### APPENDIX E

HOUSTON

- GALVESTON

- BRAZORIA 1997 EIGHT

- HOUR OZONE

NONATTAINMENT AREA V EHICLE MILES TRAVELE D OFFSET ON

- ROAD

MOBILE SOURCE EMISSI ONS INVENTORIES ANAL YSIS

2012

- 002

- SIP

- NR

Adoption

April 23 , 2013

### HOUSTON

- GALVESTON

- BRAZORIA 1997 EIGHT

- HOUR OZONE

NONATTAINMENT AREA V EHICLE MILES TRAVELE D OFFSET ON

- ROAD

MOBILE SOURCE EMISSI ONS INVENTORIES ANAL YSIS

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

on

- road mobile emissions inventories (EI) to support up dates to the Houston

- Galveston

Brazoria (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 Transpo rtation 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 docum entation of the development

and resulting VMT offset EI s 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 R evision 1 

### b y 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 Environme ntal 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):

Transportat ion Control Measures and Transportation Control Strategies to Offset Growth in

Emissions Due to Growth in Vehicle Miles Travelled 2 

### ( August 2012 )

, the inventory development

for the HGB VMT offset analysis in this SIP revision include s 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

• actu al 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) Houston

Galveston

- Brazoria Reasonable Further Progress State Implementation Plan Revision for the 1997 Eight

Hour 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 Agen cy; EPA

- 420

- B

12

- 053, August 2012

### The four inventories were developed maintaining consistency with the 2012 HGB Reasonable

Further Progress ( RFP ) inventories a nd in accordance with the EPA 2012 V MT offset guidance

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

MOVES2010bROP . The inventory methodology , a ctivity inputs, and MOVES model inputs used

were consistent with the proposed 2 012 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

**T**

**r**

**a n**  **s**

**p o**  **r t**  **a t**  **i o**  **n I**  **n s**  **t i**  **t**

**u t**  **e T** 

**e**

**x**

**a**

**s 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 Implementat ion Division

Prepared by the

Transportation Modeling Program

Texas A&M Transportation Institute

TTI Study No.: 60 1391

- 0001

Grant Activities: 582

- 11

- 11226

– FY1 3

- 08

January 2013

TEXAS A&M TRANSPORTATION INSTITUTE

The Texas A&M University S ystem

College Station, Texas 77843

- 3135iii 

## 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 Summar y

................................ ................................ ....... 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 Ty pe

................................ ................................ ............. 26

SHO by SUT/Fuel Type

................................ ................................ ................................ ......... 26

Estimation of Starts

................................ ................................ ................................ .................... 26

Estimation of SHI

................................ ................................ ................................ ...................... 27

SHI Scaling Factors

................................ ................................ ................................ ............... 27

SHI Hour ly 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

................................ ................................ ................................ ........ 34iv 

### 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. Projec t 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 Emi ssions Estimation Utilities for MOVES

- Based Emissions Inventories

........................... 69

TRANSVMT

................................ ................................ ................................ ............................. 69

VirtualLinkVMT

................................ ................................ ................................ ........................ 69

MOVESactivityInputBuild

................................ ................................ ................................ ........ 70

MOVESpopul ationBuild

................................ ................................ ................................ ........... 72

MOVESfleetInputBuild

................................ ................................ ................................ ............. 75

MOVESratesCalc

................................ ................................ ................................ ....................... 78

MOVESratesAdj

................................ ................................ ................................ ........................ 79

ShpExtIdleStartActBld

................................ ................................ ................................ .............. 80

ExtIdleH rsCalc

................................ ................................ ................................ ........................... 80

MOVESemscalc

................................ ................................ ................................ ......................... 81

Emissions Factor Interpolation Methodology

................................ ................................ ........ 82

MOVEStabcomb

................................ ................................ ................................ ........................ 83

Appendix C: TxDOT District Weekday VMT Mix by MO VES 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: Vehic le 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

................................ ................................ ....................... 139v 

## LIST OF TABLES

Table 1. HGB VMT Offset Demonstration Emissions Inventory Scenarios.

..................... 1

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

... 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 F actors.

..... 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 Navigat ion 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 Of fset Demonstration Emissions Inventory Scenarios.

................. 52

Table 29. HGB VMT Offset Demonstration Emissions Inventory Scenarios.

................. 63

Table 30. Registration Categories.

................................ ................................ .................... 74

Ta ble 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.

................................ ................................ ........................... 78vi1 

### EXECUTIVE SUMMARY

The Texas Commission on Environmental Quality (TCEQ) recently approved the Houston

Galveston

- 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 S IP revision replace s the HGB area’s

MOBILE model

- based on

- road mobile inventories and MVEBs of volatile organic compounds

(VOC) and oxides of n it rogen (NO

x 

### ) in the current SIP with inventories and MVEBs based on

MOVES, the U

. S

. Environmental Protection Age ncy’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 inventor ies 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 nona ttainment 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 performi ng VMT offset calculations. In consultation with TCEQ, f ollowing the

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

needed for the VMT offset demonstration analysis. As Table 1 shows , these fou r 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 Emission s Inventory Scenario s.

Inventory Activity

Input Year Emissions Factor Input 1



### 2002 Base Year 2002 2002 Base Year

2018 Fleet and Pre

- 2003

Controls with Growth 2018 2018 Fleet and Pre

- 2003

Controls 1 

### 2018 Fleet and Pre

- 2003

Controls without Growth 2002

2018 Attainment Year C ontrol

Strategy 2018 2018 Fleet and Control

Strategy

1 MOVES2010bROP provides the capability to model this emission s rate scenario required for the

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

allowed to occu r through 2018, while “freezing” emission s 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 On

Road Inventories and Control Strategy Reductions

, TTI, March 2012) developed for the HGB

RFP SI P 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

MOVES2 010bROP

- based inventories produced for the VMT offset analysis.

The two “2018 fleet and pre

- 2003 controls” scenario s ( 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 “ceilin g s

. ” Any

increased VOC and NO

x 

### 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 th e 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 propose d 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: Question s 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, NO

x 

### , carbon

monoxide (CO), and carbon dioxide ( CO 2 

### ) estimates were very small, within the realm of

rounding error.

The inventory method used was the detailed, hou rly, MOVES rates

- per

- activity, traffic

demand model (TDM) link

- based method. Network link level (roadway segment) and off

network (e.g., parked vehicle) emissions estimates were calculated by hour, external to the

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

estimates were link

- based VMT and operational speed estimates, and off

- network source

- hours

parked (SHP), starts, and source hours extended idling (SHI) for diesel long

- haul combination

trucks only. Post

- p rocessing 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 vehicl e 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 MOVES 2010, 2010a and 2010b. EPA, April 201 2 ) and Explanation of S pecial

VMT O ffset V ersion 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 t he resulting HGB VMT offset inventories of VOC and NO

x 

### .

Table 2

. HGB Emission s Inventories for the VMT Offset Demonstration ( T ons/ D ay)

.

