputs were included via the Manage Input Datasets panel.

The “Strategies – Rate-of-Progress” feature was used for the 2018 calendar year pre-2003

controls emissions rates modeling scenario. The check-box Compute Rate-of-Progress “No 

Clean Air Act Amendments” Emissions was selected for this scenario in combination with other 

required inputs (via modelyearcutpoints, imcoverage, and fuels tables in the input CDB, 
35

discussed later), to set, or “freeze,” emissions rates at a 2002 baseline by assigning 2002 modelyear emissions rates to all post-2002 vehicles.

Output

The output units were pounds (converted in a later step to grams), kilojoules, and miles. All of 

the activity categories were chosen for inclusion in the output database. The activity output was 

needed along with the emissions output to calculate the rates/activity emissions rates look-up 

tables via post-processing. The output detail level was by hour, link (i.e., county/road type/speed 

bin combination), pollutant, process, SUT, and fuel type.

Appendix A lists the electronic data files provided in support of this analysis, which includes 

the MRSs used.

MOVES County Input Databases

The locality-specific input data for the county scale runs are entered through the CDB. 

Additional user input data may be entered through separate databases via the Manage Input 

Datasets feature, although this feature was not used.

TTI developed procedures to facilitate building and checking CDBs for large scale emissions 

inventory projects. The basic procedure was to write a CDB builder-MySQL script that was then 

converted to a template by replacing particular scenario-specific values (e.g., year, input file 

paths, county name/FIPS) with variables. The template was looped through for each scenario (in 

this case for each year, county, and control scenario) to produce the MySQL scripts needed to 

create all of the required CDBs. After building the CDBs, a CDB checker utility was run to 

verify that all CDB tables (listed in Table 19) were built and populated as intended.

To build all of the CDBs needed for the analysis, the required data were first prepared and 

organized. The input data for populating the CDBs includes local data from prepared text files 

and databases (e.g., for local fuels and weather data), and MOVES default data from 

MOVESDB20120410 (e.g., for default activity data), and some values provided directly in the 

CDB builder MySQL script.

Table 19 provides an outline and brief description of the CDBs, followed by discussion of the 

development of the local data and the defaults contained therein. Unless otherwise stated, the 

CDB table data applies to all counties, years, and control scenarios.
36

Table 19. MOVES2010bROP County Database (CDB) Inputs.

MOVES Input Table Data 

Category Notes

year Time

Sets analysis year, designates it as a base year (i.e., activity input to be supplied 

rather than forecast by MOVES), and sets fuelyearID value (2002 for base 

year/pre-2003 control scenarios, and 2012 for 2018 control strategy scenario).

state Geography Identifies the state (Texas) for the analysis.

county Geography/ 

Meteorology

Identifies county of analysis and local altitude (“low”) and barometric pressure

(used input values from FY2012 HGB RFP inventories).

zonemonthhour Meteorology Contains local, hourly temperature and relative humidity data for the county 

(used input values from FY2012 HGB RFP inventories).

roadtype1 Activity Lists MOVES road types with ramp activity fractions (which was set to 0).1

hpmsvtypeyear2

Activity

(Defaults)

Used MOVES defaults – 1999 national annual VMT by HPMS vehicle 

category, except yearID was set to analysis year.

roadtypedistribution2 Used MOVES default road type VMT fractions.

monthvmtfraction2 Used MOVES default month VMT fractions.

dayvmtfraction2 Used MOVES default day VMT fractions.

hourvmtfraction2 Used MOVES default hour VMT fractions.

avgspeeddistribution2 Used MOVES default average speed distributions.

sourcetypeyear2 Fleet

(Defaults)

Used MOVES default – 1999 national SUT populations, except yearID was

set equal to the analysis year value.

sourcetypeagedistribution Fleet

Used county SUT age fractions consistent with the FY2012 HGB RFP analysis, 

i.e., based on mid-year 2002 historical registrations for the 2002 base year, and 

on mid-year 2011 registrations for both 2018 analysis scenarios. 

avft Fleet

Used the Texas statewide fuel fractions inputs (i.e., diesel fractions) consistent 

with the FY2012 HGB RFP inventories, using the same local registration 

datasets for each scenario as used for the sourcetypeagdistributions.

zone Activity Start, idle, SHP zone allocation factors. County = zone; all factors set to 1.0.

zoneroadtype Activity SHO zone/roadtype allocation factors. County = zone; all factors set to 1.0.

fuelsupply Fuel

Contains marketshare information for gasoline and diesel formulations. Used 

values consistent with the FY2012 HGB RFP inventories, 2002 base year data 

for the 2002 base year and the 2018 pre-2003 control scenarios, and the 2012 

data (latest fuelyearID in MOVES) for the 2018 control strategy scenario.

fuelformulation Fuel

Contains gasoline and diesel fuel formulations used, as specified by 

fuelformulationID in the fuelsupply records, and consistent with the FY2012 

HGB RFP inventories. (Although T50 and T90 fields were added as required 

for MOVES2010b, the original E200 and E300 values were used).

imcoverage I/M

Used I/M parameters consistent with the FY2012 HGB RFP inventory analysis, 

and modified inputs reflecting the Harris County program in place in 2002 as 

input for the 2018 attainment year pre-2003 control scenario run. 

modelyearcutpoints VMT Offset

Per EPA’s 2012 VMT offset guidance, set model year at which to freeze 

FMVCP for all later model years (i.e., 2002), and set start and cutoff model 

years for particular fuel effects algorithms for consistency with the pre-2003 

control scenario. Only used for the 2018 fleet pre-2003 control scenario.

1 MOVES will not produce “ramp road type” rates in a single run with all road types. To calculate emissions for 

certain travel model links coded as ramps, MOVES Unrestricted Access road type emissions rates were used. 2 Use of a default set of activity and population inputs for all MOVES runs is basic to the inventory method. The 

MOVES default activity is normalized in the emissions rates calculation post-processing procedure (i.e., MOVES 

activity output is divided into the MOVES emissions output to produce emissions rates in the desired “rates per 

activity” units), and actual local scenario-specific activity estimates are used in the external emissions calculations.
37

User Inputs to MOVES via CDB

All inputs discussed in this section are input via the CDB. Unless otherwise stated, the inputs 

apply to all counties, years, and control scenarios.

Year, State, and County Inputs to MOVES

The year, state, and county tables are populated with data identifying the year, state, and county 

of the run.

StateID “48” (Texas) was inserted in the state table. The yearID field of the “year” table was 

populated with the analysis year value, and the year was set as a base year (to specify that 

particular user-input fleet and activity data were to be used, rather than forecast by MOVES). As 

part of designating the appropriate fuel supply for the modeling scenario, the fuelyearid in the 

year table was set: for the 2002 base year and 2018 pre-2003 controls runs, fuelyearid was set to 

2002, and for the 2018 control strategy run the fuelyearid was set to 2012 (i.e., currently the 

latest fuel year in MOVES which applies to 2012 an later analysis years). The county table 

identifies the county of analysis and contains barometric pressure and altitude information 

(discussed further with other meteorological inputs). The county data was selected from a 

prepared local “meteorology” database containing tables of weather data records (i.e., “county” 

and “zonemonthhour” tables) for the analysis.

Roadtype Table Inputs to MOVES

Currently the MOVES model contains “ramp” emissions rates, but not a road type for ramps 

specifically. In the roadtype table, MOVES provides a field “rampFraction” for including a 

fraction of estimated ramp activity as a fraction of SHO on each of the MOVES road types. For 

this analysis, the MOVES default roadtype table data were used, but with the ramp fractions set 

to zero (i.e., 100 percent of activity on each MOVES road type was based on the road type drive 

cycles assigned to that road type by MOVES, exclusive of ramp activity; currently the MOVES 

Unrestricted Access road type rates are used with activity for particular ramp links in the external 

emissions calculations). The treatment of ramps for subsequent emissions inventory

development projects will be updated to incorporate use of ramp rates.

Default Activity and Population CDB Inputs to MOVES

The activity and vehicle population input parameters under the methodology use the MOVES 

defaults. The tables are: hpmsvtypeyear, roadtpyedistribution, monthvmtfaction, 

dayvmtfraction, hourvmtfraction, avgspeeddistribution, and sourcetypeyear. Data for all of these 

tables were selected and inserted from the MOVES default database. For the two tables 

dependent on year (i.e., hpmsvtypeyear and sourcetypeyear include yearID), the 1999 default 

data were used, except the yearID value was updated to the analysis year value.

The zone and zoneroadtype tables contain zonal sub-allocation activity factors. For county 

scale analyses, county is equal to zone, therefore these allocation factors were set to 1.0.

Local Fleet Age Distributions and Fuel Fractions Inputs to MOVES

All of the age distributions and fuel fractions inputs used were from the FY 2012 HGB RFP SIP 

inventories analysis. The locality-specific fleet inputs to MOVES consist of county age 

distributions input datasets and statewide diesel fractions (or fuel/engine fractions) input datasets. 
38

The age distributions and fuel fractions inputs were calculated and written to text files in 

preparation for loading the data into the appropriate CDB input tables: the 

sourcetypeagedistribution table for vehicle age distributions, and the AVFT table for fuel/engine 

fractions. The MOVESfleetInputBuild utility was used to produce these fleet inputs to MOVES 

in the required formats (see utility description in Appendix B), and MySQL scripts were used to 

populate the CDB input tables.

The age distributions and fuel fractions were based on TxDOT mid-year county registrations 

data and MOVES model defaults, where needed. The fuel/engine fractions were developed 

consistent with the SUT/fuel types in the VMT mix (e.g., no CNG vehicles are in the SUT/fuel 

type VMT mix resulting in fuel/engine fractions for CNG of zero). Locality-specific SUT age 

distributions were produced based on the TxDOT county vehicle registration category

aggregations consistent with the vehicle registration category aggregations used in estimating the 

SUT/fuel type VMT mix (see Appendix B). The age distributions and diesel fractions 

summaries are included in Appendix G.

Table 20 summarizes the data sources and aggregation levels used to estimate the HGB 

county sourcetypeagedistributions and fuel/engine fractions.
39

Table 20. Data Sources and Aggregations for SUT Age Distributions and Fuel/Engine 

Fractions.

SUT Name SUT ID

TxDOT Category1

Aggregations for Age 

Distributions and 

Fuel/Engine Fractions

Geographic 

Aggregation for 

Age Distributions

Geographic 

Aggregation for 

Fuel/Engine 

Fractions2

Motorcycle 11 Motorcycles County

NA – 100 percent 

gas, no Fuel/Engine 

Fractions

Passenger Car 21 Passenger County MOVES default

Passenger Truck 31 Total Trucks<=8500 County MOVES default

Light Commercial 

Truck 32 Total Trucks<=8500 County MOVES default

Single-Unit ShortHaul Truck 52 >8500+ >10000+ 

>14000+>16000 HGB Region Texas Statewide

Single-Unit LongHaul Truck 53 >8500+ >10000+ 

>14000+>16000 Texas Statewide Texas Statewide

Refuse Truck 51

MOVES default

Motor Home 54

Intercity Bus 41

Transit Bus2 42

School Bus 43

Combination 

Short-Haul Truck 61 >19500+ >26000+ >33000+ >60000 HGB Region Texas Statewide

Combination 

Long-Haul Truck 62 >19500+ >26000+ >33000+ >60000 Texas statewide

NA – 100 percent 

diesel, no 

Fuel/Engine 

Fractions

1 TxDOT mid-year 2002 and 2011(latest available) county vehicle registrations data (i.e., three-file data set: 

composite fuel light-duty categories; heavy-duty gas by eight weight categories; and heavy-duty diesel by eight 

weight categories) were used for developing local inputs for 2002 and 2018, respectively (weights are GVWR in 

units of lbs.). Based on the current MOVES2010a model and MOVESDB20100830 database, TTI updated the 

MOVES default age distributions from EPA’s MOVES tools webpage,

http://www.epa.gov/otaq/models/moves/tools.htm (which were based on the previous MOVES version), and used 

the default updates in this analysis. 2 MOVES default fuel/engine fractions for transit buses were revised to exclude the CNG and gasoline-fueled

components, consistent with the local SUT/fuel type VMT mixes.

Local Meteorological (County and Zonemonthhour Table) Inputs to MOVES

All of the meteorological inputs used were from the FY 2012 HGB RFP SIP inventories analysis. 

TCEQ provided the Houston peak-ozone season meteorological inputs values, which TTI 

processed into the MOVES input format, and loaded into the “county” (barometric pressure) and 

“zonemonthhour” (temperature and relative humidity) tables. These input data were developed 

by TCEQ as one set of hourly temperature and relative humidity, and 24-hour barometric 

pressure averages for the HGB region, using recent multiple year, June through August hourly 
40

weather station data from the Houston George Bush Intercontinental Airport. Low altitude was 

designated for all counties. Table 21 summarizes the temperatures, relative humidity, and 

barometric pressure input values.

Table 21. Meteorological Inputs to MOVES.

Hour

Temperature 

(Degrees 

Fahrenheit)

Relative 

Humidity 

(Percent)

24-Hour Average 

Barometric Pressure 

(Inches of Mercury)

1 77.1 73.0

29.87

2 75.7 77.2

3 75.2 78.7

4 74.1 80.1

5 72.7 83.6

6 72.5 84.4

7 72.4 84.5

8 76.0 78.0

9 79.6 68.9

10 83.1 59.5

11 85.6 52.1

12 87.8 47.3

13 89.0 43.0

14 90.1 40.6

15 91.2 39.3

16 91.2 39.5

17 91.4 40.3

18 90.9 40.2

19 88.8 44.3

20 85.4 51.4

21 82.7 57.7

22 81.0 60.1

23 80.3 62.0

24 78.8 66.6
41

Fuels Inputs to MOVES

The local fuels inputs to MOVES are input via the CDB in the fuelsupply and fuelformulation 

tables. These fuels inputs were consistent with the FY 2012 HGB RFP SIP inventories; and in 

the fuelformulation table, as required with MOVES2010b, the T50 and T90 fields were added. 

TTI prepared the HGB area-level input data for each year in spreadsheets, saved the input data to 

text files in the MOVES-required format. These data were then imported to the fuelsupply and 

fuelformulation tables in the appropriate MOVES scenario CDBs. The following describes the 

procedure used to populate the fuels tables.

• Selected and inserted into the CDB fuelsupply table all MOVES default fuelsupply 

records associated with the scenario (i.e., for the countyID, fuelyearID, monthgroupID) 

and set their marketshare field values to zero (to prevent MOVES from applying the 

default fuels data in addition to the desired local fuels input data).

• Loaded all local fuel formulations from the specified local fuels inputs text files into the 

CDB fuelformulation table (these were given fuelformulationIDs different than the set of 

IDs used as MOVES defaults). (Note that prior to loading, the new T50 and T90 fields 

were added as required for MOVES2010b, but were populated with zero values, which 

directed MOVES to give precedence to the original E200 and E300 field values.)

• Loaded the scenario (i.e., for the subject countyID, fuelyearID, monthgroupID) local fuel 

supply records (i.e., the marketshares of the specified fuel formulations) from the input 

data text files into the CDB fuelsupply table.

Data Sources – The EPA provided TTI with the Houston reformulated gasoline (RFG) retail 

outlet survey samples by fuel grade for 2002and 2011, collected by the RFG Survey Association 

(for more information see: http://www.epa.gov/otaq/regs/fuels/rfg/properf/perfmeth.htm). TTI 

processed the sample data to estimate the Houston summer season average RFG fuel property 

inputs by year, which were used for all counties. Some historical and future year MOVES 

default fuel properties for the HGB counties were also used. For average diesel sulfur content

data sources include a TCEQ summer retail fuel survey summary and MOVES defaults. See the 

Table 22 and Table 23 footnotes for data source specifics.

Development of Fuel Formulations Inputs from RFG Survey Samples – On average, 

each summer period survey included 345 total samples taken during June 1 – September 15 (by 

grade: Regular – 291, Mid-grade – 19, and Premium – 36). The RFG sample data used were 

already in the units specified for MOVES, except for the 2002 MTBE and TAME values, which 

TTI converted from oxygenate weight percent to the required oxygenate volume percent form 

using appropriate conversion factors (i.e., vol% oxygenate-per-wt% oxygenate factors of 1.0162 

for MTBE, and 0.9572 for TAME, based on conversion factors used in MOVES and provided by 

EPA OTAQ). TTI used the standard method of averaging the fuel properties by grade, and 

combining them into overall RFG averages using relative sales volumes by grade as weights. 

The relative sales volumes by grade were estimated using annual average sales volumes per day 

through retail outlets statistics for Texas, taken from the Energy Information Administration’s 

(EIA) Petroleum Marketing Annuals (for 2002 and latest available 2009).
42

The fuel supply value for each fuel formulation used was 1.0, which means that for each 

modeling scenario there was only one diesel and one RFG fuel formulation used. Table 22 and 

Table 23 show the RFG and diesel fuel formulations used.

Table 22. MOVES Gasoline Inputs – HGB Summer Emissions Rates Analysis.

Fuel Formulation Field1 2002 Base Year; and

2018 pre-2003 Controls 2018 Control Strategy

fuelFormulationID 10002 10005

fuelSubtypeID 11 12

RVP 6.85 7.06

sulfurLevel 131.11 29.42

ETOHVolume 0 10

MTBEVolume 11.13 0

ETBEVolume 0 0

TAMEVolume 0.81 0

aromaticContent 20.49 14.65

olefinContent 11.24 13.27

benzeneContent 0.601 0.55

e200 48.51 49.32

e300 83.27 84.61

volToWtPercentOxy 0.1786 0.3488

BioDieselEsterVolume \N \N

CetaneIndex \N \N

PAHContent \N \N

T50 0 0

T90 0 0

1 Data sources: 2002 – based on EPA Houston Summer 2002 retail outlet RFG survey data; 

2018 future year – all values are based on latest available summer (2011) Houston RFG 

survey data, except for benzene and ethanol, which are MOVES defaults for July 2012

(latest fuel year in MOVES).
43

Table 23. MOVES Diesel Inputs – HGB RFP Summer Emissions Rates Analysis.

Fuel Formulation Field1

2002 Base Year; and

2018 pre-2003 

Controls

2018 Control 

Strategy

fuelFormulationID 30307 30011

fuelSubtypeID 20 20

RVP 0 0

sulfurLevel 306.7 11

ETOHVolume 0 0

MTBEVolume 0 0

ETBEVolume 0 0

TAMEVolume 0 0

aromaticContent 0 0

olefinContent 0 0

benzeneContent 0 0

e200 0 0

e300 0 0

volToWtPercentOxy 0 0

BioDieselEsterVolume \N \N

CetaneIndex \N \N

PAHContent \N \N

T50 0 0

T90 0 0

1 Diesel “sulfurLevel” data sources for the 2002 and 2018 analyses, respectively, are

TCEQ Summer diesel survey data for Houston from 2003 and the July 2012 MOVES 

default.

Local I/M Inputs to MOVES

The current I/M program is administered to reduce vehicle emissions in five of the eight HGB 

counties. In 2002, only Harris County administered an I/M program. MOVES calculates county 

emissions rates that reflect the emissions-reducing benefits of the I/M program designs reflected 

in parameters specified in the MOVES IMcoverage table. These I/M input parameters are 

consistent with the FY 2012 HGB RFP SIP inventories I/M inputs. TTI produced a set of Texas 

I/M county MOVES imcoverage records to replace the MOVES default imcoverage table 
44

records for Texas (that were populated using information from the EPA’s 2005 National 

Emissions Inventory).

The imcoverage table data parameters (by field headers) are:

• polProcessID (pollutant and emissions process affected by the program);

• stateID (state subject to the I/M program);

• countyID (county number);

• yearID (year administered);

• sourceTypeID (SUT covered);

• fuelTypeID (fuel type subject to the program);

• IMProgramID (arbitrary ID number);

• begModelYearID (first model year covered);

• endModelYearID (last model year covered);

• inspectFreq (inspection frequency for the program);

• testStandardsID (I/M test type);

• useIMyn (a Y/N [yes/no] switch that specifies whether or not to use the record); and

• complianceFactor (an adjustment factor reducing the effects for compliance rate, waiver 

rates, or other adjustments).

