### APPENDIX D

REVISIONS TO APPENDI X 9

, HOUSTON

- GALVESTION

- BRAZORIA 1997 EIGHT

HOUR OZONE NONATTAIN MENT AREA REASONABLE FURTHER PROGRESS

ON

- ROAD MOBILE SOURCE E MISSIONS INVENTORIES

, ADOPTED MARCH 10, 20 10

Project Number 2012

- 002

- SIP

- NR

Adoption

April 23, 2013

### HOUSTON

- GALVESTON

- BRAZORIA 1997 EIGHT

- HOUR OZONE

NONATTAINMENT AREA REASONABLE FURTHER P ROGRESS ON

- ROAD

MOBILE SOURCE EMISSI ONS INVENTORIES

This appendix documents the development of the on

- road mobile emissions inventory for the

updates t o the Houston

- Galveston

- Brazoria (HGB ) Reasonable Further Progress (RFP) State

Implementation Plan for the 1997 Eight

- Hour Ozone Standard

.

T he Texas Transportation Institute (TTI ) develop ed the RFP emissions inventories at the

request and under the direction of the Texas Commission on Environmental Quality (TCEQ).

The on

- road mobile source emissions inventories and control strategy reduction estimates

reflect the most recent planning assumptions for the HGB transportation network. Complete

documentat ion of the development and resulting emissions inventory is provided in the

attached document, HGB MOVES

- Based RFP On

- R oad Inventories and Control Strategy

Reductions , February 2012 . The final emissions estimates are summarized in the Executive

Summary in Table 6 : HGB RFP Ozone Season Weekday On

- Road Mobile Source Emissions

Inventories

, Table 7 : HGB Ozone Season Weekday VOC Emissions

– RFP Control Scenario

Inventories (Tons)

, and Table 8 : HGB Ozone Season Weekday NO

x 

### Emissions

– RFP Control

Scenario Invento ries and Reductions (Tons)

. The supporting electronic documents for the

emissions inventory development, including MOVES input and output files and the post

processing spreadsheets

, are available in electronic format upon request. Please contact the

TCEQ, Air Quality Division, Mobile Source Programs Team for a copy of the electronic

information.

The report also documents the development of control strategy reduction estimates for each of

the RFP miles tone years between 2008 and 2018 , and th e contingency analysis year 2019

. Control strategy emission reduction estimates include the effects of the federal motor vehicle

control program (FMVCP) emissions standar ds, the HGB vehicle inspection and maintenance

(I/M) program, federal reform ulated gasoline (RFG)

, and the Texas low

- emission diesel

(TxLED) program . The emissions summaries include estimates for all control scenarios. The

control scenarios are the basis f or quantifying the reductions for each control strategy. The final

emissions estimates for each control scenario are summarized in the Executive Summary in

Tables 7 and 8

.

### February 2012 **Prepared by the** 

## 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** 

# HGB MOVES-Based RFP

On-Road Inventories and

Control Strategy Reductions

## HGB MOVES

- BASED RFP ON

- ROAD INVENTORIES AND

CONTROL STRATEGY REDUCTIONS

TECHNICAL REPORT

FINAL

Prepared for the

Texas Commission on Environmental Quality

Air Quality Planning and Implementation Division

Prepared by the

Transportation Modeling Program

Texas Transportation Institute

TTI Study No.: 600211

- 0001

Grant Activities: 582

- 11

- 11226

– FY 12

- 01

March 2012

TEXAS TRANSPORTATION INSTITUTE

The Texas A&M University System

College Station, Texas 77843

- 3135iii 

## TABLE OF CONTENTS

List of Tables

................................ ................................ ................................ .................... viii

Executive Summary

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

Purpose

................................ ................................ ................................ ............................... 3

Background

................................ ................................ ................................ ......................... 3

Development of On

- Road Mobile Source, Eight

- Hour RFP Emissions Inventories for

the HGB Eight

- Hour Ozone Nonattainment Counties for the Eight

- Hour Ozone Base

Year, Four RFP Milestone Years, th e Attainment Year, and the Contingency Year,

2002, 2008, 2011, 2014, 2017, 2018 and 2019

................................ ............................. 4

Quantification of Individual On

- Road Mobile Eight

- Hour RFP Control Redu ctions

for the HGB Eight

- Hour Ozone Nonattainment Counties for the RFP Milestone

Years, 2008, 2011, 2014, 2017, and 2018

................................ ................................ ...... 7

Development of On

- Road Mobile Source, E ight

- Hour RFP Contingency Reduction

Estimates for the HGB Eight

- Hour Ozone Nonattainment Counties for the Eight

Hour Ozone Contingency Year, 2018 to 2019

................................ ............................... 7

Acknowl edgments

................................ ................................ ................................ ......... 8

Summary of Results

................................ ................................ ................................ ............ 9

Overview of Methodology

................................ ................................ ................................ .. 11

Major EI Process Components

................................ ................................ .................... 14

VMT Mix

................................ ................................ ................................ ...................... 14

On

- Road Fleet Link

- VMT and Speeds

................................ ................................ ......... 15

Vehicle Population and Off

- Network Vehicle Activity Estimates

................................ 15

MOVES Emissions Factors

................................ ................................ .......................... 16

Emissions Calculations

................................ ................................ ................................ 17

Development of SUT/Fuel Type VMT Mix

................................ ................................ ........ 17

Estimation of VMT

................................ ................................ ................................ ............. 19

Data Sources

................................ ................................ ................................ ............... 20

VMT Adjustments

................................ ................................ ................................ ....... 20

Historical Year Analyses

– VMT Control Totals and VMT Adjustments

............. 22

Future Year Analyses

– HPMS Adjustment Factor

................................ .............. 24

Future Year Analyses

– Seasonal Adjustment Factors

................................ ......... 24

Future Year Analyses

– Intermediate Year Adjustment Factors

.......................... 25

Future Year Analyses

– VMT Summary

................................ ............................... 25

Hourly Travel Factors

................................ ................................ ................................ . 26iv 

### Estimation of Link Speeds

................................ ................................ ................................ 29

Estimation of Off

- Network Activity

................................ ................................ .................. 30

Estimation of Vehicle Population

................................ ................................ ............... 30

Historical Vehicle Population Estimates

................................ ............................... 31

Future Vehicle Population Estimates

................................ ................................ ... 32

Estimation of SHP

................................ ................................ ................................ ...... 33

Total Available Hours by SUT/Fuel Type

................................ ............................. 33

SHO by SUT/Fuel Type

................................ ................................ ........................ 33

Estimation of Starts

................................ ................................ ................................ .... 33

Estimation of SHI

................................ ................................ ................................ ....... 34

SHI Scaling Factors

................................ ................................ ............................... 34

SHI Hourly Factors

................................ ................................ ............................... 34

County

- Level CLhT_Diesel SHI by Hour Estimation

................................ .......... 35

Estimation of Emissions Factors

................................ ................................ ...................... 35

Development of Pre

- 1990 Controls and Control Strategy Scenario Emissions Factors

................................ ................................ ................................ ................................ ..... 37

MOVES Inputs, Outputs and Post

- Processing

................................ ........................... 37

Summary of Control Programs Modeled

................................ ................................ .... 38

MOVES Emissions Factor Aggregation Levels

................................ ........................... 38

MOVES Run Specifications

................................ ................................ ........................ 39

Scale, Time Spans, and Geographic Bounds

................................ ......................... 42

On

- Road Vehicle Equipment and Road Type

................................ ....................... 42

Pollutants and Processes

................................ ................................ ...................... 42

Manage Input Data Sets and Strategies

................................ ................................ 42

Output

................................ ................................ ................................ ................... 43

MOVES County Input Databases

................................ ................................ ................ 43

User Inputs to MOVES via CDB

– Locality

- Specific Inputs and Defaults Used

........ 45

Year, State, and County Inputs to MOVES

................................ ........................... 45

Roadtype Table Inputs to MOVES

................................ ................................ ........ 45

Default Activity and Population CDB Inp uts to MOVES

................................ ...... 45

Local Fleet Age Distributions and Fuel Fractions Inputs to MOVES

................... 46

Local Meteorological (County and Zonemonthhour Table) Inputs to MOVES

... 47

Fuels Inputs to MOVES

................................ ................................ ........................ 49v 

### Local I/M Inputs to MOVES

................................ ................................ .................. 51

Checks and Runs

................................ ................................ ................................ ......... 54

Post

- Processing Runs

................................ ................................ ................................ . 55

Emissions Factors for Estimation of Individual Control Reductio ns

......................... 56

Emissions Calculations

................................ ................................ ................................ ..... 58

Hourly Link

- Based Emissions Calculations

................................ ............................... 58

HGB RFP Emissions Inventories

................................ ................................ ................. 61

Individual Control Measure Emissions Reductions Estimation

................................ 62

Quality Assurance

................................ ................................ ................................ ............. 64

A. Project Management

................................ ................................ .............................. 64

B. Data Generation and Acquisition

................................ ................................ ........... 65

C. Assessment and Oversight

................................ ................................ ...................... 66

D. Data Validation and Usability

................................ ................................ ................ 67

References

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

Appendix A: HGB RFP On

- Road Inventories Electronic Data Submittal

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

Appendix B: Emissions Estimation Utilities for MOVES

- Based Emissions Inventories

. 80

TTI Emissions Estimation Utilities for MOVES

- Based Emissions Inventories

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

TRANSVMT

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

VirtualLinkVMT

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

MOVESactivityInputBuild

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

MOVESpopulationBuild

................................ ................................ ............................. 85

MOVESfleetInputBuild

................................ ................................ ............................... 88

MOVESratesCalc

................................ ................................ ................................ .......... 91

MOVESratesAdj

................................ ................................ ................................ .......... 92

ShpExtIdleStartActBld

................................ ................................ ............................... 93

ExtIdle HrsCalc

................................ ................................ ................................ ............ 93

MOVESemscalc

................................ ................................ ................................ ........... 94

Emissions Factor Interpolation Methodology

................................ ...................... 95

MOVEStabcomb

................................ ................................ ................................ ......... 96

Appendix C: TxDOT District VMT Mix by Day of Week

................................ .................. 98

Appendix D: TxDOT District Aggregate Weekday VMT Mix

................................ .......... 110

Appendix E: Annually Compounded Growth Rates and Intermediate Year Adjustment

Factors

................................ ................................ ................................ ............................. 118vi 

### Appendix F: Capacity Factors, Speed Factors, and Speed Reduction Factors

................ 124

Appendix G: Vehicle Population Estimates and 24

- Hour SHP, Starts, and SHI

Summaries

................................ ................................ ................................ ....................... 134

Appendix H: Source Type Age and Fuel/Engine Fractions Inputs to MOVES

............... 158

Appendix I: MOVES Run Summaries

................................ ................................ .............. 198viii 

## LIST OF TABLES

Table 1. HGB Region Ozone Season Weekday RFP Adjusted Base Year Emissions

(Tons/Day).

................................ ................................ ................................ ............... 2

Table 2. HGB Region Ozone Season Weekday RFP Pre

- 1990 Controls Emissions

(Tons/Day).

................................ ................................ ................................ ............... 2

Table 3. HGB Region Ozone Season Weekday RFP Control Strategy Emissions

(Tons/Day).

................................ ................................ ................................ ............... 2

Table 4. HGB Ozone Season Weekday VOC and NO

x 

### Emissions

– RFP Control Scenario

Inventories and Reductions (Tons).

................................ ................................ .......... 3

Table 5. HGB RFP Emissions Inventory Scenarios.

................................ ................................ ........ 5

Table 6. HGB RFP Ozone Season Weekday On

- Road M obile Source Emissions

Inventories (Tons).

................................ ................................ ................................ ... 10

Table 7. HGB Ozone Season Weekday VOC Emissions

– RFP Control Scenario

Inventories and Reductions (Tons).

................................ ................................ ......... 11

Table 8. HGB Ozone Season Weekday NO

x 

### Emissions

– RFP Control Scenario

Inventories and Reductions (Tons).

................................ ................................ ......... 11

Table 9. MOVES Source Use Type/Fuel Types.

................................ ................................ ............. 12

Table 10. MOVES Model Emissions Processes.

................................ ................................ ............. 13

Table 11. Emissions Rates by Process and Activity Factor.

................................ ........................... 14

Table 12. VMT Mix Year/Analysis Year Correlations.

................................ ................................ ... 19

Table 13. Analysis Year/TDM Year Designatio n.

................................ ................................ .......... 22

Table 14. HGB Weekday Factors for Control Total Development.

................................ ............... 23

Table 15. HGB 2002 Weekday VMT Control Totals and VMT Adjustment Factors.

................... 23

Table 16. HGB 2008 Weekday VMT Control Totals and VMT Adjustment Factors

.................... 24

Table 17. HGB Weekday Seasonal Adjustment Factors for Future Year Analyses.

....................... 25

Table 18. HGB 2011, 2014, and 2017 VMT Summary.

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

Table 19. HGB 2018 and 2019 VMT Summary.

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

Table 20. Weekday Hourly Travel Factors.

................................ ................................ .................. 28

Table 21. Registration Data Categories.

................................ ................................ ......................... 31

Table 22. TxDOT Vehicle Registration Aggregations and Associated SUT/Fuel Types for

Estimating SUT/Fuel Type Populations.

................................ ................................ 32

Table 23. Emissions Rates by Process and Activity Factor.

................................ .......................... 36

Table 24. Control Measure Modeling by RFP Control Scenari o.

................................ ................. 38

Table 25. Pre

- 1990 Controls and Control Strategy Scenarios

– MRS GUI Panel

Selections.

................................ ................................ ................................ ................ 40

Table 26. MOVES CDB Input Tables.

................................ ................................ ........................... 44

Table 27. Data Sources and Aggregations for SUT Age Distributions and Fuel/Engine

Fractions.

................................ ................................ ................................ .................. 47

Table 28. Meteorological Inputs to MOVES.

................................ ................................ ................ 48

Table 29. MOVES Gasol ine Inputs

– HGB RFP Summer Emissions Rates Analysis.

................. 50ix 

### Table 30. MOVES Diesel Inputs

– HGB RFP Summer Emissions Rates Analysis.

...................... 51

Table 31. MOVES IMCoverage Table Input Descriptions for HGB I/M Counties.

....................... 54

Table 32. RFP Analysis Year TxLED Adjustment Factor Summary.

................................ ............. 56

Table 33. Harris County Emissions Factor Modeling Control Scenarios and Sequence.

............. 57

Table 34. HGAC TDM Road Type/Area Type to MOVES Road Type Designations.

................... 60

Table 35. HGB RFP Emissions Inventories.

................................ ................................ ................. 62

Table 36. HGB RFP EI Control Scenarios for Indvidual Control Measure Emissions

Reductions Estimates.

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

Table 37. HGB RFP On

- road Mobile Inventory Scenario Descriptions and Data Labels.

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

Table 38. Registr ation Categories.

................................ ................................ ................................ 87

Table 39. SUT/Registration Category Correlation.

................................ ................................ ...... 87

Table 40. SUTs/Registration Categories Correlation for SUT Age Distribution.

......................... 89

Table 41. SUTs/Registration Categories Correlation for Fuel/Engine Fractions.

........................ 90

Table 42. MOVES2010a Emissions Process and Corresponding Activity for Grams per

Activity Emissions Rates.

................................ ................................ ......................... 9 11 

### EXECUTIVE SUMMARY

The Motor Vehicle Emission Simulator (MOVES) model for production of on

- road emissions

inventories (EIs) to be included with future State Implementation Plan (SIP) submissions was

officially released by the U.S. Environmental Protection Agency (EPA) on March 2, 2010. The

Texas Commission on Environmental Quality (TCEQ) will submit future SIPs to the EPA as

required under the eight

- hour ozon e standard and these submissions must be developed using the

MOVES model. T he HGB R easonable Further Progress (RFP) SIP will require an RFP analysis

from the 2002 base year to the 2018 attainment year, to demonstrate the HGB nonattainment

area’s (i.e., th e eight counties of Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty,

Montgomery, and Waller ) progress toward a ttainment of the EPA’s eight

- hour ozone standard.

The HGB RFP SIP analysis will require RFP EIs for: the 2002 base year, the 2018 atta inment

year, the four milestone years (2008, 2011, 2014, 2017), and for the year after the attainment

year (2019), for contingency measure analyses. Individual control measure reductions will be

needed for each inventory year, excluding the base year. Th e inventor ies require the latest

planning assumptions to assure the motor vehicle emissions budgets set by the SIP will be

consistent with transportation conformity analysis assumptions

.

The Texas Transportation Institute (TTI) developed and produced RFP on

- road mobile

source EIs for the seven HGB RFP analysis years of 2002, 2008, 2011, 2014, 2017

, 2018 , and

2019 . The required on

- road inventories include: a 2002 base year; 2002, 2008, 2011, 2014, 2017

and 2018 adjusted base year s ; future year s without po st

- 1990 controls for 2008, 2011, 2014,

2017

, 2018 , and 2019 ; and future year s with control strategies (i.e., with post

- 1990 controls) for

2008, 2011, 2014, 2017

, 2018 , and 2019. TTI also estimated volatile organic compounds ( VOC )

and oxides of nitrogen ( NO

x 

### ) emissions aggregate control measure reductions for the future

control scenarios (difference between pre

- 1990 and post

- 1990 control scenarios), as well as for

individual control measures. Since MOVES does not provide for separating the effects due to

the individual components of the federal motor vehicle control program (FMVCP) such as Tier

1, Tier 2, and the 2007 heavy

- duty diesel vehicle certification standard, TTI calculated the effect

of FMVCP as one control reduction.

TTI used the hourly, travel demand model (TDM), MOVES

- based “rates

- per

- activity” EI

method (a TTI

- developed alternative “external” EI calculation process) to produce hourly

emissions estimates by MOVES source use type (SUT) and fuel type, pollutant and emissions

process for each of the HGB nonattainment area counties for the ozone season weekday. The

inventory process employs methods consistent with the EPA guidance on the use of MOVES for

emissions inventory preparation in SIPs and transportation conformity.

Table 1 through Table 4 show summaries o f the HGB ozone season w eekday adjusted base

year, pre

- 1990 control, and control strategy EIs of VOC and NO

x 

### , and aggregate control measure

reductions. A more detailed summary is provided in the following sections , along with the

methods used and details of mo deling input usage and development

.2 

### Table 1

. HGB Region Ozone Season Weekday RFP Adjusted Base Year Emissions

( T ons/ D ay)

.

Year VMT Speed VOC NO

x



### 2002 128,145,285 36.40 205.76 552.30

2008 128,145,285 36.40 205.89 578.29

2011 128,145,285 36.40 214.76 601.92

2014 128,145,285 36.40 210.66 600.98

2017 128,145,285 36.40 208.69 599.24

2018 128,145,285 36.40 208.33 598.95



### Table 2

. HGB Region Ozone Season Weekday RFP Pre

- 1990 Controls Emissions

( T ons/ D ay)

.

Year VMT Speed VOC NO

x



### 2008 145,079,180 37.04 233.94 653.33

2011 157,480,120 36.64 266.44 744.52

2014 168,350,216 36.63 279.29 793.84

2017 179,999,154 36.07 298.20 850.60

2018 184,065,162 35.87 304.27 870.89

2019 188,226,423 35.65 311.70 891.79



### Table 3

. HGB Region Ozone Season Weekday RFP Control Strategy Emissions

( T ons/ D ay)

.

Year VMT Speed VOC NO

x



### 2002 128,145,285 36.40 124.47 371.89

2008 145,079,180 37.04 92.91 238.13

2011 157,480,120 36.64 85.05 213.57

2014 168,350,216 36.63 65.04 156.03

2017 179,999,154 36.07 54.34 118.17

2018 184,065,162 35.87 51.84 109.98

2019 188,226,423 35.65 50.28 102.683 

### Table 4 . HGB Ozone Season Weekday VOC and NO

x 

### Emissions

– RFP Control Scenario

Inventories and Reductions (Tons).

Emissions Analysis 2008 2011 2014 2017 2018 2019

VOC Pre

- 90 Control 233.94 266.44 279.29 298.20 304.27 311.70

Control Strategy 92.91 85.05 65.04 54.34 51.84 50.28

Reductions 141.03 181.38 214.25 243.86 252.44 261.42

NO

x 

### Pre

- 90 Control 653.33 744.52 793.84 850.60 870.89 891.79

Control Strategy 238.13 213.57 156.03 118.17 109.98 102.68

Reductions 415.20 530.95 637.81 732.43 760.91 789.11



### PURPOSE

The purpose of this work was to develop RFP on

- road mobile source EIs , control strategy

reduction estimates, and contingency plan reduction estimates for the 2002, 2008, 2011, 2014,

2017, 2018 and 2019 analysis years , for the HGB ozone nonattainment area

. The inventories

will be use d to support HGB RFP SIP analysis, planning, and development. The development of

the inventories included consideration of the requirements for transportation conformity.