Inventory 1 

### VMT Speed VOC NO

x



### 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 e stimates 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 )

. A ccording 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 inven tories

developed by TTI for TCEQ in support of the p roposed 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 nonat tainment 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 attainmen t 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 H GB SIP

, which was proposed in October 2012. This

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

of replacing current MOBILE6

- based inventories and MVEBs with MOVES

- based4 

### 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 revision s 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. T he EPA ’s new VMT

- offset demonstration g uidance specifies

that four on

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

produced a spec ial version of the MOVES on

- road inventory tool, MOVES2010bROP (EPA,

September 2012) , that must be used in the VMT offset calculation s

.

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 at tainment 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 fo r 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 emi ssions

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 ye ar 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 emission s 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 R eferences 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 th e

analysis years consistent with the EPA’s 2012 VMT offset demonstration g uidance, 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).

TT I 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 ha d already been completed, the activity information base d

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 release d a special version of the on

road mobile source emission s model, MOVES20 10b, designed to develop emission s factors and

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

referred to as MOVES2010b ROP . MOVES2010b ROP 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 inventor ies to be as consistent as possible with the other HGB

SIP inventories, some MOVES2010b ROP inputs were the same as the recently developed RFP

inventories as indicated in the following

. The MOVES inputs for the FY 2012 HGB RFP on

road mobile inventories a re documented in the project report HGB MOVES

- Based RFP On

- Road

Invent ories and Control Strategy Reduc tions, 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

MOVES201 0 b

- 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 NO

x 

### , and

carbon dio xide CO 2



### .

• 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 inve ntory 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 EP A 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 ana lysis 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 V apor P ressure (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 M emorandum

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 Emission s Inventory Sc enarios.

Inventory Activity

Input Year 1 

### Emissions Factor Input 2



### 2002 Base Year 2002 2002 Base Year

2018 Fleet and Pre

- 2003

Controls with Growth 2018 2018 Fleet and Pre

- 2003

Controls 2018 Fleet and Pre

- 2003

Controls without Growth 2002

2018 Attainmen t Year Control

Strategy 2018 2018 Fleet and Control

Strategy

1 Activity input s ( VMT mix ; link VMT/sp eeds ; and off

- network activity : SHP, starts, SHI ) were

consistent with the FY 2012 HGB RFP SIP inventories

. 2 E missions factors were based on the EPA’s spe cial model “ MOVES2010bROP ” provided to

TCEQ for use in the VMT offset calculations. “Pre

- 2003 Controls” means attainment year

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

already credited.



### The following act ivities 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), aver age speed, SHP, vehicle

starts, SHI, and totals for the pollutants VOC, CO, NO

x 

### , and C O 2 

### 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

f iles, 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 developmen t 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 a nd 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,

d esign, 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 con tract 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,

Estimati on of Off

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

Quality Assurance , and References

.

SUMMARY OF RESULTS

Table 4 summarizes t he MOVES2010bROP

- based VMT offset inventories (VMT and speed, and

VOC, CO, NO

x 

### , and CO 2 

### emissions) for the HGB region

.

Table 4 . HGB VMT Offset Ozone Season Weekday On

- Road Mobile Source

MOVES2010bROP

- Based Emissions Inventories (Tons)

.

Inventory VMT Speed 1 

### VOC CO NO

x 

### CO 2



### 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 184,06 5,162 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).

OVERV IEW 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 VM T 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 bas e year and RFP 2018 attainment year control

strategy inventories. Adjustments to inventory inputs were made to convert MOVES2010a

based inputs to MOVES2010bROP model input specifications, where needed, and as required

specifically for VMT offset calculati ons.

The inventory method is the detailed, hourly, MOVES rates

- per

- activity

- based, TDM link

based 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, whi ch 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

inventori es (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 activ ity 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 MOVE S 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

Abbreviation 1



### 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

- H aul Truck SUShT

53 Single Unit Long

- H aul Truck SULhT

54 Motor Home MH

61 Combination Short

- H aul Truck CShT

62 Combination Long

- H aul Truck CLhT

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

( e.g., MC_Gas, RT _Diesel, and SBus_Gas are m otorcycles, 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

18 1 

### Refueling Displacement Vapor Los s

19 1 

### Refueling Spillage Loss

90 Extended Idle Exhaust

99 1 

### 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 Process es Activity 1 

### 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 b asic 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 devel opment 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 ra tes: 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.

T he SUT/ fuel type VMT mixes were e stimated 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 deve loping 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

, estima ted 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 T xDOT/TxDM V registration data , along with MOVES defau lt

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 roadway

based emissions calculations (product of “mass per mile” emissions factors and VMT)

.

TTI post

- processed direc tional, 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 estimate s, 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 h ourly average

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

Houston speed model, w hich estimates operational speeds based on a link’s estimated free

- flow

speed and congestion

- related speed reduction.12 

### Vehicle Population a nd Of f

- 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 a nd 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 f actors, 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 population s 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) . T he SUT/ fuel type SHP

estimates were based on VMT mix , link VMT and speeds

, and the veh icle population estimates.

The VMT mix was applied to the link VMT to produce SUT/ fuel type

- specific VMT estimate s

. 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 na tional 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/v ehicle 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 e xtended i dling s tudy, and additional scaling factors developed by TTI. T he 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 th e day.

MOVES Emissions Factors

TTI post

- process ed the MOVES 2010bROP (emissions and activity) output to calculate the

emission s rates in the needed “per activity” unit s (as summarized in Table 7 ). A fundamental

aspect of the emiss ions 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 emissions13 

### rates, via post

- processing of the MOVES activity and emissions output (i.e., emission s 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

“emission s rate ca lculation 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

cal culate 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 NO

x 

### emissions rates (depending on anal ysis 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

emission s factors re flective of the local scenario conditions (e.g., weather and fleet

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

Emissions Calculations

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

as described previo usly, 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

coun ty

- 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 th e 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 s peeds 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 co unty 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 off

network activity, VMT, VHT, and speed by roadway. Appendix A contains more detailed outp ut

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 t hese applications

.



### DEVELOPMENT OF SUT/F UEL 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 VM T 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, w eekday SUT/ fuel type VMT mixe s (for gasoline

powered 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 Count s 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 da ta 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 catego ry (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 cate gories

– 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 dat a 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 a s 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 mod ified 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 Allocatio n

- 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 a pply day

- of

- week factors from urban area

classification count da ta (Friday, Saturday, and Sunday).

TxDOT district

- level w eekday SUT / fuel type VMT mix es 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 w eekday

SUT / fuel type VMT mix es (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 estima ted 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) wee kday (Monday through Friday). T he TRANSVMT utility (see

Appendix B for a description of the utility), the latest available HGB 200 2 and 20 1 8 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 200 2 (dated July 3, 2007 ) and 20 18 ( 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 oth er 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 e stimates,

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 o f

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 through out Texas on a continuous basis. T hese vehicle counts are collected on a17 

### 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

- of

week

, and ti me

- 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 20 10 ) of data from the ATR stations were grouped for th is

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 Ch ambers 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

- da y (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., ozon e season summer weekday). For 2002, which by definition is a historical year (i.e.,

HPMS VMT data exists for 20 0 2 ), 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 exi st), 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 Analys i s

– 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, Mont gomery, 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 y ear 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

Beaumon t 1 

### 1.08998

Houston 2 

### 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

V MT 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 respect ive 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. T able 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 VMT 1 

### VMT Adjustment F actor

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, i ncluding intrazonal VMT.