TTI produced the set of Texas counties imcoverage table input records for all MOVES 

analysis years and stored them in a database for use in building the CDBs for emissions rates 

modeling. In addition to selecting the appropriate local user-input imcoverage records for the 

modeling scenario from the Texas imcoverage database, all MOVES default imcoverage records 

for the modeling scenario must be excluded. To prepare the appropriate county imcoverage 

inputs, the following two general steps were performed for all eight counties:

• Selected and inserted all MOVES default imcoverage records for the scenario’s countyID 

and yearID into the CDB imcoverage table, and flagged them for non-use (i.e., set 

useIMyn = N) in the modeling run; and

• From the current, updated Texas MOVES imcoverage database, selected and inserted the 

imcoverage records for the scenario (excluding the 2018 pre-2003 control scenario) 

yearID and countyID into the CDB imcoverage table (with useIMyn = Y). For the 2018 

pre-2003 control scenario, a special set of imcoverage records were produced and used

for this VMT offset analysis, which reflected the I/M program in place for HGB in the 

2002 base year that included only Harris County.

Data Sources – TTI produced the I/M coverage input parameters to best represent Texas I/M 

program designs as specified in the Texas I/M SIP and Texas rules (using current Texas I/M 

modeling protocol compliance and waiver rates), and where the pertinent I/M coverage modeling 
45

parameters existed in MOVES (e.g., only for SUT and fuel type categories for which MOVES 

contained I/M effects). The HGB I/M program requires annual emissions testing of gasoline 

vehicles within a 2-through-24 year vehicle age coverage window (motorcycles, military tactical 

vehicles, diesel-powered vehicles, and antique vehicles are excluded). A gas cap integrity test is 

required on all these vehicles, and, depending on the vehicle class and model year, the vehicle 

emissions testing may utilize on-board diagnostics (OBD), the Accelerated Simulated Mode 

(ASM-2) test, or the Two-speed Idle (TSI) test. For additional Texas I/M program details, see 

the current I/M SIP, Revision to the State Implementation Plan Mobile Source Strategies, Texas 

Inspection and Maintenance State Implementation Plan, TCEQ, November 18, 2010.

Approach – Following is the general approach used to build the current Texas imcoverage 

tables.

• Identified the MOVES I/M test standards applicable to Texas (see Table 24, column 5, 

which lists the five I/M test standards pertinent to this HGB analysis).

• Queried the MOVES default imfactor table (contains adjustments to emissions rates per 

various I/M scenarios by SUT/fuel type, age, etc.) on the Texas I/M test frequency and 

fuel type (i.e., annual and gasoline) and on the imteststandards applicable to Texas – from 

this query, listed the SUTs, test standards, pollutant, and emissions process combinations 

with non-zero MOVES imfactors and corresponding base rates with non-zero standard 

I/M difference (i.e., I/M effects) available in MOVES (see Table 24, note 4).

• Categorized counties and years in groups under the same MOVES test standards.

• Assigned MOVES improgramIDs such that: 1) all MOVES default improgramIDs were 

excluded, and 2) per MOVES User’s Guide, for each yearID, each IMprogramID 

represented a unique combination of test standard, test frequency, begin model year, and 

end model year.

Table 24 and the associated table notes describe the MOVES imcoverage records developed 

by TTI and used for the HGB analyses. Note that a review of the pertinent MOVES data 

(IMfactors and mean base rates for non-I/M and reference-I/M) showed that in the current 

MOVES model there are no I/M effects included for heavy-duty vehicle categories (i.e., vehicles 

with GVWR > 8,500 pounds – see Table 24, note 4). Although the Texas I/M program design 

includes heavy-duty vehicles, the current version of MOVES provides no means to model a 

potential benefit.
46

Table 24. MOVES IMCoverage Table Input Descriptions for HGB I/M Counties.

YearID1 IMprogamID2 begModel

YearID3

endModel

YearID3 testStandardsID Sourcetypeid4

Harris County

21

(PC – Passenger 

Car)

31

(PT – Passenger 

Truck)

32

(LCT – Light 

Commercial 

Truck)

1999 through 

4/2002

20 X X 12 (2500 RPM/Idle)

50 X X 41 (Evp Cap)

Harris, Brazoria, Fort Bend, Galveston, Montgomery Counties

5/2003 (5/2002, 

for Harris)

through 2019

30 X 1995 23 (A2525/5015 

Phase)

51 X 1995 41 (Evp Cap)

40 1996 X 51 (Exh OBD)

60 1996 X 45 (Evp Cap, OBD)

2020 through 

2050

41 X X 51 (Exh OBD)

61 X X 45 (Evp Cap, OBD)

1 County I/M implementation dates: Harris – 1/1/1997 with transition to the new I/M test types on 5/1/2002; Brazoria, 

Fort Bend, Galveston, Montgomery – 5/1/2003. 2 Common parameters for Texas MOVES imcoverage records not shown include: annual test cycle, gasoline fuel 

type, use IMyn = Y. Aside from any non-standard adjustments, the compliancefactor values are common across 

areas: PC – 93.12%; PT – 87.53%; and LCT – 81.95%. Using the MOVES Emissions Inventory Technical 

Guidance compliance factor equation (Section 3.10.6), compliance factors were calculated as the product of the 

percent compliance rate, 100% percent waiver rate, and the regulatory class coverage adjustment. The current Texas 

I/M program modeling protocol compliance and waiver rates are 96% and 3%. The regulatory class adjustments 

used were taken directly from the MOVES Emissions Inventory Technical Guidance, and for PC, PT, and LCT, 

respectively are 100%, 94%, and 88%.

3 begmodelyearid and endmodelyearid, which define the range of vehicle model years covered, where represented by 

“x” are calculated as YearID – 24, and YearID – 2, respectively. 4 For heavy-duty gasoline vehicles (i.e., > 8,500 pounds GVWR), MOVES does not contain any combinations of I/M 

factors and mean base rates that yield I/M effects; for light-duty gasoline vehicles, MOVES includes both exhaust 

and evaporative I/M factors and mean base rates with I/M effects, therefore, only light-duty gasoline vehicles (SUTs 

21, 31, and 32) were included in the user input imcoverage records. The processes/pollutants affected by I/M are 

exhaust running and exhaust start THC, CO, NOx, and tank vapor venting THC.

Model Year Cut Points

Per EPA’s 2012 VMT offset guidance, TTI set the model year at which to freeze FMVCP for all 

later model years (i.e., 2002), and set start and cutoff model years for particular fuel effects 

algorithms for consistency with the pre-2003 control scenario. These set ups provided by EPA 

(see item 3 in Table 25, which is EPA’s technical sheet explaining the VMT offset version of 

MOVES, MOVES2010bROP) were applied only for the 2018 fleet pre-2003 control scenario.
47

Table 25. Explanation of Special VMT Offset Version of MOVES (Provided by EPA with 

the MOVES2010bROP Model, August 2012)

Explanation of special VMT offset version of MOVES

This version of MOVES, which will be referred to as MOVES2010bROP, is to be used only for 

VMT offset calculations according to EPA’s 2012 VMToffset guidance. It should not be used 

for reasonable further progress calculations or for any other regulatory purpose.

As background, MOVES includes the option to disable all motor vehicle requirements of the 

1990 Clean Air Act Amendments. This option was created to calculate adjusted base year 

emissions for the reasonable further progress provisions of the Clean Air Act. This option is 

selected in the MOVES RunSpec by checking the box in the Rate of Progress panel. When this 

box is checked, emissions rates are frozen after 1990 although activity inputs in MOVES 

continue to change.

MOVES2010bROP includes both code and database changes that allow the user to set a baseline 

year other than 1990. For the analysis described in EPA’s 2012 VMT offset guidance, the new 

baseline year should be 2002. Following are the steps needed to make this change:

1. Complete the local RunSpec as you normally would, but check the box in the Rate of 

Progress Panel.

2. Enter all appropriate activity inputs in the County Data Manager for the local area in the 

calendar year of analysis. I/M program inputs should reflect the program in place in 2002. 

In addition, select the tab labeled “Generic” and the table labeled “modeIYearCutPoints.”

3. Export the default table, open it in Excel, and modify it so that it appears like this:

cutPointName modelYearlD

RateOfProgress 2002

HighestFuelPredictiveModelYear 2050

sulfurModelTHCNOxStart 1975

sulfurModelTHCNOxEnd 2050

sulfurModelCOStart 1975

sulfurModelGPAPhaselnStart 2004

sulfurModelGPAPhaselnEnd 2006

4. Save the file, browse for it, select the modelYearCutPoints table, and import.

Run MOVES2010bROP. The results will be the hypothetical emissions in the calendar year of 

analysis assuming no changes in emission factors after 2002.

The MOVES input files (MRSs and CDBs) were provided as a part of the electronic data 

submittal (Appendix A) of this Technical Note.
48

Checks and Runs

After completing the input data preparation, the CDBs were checked to verify that all 19 tables

(or 20 tables for the “pre-2003 controls” runs) were in the appropriate CDBs and the tables were 

populated with data as intended. The MOVES RunSpecs were executed in batches using the 

MOVES command line tool. The batches were set up to write each MOVES run log to a text file 

for later access. After completion, TTI searched the MOVES run logs for error and warning 

messages, for which none were found. The MOVES run summaries are included as Appendix H.

Post-Processing Runs

• Rates Per Activity: Using the MOVESratescalc utility, TTI calculated “rates-per-activity” 

for each county from the MOVES output (i.e., emissions divided by activity, using the 

movesoutput [emissions] and movesactivityoutput [activity] tables). The process created 

two emissions rate tables (per run) that were added to the MOVES output database: 

“ttirateperdistance” containing mass/mile emissions rates, and “ttirateperactivity” 

containing mass/SHP, mass/SHI, and mass/start emissions rates. This was performed for 

each county, year, and control scenario. See MOVESratescalc utility description in 

Appendix B for more details.

• Rates Adjustments: From the two calculated rate tables output from each 

MOVESratescalc run, emissions rates were extracted for only those pollutants needed in 

the emissions calculations. For the Harris County 2002 base year scenario, the pre-May 

and post-April I/M scenario rates were combined using factors representing the portion of 

the fleet estimated to have been tested under the previous and current I/M tests, during 

the prior complete I/M cycle (12 months). Using a July 1, 2002 evaluation reference, the 

proportions used were 10/12 for the pre-May I/M and 2/12 for the post-April I/M 

scenarios. For the 2018 control strategy scenario, TxLED adjustments were applied to all 

diesel vehicle NOx emissions rates and the extracted and adjusted rate tables for each 

county and calendar year control scenario (i.e., 24) were placed in a separate database for 

input to the emissions calculations. Table 26 shows the TxLED factors used (provided 

by TCEQ). TCEQ produced these average diesel SUT NOx adjustments using 4.8 percent

and 6.2 percent reductions for 2002 and later, and 2001 and earlier model years, respectively. 

More details on TCEQ’s TxLED factors analysis may be found at, 

ftp://amdaftp.tceq.texas.gov/pub/Mobile_EI/Statewide/mvs/TxLED/, in the file:

o mvs10a-statewide-txled-2011-analysis-11-14-17-18-19-21.zip.
49

Table 26. 2018 Analysis Year TxLED Adjustment Factor Summary.

Diesel Fuel Source Use Type NOx Adjustment 

Factor

Passenger Car 0.9516

Passenger Truck 0.9496

Light Commercial Truck 0.9491

Intercity Bus 0.9438

Transit Bus 0.9447

School Bus 0.9444

Refuse Truck 0.9461

Single Unit Short-Haul Truck 0.9510

Single Unit Long-Haul Truck 0.9507

Motor Home 0.9465

Combination Short-Haul Truck 0.9483

Combination Long-Haul Truck 0.9488

Source: TCEQ, Fall 2011. See 

ftp://amdaftp.tceq.texas.gov/pub/Mobile_EI/Statewide/mvs/TxLED/ for 

TCEQ’s TxLED analysis spreadsheets and other information.

Appendix A describes the electronic data submittal for this VMT offset inventory analysis, 

which includes the TxLED and I/M test-type switch adjustment factor files used in the 

MOVESratesAdj utility runs that produced the final emissions rate look-up table inputs to the 

emissions calculations.

The emissions factor MOVES set-ups used (MRS files and CDBs) and final adjusted 

emission rate look-up table databases were provided as a part of the electronic data submittal 

(see Appendix A).

EMISSIONS CALCULATIONS

TTI calculated hourly, ozone season weekday, link-based emissions inventories by county for 

each VMT offset demonstration analysis inventory scenario using the MOVESemscalc utility. 

The emissions calculations fall into two categories: VMT-based emissions calculations and offnetwork emissions calculations. The VMT-based emissions calculations use the TDM VMT and 

speeds to estimate emissions at the TDM link (or roadway segment) level. The off-network 

emissions process calculations use off-network activity (SHP, starts, and SHI) to estimate 

emissions at the county level.
50

Hourly Link-Based Emissions Calculations

The ozone season weekday, hourly link-based emissions, by county, for each analysis year and 

inventory type, were calculated with the MOVESemscalc utility using the following major 

inputs:

• TxDOT district-level SUT/fuel type VMT mix by MOVES roadway type;

• TDM link and intrazonal link VMT and speeds estimates, which contain the link-specific, 

hourly, directional, operational VMT and speed estimates as developed by the 

TRANSVMT utility to include: A-node, B-node, county number, TDM road type 

(functional class) code, link length, congested (operational) speed, VMT, and TDM area 

type code;

• County-level hourly SUT/fuel type off-network activity estimates (SHP, starts, and SHI);

• MOVES-based off-network emissions factors by pollutant, process, hour, SUT, and fuel 

type;

• MOVES-based “on-network” (VMT-based) emissions factors by pollutant, process, hour, 

average speed, MOVES roadway type, SUT, and fuel type; and

• TDM road type/area type code combinations to MOVES road type designations (and 

VMT mix road type and rates road type designations) (see Table 27).

The VMT-based emissions were calculated for each hour using the TxDOT district-level 

SUT/fuel type VMT mix, the TDM link and intrazonal link VMT and speeds estimates, the 

MOVES-based “on-network” emissions factors, and the TDM road type/area type to MOVES 

road types designations. For each link, the link was assigned a MOVES road type (and VMT 

mix and rates road types, which for this analysis were the same as MOVES road type) based on 

the link’s road type and area type. The link VMT was then distributed to each SUT/fuel type 

using the VMT mix, based on the link’s designated VMT mix road type and its associated 

TxDOT district.

The emissions factors for each SUT/fuel type were selected based on the link’s designated 

rates road type code (same as MOVES road type code) and the link speed. For link speeds 

falling between MOVES speed bin average speeds, emissions factors were interpolated from 

bounding speeds. For link speeds falling outside of the MOVES speed range (less than 2.5 mph 

and greater than 75 mph), the emissions factors for the associated bounding speeds were used. 

The g/mi rates were multiplied by the link SUT/fuel type VMT producing the link-level 

emissions estimates.
51

Table 27. H-GAC TDM Road Type/Area Type to MOVES Road Type Designations.

TDM Road Type (Code -

Name)1 TDM Area Type (Code - Name)1 MOVES Road Type (Code -Name)1

3 - Toll Roads 5 – Rural

10 - Rural Interstate 5 – Rural 2 – Rural Restricted Access

11 - Rural Other Freeway 5 – Rural

4 - Ramps (Fwy/Toll/Frnt) 5 – Rural

3 – Rural Unrestricted Access

8 - Local (Centroid Connector) 5 – Rural

12 - Rural Principal Arterial 5 – Rural

13 - Rural Other Arterial 5 – Rural

14 - Rural Major Collector 5 – Rural

15 - Rural Collector 5 – Rural

1 - Urban Interstate 1 – CBD; 2 – Urban; 3 – Urban 

Fringe

4 – Urban Restricted Access

2 - Urban Other Freeway 2 – Urban; 3 – Urban Fringe

3 - Toll Roads 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban 

10 - Rural Interstate 2 – Urban; 3 – Urban Fringe; 4 –

Suburban

11 - Rural Other Freeway 3 - Urban Fringe; 4 – Suburban

4 - Ramps (Fwy/Toll/Frnt) 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban

5 – Urban Unrestricted Access

5 - Urban Principal Arterial 1 – CBD; 2 – Urban; 3 – Urban 

Fringe

6 - Urban Other Arterial 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban

7 - Urban Collector 1 – CBD; 2 – Urban; 3 – Urban 

Fringe

8 - Local (Centroid Connector 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban

12 - Rural Principal Arterial 3 – Urban Fringe; 4 – Suburban

13 - Rural Other Arterial 3 – Urban Fringe; 4 – Suburban

14 - Rural Major Collector 3 – Urban Fringe; 4 – Suburban

15 - Rural Collector 3 – Urban Fringe; 4 –

Suburban

40 - Local (Intrazonal) 40 – Local (Intrazonal)

1

The TDM road type and area type code combinations are also correlated to VMT mix road type codes and 

emissions rate road type codes, which, for this analysis, are identical to the MOVES road type codes.

The off-network emissions were calculated for each hour using the hourly MOVES-based 

off-network emissions factors by SUT/fuel type and the county-level hourly SUT/fuel type offnetwork activity estimates (SHP, starts, and SHI). The off-network emissions were calculated at 

the county level by multiplying the hourly MOVES-based off-network emissions factors by the 

appropriate county-level hourly SUT/fuel type off-network activity, which was determined by 

the pollutant process.
52

The MOVESemscalc utility outputs for this HGB VMT offset inventory project consist of a 

listing file (summarizing all pertinent information regarding the execution of the utility), and a

tab-delimited emissions report summary file for each run including both hourly and 24-hour 

activity and emissions estimates by SUT/fuel type and TDM road type, with emissions tables of 

pollutant composites and individual emissions process totals.

Table 28 (shown in a previous section, but provided again here for convenience) lists the 

activity and emissions rate input data components used in calculating each of the four VMT 

offset analysis inventories.

Table 28. HGB VMT Offset Demonstration Emissions Inventory Scenarios.

Inventory Activity 

Input Year1 Emissions Factor Input2

2002 Base Year 2002 2002 Base Year

2018 Fleet and Pre-2003 

Controls with Growth 2018 2018 Fleet and Pre-2003 

2018 Fleet and Pre-2003 Controls

Controls without Growth 2002

2018 Attainment Year Control 

Strategy 2018 2018 Fleet and Control Strategy 1 Activity inputs (VMT mix; link VMT/speeds; and off-network activity: SHP, starts, SHI) were 

consistent with the FY 2012 HGB RFP SIP inventories. 2 Emissions factors were based on the EPA’s special model “MOVES2010bROP” provided to 

TCEQ for use in the VMT offset calculations. “Pre-2003 Controls” means attainment year 

emission factors were modeled to reflect no new controls after the base year beyond those 

already credited.

See Appendix A (Electronic Data Submittal) for the listing of emissions inventory data files 

submitted as a part of this project report. See Appendix B for additional MOVESemscalc utility 

information and the emissions calculation process flow diagram.

QUALITY ASSURANCE

Analyses and results were subjected to appropriate internal review and QA/QC procedures, 

including independent verification and reasonableness checks. All work was completed 

consistent with applicable elements of ANSI/ASQ E4-2004: Specifications and Guidelines for 

Quality Systems for Environmental Data Collection and Technology Programs and the TCEQ 

Quality Management Plan.

Quality Assurance Project Plan (QAPP) Category II (Modeling for NAAQS Compliance) is 

the QAPP category that most closely matches these objectives and establishes QAPP 

requirements for projects involving applied research or technology evaluations. Internal review 

and quality control measures consistent with applicable NRML QAPP requirements, along with 

appropriate audits or assessments of data and reporting of findings, were employed. These 

include, but are not limited to, the elements outlined in the following description.
53

A. Project Management

The project management was as listed previously in the Acknowledgments section.

The definition and background of the problem addressed by this project, the project/task 

description, and project documents and records produced are as described previously in the 

Purpose and Background sections. No special training or certifications were required. The TTI 

project manager assured that the appropriate project personnel had and used the most current, 

approved version of the QAPP.