BACKGROUND

The TCEQ works with local planning districts, the Texas Department of Tra nsportation

(TxDOT), and TTI to provide on

- road mobile source EIs 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 wo rk in support of the Federal

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

Standards (NAAQS, 42 U.S. Code Annotated 7409), and the study and control of hazardous air

pollutants, including those from motor vehicle s and/or motor vehicle fuels (as mandated under

CAA sections 202 and 211).

TCEQ is planning an update to the HGB RFP SIP which will require RFP analysis from the

base year of 2002 to an attainment year of 2018 to demonstrate continued progress toward

att ainment of the EPA’s eight

- hour ozone standard for the eight

- county nonattainment area of

HGB: Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, and Waller

c ounties. To complete the HGB RFP SIP analysis, RFP inventories are required f or 2002, 2008,

2011, 2014, 2017 and 2018. Individual control measure reduction estimates are required for

2008, 2011, 2014, 2017 and 2018. Contingency measure control reduction estimates are

required for 2019.

Although RFP inventories and control reduc tions were previously developed to support a

2018 HGB RFP SIP, those inventories and control strategy estimates were developed using

MOBILE6.2.03. At the time, MOBILE6.2.03 was the newest version of the EPA’s on

- road

mobile emission s factor model. In Mar ch 2010, the EPA released a new mobil e source4 

### emissions model, MOVES

. T o incorporate MOVES, the HGB RFP SIP inventories will need to

be updated. The inventory updates will also include the latest planning assumptions to assure the

motor vehicle emissions budgets set by the SIP will be consistent with transportation c onformity

analysis assumptions.

As part of the inventory development for the RFP analysis, on

- road mobile inventories for

the eight

- hour RFP base, milestone, and attainment years need ed to be developed. TTI

developed and produced the required eight

- hour RFP, on

- road mobile source EIs for the HGB

eight

- hour RFP 2002, 2008, 2011, 2014, 2017 and 2018 analysis years . The required on

- road

inventories include: a base year inventory for 2002; adjusted base year inventories with a 2002

base year for 2002, 2008, 2011, 2014, 2017 and 2018; future year inventories without post

- 1990

controls for 2008, 2011, 2014, 2017 and 2018; and future year inventories with control strategies

for 2 008, 2011, 201 4, 2017 and 2018.

The RFP demonstration includes quantifying control reductions that will meet the RFP

requirements for each milestone year. Generally, it is best to have control strategy reductions

estimated for each control strategy rather than bundle d as a total reduction for a given source. As

a part of the inventory work, TTI developed and provided emissions reduction estimates for each

on

- road mobile source control for the four eight

- hour RFP milestone years and the attainment

year , 2008, 2011, 20 14, 2017 and 2018. TTI used standard methodology for quantifying

individual control reductions for on

- road mobile sources using MOVES2010a. Since MOVES

does not provide for separating the effects due to the individual components of the FMVCP such

as Tier 1, Tier 2, and the 2007 heavy

- duty diesel vehicle certification standard, the effect of

FMVCP was calculated as one control reduction.

The RFP demonstration also includes quantifying control reductions that will meet the RFP

contingency measure requirem ents for the year following the attainment year. There are

particular methods that have been established for quantifying individual control reductions for

on

- road mobile sources for RFP contingency analysis. The contingency reduction estimate

procedure w as based on methods agreed upon during consultation between TTI and TCEQ

. TTI

developed and furnished contingency measure emissions reduction estimates for each on

- road

mobile source control between the attainment year and the year following the attainmen t year,

2018 to 2019. This work was conducted by and completed consistent with the list of references.

Development of On

- Road Mobile Source, Eight

- Hour RFP Emissions Inventories for the

HGB Eight

- Hour Ozone Nonattainment Counties for the Eight

- Hour Ozone Base Year,

Four RFP Milestone Years, the Attainment Year, and the Contingency Year, 2002, 2008,

2011, 2014, 2017, 2018 and 2019

TTI developed link

- based emissions inventory estimates for the eight HGB ozone nonattainment

counties, for the seven RFP analysi s years, 2002, 2008, 2011, 2014, 2017, 2018 and 2019. For

the 2002 eight

- hour RFP base year, there are two inventories: an RFP base year inventory

, and

an RFP adjusted base year inventory. For each of the four RFP milestone years, 2008, 2011,

2014 and 20 17, and the attainment year, 2018, there are three inventories: an RFP adju sted base

year based upon 2002, an RFP inventory with pre

- 1990 controls only

, and an RFP inventory with

pre and post

- 1990 control strategies. For the 2019 contingency year , there a re two inventories:

an RFP inventory with pre

- 1990 controls only

, and an RFP inventory with pre and post

- 1990

control strategies.5 

### F or the HGB area RFP inventories to be consistent with inventory development for other SIP

analyses, the most recent activit y information, based upon current travel demand modeling, and

the newest version of the EPA’s on

- road mobile source emission model, MOVES2010a, were

used to complete the work . The RFP inventories were produced based on methods agreed upon

in consultation with the TCEQ Project Representative. The methods were consistent with the

EPA’s RFP guidance. Individual control reduction calculations were consistent with the

capabilities of MOVES.

T TI conformed to the following:

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

newest version of MOVES, MOVES2010a, released August 2010.

• The pollutants included in the inventory work are VOC, carbon monoxide (CO), NO

x 

### , and carbon dioxide (CO 2 

### ).

• The day type for all the inventories was ozone

- season, daily. Activity levels were

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

• The temperatures were consistent with the eight

- hour, ozone season temperatures as

determined using the EPA guidance and provided by TCEQ

.

• The humidity input was developed using the same methodology as the ozone season

temperatures and was provided by TCEQ.

• The vehicle miles traveled (VMT) mixes were consistent with the EPA MOVES

source use types. The most current VMT mix was used.

• TTI u se d 2002 and 2008 vehicle registration distributions for locality

- specific age

distributions for the RFP analysis years 2002 and 2008 , and u se d the most recent year

vehicle registration distributions developed from the TxDOT registration data as input

for lo cality

- specific MOVES age distributions for future RFP analysis years, 2011,

2014, 2017, 2018 and 2019.

• A link

- based, time

- of

- day emissions analysis methodology was used for all of the

referenced counties.

• Control program parameters, including Reid V apor P ressure (RVP) and fuel settings

were determined based upon the inventory type, adjusted base year, pre

- 1990 control

, and control strategy. The control program parameters and fuel settings used by TTI

for this analysis were developed in consultation with T CEQ

.

• Y ear

- specific TxLED adjustment factors developed using the established method

were used. These were developed and provided by TCEQ

.

Table 5 lists the RFP EI sc enarios, along with the activity and emissions rate components

comprising each inventory.

Table 5 . HGB RFP Emission s Inventory Scenarios.

Number RFP Inventory Activity Input Emissions Factor6 

### Year* Input 1



### 1 2002 Base Year

2002 2002 Base Year

2 2002 Adjusted Base Year 2002 Pre

- 1990 Control

3 2008 Adjusted Base Year 2008 Pre

- 1990 Control

4 2011 Adjusted Base Year 2011 Pre

- 1990 Control

5 2014 Adjusted Base Year 2014 Pre

- 1990 Control

6 2017 Adjusted Base Year 2017 Pre

- 1990 Control

7 2018 Adjusted Base Year 2018 Pre

- 1990 Control

8 2008 Pre

- 1990 Control 2008 2008 Pre

- 1990 Control

9 2011 Pre

- 1990 Control 2011 2011 Pre

- 1990 Control

10 2014 Pre

- 1990 Control 2014 2014 Pre

- 1990 Control

11 2017 Pre

- 1990 Control 2017 2017 Pre

- 1990 Control

12 2018 Pre

- 1990 Control 2018 2018 Pre

- 1990 Control

13 2019 Pre

- 1990 Control 2019 2019 Pre

- 1990 Control

14 2008 Control Strategy 2008 2008 Control Strategy

15 2011 Control Strategy 2011 2011 Control Strategy

16 2014 Control Strategy 2014 2014 Control Strategy

17 2017 Control Strategy 2017 2017 Control Strategy

18 2018 Control Strategy 2018 2018 Control Strategy

19 2019 Control Strategy 2019 2019 Control Strategy

1 “Pre

- 1990 Control” emissions factors model future evaluation year fleets with pre

- 1990 controls. Th erefore , no

Inspection and Maintenance (I/M) programs was modeled, and the MOVES model switch to “turn off” post

- 1990

FMVCP emissions standards was used, along with summer 1992 fuel parameters (which includes the 1992 RVP

limit promulgated prior to enactment of the 1990 Clean Air Act Amendments [CAAA]). “Control Strategy”

emissions factors model the controls based on the current regulations (i.e., pre

- 1990 and all subsequent controls).

Activity input includes VMT mix, link VMT/speeds, and off

- network activity ( source hours parked [ SHP ] , starts,

source hours idling [ SHI ] ).7 

### Quantification of Individual On

- Road Mobile Eight

- Hour RFP Control Reductions for the

HGB Eight

- Hour Ozone Nonattainment Counties for the RFP Milestone Years, 2008, 2011,

2014, 2017, and 2018

TTI developed emissions reduction estimates for each on

- road mobile source control strategy for

the four RFP milestone years, 2008, 2011, 2014 and 2017 and the attainment year, 2018. The

entire MOVES2010a control strategy reduction was subdivided into individual control

reductions using a methodology with MOVES2010a agreed to by the TCEQ

. The methods were

consistent with the EPA’s RFP guidance. T he methodology included turning on successive

control strategies and rerunning the emission s model. F or the HGB area RFP control reduction

estimates to be consistent with other SIP analyses, the emissions reduction estimates were

developed using the newes t version of MOVES, MOVES2010a, released August 2010. The

MOVES2010a inputs used for this work were consistent with the associated inventory work

described previously.

The order in which the individual controls were added sequentially into the model run s for

individual control measure reduction estimates was:

• Pre

- 1990 Control (Initial “uncontrolled” run);

• Reformulated Gasoline (RFG) (i.e., Pre

- 1990 Controls and RFG);

• FVMCP (i.e., Pre

- 1990 Controls, RFG, and post

- 1990 FMVCP);

• I/M Program (i.e., Pre

- 1990 Controls, RFG, post

- 1990 FMVCP, and I/M program);

and

• TxLED (i.e., all of the above, and TxLED).

Development of On

- Road Mobile Source, Eight

- Hour RFP Contingency Reduction

Estimates for the HGB Eight

- Hour Ozone Nonattainment Counties for the Eight

- Hour

Oz one Contingency Year, 2018 to 2019

TTI developed emissions reduction estimates for each on

- road mobile source control strategy for

the HGB eight

- hour RFP contingency measure analysis year, 2018 to 2019. The entire

MOVES2010a control strategy reduction for 2019 was subdivided into individual control

strategy reductions using a methodology with MOVES2010a agreed to by TCEQ. The methods

were consistent with the EPA’s RFP guidance. The methodology include d turning on successive

control strategies and re

- runn ing the emission s factor model. F or the HGB area RFP

contingency measure reduction estimates to be consistent with other SIP analyses, the emissions

reduction estimates were developed using the newest version of MOVES, MOVES201a, released

August 2010. Th e MOVES2010a inputs were consistent with the HGB RFP inventory work, and

the RFP control reductions work for the other analysis years, described previously.

The order in which the individual controls were added sequentially into the model runs for

the pu rpose of individual control measure reduction estimates for the contingency was the same

as the individual control measure modeling protocol previously outlined for the milestone years.8 

### To achieve maximum flexibility for subsequent analyses and use of th e on

- road mobile

source inventories produced under this task, TTI provided the detailed, link

- based inventories

summarized for each county and analysis year in the most recent tab

- delimited EI summary file

format provided by TTI to TCEQ; and in a format co mpatible for uploading to TCEQ’s Texas

Air Emissions Repository (TexAER). TexAER has recently been updated to be compatible with

the most recent version of the EPA’s National Emission Inventory Format, the Consolidated

Emissions Reporting Schema (CERS) wri tten in Extensible Markup Language (XML). MOVES

source use types are not consistent with the EPAs source classification code (SCC) system for

reporting inventories in CERS. A mapping of the MOVES source use types to the EPAs SCCs

will need to be establis hed. TCEQ will work with the EPA to establish the methodology to

incorporate MOVES

- based inventories into the SCCs and the CERS XML format. The

methodology is to be provided to TTI. The schedule for completion of the XML files is

contingent upon when TTI receives the required mapping information from TCEQ. This

deliverable can be completed within two months after receipt of the mapping information.

The following activities were completed:

• Prepared county

- level hourly and 24

- hour day tables that provide roadway and source

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

hours parked (SHP), vehicle starts, extended idle hours (SHI), and totals for the

pollutants VOC, CO, NOx, and CO2 by associated emissions processes. These files

are tab

- delimited for ease of loading into spreadsheet software such as Microsoft®

Excel.

• Prepared county

- level 24

- hour day spreadsheet tables that provide estimates of VOC

and NOx emissions reductions due to post

- 1990 CAA on

- road mobile source

emiss ions control programs, in aggregate, and by individual post

- 1990 CAA control

programs.

• Prepared TexAER ready formatted inventory files (to be provided within two months

subsequent to TTI’s receipt of the needed mapping convention from TCEQ for use in

conve rsion of MOVES

- based EIs to CERS XML format).

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

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

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

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

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

–

Electronic Data Submittal.

Acknowledgments

Dennis Perkinson, Ph.D., L.D. White

, Gary Lobaugh, and Martin Boardman, all of TTI,

contributed to the development of the MOVES link

- based emissions estimates. Dr. Perkinson

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

categories, county VMT control totals, and hourly VMT factors. Houston

- Galveston Area

Council (HGAC) staff provided the HGB regional travel model data sets. White processed

roadway based activity (VMT and speeds) and off

- network vehicle activity estimates (and9 

### vehicle population estimates) needed for the emissions calculations. Lobaugh produced MOVES

model and MOVES output post

- processor set

- ups, and Boardman produced MOVES

- based

emissions factors with adj ustments for Texas Low

- Emissions Diesel (TxLED) fuel. Boardman

prepared MOVES model county input databases and performed the emissions runs. Chris Kite

of TCEQ provided the TxLED fuel adjustment factors and Mary McGarry

- Barber provided

meteorological inp ut data. Lobaugh was additionally responsible for editing, design, and

production of this Technical Report. Each member of the assigned TTI staff contributed to the

quality assurance of the EI elements

. Dr. Perkinson was the principle investigator for t his

project

. This work was performed by TTI under contract to TCEQ . Mary McGarry

- Barber was

the TCEQ project technical manager.

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

Methodology, Development of SUT/Fuel Type VMT Mix, Estimation of VMT , Estimation of

Link Speeds, Estimation of Off

- Network Activity, Estimation of Emissions Factors, Emissions

Calculations, and Quality Assurance.

SUMMARY OF RESULTS

Table 6 summarizes the RFP inventories (VMT and speed, and VOC, CO, NO

x 

### , and CO 2 

### emissions) for the HGB region. The emissions reductions estimates from the incremental

inclusion of control measures in t he modeling are summarized for VOC in Table 7 , and for NO

x 

### in Table 8

.10 

### Tab le 6 . HGB RFP Ozone Season Weekday On

- Road Mobile Source Emissions Inventories

(Tons).

Inventory

Type Year VMT Speed 1 

### VOC CO NO

x 

### CO 2



### Adjusted

Base Year 2002 128,145,285 36.40 205.76 2,571.38 552.30 70,360.17

2008 128,145,285 36.40 205.89 2,544.53 578.29 69,761.69

2011 128,145,285 36.40 214.76 2,680.25 601.92 69,573.04

2014 128,145,285 36.40 210.66 2,658.41 600.98 69,500.22

2017 128,145,285 36.40 208.69 2,639.31 599.24 69,476.11

2018 128,145,285 36.40 208.33 2,638.01 598.95 69,471.74

Pre

- 1990

Controls 2008 145,079,180 37.04 233.94 2,878.39 653.33 78,547.29

2011 157,480,120 36.64 266.44 3,301.92 744.52 85,648.60

2014 168,350,216 36.63 279.29 3,493.90 793.84 91,383.35

2017 179,999,154 36.07 298.20 3,728.70 850.60 98,223.10

2018 184,065,162 35.87 304.27 3,801.47 870.89 100,688.94

2019 188,226,423 35.65 311.70 3,893.88 891.79 103,190.26

Base Year

and Control

Strategy 2002 128,145,285 36.40 124.47 1,414.97 371.89 72,346.10

2008 145,079,180 37.04 92.91 896.04 238.13 82,009.28

2011 157,480,120 36.64 85.05 858.00 213.57 89,258.69

2014 168,350,216 36.63 65.04 737.30 156.03 93,685.20

2017 179,999,154 36.07 54.34 672.20 118.17 96,938.26

2018 184,065,162 35.87 51.84 663.10 109.98 97,995.85

2019 188,226,423 35.65 50.28 660.53 102.68 98,955.50

1 Average speed in miles

- per

- hour.11 

### Table 7 . HGB Ozone Season Weekday VOC Emissions

– RFP Control Scenario Inventories

and Reductions (Tons).

Emissions Analysis 2008 2011 2014 2017 2018 2019

Inventory Pre

- 90 Control 233.94 266.44 279.29 298.20 304.27 311.70

Control Strategy 92.91 85.05 65.04 54.34 51.84 50.28

Reductions Total 141.03 181.38 214.25 243.86 252.44 261.42

RFG 22.30 22.79 17.27 14.12 13.48 13.23

FMVCP 109.17 148.83 188.98 222.89 232.44 241.94

I/M 9.56 9.77 7.99 6.86 6.51 6.25

TxLED 0.00 0.00 0.00 0.00 0.00 0.00



### Table 8 . HGB Ozone Season Weekday NO

x 

### Emissions

– RFP Control Scenario Inventories

and Reductions (Tons).

Emissions Analysis 2008 2011 2014 2017 2018 2019

Inventory Pre

- 90 Control 653.33 744.52 793.84 850.60 870.89 891.79

Control Strategy 238.13 213.57 156.03 118.17 109.98 102.68

Reductions Total 415.20 530.95 637.81 732.43 760.91 789.11

RFG 150.64 189.54 213.44 235.00 241.29 248.09

FMVCP 241.17 319.72 409.05 486.84 510.15 532.43

I/M 17.35 16.62 11.80 8.03 7.10 6.43

TxLED 6.03 5.08 3.52 2.55 2.36 2.16

RFP inventory and individual control measure emissions reductions estimates with more

detail (e.g., by county, SUT/fuel type) may be found in the electronic data submittal (see

description in Appendix A).

OVERVIEW OF METHODOLOGY

A detailed, hourly, TDM link

- based, MOVES “rates

- per

- activity”

- based EI method was used to

produce the hourly emissions estimates by MOVES SUT/fuel type (see Table 9 ), pollutant, and

emission process (see Table 10 ) for each county and year, and for each RFP inventory type.

This method is an adaptation of the previous TDM link

- bas ed emissions inventory method

used with MOBILE6. In addition to the VMT

- based emissions calculations for roadway

- based

emissions processes, the model now uses off

- network activity measures (i.e., starts, hours12 

### parked, extended idling hours) with MOVES mod el

- based emissions rates in these off

- network

activity units ( Update of On

- Road Inventory Development Methodologies for MOVES Model

Compatibility , TTI, July 2011). “Rates

- per

- activity” is emphasized because the standard

MOVES off

- network emission rate loo k

- up table output for external emissions calculations

provides rates in terms of pollutant mass per vehicle; however, the TTI method requires post

processing of the MOVES output into rate tables that include the off

- network process emissions

rates based in units of activity, rather than the MOVES “per vehicle” type of rates. This method

was first implemented by TTI in the development of link

- based emissions inventories as

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).

Table 9 . MOVES Source Use Type/Fuel Types.

Source Use Type ID Source Use Type Description Source Use T ype

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 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.13 

### Table 10

. 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 Loss

19 1 

### Refueling Spillage Loss

90 Extended Idle Exhaust

99 1 

### Well

- to

- Pump

1 Not subject to on

- road mobile emissions analysis.



### W ith TTI’s MOVES link

- based emissions estimation utility, in addition to VMT for

roadway

- based emission s processes, either vehicle populations (numbers of vehicles) or off

network activity measures are needed to estimate emission s for the off

- network emissions

processes . The method of choice requires rates

- per

- activity as opposed to rates

- per

- vehicle. The

major difference is that t his “rates

- per

- activity” method require s: 1) estimation of the associated

off

- network activity, rather than just numbers of vehicles , and 2) post

- processing of the MOVES

output into rate tables that include the off

- network process emissions rates based in units of

activity, rather than the MOVES “per vehicle” type of rates. Table 11 shows t he emission s rate

units with associate d processes and activity factors used in this MOVES “rates

- per

- activity”

-

based analysis.14 

### Table 11

. Emission s Rates by Process and Activity Factor

.