### 2018 Future Year Analysi s

– HPMS Adjustment Factor

For the 2018 future year analys i s, a n 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 , 20 11), 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 da ta 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 Factor19 

### 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 Analysi s

– Seasonal Adjustment Factors

For the 2018 future year analys i s, seasonal adjustment factors were used to adjust the T DM

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 adjustme nt 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 lo cated 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 Analys i s.

TxDOT District Weekday Seasonal Adjustment Factor

Beaumont 1 

### 1.06428

Houston 2 

### 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 Analys i s

– 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 se ction). 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 201 8 VMT Summary

.

County 201 8

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 throu gh 20 10 )

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 ho urly 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. 0.143 563

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. 0.13389622 

### ESTIMATION OF LINK S PEEDS

The estimated link speeds input s 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 conn ectors 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 ) ratio

based 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 t he

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

S unday); 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 S RF 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 SRF s. The link

- specific SRF was calculated using linear interpola tion. 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:

S V/C 

### = S 0.0



### - SRF V/C 

### × (S 0.0



### - S 1.0 

### )

Where:

S V/C 

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

direction;

S 0.0 

### = estimated free

- flow speed for V/C ratio equal to 0.0;

S 1.0 

### = estimated LOS E speed for V/C ratio equal to 1.0; and

SRF V/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:

S V/C 

### = S 1.0 

### × (1.15/(1.0 + (0.15 × (v/c) 4 

### )))

Where:

S v/c 

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

direction;

S 1.0 

### = 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

e xtension, 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 connect or 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 s peed (VMT/VHT) summaries by county and road type were

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

ESTIMATION OF O FF

- N ETWORK A CTIVITY

The estimated off

- network (or parked vehicle) activity inputs to the emissions inven tory

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 activit y

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 of f

- network activity estimation is

the county

- level vehicle population for each analysis year. Summaries of t he vehicle population

and 24

- hour SHP, starts, and SHI off

- network activity are included as Appendix F . Hourly SHP,

starts, and SHI activity estima tes 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 star ts 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 utili ty. Appendix B

contains a description of the utilities.

E stimation 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 ) were24 

### produced for each county and analysis ye ar. 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 200 2 analysis year was considered a historical year and the

vehicle population estimates were based on the Tx DOT 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 factor s

.

The V MT mix used to estimate the vehicle population is the aggregate (i.e., all road

- type

categories) TxDOT district

- level w eekday SUT / fuel type VMT mix es. 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

c alculated 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 estima tes 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 o f 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 w eight

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 calc ulated for each

SUT/ fuel type combination

. For the non

- long

- haul SUT categories, the SUT/fuel population25 

### 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 reg istration 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 lo ng

- 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 type s.

Table 15

. TxDOT Vehicle Registration Aggregations and Associated SUT/ Fuel Type s for

Estimating SUT/ Fuel Type Populations.

Vehicle Registration 1 

### Aggregation Associated MOVES SUT/ Fuel Type 2



### 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

NA 1 

### 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 w eekday SUT VMT

mix ratio applied to the short

- haul SUT population estimate.

2 The mid

- y ear 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 type s (SULhT_Gas,

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

corresponding short

- haul category vehic le 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

re gistration data sets for which HPMS data exists were used

. Using these registration data sets26 

### 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 county

level 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 fo r 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 estimat e the off

- network emissions using the grams per

activity emissions rates are county

- level w eekday estimate s 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 differen ce 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 mix es 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 w eekday esti mate s 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 t otal available hours by SUT/ fuel type is typ ically 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 b y SUT/ fuel type for the analysis year

.

SHO by SUT/ Fuel Type

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

using the TxDOT district

- level SUT / fuel type VMT mix es by MOVES road

- type category

, which was then divid ed by the link speed to calculate the link SHO by SUT/ fuel type

. Th ese

VMT mix es are the same VMT mix es used to estimate emissions in the emissions estimation

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

agg regating 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 estimate s of starts by hour and SUT/ fuel type

for each analysis year . T he 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 county

level starts by hour and SUT/ fuel type for each analysis year (hourly summaries were provided

electronically to TCEQ; see Appe ndix 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 MOV ES 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 w eekday, 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 emis sions 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 fr om a TCEQ e xtended i dling s tudy, 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., ho urly

SHI values cannot exceed the hourly SHP values). Appendix F includes the 24

- hour summaries

of the county

- level estimate s of SHI by hour and SUT/ fuel type for each analysis year (hourly

summaries were provided electronically to TCEQ; see Appendix A fo r 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 distri ct

- 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 li nk

- 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 tota l s.

The SUT/ fuel type VMT mixes were the same VMT mix es used to estimate emissions in the

emissions estimation process (see Appendix C ). For the base weekday SUT/ fuel type VMT mix,

the 200 5 weekday SUT/ fuel type VMT mix was used.

For each link in the 2 004 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 sp eeds, 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

analys is 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 factor s for 2002 and 2018 were calculated28 

### by div id ing the analysis year and day type CLhT_Diesel 24

- hour VMT by the C LhT_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 analysi s

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, th erefore 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 ca lculate 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 analys is 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 calcul ations (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 inventor y 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 inventorie s 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 mo dified 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

emission s rates are “frozen” (i.e., as in this analysis, 2002)

.

The em issions 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 in29 

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

Transport ation 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 t he

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 201 2

. This MOVES

emissions inventory Technical Guidance and EPA’s 2012 VMT offset guidance, along with

Motor Vehicle Emission Simulator (MOVES) User Guide for MOVES1010 b , EPA, March 201 2 ;

and Update Of On

- Road Inventory Development Methodologies For Motor Vehicle Emissions

Simulator ( Moves) Model Compatibility Technical Report , TTI

, July 2011 (MOVES output post

processing utilities used are summarized in Appendix B of this Tech nical Report ) , are the main

references where additional detail may be found , if desired

.

The thr ee 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 st rategy 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 pollut ant emissions from MOVES2010”)

. In comparing the rates between the two projects, TTI found that the VOC, CO, NO

x 

### , and CO 2



### emission s rates for the scenarios are essentially the same, e.g., within rounding error.

The detailed link

- based emissions inventor y 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 emi ssions

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 . Emission s Rates by Process and Activity Factor.

Emission Process es Activity 1 

### 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

S tart 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 Tru cks 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

aff ect emissions factors

. MOVES default s 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]) an d MOVES CDB (county input database)

. (The MOVES

RunSpecs, CDBs, and MOVES default database provide the data for each local scenario model

run.) L ocal inputs were developed and used to produce emissions factors c haracteristic of the

June through August pe riod peak ozone season average weather conditions, summer fuel

properties

, vehicle fleet characteristics

, and emissi ons 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

- P r ocessing

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 c ontrol 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 e missions rates , by

performing NO

x 

### TxLED effect adjustments for attainment year control strategy scenario

, and for

the Harris 2002 I/M tes t

- type switch effects combines two sets of rates into one, and extracts and

stores the rates for the inventor ied pollut ants 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

(s ee descriptions in Appendix B).