After receiving the Notice to Commence (NTC) from TCEQ, the TTI project manager 

provided a detailed pre-analysis plan to the TCEQ project manager for review and concurrence. 

Upon concurrence of the pre-analysis plan, the TTI project manager distributed the pre-analysis 

plan to the TTI inventory developers for use in both the inventory development and QA review 

process. TTI maintains records of the project QA checks as a part of the project archive, for at 

least five years.

The objective was to produce the emissions inventory product of the quality suited to its 

purpose as specified (i.e., inventories for use in the HGB VMT-offset demonstration analysis to 

be included in the TCEQ’s SIP Revision), in accordance with the appropriate guidance and 

methods documents as referenced, as detailed in the pre-analysis plan, and in consultation with 

the TCEQ project manager.

Basic criteria were used to assure that the acceptable quality of the product was met –

product developers verified that the process and product were as specified, to include:

• The product met the purpose of the emissions analysis (e.g., photochemical modeling, 

reasonable further progress, transportation conformity, model comparison);

• The full extent of the modeling domain (e.g., analysis years, geographic coverage, 

seasonal periods, days, sources, pollutants) was included;

• Agreed methods, models, tools, and data were used (e.g., as listed in the Grant Activities 

Description, and as listed in the more detailed pre-analysis plan – any change from this 

plan, if needed, was made in consultation with the TCEQ project manager);

• The required output data sets were produced in the appropriate formats in accordance 

with the pre-analysis plan;

• Any deficiencies found during development and end-product quality checks (as discussed 

in QAPP Section D) were corrected; and

• Aggregate emissions estimate results assessed for comparability with available, similarly 

produced emissions estimates.

B. Data Generation and Acquisition

Note that no sampling of data was involved in the emissions inventory development, thus only 

existing data (non-direct measurements) were used for this project.
54

The data needed for project implementation were in the categories needed for development 

of emissions rate model inputs and adjustment factors, and development of the activity inputs for 

external emissions calculations. These emissions factor model inputs and activity inputs were 

developed using data sources as outlined previously and/or methods and procedures as detailed 

in the references listed, and as provided in the pre-analysis plan.

All data used either as direct input or to produce inputs (e.g., to the MOVES model or to 

TTI’s emissions inventory development utilities used, which were listed in the pre-analysis plan) 

were reviewed by TTI for suitability before use. The data sets for the project were provided by 

the Texas Department of Transportation (TxDOT), a Metropolitan Planning Organization (MPO) 

or Council of Governments (COG), TCEQ, and/or the EPA, and in most cases were QA’d by the 

providing agency. The data needed may include: Highway Performance Monitoring System 

(HPMS) data (from TxDOT’s Roadway Inventory Functional Classification Record [RIFCREC] 

report); regional travel demand model data; speed model data; vehicle registration data; 

automatic traffic recorder data; vehicle classification count data; meteorological data; fuels data; 

MOVES emissions model data; extended idling activity data; and vehicle inspection and 

maintenance program design data.

Any significant problems found during data review, verification, and/or validation (see QA 

criteria and methods discussion in section D) were to be corrected, and the QA procedure was 

repeated until satisfied. No significant problems were found.

Data Management: TTI emissions inventory data developers work as a closely coordinated 

team. The assigned staff used the same electronic project folder structure on their individual 

workstations. As various scripts, inputs, and outputs were developed in the emissions inventory 

development process, data were shared within the team for crosschecking via an intra-net, flash 

drive, or external hard drive. To perform the MOVES model runs, a computer cluster (multiple 

computer) configuration or individual workstation configuration was used. After input data were

QA’d, depending on the size of the data set, the data sets were backed up and stored in 

compressed files. These activities were performed throughout the process until the final products 

were produced.

For MOVES model runs to produce emissions factor look-up tables for the emissions 

inventories, all run files (MOVES model inputs and batch files) were produced on an individual 

workstation. After the MOVES input data and batch files (i.e., Run Files) were QA’d, they were 

either executed on an individual workstation, or they were copied (via external hard drive) to the 

cluster’s Master computer and executed. Upon execution, completion, and error checking, the 

MOVES output databases and run log text files were (for cluster runs first copied to an 

individual workstation), archived and processed further in preparation for input to the emissions 

calculations.

After the final product was completed, all the project data archives were compiled on a set of 

optical data discs (CD-ROM or DVD, depending on size), or on an external drive for very large 

project data sets. A complete archive of the project data is kept by TTI (the computer models 

and emissions inventory development utilities used in the process are included). An electronic 

data submittal package (containing the project deliverables as listed in Appendix A) was 

produced along with data description (on CD-ROM, DVDs, or external hard drive, depending on 

needed storage space) and delivered to TCEQ.
55

C. Assessment and Oversight

The following assessments were performed.

• Verified that the overall scope was met (consistent with the intended purpose, for 

specified temporal resolution and geographic coverage, for specified sources, pollutants, 

and emissions processes).

• Checked input data preparation, and model or utility execution instructions (e.g., run 

specifications, scripts, JCFs, command files) were prepared according to the plan; and

• Checked that correct output data were produced (includes interim output [output that 

becomes input to a subsequent step in the inventory development process], as well as the 

final product). Records were kept of the checks performed.

In the case that any inconsistencies or deficiencies were found, the issue was directly 

communicated to the responsible staff for corrections (or the outside agency staff involved, if 

provided from outside of TTI, if needed). After a correction was made, the QA checks were

performed again to ensure that the additional work resulted in the intended quality assured result, 

and the correction was noted in the QA record (process was performed until QA check was

satisfied).

Any major problem was reported to the project manager and communicated to the project 

team as needed, as well as when the various data elements in the process passed QA checks and 

were ready for further processing according to the project pre-analysis plan. The project 

manager ensured that all of the QA checks performed were compiled, and maintained in the 

project archives.

In addition, technical systems audits were performed as appropriate. Audits of data quality at 

the requisite 25% level were performed for any data collected or produced as part of this study. 

QA findings were reported in both the draft and the final reports.

D. Data Validation and Usability

Development of the detailed on-road mobile source emissions estimates is a multi-staged process 

that involves many data sets and data processing steps. In the interest of product quality and 

process efficiency, thorough quality assurance checks were performed during emissions 

inventory development.

Data for the project, whether provided for direct use or processed by TTI, were reviewed, 

verified, and validated to ensure that they conform to their particular specifications and TCEQ’s 

requirements for the intended use. The data specifications and requirements where not stated 

specifically, are included in the documents listed in the References section, or are outlined or 

referenced in the detailed pre-analysis plan.

The criteria for passing quality checks and the checks typically performed on each major 

inventory input component (i.e., estimates of source activity, activity distributions, and emissions 

factors) as well as on the resulting emissions estimates, are summarized in the following lists. 
56

These QA guidelines were used to ensure the development of emissions inventory estimates that 

are as accurate as possible and meet the requirements of TCEQ’s intended use.

Verified that the overall scope of the emissions analysis has been met as prescribed in the 

pre-analysis plan, to include:

• Purpose of the emissions analysis (e.g., photochemical modeling, reasonable further 

progress, transportation conformity, model comparison);

• Extent of the modeling domain (e.g., analysis years, geographic coverage, seasonal 

periods, days, sources, pollutants);

• Methods, models, and data used (e.g., default versus local input data sources); and

• Procedures and tools used and all required emissions output data sets were produced.

Performed checks on input data preparation, model or utility execution instructions (e.g., run 

specifications, scripts, JCFs, command files), and output, as appropriate to the component:

• Input data preparation checks:

o Verified the basis of input data sets against the pre-analysis plan: Actual historical or 

latest available data, validated model, expected values or regulated limits, regulatory 

program design, model defaults, surrogates, professional judgment; check 

aggregation levels.

o Data development: Depending on the procedure and particular input data set, 

calculations were verified (e.g., re-calculated independently and compared with 

originally prepared values – if spot-checking a series of results, included extremes 

and intermediate values).

o Completeness: Verified that input data sets were within the required dimensions, and 

all required fields were populated and properly coded or labeled.

o Format: Verified that formats were within required specifications (e.g., field 

positions, data types and formats, and file formats), if any.

o Reasonability checks: (discussed in the next section).

o Ensured that any inputs provided from external sources were quality assured, as listed 

previously.

• Checked the model or utility execution instructions:

o Verified that the correct number of utility or model run specifications were prepared 

for each application (e.g., by year, county, season, day type).

o Verified that each utility or model run script included the correct modeling 

specifications (e.g., commands, input values, input and output file paths, output 

options) for the application per applicable user guide.

• Checked for the successful completion of model and utility executions:

o Verified that the correct number of each type of output file was produced by the 

particular model or utility.
57

o Checked for any unusual output file sizes.

o Searched output (e.g., utility listing files or model execution logs that contain error 

and warning records) for warnings/errors.

o Checked the summary information provided in output listing files for any unusual 

results.

Performed further checks for consistency, completeness, and reasonability of data output 

from model or utility applications:

• Verified that the data distributions and allocation factors produced or used sum to 1.0, as 

appropriate (e.g., hourly travel factors within a time period, proportion of travel by 

vehicle categories on a particular roadway category).

• Verified that the required data fields were present, populated, and properly coded or 

labeled; verified that data and file formats were within specifications.

• Verified that any activity, emissions rate, or emissions adjustments were performed as 

intended (e.g., seasonal activity factor, emissions control program adjustment).

• For data sets prepared with temporal or geographic variation (e.g., activity distributions 

between weekends/weekdays, vehicle mix by day-type, or average speeds between road 

types or time periods), compared and noted whether directional differences were as 

expected.

• Checked for consistency between data sets (e.g., compared detailed spatially and 

temporally disaggregated activity estimates [e.g., link VMT] to original aggregate totals, 

activity total summaries between utility applications [e.g., link-VMT producer and 

emissions calculator], and input hourly distributions versus hourly summaries from the 

link activity output data).

• Calculated county, 24-hour, aggregate emissions rates (from aggregate VMT and 

emissions output) and compared the rates between counties examining the results for 

outliers while assessing the reasonability of any relative and directional differences (e.g., 

qualify based on activity distributions by road type and speed, mix of vehicles by road 

type, meteorological variation, control program coverage). Compared the results to 

results from previous emissions analyses if available.

• Calculated county, 24-hour aggregate rates by vehicle class and compared between 

vehicle classes. Examined the results for consistent patterns, e.g., between gasoline 

versus diesel, heavy versus light.

• Verified summed link emissions output against tabular emissions output summaries –

differences should be within rounding error (N/A).

Any additional data products required for the emissions analysis were subjected to the 

appropriate QA checks previously listed. Any issues found needing resolution were corrected 

and appropriate QA checks were performed until satisfied.
58

REFERENCES

Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National 

Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations, EPA-454/R-05-001. 

Issued by the Emissions Inventory Group, Emissions, Monitoring and Analysis Division, Office 

of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 27711. August 2005.

Draft Motor Vehicle Emission Simulator (MOVES) 2009, Software Design and Reference 

Manual. Assessment and Standards Division, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency, EPA420-B-09-007. March 2009.

Motor Vehicle Emissions Simulator (MOVES), User Guide Version MOVES2010b. 

Assessment and Standards Division, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency, EPA420-B-12-001. March 2012 (Update).

Using MOVES to Prepare Emission Inventories in State Implementation Plans and 

Transportation Conformity: Technical Guidance for MOVES2010, 2010a and 2010b. 

Transportation and Climate Division, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency, EPA420-B-12-028. April 2012 (Update).

Federal Register. Friday, April 30, 2004. Part II, Environmental Protection Agency, 40 CFR 

Parts 50, 51 and 81, [OAR 2003-0079, FRL-7651-7], RIN 2060-AJ99, Final Rule To Implement 

the 8-Hour Ozone National Ambient Air, Quality Standard–Phase 1.

Federal Register. Tuesday, November 29, 2005. Part II, Environmental Protection Agency, 40 

CFR Parts 51, 52, and 80 Final Rule To Implement the 8-Hour Ozone National Ambient Air 

Quality Standard. Final Rule.

Memorandum. U.S. Environmental Protection Agency, National Vehicle and Fuel Emissions 

Laboratory, Ann Arbor, Michigan, September 27, 2001, To Karl Edlund, Director, Multimedia 

Planning and Permitting Division, Region VI, From Robert Larson, Acting Director, 

Transportation and Regional Programs Division, Office of Transportation and Air Quality 

(OTAQ). RE: Texas Low Emission Diesel (LED) Fuel Benefits.

Memorandum. U.S. Environmental Protection Agency, Transportation and Climate Division, 

Office of Transportation and Air Quality, Washington, D.C., August 30, 2012, To Carl Edlund, 

Director, Multimedia Planning and Permitting Division, EPA Region 6, From Karl Simon, 

Director, Transportation and Climate Division, Office of Transportation and Air Quality. 

SUBJECT: Guidance on Implementing Clean Air Act Section 182(d)(1)(A): Transportation 

Control Measures and Transportation Control Strategies to Offset Growth in Emissions Due to 

Growth in Vehicle Miles Travelled.

Implementing Clean Air Act Section 182(d)(1)(A): Transportation Control Measures and 

Transportation Control Strategies to Offset Growth in Emissions Due to Growth in Vehicle 

Miles Travelled. Office of Transportation and Air Quality, U.S. Environmental Protection 

Agency, EPA420-B-12-053. August 2012.
59

Explanation of Special VMT Offset Version of MOVES. Office of Transportation and Air 

Quality, U.S. Environmental Protection Agency. (Note: this undated technical sheet was 

provided by the EPA along with the MOVES2010bROP model through the TCEQ, September 

2012.)

Revisions to the State of Texas Air Quality Implementation Plan for the Control of Ozone Air 

Pollution, Houston-Galveston-Brazoria 1997 Eight-Hour Ozone Standard Nonattainment Area. 

Texas Commission on Environmental Quality, Austin, Texas. Houston-Galveston-Brazoria 1997 

Eight-Hour Ozone Standard Nonattainment Area Motor Vehicle Emissions Budgets Update State 

Implementation Plan Revision. Project Number 2012-002-SIP-NR. Proposal, October 17, 2012.

EPA Releases MOVES2010b Mobile Source Emissions Model Update: Questions and Answers. 

Office of Transportation and Air Quality, U.S. Environmental Protection Agency, EPA420-F-12-

014, EPA. April 2012.

HGB MOVES-Based RFP On-Road Inventories and Control Strategy Reductions. Texas 

Transportation Institute, The Texas A&M University System, College Station, TX. March 2012.

Update Of On-Road Inventory Development Methodologies for Motor Vehicle Emissions 

Simulator (Moves) Model Compatibility Technical Report. Texas Transportation Institute, The 

Texas A&M University System, College Station, TX. July 2011.

Development and Production of 2006 Base Case and 2008 Baseline On-road Mobile Source 

Emissions Inventories for the HGB Nonattainment Area. Texas Transportation Institute, The 

Texas A&M University System, College Station, TX. July 2011.

Methodologies for Conversion of Data Sets for MOVES Model Compatibility. Texas 

Transportation Institute, The Texas A&M University System, College Station, TX. August 

2009.

Heavy-Duty Vehicle Idle Activity and Emissions Characterization Study. ERG. August 2004.

Energy Information Administration’s (EIA) Petroleum Marketing Annuals.

Revision to the State Implementation Plan Mobile Source Strategies, Texas Inspection and 

Maintenance State Implementation Plan. Texas Commission on Environmental Quality, Austin, 

TX. November 18, 2010.

HGB RFP SIP Revision for the 1997 Eight-Hour Ozone Standard. Texas Commission on 

Environmental Quality, Austin, TX. March 2010.

61

APPENDIX A:

HGB VMT OFFSET ON-ROAD INVENTORIES ELECTRONIC DATA 

SUBMITTAL

63

HGB VMT Offset MOVES2010bROP-Based, County-Level, Emissions Inventories –

Electronic Data Submittal

This appendix contains a description of the electronic data package that TTI submitted to TCEQ, 

per Proposal for Grant Activities No. 582-11-11226-FY13-08.

The MOVES rates-per-activity, TDM link-based method was used to produce ozone season 

weekday emissions inventories of VOC, CO, NOx, and CO2. The four HGB emissions 

inventories submitted consist of one base year and three attainment year scenarios (i.e., 32 

county-level emissions inventories), described by activity and emission factor components in the 

following table.

Table 29. HGB VMT Offset Demonstration Emissions Inventory Scenarios.

Inventory Activity 

Input Year Emissions Factor Input1

2002 Base Year 2002 2002 Base Year (CSS)

2018 Attainment Year Fleet and 

Pre-2003 Controls with Growth 2018 2018 Fleet and Pre-2003 

2018 Attainment Year Fleet and Controls (P03)

Pre-2003 Controls without Growth 2002

2018 Attainment Year Control 

Strategy 2018 2018 Fleet and Control Strategy (CSS) 1 Emissions factors were based on the EPA’s special model “MOVES2010bROP” provided to TCEQ 

for use in the VMT offset calculations. “Pre-2003 Controls” means attainment year emissions factors 

were modeled to reflect no new controls after the base year beyond those already credited.

Electronic Media

The electronic data submittal files and databases, summarized in the following, were compressed 

and submitted on one CD-ROM, entitled:

“HGB MOVES2010bROP- Based On-Road Mobile Source Emissions Inventories for VMT 

Offset Analysis – TTI FY2013.”

• Emissions Files:

o Emissions calculator output – MOVESemscalc utility TAB-delimited, hourly/24-hour 

emissions inventory report summary files;

o 24-hour aggregate inventory extracts – 24-hour totals summaries extracted from 

MOVESemscalc emissions inventory data files listed in previous bullet; and

o Emissions summary spreadsheet produced from inventory extracts listed in previous 

bullet, and used to produce inventory summaries in report;

• MOVES inputs (MOVES run specifications and county domain input databases); and

• Final, MOVES-based, rate-per-activity emissions factors (emissions factor database 

inputs to the emissions calculations, produced with TTI MOVES output post-processors), 

and TxLED adjustment factor file.
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File-Naming Conventions

In the file and database names:

• SSSS is: BYR, AYR (or inventory type where BYR and AYR are base year [2002] and 

attainment year [2018], respectively);

• YYYY: is 2002, 2018 (calendar or analysis year value for HGB base year and attainment 

year);

• QQQQ is: CSS, P03 (control scenario where CSS and P03 are analysis year control 

strategy scenario and pre-2003 control scenario, respectively);

• KKKK is: 2002, 2018 (year of VMT inputs to the inventory runs); and

• FFFF is: 48039, 48071.... (or county FIPS code for each of the eight counties).

Note: databases are MySQL databases. Each MySQL database consists of one “db.opt” file 

and a set of tables, where each table is composed of three files of the type: *.frm, *.MYD, and 

*.MYI.

Emissions Data Files

Emissions inventory output – Four MOVESemscalc utility emissions inventory runs per county 

(see the previous table for four inventory types). Each run produced two output file types (i.e., 

*.TAB and *.LST files) compressed in: “HGBvo_mvs10brop_4EIs.zip”:

• “HGBvo_mvs10brop_BYR_CSS2002swkd_FFFF_ems.TAB,” 

“HGBvo_mvs10brop_AYR_P03KKKKswkd_FFFF_ems.TAB,” and 

“HGBvo_mvs10brop_AYR_CSS2018swkd_FFFF_ems.TAB”: 32 tab-delimited county 

emissions inventory data summary reports including hourly and 24-hour activity and 

emissions tables. For roadway-based processes by roadway and vehicle type (SUT/Fuel 

Type) – VMT, VHT (vehicle hours traveled), average speed (VMT/VHT), and 

pollutant/process emissions totals; for off-network-based processes by vehicle type –

SHP, SHI, starts, and pollutant/process emissions totals (SHI for combination long-haul 

trucks only); and

• *.LST”: 32 list files corresponding to the above *.TAB files and with the same file name 

prefixes as the *.TABs, listing run execution times; run script; file locations; data 

descriptions; and varied data summaries including hourly and 24-hour activity, 

pollutant/process emissions totals, and average speed (VMT/VHT).