Emissions Process Activity Factor 1 

### Emission s Rate Units

Running Exh aust VMT Grams per mile (g/mi)

Brakewear VMT g/mi

Tirewear VMT g/mi

Evap orative Permeation VMT; SHP g/mi; g/shp

Evaporative Fuel Vapor Venting VMT; SHP g/mi; g/shp

Evap orative Fuel Leak s VMT; SHP g/mi; g/shp

Crank case Run ning Exh aust VMT g/mi

Start Exh aust Starts g/start

Crank case Start Exh aust Starts g/start

Crank case Ext ended Idle Exhaust SHI g/shi

Ext ended Idle Exh aust SHI g/shi

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

factors

. SHI is for Combination Long

- Haul Trucks only.



### Major EI Process Components

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

components

. All are inputs to the emissions calculations, except for vehicle populations, which

are intermediate input s needed for calculating estimates of SHP and vehicle starts.

• Area SUT/ fuel type VMT m ix ;

• County, hourly link f leet VMT and average speeds ;

• County SUT/ fuel type v ehicle p opulations ;

• County, hourly SUT/ fuel type SHP ;

• County, hourly SUT/ fuel type s tarts ;

• County, hourly SUT/ fuel type SHI ; and

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

.

VMT Mix

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

of on

- road flee t VMT attributable to each SUT / fuel type by MOVES road type.15 

### T he SUT/ fuel type mixes were estimated using TTI’s SUT/ fuel type mix method

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

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

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

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

meth od produced a set of 24

- hour average SUT/ fuel type VMT allocations by MOVES road

type

, estimated for each TxDOT district associated with the eight

- county HGB region (i.e.,

Houston and Beaumont Districts) , for use by analysis year

. The data sources used were recent,

multi

- year TxDOT vehicle classification counts , year

- end T xDOT/TxDM V registration data

, along with MOVES default data where needed.

On

- Road Fleet Link

- VMT and Speeds

The TDM link

- based method (as used in the prior, recent MOBILE6 analyses) 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 directional, four

- period time

- of

- day, TDM network traffic assignment s

and trip matrix data provided by HGAC to produce the hourly, directional, link VMT (consistent

with Highway Performance Monitoring System [HPMS] VMT estimates for each year) and

associated average fleet speed estimate s, reflective of the ozone season wee kday (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 ou rly average operational fleet speeds were

estimated corresponding to the link VMT estimates using the Houston speed model, w hich use s

the link’s free

- flow speed in combination with the link’s congestion

- related speed reduction

estimate.

Vehicle Population a nd Off

- Network Vehicle Activity Estimates

The non

- roadway travel

- related emissions estimates (e.g., from vehicle starts, parked vehicle

evaporative permeation and tank vapor venting, and extended idling) were calculated as the

product of the amount of ass ociated activity and the pollutant mass per unit of activity (rate units

as shown in Table 11 ). To estimate the SHP and vehicle starts, SUT/ fuel type category

populat ion estimates were needed, whereas SHI was based on HGB county

- specific actual

estimates ( Heavy

- Duty Vehicle Idle Activity and Emissions Characterization Study

, ERG, August

2004)

.

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

SUT/ fuel type VMT mix

- based vehicle population factors, and for future years, VMT

- based

growth estimates. For historical years, the SUT/ fuel type vehicle population estimates were

based solely on mid

- year TxDOT (or Texas Division of Mot or Vehicles [ TxDMV ] ) county

registrations data and regional, all roads

- weekday SUT/ fuel type VMT mix

- based population

factors for the analysis year . For future years, SUT/ fuel type population s were estimated as a

function of base (e.g., latest available, mid

- year) registrations, grown to a 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.16 

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

source hours of operation (SHO) on roads (SHO, which is the same as VHT) . For historical

years, the 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 estimates

. Link VMT was divided by the associated speed to produce SHO

estimates

, which were subtracted from source hours resulting in SHP estimates

. This was

performed for eac h county by year and hour . For the future years, the SUT/ fuel type SHP was

estimated in the same manner as for historical years, except using the future year link VMT and

speeds, VMT mix, and the vehicle population estimates.

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

local , county SUT/ fuel type vehicle population estimates

. MOVES default weekday starts per

vehicle are for the applicable day type and were used for this ozone season weekday analysis.

The starts were calculated as the product of starts/v ehicle from MOVES, and the county

SUT/ fuel type population estimates.

SHI : The SHI (for Combination Long

- Haul Trucks) was estimated based on information

from a TCEQ e xtended i dling s tudy, and additional sc aling factors developed by TTI. The 2004

idle activity study produced 2004 summer weekday extended idling hours 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 extended idling

activity for each analysis year summer weekday s cenario . SHI hourly factors (estimated as the

inverse of the hourly VMT factors) were used to allocate the 24

- hour SHI to each hour of the

day.

MOVES Emissions Factors

TTI post

- process ed the MOVES (emissions and activity) output to calculate the emission s rates

in the needed activity unit s (as summarized in Table 11 ).

Basic to the rates

- per

- activity emissions rates modeling method, the activity inputs used in

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

of emissions rates, via post

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

emissions divided by activity). The actual local, year and scenario

- specific activity estimates for

each county are then used in the EI calculations outside of MOVES.

Look

- up tables of MOVES emissions factors were developed (post

- processed from

“emission rate calculation type” MOVES model runs) by pollutant, process, speed (for roadway

based processes), hour, road type, and average S UT/ fuel type . MOVES outputs were post

processed in two ways: 1) to calculate the emissions rates from emissions and activity output,

and 2) to extract the rates for only those pollutants needed in the emissions calculations , and

apply TxLED adjustments to each county’s diesel vehicle NO

x 

### emission s rates as well as

combining effects of two I/M test

- types where the test

- type change occurred in the preceding 12

month I/M cycle (e.g., Harris County, 2002 Base Year).

County

- level emissions factors were develo ped for each evaluation year, and for the two RFP

control scenarios: 1) Pre

- 1990 Controls, and 2) Control Strategy (or “current controls”). Local17 

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

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

properties, and I/M program). To estimate the individual control reductions, an additional set of

MOVES runs was performed by sequentially adding in post

- 1990 CAA controls to th e Pre

- 1990

Scenario; the order used was: RFG, FMVCP, I/M Program, and TxLED.

Emissions Calculations

In general, emissions were calculated for each county/year/RFP inventory type scenario using

the major inputs as described previously, and summarized here: TxDOT district

- level 24

- hour

SUT/ fuel type VMT mix by MOVES road type; county, hourly link on

- road fleet VMT and

speed estimates; county hourly off

- network activity estimates by SUT/ fuel type of SHP, starts,

and SHI; and the county

- level look

- up tables of activity

- based hourly emissions rates by

SUT/ fuel type and emissions process.

For the VMT

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

designation was used to ma tch 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

SUT/ fuel types . Emissions rates for each link’s average speed were interpolated (see procedure

in Appe ndix B) from the set of look

- up table rates and corresponding index speeds (i.e., the

average bin speeds of 2.5, 5.0, 10.0, 15.0, ... 75.0 mph), bounding the link’s average speed. For

link speeds below or above the minimum and maximum average bin speeds of 2.5 and 75 mph,

the rates for those bounding speeds were used. The estimated SUT/ fuel type/MOVES road

type/link speed

- specific emissions factors for each pollutant process were then multiplied by the

associated VMT to produce the link

- based emissions esti mates. This process was executed for

each hour.

For the off

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

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

the pollutant process. This process was executed for each hour.

The emissions estimates are organized in a tab

- delimited output file for the specified 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, vehicle hours traveled (VHT), and speed by roadway (more detailed

output definitions and specifications are listed in Appendix A).

TTI developed and mai ntains a series of computer utilities to calculate and summarize

detailed on

- road mobile source EIs in various formats, such as those used in this analysis.

Appendix B describes these applications.

DEVELOPMENT OF SUT/F UEL TYPE VMT MIX

The SUT/ fuel type VM T mix is a major input to the MOVES link

- based emissions estimation

process. It is an estimate of the fraction of on

- road fleet VMT attributable to each SUT by fuel

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

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

factors in the link

- emissions calculations.18 

### TxDOT district

- level, 24

- hour average, w eekday SUT/ fuel type VMT mixes (for gasoline

powered and diesel

- powered vehi cles) 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/ fue l 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 data

into the five MOVES road types.

• Define MOVES vehicle categories. For example, PV21

– Passenger vehicles

equivalent to FHWA C minus .001 for MCs.

• Define MOVES vehicle categories

- Passenger and Light Commercial Trucks

— Separates FHWA light

- truck category (P) into passenger trucks and li ght commercial

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

disaggregation is similar to the MOBILE6 distinction between the two primary LDT

categories (LDT12 and LDT34).

• Define MOVES vehicle categories

– Single

- Unit Trucks RTF51

— These are refuse

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

combined FHWA single

- unit truck category total (SU2, SU3

, and SU4). To be

modified as improved or locally

- specific data become available.

• Define MOVES ve hicle 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 c ounts. 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 combine d FHWA single

- unit truck category total (SU2, SU3

, and SU4). To be modified as improved or locally

- specific data become available.

• Define MOVES vehicle categories

- Buses (approximate MOBILE6 defaults)

— To

be modified as improved or locally

- specific data become available.

• Define MOVES vehicle categories

- Combination Trucks Short

- Haul versus Long

Haul (CSH61 and CLH62) per SUT_HDX9 and SUT_CSHX

— Separates

combination trucks into short

- haul and long

- haul based on local (TxDOT district)

registrations versu s observed vehicles from the classification counts. District

allocations verified against statewide allocation.19 

### • Define MOVES vehicle categories

- MCs

— Nominal default value taken from

passenger cars (FHWA C). To be modified as improved or locally

- specif ic data

become available.

• Fuel Type Allocation

- PV and LDT fuel type allocation per TxDOT registration data

and MOVES defaults (21, 31, and 32) per AgeReg9X and MF_Fuel

— Other fuel

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

local or regional data become available, or from the MOVES national default

database (though this latter option is not recommended). Note allocation of fuel type

varies with analysis year.

• Fuel Type Allocation

- Single Unit and Combination Trucks per TxDOT registration

data per SUT_HDV9

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

minimus

.

• Aggregate and Calculate MOVES SUTs and a pply day

- of

- week factors from urban

area classification count data (Friday, Saturday, and Sunda y).

TxDOT district

- level weekday 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 weekday

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

included as Appendix D. To ensure general applicabil ity 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 12

.

Table 12 . 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 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 charact eristic of a typical summer ozone season (June

through August) weekday (Monday through Friday). T he TRANSVMT utility (see Appendix B

for a description of the utility), the latest available HGB 200 2, 2008, and 20 1 8 TDMs, and post

processing factors develop ed from several other data sources, were used to produce this hourly20 

### 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), 2008 ( dated April 25, 2011)

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

estimate the seasonal sub

- period day types

. The first data source is HPMS VMT 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 of

VMT and require that VMT used to construct on

- road mobile source emissions estimates be

consistent with that reported through HPMS.)

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

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

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

average daily basis (i.e., AADT) . The counts are very well suited for making seasonal, day

- of

week

, and time

- of

- day comparisons (e.g., seasonal adjustment and hourly allocation factors),

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

Multiple years ( 2000 through 20 10 ) of data from the ATR stations were grouped for this

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

to produce the day

- type

- specific adjustment fac tor, in which the data from the ATR stations

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

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

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

This data source was also used to product the time

- of

- day (hourly) allocation factors, in which

the data from the ATR stations with in the eight

- county region was combined.

VMT Adjustments

For each analysis year, the designated TDM VMT (see Table 13 ) was adjusted for HPMS

consistency and for seasonality (i.e., ozone season summer weekday). For 2002 and 2008, which

by definition are historical years (i.e., HPMS VMT data exists for those years), county

- level

VMT control totals were used. For the remaining analysis years (2011, 2014, 2017, 2018, and

2019), which are considered future years (i.e., HPMS VMT data does not exist), a regional

HPMS factor and seasonal weekday factors were used. However, a TDM does not exist for

2011, 2014, 2017, and 2019. For 2011, 2014, and 2017, intermediate year factors were

developed using the bounding TDMs (i.e., 2008 and 2018) and applied to the analysis year’s

respective TDM. Since 2019 does not have bo unding TDMs, intermediate year factors were

developed using the 2008 and 2018 TDMs for a one year increment and applied to the 2018

TDM. Hourly travel factors were also applied to distribute this adjusted VMT over each hour of

the day.2122 

### Table 13 . Analysis Year/TDM Year Designation

.

Analysis Year TDM Year

2002 2002

2008 2008

2011 2008

2014 2018

2017 2018

2018 2018

2019 2018

Historical Year Analyses

– VMT Control Totals and VMT Adjustments

To estimate the HPMS

- consistent ozone season summer weekday for each 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 the respective a nalysis year 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 distri cts, two

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

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

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

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

calculated by dividing the average day

- of

- week count by the AADT traffic count. The same

weekday adjustment factors were used for each historic al year analysis. Table 14 shows the

HGB weekday factors used in developing the VMT control totals.23 

### Table 14 . HGB Weekday Factors for Contr ol Total Development

.

TxDOT District Weekday Adjustment Factor

Beaumont 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.



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

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

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

dev elop the county

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

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

analysis year TDM to produce eight county

- level VMT adjustment factors for each his torical

analysis year. 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 produc e the link

- level HPMS consistent,

period day

- type

- specific VMT estimates for each analysis year. Table 15 and Table 16 show the

weekday VMT control totals, the total TDM VMT, and the VMT adjustment factors for 2002

and 2008 respectively.

Table 15 . HGB 2002 Weekday VMT Control Totals and VMT Adjustment Fact ors

.

County VMT Control Total TDM VMT 1 

### VMT Adjustment Factor

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

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

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

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

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

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

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

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

1 2002 TDM, i ncluding intrazonal VMT.24 

### Table 16 . HGB 200 8 Weekday VMT Control Totals and VMT Adjustment Factors

County VMT Control Total TDM VMT 1 

### VMT Adjustment Factor

Harris 106,762,742 108,268,747.25 0.986090120

Brazoria 6,106,303 6,713,738.53 0.909523505

Fort Bend 9,047,192 10,367,818.80 0.872622504

Waller 2,003,832 2,142,409.75 0.935316877

Montgomery 10,219,441 11,554,714.19 0.884439098

Liberty 2,391,466 2,679,389.43 0.892541401

Chambers 2,630,376 2,909,594.51 0.904035249

Galveston 5,917,828 5,854,126.47 1.010881474

1 2008 TDM, i ncluding intrazonal VMT.



### Future Year Analyses

– HPMS Adjustment Factor

For the future year analyses, a n HPMS adjustment factor was used to adjust the total VMT

(TDM assignment VMT plus intrazonal VMT estimate) for HPMS consistency for each 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 produc e the annual non

- summer weekday traffic (ANSWT) adjustment factor

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

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

f

ormula for the HPMS factor calculation is:

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

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



### 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)



### Future Year Analyses

– Seasonal Adjustment Factors

For the future year analyses, s easonal adjustment factors were used to adjust the TDM and

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

factors were developed using aggregated ATR data for the years 2000

– 2010. Since the HGB

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

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

the Bea umont TxDOT District) and one factor was developed for Harris, Galveston, Fort Bend,

Brazoria, Waller, and Montgomery counties (which are located in the Houston TxDOT District).

These factors were calculated by dividing the average day

- of

- week (weekday) c ount by the

ANSWT traffic count. Table 17 shows the seasonal adjustment factors by TxDOT district.25 

### Table 17 . HGB Weekday Seasonal Adjustment Factors for Future Year Analyses.

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.



### Future Year Analyses

– Intermediate Year Adjustment Factors

For those analysis years where a TDM does not currently exist (i.e., 2011, 2014, 2017, and

2019), intermediate year adjustment factors were used to estimate the analysis year VMT from

an existing TDM. These adjustment factors were developed using the bounding year TDMs

(2008 and 2018) and applied to the TDM as specified in Table 12 . However, the 2019 analysis

year does not fall between the two available TDMs. For this analysis year, these adjustment

factors were calculated for a one year increment (i.e., 2009 fr om the 2008 TDM) and applied to

the 2018 TDM. The intermediate year adjustment factors were based on the annually

compounded growth rates between the 2008 and 2018 TDM. The annual growth rates were then

converted into the intermediate year adjustment fac tors using the following equation:

Intermediate Year Adj. Factor = Growth Rate Target Year

- Base Year



### Where:

Target Year

= the desired intermediate year;

Base Year

= the year of the TDM used for estimating the VMT; and

Growth Rate

= the annual growth rate from the range of TDM years encompassing

the Target Year.



### To maintain the consistency between counties and the four time periods in the TDM, these

adjustment factors were developed for each time period and county. Appendix C shows the

annually com pounded growth rates and intermediate year adjustment factors for each analysis

year without a TDM by county and time period.

Future Year Analyses

– VMT Summary

For each future year (i.e., 2011, 2014, 2017, 2018, and 2019), t he final HPMS

- consistent, day

t ype specific VMT is comprised of two parts

– the link

- level VMT and the estimated intrazonal

VMT. The volume for each link was multiplied by the HPMS factor , the seasonal adjustment

factor, and the link’s respective length to estimate the link

- level VMT ( hourly factors were

applied to distribute the resulting VMT over each hour of the day, discussed in a later section).

The HPMS and seasonal adjustment factors (as well as the hourly factors mentioned previously )

were also applied to the estimated intrazon al VMT. For those future years where TDMs do not

exist (i.e., 2011, 2014, 2017, and 2019), the appropriate intermediate year VMT factors were

applied to the volume for each link prior to the VMT calculation and to the estimated intrazonal

VMT. Table 18 and Table 19 show the TDM and ozone season weekday VMT summaries.26 

### Table 18 . HGB 2011, 2014, and 2017 VMT Summary

.

County 2011 1 

### 2014 2 

### 2017 2



### TDM Weekday TDM Weekday TDM Weekday

Harris 108,268,747 112,150,530 132,292,219 119,099,696 132,292,219 126,479,514

Brazoria 6,713,739 7,112,391 8,841,141 7,724,647 8,841,141 8,389,625

Fort Bend 10,367,819 11,163,056 14,411,651 12,322,273 14,411,651 13,601,903

Waller 2,142,410 2,252,971 2,752,993 2,428,988 2,752,993 2,618,792

Montgomery 11,554,714 12,311,835 15,512,473 13,449,280 15,512,473 14,691,870

Liberty 2,679,389 3,081,051 3,377,161 3,302,582 3,377,161 3,540,045

Chambers 2,909,595 3,333,782 3,623,749 3,560,694 3,623,749 3,803,063

Galveston 5,854,126 6,074,503 7,194,417 6,462,057 7,194,417 6,874,342

1 Based on 2008 TDM with intermediate year VMT factors

. 2 Based on 2018 TDM with intermediate year VMT factors

.



### Table 19 . HGB 201 8 and 201 9 VMT Summary

.

County 201 8 201 9 1



### TDM Weekday TDM Weekday

Harris 132,292,219 129,039,721 132,292,219 131,651,759

Brazoria 8,841,141 8,623,775 8,841,141 8,864,461

Fort Bend 14,411,651 14,057,330 14,411,651 14,528,011

Waller 2,752,993 2,685,309 2,752,993 2,753,519

Montgomery 15,512,473 15,131,088 15,512,473 15,583,443

Liberty 3,377,161 3,622,935 3,377,161 3,707,767

Chambers 3,623,749 3,887,468 3,623,749 3,973,748

Galveston 7,194,417 7,017,537 7,194,417 7,163,715

1 Based on 2018 TDM with intermediate year VMT factors

.



### Hourly Travel Factors

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

day. These hourly travel factors were developed using multi

- year (2000 through 20 10 )

aggregated ATR station data for the eight

- county HGB region. To maintain VMT proportions

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

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

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

then multiplied by the link volume (i n 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 were27 

### also multi plied by the estimated intrazonal VMT to produce the final hourly

- adjusted VMT.

Table 20 shows the weekday hourly travel factors.28 

### Table 20

. Weekday Hourly Travel Factors

.

Assignment Hour Weekday

AM Peak 6:00 a.m. 0.319975

7:00 a.m. 0.368425

8:00 a.m. 0.311600

Mid

- Day 9:00 a.m. 0.160270

10:00 a.m. 0.155898

11:00 a.m. 0.162983

12:00 p.m. 0.168764

1:00 p.m. 0.171777

2:00 p.m. 0.180308

PM Peak 3:00 p.m. 0.238603

4:00 p.m. 0.259721

5:00 p.m. 0.275395

6:00 p.m. 0.226281

Overnight 7:00 p.m. 0.204604

8:00 p.m. 0.159440

9:00 p.m. 0.143563

10:00 p.m. 0.113907

11:00 p.m. 0.077553

12:00 a.m. 0.043044

1:00 a.m. 0.028404

2:00 a.m. 0.026089

3:00 a.m. 0.024941

4:00 a.m. 0.044559

5:00 a.m. 0.13389629 

### ESTIMATION OF LINK S PEEDS

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

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

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

- flow speed factor

and level of service [LOS] E speed factor) and a volume

- to

- capacity (V/C) 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 in to seven functional groups. Appendix D shows the speed factors and the network

functional class and functional group relationship.