Summary of Control Programs Modeled

Table 17 sh ows 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 Measures 1 

### Method Calendar Year Control Scenario

2002 Base

Year 2018 Pre

- 2003

Controls 2 018 Control

Strategy

Pre

- 2003 FMVCP

Tier 0

Tier 1

National Low Emission

Vehicle Program 

### MOVES √ √ √

Post

- 2002 FMVCP 1



### Tier 2

Heavy

- Duty

2004 Diesel

2005 Gasoline

2007 Gasoline and Diesel



### Highway Motorcycle 2006 MOVES N/A N/A √

RFG 1 

### 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

m aximum, with provisions) MOVES N/A N/A √ (2011 fuel

formulation)

I/M Program 1 

### MOVES √ (2002

program) √ (2002

program) √ ( latest

program )

TxLED Fuel 1 

### Post

- process

diesel vehicle

NO

x 

### 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 pre

2003 scenario, and MOVES default for 2018 control strategy). The only I/M county in 2002 w as Harris County

, which was modeled for the 2002 base year and for the 2018 pre

- 2003 controls scenario ; for the 2018 cont r ol

strategy scenario, I/M was modeled for the five HGB counties under the current I/M program. TxLED effects

were modeled as a post

- p rocessing procedure adjustment to diesel vehicle NO

x emissions for all coun ties, for the

2018 control strategy scenario only.32 

### M OVES Emission s Factor Aggregation Levels

The summer weekday emissions factor look

- up tables produced provide the MOVES emission s

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

- ba sed 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, an d 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 ) (XM L 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 th rough 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 ex tra

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 Spans 1 Time Aggregation Level ;

Years

– Months

– Days

– H ours Hour ;

<YEAR> 1

- July

- Weekday

- All

Geographic

Bounds 2 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

-

Passe nger 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

- Ha ul 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

Processes 3 VOC ; CO ; NO

x ;

Atmospheric CO 2 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

Dat a Sets Additional input database

selections None

Strategies 4 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 NA ME> ;

Pounds , KiloJoules, Miles ;

Distance T raveled, 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

– Polluta nt;

Fuel Type , Emissions Process;

Source Use Type

Advanced

Performance

Measures Aggregation and Data Handling All check boxes are to be “un

- checked”

1 County s cale allows one year and county per run. The years are 2002 (base year) and 2018 ( for the atta inment year

pre

- 2003 controls and control strategy runs).

2 T he counties are Brazoria, Chambers, Fort Bend, Galveston, Harris, L iberty, 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: CO 2 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 bas eline, 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 se lected 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 (s ee 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.

Und er 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

- R oad Vehicle Equipment and Road Type

All of the SUTs assoc iated 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

inclu des 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 analy sis, 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 CO 2 

### 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 tho se included in the CDB.

No additional inputs were included via the Manage Input Datasets panel.

The “ Strat egies

– Rate

- of

- Progress ” feature was used for the 2018 calendar year p re

- 2003

c ontrol s emission s rates modeling scenario

. The check

- box Compute R ate

- 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 model

year 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 out put 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), pol lutant, process, SUT

, and fuel type

.

Appendix A lists the electronic data files provided in support of this analysis, which includes

the M RSs used.

MOVES County Input Databases

T he locality

- specific input data for the county scale runs are entered throug h 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

invent ory 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 thro ugh 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 file s

and databases (e.g., for local fuels and weather data), and MOVES default data from

MOVESDB201 20410 (e.g., for default activity data), and some values provided directly in the

CDB builder MySQL script.

Table 19 provides an out line and brief description of the CDBs, followed by discussion of the

development of the local data and the def aults contained therein. Unless otherwise stated, the

CDB table data applies to all counties, years, and control scenarios.36 

### Table 19

. MOVES2010b ROP County Database (CDB) Inputs.

MOVES Input Table Data

Category Notes

y ear 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 (2 002 for base

year/pre

- 2003 control scenarios, and 2012 for 2018 control strategy scenario)

.

s tate Geography Identifies the state (Texas) for the analysis.

c ounty Geography/

Meteorology Identifies county of analysis and local altitude (“low”) and barometr ic pressure

(used input values from FY2012 HGB RFP inventories).

z onemonthhour Meteorology Contains l ocal, hourly temperature an d relative humidity data for the county

(used input values from FY2012 HGB RFP inventories).

r oadtype 1 Activity Lists MOVES ro ad types with ramp activity fractions (which was set to 0 ). 1

h pmsvtypeyear 2

Activity

(Defaults) Used MOVES defaults

– 1999 national annual VMT by HPMS vehicle

category, except yearID was set to analysis year.

r oadtypedistribution 2 Used MOVES default roa d type VMT fractions.

m onthvmtfraction 2 Used MOVES default month VMT fractions.

d ayvmtfraction 2 Used MOVES default day VMT fractions.

h ourvmtfraction 2 Used MOVES default hour VMT fractions.

a vgspeeddistribution 2 Used MOVES default average speed dis tributions.

s ourcetypeyear 2 Fleet

(Defaults) Used MOVES default

– 1999 national SUT populations, except yearID was

set equal to the analysis year value.

s ourcetypeage

distribution Fleet Used county SUT age fractions consistent with the FY2012 HGB RFP ana lysis,

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.

z one Activity Start, idle, SHP zone allocation factors

. County = zone ; all factors set to 1.0

.

z oneroadtype Activity SHO zone/roadtype allocation factors

. County = zone ; all factors set to 1.0

.

f uelsupply Fuel Contains marketshare information for gasoline and diesel formulations. Used

values consistent with the FY2012 HGB RFP inventories, 2002 base year data

for the 20 02 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 R FP 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 whic h 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 U nrestricted A ccess road type emissions rates were used.

2 U se 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 r ates 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 otherwi se 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 ins erted 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 fuelyeari d 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 met eorological 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” emission s rates, but not a road type for ramps

specifically. In the roadtype table, M OVES 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 activit y; 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 incorpor ate use of ramp rates.

Default Activity and Population CDB Inputs to MOVES

The activity and vehicle population input parameter s under the methodology use the MOVES

defaults. The tables are: hpmsvtypeyear, roadtpyedistribution, monthvmtfaction,

dayvmtfract ion, 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 199 9 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 fact ors 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 o f 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 appr opriate 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 utilit y 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 Distribu tions and Fuel / Engine

Fractions.

SUT Name SUT ID TxDOT Category 1



### Aggregations for Age

Distributions and

Fuel/Engine Fractions Geographic

Aggregation for

Age Distributions Geographic

Aggregation for

Fuel/Engine

Fractions 2



### Motorcycle 11 Motorcycles County N A

– 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 Short

Haul Truck 52 > 8500+ >10000+

>14000+>16000 HGB Region Texas Statewide

Single

- Unit Long

Haul Truck 53 >8500+ >10000+

>14000+>16000 Texas Statewide Texas Statewide

Refuse Truck 51

MOVES default Motor Home 54

Intercity Bus 41

Transit Bus 2 

### 42

School Bus 43

Combina tion

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 200 2 and 2011 (latest avail able) 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 200 2 and 201 8, respectively (weights are GVWR in

units of lbs.). Based on the current MOVES 2010a 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 re vised to exclude the CNG and gasoline

- fueled

component s, 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 hourly40 

### weather station data from the Houston George Bush Intercontinental Airport. Low altitu de 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

Fahre nheit) 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 66.641 

### 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 a rea

- 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 describe s the

procedure used to populate the fuels tables.