24-Hour aggregate inventory extracts and spreadsheet – 24-hour emissions inventory totals by 

SUT/fuel type extracted from the MOVESemscalc tab-file output, used for emissions summaries. 

Four county total and four county-level by SUT/fuel type inventory extracts were provided 

compressed in “HGBvo_mvs10brop_4EIs.zip”:

• “HGBvo_mvs10brop_BYR_CSS2002swkd_tabtots_ST.tab,” 

“HGBvo_mvs10brop_AYR_P03KKKKswkd_tabtots_ST.tab,” and 

“HGBvo_mvs10brop_AYR_CSS2018swkd_tabtots_ST.tab”: Four inventory type tab-
65

delimited text file of 24-hour emissions inventory data summaries for the eight HGB 

counties, by SUT/fuel type, pollutant, and process;

• “HGBvo_mvs10brop_BYR_CSS2002swkd_tabtots.tab,” 

“HGBvo_mvs10brop_AYR_P03KKKKswkd_tabtots.tab,” and 

“HGBvo_mvs10brop_AYR_CSS2018swkd_tabtots.tab”: Four inventory type tabdelimited text file of 24-hour emissions inventory data summaries for eight counties, by 

pollutant and process; and

• “HGBvo_mvs10brop_OverallSummaries.xls”: spreadsheet file of 24-hour emissions 

inventory data summaries for eight counties, by pollutant and process.

Emissions Factors – MOVES Input Files (MRSs and CDBs)

MOVES Input Files: There were three rate analysis year/control scenario combinations (the 

2002/CSS, 2018/P03, and 2018/CSS), for a total of 25 county level runs. The MOVES inputs 

used – run specification files (RunSpec or MRS) and county input databases (CDB) – were 

provided. (The modified MOVES default database made available with the MOVES2010bROP 

model by TCEQ, was not included in this submittal.):

• “MVS10BROP_HGBVO_2002SWKD_CSS_FFFF_ER.MRS,” and 

“MVS10BROP_HGBVO_2018SWKD_QQQQ_FFFF_ER.MRS”: 25 MRS files, i.e., 

three analysis year/control scenarios x eight counties + one extra Harris County 2002 

base year MRS file for I/M test-type switch effects post-processing procedure [Harris 

County 2002CSS has an “IM1” and an “IM2” run], compressed in 

“HGBvo_mvs10brop_mrs25.zip;” and

• “*_CDB_IN” (25 MySQL database folders containing 1,474 total files, where each 

database corresponds to one of the MRS files and the “*” in the database name is the 

same as the MRS filename prefix). Along with “CDBchecker” utility files (three files –

an LST and TAB output along with *.doc file listing of utility checks), the CDBs are 

compressed in “HGBvo_mvs10brop_CDB25.zip.”

Emissions Factors – Final, Adjusted MOVES2010bROP-based Emissions Rates

Final MOVES-based, Emissions Factor Look-up Tables: The MOVESratesAdj utility performed 

adjustments as specified (e.g., NOx TxLED effects and I/M test-type switch effects), and 

produced the final emissions rate look-up table inputs to the emissions calculations – MySQL 

databases containing two emissions rate lookup tables, ttirateperdistance for roadway-based 

emissions processes, and ttirateperactivity for the “off-network” processes. The ttirateperactivity 

table fields are: pollutantID, processID, hourID, sourceTypeID, fuelTypeID, rateperactivity. The 

ttirateperdistance table fields are: pollutantID, avgSpeedBinID, processID, hourID, roadTypeID, 

sourceTypeID, fuelTypeID, ratePerDistance.

• “MVS10BROP_HGBVO_2002SWKD_CSS_FFFF_ER_outratesadj,” and 

“MVS10BROP_HGBVO_2018SWKD_QQQQ_FFFF_ER_outratesadj”: (24 MySQL 

database folders containing 168 total files). This is the set of 2002 base year control 

strategy and 2018 control strategy and 2018 pre-2003 control scenario rates database 

look-up tables for all counties, used in the four emissions inventories. Each database 

contains the ttirateperdistance and ttirateperactivity emissions rate look-up tables used in 
66

the emissions calculations. They are compressed in 

“hgbvo_mvs10brop_MOVESratesadjDB24.zip;” and

• “tx_mvs10a_2018_txled_facts.txt” (TxLED adjustment factor file) and 

“48201im2002_hgbvo_adjfactspath.txt”(one I/M test-type switch factor file used for 

Harris County 2002 run) are compressed in “hgbvo_mvs10brop_adjfacts.zip.”

Note that the MOVES output databases were post-processed in two main steps to calculate 

the final emissions rates used in the external emissions calculations:

1) Rate Calculations: Using the TTI’s MOVESratesCalc utility, TTI calculated “rates-peractivity” as “emissions/activity” from the movesoutput (emissions) and

movesactivityoutput (activity) tables, performed unit conversions, and added two new 

tables to the MOVES output database: “ttirateperdistance” with grams/mile rates, and 

“ttirateperactivity” with grams/SHP, grams/SHI, and grams/start rates.

2) Final Rates Adjustments: Using TTI’s MOVESratesAdj utility, TTI-calculated rates were

extracted for only those pollutants needed in the emissions calculations. The I/M testtype switch effects adjustment (post-processing of rates from two runs with different I/M 

test-types into a single set of rates with the combined I/M test-type effects) was 

performed for the Harris County 2002 base year, and TxLED adjustments were applied to 

diesel vehicle NOx rates for the 2018 attainment year control strategy scenario, and the 

extracted and adjusted rate tables were placed in a separate database (by 

county/year/control scenario) for input to the emissions calculations.
67

APPENDIX B:

EMISSIONS ESTIMATION UTILITIES FOR MOVES-BASED EMISSIONS 

INVENTORIES

69

TTI EMISSIONS ESTIMATION UTILITIES FOR MOVES-BASED EMISSIONS 

INVENTORIES

The following is a summary of utilities developed by TTI (written in the Visual Basic 

programming language) for producing detailed, link-based, hourly, and 24-hour emissions

estimates for on-road mobile sources using the latest version of EPA’s MOVES model. These 

utilities produce inputs used with the MOVES model, make special adjustments to the emissions 

factors (when required), and multiply them with travel model link-based or HPMS-based (virtual 

link) activity estimates to produce emissions at user-specified temporal and spatial scales.

The main utilities for calculating hourly and 24-hour emissions using MOVES are 

TRANSVMT, VirtualLinkVMT, MOVESactivityInputBuild, MOVESpopulationBuild, 

MOVESfleetInputBuild, MOVESratesCalc, MOVESratesAdj, ShpExtIdleStartActBld, 

ExtIdleHrsCalc, MOVESemscalc, and MOVEStabcomb. The TRANSVMT and 

VirtualLinkVMT prepare the link VMT and speeds activity input. The 

MOVESactivityInputBuild, MOVESpopulationBuild, and MOVESfleetInputBuild utilities build 

inputs used in MOVES. The MOVESratesCalc utility calculates the emissions rates from the 

MOVES output in terms of grams per activity, rather than the grams per vehicle emissions rates 

produced by MOVES. The MOVESratesadj utility makes special adjustments to the emissions 

rates when required. The ShpExtIdleStartActBld utility builds the SHP and starts activity 

required to estimate emissions using the grams per activity emissions rates produced by the 

MOVESratesCalc utility. The ExtIdleHrsCalc utility builds the SHI activity required to estimate 

emissions using the grams per activity emissions rates produced by the MOVESratesCalc utility. 

The MOVESemscalc utility calculates emissions by hourly time periods, producing a tabdelimited summary file (including 24-hour totals) and hourly link emissions output files 

(optional). The MOVEStabcomb utility combines multiple MOVESemscalc tab-delimited 

output files into one regional tab-delimited output file.

A process flow diagram follows the utility descriptions.

TRANSVMT

The TRANSVMT utility post-processes TDMs to produce hourly, on-road vehicle, seasonal and 

day-of-week specific, directional link VMT, and speed estimates. The TRANSVMT utility 

processes a TDM traffic assignment by multiplying the link volumes by the appropriate HPMS, 

seasonal, or other VMT factors. Hourly factors are then used to distribute the link VMT to each 

hour in the day. The TTI speed model is used to estimate the operational time-of-day link speeds 

for each direction. Since intrazonal links are not included in the TDM, special intrazonal links 

are created and the VMT and speeds for these special links are estimated using the intrazonal 

trips from the trip matrix and the zonal radii. The link VMT and speeds produced by 

TRANSVMT are subsequently input to the EMSCALC utility for applying the MOVES-based

emissions factors.

VirtualLinkVMT

The VirtualLinkVMT utility post-processes county HPMS AADT VMT, centerline miles, and 

lane miles by functional classification and area type (from TxDOT’s annual RIFCREC report) to 

produce hourly, on-road vehicle fleet, seasonal, and day-of-week specific actual or projected 
70

VMT, and directional operational speed estimates. These estimated VMT and speeds are 

produced for up to 42 directional HPMS functional classification/area type combinations, or 

“links.” The VirtualLinkVMT utility was developed for use in areas that do not have TDM 

networks, as well as for emissions inventory applications for which network link-based detail is 

not required. The main inputs to VirtualLinkVMT are:

• County HPMS data sets, which include AADT VMT, centerline miles, and lane miles by 

HPMS area type and functional class;

• County-level VMT control totals;

• Hourly VMT distributions; and

• Speed model inputs to include volume/delay equation parameters adapted for HPMS, and 

free-flow speeds and lane capacities by HPMS functional classification and area type.

VirtualLinkVMT initially scales the county HPMS AADT VMT at the link level to the 

appropriate VMT (e.g., uses county-level VMT control total-to-AADT ratios to produce 

seasonal, day-of-week specific VMT). Hourly factors and directional split factors are applied to 

the adjusted VMT on each link to estimate the hourly, directional VMT (and volumes) by HPMS 

link. Congested speed models, each for the high- and low-capacity links, are used to estimate the 

hourly operational speeds by direction for each link. The operational speeds are based on V/Cderived directional delay (minutes/mile) applied to the estimated free-flow speeds for each link. 

The virtual link VMT and speeds produced using the VirtualLinkVMT utility are an input to the 

emissions calculation utility, EMSCALC.

MOVESactivityInputBuild

The MOVESactivityInputBuild utility builds the roadtypedistribution, hourvmtfraction, 

avgspeeddistribution, roadtype, hpmsvtypeyear, year, state, zone, zoneroadtype, 

monthvmtfraction, and dayvmtfraction MOVES input database tables using the link-based 

hourly VMT and speeds developed with the TRANSVMT or VirtualLinkVMT utility, the VMT 

mix, and the MOVES defaults. The primary inputs to this utility are:

• Link-based hourly VMT and speeds developed with the TRANSVMT or 

VirtualLinkVMT utility;

• County ID file which specifies the county number in the link-based hourly VMT and 

speeds for which the output will be calculated;

• Link/MOVES roadway type designations, which lists associations of the link roadway 

types/area type combination to the MOVES roadway types (same as used with the 

MOVESemscalc utility);

• VMT mix by MOVES roadway type, MOVES source type, and MOVES fuel type (same 

as used with the MOVESemscalc utility);

• Day ID, which specifies the MOVES day ID for calculating the output;

• Year ID, which specifies the year for calculating the output;
71

• Link/Ramp designations, which designates each link roadway type/area type combination 

to either ramp or non-ramp; and

• MOVES default database.

For each link in the link-based hourly VMT and speeds in which the county number matches 

the desired county ID, the link VMT is saved in a VMT summary array based on hour, link 

functional class, and link area type. The link VHT (link VMT/link speed) is saved in a VHT 

summary array based on hour, link functional class, link area type, and MOVES average speed 

bin ID (determined using the MOVES average speed bins and the link speed). The link VHT is 

also saved in a road type VHT array based on link functional class and link area type, and if the 

link is specified as ramp by the link/ramp designations specified by the user, then the VHT is 

also saved in the ramp segment of the road type VHT array.

A MOVES roadway type array (by MOVES roadway type) is also formed using the data in 

the VMT summary array and the link/MOVES roadway type designations. An hourly VMT 

array (by MOVES SUT, MOVES roadway type, and hour) is formed using the data in the VMT 

summary array, the link/MOVES roadway type designations, and the VMT mix. An average 

speed distribution array (by MOVES SUT, MOVES roadway type, hour, and MOVES speed bin) 

is created using the VHT summary array and the VMT mix. Using the appropriate MySQL 

code, the MOVES roadtypedistribution, hourvmtfraction, and avgspeeddistribution default 

values are extracted and saved for later use.

The VMT in the hour VMT array is aggregated by hour to produce the roadway type 

distribution array by MOVES SUT and MOVES roadway type. This VMT is then converted to a 

distribution by MOVES SUT (i.e., the total for a SUT over the five MOVES roadway types 

should equal 1), with the distribution value for MOVES roadway type 0 (Off-Network) equal to 

0. Using the appropriate MySQL code, the roadtypedistribution database table is written. A tabdelimited version is also written (optional).

The VMT in the hourly VMT array is added to the hourly VMT fraction array (by SUT, 

MOVES roadway type, and hour) and for those roadway types where the VMT for all hours is 

greater than 0, this VMT is converted to an hourly distribution. For those roadway types where 

the VMT is equal to 0, a value of 1 is placed in the first hour, followed by 0 in the remaining 

hours. Using the appropriate MySQL code, the hourvmtfraction database table is written. For 

those SUTs where the VMT mix is greater than 0, the hourly VMT fraction array is used. 

Otherwise, the MOVES hourvmtfraction default values are used. A tab-delimited version is also 

written (optional).

The VHT in the average speed distribution array is converted to a distribution by SUT, 

MOVES roadway type, hour/day (combination of hour and the day ID specified by the user), and 

MOVES average speed bin. Using the appropriate MySQL code, the avgspeeddistribution 

database table is written. For those SUTs where the VMT mix is greater than 0, the average 

speed distribution array is used. Otherwise, the MOVES avgspeeddistribution default values are 

used. A tab-delimited version is also written (optional).

The VHT in the road type VHT array is converted to a proportion of ramp VHT by dividing 

the ramp segment of the road type VHT array by the total VHT for the road type in the road type 
72

VHT. Using the appropriate MySQL code, the road type database table is written using the 

proportions from the road type VHT array. A tab-delimited version is also written (optional).

The VMT in the hourly VMT array is aggregated to form the HPMS vehicle type VMT 

array. Each SUT is assigned an HPMS vehicle type (SUT 11 is HPMS vehicle type 10; SUT 21 

is HPMS vehicle type 20; SUTs 31 and 32 are HPMS vehicle type 30; SUTs 41, 42, and 43 are 

HPMS vehicle type 40; SUTs 51, 52, 53, and 54 are HPMS vehicle type 50; and SUTs 61 and 62 

are HPMS vehicle type 60). Using the appropriate MySQL code, the hpmsvtypeyear database 

table is written using the VMT from the HPMS vehicle type VMT array, along with the user 

supplied year ID, the VMT growth factor (automatically set to “Null”), and the base year OffNetwork VMT (automatically set to 0). A tab-delimited version is also written (optional).

Using the appropriate MySQL code, the fuel year ID is extracted from the MOVES default 

year database table for the user-supplied year ID and the new year database table is written using 

the user-supplied year ID and the extracted fuel year ID. The “isbaseYear” data is written as 

well (automatically set to “Y”). A tab-delimited version is also written (optional).

The utility also produces two tab-delimited summary output files. A tab-delimited VMT 

summary is output by hour, link road type, and link area type for the user-specified county. A 

tab-delimited VHT summary is output by hour, link road type, link area type, and MOVES 

average speed bin for the user-specified county.

The utility outputs five other database tables (state, zone, zoneroadtype, monthvmtfraction, 

and dayvmtfraction) using the appropriate MySQL code and the user-supplied inputs. For the 

state database table, a new state database table is created and the data from the MOVES default 

state database table is copied to the new table for the state ID of 48. For the zone database table, 

a new zone database table is created and the data from the MOVES default zone data base table 

is copied to the new table for the county ID greater than 48000 and county ID less than 49000. 

The start allocation factors, idle allocation factors, and SHP allocation factors are all then 

replaced with values of 1 in the new table.

For the zoneroadtype database table, a new zoneroadtype database table is created and the 

data from the MOVES default zoneroadtype data base table is copied to the new table for the 

zone ID greater than 480000 and zone ID less than 490000. The SHO allocation factors are all 

then replaced with values of 1 in the new table. For the monthvmtfraction database table, a new 

monthvmtfraction database table is created and the data from the MOVES default 

monthvmtfraction database table is copied to the new database table and the month VMT 

fraction is set to 1 for the user-supplied month ID and 0 for all other months. For the 

dayvmtfraction database table, a new dayvmtfraction database table is created and the data from 

the MOVES default dayvmtfraction database table is copied to the new and the day VMT 

fraction is set to 1 for the user-supplied day ID and 0 for all other months.

MOVESpopulationBuild

The MOVESpopulationBuild utility builds the sourcetypeyear MOVES input database table and 

the source type/fuel type population input file used with the MOVESemscalc utility to estimate 

emissions using the VMT mix and the TxDOT registration data sets. The TxDOT registration 

data sets are three sets of registration data (an age registration data file, a gasoline trucks 
73

registration data file, and a diesel trucks registration data file) that list 31 years of registration 

data. The primary inputs to this utility are:

• County ID file, which specifies the county for which the output will be calculated;

• Age registration data file, which lists 31 years of registration data for the Passenger 

Vehicle, Motorcycles, Trucks <=6000, Trucks >6000 <=8500, Total Trucks <=8500, Gas 

Trucks >8500, Diesel Trucks >8500, Total Trucks >8500, and Total All Trucks vehicle 

categories;

• Gasoline trucks registration data file, which lists 31 years of registration data for the Gas 

> 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500, Gas > 26000, Gas > 

33000, Gas > 60000, and Gas Totals gasoline truck categories;

• Diesel trucks registration data file, which lists 31 years of registration data for the Diesel 

> 8500, Diesel > 10000, Diesel > 14000, Diesel > 16000, Diesel > 19500, Diesel > 

26000, Diesel > 33000, Diesel > 60000, and Diesel Totals diesel truck categories;

• VMT mix by TxDOT district, MOVES source type, and MOVES fuel type;

• TxDOT district name file, which specifies the VMT mix TxDOT district;

• MOVES default database;

• Population factor file (optional); and

• Year ID file (optional, only used if population factors are used), which specifies the year

for calculating the output.

For the desired county (from the county ID file), the age registration data (for the Passenger

Vehicle, Motorcycles, Trucks <=6000, Trucks >6000 <=8500, and Total Trucks <=8500 vehicle 

categories) are saved in an age registration data array. The gasoline truck registration data (for 

the Gas > 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500, Gas > 26000, Gas > 

33000, and Gas > 60000 gasoline truck categories) are saved in the gasoline truck section of the 

diesel/gasoline registration data array. The diesel truck registration data (for the Diesel > 8500, 

Diesel > 10000, Diesel > 14000, Diesel > 16000, Diesel > 19500, Diesel > 26000, Diesel > 

33000, and Diesel > 60000 diesel truck categories) are saved in the diesel truck section of the 

diesel/gasoline registration data array. The age registration data array and the diesel/gasoline

registration data array are combined to form the registration category data array (a total of seven 

categories for 31 years of data and the total) using the combinations in Table 30.
74

Table 30. Registration Categories.

Registration 

Category Vehicle Category Data Location

1 Passenger Vehicle

2 Motorcycles Age registration data array

3 Total Trucks <=8500

4 Diesel > 8500, Diesel > 10000, 

Diesel > 14000, Diesel > 16000

Diesel/gas registration data 

array

5 Diesel > 19500, Diesel > 26000, 

Diesel > 33000, Diesel > 60000

6 Gas > 8500, Gas > 10000, Gas > 

14000, Gas > 16000

7 Gas > 19500, Gas > 26000, Gas 

> 33000, Gas > 60000

The registration category data array is used to fill the SUT population array (by SUT and fuel 

type) for everything except the long-haul trucks. Each SUT/fuel type combination is assigned 

the total registrations from one or more of the registration categories in the registration category 

data array. Table 31 shows the SUTs and their associated registration category in the registration 

category data array.