The link

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

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

v/c ratio = Vh / Ch



### Where:

Vh

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

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

Sunday); and

Ch

= the hourly lin k capacity (travel model capacity × hourly capacity factor).

Appendix F shows the hourly capacity factors.



### After the V/C ratio was calculated, the link

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

the link

- specific SRF area type, the link

- specific S RF 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 D

shows these speed reduction factors. The link

- specific SRF was calculated using linear

interpolation. For V/C ratios greater than 1.0, a SRF is not required.

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

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

.



### For V/C ratios g reater 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 

### )))30 

### Where:

S v/c 

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

direc tion;

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

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

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

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

centroid connector travel m odel input speeds were used as the centroid connector operational

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

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

The hourly and 24

- hour speed (VMT/V HT) summaries by county and road type were

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

ESTIMATION OF OFF

- NETWORK ACTIVITY

To estimate the off

- network (or parked vehicle) emissions using the grams per activity emission s

rates (i.e., grams per SHP, grams per start, and grams per SHI), county

- level estimates of the

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

One of the main components of the SHP and starts off

- network activity estimation is the county

level vehicle population for each analysis year. Summaries of t he vehicle population and 24

-

hour SHP, starts, and SHI off

- network activity are included as Appendix G. Hourly SHP, starts,

and SHI activity estimates are in cluded with the detailed EI data provided (see inventory data file

descriptions in Appendix A).

The county

- level vehicle population estimates were developed using the

MOVESpopulationBuild utility. The county

- level SHP and starts by hour and SUT/fuel type

estimates were developed using the ShpExtIdleStartActBld utility. The county

- level SHI by

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

contains a de scription of the utilities.

Estimation of Vehicle Population

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

starts off

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

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

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

applicable), and SUT/fuel type VMT mix.

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

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

factors) exists. Therefore, the 200 2 and 2008 analysis years were considered historical years and

the vehicle po pulation estimates were based on the TxDOT registration data for the analysis

year . Since the 2011 HPMS VMT data was not available, the 2011 analysis year (along with the

2014, 2018, 2018, and 2019 analysis years) were considered future analysis years (i. e., TxDOT31 

### registration data does not exist) . For the future analysis years, 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 VMT mix used to estimate the vehicle population is the aggregate (i.e., all road

- type

categories) TxDOT district

- level weekday SUT / fuel type VMT mix es. The development of

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

Mix” section and included as Appendix D.

Historical Vehicle Population Estimates

The county

- level vehicle population estimates for the historical analysis years (2002 and 2008)

were calculated using the analysis year county

- level

, mid

- year TxDOT v ehicle registrations and

the assigned aggregate SUT/ fuel type VMT mix (see Table 12 and Appendix F). The vehicle

estimation process assumes that all of the non

- long

- h aul SUT category populations for a county

are represented in the county vehicle registrations data

. This process also estimates the long

- haul

category populations as an expansion of the county registrations

. There are three main steps in

the vehicle estimation process: registration data category aggregation, calculation of the

SUT/ fuel type population factors, and estimation of the county

- level vehicle population by

SUT/ fuel type

.

The first step in the vehic le estimation process is the registration data category aggregation

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

. Table 21 shows these five categories.

Table 21

. Registration Data Categories

.

Registration Data Category Vehicle Registration Aggregation

1 Motorcycles

2 Passenger Cars (PC)

3 Trucks <= 8.5 K GVWR (pounds)

4 Trucks > 8.5 and <= 19.5 K GVWR

5 Trucks > 19.5 K GVWR



### The second step is calculating the SUT/ fuel type population factors

. Using the assigned

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

SUT/ fuel type combination

. For the non

- long

- haul SUT categories, the SUT/fuel population

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

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

. For

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

PT_Diesel + LCT_Gas + LCT_Diesel)

. For the long

- haul SUTs, the SUT/ fuel type population

factors were calculated by taking the ratio of the long

- haul and short

- haul VMT mix values

. For32 

### examp le, the SULhT_Gas population factor using SUT mix fractions is

SULhT _Gas/SUShT_Gas

. Table 22 shows the vehicle registration aggregations and their

associated MOVES SUT/ fuel type s.

Table 22

. 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

- year TxDOT county registrations data extracts were used (i.e., the three

- file data set consisting

of: 1

- ligh t

- 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 vehicle population and the associated long

- haul population

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

SUT mix fraction/ SUShT_Gas SUT mix fraction]).

Future Vehicle Population Estimates

The process for estimating the county

- level vehicle population estimates for the future analysis

years (2011, 2014, 2017, 2018, and 2019) is very similar to the historical vehicle population

estimates except that instead of using the analysis year registration data sets, the most recent

(2010) mid

- year TxDOT registration data sets for which HPMS data exists were used

. Using

these registration data sets and the assigned VMT mix, the base SUT/ fuel type population for

2010 was calculated

. To estimate the future analysis year county

- level vehicle populations,

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 for the analysis year to the county

- level33 

### weekday VMT for the year of the most recent (2010) mid

- year TxDOT registration data (i.e.,

vehicle population increases linearly with VMT).

Estimation of SHP

The first activity measure needed to estimate the off

- network emissions using the grams per

activity emissions rates are county

- level weekday 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 difference between the total available hours minus the source hours operating (SHO)

by SUT/ fuel type

. Since this ca lculation 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 MO VES road

- type category (see the “Development of SUT/Fuel Type VMT Mix”

section for more details)

. Appendix G includes the 24

- hour summaries of the county

- level

weekday estimate s of SHP by hour and SUT/ fuel type for each analysis year (hourly summaries

wer e 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 typically calculated as the vehicle population times

the number of hours in the time p eriod

. Since this calculation was performed at the hourly level,

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

population by SUT/ fuel type for the analysis year

.

SHO by SUT/ Fuel Type

To calculate the VHT (or SHO) for 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 divided 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 Table 12 and Appendix E). This SHO calculation was performed for each link in a

given hour, aggregating the SHO to one value per SUT/ fuel type per hour

. The hourly SHO by

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

.

Estimation of Starts

The second activity measure needed to estimate the off

- network emissions using the grams per

activity emissions rates are county

- level weekday 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 G 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 Appendix A for electronic data descriptions).

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

. The MOVES

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

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

output

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

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

weekday default starts per vehicle were used.34 

### Estimation of SHI

The third activity measure needed to estimate the off

- network emissions using the grams per

activity emissions rates are county

- level weekday estimat e of SHI by hour and SUT/ fuel type for

each analysis year

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

only

. The SHI was based on information from a TCEQ e xtended i dling s tudy, which produced

2004 summer weekday SHI estimates f or each Texas county

. SHI scaling factors (by analysis)

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 are used in the

emissions estimation, the hourly SHI was compared to the CLhT_Diesel hourly SHP (i.e., hourly

SHI values cannot exceed the hourly SHP values). Appendix G incudes the 24

- hour summ aries

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 for electronic data

descriptions).

SHI Scaling Factors

To estimate the county

- level 24

- hour S HI by analysis year, county

- level SHI scaling factors were

developed using county

- level 2004 summer weekday link

- level VMT and speeds, the TxDOT

district

- level base weekday SUT/ fuel type VMT mix (by MOVES road type), the county

- level

analysis year weekday link

- level VMT and speeds, and the TxDOT district

- level analysis year

SUT/ fuel type VMT mix (by MOVES road type)

. The 2004 summer weekday link

- level VMT

and speeds were developed using a similar to process as the historical analysis years (200 2 and

2008) analysis year weekday link

- level VMT speeds using the HGB 2005 TDM (run date

November 16, 2011) and a 2004 summer weekday VMT control total s. The SUT/ fuel type VMT

mixes were the same VMT mix es used to estimate emissions in the emissions estimation proces s

(see Table 12 and Appendix E). 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 2004 summer weekday link

- level 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 VMT

and speeds, with the individual link VMT aggregated by hour to produce the CLhT_Diesel

hourly and 24

- hour 2004 summer weekday VMT

. Using a similar allocation process, the

analysis year CLhT_Diesel hourly and 24

- hour VMT was calculated using the analysis y ear

weekday link

- level VMT and speeds and the analysis year SUT/ fuel type VMT mix

. The

county

- level 24

- hour SHI scaling factors by analysis year were calculated by div id ing the

analysis year and day type CLhT_Diesel 24

- hour VMT by the CLhT_Diesel 24

- hour 2004

summer weekday VMT.

SHI Hourly Factors

To allocate the analysis year and weekday county

- level 24

- hour SHI to each hour of the day, SHI

hourly factors were used

. These SHI hourly factors were calculated as the inverse of the analysis

year weekday CLhT _Diesel hourly VMT fractions

. The analysis year weekday CLhT_Diesel

hourly VMT fractions were calculated using the analysis year weekday CLhT_Diesel hourly

VMT

. The analysis year weekday CLhT_Diesel hourly VMT were converted to hourly

fractions, th erefor e creating analysis year weekday CLhT_Diesel hourly VMT fractions

. The35 

### inverse of these hourly VMT fractions were calculated and the inverse for each hour was divided

by the sum of the inverse hourly VMT fractions across all hours to calculate the county

- level

analysis year weekday SHI hourly factors.

County

- Level CLhT_Diesel SHI by Hour Estimation

The base analysis year weekday CLhT_Diesel SHI by hour was calculated by multiplying the

24

- hour 2004 summer weekday SHI by the SHI scaling factor and by the SH I hourly factors

. For each hour, the base analysis year weekday CLhT_Diesel SHI was then compared to the

analysis year weekday CLhT_Diesel SHP to estimate the final analysis year weekday

CLhT_Diesel SHI by hour

. If the base analysis year weekday CLhT_Die sel SHI value was

greater than the analysis year weekday CLhT_Diesel SHP value, then the final analysis year

weekday CLhT_Diesel SHI for that hour was set to the analysis year weekday CLhT_Diesel

SHP value

. Otherwise, the final analysis year weekday CLhT_ Diesel SHI for that hour was set

to the base analysis year weekday CLhT_Diesel SHI value

. All calculations (scaling factors,

SHI hourly factors, and SHI by hour calculations) were performed by county and analysis year

(i.e., eight SHI scaling factors were calculated per analysis year).

ESTIMATION OF EMISSIONS FACTORS

TTI developed emissions factors using MOVES2010a (software and database released in EPA’s

MOVES2010a Installation Suite, revised September 23, 2010, downloadable from

http://www.epa.gov/otaq/models/moves/index.htm ). The emissions factors were developed

based on the current MOVES guidance as documented in Technical Guidance on the Use of

MOVES2010 for Emissi on Inventory Preparation in State Implementation Plans and

Transportation Conformity , EPA, April 2010. This MOVES EI Technical Guidance along with

Motor Vehicle Emission Simulator (MOVES) User Guide for MOVES1010a , EPA, August 2010;

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

referen ces where additional detail may be found, if desired

.

The detailed link

- based EI method of analysis requires emissions rates by speed in look

- up

table form; the MOVES Calculation Type of Emission Rate 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 EI method required that all rates be in terms of mass/activity

(as opposed to the off

- network rates in terms of mass/vehicle as output by MOVES) for the

external emissions calculations. TTI post processed the MOVES emissions and activity output

to produce all emissions rates in mass/activity terms (see Table 23

, which was included in a

previous section, but is provided again here for convenience).36 

### Table 23

. Emission s Rates by Process and Activity Factor

.

Emissions Process Activity 1 

### Emission s Rate Units

Running Exh aust VMT grams/mile (g/mi)

Brakewear VMT g/mi

Tirewear VMT g/mi

Evap orative Permeation VMT; SHP g/mi; g/shp

Evaporative Fuel Vapor Venting VMT; SHP g/mi; g/shp

Evap orative Fuel Leak s VMT; SHP g/mi; g/shp

Crank case Run ning Exh aust VMT g/mi

Start Exh aust starts g/start

Crank case Start Exh aust starts g/start

Crank case Ext ended Idle Exhaust SHI g/shi

Ext ended Idle Exh aust SHI g/shi

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

. SHI is

for Combination Long

- Haul Trucks only.



### The RFP inventory analysis required sets of emissions factors for the two main RFP control

scenarios: Pre

- 1990 Controls, and Control Strategy

. Since MOVES does not model TxLED fuel,

emissions rates were post

- processed to include TxLED effects in the Control Strategy emissions

rates. Additional post

- processing was performed specifically for the Harris County 2002 Control

Strategy (or Base Year) scenar io, to account for effects of the I/M test type switch (effective May

1, 2002) during the modeled I/M cycle.

The difference between Pre

- 1990 Controls and Control Strategy emissions are emissions

reductions due to the Post

- 1990 CAA controls. To estimate emissions reductions from

individual control measures, an additional set of MOVES runs was performed. A single county

(Harris) was selected, and additional scenarios were set up by adding sequentially to the Pre

1990 Controls scenario: RFG, post

- 1990 FMVC P, I/M, and TxLED. The rates from these runs

were used in a procedure discussed in a later section for estimating the individual control

program emissions reductions.

In total, these are the five control scenarios (listed by label used in the modeling i nput/output

files and databases):

• 1CS

– Pre

- 1990 Controls (7.8 RVP conventional gasoline and no Post

- 1990 CAA

FMVCP);

• 2CS

– Pre

- 1990 FMVCP + RFG;

• 3CS

– Pre

- 1990 FMVCP + RFG + Post

- 1990 FMVCP;37 

### • 4CS

– Pre

- 1990 FMVCP + RFG + Post

- 1990 FMVCP + I/M Program; and

• CSS

– Control Strategy (Pre

- 1990 FMVCP + RFG + Post

- 1990 FMVCP + I/M

Program +TxLED fuel).

Development of RFP emissions rates required for estimating emissions reductions from all

Post

- 1990 CAA controls (the 1CS and CSS scenarios) will be discussed firs t, followed by

discussion of the emissions rates modeling performed to estimate the emissions reductions for

individual control programs.

The utilities used to calculate the emissions rates from the MOVES emissions and activity

output and to adjust the e missions rates are MOVESratescalc and MOVESratesadj, respectively

(see descriptions in Appendix B).

Development of Pre

- 1990 Control s and Control Strategy Scenario Emissions Factors

The main purpose of this section is two

- fold: 1) explain the overall RFP oz one season weekday

MOVES

- based emissions rate look

- up table development process, and 2) provide the specifics

on modeling emissions rates for the two main RFP control scenarios, Pre

- 1990 Controls (1CS),

and Control Strategy (CSS). All emissions rates for this RFP analysis were produced consistent

with the methods and procedures presented in this section. The other control scenarios modeled

(2CS, 3CS, and 4CS), are discussed in a later section.

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 th e MOVES Run Specifications file (RunSpec or MOVES

Run Specifications [MRS]) and MOVES CDB (county input database)

. (The MOVES

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

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

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

properties

, vehicle fleet characteristics, and emissi ons control programs (depending on local

particular RFP contro l 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 rocessing

There is one RunSpec required per county, year and control scenario, and a corresponding

number of CDBs (county input databases), and output databases (i.e., one output database per

run). Therefore, for eight counties, seven years, and two control scenarios (plus the additional

I/M scenario for Harris 2002 needed for the I/M test

- type switch modeling procedure) there are

113 RunSpecs, 113 CDBs, 113 MOVES outpu t databases, 113 MOVESRatesCalc runs, and 112

MOVESRatesAdj runs (for NO

x 

### TxLED effect adjustments, for Harris 2002 I/M test

- type switch

effects modeling, which combines two sets of rates into one, and for extracting and storing only

the rates for the inve ntory pollutants in a separate, smaller database for input to the emissions

runs).38 

### Summary of Control Programs Modeled

Table 24 shows the control measures modeled (“ √ ”) in each of the RFP control scenarios, Pre

1990 Controls (1CS) and Current Controls (CSS).

Table 24 . Control Measure Modeling by RFP Control Scenario.

Individual Control Measures 1 

### Method RFP Control Scenario

Pre

- 1990

Controls

(1CS) Current

Controls

(CSS)

Pre

- 1990 CAA FMVCP MOVES inputs √ √

1992 Federal Controls on Gasoline

Volatility MOVES inputs √ Not a pplied

RFG MOVES inputs Not a pplied √

Post

- 1990 CAA FMVCP 1



### Tier 1

National Low Emission Vehicle

Program

Tier 2

Heavy

- Duty

2004 Diesel

2005 Gasoline

2007 Gasoline and Diesel

Highway Motorcycle 2006 MOVES inputs Not a pplied √

I/M Program 1 

### MOVES inputs Not a pplied √

TxLED Fuel 1 

### Post

- process

diesel vehicle

NOx rates Not a pplied √

1 Post

- 1990 FMVCP was modeled all together per MOVES limitation, and I/M was modeled in Harris Count y for all

years, and in 2008 and later years for the I/M expansion counties of Brazoria, Fort Bend, Galveston, and

Montgomery

. TxLED effects were modeled as a post

- processing procedure adjustment to diesel vehicle NO

x emissions for all coun ties, for 2008 and later years.



### M OVES Emission s Factor Aggregation Levels

The MOVES emissions factors for the analysis are produced at the following levels. The

summer weekday emissions factor look

- up tables provide the emission rates by:

• Up to 13 source types (i.e.

, vehicle types) ;

• Up to 4 fuel types ;

• Up to 5 road types (four actual MOVES road categories and “off

- network”) ;

• Each of the 24 hours in a day ;

• 16 speed bins (i.e., 2.5, 5, 10, 15,.... 75 mph) (only included in miles

- based rate tables) ;39 

### • 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 f ive road type categories in MOVES are Off

- Network (not actually a

road type), Rural Restricted Access, Rural Unrestricted Access, Urban Restricted Access, and

Urban Unrestricted Access. Of the two rate tables produced (by post

- processing) for input to the

emissions calculations; one rate table contains off

- network rates, and the other includes rates for

each of the actual four MOVES road types, indexed by 16 speeds. The speeds index corresponds

to the 16 MOVES speed bin average speeds: 2.5, 5, 10, 15,.... 7 5 mph.

MOVES Run Specifications

The MRS (XML file) defines the 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 for ea ch year using the MOVES Graphical User Interface (GUI),

then looping through the templates with a basic file

- building utility to create one MRS for each

county, year, and control scenario. This process was used to produce the 113 MRSs for the

analysis (tw o control scenarios x eight counties x seven years, plus one extra for the Harris

County 2002 Control Strategy scenario needed for the I/M test

- type switch effects modeling

procedure).

Table 25 describes the MRS selections TTI used to produce MOVES emissions and activity

output for calculating the emission s rates for the two main RFP control scenarios.40 

### Table 25

. Pre

- 1990 Controls and Control Strategy Scenarios

– MRS GUI Panel Selections.

Navigation

Panel Detail Panel Selection

Scale 1 Domain/Scale ; Calculation Type County ;

Emissions Rates

Time Spans 1 Time Aggregation Level ;

Years

– Months

– Days

– H ours Hour ;

2002 1

- July

- Weekday

- All

Geographic

Bounds 1 Region ;

Selections ; Domain Input Database Zone and Link ;

Brazoria 1 ;

<d atabase name for county/year scenario >

On

- Road

Vehicle

Equipment SUT/fuel combinations SUT Gasoline Diesel

Motorcycle X

- Passenger Car X X

Passenger Truck X X

Light Commercial Truck X X

Intercity Bus

- X

Transit Bus

- X

School Bus X X

Refuse Truck X X

Single Unit Short

- Haul Truck X X

Single Unit Long

- Haul Truck X X

Motor Home X X

Combination Short

- Haul Truck X X

Combination Long

- Haul Truck

- X

Road Type Selected Road Types Off

- Network

– Rural Restricted Access

– Rural Unrestricted Access

– Urban

Restricted Access

– Urban Unrestricted Access

Pollutants and

Processes 2 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 Fue l Leaks

Manage Input

Data Sets Additional input database

selections None

Strategies 3 Alternate Vehicle Fuels

Technologies ( AVFT ) “New” button is gr a yed out , which m eans that an AVFT data

set (local fuel fractions) was imported to the R unspec

Rate

- of

- Progress “No Clean Air Act Amendments” box is checked only for the

Pre

- 1990 Control scenario runs

General Output Output Database ;

Units ; Activity <database name for county /year/ scenario> ;

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

– Pollutant;

Fuel Type , Emissions Process;

Source Use Type

Advanced

Performance

Measures Aggregation and Data

Handling All check boxes are “un

- checked”

1 County s cale allows one county and year per run

– the evaluation years and counties are 2002, 2008, 2011,

2014, 2017, 2018, and 2019; Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, and

Waller.41 2 Chained pollutants require other pollutants (not listed in the tabl e) to be selected (e.g., VOC requires Total

Gaseous Hydrocarbons and Non

- Methane Hydrocarbons: CO 2 requires Total Energy Consumption).