• S elected 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 val ues 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 or iginal 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. S ome histor ical 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. S ee 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

usi ng 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 b y 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 t he 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 Emission s Rates Analysis

.

Fuel Formulat ion Field 1 

### 2002 B ase Y ear; and

2018 pre

- 2003 C ontrols 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

aromaticConten t 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 

### T able 23

. MOVES Diesel Inputs

– HGB RFP Summer Emission s Rates Analysis

.

Fuel Formulation Field 1 

### 2002 Base Year; and

2018 pre

- 2003

Controls 2018 Control

Strategy

fuelFormulationID 30307 30011

fuelSubtypeID 20 20

RVP 0 0

sulfurLev el 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

T9 0 0 0

1 D iesel “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 specifie d 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 table44 

### records for Texa s ( that were populated using information from the EPA’s 2005 National

E missions Inventory )

.

The imcoverage table data parameters (by field headers) are:

• polProcessID (pollutant and emission s process affected by the program);

• stateID (state subject to th e 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 c overed);

• 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 ra te, 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 fo llowing 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 m odeling 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 pr ogram in place for HGB in the

2002 base year that included only Harris County.

Data Sources

– TTI p roduced 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 modeling45 

### p arameters 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 di agnostics (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 th e State Implementation Plan Mobile Source Strategies , Texas

Inspection and Mai ntenance 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 ta ble notes describe the MOVES imcoverage records developed

by TTI and used for the HGB analys e s. 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 n o 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.

YearID 1 

### IMprogamID 2 

### begModel

YearID 3 

### endModel

YearID 3 

### testStandardsID Sourcetypeid 4



### Harris County

2 1

(PC

– P assenge r

Car)

31

(PT

– P assenger

Truck)

3 2

(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

perce nt 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 MOV ES 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 s tart THC, CO, NO

x , 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 effec ts

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 f or 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 b ackground, 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. Th is 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 datab ase 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 200 2.

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

HighestFuelPredict iveModelYear 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 (Appendi x 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 dat a 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

me ssages, 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 calculate d “rates

- per

- activity”

for each county from the MOVES output (i.e., emissions di vided by activity

, using the

movesoutput [emissions] and movesactivityoutput [activity] tables) . The process created

two emission s rate tables (per run) that were added to the MOVES output database :

“ttirateperdistance” containing mass/mile emission s rat es, and “ttirateperactivity”

containing mass/SHP, mass/SHI, and mass/start emission s rates. This was performed for

each county, year, and control scenario. See MOVESratescalc utility description in

Appendix B for more details.

• Rates Adjustments : F rom 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 we re 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 1 0/12 for the pre

- May I/M and 2/12 for the post

- April I/M

scenarios . For the 20 1 8 control strategy scenario

, TxLED adjustments were applied to all

diesel vehicle NO

x 

### emissions rates and the extracted and adjus ted rate tables for each

county and calendar ye ar 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 NO

x 

### 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://amd aftp.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 Adjust ment

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/ fo r

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 ut ility 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 ele ctronic data submittal

(see Appendix A).

EMISSIONS CALCULATIO NS

TTI calculated hourly , ozone season weekday, link

- based emissions inventories by county for

each VMT offset demonstration analysis inventory scenario us ing the MOVESemscalc utility

. The emiss ions calculations fall into two categories: VMT

- based emissions calculations and off

network 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

- specif ic,

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;

• Cou nty

- 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, h our,

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 emi ssions 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 design ations. 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 ty pe

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 th e 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 fa ctors 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 Designation s

.

TDM Road Type (Code

- Name) 1 

### TDM Area Type (Code

- Name) 1 

### MOVES Road Type (Code

- Name) 1



### 3

- Toll Roads 5

– Rural

2

– Rural Restricted Access 10

- Rural Interstate 5

– Rural

11

- Rural Other F reeway 5

– Rural

4

- Ramps (Fwy/Toll/Frnt) 5

– Rural

3

– Rural Unrestricted Access 8

- Local (Cent roid Connector) 5

– Rural

12

- Rural Prin cipal Arterial 5

– Rural

13

- Rural Other Art erial 5

– Rural

14

- Rural Major Co llector 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 O ther F reeway 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 Prin cipal Arterial 1

– CBD ; 2

– Urban; 3

– Urban

Fringe

6

- Urban Other Art erial 1

– CBD ; 2

– Urban; 3

– Urban

Fringe; 4

– Suburban

7

- Urban Collector 1

– CBD ; 2

– Urban; 3

– Urban

Fringe

8

- Local (Cent roid Connector 1

– CBD ; 2

– Urban; 3

– Urban

Fringe; 4

– Suburban

12

- Rural Prin cipal Arterial 3

– Urban Fringe; 4

– Suburban

13

- Ru ral Other Art erial 3

– Urban Fringe; 4

– Suburban

14

- Rural Major Col lector 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 comb inations are also correlated to VMT mix road type codes and

emission s 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

- ne twork emissions factors by SUT/fuel type and the county

- level hourly SUT/fuel type off

network activity estimates (SHP, starts, and SHI). The off

- network emissions were calculated at

the county level by multiplying the hourly MOVES

- based off

- network emiss ions 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 p ertinent 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

pol lutant 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 t he four VMT

offset analysis inventories.

Table 28 . HGB VMT Offset Demonstration Emission s Inventory Scenarios.

Inventory Activity

Input Year 1 

### Emissions Factor Input 2



### 2002 Base Year 2002 2002 Base Year

2018 Fleet and Pre

- 2003

Cont rols with Growth 2018 2018 Fleet and Pre

- 2003

Controls 2018 Fleet and Pre

- 2003

Controls without Growth 2002

2018 Attainment Year Control

Strategy 2018 2018 Fleet and Control

Strategy

1 Activity input s ( VMT mix ; link VMT/sp eeds; 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

emiss ion 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 ve rification 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 Manageme nt 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 document s 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 projec t 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 ob jective 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 tha t 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 p roject m anager);

• The required output data sets were produced in the appropriate formats in accordance

with the pre

- a nalysis 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 sa mpling 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 m odel 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

- a nalysis 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; spe ed 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 use d 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 wor kstation 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 file s 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

opt ical 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. A ssessment 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 pr ocesses).

• 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

comm unicated 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 intend ed 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 vario us data elements in the process passed QA checks and

were ready for further processing according to the project pre

- analysis plan. The project

manager ensure d 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 specificatio ns 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 summari zed 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 anal ysis 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 t he 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 t he 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: Verifie d 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 for mats), 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 o f 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

re sults.

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 tra vel 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 s pecifications.

• 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., activ ity 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 deta iled 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 ho urly 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 need ing 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 Vehic le 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 Implemen tation 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).

Fed eral 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 Reg ister

. 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, Nati onal 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, Offic e 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 18 2(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 C ontrol 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

. O ffice 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 Im plementation 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 Nona ttainment 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 Transpor tation 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 f or Motor Vehicle Emissions

Simulator (Moves) Model Compatibility Technical Report. Texas Transportation Institute, The

Texas A&M University System, College Station, TX. July 2011

.