Table 31. SUT/Registration Category Correlation.

SUT Registration Category

11 2

21 1

31, 32 3

41, 42, 43, 51, 52, 54 4 + 6

61 5 + 7

SUT population factors are calculated by SUT/fuel type using the data from the VMT mix 

input for all SUTs except motorcycles (SUT 11) and the long-haul trucks (SUTs 53 and 62) and 

saved in the SUT population factors array. For SUT 21, the fuel type VMT mix is divided by the 

total VMT mix for SUT 21. For SUT 31, the fuel type VMT mix is divided by the total VMT 

mix for SUTs 31 and 32. The same process applies to SUT 32. For SUT 41, the fuel type VMT 

mix is divided by the total VMT mix for SUTs 41, 42, 43, 51, 52, and 54. The same process 

applies to SUTs42, 43, 51, 52, and 54. For SUT 61, the fuel type VMT mix is divided by the 

total VMT mix for SUT 61.

For SUT 11, the source type population factor for fuel type 1 (gasoline) is set 1 with all other 

factors set to 0. For SUT 53, the SUT population factors by fuel type are calculated by dividing 
75

the fuel type VMT mix for SUT 53 by the fuel type VMT mix for SUT 52. For SUT 62, the 

SUT population factors by fuel type are calculated by dividing the fuel type VMT mix for SUT 

62 by the fuel type VMT mix for SUT 61, therefore creating a ratio of long-haul and short-haul 

trucks.

The SUT population factors and the population factor (if desired) are applied to the SUT 

population array for all SUTs except SUT 53 and 62. For SUT 53, the SUT population factors 

for SUT 53 are applied to the SUT population array for SUT 52. For SUT 62, the SUT 

population factors for SUT 62 are applied to the SUT population array for SUT 61.

Using the appropriate MySQL code, a new sourcetypeyear database table is created. The 

data in the SUT population array is aggregated by fuel type and used to fill the sourcetypeyear 

database table, along with the yearID, salesGrowthFactor, and migrationrate. For the yearID, the 

year of the registration data is used, unless a population factor is used, in which case the year 

from the year ID input is used. The salesGrowthFactor and migrationrate for each SUT is set to 

1. A text format of this database table is written by the utility as well. The source type/fuel type 

population input file used with the MOVESemscalc utility is written using the SUT population 

array.

MOVESfleetInputBuild

The MOVESfleetInputBuild utility builds the sourcetypeagedistribution database table and 

fuel/engine fraction inputs to MOVES using the TxDOT registration data sets and the MOVES 

default database tables. The TxDOT registration data sets are three sets of registration data (an 

age registration data file, a gasoline trucks registration data file, and a diesel trucks registration 

data file) that list 31 years of registration data. The primary inputs to this utility are:

• Age registration data file, which lists 31 years of registration data for the Passenger

Vehicles, Motorcycles, Trucks <=6000, Trucks >6000 <=8500, Total Trucks <=8500, 

Gas Trucks >8500, Diesel Trucks >8500, Total Trucks >8500, and Total All Trucks

vehicle categories;

• Gasoline trucks registration data file, which lists 31 years of registration data for the Gas 

> 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500, Gas > 26000, Gas > 

33000, Gas > 60000, and Gas Totals gasoline truck categories;

• Diesel trucks registration data file, which lists 31 years of registration data for the Diesel 

> 8500, Diesel > 10000, Diesel > 14000, Diesel > 16000, Diesel > 19500, Diesel > 

26000, Diesel > 33000, Diesel > 60000, and Diesel Totals diesel truck categories;

• SUT data sources input, which specifies the data source for each SUT to use when 

building the souretypeagedistribution database table;

• Fuel/engine fractions data sources input, which specifies the data source for each SUT to 

use when building the fuel/engine fractions;

• Default sourcetypeage distribution input;

• MOVES default database; and

• Year ID file (optional, only if year is not the registration data year as in a future year 

analysis), which specifies the year for calculating the output.
76

The SUT data sources input lists the data source for each SUT, either a single county, 

multiple counties, state, or MOVES default. As this input is processed, the utility keeps a list of 

the input sources. The same applies to the fuel/engine fractions, except data source inputs are 

only valid for source types 52, 53, and 61 (other are not valid due to data limitations and source 

type 62 are all considered diesel).

For each county (or state total) in the list of the input sources, the age registration data (for 

the Passenger Vehicle, Motorcycles, Trucks <=6000, Trucks >6000 <=8500, and Total Trucks 

<=8500 vehicle categories) are saved in an age registration data array. The gasoline truck 

registration data (for the Gas > 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500, 

Gas > 26000, Gas > 33000, and Gas > 60000 gasoline truck categories) are saved in the gasoline

truck section of the diesel/gasoline registration data array. The diesel truck registration data (for 

the Diesel > 8500, Diesel > 10000, Diesel > 14000, Diesel > 16000, Diesel > 19500, Diesel > 

26000, Diesel > 33000, and Diesel > 60000 diesel truck categories) are saved in the diesel truck 

section of the diesel/gasoline registration data array.

The age registration data array and the diesel/gas registration data array are combined to form 

the registration category data array (a total of seven categories for 31 years of data and the total) 

using the combinations in Table 32 (Registration Categories). The county is compared to the 

data sources for each SUT in the SUT data sources input. If the county is found for a given 

source type, then the 31 years of registration data from the source type’s corresponding category 

in the registration category data array are added to the SUT age distribution array. Table 33

shows the source types and their corresponding registration categories.

Table 32. SUTs/Registration Categories Correlation for SUT Age Distribution.

SUT Registration Category

11 2

21 1

31, 32 3

52, 53 4

61, 62 5

A similar process is followed for the fuel/engine fractions array. However, only SUTs 52, 

53, 61, and 62 are processed due to data limitations. The registration data are saved in the 

fuel/engine fractions array based on fuel type. Table 33 shows the SUTs and their corresponding 

registration categories.
77

Table 33. SUTs/Registration Categories Correlation for Fuel/Engine Fractions.

SUT Fuel Type Registration Category

52, 53

Diesel 4

Gasoline 6

61

Diesel 5

Gasoline 7

62

Diesel 5 + 7

Gasoline None – all are assumed diesel

After processing all of the counties, the data from the default sourcetypeage distribution input 

are processed and the data for the registration data year are saved in the default age distribution 

array. For each source type in which the registration data are to be used for the age distribution, 

the 31 years of registration data in the SUT age distribution array are converted to a distribution 

by dividing the source type yearly registration data by the source type total registration data. For 

each source type in which the defaults are to be used, the defaults values from the default age 

distribution array are copied to the SUT age distribution array.

The MOVES default fuel/engine fractions are extracted from the MOVES default database 

(using the appropriate code for MySQL) and saved in the default fuel/engine fractions array. For 

source types 52, 53, and 61, the source type yearly registration data in the fuel/engine fractions 

array are converted to fuel/engine fractions by dividing the yearly source type diesel registration 

data by the sum of the yearly source type diesel registration data and the yearly source type 

gasoline registration data.

If the year ID input is used, then these fuel/engine fractions are adjusted to match the year 

from the year ID input. If the year from the year ID input is greater than the registration data 

year, then the first fuel/engine fraction is extended to match the year from the year ID input and 

the appropriate number of years is dropped from the end of the fuel/engine fractions to maintain 

the appropriate distribution. If the year from the year ID input is less than the registration data 

year, then the last fuel/engine fraction is extended to match the year from the year ID input and 

the appropriate number of years is dropped from the beginning of the fuel/engine fractions to 

maintain the appropriate distribution. For source type 62, all of the fuel/engine fractions in the 

fuel/engine fractions array are set to a value of 1.

Using the appropriate MySQL code, a new sourcetypeagedistribution database table is 

created and the data from the SUT age distribution array, along with the year ID (either from the 

registration data or the year ID input), are used to fill the new database table. A text format of 

this database table may be written as well. Using the appropriate MySQL code, a new 

AVFTfuelengfraction database table is created and the data from the fuel/engine fractions array 

are used to fill the new database table for SUTs 52, 53, 61, and 62. For all other SUTs, the 

default fuel/engine fraction array data for the appropriate year (either the registration data year or 

the year ID input) are used to fill the new database table. A text format of this database table 

may be written as well.
78

MOVESratesCalc

The MOVESratesCalc utility estimates emissions rates in terms of grams per activity (i.e., grams 

per mile, grams per SHP, grams per start, and grams per SHI) using the data in the movesoutput 

(emissions output) and movesactivityoutput (activity output) database tables produced by a 

MOVES emissions rate run. The utility also has the option of calculating the SHP, starts, and 

SHI activity per vehicle using the movesactivityoutput database table. If not specified, emissions 

rates are calculated for each pollutant and process combination (excluding total energy) in the 

movesoutput database table. The utility also uses the movesrun database table to determine the 

units of the emissions in the movesoutput table, which will then be converted to grams per 

activity during the emissions rate calculations; therefore allowing the user to specify any of the 

units available in MOVES for the MOVES emissions rate run. The type of activity used for the 

emissions rate calculation is determined by the process, as Table 34 shows.

Table 34. MOVES2010a Emissions Process and Corresponding Activity for Grams per 

Activity Emissions Rates.

MOVES2010a Emissions Process Activity Emissions Rate Units

Running Exhaust Miles Traveled Grams/Mile

Crankcase Running Exhaust Miles Traveled Grams/Mile

Start Exhaust Starts Grams/Start

Crankcase Start Exhaust Starts Grams/Start

Extended Idle Exhaust Extended Idle Hours Grams/Extended Idle Hour

Crankcase Extended Idle Exhaust Extended Idle Hours Grams/Extended Idle Hour

Evaporative Permeation Miles Traveled

Source Hours Parked

Grams/Mile

Grams/ Source Hour Parked

Evaporative Fuel Vapor Venting Miles Traveled

Source Hours Parked

Grams/Mile

Grams/ Source Hour Parked

Evaporative Fuel Leaks Miles Traveled

Source Hours Parked

Grams/Mile

Grams/ Source Hour Parked

Brake Wear Miles Traveled Grams/Mile

Tire Wear Miles Traveled Grams/Mile

For the distance based emissions rates (i.e., grams per mile), the utility extracts the emissions 

data (by pollutant, pollutant process, roadway type, average speed bin, SUT, and fuel type) from 

the movesoutput database for the specified pollutants (or all of the pollutants if not specified) and 

the corresponding miles traveled activity (MOVES activityTypeID = 1) from the 

movesactivityoutput database table. The utility divides the emissions data by the corresponding 

activity data, applies the appropriate units conversion factors and saves the emissions rates in the 

ttirateperdistance database table.
79

For the off-network emissions rates (i.e., grams per SHP, grams per start, and grams per SHI) 

the utility calculates the emissions rates based on the pollutant process. For processID = 12 

(evaporative fuel vapor venting), the utility extracts the emissions data (by hour, pollutant, 

pollutant process, source type, and fuel type) from the movesoutput database table for the 

specified pollutants pollutants (or all of the pollutants if not specified) where roadTypeID = 1 

(“off-network” emissions) and the corresponding SHP activity (activityTypeID = 5) from the 

movesactivityoutput database table. The utility divides the emissions data by the corresponding 

activity data, applies the appropriate units conversion factor and saves the emissions rates in the 

ttirateperactivity database table.

Using the same calculation process, the utility also calculates the emissions rates for 

processID = 17 (crankcase extended idle exhaust) and processID = 90 (extended idle exhaust) 

using the corresponding SHI activity (activityTypeID = 3), for processID = 11 (evaporative 

permeation) and processID = 13 (evaporative fuel leaks) using the corresponding SHP activity 

(activityTypeID = 5), and for processID = 2 (start exhaust) and processID = 16 (crankcase start 

exhaust) using the corresponding starts activity (activityTypeID = 7).

For the SHP, starts, and SHI activity per vehicle, the utility extracts the SHP, starts, SHI, and 

population activity data (by hour, SUT, and fuel type) from the movesactivityoutput database 

table. The utility divides the SHP activity by the population and saves the SHP per vehicle in the 

ttiactpervehicle database table. Using the same calculation process, the utility also calculates the 

starts per vehicle and SHI per vehicle using the starts and SHI activity data.

MOVESratesAdj

The MOVESratesAdj utility applies emissions rate adjustments to an emissions rate database 

table produced by MOVES (rateperdistance, ratepervehicle, rateperprofile), the 

MOVESratesCalc utility (ttirateperdistance, ttirateperactivity) or by this utility to produce a new 

emissions rate database table in the same format as the input emissions rate database table. The 

emissions rate adjustments can be linear adjustments that are applied to all emissions rates or can 

be applied by SUT, fuel type, pollutant, and process (adjustments may also include roadway 

type, average speed bin, and hour). The user has the option of selecting which pollutants will be 

in the new emissions rate database table. Otherwise, all of the pollutants in the input emissions 

rate database table will be in the new emissions rate database table. The utility also has the 

option for combining multiple emissions rate database tables into one new emissions rate 

database table, provided that the input emissions rate database tables are in the same format.

For the first input emissions rate database table, the utility extracts the emissions rates for the 

specified pollutants (or all the pollutants if not specified) from the input database emissions rate 

table, applies the emissions rate adjustments (if necessary) and saves these adjusted emissions 

rates. If more than one emissions rate database table is input, then the utility performs a similar 

calculation process to the first input emissions rate database table for each input emissions rate 

database table. If pollutants are found in more than one input emissions rate database table, the 

adjusted emissions rates are summed to produce one emissions rate.

After processing all of the input emissions rate database tables, the utility creates a new 

emissions rate database table in the same format as the first input emissions rate database table 

and writes the adjusted emissions rates to this new emissions rate database table. A tab-
80

delimited form of this database table may also be output that includes the text description of the 

pollutant, process, and roadway type instead of the MOVES codes that are included in the 

database tables.

ShpExtIdleStartActBld

The ShpExtIdleStartActBld utility calculates the SHP and starts activity by hour, SUT, and fuel 

type used to estimate emissions using the MOVESratesCalc emissions rates. The SHP is 

calculated using the either the TDM or virtual link based link VMT and speeds (same as used in 

the distance-based emissions estimation), the VMT mix (by roadway type), and the SUT/fuel 

type population (from the MOVESpopulationBuild). The starts activity is calculated using the 

SUT/fuel type population and the starts per vehicle (typically from the ttiactpervehicle database 

table created by the MOVESratesCalc utility). The utility also has the option of calculating the 

SHI activity used to estimate emissions using the MOVESratesCalc emissions rates. However, 

this method of estimating the extended idle hours is a direct function of the SHO and does not 

consider the availability of locations where extended idling may occur. The suggested method 

for estimating the SHI is discussed in the “ExtIdleHrsCalc” section.

For each link in the first VMT and speeds input with the desired county code, the utility 

applies the appropriate VMT mix to distribute the link VMT to each SUT/fuel type, which is 

added to the hourly SUT/fuel type VMT. The link VMT by SUT/fuel type is divided by the link 

speed to calculate the link VHT (or SHO) by SUT/fuel type, which is added to the SUT fuel/type 

VHT. This calculation process is repeated for each VMT and speeds input; therefore producing 

24-hourly values for VMT by SUT/fuel type and for VHT by SUT/fuel type.

The hourly SUT/fuel type speed, total hours (or source hours), and SHP are then calculated. 

For each hour and SUT/fuel type, the hourly SUT/fuel type VMT is divided by the hourly 

SUT/fuel type VHT to calculate the hourly SUT/fuel type speed. The hourly SUT/fuel type total 

hours are set equal to the SUT/fuel type population. The hourly SUT/fuel type SHP by are 

calculated subtracting the hourly SUT/fuel type VHT (or SHO) from the hourly SUT/fuel type 

total hours.

The hourly SUT/fuel type starts are calculated using the hourly starts per vehicle and the 

SUT/fuel type population. For each hour, the hourly SUT/fuel type starts are calculated by 

multiplying the hourly starts per vehicle by the SUT/fuel type population.

The hourly SUT/fuel type SHIs are calculated for source type 62, fuel type 2 (CLhT_Diesel) 

only. The CLhT_Diesel 24-hour SHI is calculated by multiplying the CLhT_Diesel 24-hour 

VHT (from the SHP calculation process) by the user-supplied extended idle factor, which

represents the amount of extended idle time that must occur per SHO. The hourly CLhT_Diesel 

VHT (from the SHP calculation process) is converted to hourly VHT fractions. The hourly SHI 

fractions are calculated as the inverse of the hourly VHT fractions. The hourly SHI fractions are 

then applied to the CLhT_Diesel 24-hour SHI to calculate the hourly SUT/fuel type SHI.

ExtIdleHrsCalc

The ExtIdleHrsCalc utility calculates the SHI activity by hour for SUT 62, fuel type 2 

(CLhT_Diesel) used to estimate emissions using the MOVESratesCalc emissions rates. This 

hourly SHI is calculated using a 24-hour base SHI for a specific year and day type, base link 
81

VMT and speeds, base VMT mix, future link VMT and speeds, future VMT mix, and the future 

tab-delimited hourly SHP by SUT/fuel type. All of the base data should be from the same year 

and day-type. Although the term future data is used, the future data can be a year previous to the 

base data (i.e., historical year) and should be from the same year and day type. The tabdelimited hourly SHP by SUT/fuel type is typically the output from the ShpExtIdleStartActBld

utility.

For each link in the first base VMT and speeds input with the desired county code, the utility 

applies the appropriate base VMT mix for CLhT_Diesel to the link VMT to calculate the link 

CLhT_Diesel VMT, which is added to the hourly CLhT_Diesel VMT. The link CLhT_Diesel 

VMT is divided by the link speed to calculate the link CLhT_Diesel VHT, which is added to the 

hourly CLhT_Diesel VHT. This calculation process is repeated for each base VMT and speeds 

input; therefore producing 24-hourly values for base CLhT_Diesel VMT and for base 

CLhT_Diesel VHT by SUT/fuel type. The same calculation process is performed for the future 

data (future VMT and speeds, future VMT mix) to calculate the hourly future CLhT_Diesel 

VMT and the hourly future CLhT_Diesel VHT.

The 24-hour future SHI is calculated using the 24-hour base CLhT_Diesel VMT, the 24-hour 

future CLhT_Diesel VMT, and the 24-hour base SHI. The scaling factor is calculated by the 

dividing the 24-hour future CLhT_Diesel VMT by the 24-hour base CLhT_Diesel VMT. The 

scaling factor is multiplied by the 24-hour base SHI to estimate the 24-hour future SHI.

The 24-hour future SHI is distributed to each hour using SHI hourly factors. The SHI hourly 

factors are calculated using the hourly CLhT_Diesel VMT. The hourly CLhT_Diesel VMT is 

converted hourly CLhT_Diesel VMT fractions. The SHI hourly factors are calculated by taking 

the inverse of the hourly CLhT_Diesel VMT fractions (i.e., more VMT implies less SHI). The 

SHI hourly factors are applied to the 24-hour future SHI to calculate the initial hourly future 

SHI, therefore distributing the 24-hour future SHI to each hour of the day.

To form the final SHI activity by hour, the initial hourly future SHI is compared to the hourly 

CLhT_Diesel SHP. If the initial hourly future SHI is greater than the hourly CLhT_Diesel SHP, 

then the final SHI activity for that hour is set to the hourly CLhT_Diesel SHP. Otherwise, the 

final SHI activity for that hour is set to the initial hourly future SHI. This comparison is 

performed for each hour.