3 Two full sets of county/year runs were performed

– one set with the Strategies Rate

- of

- Progress panel “No

Cle an Air Act Amendments” check

- box checked, and one set with this box un

- checked.42 

### Scale, Time Spans , and Geographic Bounds

The MOVES “County Domain/Scale” was selected as is required for SIP EI analyses. The

MOVES Calculation Type “Emissions Rates” was sele cted (with other required selections) for

MOVES to produce the activity and emissions output needed for input to the post

- processing

algorithms (see MOVESratescalc utility description in Appendix B) that calculate the emissions

rate look

- up tables with spe ed bin indexing, as needed for the detailed link

- based emissions

estimation process.

The Time Spans parameters were specified to provide the most detail available, which is the

hourly aggregation level, for all hours of the day. One analysis year per ru n 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 for this

analysis) and one “Days” (Weekdays) selection was made.

Under Geographic Bounds for the County Domain Scale, only one county may be selected.

The user

- produced CDB containing the scenario

- specific input data for the county was specified

as the County Domain Input Database, and under Region, “Zone & Link” wa s selected as

required for the emissions rates calculation type. With these required set

- ups, only one county,

one year, and one day type was modeled per run.

On

- R oad Vehicle Equipment and Road Type

All of the SUTs associated with gasoline and diesel fuel s were specified. Note that for this

analysis, the MOVES default fuel/engine fractions were replaced with local inputs, which

showed no compressed natural gas (CNG) vehicles in the fleet (the MOVES default database

includes some CNG Transit Buses), and no gasoline Transit Buses. (The local SUT/fuel type

VMT mixes developed for the study define the SUT/fuel type combinations included in the

MOVES runs.) For emissions rate calculations, all five MOVES road type categories were

selected.

Pollutants and Proce sses

In MOVES, VOC is a “chained” pollutant, meaning that it is calculated based on one or more

other pollutants that must also be calculated. In addition to the pollutants called for in this

analysis, the following pollutants were additionally required f or MOVES to produce the VOC

emissions rates: Total Gaseous 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 avail able by the selected pollutants were

included, except for the two refueling emissions processes.

Manage Input Data Sets and Strategies

The Manage Input Datasets feature allows alternate inputs other than those included in the CDB.

No additional inputs wer e included via the Manage Input Datasets panel.

The Strategies, Alternative Vehicle Fuels & Technologies feature was used to import the

local fuel/engine fractions (i.e., equivalent of diesel fractions inputs to MOBILE6) into the

MOVES MRS. A year

- speci fic fuel/engine fractions data set (e.g., year of local vehicle43 

### registration data extract used

– 2002, 2008, and latest available 2011 for this analysis) was

imported to each of three year

- specific MRS templates (i.e., 2002, 2008, and 2011 for

subsequent a nalysis years), prior to creating the individual scenario RunSpecs for each year.

The Strategies, Rate

- of

- Progress feature was used for the Pre

- 1990 Control emissions rates

modeling scenario. The check

- box Compute Rate

- of

- Progress “No Clean Air Act Amen dments”

Emissions was selected, which models a “No Clean Air Act Amendments” scenario by assigning

1993 model year emissions rates to all post

- 1993 vehicles.

Output

The output units were pounds (converted in a later step to grams), kilojoules, and miles. All of

the activity categories were chosen for inclusion in the output database. The activity output was

needed along with the emissions output to calculate the rates/activity emissions rates look

- up

tables via post

- processing. The output detail level wa s by hour, link (i.e., county/road type/speed

bin combination), pollutant, process, SUT and fuel type.

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

MOVES RunSpecs used.

MOVES County Input Databases

Most of the locality

- specific input data for the county scale runs are entered through the CDB

(the exception is the alternate or local fuel/engine fractions which are input via the MRS).

Additional user input data may be entered through separate database s via the Manage Input

Datasets feature, although this feature was not used.

TTI developed procedures to facilitate building and checking CDBs for large scale EI

projects. The basic procedure was to write a CDB builder

- MySQL script that was then

convert ed to a template by replacing particular scenario

- specific values (e.g., year, input file

paths, county name/FIPS) with variables. The template was looped through for each scenario (in

this case for each year, county, and control scenario) to produce the MySQL scripts needed to

create all of the required CDBs. After building the CDBs, a CDB checker utility was run to

verify that 18 CDB tables (listed in Table 26 ) 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 include s local data from prepared text files

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

MOVESDB20100830 (e.g., for default activity data), and some values provided directly in the

CDB builder MySQL script.

Table 26 provides an outline and brief description of the CDBs, followed by discussion of the

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

CDB table data applies to all counties, years, and to both control scenarios.44 

### Table 26

. MOVES CDB Input Tables

.

Input Table 1 Category Notes

Y

ear Time D esignate d analysis year as a base year ( i.e., specifies that local

activity inputs were supplied rather than forecast by the model).

S tate Geography Identified the state (Texas) for the analysis.

C ounty Geography

/

Meteorolog

y Identified county of analysis. Contains local altitude and

barometric pressure ( TCEQ, September 2011 ).

Zonemonthhour Meteorolog

y H ourly temperature an d relative humidity ( TCEQ, September

2011 ).

Roadtype 2 Activity Lists the MOVES road types and associated ramp activity

fractions . Road type ramp fractions were se t to 0.

H pmsvtypeyear 3

Activity

(Defaults) Used MOVES defaults

– 1999 national annual VMT by HPMS

vehicle category, except yearID was set to analysis year.

roadtypedistributio n

3 Used MOVES default road type VMT fractions.

monthvmtfracti on 3 Used MOVES default month VMT fractions.

d ayvmtfractio n 3 Used MOVES default day VMT fractions.

Hourvmtfractio n 3 Used MOVES default hour VMT fractions.

Avgspeeddistributi o

n 3 Used MOVES default average speed distributions.

sourcetypeyea r 3 Fleet

(Defaults) Used MOVES default

– 1999 national SUT populations,

except yearID was set equal to the analysis year value.

s ourcetypeage

distribution Fleet TTI estimated SUT age fractions using mid

- year TxDOT/TxDMV

vehicle registration data and MOVES defaults, as needed

. Analysis year

- specific re gistration data were used for historical

years and latest available for future years.

Fuelengfraction (text

file import) Fleet TTI estimated SUT fuel fractions using TxDOT /TxDMV vehicle

registration data and defaults where needed . Analysis year

specific re gistration data were used for historical years and latest

available for future years.

Zone Activity Start, idle, and SHP zone allocation factors

. County = zone, and

all factors were set to 1.0 (required for county scale analys es).

zoneroadtype Activity SHO zone/roadtype allocation factors

. County = zone, and all

factors were set to 1.0 (required for county scale analyses).

fuelsupply Fuel Control scenario

- specific . Contains market share information

for gasoline and diesel f ormations, developed by TTI in

consultation with TCEQ. (7.8 RVP conventional gasoline for

Pre

- 1990 Control scenario, and RFG for Current Control

scenario.)

fuelformulation Fuel Control scenario

- specific . Contains gasoline and diesel fuel

formulations developed by TTI in consultation with TCEQ. (7.8

RVP conventional gasoline for Pre

- 1990 Control scenario, and

RFG for Current Control scenario.)

imcoverage I/M Control scenario

- specific

. TTI prepared input s to represent I/M

program design for counties and year s based on current I/M

rules and modeling protocols , and on available MOVES I/M

parameters (i.e., “teststandards” and “imfactors”) across the

domain of I/M vehicles. Regulatory class adjustments were

made per MOVES EI Preparation Guidance . (No I/M modeled

for Pre

- 1990 Controls.)

1 All of these datasets are CDB tables except for the fuel/engine fractions, which are imported to

RunSpecs.45 2 MOVES does not produce “ramp road type” rates in a single run with the other road types. To calculate

emissions for particular travel model ramp links, MOVES unrestricted access road type rates were used.

3 U se of default activity and population inputs to MOVES is basic to the inventory method. It allows

simultaneous development of emission s factors and local activity parameters required in the external

emissions calculations . The MOVES default activity is normalized in the emissions rates calculation

post

- processing procedure (i.e., emissions/activity), and the actual local activity estimate s are used in

the external emissions calculations.



### User Inputs to MOVES via CDB

– Locality

- Specific Inputs and Defaults Used

All inputs discussed in this section are input via the CDB unless otherwise noted (e.g.,

fuel/engine fractions). Unless otherwise stated, the inputs apply to all counties, years, and the

two base RFP control scenarios.

Year , State, and County Inputs to MOVES

The year, state, and county tables are populated with data identifying the subject year, state, and

county of the run.

The ye arID 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). StateID “48” (Texas) was inserte d in the state table. The

county table identifies the county of analysis and contains barometric pressure and altitude

information (discussed further with other meteorological inputs). The county data was selected

from a prepared local “meteorology” data base 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 rates, but not a road type for ramps

specifically. In the roadty pe 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

t o zero (i.e., 100% of activity on each MOVES road type was based on the road type drive cycles

assigned to that road type by MOVES, exclusive of ramp activity; currently the MOVES

Unrestricted Access road type rates are used with activity for particular ra mp links in the external

emissions calculations). The treatment of ramps for subsequent EI development projects will be

updated to incorporate use of ramp rates.

Default Activity and Population CDB Inputs to MOVES

The activity and vehicle population input parameters under the methodology use the MOVES

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

dayvmtfraction, hourvmtfraction, avgspeeddistribution, and sourcetypeyear. Data for all of these

tables were selected and inse rted from the MOVES default database. For the two tables

dependent on year (i.e., hpmsvtypeyear and sourcetypeyear include yearID), the 1999 default

data were used, and the yearID value was updated by setting it to the analysis year value.

The zone and zoneroadtype tables contain zonal sub

- allocation activity factors. For county

scale analyses, county is equal to zone, therefore these allocation factors were set to 1.0.46 

### Local Fleet Age Distributions and Fuel Fractions Inputs to MOVES

The locality

- specif ic fleet inputs to MOVES consist of age distributions and diesel fractions (or

fuel/engine fractions). The age distributions and fuel fractions inputs were calculated and written

to text files in preparation for loading the data into the appropriate model input files or tables.

The MOVESfleetInputBuild utility was used to produce these fleet inputs to MOVES in the

required formats (see utility description in Appendix B).

One statewide level fuel fractions data set was produced for each historical year a nd imported

to its particular year

- specific MRS template prior to creation of all of the MRS files for each

year. For 2011 and all future analysis years MRS templates, the latest available (2011) local

dataset was used. The county

- level SUT age distribut ion inputs for each year were loaded into

the appropriate county scenario CDB sourcetypeagedistribution tables via the CDB builder

MySQL scripts.

The age distributions and fuel/engine fractions were based on TxDOT mid

- year county

registrations data and M OVES model defaults, as 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

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). See age distributions and diesel fractions summaries

in Appendix H.

Table 27 summarizes the data sources and aggregation levels used to estimate the HGB

county sourcetypeagedistributions and fuel/engine fractions.47 

### Table 27 . Data Sources and Aggregations for SUT Age Distributions 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 NA

– 100 percent

gas, no Fuel/Engine

Fractions

Passenger Car 21 Passenger County MOVES default

Passenger Truck 31 Total Trucks<=8500 County MOVES default

Light

Commercial

Truck 32 Total Trucks<=8500 County MOVES default

Single

- Unit

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

Combination

Short

- Haul

Truck 61 >19500+ >26000+

>33000+ >60000 HGB Region Texas Statewide

Combination

Long

- Haul Truck 62 >19500+ >26000+

>33000+ >60000 Texas statewide NA

– 100 percent

diesel, no

Fuel/Engine

Fractions

1 TxDOT mid

- year 200 2, 2008, and 2011 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 in puts for 200 2, 2008 and 2011 (and future

years), respectively (weights are gross vehicle weight rating [ GVWR ] in units of lbs.). Based on the

current MOVES model and 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 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

The TCEQ provided the local 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 set48 

### of hourly temperature and relative humidity, and 24

- hour barometric pressure averages for the

HGB region, using recent multiple year, June through August hourly weather station data from

the Houston George Bush Intercontinental Airport. These Houston peak

- ozone season

meteorological inputs are from the recently adopted HGB RFP SIP Revision for the 1997 Eight

Hour Ozone Standard (TCEQ, March 2010). Low altitude was designated for all counties.

Table 28 summarize s the temperatures, relative humidity

, and barometric pressure input values

.

Table 28

. Meteorological Inputs to MOVES

.

Hour Temperature

(Degrees

Fahrenheit) Relative

Humidity

(Percent) 24

- Hour Average

Barometric Pressure

(Inches of Mercury)

1 77.1 73.0

29.87 2 75.7 77.2

3 75.2 78.7

4 74.1 80.1

5 72.7 83.6

6 72.5 84.4

7 72.4 84.5

8 76.0 78.0

9 79.6 68.9

10 83.1 59.5

11 85.6 52.1

12 87.8 47.3

13 89.0 43.0

14 90.1 40.6

15 91.2 39.3

16 91.2 39.5

17 91.4 40.3

18 90.9 40.2

19 88.8 44.3

20 85.4 51.4

21 82.7 57.7

22 81.0 60.149 

### 23 80.3 62.0

24 78.8 66.6



### Fuels Inputs to MOVES

The local fuels inputs to MOVES are input via the CDB in the fuelsupply and fuelformulation

tables. TTI prepared the HGB area level input data for each year in spreadsheets, saved the input

data to text files in the MOVES

- required format. These data were then imported to the

fuelsupply and fuelformulation tables in the appropriate MOVES scenar io CDBs. The following

describes 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 values to zero ( to prevent MOVES from applying the

default fuels data in addition to the desired local fuels input data).

• Loaded all local fuel formulations from the specified local fuels inputs text files into the

CDB fuelform ulation table (these were given fuelformulationIDs different than the set of

IDs used as MOVES defaults).

• Loaded the subject scenario (i.e., for the subject countyID, fuelyearID, monthgroupID)

local fuel supply records (i.e., the market shares of the speci fied fuel formulations) from

the input data text files into the CDB fuelsupply table.

Data Sources

– The EPA provided TTI with the Houston RFG retail outlet survey samples

by fuel grade for 2002, 2008, and 2011, collected by the RFG Survey Association (for more

information see: http://www.epa.gov/otaq/regs/fuels/rfg/properf/perfmeth.htm ). TTI proces sed

the sample data to estimate the Houston summer season average RFG fuel property inputs by

year, which were used for all counties. Other data sources used include regulated RVP limit

value, and some historical and future year MOVES default fuel propert ies for the HGB counties.

For average diesel sulfur content, various data sources were used including TCEQ summer retail

fuel survey summaries, MOVES defaults, and other available information. See the Table 29 and

Table 30 footnotes for data source specifics.

Development of Fuel Formulations Inputs from RFG Survey Samples

– On a verage,

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 a nd TAME values, which

TTI converted from oxygenate weight percent to the required oxygenate volume percent form

using appropriate conversion factors (i.e., vol% oxygenate

- per

- wt% oxygenate factors of 1.0162

for MTBE, and 0.9572 for TAME, based on conversio n factors used in MOVES and provided by

EPA OTAQ). TTI used the standard method of averaging the fuel properties by grade, and

combining them into overall RFG averages using relative sales volumes by grade as weights.

The relative sales volumes by grade were estimated using annual average sales volumes per day

through retail outlets statistics for Texas, taken from the Energy Information Administration’s

(EIA) Petroleum Marketing Annuals (for 2002, 2008 and latest available 2009).50 

### The fuel supply value for each fuel formulation used was 1.0, meaning that for each

modeling scenario there was only one diesel and one RFG fuel formulation used. The RFG and

diesel fuel formulations used are shown in Table 29 and Table 30

.

Table 29

. MOVES Gasoline Inputs

– HGB RFP Summer Emissions Rates Analysis

.

Fuel Formulation Field 1 

### Pre

- 1990 CSS 2002 CSS 2008 CSS 2011 CSS Future

Years

fuelFormulationID 10001 10002 10003 10004 10005

fuelSubtypeID 10 11 12 12 12

RVP 7.8 6.85 6.93 7.06 7.06

sulfurLevel 402 131.11 29.38 29.42 29.42

ETOHVolume 0 0 9.85 9.76 10

MTBEVolume 0 11.13 0 0 0

ETBEVolume 0 0 0 0 0

TAMEVolume 0 0.81 0 0 0

aromaticContent 26.4 20.49 15.15 14.65 14.65

olefinContent 11.9 11.24 11.93 13.27 13.27

benzeneContent 1.64 0.601 0.582 0.532 0.55

e200 50 48.51 49.12 49.32 49.32

e300 83 83.27 86.25 84.61 84.61

volToWtPercentOxy 0 0.1786 0.3488 0.3488 0.3488

BioDieselEsterVolume \ N \ N \ N \ N \ N

CetaneIndex \ N \ N \ N \ N \ N

PAHContent \ N \ N \ N \ N \ N

1 Data sources: Pre

- 1990

– July 1990 MOVES defaults; 2002, 2008, and 2011

– based on EPA Houston

Summer 2002, 2008, and 2011 retail outlet RFG survey data; Future Years

– all values are based on

latest available summer (2011) Houston RFG survey data, except for benzene and ethanol, which are

MOVES defaults for July 2012.51 

### Table 30

. MOVES Diesel Inputs

– HGB RFP Summer Emissions Rates Analysis

.

Fuel Formulation Field 1 

### Pre

- 1990 CSS 2002 CSS 2008 CSS 2011 CSS Future

Years

fuelFormulationID 32500 30307 30061 30006 30011

fuelSubtypeID 20 20 20 20 20

RVP 0 0 0 0 0

sulfurLevel 2500 306.7 60.5 6.0 11

ETOHVolume 0 0 0 0 0

MTBEVolume 0 0 0 0 0

ETBEVolume 0 0 0 0 0

TAMEVolume 0 0 0 0 0

aromaticContent 0 0 0 0 0

olefinContent 0 0 0 0 0

benzeneContent 0 0 0 0 0

e200 0 0 0 0 0

e300 0 0 0 0 0

volToWtPercentOxy 0 0 0 0 0

BioDieselEsterVolume \ N \ N \ N \ N \ N

CetaneIndex \ N \ N \ N \ N \ N

PAHContent \ N \ N \ N \ N \ N

1 These are the diesel “sulfurLevel” data sources, beginning with “Pre

- 1990

, ” respectively: an approximate, typical,

post

- 1979/pre

- 1993 regulation average sulfur value (i.e., the low end of the range for average sulfur level in No. 2

diesel [2500

- 3000 parts per million ( ppm ) ], based on National Institute f or Petroleum and Energy Research

[NIPER] U.S. refiner survey summary information, referenced in an article from Energy Information

Administration/Monthly Energy Review, August 1993 ) ; TCEQ Summer diesel survey data for Houston from

2003, 2008, and 2011; MOV ES default.



### Local I/M Inputs to MOVES

The I/M program is required and administered to reduce vehicle emissions in five of the eight

HGB counties. MOVES calculates county emissions rates that reflect the emissions

- reducing

benefits of the I/M program designs reflected in parameters specified in the MOVES IMcoverage

table. TTI produced a new and updated set of Texas I/M county MOVES imcoverage records to

replace the MOVES default imcoverage table records for Texas ( that were populated u sing

information from the EPA’s 2005 National EI )

.

The imcoverage table data parameters (by field headers) are:52 

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

• stateID (state subject to the I/M program);

• countyID (subject county);

• yearID (year administered);

• sourceTypeID (SUT covered);

• fuelTypeID (fuel type subject to the program);

• IMProgramID (arbitrary ID number);

• begModelYearID (first model year covered);

• endModelYearID (last model year covered);

• inspectFreq (inspection frequenc y for the program);

• testStandardsID (I/M test type);

• useIMyn (a Y/N [yes/no] switch that specifies whether or not to use the record); and

• complianceFactor (an adjustment factor reducing the effects for compliance rate,

waiver rates, or other adjustments).

TTI produced the new 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 imc overage records for the

modeling scenario from the Texas imcoverage database, all MOVES default imcoverage records

pertaining to the modeling scenario must be excluded. To prepare the appropriate county

imcoverage inputs, the following two general steps w ere performed for all eight counties:

• Selected and inserted all MOVES default imcoverage records for the scenario’s countyID

and yearID into the CDB imcoverage table, and flagged them for non

- use (i.e., set

useIMyn = N) in the modeling run; and

• From the c urrent, updated Texas MOVES imcoverage database, selected and inserted the

imcoverage records for the scenario’s yearID and countyID into the CDB imcoverage

table (with useIMyn = Y).