De velopment 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 C onversion 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 I nformation 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

. Novembe r 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 ELE CTRONIC DATA

SUBMITTAL63 

### HGB VMT Offset MOVES 2010bROP

- Ba sed, County

- Level, Emissions Inventorie s

– 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, NO

x 

### , and CO 2 

### . The four HGB emissions

inventories submitted consist of one base year and three attainment year scenarios (i.e., 32

county

- level emissions inv entories), described by activity and emission factor components in the

following table.

Table 29 . HGB VMT Offset Demonstration Emission s Inventory Scenarios.

Inventory Activity

Input Year Emissions Factor Input 1



### 2002 Base Year 200 2 2002 Base Year (CSS)

2018 Attainment Year Fleet and

Pre

- 2003 Controls with Growth 2018 2018 Fleet and Pre

- 2003

Controls (P03) 2018 Attainment Year Fleet and

Pre

- 2003 Controls without Growth 2002

2018 Attainment Year Control

Strategy 2018 2018 Fleet a nd 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 control s 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 FY201 3. ”

• Emissions Files :

o Emissions calculator output

– MOVESemscalc utility TAB

- delimited, hourly/24

- hour

emissions inventory report summary files;

o 24

- hour aggregate inventory e xtracts

– 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 (emission s factor database

inputs to the emissions calculations , produced with TTI MOVES output post

- processors ),

and TxLED adjustment factor file.64 

### File

- N aming 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, 4 8071.... (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.

E missions 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_m vs10brop_4EIs.zip”:

• “HGBvo_mvs10brop_BYR_CSS2002 s wkd_ FFFF _ems.TAB,”

“HGBvo_mvs10brop_AYR_P03 KKKK s wkd_ FFFF _ems.TAB,” and

“HGBvo_mvs10brop_AYR_CSS2018 s wkd_ FFFF _ems.TAB”: 32 tab

- delimited county

emissions inventory data summary reports including hourly and 2 4

- 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; r un 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

- H our aggregate inventory extracts and spreadsheet

– 24

- hour emissions invent ory 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_mvs10bro p_ BYR_CSS2002 s wkd_tabtots_ST.tab,”

“HGBvo_mvs10brop_AYR_P03 KKKK s wkd_tabtots_ST.tab,” and

“HGBvo_mvs10brop_AYR_CSS2018 s wkd_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_CSS2002 s wkd_tabtots.tab,”

“HGBvo_mvs10brop_AYR_P03 KKKK s wkd_tabtots.tab,” and

“HGBvo_mvs10brop_AYR_CSS2018 s wkd_tabtots.tab”: Four inventory type tab

delimited text file of 24

- hour emissions inv entory 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 Fi les (MRSs and CDBs)

MOVES Input File s: 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 count y input databases (CDB)

– were

provided. (The modified MOVES default database made available with the MOVES2010bROP

model by TCEQ, was not included in this submittal.):

• “ MVS10 BROP _HGB VO

_ 2002 SWKD_ CSS

_ FFFF _ER.MRS ,” and

“ MVS10 BROP _HGB VO

_ 2018 SWKD_ 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 “CDBcheck er” 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 MOVES 2010bROP

- based Emission s Rates

Final MOVES

- based, Emi ssions 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

database s 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, fuelTyp eID, rateperactivity . The

ttirateperdistance table fields are: pollutantID, av gSpeedBinID, processID, hourID, roadTypeID,

sourceTypeID, fuelTypeID, ratePerDistance

.

• “ MVS10 BROP _HGB VO

_ 2002 SWKD_ CSS

_ FFFF _ER _outratesadj,” and

“ MVS10 BROP _HGB VO

_ 2018 SWKD_ QQQQ

_ FF FF _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 em issions inventories. Each database

contains the ttirateperdistance and ttirateperactivity emissions rate look

- up tables used in66 

### the emissions calculations. They are compressed in

“hgbvo_mvs10brop_MOVESratesadjDB24.zip;” and

• “ tx_mvs10a_ 2018_ txled_facts.tx t ” (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

- per

activity” 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 Ad justments : Using TTI’s MOVESratesAdj utility, TTI

- calculated rates were

extracted for only those pollutants needed in the emissions calculations . The I/M test

type switch effects adjustment (post

- processing of rates from two runs with different I/M

test

- t ypes 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 NO

x 

### rates for the 2018 attainment year control strategy scenario , and the

extr acted and adjusted rate tables were placed in a separate database (by

county/year/control scenario) for input to the emissions calculations.67 

## APPENDIX B:

E MISSIONS E STIMATION U TILITIES FOR MOVES

- B ASED E MISSIONS

I NVENTORIES69 

### TTI E MISSIONS E STIMATION U TILITIES FOR MOVES

- B ASED E MISSIONS

I NVENTORIES

The f ollowing 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 sourc es 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) acti vity 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,

MOVESfleetInputBui ld, MOVESratesCalc, MOVESratesAdj, ShpExtIdleStartActBld,

ExtIdleHrsCalc , MOVESemscalc, and MOVEStabcomb. The TRANSVMT and

VirtualLinkVMT prepare the link VMT and speeds activity input. The

MOVESactivityInputBuild, MOVESpopulationBuild, and MOVESfleetInp utBuild 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 m akes 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 Ex tIdleHrsCalc 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 period s, producing a tab

delimite d 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 t he 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 multipl ying 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 direc tion

. 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 produ ced by

TRANSVMT are subsequently input to the EMSCALC utility for applying the MO VES

- based

emissions factors.

VirtualLinkVMT

The VirtualLinkVMT utility post

- processes county HPMS AADT VMT, centerline miles, and

lane miles by functional classification and a rea type (from TxDOT’s annual RIFCREC r eport) to

produce hourly, on

- road vehicle fleet, seasonal

, and day

- of

- week specific actual or projected7 0 

### VMT, and directional operational speed estimates

. These estimated VMT and speeds are

produced for up to 42 direc tional 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 r equired

. The main inputs to VirtualLinkVMT are:

• C ounty HPMS data sets, which include AADT VMT, centerline miles, and lane miles by

HPMS area type and functional class;

• C ounty

- level VMT control totals;

• H ourly VMT distributions; and

• S peed model inputs to i nclude 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., u ses county

- level VMT control total

- to

- AADT ratio s 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/C

derived directional delay (minutes/mile) applied to the estimated f ree

- 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 roadtypedistributio n, 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 TRA NSVMT or VirtualLinkVMT ut ility, the VMT

mix, and the MOVES defaults. The primary inputs to this utility are:

• L ink

- based hourly VMT and speeds developed with the TRA NSVMT 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 des ignates 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 su mmary 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 avera ge 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 ar ray 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 roa dway 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 tab

delimited 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 ar e 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 type72 

### VHT. U sing 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 Off

Network 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

av erage 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 da ta 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 t he 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 f actors 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 tab le 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 popula tion 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 gas oline trucks73 

### registr ation data file, and a diesel trucks registration data file) that list 31 years of registration

data. The primary inputs to this utility are:

• C ounty ID file, which specifies the county for which the output will be calculated;

• A ge 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;

• G as oline trucks r egistration 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 gas oline truck categories;