MOVESemscalc

The MOVESemscalc utility estimates the hourly link emissions for one user-specified county 

using the emissions factors (either from MOVES, MOVES ratesCalc or MOVESratesAdj), the 

VMT mix, the hourly link VMT and speeds activity estimates (either from TRANSVMT or 

VirtualLinkVMT), and the off-network activity (either vehicle population or SHP, starts, and 

SHI). This utility produces a tab-delimited output summary (including hourly and 24-hour 

totals) and hourly link emissions output files (optional). The primary inputs to MOVESemscalc 

are:

• Emissions factors from MOVES, MOVES ratesCalc or MOVESratesAdj;
82

• Link-based hourly VMT and speeds developed with the TRANSVMT or 

VirtualLinkVMT utility. For each link, the following information is input to 

MOVESemscalc: link start node, link end node, link county number, link roadway type 

number, link area type number, link VMT, and link operational speed estimate;

• VMT mix by MOVES roadway type, MOVES SUT, and MOVES fuel type;

• Off-network activity. If the emissions factors are in the MOVES format, vehicle 

population by SUT/fuel type is required. If the emissions factors are in the 

MOVESratesCalc format, the SHP, starts, SHI by hour, and SUT/fuel type are required; 

and

• Link/MOVES roadway type designations, which associates the link roadway/area type 

combination to the appropriate MOVES roadway type.

The emissions estimation can be categorized by two basic types based on the type of 

emissions factors: the VMT-based emissions and the off-network emissions. For the VMTbased emissions (rateperdistance or ttirateperdistance emissions factors), the VMT for each link 

is distributed to each of the SUT/fuel type combinations listed in the VMT mix by MOVES 

roadway type (as designated in the link/MOVES roadway type designations). The emissions 

factors are selected based on the MOVES roadway type and the link speed for each SUT/fuel 

type combinations listed in the VMT mix. For link speeds greater than 75 mph, the emissions

factors for 75 mph are used. For link speeds less than 2.5 mph, the emissions factors for 2.5 mph 

are used. For those link speeds that fall between the 16 MOVES speeds, the emissions factors 

are interpolated using the emissions factor interpolation methodology in the following section.

These SUT/fuel type combination-specific emissions factors are multiplied by the SUT/fuel type 

combination-specific VMT to estimate the mobile source emissions for that link by SUT/fuel 

type combination.

The off-network emissions calculation depends on the format of the input emissions factors. 

If the emissions factors are in the MOVES format (rateperprofile and ratepervehicle), the 

emissions factors by SUT/fuel type are multiplied by their associated vehicle population to 

estimate emissions. If the emissions factors are in the MOVESratesCalc format, the emissions 

factors by SUT/fuel type are multiplied by the appropriate activity, which is determined by the 

pollutant process (see Table 34).

The emissions estimates are output in a tab-delimited file (including all of the SUT/fuel type 

combinations listed in the VMT mix on a single line, separated by a tab character) for the 

specified county by pollutant, roadway type, and SUT/fuel type combination for each of the 

specified episode time periods. A 24-hour (or total if all 24 hours are not specified) output is 

also included in the tab-delimited file. This tab-delimited file also includes hourly and 24-hour 

summaries of the off-network activity and VMT, VHT, and speed by roadway. Link emissions 

may also be output by county, pollutant, process, and each SUT/fuel type combination.

Emissions Factor Interpolation Methodology

To calculate emissions factors for link speeds that fall between two of the 16 MOVES speed bin 

speeds, an interpolation methodology similar to the methodology used with MOBILE6 is used. 

This methodology interpolates each emissions factor using a factor developed from the inverse 
83

link speed and the inverse high and low bounding speed bin speeds. The following is an 

example for a link speed of 41.2 mph.

The interpolated emissions factor (EFInterp) is expressed as:

EFInterp = EFLowSpeed - FACInterp Η (EFLowSpeed - EFHighSpeed )

Where:

EFLowSpeed = emissions factor (EF) corresponding to the speed below the link speed;

EFHighSpeed = EF corresponding to the speed above the link speed; and

FACInterp = 1 1 1 1

Speedlink Speedlow Speedhigh Speedlow

− 



 



 − 



 





Given that:

EFLowSpeed = 0.7413 g/mi;

EFHighSpeed = 0.7274 g/mi;

Speedlnk = 41.2 mph;

Speedlow = 40 mph; and

Speedhigh = 45 mph.

FACInterp = 1

412

1

40

1

45

1

. mph mph mph 40mph − 



 



 − 



 



 = −

−

0 00073

0 00278

.

. = 0.26214;

EFInterp = 0.7413 g/mi - (0.26214) Η (0.7413 g/mi - 0.7274 g/mi);

= 0.7377 g/mi.

MOVEStabcomb

The MOVEStabcomb utility combines the tab-delimited output from multiple runs of the 

MOVESemscac utility to produce a regional summary of the VMT, VHT, speed (VMT/VHT), 

off-network activity, and emissions for each hour along with a 24-hour summary. A maximum 

of 1,000 tab files can be combined in one run of the MOVEStabcomb utility. However, each tab 

file must have the same roadway types or the utility will not function properly.

The new regional tab-delimited file is in the same form as the individual county files taken as 

input, except that each time period includes the individual county data summaries plus the 

regional summary. The individual county summaries are taken directly from the input tabdelimited files. The regional data summaries are summations of the county data except for 

“speed,” which is calculated as regional VMT/regional VHT.
84

START

Transportation

Network Data

Set

TRANSVMT

Time Period Directional

VMT and Speed

for Each Link

VMT Mix

Adjusted Hourly

Emissions Factors

MOVESratesadj

VMT Factors,

Capacity Factors, and

Speed Model Parameters

Hourly MOVES Emissions Factors

MOVES Road-Type

Emissions Factor-to-Network

Functional Class Definitions

Travel Demand Model Network Link-Based

Hourly MOVES Emissions Estimates

Hourly Link

Emissions

Records

End

Tabulated Hourly and

Total County Emissions

Summaries

MOVEStabcomb

Tabulated Hourly and

Total County and

Regional EI Summaries

1

2

3

 MOVESactivityInputBuild,

 MOVESfleetInputBuild,

 MOVESmetInputBuild, and

 MOVESpopulationBuild.

 Tabulated EI summaries for other counties

 from additional MOVESemscalc runs (same

 functional class system required for combining).

 MOVESpopulationBuild,

 ShpExtIdleStartActBld,

 ExtIdleHrsCalc

Tabulated Hourly

and Total County EI

Summaries2

MOVES Input

Builders1

Off-Network

Activity3

MOVES

Inputs

MOVESemscalc

MOVES

Emissions and

Activity MOVESratesCalc
85

APPENDIX C:

TXDOT DISTRICT WEEKDAY VMT MIX BY MOVES ROAD TYPE

87

TxDOT District/HGB Counties

TxDOT District HGB County

Beaumont

Liberty

Chambers

Houston

Harris

Galveston

Fort Bend

Brazoria

Montgomery

Waller

VMT Mix Year/Analysis Year Correlations

VMT Mix Year Analysis Years

2000 1998 through 2002

2005 2003 through 2007

2010 2008 through 2012

2015 2013 through 2017

2020 2018 through 2022
88

2000 Weekday VMT Mix by TxDOT District (2002 Analysis Year)

SUT/FT

Beaumont Houston

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5

21_D 0.00218 0.00228 0.00232 0.00258 0.00199 0.00191 0.00223 0.00216

21_G 0.54312 0.56709 0.57688 0.64189 0.66078 0.63605 0.74264 0.71796

31_D 0.00248 0.00328 0.00263 0.00333 0.00244 0.00299 0.00216 0.00257

31_G 0.15223 0.20141 0.16175 0.20477 0.14991 0.18374 0.13311 0.15782

32_D 0.00449 0.00594 0.00477 0.00603 0.00442 0.00541 0.00392 0.00465

32_G 0.04708 0.06229 0.05003 0.06333 0.04637 0.05683 0.04117 0.04881

51_D 0.00131 0.00157 0.00117 0.00111 0.00080 0.00109 0.00069 0.00082

51_G 0.00033 0.00040 0.00030 0.00028 0.00041 0.00056 0.00035 0.00042

52_D 0.03591 0.04302 0.03215 0.03053 0.02090 0.02822 0.01797 0.02124

52_G 0.00920 0.01103 0.00824 0.00783 0.01067 0.01441 0.00917 0.01085

53_D 0.00173 0.00207 0.00155 0.00147 0.00230 0.00310 0.00197 0.00233

53_G 0.00044 0.00053 0.00040 0.00038 0.00117 0.00158 0.00101 0.00119

54_D 0.00188 0.00225 0.00168 0.00160 0.00116 0.00156 0.00099 0.00118

54_G 0.00048 0.00058 0.00043 0.00041 0.00059 0.00080 0.00051 0.00060

41_D 0.00188 0.00176 0.00164 0.00128 0.00090 0.00099 0.00121 0.00127

42_D 0.00072 0.00067 0.00062 0.00049 0.00034 0.00038 0.00046 0.00048

42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

43_D 0.00215 0.00202 0.00188 0.00147 0.00103 0.00114 0.00138 0.00145

43_G 0.00002 0.00002 0.00002 0.00001 0.00001 0.00001 0.00001 0.00001

61_D 0.06570 0.03125 0.05171 0.01047 0.04660 0.02931 0.01915 0.01174

61_G 0.00738 0.00351 0.00581 0.00118 0.00362 0.00227 0.00149 0.00091

62_D 0.11873 0.05647 0.09345 0.01891 0.04295 0.02701 0.01765 0.01082

62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

11_G 0.00055 0.00057 0.00058 0.00065 0.00066 0.00064 0.00075 0.00072
89

2020 Weekday VMT Mix by TxDOT District (2018 Analysis Year)

SUT/FT

Beaumont Houston

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5

21_D 0.00164 0.00171 0.00174 0.00193 0.00199 0.00191 0.00223 0.00216

21_G 0.54366 0.56766 0.57746 0.64254 0.66078 0.63605 0.74264 0.71796

31_D 0.00866 0.01146 0.00921 0.01165 0.00868 0.01064 0.00771 0.00914

31_G 0.14604 0.19323 0.15517 0.19645 0.14367 0.17608 0.12756 0.15124

32_D 0.00346 0.00457 0.00367 0.00465 0.00340 0.00417 0.00302 0.00358

32_G 0.04811 0.06366 0.05112 0.06472 0.04738 0.05807 0.04207 0.04988

51_D 0.00037 0.00044 0.00033 0.00031 0.00023 0.00031 0.00019 0.00023

51_G 0.00009 0.00011 0.00008 0.00008 0.00012 0.00016 0.00010 0.00012

52_D 0.03681 0.04410 0.03295 0.03130 0.02142 0.02893 0.01841 0.02177

52_G 0.00943 0.01130 0.00844 0.00802 0.01093 0.01477 0.00940 0.01112

53_D 0.00177 0.00213 0.00159 0.00151 0.00235 0.00318 0.00202 0.00239

53_G 0.00045 0.00054 0.00041 0.00039 0.00120 0.00162 0.00103 0.00122

54_D 0.00188 0.00225 0.00168 0.00160 0.00116 0.00156 0.00099 0.00118

54_G 0.00048 0.00058 0.00043 0.00041 0.00059 0.00080 0.00051 0.00060

41_D 0.00199 0.00186 0.00173 0.00136 0.00095 0.00105 0.00128 0.00134

42_D 0.00057 0.00054 0.00050 0.00039 0.00027 0.00030 0.00037 0.00039

42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

43_D 0.00219 0.00205 0.00191 0.00149 0.00104 0.00115 0.00140 0.00148

43_G 0.00002 0.00002 0.00002 0.00002 0.00001 0.00001 0.00001 0.00001

61_D 0.06570 0.03125 0.05171 0.01047 0.04660 0.02931 0.01915 0.01174

61_G 0.00738 0.00351 0.00581 0.00118 0.00362 0.00227 0.00149 0.00091

62_D 0.11873 0.05647 0.09345 0.01891 0.04295 0.02701 0.01765 0.01082

62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

11_G 0.00055 0.00057 0.00058 0.00065 0.00066 0.00064 0.00075 0.00072

91

APPENDIX D:

TXDOT DISTRICT AGGREGATE WEEKDAY VMT MIX

93

TxDOT District/HGB Counties

TxDOT District HGB County

Beaumont

Liberty

Chambers

Houston

Harris

Galveston

Fort Bend

Brazoria

Montgomery

Waller

VMT Mix Year/Analysis Year Correlations

VMT Mix Year Analysis Years

2000 1998 through 2002

2005 2003 through 2007

2010 2008 through 2012

2015 2013 through 2017

2020 2018 through 2022
94

2000 Aggregate Weekday VMT Mix by TxDOT District (2002 Analysis Year)

SUT/FT Beaumont Houston

21_D 0.002280 0.002200

21_G 0.568760 0.732370

31_D 0.002820 0.002240

31_G 0.173240 0.137890

32_D 0.005110 0.004060

32_G 0.053580 0.042650

51_D 0.001320 0.000720

51_G 0.000340 0.000370

52_D 0.036230 0.018810

52_G 0.009280 0.009610

53_D 0.001750 0.002070

53_G 0.000450 0.001060

54_D 0.001890 0.001040

54_G 0.000490 0.000530

41_D 0.001720 0.001190

42_D 0.000660 0.000450

42_G 0.000000 0.000000

43_D 0.001970 0.001360

43_G 0.000020 0.000010

61_D 0.047110 0.020320

61_G 0.005290 0.001580

62_D 0.085140 0.018730

62_G 0.000000 0.000000

11_G 0.000570 0.000740
95

2020 Aggregate Weekday VMT Mix by TxDOT District (2018 Analysis Year)

SUT/FT Beaumont Houston

21_D 0.001710 0.002200

21_G 0.569330 0.732370

31_D 0.009860 0.007990

31_G 0.166190 0.132140

32_D 0.003930 0.003130

32_G 0.054750 0.043580

51_D 0.000370 0.000200

51_G 0.000090 0.000100

52_D 0.037130 0.019280

52_G 0.009520 0.009850

53_D 0.001790 0.002120

53_G 0.000460 0.001080

54_D 0.001890 0.001040

54_G 0.000490 0.000530

41_D 0.001820 0.001260

42_D 0.000520 0.000360

42_G 0.000000 0.000000

43_D 0.002000 0.001380

43_G 0.000020 0.000010

61_D 0.047110 0.020320

61_G 0.005290 0.001580

62_D 0.085140 0.018730

62_G 0.000000 0.000000

11_G 0.000570 0.000740

97

APPENDIX E:

CAPACITY FACTORS, SPEED FACTORS, AND SPEED REDUCTION 

FACTORS

99

Capacity Factors

Time of Day Assignment Capacity Factor1

AM Peak 0.3333333

Mid-Day 0.1666667

PM Peak 0.2500000

Overnight 0.0909091

Freeflow (Volume = 1) Speed Factors for Houston/Galveston Speed Model

Functional Group

Area Type

CBD Urban Urban Fringe Suburban Rural

Freeways, Interstates 1.147400 1.177508 1.157272 1.197842 1.137903

Principal Arterials 1.136702 0.860413 0.887801 1.002511 1.018546

Other Arterials, Major 

Collectors 1.145946 0.811981 0.813370 0.813370 1.112661

Collectors 1.235367 0.857297 0.889344 0.974734 1.033400

Toll Roads 1.054545 1.100000 1.081379 1.057097 1.077846

Ramps 1.235367 0.857297 0.889344 0.974734 1.033400

Locals 1.000000 1.000000 1.000000 1.000000 1.000000

 1 To obtain hourly capacities, a single capacity factor for each time-of-day assignment is used for all area types and 

function classes.
100

LOS E (V/C = 1.0) Speed Factors for Houston/Galveston Speed Model

Functional Group

Area Type

CBD Urban Urban Fringe Suburban Rural

Freeways, Interstates 0.700000 0.768691 0.737693 0.715108 0.645161

Principal Arterials 0.703191 0.578709 0.633464 0.750628 0.458973

Other Arterials, Major 

Collectors 0.677477 0.560596 0.613733 0.870193 0.517909

Collectors 0.727049 0.567228 0.642497 0.761987 0.554081

Toll Roads 0.636364 0.636364 0.689705 0.645161 0.615385

Ramps 0.727049 0.567228 0.642497 0.761987 0.554081

Locals 1.000000 1.000000 1.000000 1.000000 1.000000
101

Functional Classification to Functional Group Relationship

for the Application of and Speed Factors

Functional Group Corresponding Network Functional 

Classifications

Freeways, Interstates

1. Urban Interstate Freeways

2. Urban Other Freeways

10. Rural Interstate Freeways

11. Rural Other Freeways

Principal Arterials 5. Urban Principal Arterials

12. Rural Principal Arterials

Other Arterials, Major 

Collectors

6. Urban Other Arterials

13. Rural Other Arterials

14. Rural Major Collectors

Collectors 7. Urban Collectors

15. Rural Collectors

Toll Roads 3. Toll Roads

Ramps 4. Ramps

Locals 8. Locals (Centroid Connectors)

40. Locals (Intrazonals)
102

Figure 1. Freeway Speed Reduction Factors by V/C Ratio.

Speed Reduction Factors by V/C Ratio

Functional Group 1 (Freeways)

CBD Urban Urban Fringe

Suburban Rural

V/C Ratios

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

0.00

0.20

0.40

0.60

0.80

1.00

Speed Reduction F

actors
103

Figure 2. Principal Arterial Speed Reduction Factors by V/C Ratio.

Speed Reduction F

actors

CBD Urban Urban Fringe

Suburban Rural
104

Figure 3. Other Arterial Speed Reduction Factors by V/C Ratio.

Spe

ed R

eduction F

actors

CBD Urban Urban Fringe

Suburban Rural
105

Figure 4. Collector Speed Reduction Factors by V/C Ratio.