Data Sources

– TTI produced the I/M coverage input parameters to best r epresent Texas I/M

program designs as specified in the Texas I/M SIP and Texas rules (using current Texas I/M

modeling protocol compliance and waiver rates), and where the pertinent I/M coverage modeling

parameters existed in MOVES (e.g., only for SUT and fuel type categories for which MOVES

contained I/M effects). The HGB I/M program requires annual emissions testing of gasoline

vehicles within a 2

- through

- 24 year vehicle age coverage window (motorcycles, military tactical

vehicles, diesel

- powered vehicle s, and antique vehicles are excluded). A gas cap integrity test is

required on all these vehicles, and, depending on the vehicle class and model year, the vehicle

emissions testing may utilize on

- board diagnostics (OBD), the Accelerated Simulated Mode

(AS M

- 2) test, or the Two

- speed Idle (TSI) test. For additional Texas I/M program details, see53 

### the current I/M SIP, Revision to th e State Implementation Plan Mobile Source Strategies , Texas

Inspection and Maintenance State Implementation Plan , TCEQ, November 18 , 20 10

.

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 31 , 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 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

- zer o MOVES imfactors and corresponding base rates with non

- zero standard I/M

difference (i.e., I/M effects) available in MOVES (see Table 31 , note 4).

• Categorized countie s 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 sta ndard, test frequency, begin model year, and

end model year.

Table 31 and the associated table notes describe the MOVES imcoverage records developed

by TTI and used for this HGB analysis. Note that a review of the pertinent MOVES data

(IMfactors and mean base rates for non

- I/M and reference

- I/M) showed that in the current

MOVES model there are no I/M effects included for heavy

- duty vehicle categories (i.e., vehicles

with GVWR > 8,500 pounds

– see Table 31 , note 4). Although the Texas I/M program design

includes heavy

- duty vehicles, the current version of MOVES provides no means t o model a

potential benefit.54 

### Table 31 . 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 assenger

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 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, gasol ine

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 EI Technical

Guidance compliance factor equation (Section 3.10.6), comp liance factors were calculated as the product

of the percent compliance rate, 100

– percent waiver rate, and the regulatory class coverage adjustment.

The current Texas I/M program modeling protocol compliance and waiver rates are 96% and 3%. The

regulat ory class adjustments used were taken directly from the MOVES EI 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 (LD) gaso line

vehicles, MOVES includes both exhaust and evaporative I/M factors and mean base rates with I/M

effects, therefore, only LD gasoline vehicles (SUTs 21, 31, and 32) were included in the user input

imcoverage records. The processes/pollutants affected b y I/M are exhaust running and exhaust start total

hydrocarbon (THC), CO, NO

x , and tank vapor venting THC.



### The MOVES input files (MRSs and CDBs) were provided as a part of the electronic data

submittal (Appendix A) of this Technical Note.

Checks and Runs

After completing the input data preparation, the CDBs were checked to verify that all 18 tables

were in the appropriate CDBs and the tables were populated with data as intended. The MOVES

RunSpecs were executed in batches using the MOVES command line too l. The batches were set

up to write each MOVES run log to a text file for later access. After completion, TTI searched55 

### the MOVES run logs for error and warning messages, for which none were found. The MOVES

run summaries are included as Appendix I.

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 divided 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 rates, and “ttirateperactivity”

containing mass/SHP, mass/SHI, and mass/start emission s rat es. 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 ex tracted for only those pollutants needed in

the emissions calculations. For the Harris County 2002 base year, the pre

- May and post

April I/M scenario rates were combined using factors representing the portion of the fleet

estimated to have been tested und er the previous and current I/M tests, during the prior

complete I/M cycle (12 months). Using the July 1, 2002 evaluation reference, the

proportions used were: 10/12 for the pre

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

- April

I/M proportion. For the 2008 and later year Control Strategy scenarios, TxLED

adjustments were applied to all diesel vehicle NO

x 

### emissions rates, and the extracted and

adjus ted rate tables for each county, year, and control scenario were placed in a separate

database for input to the emissions calculations. Table 32 shows the TxLED factors used

(provided by TCEQ). TCEQ produced these average diesel SUT NO

x 

### adjustments using

4.8% and 6.2% reduct ions for 2002 and later, and 2001 and earlier model years, respectively.

More details on TCEQ’s 

### TxLED factors analysis may be found at

, ftp://amdaftp.tceq.texas.gov/pub/Mobile_EI/Statewide/mvs/TxLED/

, in the files:

o mvs10a

- statewide

- txled

- 2010

- analysis

- 06

- 08

- 11

- 12

- 13

- 18.zip

, and

o mvs10a

- statewide

- txled

- 2011

- analysis

- 11

- 14

- 17

- 18

- 19

- 21.zip.

Note that both TxLED analysis zip files contain dif ferent sets of estimated TxLED NO

x 

### adjustment factors for future years. The distinction is that the “2011

- analysis” fleet inputs

(vehicle age distributions) used were based on the most current mid

- year (2011) TxDMV vehicle

registration data. The “txled

- 2 011

- analysis” zip file contains the analyses of 2011, 2014, 2017,

2018, and 2019 TxLED factors used in this RFP analysis. The “ txled

- 2010

- analysis ” zip file

listed contains the analysis of 2008 TxLED adjustment factors used in this RFP analysis.56 

### Table 32 . RFP Analysis Year TxLED Adjustment Factor Summary

.

Diesel Fuel Source Use

Type 2008 2011 2014 2017 2018 2019

Passenger Car 0.9499 0.9494 0.9509 0.9515 0.9516 0.9517

Passenger Truck 0.9445 0.9456 0.9479 0.9492 0.9496 0.9499

Light Commercial Truck 0.9452 0.9460 0.9479 0.9488 0.9491 0.9494

Intercity Bus 0.9410 0.9418 0.9426 0.9433 0.9438 0.9440

Transit Bus 0.9413 0.9422 0.9428 0.9442 0.9447 0.9452

School Bus 0.9416 0.9423 0.9430 0.9438 0.9444 0.9445

Refuse Truck 0.9422 0.9431 0.9438 0.9451 0.9461 0.9466

Single Unit Short

- Haul Truck 0.9483 0.9491 0.9503 0.9508 0.9510 0.9512

Single Unit Long

- Haul Truck 0.9479 0.9487 0.9499 0.9505 0.9507 0.9509

Motor Home 0.9435 0.9445 0.9454 0.9462 0.9465 0.9468

Combination Short

- Haul

Truck 0.9432 0.9445 0.9462 0.9476 0.9483 0.9487

Combination Long

- Haul

Truck 0.9431 0.9445 0.9465 0.9481 0.9488 0.9493

Source: TCEQ, Fall 2011. See

ftp://amdaftp.tceq.texas.gov/pub/Mobile_EI/Statewide/mvs/TxLED/ for TCEQ’s TxLED

analysis spreadsheets and other information.



### Appendix A describes the electronic data submittal for this RFP inventory analysis, which

includes the TxLED and I/M test

- type switch adjustment factor files used in the

MOVESratesAdj utility runs that produced the final emissions rate look

- up table inputs to the

emissions calculations.

Emissions Factors for Estimation of Individual Control Reductions

The remaining emissions factor control scenarios (i.e., 2CS, 3CS, and 4CS) are covered in this

section. The previous discussion on development of 1CS and CSS control scenario emissions

factors included the general process used for all RFP analysis emissions factors estimates

– the

previous section may be referred to for any questions on the general process not included here.

The objective is to estimate emissions reductions by individual control measure (listed), for

each county and the region. The method is to estimate incremental control emissions reductions

for a single county (Harris), and apply the individual relative reductions to the total reducti ons

(1CS

– CSS) for each other county to produce the estimates for all counties. Two sets of relative

reductions were developed: one with I/M and one without I/M, for use, respectively, with I/M

and with non

- I/M counties.57 

### Table 33 summarizes the run sequence. Note that existing MOVES and MOVES post

processor utility runs from the 1CS and CSS scenarios were used in combination with output

from the extra runs needed, to produce the required five scenarios of Harris County emissions

estimates. Existing runs and new runs are summarized together for the overall emissions rates

development process, which includes development of MOVES setups (MRSs, CDBs),

MOVESratescalc se t

- ups, and MOVESratesadj set

- ups. (MOVESemscalc runs to calculate the

emissions estimates are discussed in the next section).

Table 33 . Harris County Emissions Factor Modeling Control Scenarios and Sequence.

Scenari

o Label Control s

Increment MOVES

CDB MRS MOVE

S

Run s RatesCa

lc

Runs RatesAd

j

Runs

1cs Pre

- 1990

Controls Existing set

- ups and r un s

2cs 1cs + RFG L ike "1cs ”

except for

current

fuels “1cs” labels

changed to

“2cs” √ √ √ (no

TxLED)

3cs 2cs + post

FMVCP “2cs” label

changed to

“3cs” “2cs” labels

changed to

“3cs”; “No

CAA” ROP

switched off √ √ √ (no

TxLED)

4cs 3cs + I/M Existing set

- ups and r un s (i.e., “css” set

- ups and

runs prior to TxLED adjustments, or CSS scenario

- TxLED) √ (no

TxLED)

css 4cs +TxLED Existing set

- ups and r un s



### As shown in Table 33 , of the five control scenarios, three (2CS, 3CS, and 4CS) required

some modeling set

- ups and runs. The 2CS and 3CS control scenarios required the full process

stream of set

- ups and runs, whe reas the 4CS control scenario only required set

- ups and runs

beginning with the MOVESratesAdj step. This is because 4CS is the same as CSS, except with

no TxLED. Therefore, the CSS MOVESratesCalc step output was input to a new “4CS”

MOVESratesAdj utility run with no TxLED adjustments applied to produce the 4CS scenario

rate tables. This series of additional emissions factor modeling set

- ups and runs was developed

and executed for 2008, 2011, 2014, 2017, 2018 and 2019 analysis years.

The Harris County e missions factors for the 2CS, 3CS and 4CS scenarios for each year were

input with appropriate activity inputs into MOVESemscalc to produce the emissions estimates

needed, along with the 1CS and CSS scenario emissions, that were used to quantify the

individ ual control measure emissions reductions, discussed in a later section.58 

### The emissions factor MOVES set

- ups used (MRS files and CDBs) were provided as a part of

the electronic data submittal (see Appendix A).

EMISSIONS CALCULATIO NS

TTI calculated hourly

, ozone season weekday, link

- based EIs by county for each RFP EI, and for

Harris County for the additional incremental control scenarios needed to estimate emissions

reductions by control measure, us ing the MOVESemscalc utility . The link

- based emissions

cal culations 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.

Hourly Link

- Based Emissions Calculations

The ozone season weekday, hourly link

- based emissions

, by county

, for each analysis year and

inventory type, were calculated with the MOVESemscalc utility using the following major

inputs:

• TxDOT district

- level SUT/fuel type VMT mix by MOVES roadway type;

• TDM link and intrazonal link VMT and speeds estimates, which contain the link

- specific,

hour ly, directional, operational VMT and speed estimates as developed by the

TRANSVMT utility to include: A

- node, B

- node, county number, TDM road type

(functional class) code, link length, congested (operational) speed, VMT, and TDM area

type code;

• County

- level hourly SUT/fuel type off

- network activity estimates (SHP, starts, and SHI);

• MOVES

- based off

- network emissions factors by pollutant, process, hour, SUT, and fuel

type;

• MOVES

- based “on

- network” (VMT

- based) emissions factors by pollutant, process , hour,

average speed, MOVES roadway type, SUT, and fuel type; and

• TDM road type/area type to MOVES road types designations (see Table 34 )

.

The VMT

- based emissions w ere calculated for each hour using the TxDOT district

- level

SUT/fuel type VMT mix, the TDM link and intrazonal link VMT and speeds estimates, the

MOVES

- based “on

- network” emissions factors, and the TDM road type/area type to MOVES

road types designations. For each link, the link was assigned a MOVES road type based on the

link’s road type and area type (see Table 34 ). The link VMT was then distributed to each

SUT/fuel type using the VMT mix

, based on the link’s designated MOVES road type and its

associated TxDOT district

.

The emissions factors for each SUT/fuel type were selected based on the link’s designated

MOVES road type and the link speed. For link speeds fall ing 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), the59 

### emissions factors for the associated bounding s peeds were used. The g/mi rates were multiplied

by the link SUT/fuel type VMT producing the link

- level emissions estimates.60 

### Table 34

. HGAC TDM Road Type/Area Type to MOVES Road Type Designations

. TDM Road Type (Code

- Name) TDM Area Type (Code

- Name) MOVES Road Type (Code

- Name)

3

- Toll Roads 5

– Rural

2

– Rural Restricted Access 10

- Rural Interstate 5

– Rural

11

- Rural Other Fwy. 5

– Rural

4

- Ramps (Fwy/Toll/Frnt) 5

– Rural

3

– Rural Unrestricted Access 8

- Local (Cent. Conn.) 5

– Rural

12

- Rural Prin. Art. 5

– Rural

13

- Rural Other Art. 5

– Rural

14

- Rural Major Col. 5

– Rural

15

- Rural Collector 5

– Rural

1

- Urban Interstate 1

– CBD ; 2

– Urban; 3

– Urban

Fringe

4

– Urban Restricted Access 2

- Urban Other Freeway 2

– Urban ; 3

– Urban Fringe

3

- Toll Roads 1

– CBD ; 2

– Urban ; 3

– Urban

Fringe; 4

– Suburban

10

- Rural Interstate 2

– Urban ; 3

– Urban Fringe; 4

– Suburban

11

- Rural Other Fwy. 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. Art. 1

– CBD ; 2

– Urban; 3

– Urban

Fringe

6

- Urban Other Art. 1

– CBD ; 2

– Urban; 3

– Urban

Fringe; 4

– Suburban

7

- Urban Collector 1

– CBD ; 2

– Urban; 3

– Urban

Fringe

8

- Local (Cent. Conn.) 1

– CBD ; 2

– Urban; 3

– Urban

Fringe; 4

– Suburban

12

- Rural Prin. Art. 3

– Urban Fringe; 4

– Suburban

13

- Rural Other Art. 3

– Urban Fringe; 4

– Suburban

14

- Rural Major Col. 3

– Urban Fringe; 4

– Suburban

15

- Rural Collector 3

– Urban Fringe; 4

– Suburban

40

- Local (Intrazonal) 40

- LOCAL (INTRAZONAL)



### The off

- network emissions were calculated for each hour using the hourly MOVES

- based

off

- network emissions factors by SUT/fuel type and the county

- level hourly SUT/fuel type 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 emissions factors by the

appropriate cou nty

- level hourly SUT/fuel type off

- network activity, which was determined by

the pollutant process.61 

### The MOVESemscalc utility outputs for this RFP inventory project consist of a listing file

(summarizing all pertinent information regarding the execution o f the utility) , and a tab

delimited emissions report summary file for each run including both hourly and 24

- hour activity

and emissions estimates by SUT/fuel type and TDM road type, with emissions tables of pollutant

composites and individual emissions pro cess totals

.

See Appendix A (Electronic Data Submittal) for the listing of EI 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.

HGB R FP Emissions Inventories

Table 35 lists the activity and emissions rate input data components (Pre

- 1990 Control or Control

Strategy) used in calculating each of the 19 HGB RFP EIs. This represents 152 county

- level EIs

(not including the extra EIs produced to quantify the individual control measure emissions

reductions).62 

### Table 35 . HGB RFP Emission s Inventories.

Number RFP Inventory Activity

Year 1 

### Emissions Factor

Input 1



### 1 2002 Base Year

2002 2002 Control Strategy

2 2002

Adjusted Base

Year 2002

Pre

- 1990 Control 3 2008 2008

4 2011 2011

5 2014 2014

6 2017 2017

7 2018 2018

8 2008

Pre

- 1990

Control 2008 2008

Pre

- 1990 Control 9 2011 2011 2011

10 2014 2014 2014

11 2017 2017 2017

12 2018 2018 2018

13 2019 2019 2019

14 2008

Control Strategy 2008 2008

Control Strategy 15 2011 2011 2011

16 2014 2014 2014

17 2017 2017 2017

18 2018 2018 2018

19 2019 2019 2019

1 “ Pre

- 1990 Control ” emissions factors model future evaluation year fleets with pre

- 1990 controls (no

I/M program, with the MOVES model switch ed to “turn off” post

- 1990 FMVCP emissions standards

, along with summer 1992 fuel parameters including 1992 RVP limit promulgated pri or to enactment of the

1990 CAAA

. “ Control Strategy ” emission s factors model the controls based on the current calendar year

regulations (i.e., pre

- 1990 and all subsequent controls). Activity input includes VMT mix, link

VMT/speeds, and off

- network activ ity (SHP, s tarts, and SHI).



### Individ ual Control Measure Emissions Reductions Estimation

Table 36 lists the five inventory control scenarios that were used to estimate the emissions

reductions for individual control measures. With the EIs listed in this table, for each HGB

county and for all the milestone years and the attainment and contingency years, emissions

reductions were estimated attributable to RFG, post

- 1990 CAA FMVCP, I/M program, and

TxLED fuel.

The general process outlined in Table 36 , involved:

• calculation of total reductions for each county;

• calculation of Harris County individual control measure emissions reductions;63 

### • calculation of individual control reduction distributions from Har ris County

individual control measure and total reductions; and

• multiplying individual control measure emissions reduction distributions estimated

from Harris County to the total reductions for each of the other HGB counties.

To perform these calculation s, the additional Harris County control scenario EIs (2CS, 3CS,

and 4CS) were needed, consistent with the 1CS and CSS inventories (i.e., the TDM link

- based

EIs numbered 8 through 19 in Table 35 ). These additional Harris County extra control scenario

EIs were produced (consistent with the inventory data and procedures previously discussed), and

the EI results for all scenarios were applied with this process yielding the individual control

measure emissions reductions estimates. Note that under the previous third bullet, two

“emissions reduction distributions” were needed, one with I/M included, and one with I/M

excluded (needed for non

- I/M county emissions reduction est imation).

Table 36 . HGB RFP EI Control Scenarios for Indvidual Control Measure Emissions

Reductions Estimates.

Scenario

Label Inventory Control

Measures 1 

### Individual Control Measure

Emission Reduction

Calculation Counties

1 CS

(or P90) Pre

- 1990 Control

– All HGB

Counties

2

CS Pre

- 1990 Control + RFG RFG

(1 CS

– 2 CS emissions) Harris

3

CS Pre

- 1990 Control + RFG +

Post

- 1990 FMVCP Post

- 1990 FMVCP

(2

CS

– 3 CS emissions) Harris

4 CS Pre

- 1990 Control + RFG +

Post

- 1990 FMVCP + I/M

Program I/M Progam

(3

CS

– 4 CS emissions) Harris

C SS Control Strategy TxLED

(4 CS

– CSS ) All HGB

Counties

1 “ Pre

- 1990 Control ” emissions factors model future evaluation year fleets with pre

- 1990 controls (no I/M

program), with the MOVES model switch ed to “turn off” post

- 1990 FMVCP emissions standards , along

with summer 1992 fuel parameters including 1992 RVP limit promulgated prior to enactment of the

1990 CAAA

. “ Control Strategy ” emission s factors model the controls based on the current calendar year

regulations (i.e., pre

- 1990 and all subsequent controls).



### The individual reduction distributions used for counties with I/M programs were calculated

from the Harris County EIs as:

(1CS

- 2CS)/(1CS

- CSS) = relative reductions from RFG;

(2CS

- 3CS)/(1CS

- CSS) = relative reductions from Post

- 1990 FMVCP;

(3CS

- 4CS)/(1CS

- CSSs) = relative reductions from I/M Program; and

(4CS

- CSS)/(1CS

- CSS) = relative reductions from TxLED fuel.



### The individual reduction distributions used for non

- I/M counties were calculated from the

Harris County EIs as:64 

### (1CS

- 2CS)/[(1CS

- CSS)

- (3CS

- 4CS)] = relative reductions from RFG;

(2CS

- 3CS)/[(1CS

- CSS)

- (3CS

- 4CS)] = relative reductions from Post

- 1990 FMVCP; and

(4CS

- CSS)/[(1CS

- CSS)

- (3CS

- 4CS)] = relative reductions from TxLED fuel.



### These facto rs were calculated and applied to county total reductions, at the SUT/fuel type

and emissions

- process level. The results were summed within reduction categories to produce

the county and region pollutant total individual reduction estimates (as shown in Table 7 and

Table 8 ). The calculations were performed in individual spreadsh eets for each year, including

2008, 2011, 2014, 2017, 2018, and 2019.

The spreadsheet calculations, with county, SUT/fuel type, and emissions

- process level

results, as well as higher level aggregations, such as the HGB region estimates previously

present ed in Table 7 and Table 8 , were included in Appendix A (Electronic Data Submittal).

QUALITY ASSURANCE

Analyses and results were subjected to appropriate internal review and QA/QC procedures,

including independent verification and reasonableness checks. All work was completed

consistent with applicable el ements of ANSI/ASQ E4

- 2004: Specifications and Guidelines for

Quality Systems for Environmental Data Collection and Technology Programs and the TCEQ

Quality Management Plan.

Quality Assurance Project Plan (QAPP) Category II (Modeling for NAAQS Compliance ) is

the QAPP category that most closely matches these objectives and establishes QAPP

requirements for projects involving applied research or technology evaluations. Internal review

and quality control measures consistent with applicable NRML QAPP requir ements, 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.