• D iesel 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 sou rce type, and MOVES fuel type;

• TxDOT district name file, which specifies the VMT mix TxDOT district;

• MOVES default database;

• P opulation factor file (optional); and

• Y ear 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 registrat ion data array. The gas oline truck registration data (for

the Gas > 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500, Gas > 26000, Gas >

33000, and Gas > 60000 gas oline truck categories) are saved in the gas oline truck section of the

diesel/gas oli ne 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 truc k section of the

diesel/gas oline registration data array. The age registration data array and the diesel/gas oline

registration data array are combined to form the registration category data array (a total of seven

categories for 31 years of data and the t otal) using the combinations in Table 30

.74 

### Table 30 . Registration Categories.

Registration

Category Vehicle Category Data Location

1 Passenger Vehicle

Age registration data array 2 Motorcycles

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

. S UT /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 a nd 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 th e

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 dividing75 

### the fuel type VMT mix for SUT 5 3 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.

Th e 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, t he 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 t he 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 sales GrowthFactor 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 s ets are three sets of registration data (an

age registration data file, a gas oline 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 registratio n 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;

• Gas oline 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 gas oline truck categories;

• Diesel trucks registrati on 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 val id 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 , Motorc ycles, Trucks <=6000, Trucks >6000 <=8500, and Total Trucks

<=8500 vehicle categories) are saved in an age registration data array. The gas oline truck

registration data (for the Gas > 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500,

Gas > 26000, Gas > 33000, and Gas > 60000 gas oline truck categories) are saved in the gas oline

truck section of the diesel/gas oline registration data array. The diesel truck registration data (for

the Diesel > 8500, Diesel > 10000, Diesel > 14000, Diesel > 16000, Dies el > 19500, Diesel >

26000, Diesel > 33000, and Diesel > 60000 diesel truck categories) are saved in the diesel truck

section of the diesel/gas oline registration data array.

The age registration data array and the diesel/gas registration data array are c ombined 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 ea ch 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 ty pe. 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 Di esel 4

Gas oline 6

61 Diesel 5

Gas oline 7

62 Diesel 5 + 7

Gas oline 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 dat a 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 div iding 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

gas oline 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 fracti ons 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

registra tion 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 ne w 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) usi ng 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 move sactivityoutput 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

uni ts 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

.

MOVES201 0 a Emission s Process Activity Emission s Rate Units

Running Exhaust Miles Traveled Grams/Mile

Crankcase Running Exhaust Miles Traveled Grams/Mile

Start Exhaust Starts G rams/ S tart

Crankcase Start Exhaust Starts G rams/ S tart

Extended Idle Exhaus t E xtended I dle H ours G rams/ E xtended I dle H our

Crankcase Extended Idle Exhaust E xtended I dle H ours G rams/ E xtended I dle H our

Evaporative Permeation Miles Traveled

S ource H ours P arked Grams/Mile

G rams/ S ource Hour P arked

Evaporative Fuel Vapor Venting Mil es Traveled

S ource H ours P arked Grams/Mile

G rams/ S ource Hour P arked

Evaporative Fuel Leaks Miles Traveled

S ource H ours P arked Grams/Mile

G rams/ S ource Hour P arked

Brake Wear Miles Traveled Grams/Mile

Tire Wear Miles Traveled Grams/Mile

For the dist ance 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 poll utants 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 convers ion factor s 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 poll utants 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 calcu la tes 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 pro cessID = 2 (start exhaust) and processID = 16 (crankcase start

exhaust) using the corresponding starts activity (activityTypeID = 7)

.

For th e SHP, starts, and SHI activity per vehicle, the utility extracts the SHP, starts, SHI, and

population activity da ta (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 als o 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, ratepervehicl e, 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 th e 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 t his 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.

ShpEx tIdleStartActBld

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 s tarts 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,

th is 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 f or 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/fue l 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 SH I activity by hour for SUT 62, fuel type 2

( CLhT_Diesel ) used to estimate emissi ons using the MOVESratesCalc emissions rates. This

hourly SHI is calculated using a 24

- hour base SHI for a specific year and day type, base link81 

### 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. T he tab

delimited 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_Dies el 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 calcul ation 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 scalin g 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 hou r 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. Other wise, 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 fac tors (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 uti lity 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 

### • L ink

- based hour ly 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

M OVESratesCalc 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 c ategorized by two basic types based on the type of

emissions factors: the VMT

- based emissions and the off

- network emissions. For the VMT

based emissions (rateperdistance or ttirateperdistance emissions factors), the VMT for each link

is distributed to eac h 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 combi nations listed in the VMT mix. For link speeds greater than 7 5 mph, the emissions

factors for 7 5 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 1 6 MOVES speeds, th e 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 mob ile 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

emissi ons 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 i s 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 charact er) 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

- deli mited 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 Metho dology

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 fa ctor developed from the inverse83 

### 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 (EF Interp 

### ) is expressed as:

EF Interp 

### = EF LowSpeed



### - FAC Interp 

### Η (EF LowSpeed



### - EF HighSpeed 

### )

Where:

EF LowSpeed 

### = emissions factor (EF) corresponding to the speed below the link speed;

EF HighSpeed 

### = EF corresponding to the speed above the link speed; and

FAC Interp 

### = 1 1 1 1

Speed Speed Speed Speed link low high low 

### − 

  

 

− 

  

 

Given that:

EF LowSpeed 

### = 0.7413 g/mi;

EF HighSpeed 

### = 0.7274 g/mi;

Speed lnk 

### = 41.2 mph;

Speed low 

### = 40 mph; and

Speed high 

### = 45 mph.

FAC Interp 

### = 1

41 2 1

40 1

45 1

40

. mph mph mph mph − 

  

 

− 

  

 

= −

− 0 00073

0 00278

.

. = 0.26214;

EF Interp 

### = 0.7413 g/mi

- (0.26214) Η (0.7 413 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 e missions for each hour along with a 24

- hour summary

. A maximum

of 1

, 0 00 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 regiona l 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 tab

delimited 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

Emissions Factors MOVES 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

Summaries 2 MOVES Input

Builders 1 Off-Network

Activity 3

MOVES

Inputs MOVESemscalc

MOVES

Emissions and

Activity MOVESratesCalc85 

## APPENDIX C:

TXDOT DISTRICT WEEKDAY VMT MIX BY MOVES ROAD TYPE87 

### TxDOT District/HGB Counties

T xDOT 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 200 8 through 2012

2015 2013 through 2017

2020 2018 through 202288 

### 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 0.0011 9

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 0.0003 4 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 0.0312 5 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 0.00 000

11_G 0.00055 0.00057 0.00058 0.00065 0.00066 0.00064 0.00075 0.0007289 

### 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 0.00 223 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 0.00 465 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 0.03 681 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 0.0007291 

## APPENDIX D:

TXDOT DISTRICT AGGRE GATE WEEKDAY VMT MIX93 

### 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 thro ugh 202294 

### 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 0.000 000

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 0.00074095 

### 2020 Aggregate Weekday VMT Mix by TxDOT District (2018 Analysis Year)

SUT/FT Be aumont 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 0.00074097 