Speed Reduction Factors by V/C Ratio

Functional Group 4 (Collectors)

V/C Ratios

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

0.00

0.20

0.40

0.60

0.80

1.00

Speed Reduct oi n F

actors

CBD Urban Urban Fringe

Suburban Rural
106

Functional Classification to Functional Group Relationship for the

Application of Speed Reduction Factors

Functional Group Corresponding Network Functional 

Classifications

1. Freeways, Interstates

1. Urban Interstate Freeways

2. Urban Other Freeways

3. Toll Roads

10. Rural Interstate Freeways

11. Rural Other Freeways

2. Principal Arterials 5. Urban Principal Arterials

12. Rural Principal Arterials

3. Other Arterials, Major 

Collectors

6. Urban Other Arterials

13. Rural Other Arterials

14. Rural Major Collectors

4. Collectors

4. Ramps

7. Urban Collectors

15. Rural Collectors
107

APPENDIX F:

VEHICLE POPULATION ESTIMATES AND 24-HOUR SHP, STARTS, 

AND SHI SUMMARIES

109

2002 Vehicle Population Estimates

SUT/FT Harris Brazoria Fort 

Bend Waller Montgomery Liberty Chambers Galveston

21_D 4,763 309 510 35 395 93 39 342

21_G 1,585,418 102,754 169,889 11,507 131,650 23,256 9,636 113,876

31_D 8,220 849 846 122 1,024 270 120 750

31_G 506,003 52,259 52,086 7,537 63,018 16,578 7,378 46,144

32_D 14,899 1,539 1,534 222 1,855 489 218 1,359

32_G 156,509 16,164 16,110 2,331 19,492 5,127 2,282 14,273

51_D 577 44 31 10 54 19 8 31

51_G 297 23 16 5 28 5 2 16

52_D 15,079 1,148 812 267 1,415 517 206 801

52_G 7,704 586 415 136 723 133 53 409

53_D 1,659 126 89 29 156 25 10 88

53_G 850 65 46 15 80 6 3 45

54_D 834 63 45 15 78 27 11 44

54_G 425 32 23 8 40 7 3 23

41_D 954 73 51 17 90 25 10 51

42_D 361 27 19 6 34 9 4 19

42_G 0 0 0 0 0 0 0 0

43_D 1,090 83 59 19 102 28 11 58

43_G 8 1 0 0 1 0 0 0

61_D 15,514 888 888 153 892 468 85 495

61_G 1,206 69 69 12 69 53 9 39

62_D 14,300 818 818 141 822 847 153 457

62_G 0 0 0 0 0 0 0 0

11_G 28,919 3,284 3,252 319 4,133 747 431 3,940
110

2018 Vehicle Population Estimates

SUT/FT Harris Brazoria Fort 

Bend Waller Montgomery Liberty Chambers Galveston

21_D 6,328 569 1,261 58 842 118 59 450

21_G 2,106,728 189,277 419,947 19,161 280,310 39,214 19,704 149,652

31_D 39,214 4,969 6,170 709 6,809 1,595 800 3,653

31_G 648,524 82,180 102,047 11,730 112,602 26,888 13,481 60,406

32_D 15,362 1,947 2,417 278 2,667 636 319 1,431

32_G 213,884 27,103 33,655 3,869 37,136 8,858 4,441 19,922

51_D 324 45 39 10 58 20 10 26

51_G 162 22 19 5 29 5 2 13

52_D 31,249 4,314 3,754 967 5,626 2,042 1,013 2,490

52_G 15,965 2,204 1,918 494 2,874 523 260 1,272

53_D 3,436 474 413 106 619 98 49 274

53_G 1,750 242 210 54 315 25 13 140

54_D 1,686 233 203 52 303 104 52 134

54_G 859 119 103 27 155 27 13 68

41_D 2,042 282 245 63 368 100 50 163

42_D 583 81 70 18 105 29 14 47

42_G 0 0 0 0 0 0 0 0

43_D 2,237 309 269 69 403 110 55 178

43_G 16 2 2 1 3 1 1 1

61_D 18,607 1,254 1,723 248 1,736 584 214 644

61_G 1,447 97 134 19 135 66 24 50

62_D 17,151 1,156 1,589 228 1,600 1,056 387 594

62_G 0 0 0 0 0 0 0 0

11_G 55,670 10,395 11,224 1,113 15,092 2,783 1,426 9,352
111

2002 24-Hour Weekday SHP Summaries

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 108,459 7,131 11,809 764 9,041 2,124 845 7,874

21_G 36,107,477 2,373,995 3,931,179 254,513 3,009,715 529,949 210,844 2,621,262

31_D 190,653 20,016 19,773 2,848 23,985 6,321 2,781 17,595

31_G 11,736,849 1,232,212 1,217,235 175,310 1,476,533 388,337 170,851 1,083,174

32_D 345,570 36,281 35,839 5,162 43,474 11,455 5,040 31,892

32_G 3,630,270 381,129 376,497 54,224 456,699 120,106 52,841 335,031

51_D 11,733 934 571 213 1,102 384 129 608

51_G 6,035 480 294 110 566 99 34 313

52_D 306,919 24,434 14,944 5,576 28,821 10,555 3,550 15,913

52_G 156,816 12,484 7,636 2,849 14,726 2,703 909 8,130

53_D 33,796 2,690 1,646 614 3,173 510 172 1,752

53_G 17,312 1,378 844 315 1,626 131 44 898

54_D 16,961 1,350 826 308 1,593 550 185 879

54_G 8,642 688 421 157 811 143 48 448

41_D 19,541 1,589 983 372 1,904 511 167 1,022

42_D 7,388 600 371 141 719 196 64 386

42_G 0 0 0 0 0 0 0 0

43_D 22,337 1,815 1,123 425 2,175 586 192 1,168

43_G 166 13 8 3 16 6 2 9

61_D 334,429 18,609 17,849 2,515 16,416 9,906 465 9,660

61_G 26,009 1,448 1,389 196 1,278 1,112 52 752

62_D 308,265 17,153 16,453 2,319 15,133 17,904 840 8,904

62_G 0 0 0 0 0 0 0 0

11_G 692,104 78,724 77,901 7,634 99,041 17,900 10,323 94,448
112

2018 24-Hour Weekday SHP Summaries

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 143,711 13,201 29,444 1,284 19,413 2,687 1,308 10,377

21_G 47,844,071 4,394,661 9,802,149 427,311 6,462,743 894,603 435,489 3,454,692

31_D 908,192 117,242 144,456 16,537 159,856 37,391 18,538 85,930

31_G 15,019,555 1,938,941 2,389,004 273,493 2,643,697 630,215 312,457 1,421,111

32_D 355,776 45,928 56,589 6,478 62,622 14,903 7,389 33,662

32_G 4,953,473 639,466 787,897 90,198 871,896 207,619 102,937 468,685

51_D 6,952 1,021 840 227 1,307 457 216 576

51_G 3,457 509 418 113 651 111 52 287

52_D 671,253 98,504 81,174 21,935 126,152 45,842 21,674 55,599

52_G 342,950 50,326 41,473 11,207 64,452 11,754 5,558 28,406

53_D 73,825 10,832 8,927 2,412 13,873 2,210 1,045 6,114

53_G 37,601 5,518 4,547 1,229 7,067 568 269 3,114

54_D 36,197 5,313 4,377 1,183 6,804 2,333 1,103 2,998

54_G 18,444 2,707 2,230 603 3,467 605 286 1,528

41_D 44,063 6,504 5,390 1,464 8,358 2,265 1,063 3,656

42_D 12,566 1,857 1,538 418 2,386 647 303 1,043

42_G 0 0 0 0 0 0 0 0

43_D 48,228 7,122 5,900 1,604 9,151 2,489 1,167 4,002

43_G 351 52 43 12 66 25 12 29

61_D 393,348 25,930 34,815 4,303 34,131 11,799 1,872 12,676

61_G 30,592 2,017 2,708 335 2,655 1,325 210 986

62_D 362,575 23,902 32,092 3,966 31,462 21,324 3,384 11,684

62_G 0 0 0 0 0 0 0 0

11_G 1,333,353 249,337 269,096 26,669 361,934 66,753 34,188 224,307
113

2002 24-Hour Weekday Starts Summaries

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 27,961 1,812 2,996 203 2,322 547 227 2,008

21_G 9,308,001 603,271 997,417 67,560 772,915 136,535 56,575 668,566

31_D 47,433 4,899 4,883 706 5,907 1,557 693 4,326

31_G 2,919,871 301,556 300,560 43,490 363,644 95,662 42,572 266,273

32_D 89,642 9,258 9,227 1,335 11,164 2,942 1,309 8,175

32_G 941,687 97,255 96,933 14,026 117,279 30,850 13,729 85,876

51_D 2,164 165 117 38 203 71 28 115

51_G 1,112 85 60 20 104 18 7 59

52_D 105,416 8,023 5,674 1,863 9,894 3,617 1,442 5,597

52_G 53,857 4,099 2,899 952 5,055 926 369 2,860

53_D 7,114 541 383 126 668 107 43 378

53_G 3,643 277 196 64 342 28 11 193

54_D 472 36 25 8 44 15 6 25

54_G 241 18 13 4 23 4 2 13

41_D 2,640 201 142 47 248 68 27 140

42_D 1,651 126 89 29 155 43 17 88

42_G 0 0 0 0 0 0 0 0

43_D 6,267 477 337 111 588 162 64 333

43_G 46 4 2 1 4 2 1 2

61_D 92,000 5,266 5,266 908 5,288 2,778 501 2,938

61_G 7,154 409 409 71 411 312 56 228

62_D 61,302 3,509 3,509 605 3,523 3,629 655 1,958

62_G 0 0 0 0 0 0 0 0

11_G 12,866 1,461 1,447 142 1,839 332 192 1,753
114

2018 24-Hour Weekday Starts Summaries

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 37,154 3,338 7,406 338 4,944 691 347 2,639

21_G 12,368,612 1,111,249 2,465,509 112,493 1,645,704 230,224 115,684 878,605

31_D 226,282 28,674 35,606 4,093 39,289 9,205 4,615 21,077

31_G 3,742,289 474,216 588,860 67,689 649,769 155,154 77,792 348,573

32_D 92,428 11,712 14,544 1,672 16,048 3,826 1,918 8,609

32_G 1,286,906 163,074 202,498 23,277 223,444 53,297 26,722 119,868

51_D 1,216 168 146 38 219 76 38 97

51_G 608 84 73 19 109 19 9 48

52_D 218,462 30,160 26,246 6,759 39,333 14,274 7,079 17,410

52_G 111,611 15,409 13,409 3,453 20,095 3,660 1,815 8,895

53_D 14,730 2,034 1,770 456 2,652 422 209 1,174

53_G 7,504 1,036 902 232 1,351 108 54 598

54_D 954 132 115 30 172 59 29 76

54_G 486 67 58 15 88 15 8 39

41_D 5,651 780 679 175 1,017 277 137 450

42_D 2,671 369 321 83 481 131 65 213

42_G 0 0 0 0 0 0 0 0

43_D 12,857 1,775 1,545 398 2,315 632 314 1,025

43_G 93 13 11 3 17 6 3 7

61_D 110,346 7,436 10,221 1,469 10,294 3,465 1,271 3,818

61_G 8,580 578 795 114 800 389 143 297

62_D 73,526 4,955 6,810 979 6,859 4,526 1,661 2,544

62_G 0 0 0 0 0 0 0 0

11_G 24,767 4,625 4,993 495 6,714 1,238 634 4,161
115

24-Hour Weekday SHI Summaries (CLhT_Diesel Only)

County 2002 2008 2011 2014 2017 2018 2019

Harris 17,032 19,490 20,474 21,667 23,010 23,475 23,951

Brazoria 153 157 182 200 218 224 230

Fort Bend 3,072 3,415 4,214 4,663 5,147 5,320 5,498

Waller 1,560 1,835 1,905 2,046 2,205 2,260 2,318

Montgomery 4,242 4,357 5,248 5,283 5,771 5,943 6,121

Liberty 449 466 600 652 699 715 732

Chambers 840 1,575 2,138 2,272 2,420 2,471 2,523

Galveston 481 503 517 550 585 597 609

117

APPENDIX G:

SOURCE TYPE AGE AND FUEL/ENGINE FRACTIONS INPUTS TO 

MOVES

119

Brazoria County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.110231 0.056917 0.077390 0.077390 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.139160 0.083299 0.106849 0.106849 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.104446 0.097620 0.091753 0.091753 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.081912 0.086239 0.088335 0.088335 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.060901 0.078020 0.067279 0.067279 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.055116 0.074382 0.074891 0.074891 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.050244 0.065494 0.058156 0.058156 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.035932 0.070753 0.060415 0.060415 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.037454 0.058576 0.058029 0.058029 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.030146 0.055878 0.045078 0.045078 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.022838 0.044788 0.038469 0.038469 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.015834 0.042236 0.036803 0.036803 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.016748 0.034882 0.032086 0.032086 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.017052 0.033426 0.030857 0.030857 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.012485 0.023723 0.024615 0.024615 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.013398 0.018067 0.016565 0.016565 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.024665 0.015524 0.018232 0.018232 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.022838 0.013225 0.014814 0.014814 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.013398 0.010023 0.013572 0.013572 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.013398 0.005967 0.007513 0.007513 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.023752 0.004065 0.008050 0.008050 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.015225 0.003619 0.006044 0.006044 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.017966 0.002620 0.002994 0.002994 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.012485 0.002950 0.003658 0.003658 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.009744 0.002329 0.003305 0.003305 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.007308 0.001611 0.002528 0.002528 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.004066 0.001223 0.001447 0.001447 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.005082 0.000828 0.001345 0.001345 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.004193 0.000876 0.001289 0.001289 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.003304 0.000693 0.000684 0.000684 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.018678 0.010148 0.006956 0.006956 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
120

Brazoria County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.029489 0.041465 0.025332 0.025332 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.035165 0.059996 0.041857 0.041857 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.084868 0.058323 0.038005 0.038005 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.097743 0.088039 0.069630 0.069630 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.113526 0.091720 0.077310 0.077310 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.102174 0.081450 0.069196 0.069196 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.078222 0.075970 0.063213 0.063213 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.059532 0.065633 0.070261 0.070261 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.071577 0.061826 0.073680 0.073680 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.064655 0.060152 0.075947 0.075947 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.049702 0.054189 0.069147 0.069147 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.033504 0.053452 0.056586 0.056586 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028243 0.043257 0.050776 0.050776 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.022290 0.034683 0.035564 0.035564 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.015368 0.027046 0.036060 0.036060 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.015921 0.020837 0.025890 0.025890 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.013014 0.019007 0.025506 0.025506 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.011076 0.013489 0.021554 0.021554 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007615 0.010277 0.013849 0.013849 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.007061 0.007808 0.011582 0.011582 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.003323 0.006053 0.009291 0.009291 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.004015 0.004439 0.007457 0.007457 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003738 0.003324 0.006937 0.006937 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.003600 0.002372 0.005215 0.005215 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002769 0.001837 0.002564 0.002564 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004984 0.001398 0.002973 0.002973 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.003600 0.001450 0.002155 0.002155 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003600 0.001034 0.002329 0.002329 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003184 0.000647 0.001102 0.001102 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.004430 0.000565 0.001028 0.001028 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.022013 0.008262 0.008002 0.008002 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
121

Chambers County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.118329 0.035245 0.050515 0.050515 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.139211 0.069871 0.099530 0.099530 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.146172 0.099845 0.098129 0.098129 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.090487 0.091266 0.091127 0.091127 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.078886 0.080517 0.076823 0.076823 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.044084 0.076176 0.083025 0.083025 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.062645 0.068320 0.059718 0.059718 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.041763 0.072351 0.063919 0.063919 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.027842 0.057778 0.061118 0.061118 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.034803 0.057364 0.046314 0.046314 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.020882 0.046305 0.041312 0.041312 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.009281 0.041240 0.037111 0.037111 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.011601 0.033592 0.032510 0.032510 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.013921 0.036072 0.026308 0.026308 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.011601 0.027390 0.023107 0.023107 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.000000 0.019018 0.014704 0.014704 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.009281 0.015917 0.016205 0.016205 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.020882 0.015090 0.015105 0.015105 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.013921 0.012610 0.014904 0.014904 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.011601 0.007235 0.008903 0.008903 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.023202 0.005478 0.008403 0.008403 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.013921 0.004651 0.007202 0.007202 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.009281 0.002791 0.003101 0.003101 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.016241 0.003824 0.004201 0.004201 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.004640 0.002687 0.003001 0.003001 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.004640 0.002481 0.002401 0.002401 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.000994 0.000986 0.001726 0.001726 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.001989 0.000807 0.001683 0.001683 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.001989 0.001435 0.001278 0.001278 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.000994 0.000717 0.000879 0.000879 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.014915 0.010939 0.005737 0.005737 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
122

Chambers County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.016832 0.043599 0.026411 0.026411 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.028713 0.062691 0.040891 0.040891 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.086139 0.063048 0.046444 0.046444 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.094059 0.103227 0.077431 0.077431 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.130693 0.098597 0.083433 0.083433 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.109901 0.091757 0.075180 0.075180 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.080198 0.077652 0.066627 0.066627 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.068317 0.062122 0.075405 0.075405 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.075248 0.052148 0.073980 0.073980 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.067327 0.055639 0.069928 0.069928 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.045545 0.048372 0.067977 0.067977 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.039604 0.045166 0.051170 0.051170 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.040594 0.037900 0.045618 0.045618 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.023762 0.028924 0.031813 0.031813 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.010891 0.025219 0.031438 0.031438 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.011881 0.019377 0.022209 0.022209 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.006931 0.017953 0.024010 0.024010 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008911 0.011968 0.020408 0.020408 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007921 0.010401 0.013205 0.013205 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.003960 0.007053 0.010579 0.010579 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.004950 0.007124 0.008929 0.008929 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.001980 0.005486 0.007428 0.007428 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003960 0.004203 0.006227 0.006227 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.002970 0.002208 0.004727 0.004727 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.001980 0.002636 0.002776 0.002776 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.001980 0.001781 0.002701 0.002701 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.001980 0.001781 0.002176 0.002176 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003960 0.001282 0.002326 0.002326 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.000000 0.000427 0.001050 0.001050 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.002970 0.000712 0.000975 0.000975 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.015842 0.009546 0.006528 0.006528 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
123

Fort Bend County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.151907 0.060388 0.093630 0.093630 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.141451 0.092442 0.123951 0.123951 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.107626 0.110740 0.111809 0.111809 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.091021 0.094678 0.100714 0.100714 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.069188 0.085928 0.076485 0.076485 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.046740 0.078293 0.075181 0.075181 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.045510 0.071256 0.053658 0.053658 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.037208 0.072659 0.056521 0.056521 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.036593 0.056825 0.051887 0.051887 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.031058 0.051550 0.040580 0.040580 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.019065 0.042365 0.032561 0.032561 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.014760 0.038568 0.028352 0.028352 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.011993 0.032265 0.024668 0.024668 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.013530 0.023938 0.022260 0.022260 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.010455 0.018644 0.018179 0.018179 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.011685 0.013797 0.012511 0.012511 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.017528 0.011080 0.013432 0.013432 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.013838 0.009930 0.012384 0.012384 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.011070 0.007435 0.010811 0.010811 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.016298 0.004378 0.005696 0.005696 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.018143 0.003421 0.006844 0.006844 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.012608 0.002430 0.005299 0.005299 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.010148 0.001831 0.002919 0.002919 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.008303 0.002171 0.004180 0.004180 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.009225 0.001755 0.003089 0.003089 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.004920 0.001291 0.002338 0.002338 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.005119 0.000776 0.001003 0.001003 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.006355 0.000784 0.001506 0.001506 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.003884 0.000542 0.001288 0.001288 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.004766 0.000537 0.000577 0.000577 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.018006 0.007302 0.005686 0.005686 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
124

Fort Bend County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.043366 0.046518 0.029637 0.029637 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.048141 0.065960 0.053790 0.053790 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.102478 0.064906 0.044450 0.044450 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.101058 0.094485 0.079934 0.079934 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.120289 0.096165 0.088972 0.088972 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.103898 0.085778 0.072731 0.072731 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.079375 0.075541 0.066153 0.066153 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.059241 0.067667 0.078277 0.078277 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.067759 0.063112 0.078099 0.078099 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.056402 0.059721 0.078037 0.078037 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.043753 0.053351 0.065736 0.065736 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.032137 0.050441 0.050913 0.050913 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028653 0.038721 0.042448 0.042448 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.019489 0.031594 0.032045 0.032045 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.011745 0.024350 0.029011 0.029011 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.011874 0.017987 0.019202 0.019202 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.008776 0.015595 0.018680 0.018680 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.006324 0.010355 0.015689 0.015689 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007486 0.007780 0.010508 0.010508 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.003098 0.006066 0.008235 0.008235 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.002710 0.004559 0.006130 0.006130 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.001678 0.003633 0.005494 0.005494 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.002194 0.002202 0.004555 0.004555 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.002323 0.001756 0.003357 0.003357 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002710 0.001362 0.002450 0.002450 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.002323 0.001013 0.002085 0.002085 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.002581 0.000971 0.001939 0.001939 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.002452 0.000847 0.001887 0.001887 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.002839 0.000591 0.001136 0.001136 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.003872 0.000505 0.001220 0.001220 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.018973 0.006470 0.007203 0.007203 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
125

Galveston County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.118528 0.059071 0.086589 0.086589 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.127157 0.082439 0.107589 0.107589 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.102538 0.090599 0.088237 0.088237 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.091624 0.084523 0.087677 0.087677 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.061168 0.076398 0.068389 0.068389 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.041878 0.071276 0.072179 0.072179 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.052538 0.065498 0.053946 0.053946 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.039340 0.070488 0.059576 0.059576 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.034264 0.058099 0.056905 0.056905 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.027157 0.055543 0.045886 0.045886 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.015736 0.046219 0.039312 0.039312 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.013452 0.043049 0.034738 0.034738 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.019797 0.037236 0.031251 0.031251 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.013959 0.033200 0.030180 0.030180 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.013959 0.024593 0.023495 0.023495 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.014721 0.019270 0.016809 0.016809 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.024873 0.016652 0.017129 0.017129 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.021320 0.014577 0.015402 0.015402 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.016751 0.011846 0.015146 0.015146 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.020305 0.006260 0.008221 0.008221 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.019036 0.004614 0.008653 0.008653 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.013706 0.003380 0.006190 0.006190 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.018020 0.002863 0.003071 0.003071 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.011675 0.003450 0.004558 0.004558 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.010660 0.002793 0.003870 0.003870 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.006345 0.001821 0.002879 0.002879 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.004645 0.001285 0.001664 0.001664 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.007047 0.001049 0.001511 0.001511 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.005286 0.000865 0.001510 0.001510 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.005606 0.000821 0.000654 0.000654 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.026908 0.010225 0.006783 0.006783 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
126