A. Project Management

The project management was as listed pr eviously in the Acknowledgments section.

The definition and background of the problem addressed by this project, the project/task

description, and project documents and records produced are as described previously in the

Purpose, Background, Project Repo rting, and Project Electronic Data 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 No tice to Commence (NTC) from TCEQ, the TTI project manager

provided a detailed pre

- analysis plan to the TCEQ project manager for review and concurrence.

Upon concurrence of the pre

- analysis plan, the TTI project manager distributed the pre

- analysis

plan to the TTI inventory developers for use in both the inventory development and QA review

process. TTI maintains records of the project QA checks as a part of the project archive, for at

least five years.

The objective was to produce the emissions inventory product of the quality suited to its

purpose as specified (e.g., inventories to be processed for input to a photochemical model, for

use in an attainment demonstration SIP revision, from which a motor vehicle emissions budget65 

### will be produced for transpor tation conformity purposes), in accordance with the appropriate

guidance and methods documents as referenced, as detailed in the pre

- analysis plan, and in

consultation with the TCEQ project manager.

Basic criteria were used to assure that the acceptable quality of the product was met

– product developers verified that the process and product were as specified, to include:

• The product met the purpose of the emissions analysis (e.g., photochemical modeling,

reasonable further progress, transportation confo rmity, model comparison);

• The full extent of the modeling domain (e.g., analysis years, geographic coverage,

seasonal periods, days, sources, pollutants) was included;

• Agreed methods, models, tools, and data were used (e.g., as listed in the Grant

Activities Description, and as listed in the more detailed pre

- analysis plan

– any

change from this plan, if needed, was made in consultation with the TCEQ Project

Manager);

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

with the pre

- analysis plan; and

• Any deficiencies found during development and end

- product quality checks (as

discussed in QAPP Section D) were corrected.

B. Data Generation and Acquisition

Note that no sampling of data was involved in the emissions inve ntory development, thus only

existing data (non

- direct measurements) were used for this project.

The data needed for project implementation were in the categories needed for development

of emissions rate model inputs and adjustment factors, and developme nt 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 prov ided in the pre

- analysis plan.

All data used either as direct input or to produce inputs (e.g., to the MOVES model or to

TTI’s emissions inventory development utilities used, which were listed in the pre

- analysis plan)

were reviewed by TTI for suitabilit y 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 age ncy. The data needed may include: Highway Performance Monitoring System

(HPMS) data (from TxDOT’s Roadway Inventory Functional Classification Record [RIFCREC]

report); regional travel demand model data; speed model data; vehicle registration data;

automat ic 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 dat a 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.66 

### Data Management: TTI emissions inventory data dev elopers 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, or

multiple computer configuration s

, 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

copied (via external hard drive) to the cluster’s Master computer . Upon execution, completion,

and error checking, the MOVES output databases and run log text files were copied to an

individual workstation, archived and processed further in preparation for input to the emissions

calculations.

After the final product was completed, all the project data archives were compiled on a set of

optical data discs (CD

- ROM or DVD, depending on size), or on an external drive for very large

project data sets. A complete archive of the project data is kept by TTI (the computer mod els

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.

C. Assessment and Oversight

The following assessments were performed.

• Verified that the overall scope was met (consistent with the intended purpose, for

specified temporal resolution an d geographic coverage, for specified sources,

pollutants, and emissions processes).

• Checked input data preparation, and model or utility execution instructions (e.g., run

specifications, scripts, JCFs, command files) were prepared according to the plan; an d

• 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 a ny inconsistencies or deficiencies were found, the issue was directly

communicated to the responsible staff for corrections (or the outside agency staff involved, if

provided from outside of TTI, if needed). After a correction was made, the QA checks were

performed again to ensure that the additional work resulted in the intended quality assured result,

and the correction was noted in the QA record (process was performed until QA check was

satisfied).67 

### Any major problem was reported to the project manager and communicated to the project

team as needed, as well as when the various data elements in the process passed QA checks and

were ready for further processing according to the project pre

- analysis plan. The project

manager ensured that all of the QA che cks 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 Us ability

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 we re performed during emissions

inventory development.

Data for the project, whether provided for direct use or processed by TTI, were reviewed,

verified, and validated to ensure that they conform to their particular specifications and TCEQ’s

requirements for the intended use. The data specifications and requirements where not stated

specifically, are included in the documents listed in the “References” section, or are outlined or

referenced in the detailed pre

- analysis plan.

The criteria for passing qua lity checks and the checks typically performed on each major

inventory input component (i.e., estimates of source activity, activity distributions, and emissions

factors) as well as on the resulting emissions estimates, are summarized in the following. Th ese

QA guidelines were used to ensure the development of emissions inventory estimates that are as

accurate as possible and meet the requirements of TCEQ’s intended use.

Verified that the overall scope of the emissions analysis has been met as prescribed in the

pre

- analysis plan, to include:

• Purpose of the emissions analysis (e.g., photochemical modeling, reasonable further

progress, transportation conformity, model comparison);

• Extent of the modeling domain (e.g., analysis years, geographic coverage, se asonal

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 preparatio n, model or utility execution instructions (e.g., run

specifications, scripts, JCFs, command files), and output, as appropriate to the component:

• Input data preparation checks:

o Verified the basis of input data sets against the pre

- analysis plan: Actual hi storical

or latest available data, validated model, expected values or regulated limits,68 

### regulatory program design, model defaults, surrogates, professional judgment;

check aggregation levels.

o Data development: Depending on the procedure and particular inp ut data set,

calculations were verified (e.g., re

- calculated independently and compared with

originally prepared values

– if spot

- checking a series of results, included extremes

and intermediate values).

o Completeness: Verified that input data sets were wit hin the required dimensions,

and all required fields were populated and properly coded or labeled.

o Format: Verified that formats were within required specifications (e.g., field

positions, data types and formats, and file formats), if any.

o Reasonability ch ecks: (discussed in the next section).

o Ensured that any inputs provided from external sources were quality assured, as

listed previously.

• Checked the model or utility execution instructions:

o Verified that the correct number of utility or model run specific ations 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.

o Checked for any unusual output file sizes.

o Searched output (e.g., utility listing files or model execution logs that contain

error and warning records) for warnings/errors.

o Checked the summary information provided in output listing files for any unusual

results.

Performed further chec ks for consistency, completeness, and reasonability of data output

from model or utility applications:

• Verified that the data distributions and allocation factors produced or used sum to 1.0,

as appropriate (e.g., hourly travel factors within a time perio d, proportion of travel by

vehicle categories on a particular roadway category).

• Verified that the required data fields were present, populated, and properly coded or

labeled; verified that data and file formats were within specifications.

• Verified that an y activity, emissions rate, or emissions adjustments were performed as

intended (e.g., seasonal activity factor, emissions control program adjustment).

• For data sets prepared with temporal or geographic variation (e.g., activity

distributions between weeke nds/weekdays, vehicle mix, or average speeds between69 

### road types or time periods), compared and noted whether directional differences were

as expected.

• Checked for consistency between data sets (e.g., compared detailed spatially and

temporally disaggregated activity estimates [e.g., link VMT] to original aggregate

totals, activity total summaries between utility applications [e.g., link

- VMT producer

and emissions calculator], and input hourly distributions versus hourly summaries

from the link activity outpu t 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 difference s

(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, 2 4

- 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 su mmaries

– differences should be within rounding error (N/A).

Any additional data products required for the emissions analysis were subjected to the

appropriate QA checks previously listed. Any issues found needing resolution were corrected

and appropria te QA checks were performed until satisfied.

REFERENCES

Guidance on the Post

- 1996 Rate

- of

- Progress Plan and the Attainment Demonstration, Corrected

Version as of February 18, 1994, U.S. Environmental Protection Agency, Ozone/Carbon

Monoxide Programs Branch , Office of Air Quality Planning and Standards, Research Triangle

Park, NC 27711.

Guidance on the Adjusted Base Year Emissions Inventory and the 1996 Target for the 15 Percent

Rate

- of

- Progress Plans, U.S. Environmental Protection Agency, Ozone/Carbon Mono xide

Programs Branch, Office of Air Quality Planning and Standards, Research Triangle Park, NC

27711.

Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National

Ambient Air Quality Standards (NAAQS) and Regional Haze Regulati ons, EPA

- 454/R

- 05

- 001,

Issued By: 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.

Procedures for Emission Inventory Preparation, Volume IV: Mobile Sources, EPA420

- R

- 92

- 009,

December 1992, U.S. Environmental Protection Agency, Emission Planning and Strategies

Division, Office of Mobile Sources and Technical Support Division, Office of Air Quality

Plann ing and Standards.70 

### Draft Motor Vehicle Emission Simulator (MOVES) 2009, Software Design and Reference

Manual, Assessment and Standards Division Office of Transportation and Air Quality U.S.

Environmental Protection Agency, EPA420

- B

- 09

- 007 March 2009.

Mot or Vehicle Emissions Simulator (MOVES), User Guide for MOVES2010a, Assessment and

Standards Division, Office of Transportation and Air Quality, U.S. Environmental Protection

Agency, , EPA420

- B

- 10

- 036, August 2010.

Technical Guidance on the Use of MOVES201 0 for Emission Inventory Preparation in State

Implementation Plans and Transportation Conformity, Transportation and Regional Programs

Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agency,

EPA420

- B

- 10

- 023, April 2010.

2 008 NEI/EIS Implementation Plan, Section 2, Transitioning from National Emission Inventory

Format to the 2008 NEI, available at http://www.epa.gov/ttn/chief/net/neip/

.

2008 NEI/EIS Implementation Plan , Appendix 2, CERS and Examples, available at

http://www.epa.gov/ttn/chief/net/neip/

.

2008 NEI/EIS Implementation Plan, Appendix 6, Reporting Code Tables, available at

http://www.epa.gov/ttn/chief/net/neip/

.

Federal Register

. Monday, June 2, 2003, Part II, Environmental Protection Agency, 40 CFR Part

51, Proposed Rule To Implement the eight

- Hour Ozone National Ambien t Air Quality Standard;

Proposed Rule.

Federal Register

. Friday, April 30, 2004, Part II, Environmental Protection Agency, 40 CFR

Parts 50, 51 and 81, [OAR 2003

- 0079, FRL

- 7651

- 7], RIN 2060

- AJ99, Final Rule To Implement

the 8

- Hour Ozone National Ambient A ir, Quality Standard

-- Phase 1.

Federal Register, Tuesday, November 29, 2005, Part II, Environmental Protection Agency, 40

CFR Parts 51, 52, and 80 Final Rule To Implement the 8

- Hour Ozone National Ambient Air

Quality Standard; Final Rule.

Memorandum. U.S . Environmental Protection Agency, National Vehicle and Fuel Emissions

Laboratory, Ann Arbor, Michigan, September 27, 2001, To Karl Edlund, Director, Multimedia

Planning and Permitting Division, Region VI, From Robert Larson, Acting Director,

Transportatio n and Regional Programs Division, Office of Transportation and Air Quality

(OTAQ), RE: Texas Low Emission Diesel (LED) Fuel Benefits.

Development and Production of 2006 Base Case and 2008 Baseline On

- road Mobile Source

Emissions Inventories for the HGB No nattainment Area. Texas Transportation Institute, The

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

Methodologies for Conversion of Data Sets for MOVES Model Compatibility. Texas

Transportation Institute, The Texas A&M University System, College Station, TX. August

2009.71 

### Heavy

- Duty Vehicle Idle Activity and Emissions Characterization Study, ERG, August 2004.

Update Of On

- Road Inventory Development Methodologies For Motor Vehicle Emissions

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

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

Energy Information Administration’s (EIA) Petroleum Marketing Annuals

.

Revision to the State Implementation Plan Mobile Source Strategies, Texas Inspection and

Maintenance State Implementation Plan . Texas Commission on Environmental Quality, Austin,

TX, November 18, 2010.

HGB RFP SIP Revision for the 1997 Eight

- Hour Ozone Standard

. Texas Commission on

Environmental Quality, Austin, TX, March 201 0

.

Energy Information Administration/Monthly Energy Review, August 1993

.72 

## APPENDIX A:

HGB RFP ON

- ROAD INVENTORIES ELE CTRONIC DATA SUBMITT AL74 

### HGB RFP MOVES

- Based, County

- Level, EIs and Control Measure Emissions Reductions

Estimates

– Electronic Data Submittal

This appendix (electronic data set and description) contains a description of the electronic data

package that TTI submitted to TCEQ, per Revised Proposal for Grant Activities No. 582

- 11

-

11226

- FY12

- 01

.

The MOVES rates

- per

- activity, TDM link

- based method was used to produce ozone season

weekday EIs of VOC, CO, NO

x 

### , and CO 2 

### . The 19 HGB RFP EIs (i.e., 152 county level EIs)

submitted are described in the following table. The individ ual control measure reduction

estimates submitted were produced for 2008, 2011, 2014, 2017, 2018, and 2019.

Table 37 . HGB RFP On

- road Mobile Inventory Scenario Descriptions and Data Labels.

Number RFP Inventory Activity 1 

### Emissions Factors 1



### 1 2002 Base Year (BYR or CSS)

2002 2002 CSS

2 2002 Adjusted Base Year (ABY) 2002 Pre

- 1990 Control (P90 or

1CS)

3 2008 ABY 2008 1CS

4 2011 ABY 2011 1CS

5 2014 ABY 2014 1CS

6 2017 ABY 2017 1CS

7 2018 ABY 2018 1CS

8 2008 Pre

- 1990 Control (P90) 2008 2008 1CS

9 2011 P90 2011 2011 1CS

10 2014 P90 2014 2014 1CS

11 2017 P90 2017 2017 1CS

12 2018 P90 2018 2018 1CS

13 2019 P90 2019 2019 1CS

14 2008 Control Strategy (CSS) 2008 2008 CSS

15 2011 CSS 2011 2011 CSS

16 2014 CSS 2014 2014 CSS

17 2017 CSS 2017 2017 CSS

18 2018 CSS 2018 2018 CSS

19 2019 CSS 2019 2019 CSS

1 “ Pre

- 1990 Control ” emissions factors model future evaluation year fleets with pre

- 1990 controls : therefore no I/M

program was modeled, post

- 1990 F MVCP emissions standards in MOVES were switched off

, and summer 1992

fuel parameters were used (i.e., the 1992 RVP limit promulgated prior to enactment of the 1990 CAAA). “ Control

Strategy ” emission s factors model the controls based on the current regulations (i.e., pre

- 1990 plus subsequent

controls per analysis year ). Activity input includes VMT mix, link VMT/speeds, and off

- network activity (SHP,

s tarts, SHI). Additional runs (Harris County only) that incrementally added in controls for estimating emissions

reductions by individual control measure, were “2CS” switching to RFG, “3CS” adding post

- 1990 FMVCP, and

“ 4CS” adding the I/M program.75 

### Electronic Media



### The electronic data submittal data files and databases, summarized in the following, were

compressed into about 630 megabytes of memory space, and were submitted on one CD

- ROM,

entitled:

“ HGB RFP MOVES

- Based On

- Road Mobile Source EIs

– TTI FY2012 ”.

• Emi ssions Files :

o RFP EIs

– MOVESemscalc utility TAB

- delimited, hourly/24

- hour EI report

summary files;

o 24

- Hour RFP EI Report Extracts

– 24

- hour totals summaries extracted from

MOVESemscalc EI data files listed in previous bullet;

o Control measure reductions calculation spreadsheets.

• MOVES inputs (MOVES run specifications and county domain input databases); and

• Final, MOVES

- based, TTI MOVES post

- processor

- utility

- produced, rate

- per

- activity

emissions factors (emission s factor database inputs to the emissions calculations), and

TxLED adjustment factor files.

File

- N aming Conventions

The following file naming convention s were used :

• SSSS is:

• EI type

– BYR, ABY, P90 (or 1CS), and CSS, respectively, are:

 base year,

 adjusted base year,

 pre

- 1990 controls, and

 contro l strategy; or for

• control scenario

– 1CS (or P90), 2CS, 3CS, 4CS, CSS, respectively, represent:

 pre

- 1990 controls,

 pre

- 1990 controls with RFG,

 pre

- 1990 controls with RFG and post

- 1990 FMVCP,

 pre

- 1990 controls with RFG and post

- 1990 FMVCP and I/M program, and

 pre

- 1990 controls with RFG and post

- 1990 FMVCP and I/M program and

TxLED fuel;

• YYYY is for analysis year ( 2002, 2008, 2011, 2014, 2017, 2018, 2019 ); and

• CCCC is county FIPS code (48 039, 48071, 48157, 48167, 48201, 48291, 48339, 48473 )

.

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.76 

### Emissions Data Files



### RFP EIs

– 19 MOVESemscalc utility RFP EI runs per county (see the previous table). Each run

produced two file types (compressed in: “HGBrfp_mvs10a_19EIs.zip”):

• “HGBrfp_mvs10a_ SSSS YYYY s wkd_ CCCC _ems.TAB” (152): tab

- delimited county EI

data summary report including hourly and 24

- ho ur activity and emissions tables. F or

roadway

- based processes by roadway and vehicle type (SUT/Fuel Type)

– VMT, VHT

(vehicle hours traveled), average speed (VMT/VHT), and pollutan t/process emissions

totals; for off

- network

- based p rocesses by vehicle type

– SHP, SHI, starts

, and

pollutant/process emission s totals (SHI for combination long

- haul trucks only)

.

• “HGBrfp_mvs10a_ SSSS YYYY s wkd_ CCCC _ems.LST” (152): list of execution times;

run script; file locations; data descriptions; and varied data summaries incl uding hourly

and 24

- hour activity, pollutant/process emissions totals, and average speed (VMT/VHT ).

24

- hour RFP EI Extracts

– 24

- hour EI totals by SUT/fuel type extracted from the

MOVESemscalc tab

- file output, used for emissions summaries and emissions reductions

calculations. 19 RFP EI extracts were provided (compressed in “HGBrfp_mvs10a_19EIs.zip”):

• “HGBrfp_mvs10a_ SSSS YYYY s wkd_tabtots_ST.tab” (19): tab

- delimited text file of 24

-

hour EI data summaries for eight counties, by SUT/fuel type, pollutant, and process.

• “HGBrfp_mvs10a_ SSSS YYYY s wkd_tabtots.tab” (19): tab

- delimited text file of 24

- hour

EI data summaries for eight counties, by pollutant and process.

Control M easure Emissions R eduction E stimates

– 18 extra Harris County MOVESemscalc EI

runs were performed, for 2008, 2011, 2014, 2017, 2018, 2019; and control scenarios 2CS, 3CS,

and 4CS. 24

- hour EI data extracts by SUT/fuel type were used in the calculations. The extra EI

data files and s preadsheets are compressed in “hgbrfp_mvs10a_indred.zip” and include:

• “HGBrfp_mvs10a_ SSSS YYYY s wkd_48201_ems.TAB” (18): MOVESemscalc tab

delimited EI data summary report file (previously described) for Harris County;

• “HGBrfp_mvs10a_ SSSS YYYY s wkd_48201_ems. LST” (18): MOVESemscalc utility

run list file ( previously described);

• “HGBrfp_mvs10a_ SSSS YYYY s wkd_tabtots_ST.tab” (18): tab

- delimited text file of

Harris County 24

- hour EI data by SUT/fuel type (MOVESemscalc EI TAB file extracts);

• “HGBrfp_m vs10a_ SSSS YYYY s wkd_tabtots.tab” (18): tab

- delimited text file of Harris

County 24

- hour EI data totals (MOVESemscalc EI TAB file extracts);

• “HGBrfp_mvs10a_ YYYY reductions.xlsx” (6): individual emissions control measure

reduction calculations spreadsheet file ; and

• “HGBrfp_mvs10a_allyrsreductions_summaries.xlsx” (1): emissions control measure

reductions summary spreadsheet.77 

### Emissions Factors

– MOVES Input Files (MRSs and CDBs)

MOVES Input Files for RFP EIs : There were 113 MOVES runs performed for the EI

develo pment process of the 152 county level RFP EIs (i.e., One set of Pre

- 1990 Control [or 1CS]

runs; and one set of Control Strategy [or CSS] runs including the 2002 base year). The MOVES

inputs used

– run specification files (RunSpec or MRS) and county input databases (CDB)

– were provided (MOVESDB20100830, the MOVES model current default input database, is

available at EPA’s MOVES model website: http://www.epa.gov/otaq/models/moves/index.htm ):

• “ MVS10A_HGBRFP_ YYYY SWKD_ SSS S

_ CCCC _ER.MRS ” (113): two RFP scenario s

(i.e., 1cs and css) x eight counties x seven years + one extra Harris County 2002 MRS

file for special I/M test

- type switch effects post

- processing procedure, compressed in

“HGBrfp_mvs10a_1cs_css_MRSs.zip;” and

• “ MVS10A_HGBRFP

_ YYYY SWKD_ SSS S

_ CCCC

_ ER_CDB_IN ” (113 MySQL

database folders containing 6,215 total files ). The CDBs are compressed in

“HGBrfp_mvs10a_1cs_css_CDBs.zip.”