## APPENDIX E:

CAPACITY FACTORS, SP EED FACTORS, AND SPE ED REDUCTION

FACTORS99 

### Capacity Factors

Time of Day Assignment Capacity Factor 1



### AM Peak 0.33 33333

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 1.081 379 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 Subur ban 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 1.000000101 

### 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

Ramp s 4. Ramps

Locals 8. Locals (Centroid Connectors)

40. Locals (Intrazonals)102



### Figure 1. Freeway Speed Reduction Factors by V/C Ratio. 

# S p e e d R e d u c t i o n F a c t o r s b y V / C R a t i o

F u n c t i o n a l G r o u p 1 ( F r e e w a y s )

C B D U r b a n U r b a n F r i n g e

S u b u r b a n R u r a l V /

C R a t i o s 0

. 0 0 0

. 1 0 0

. 2 0 0

. 3 0 0

. 4 0 0

. 5 0 0

. 6 0 0

. 7 0 0

. 8 0 0

. 9 0 1

. 0 0 0

. 0 0 0

. 2 0 0

. 4 0 0

. 6 0 0

. 8 0 1

. 0 0SpeedReductionFactors103



### Figure 2. Principal Arterial Speed Reduction Factors by V/C Ratio.SpeedReductionFactors

C B D U r b a n U r b a n F r i n g e

S u b u r b a n R u r a l104



### Figure 3. Other Arterial Speed Reduction Fa ctors by V/C Ratio.SpeedReductionFactors

C B D U r b a n U r b a n F r i n g e

S u b u r b a n R u r a l105



### 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 1.00SpeedReductionFactors

C B D U r b a n U r b a n F r i n g e

S u b u r b a n R u r a l106 

### Functional Classification to Functional Group Relationship for the

Application of Speed Reduction Factors

Functional Group Corresponding Network Func tional

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 Collectors107 

## APPENDIX F :

VEHICLE POPULATION E STIMATES AND 24

- HOUR SHP , STARTS,

AND SH I SUMMARIES109 

### 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 3,940110 

### 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 6,17 0 709 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

4 1_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 1,58 9 228 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 9,352111 

### 2002 24

- H our 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 94,448112 

### 20 18 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Ch ambers 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 2,4 89 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 2 49,337 269,096 26,669 361,934 66,753 34,188 224,307113 

### 2002 24

- H our 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 3 0,850 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 1,753114 

### 20 18 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazoria Fort Be nd 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 218,46 2 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 5 78 795 114 800 389 143 297

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 4,161115 

### 24

- H our 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 44 9 466 600 652 699 715 732

Chambers 840 1,575 2,138 2,272 2,420 2,471 2,523

Galveston 481 503 517 550 585 597 609117 

## APPENDIX G :

SOURCE TYPE AGE AND FUEL /E NGINE FRACTIONS INPU TS TO

MOVES119 

### Brazoria County 200 2 Age Distributions Inputs to MOVES

Age M C 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 0.1378 69 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 0.06 8539 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 0.0374 54 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 0.0 38469 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 0.04 0918 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 0.008 449 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 0.0106 17 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

2 5 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 0.0 00828 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 0.016715120 

### Brazoria County 2011 Age Distribution Inp uts 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 0.05465 4 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 0.063 794 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 0.0 43562 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 0.025 506 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 0.0189 11 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 0.00174 7 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 0.0 13089 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 0.020875121 

### C hambers County 200 2 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 0.09112 7 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 0.067 887 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 0.0 87457 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 0.0265 85 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 0.0 09281 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 0.00 8403 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 0.007 108 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 0.0020 35 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 0.016715122 

### 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 0.0339 60

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 0.0356 96 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 0.03390 4 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 0.0 09689 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 0.01 7590

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 0.003 960 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 0.0021 76 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 0.00402 3 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875123 

### 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 0.101 906 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 0.0 67163 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 0.037 208 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 0.0 40580 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 0.04 9789 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 0.009 848 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 0.0132 48 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

2 4 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 0.0 00776 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 0.00 2922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715124 

### 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 0.05397 7 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 0.032 867 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 0.0 91233 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 0.05210 7

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 0.01 9202 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 0.018 696 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 0.0026 98 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 0.00373 1 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 0.020875125 

### 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 0.05907 1 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 0.088 237 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 0.0 72023 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 0.034 250 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 0.01457 7 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 0.0 08221 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 0.00 8149 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 0.002 707 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 0.016715126 

### Galveston County 2011 Age Distribution Inputs to MOVES (2018 Analysis Year)

Age MC PC PT LCT IBus TBus SBus RT SUS hT 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 0.0 18938

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 0.10816 6 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 0.063 120 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 0.0 41523 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 0.044 739 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 0.0266 44 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 0.0 03786 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 0.00 2404 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 0.002 344 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 0.020875127 

### 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 0.06 0557 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 0.0494 83 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 0.05 4443 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 0.0626 90 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 0.00984 8 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 0.0 40114 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 0.001 577 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 0.0025 72 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 0.016715128 

### 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 0.0 62014 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 0.0510 25 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 0.046 624 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 0.0275 72 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 0.04076 1 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 0.0 03071 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 0.00 8281

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 0.003 982 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 0.020875129 

### Liberty County 2002 A ge 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 0.0 67160 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 0.0538 93 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 0.04 9692 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 0.0192 75 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 0.01342 9 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 0.0 03160 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 0.01 1604 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 0.016715130 

### Li berty 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 0.07986 5 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 0.065 269 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 0.0 46621 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 0.0382 21 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 0.03043 5 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 0.01 1716 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 0.015 702 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 0.0056 41 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 0.020875131 

### 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 0.07832 9

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 0.04016 1 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 0.0 47487 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 0.01 2053 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 0.011 549

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 0.0120 98 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 0.00172 1 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 0.0 00582 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 0.016715132 

### 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 0.0 67376 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 0.0683 33 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 0.0497 92 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 0.02121 5 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 0.0 15864 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 0.00 3655 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 0.008 763

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 0.0037 35 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 0.020875133 

### 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 0.05 7086 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 0.04043 6 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 0.050 170 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

1 1 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 0.0 35609 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 0.03 6115 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 0.012 328 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 0.0185 03 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 0.0 13080 0.013092 0.009332 0.009332 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715134 

### 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 0.06941 5 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 0.059 523 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 0.0 28087 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 0.040 292 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 0.01614 1 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 0.0 09916 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 0.01 3214 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 0.001 073 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 0.020875135 

### Texas Statewide 2002 Fuel / Engine Fractions Summary 1

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 0.0 00 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 0.97 7 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 0.50 7 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

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### Texas Statewide 2002 Fuel / Engine Fractions Summary 1 

### (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 0.0 17 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 0.04 2 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 1.00 0 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 1.0 00 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 0.15 0 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 Summary 1

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 0.9 77 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 0.9 58 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 0.7 50

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 0.50 0 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 Convention al internal combustion engine technology only.138 

### Texas Statewide 2018 Fuel / Engine Fractions Summary 1 

### (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 0.99 5

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 1.0 00 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 0.04 2 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 0.6 88 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 SUMMARIES141 

### Appendix H Available Upon Request

Mary McGarry

- Barber

Emissions Assessment Section

mary.mcgarry

- barber@tceq.texas.gov

512.239.1987