Galveston County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.025243 0.043574 0.026319 0.026319 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.037738 0.058903 0.049632 0.049632 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.100719 0.063386 0.042001 0.042001 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.098574 0.088516 0.079627 0.079627 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.108166 0.090634 0.082367 0.082367 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.097816 0.083909 0.072569 0.072569 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.080020 0.075971 0.063120 0.063120 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.058437 0.064917 0.073910 0.073910 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.073457 0.062605 0.074861 0.074861 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.059447 0.059629 0.076888 0.076888 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.044806 0.052487 0.068292 0.068292 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.034709 0.050206 0.051254 0.051254 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.030418 0.041696 0.045928 0.045928 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.020825 0.033502 0.032860 0.032860 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.015777 0.026784 0.031798 0.031798 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.016534 0.020083 0.022671 0.022671 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.009719 0.018745 0.021832 0.021832 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008709 0.012337 0.017681 0.017681 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007952 0.010034 0.012957 0.012957 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.004670 0.007490 0.008889 0.008889 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.004039 0.005813 0.007785 0.007785 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.003786 0.005017 0.007548 0.007548 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.004670 0.003564 0.005996 0.005996 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.004670 0.002427 0.004025 0.004025 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.003155 0.002056 0.002376 0.002376 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004670 0.001778 0.002404 0.002404 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.003155 0.001662 0.002111 0.002111 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.002524 0.001376 0.002264 0.002264 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003282 0.000873 0.001384 0.001384 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.005301 0.000657 0.001132 0.001132 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.027010 0.009369 0.007520 0.007520 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
127

Harris County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.133995 0.076273 0.100452 0.100452 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.145925 0.093193 0.114137 0.114137 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.114043 0.092654 0.096030 0.096030 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.093503 0.080164 0.090982 0.090982 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.066704 0.074951 0.072659 0.072659 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.051592 0.070560 0.074424 0.074424 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.049483 0.064047 0.052854 0.052854 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.043293 0.069245 0.058809 0.058809 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.033507 0.055981 0.054443 0.054443 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.031087 0.052927 0.044816 0.044816 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.018742 0.046193 0.036332 0.036332 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.012656 0.042864 0.030902 0.030902 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.013140 0.037605 0.025900 0.025900 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.012241 0.031161 0.025331 0.025331 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.012449 0.024683 0.020578 0.020578 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.010685 0.018216 0.014423 0.014423 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.020263 0.014785 0.014715 0.014715 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.015664 0.012829 0.013796 0.013796 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.012794 0.009622 0.011971 0.011971 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.014074 0.005572 0.006788 0.006788 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.017808 0.004139 0.008045 0.008045 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.011515 0.003006 0.005852 0.005852 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.011169 0.002237 0.003099 0.003099 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.009267 0.002831 0.004308 0.004308 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.007089 0.002124 0.003630 0.003630 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.005913 0.001577 0.002999 0.002999 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.003569 0.001136 0.001504 0.001504 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.004734 0.000845 0.001815 0.001815 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.002977 0.000729 0.001364 0.001364 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.002903 0.000559 0.000656 0.000656 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.017215 0.007291 0.006386 0.006386 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
128

Harris County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.035445 0.047178 0.027418 0.027418 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.042013 0.057736 0.040492 0.040492 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.102145 0.052988 0.035349 0.035349 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.098830 0.078010 0.069539 0.069539 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.117470 0.082164 0.077581 0.077581 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.098789 0.076988 0.066823 0.066823 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.077021 0.071964 0.064280 0.064280 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.055649 0.062982 0.073246 0.073246 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.068660 0.060872 0.076613 0.076613 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.057818 0.061481 0.079337 0.079337 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.044703 0.058503 0.071480 0.071480 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.035425 0.057776 0.057650 0.057650 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028273 0.046624 0.051219 0.051219 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.020850 0.039183 0.038065 0.038065 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.014449 0.031811 0.035592 0.035592 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.013490 0.024008 0.023130 0.023130 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.011238 0.021505 0.023112 0.023112 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008924 0.015239 0.019319 0.019319 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.006484 0.011823 0.013504 0.013504 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.004962 0.009001 0.010016 0.010016 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.003419 0.006878 0.007798 0.007798 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.003482 0.005110 0.006732 0.006732 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003336 0.003481 0.005890 0.005890 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.003607 0.002469 0.004190 0.004190 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002877 0.001852 0.002588 0.002588 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004253 0.001431 0.002746 0.002746 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.004024 0.001468 0.002465 0.002465 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003440 0.001175 0.002283 0.002283 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.002648 0.000789 0.001410 0.001410 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.003982 0.000576 0.001734 0.001734 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.022289 0.006934 0.008398 0.008398 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
129

Liberty County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.096386 0.042443 0.079282 0.079282 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.129853 0.073665 0.103321 0.103321 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.115127 0.087241 0.082888 0.082888 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.100402 0.078119 0.081063 0.081063 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.065596 0.070581 0.067530 0.067530 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.048193 0.065527 0.075231 0.075231 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.058902 0.059960 0.054220 0.054220 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.048193 0.067926 0.058894 0.058894 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.038822 0.055848 0.053953 0.053953 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.029451 0.056876 0.043269 0.043269 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.020080 0.047240 0.038105 0.038105 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.010710 0.049081 0.035256 0.035256 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.009371 0.042614 0.029870 0.029870 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.010710 0.040558 0.031116 0.031116 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.014726 0.030622 0.026309 0.026309 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.014726 0.025440 0.016248 0.016248 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.022758 0.021628 0.019275 0.019275 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.021419 0.020686 0.019053 0.019053 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.010710 0.015033 0.017673 0.017673 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.018742 0.009422 0.011574 0.011574 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.020080 0.006467 0.011619 0.011619 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.021419 0.004411 0.008859 0.008859 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.016064 0.003041 0.003962 0.003962 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.004016 0.005097 0.007434 0.007434 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.004016 0.004668 0.005253 0.005253 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.005355 0.002398 0.003828 0.003828 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.001893 0.001305 0.002325 0.002325 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.006311 0.001395 0.002582 0.002582 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.007573 0.001080 0.001763 0.001763 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.006311 0.000720 0.000819 0.000819 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.022088 0.008907 0.007424 0.007424 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
130

Liberty County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.023510 0.031586 0.022295 0.022295 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.029524 0.046288 0.032190 0.032190 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.083652 0.045982 0.031827 0.031827 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.092947 0.079865 0.065471 0.065471 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.107709 0.080018 0.072378 0.072378 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.109896 0.074275 0.065269 0.065269 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.082559 0.070868 0.054768 0.054768 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.061236 0.056013 0.062038 0.062038 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.061782 0.054520 0.069147 0.069147 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.064516 0.052950 0.070883 0.070883 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.057408 0.057736 0.069712 0.069712 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.041006 0.056893 0.054606 0.054606 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.035539 0.051725 0.051133 0.051133 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.025150 0.042536 0.042974 0.042974 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.019683 0.038707 0.044428 0.044428 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.015309 0.030476 0.030373 0.030373 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.009841 0.028294 0.032635 0.032635 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.007654 0.021134 0.027546 0.027546 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.006014 0.017191 0.017812 0.017812 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.003827 0.013668 0.016156 0.016156 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.002734 0.011716 0.011753 0.011753 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.002734 0.008002 0.010663 0.010663 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.004921 0.006394 0.009088 0.009088 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.004374 0.005054 0.007634 0.007634 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002187 0.002948 0.004241 0.004241 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.009841 0.002259 0.003918 0.003918 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.003827 0.002106 0.003958 0.003958 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.004374 0.001646 0.003110 0.003110 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003827 0.000536 0.001494 0.001494 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.001640 0.000689 0.001535 0.001535 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.020776 0.007925 0.008966 0.008966 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
131

Montgomery County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.100411 0.058798 0.088606 0.088606 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.145899 0.091847 0.120742 0.120742 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.126301 0.102367 0.099240 0.099240 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.098476 0.088076 0.093396 0.093396 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.072103 0.079640 0.071707 0.071707 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.049117 0.074316 0.073276 0.073276 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.049601 0.065947 0.054375 0.054375 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.042342 0.070309 0.059036 0.059036 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.035325 0.056640 0.052114 0.052114 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.027583 0.052346 0.043073 0.043073 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.015485 0.042947 0.034196 0.034196 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.012098 0.039221 0.030835 0.030835 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.015727 0.033928 0.026268 0.026268 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.014759 0.029263 0.023856 0.023856 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.010888 0.023000 0.019675 0.019675 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.014275 0.017373 0.013550 0.013550 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.020082 0.013995 0.015377 0.015377 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.019114 0.012844 0.014627 0.014627 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.016211 0.009557 0.014053 0.014053 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.016453 0.005915 0.007718 0.007718 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.018872 0.004264 0.008514 0.008514 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.013549 0.003135 0.006324 0.006324 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.012098 0.002507 0.002939 0.002939 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.011130 0.003181 0.005446 0.005446 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.006049 0.002810 0.004345 0.004345 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.005081 0.001772 0.003115 0.003115 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.004757 0.001184 0.001721 0.001721 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.004272 0.000897 0.001642 0.001642 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.003592 0.000791 0.001323 0.001323 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.002815 0.000654 0.000714 0.000714 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.015534 0.010477 0.008197 0.008197 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
132

Montgomery County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.040452 0.047069 0.031806 0.031806 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.035805 0.066982 0.050435 0.050435 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.089468 0.060890 0.043266 0.043266 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.087281 0.093845 0.077591 0.077591 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.110787 0.092229 0.083330 0.083330 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.105047 0.083485 0.068758 0.068758 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.082088 0.074366 0.064179 0.064179 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.061771 0.066269 0.073435 0.073435 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.074708 0.060237 0.076143 0.076143 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.056669 0.058257 0.073597 0.073597 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.047103 0.052050 0.068659 0.068659 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.038539 0.049792 0.050687 0.050687 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028972 0.040304 0.044102 0.044102 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.019679 0.032307 0.033524 0.033524 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.015853 0.025846 0.032391 0.032391 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.015306 0.018497 0.023396 0.023396 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.011935 0.017126 0.021777 0.021777 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008746 0.012005 0.017801 0.017801 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.008200 0.009174 0.012323 0.012323 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.006013 0.007304 0.009139 0.009139 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.003462 0.005529 0.007088 0.007088 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.003371 0.004268 0.006494 0.006494 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003644 0.003410 0.005325 0.005325 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.003735 0.002438 0.003562 0.003562 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.003098 0.001875 0.002276 0.002276 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004191 0.001436 0.002546 0.002546 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.004282 0.001256 0.002393 0.002393 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003735 0.001077 0.002357 0.002357 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003735 0.000858 0.001520 0.001520 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.004191 0.000653 0.001250 0.001250 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.018130 0.009169 0.008851 0.008851 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
133

Waller County 2002 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.081505 0.041587 0.074618 0.074618 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.115987 0.068359 0.098903 0.098903 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.094044 0.086294 0.084508 0.084508 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.090909 0.070612 0.085879 0.085879 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.056426 0.071391 0.066882 0.066882 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.062696 0.067233 0.071582 0.071582 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.053292 0.061601 0.058167 0.058167 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.053292 0.061428 0.061105 0.061105 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.031348 0.055623 0.055817 0.055817 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.043887 0.054757 0.045339 0.045339 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.006270 0.045833 0.036428 0.036428 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.018809 0.046959 0.033000 0.033000 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.021944 0.046266 0.028692 0.028692 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.025078 0.035609 0.027810 0.027810 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.028213 0.036475 0.024089 0.024089 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.015674 0.028331 0.018606 0.018606 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.012539 0.020880 0.017235 0.017235 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.031348 0.020447 0.016843 0.016843 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.028213 0.017848 0.018410 0.018410 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.018809 0.011090 0.011653 0.011653 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.015674 0.007971 0.012241 0.012241 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.012539 0.005372 0.009792 0.009792 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.015674 0.005285 0.005092 0.005092 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.015674 0.006585 0.007736 0.007736 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.009404 0.003726 0.006365 0.006365 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.006270 0.002253 0.004015 0.004015 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.009512 0.002779 0.003055 0.003055 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.005945 0.001902 0.002788 0.002788 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.003567 0.001243 0.002874 0.002874 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.002378 0.001170 0.001145 0.001145 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.013080 0.013092 0.009332 0.009332 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715
134

Waller County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.032184 0.023292 0.017334 0.017334 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.031034 0.039236 0.032092 0.032092 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.078161 0.041730 0.029125 0.029125 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.072414 0.072239 0.061138 0.061138 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.120690 0.071583 0.069415 0.069415 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.101149 0.070009 0.063012 0.063012 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.083908 0.068959 0.057469 0.057469 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.058621 0.055902 0.071133 0.071133 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.074713 0.054130 0.069806 0.069806 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.050575 0.059051 0.073007 0.073007 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.044828 0.059839 0.069962 0.069962 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.042529 0.062594 0.050051 0.050051 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.032184 0.054721 0.052393 0.052393 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.025287 0.043435 0.042008 0.042008 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.013793 0.038843 0.044741 0.044741 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.016092 0.033134 0.031233 0.031233 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.018391 0.031560 0.033497 0.033497 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.009195 0.024014 0.026860 0.026860 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.011494 0.020471 0.017569 0.017569 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.004598 0.016141 0.016710 0.016710 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.004598 0.012204 0.010541 0.010541 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.005747 0.008792 0.009916 0.009916 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.004598 0.007086 0.008745 0.008745 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.006897 0.005118 0.007340 0.007340 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.004598 0.004462 0.004919 0.004919 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.008046 0.001968 0.004216 0.004216 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.006897 0.002428 0.004216 0.004216 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.004598 0.002493 0.003904 0.003904 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.009195 0.001378 0.002342 0.002342 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.004598 0.001115 0.002577 0.002577 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.018391 0.012073 0.012727 0.012727 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875
135

Texas Statewide 2002 Fuel/Engine Fractions Summary1

SUT Fuel 

Type

Model Year

2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1900 1989 1988 1987

PC Gas 0.996 0.996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

PC Diesel 0.004 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

PT Gas 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010

PT Gas 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250 0.266

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750 0.734

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.202 0.265 0.346 0.330 0.413 0.450 0.456 0.703 0.492 0.493 0.504 0.517 0.570 0.606 0.723 0.775

SUShT Diesel 0.798 0.735 0.654 0.670 0.587 0.550 0.544 0.297 0.508 0.507 0.496 0.483 0.430 0.394 0.277 0.225

SULhT Gas 0.202 0.265 0.346 0.330 0.413 0.450 0.456 0.703 0.492 0.493 0.504 0.517 0.570 0.606 0.723 0.775

SULhT Diesel 0.798 0.735 0.654 0.670 0.587 0.550 0.544 0.297 0.508 0.507 0.496 0.483 0.430 0.394 0.277 0.225

MH Gas 0.600 0.630 0.660 0.680 0.710 0.740 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.400 0.370 0.340 0.320 0.290 0.260 0.230 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.062 0.102 0.094 0.100 0.131 0.202 0.156 0.403 0.125 0.135 0.213 0.175 0.159 0.155 0.199 0.207

CShT Diesel 0.938 0.898 0.906 0.900 0.869 0.798 0.844 0.597 0.875 0.865 0.787 0.825 0.841 0.845 0.801 0.793

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional internal combustion engine technology only.
136

Texas Statewide 2002 Fuel/Engine Fractions Summary1 (Continued)

SUT Fuel 

Type

Model Year

1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 1973 1972

PC Gas 1.000 0.988 0.981 0.969 0.939 0.924 0.953 0.969 0.985 0.987 0.983 0.982 0.993 0.993 0.993

PC Diesel 0.000 0.012 0.019 0.031 0.061 0.076 0.047 0.031 0.015 0.013 0.017 0.018 0.007 0.007 0.007

PT Gas 0.978 0.983 0.985 0.979 0.960 0.982 0.988 0.986 0.986 0.986 0.986 0.986 0.986 0.986 0.986

PT Diesel 0.022 0.017 0.015 0.021 0.040 0.018 0.012 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014

PT Gas 0.978 0.983 0.985 0.979 0.960 0.982 0.988 0.986 0.986 0.986 0.986 0.986 0.986 0.986 0.986

PT Diesel 0.022 0.017 0.015 0.021 0.040 0.018 0.012 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014

LCT Gas 0.919 0.900 0.852 0.795 0.780 0.929 0.893 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

LCT Diesel 0.081 0.100 0.148 0.205 0.220 0.071 0.107 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

LCT Gas 0.919 0.900 0.852 0.795 0.780 0.929 0.893 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

LCT Diesel 0.081 0.100 0.148 0.205 0.220 0.071 0.107 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.327 0.485 0.615 0.676 0.674 0.736 0.941 0.954 0.971 0.976 0.991 0.991 0.991 0.991 0.991

SBus Diesel 0.673 0.515 0.385 0.324 0.326 0.264 0.059 0.046 0.029 0.024 0.009 0.009 0.009 0.009 0.009

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.775 0.814 0.791 0.806 0.839 0.932 0.968 0.969 0.984 0.977 0.969 0.993 0.987 0.994 0.999

SUShT Diesel 0.225 0.186 0.209 0.194 0.161 0.068 0.032 0.031 0.016 0.023 0.031 0.007 0.013 0.006 0.001

SULhT Gas 0.775 0.814 0.791 0.806 0.839 0.932 0.968 0.969 0.984 0.977 0.969 0.993 0.987 0.994 0.999

SULhT Diesel 0.225 0.186 0.209 0.194 0.161 0.068 0.032 0.031 0.016 0.023 0.031 0.007 0.013 0.006 0.001

MH Gas 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.254 0.277 0.282 0.351 0.348 0.311 0.590 0.647 0.640 0.697 0.734 0.614 0.577 0.802 0.894

CShT Diesel 0.746 0.723 0.718 0.649 0.652 0.689 0.410 0.353 0.360 0.303 0.266 0.386 0.423 0.198 0.106

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional internal combustion engine technology only.
137

Texas Statewide 2018 Fuel/Engine Fractions Summary1

SUT Fuel 

Type

Model Year

2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1900 1989 1988 1987

PC Gas 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996

PC Diesel 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013

LCT Gas 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133

LCT Gas 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250

SUShT Diesel 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750

SULhT Gas 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250

SULhT Diesel 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590

MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.490 0.470 0.460 0.440 0.430 0.410

CShT Gas 0.046 0.046 0.046 0.046 0.046 0.046 0.046 0.046 0.071 0.047 0.053 0.026 0.059 0.048 0.050 0.050

CShT Diesel 0.954 0.954 0.954 0.954 0.954 0.954 0.954 0.954 0.929 0.953 0.947 0.974 0.941 0.952 0.950 0.950

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional internal combustion engine technology only.
138

Texas Statewide 2018 Fuel/Engine Fractions Summary1 (Continued)

SUT Fuel 

Type

Model Year

1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 1973 1972

PC Gas 0.996 0.996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

PC Diesel 0.004 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

PT Gas 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005

PT Gas 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.266 0.312 0.348 0.359 0.426 0.423 0.435 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658

SUShT Diesel 0.734 0.688 0.652 0.641 0.574 0.577 0.565 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342

SULhT Gas 0.266 0.312 0.348 0.359 0.426 0.423 0.435 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658

SULhT Diesel 0.734 0.688 0.652 0.641 0.574 0.577 0.565 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342

MH Gas 0.600 0.630 0.660 0.680 0.710 0.740 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.400 0.370 0.340 0.320 0.290 0.260 0.230 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.078 0.077 0.083 0.102 0.131 0.152 0.146 0.306 0.112 0.123 0.164 0.161 0.153 0.124 0.170

CShT Diesel 0.922 0.923 0.917 0.898 0.869 0.848 0.854 0.694 0.888 0.877 0.836 0.839 0.847 0.876 0.830

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional internal combustion engine technology only.
139

APPENDIX H:

MOVES RUN SUMMARIES

141

Appendix H Available Upon Request

Mary McGarry-Barber

Emissions Assessment Section

mary.mcgarry-barber@tceq.texas.gov

512.239.1987