Additional MOVES Input Files for Incremental Control Measure Emissions Reductions

Estimates Procedure : To estimate control measure reductions, extra MOVES runs were

performed for Harris County, for 2008, 2011, 2014, 2017, 2018, 2019, and control scenarios 2CS

and 3CS. The extra RunSpecs and CDBs are:

• “ MVS10A_HGBRFP_ YYYY SWKD_ SSS S

_ 48201 _ER.MRS ” (12: 2 sce narios x 1 county

x 6 years): compressed in “HGBrfp_mvs10a_2cs_3cs_MRSs.zip.”

• “ MVS10A_HGBRFP

_ YYYY SWKD_ SSS S

_ 48201

_ ER_CDB_IN ” (12 corresponding

MySQL database folders containing 660 total files ). The CDBs are compressed in

“HGBrfp_mvs10a_2cs_3cs_CDBs.zip.”

Emissions Factors

– Final, Adjusted MOVES

- based Emission Rates

Final MOVES

- based, Emissions Factor Look

- up Tables : The MOVESratesAdj utility performed

adjustments (e.g., NOx TxLED effects, I/M test

- type switch effects) as specified, and produced

the final emissions rate look

- up table inputs to the emissions calculations

– MySQL databases

containing two emissions rate lookup tables, ttirateperdistance for roadway

- based emissions

processes, and ttirateperactivity for the “off

- network” processes. The ttirate peractivity table

fields are: pollutantID, processID, hourID, sourceTypeID, fuelTypeID, rateperactivity . The

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

sourceTypeID, fuelTypeID, ratePerDistance

.

• “ mvs10 a_hgbrfp_ YYYY SWKD_ SSS S

_ CCCC

_ er_outratesadj ” (112 MySQL database

folders containing 784 total files ). This is the set of 1CS and CSS control scenario rates

database look

- up tables for all counties, used in the 19 RFP EIs. Each database contains

the ttirateperdistance and ttirateperactivity emissions rate look

- up tables used in the

emissions calculations. They are compressed in “hgbrfp_mvs10a_ratesadj_outdbs.zip.”78 

### • “ mvs10a_hgbrfp_ YYYY SWKD_ SSSS

_ 48201 _er_outratesadj ” (18 MySQL database

folders contain ing 126 total files). This is the set of 2CS, 3CS, and 4CS control scenario

Harris County rates used in incremental control measure emissions reductions estimates.

Each database contains the ttirateperdistance and ttirateperactivity emissions rate look

- u p

tables used in the emissions calculations. They are compressed in

“hgbrfp_mvs10a_xcs_ratesadj_outdbs.zip.”

• “ tx_mvs10a_ YYY Y _txled_facts.txt ” (six TxLED adjustment factor files), and

“ 48201im2002_hgbrfp_adjfactspath.txt ” (one I/M test

- type switch factor f ile used) are

compressed in “hgbrfp_mvs10a_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 MO VESratesCalc 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 : “ttirateperdist ance” with grams/mile rates, and

“ttirateperactivity” with grams /SHP, grams /SHI, and grams /start rates.

2) Final Rates Adjustments : Using TTI’s MOVESratesAdj utility, TTI

- calculated rates were

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

type switch effects adjustment (post

- processing of rates from two

- runs with different I/M

test

- types into a single set of rates with the combined I/M test

- type effects ) was

performed for the Harris County 2002 Base Year, and TxLED adjustments were applied

to diesel vehicle NO

x 

### rates for all appropriate years/control scenarios , and the extracted

and adjusted rate tables were placed in a separate database (by county/year/ control

scenario) for input to the emissions calculations.80 

## APPENDIX B:

EMISSIONS ESTIMATION UTILITIES FOR MOVES

- BASED EMISSIONS

INVENTORIES82 

### TTI EMISSIONS ESTIMATION UTILITIES FOR MOVES

- BASED EMISSIONS

INVENTORIES

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 sources using the latest version of EPA’s MOVES model. These

utilities produce inputs used with the MOVES model, make special adjustments to the emissions

factors (when required) , and multiply them with travel model link

- base d or HPMS

- based (virtual

link) activity estimates to produce emissions at user

- specified temporal and spatial scales.

The main utilities for calculating hourly and 24

- hour emissions using MOVES are

TRANSVMT, VirtualLinkVMT, MOVESactivityInputBuild, MOVES populationBuild,

MOVESfleetInputBuild, MOVESratesCalc, MOVESratesAdj, ShpExtIdleStartActBld,

ExtIdleHrsCalc , MOVESemscalc, and MOVEStabcomb. The TRANSVMT and

VirtualLinkVMT prepare the link VMT and speeds activity input. The

MOVESactivityInputBuild, MOVE SpopulationBuild, and MOVESfleetInputBuild utilities build

inputs used in MOVES. The MOVESratesCalc utility calculates the emissions rates from the

MOVES output in terms of grams per activity, rather than the grams per vehicle emissions rates

produced by MOVES. The MOVESratesadj utility makes special adjustments to the emissions

rates when required. The ShpExtIdleStartActBld utility builds the SHP and starts activity

required to estimate emissions using the grams per activity emissions rates produced by the

MOVESratesCalc utility. The ExtIdleHrsCalc utility builds the SHI activity required to estimate

emissions using the grams per activity emissions rates produced by the MOVESratesCalc utility.

The MOVESemscalc utility calculates emissions by hourly tim e period s, producing a tab

delimited summary file (including 24

- hour totals) and hourly link emissions output files

(optional). The MOVEStabcomb utility combines multiple MOVESemscalc tab

- delimited

output files into one regional tab

- delimited output file.

A process flow diagram follows the utility descriptions.

TRANSVMT

The TRANSVMT utility post

- processes TDMs to produce hourly, on

- road vehicle, seasonal and

day

- of

- week specific, directional link VMT

, and speed estimates

. The TRANSVMT utility

processes a travel demand model ( TDM ) traffic assignment by multiplying the link volumes by

the appropriate HPMS, seasonal, or other VMT factors

. Hourly factors are then used to

distribute the link VMT to each hour in the day

. The TTI speed model is used to estima te the

operational time

- of

- day link speeds for each direction

. Since intrazonal links are not included in

the TDM, special intrazonal links are created and the VMT and speeds for these special links are

estimated using the intrazonal trips from the trip m atrix and the zonal radii

. The link VMT and

speeds produced 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 area type (from TxDOT’s annual RIFCREC r eport) to

produce hourly, on

- road vehicle fleet, seasonal and day

- of

- week specific actual or projected83 

### VMT, and directional operational speed estimates

. These estimated VMT and speeds are

produced for up to 42 directional HPMS functional classification/area type combinations, or

“links.” The VirtualLinkVMT utility was developed for use in areas that do not have TDM

networks, as well as for EI applications for w hich network link

- based detail is not required

. 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 dis tributions; and

• S peed model inputs to include volume/delay equation parameters adapted for HPMS,

and free

- flow speeds and lane capacities by HPMS functional classification and area

type.

VirtualLinkVMT initially scales the county HPMS AADT VMT at the link level to the

appropriate VMT (e.g., uses county

- level VMT control total

- to

- AADT ratio to produce seasonal,

day

- of

- week specific VMT)

. Hourly factors and directional split factors are applied to the

ad justed 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 freeflow speeds for each link

. The virtual link VMT and speeds produced using the VirtualLinkVMT utility are an input to the

emissions calculation ut ility

, EMSCALC

.

MOVESactivityInputBuild

The MOVESactivityInputBuild utility builds the roadtypedistribution, hourvmtfraction,

avgspeeddistribution, roadtype, hpmsvtypeyear, year, state, zone, zoneroadtype,

monthvmtfraction, and dayvmtfraction MOVES input d atabase tables using the link

- based

hourly VMT and speeds developed with the TRA NSVMT or VirtualLinkVMT utility, 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 com bination 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 t he output;

• Year ID, which specifies the year for calculating the output;84 

### • Link/Ramp designations, which designates each link roadway type/area type combination

to either ramp or non

- ramp, and

• MOVES default database.

For each link in the link

- based hourly VMT and speeds in which the county number matches

the desired county ID, the link VMT is saved in a VMT summary array based on hour, link

functional class, and link area type. The link VHT (link VMT/link speed) is saved in a VHT

summary array based on hou r, link functional class, link area type, and MOVES average speed

bin ID (determined using the MOVES average speed bins and the link speed). The link VHT is

also saved in a road type VHT array based on link functional class and link area type, and if the

link is specified as ramp by the link/ramp designations specified by the user, then the VHT is

also saved in the ramp segment of the road type VHT array.

A MOVES roadway type array (by MOVES roadway type) is also formed using the data in

the VMT summary array and the link/MOVES roadway type designations. An hourly VMT

array (by MOVES SUT, MOVES roadway type, and hour) is formed using the data in the VMT

summary array, the link/MOVES roadway type designations, and the VMT mix. An average

speed distributi on array (by MOVES SUT, MOVES roadway type, hour, and MOVES speed bin)

is created using the VHT summary array and the VMT mix. Using the appropriate MySQL

code, the MOVES roadtypedistribution, hourvmtfraction, and avgspeeddistribution default

values are e xtracted and saved for later use.

The VMT in the hour VMT array is aggregated by hour to produce the roadway type

distribution array by MOVES SUT and MOVES roadway type. This VMT is then converted to a

distribution by MOVES SUT (i.e., the total for a SU T 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 writ ten (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 grea ter than 0, the hourly VMT fraction array is used.

Otherwise, the MOVES hourvmtfraction default values are used. A tab

- delimited version is also

written (optional).

The VHT in the average speed distribution array is converted to a distribution by SUT,

MOVES roadway type, hour/day (combination of hour and the day ID specified by the user), and

MOVES average speed bin. Using the appropriate MySQL code, the avgspeeddistribution

database table is written. For those SUTs where the VMT mix is greater than 0 , the average

speed distribution array is used. Otherwise, the MOVES avgspeeddistribution default values are

used. A tab

- delimited version is also written (optional).

The VHT in the road type VHT array is converted to a proportion of ramp VHT by dividi ng

the ramp segment of the road type VHT array by the total VHT for the road type in the road type85 

### VHT. Using the appropriate MySQL code, the road type database table is written using the

proportions from the road type VHT array. A tab

- delimited version is also written (optional).

The VMT in the hourly VMT array is aggregated to form the HPMS vehicle type VMT

array. Each SUT is assigned an HPMS vehicle type (SUT 11 is HPMS vehicle type 10; SUT 21

is HPMS vehicle type 20; SUTs 31 and 32 are HPMS vehicle type 30; SUTs 41, 42, and 43 are

HPMS vehicle type 40; SUTs 51, 52, 53, and 54 are HPMS vehicle type 50; and SUTs 61 and 62

are HPMS vehicle type 60). Using the appropriate MySQL code, the hpmsvtypeyear database

table is written using the VMT from the HPMS vehicle type VMT array, alo ng 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

average speed bin for the user

- specified county.

The utility outputs five other database tables (state, zone, zoneroadtype, monthvmtfraction,

and dayvmtfraction) using the appropri ate 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 ne w zone database table is created and the data from the MOVES default zone data base table

is copied to the new table for the county ID greater than 48000 and county ID less than 49000.

The start allocation factors, idle allocation factors, and SHP allocat ion factors are all then

replaced with values of 1 in the new table.

For the zoneroadtype database table, a new zoneroadtype database table is created and the

data from the MOVES default zoneroadtype data base table is copied to the new table for the

zon e ID greater than 480000 and zone ID less than 490000. The SHO allocation factors are all

then replaced with values of 1 in the new table. For the monthvmtfraction database table, a new

monthvmtfraction database table is created and the data from the MOV ES default

monthvmtfraction database table is copied to the new database table and the month VMT

fraction is set to 1 for the user

- supplied month ID and 0 for all other months. For the

dayvmtfraction database table, a new dayvmtfraction database table is created and the data from

the MOVES default dayvmtfraction database table is copied to the new and the day VMT

fraction is set to 1 for the user

- supplied day ID and 0 for all other months.

MOVESpopulationBuild

The MOVESpopulationBuild utility builds the so urcetypeyear MOVES input database table and

the source type/fuel type population input file used with the MOVESemscalc utility to estimate

emissions using the VMT mix and the TxDOT registration data sets. The TxDOT registration

data sets are three sets of registration data (an age registration data file, a gas trucks registration86 

### 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 count y 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 T rucks >8500, and Total All Trucks vehicle

categories;

• G as 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 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 source 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 us ed 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 Tr ucks <=8500 vehicle

categories) are saved in an age registration data array. The gas truck registration data (for the

Gas > 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500, Gas > 26000, Gas > 33000,

and Gas > 60000 gas truck categories) are saved in the gas truck section of the diesel/gas

registration data array. The diesel truck registration data (for the Diesel > 8500, Diesel > 10000,

Diesel > 14000, Diesel > 16000, Diesel > 19500, Diesel > 26000, Diesel > 33000, and Diesel >

60000 diesel truck categories) are saved in the diesel truck section of the diesel/gas registration

data array. The age registration data array and the diesel/gas registration data array are combined

to form the registration category data array (a total of seven categories for 31 years of data and

the total) using the combinations in Table 38

.87 

### Table 38 . 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 catego ries in the registration category

data array. Table 39 shows the SUTs and their associated registration category in the registration

category data array.

Table 39

. SUT /Registration Category Correlation

.

SUT Registration

Category

11 2

21 1

31, 32 3

41, 42, 43, 51, 52, 54 4 + 6

61 5 + 7



### SUT population factors are calculated by SUT/fuel type using the data from the VMT mix

input for all SUTs except motorcycles (SUT 11) and the long

- haul trucks (SUTs 53 and 62) and

saved in the SUT population factors array. For SUT 21, the fuel type VMT mix is divided by the

total VMT mix for SUT 21. For SUT 31, the fuel type VMT mix is divided by th e total VMT

mix for SUTs 31 and 32. The same process applies to SUT 32. For SUT 41, the fuel type VMT

mix is divided by the total VMT mix for SUTs 41, 42, 43, 51, 52, and 54. The same process

applies to SUTs42, 43, 51, 52, and 54. For SUT 61, the fuel type VMT mix is divided by the

total VMT mix for SUT 61.

For SUT 11, the source type population factor for fuel type 1 (gasoline) is set 1 with all other

factors set to 0. For SUT 53, the SUT population factors by fuel type are calculated by dividing

th e fuel type VMT mix for SUT 53 by the fuel type VMT mix for SUT 52. For SUT 62, the

SUT population factors by fuel type are calculated by dividing the fuel type VMT mix for SUT88 

### 62 by the fuel type VMT mix for SUT 61, therefore creating a ratio of long

- hau l and short

- haul

trucks.

The SUT population factors and the population factor (if desired) are applied to the SUT

population array for all SUTs except SUT 53 and 62. For SUT 53, the SUT population factors

for SUT 53 are applied to the SUT population arr ay for SUT 52. For SUT 62, the SUT

population factors for SUT 62 are applied to the SUT population array for SUT 61.

Using the appropriate MySQL code, a new sourcetypeyear database table is created. The

data in the SUT population array is aggregated by fuel type and used to fill the sourcetypeyear

database table, along with the yearID, salesGrowthFactor, and migrationrate . For the yearID, the

year of the registration data is used, unless a population factor is used, in which case the year

from the year ID input is used. The salesGrowthFactor and migrationrate for each SUT is set 1.

A text format of this database table is written by the utility as well. The source type/fuel type

population input file used with the MOVESemscalc utility is written using the SUT population

array.

MOVESfleetInputBuild

The MOVESfleetInputBuild utility builds the sourcetypeagedistribution database table and

fuel/engine fraction inputs to MOVES using the TxDOT registration data sets and the MOVES

default database tables. The TxDOT registration data sets are three sets of registration data (an

age registration data file, a gas trucks registration data file, and a diesel trucks registration data

file) that list 31 years of registration data. The primary inputs to this utility are:

• Age registration data file, which lists 31 years of registration data for the Passenger

Vehicles , Motorcycles, Trucks <=6000, Trucks >6000 <=8500, Total Trucks <=8500,

Gas Trucks >8500, Diesel Trucks >8500, Total Trucks >8500, and Total All Trucks

vehicle categories;

• Gas 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 truck categories;

• Diesel tru cks registration data file, which lists 31 years of registration data for the Diesel

> 8500, Diesel > 10000, Diesel > 14000, Diesel > 16000, Diesel > 19500, Diesel >

26000, Diesel > 33000, Diesel > 60000, and Diesel Totals diesel truck categories;

• SUT data sources input, which specifies the data source for each SUT to use when

building the souretypeagedistribution database table;

• Fuel/engine fractions data sources input, which specifies the data source for each SUT to

use when building the fuel/engine fract ions;

• 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.89 

### The SUT data sources in put 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 input s are

only valid for source types 52, 53, and 61 (other are not valid due to data limitations and source

type 62 are all considered diesel).

For each county (or state total) in the list of the input sources, the age registration data (for

the Passenger Vehicle , Motorcycles, Trucks <=6000, Trucks >6000 <=8500, and Total Trucks

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

data (for the Gas > 8500, Gas > 10000, Gas > 14000, Gas > 16000, Gas > 19500, Ga s > 26000,

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

diesel/gas registration data array. The diesel truck registration data (for the Diesel > 8500,

Diesel > 10000, Diesel > 14000, Diesel > 16000, Diesel > 19500, Diesel > 26000, Diesel >

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

diesel/gas registration data array.

The age registration data array and the diesel/gas registration data array are combined t o form

the registration category data array (a total of seven categories for 31 years of data and the total)

using the combinations in Table 38 ( Registration Categorie s ) . The county is compared to the

data sources for each SUT in the SUT data sources input. If the county is found for a given

source type, then the 31 years of registration data from the source type’s corresponding category

in the registration category d ata array are added to the SUT age distribution array. Table 40

shows the source types and their corresponding registration categories.

Table 40

. SUTs /Registration Categories Correlation for SUT Age Distribution

.

SUT Registration Category

11 2

21 1

31, 32 3

52, 53 4

61, 62 5

A similar process is followed for the fuel/engine fractions array. However, only SUTs 52,

53, 61, and 62 are processed due to data limitations. The registration data are saved in the

fuel/engine fractions array based on fuel type. Table 41 shows the SUTs and their corresponding

registration categories.90 

### Table 41

. SUTs /Registration Categories Correlation for Fuel/Engine Fractions.

SUT Fuel Type Registration Category

52, 53 Diesel 4

Gas 6

61 Diesel 5

Gas 7

62 Diesel 5 + 7

Gas None

– all are assumed diesel



### After processing all of the counties, the data from the default sourcetypeage distribution input

are processed and the data for the registration data year are saved in the default age distribution

array. For each source type in which the registration data are to be used for the age distribution,

the 31 years of registration data i n the SUT age distribution array are converted to a distribution

by dividing the source type yearly registration data by the source type total registration data. For

each source type in which the defaults are to be used, the defaults values from the defau lt 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. Fo r

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 regist ration data and the yearly source type gas

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 appropriat e distribution. For source type 62, all of the fuel/engine fractions in the

fuel/engine fractions array are set to a value of 1.

Using the appropriate MySQL code, a new sourcetypeagedistribution database table is

created and the data from the SUT age di stribution array, along with the year ID (either from the

registration data or the year ID input), are used to fill the new database table. A text format of

this database table may be written as well. Using the appropriate MySQL code, a new

AVFTfuelengfr action 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 re gistration data year or

the year ID input) are used to fill the new database table. A text format of this database table

may be written as well.91 

### MOVESratesCalc

The MOVESratesCalc utility estimates emissions rates in terms of grams per activity (i.e., gram s

per mile, grams per SHP, grams per start, and grams per SHI) using the data in the movesoutput

(emissions output) and movesactivityoutput (activity output) database tables produced by a

MOVES emissions rate run. The utility also has the option of calcul ating the SHP, starts, and

SHI activity per vehicle using the movesactivityoutput database table. If not specified, emissions

rates are calculated for each pollutant and process combination (excluding total energy) in the

movesoutput database table. The utility also uses the movesrun database table to determine the

units of the emissions in the movesoutput table, which will then be converted to grams per

activity during the emissions rate calculations; therefore allowing the user to specify any of the

uni ts 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 42 shows.

Table 42

. 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 Exhaust 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 Miles 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 distance based emissions rates (i.e., grams per mile), the utility extracts the emissions

data (by pollutant, pollutant process, roadway type, average speed bin, SUT, and fuel type) from

the movesoutput database for the specified pollutants (or all of the pollutants if not specified) and

the corresponding miles traveled activity (MOVES activityTypeID = 1) from the

movesactivityoutput database table. The utility divides the emissions data by the corresponding

activity data, applies the appropriate u nits conversion factor and saves the emissions rates in the

ttirateperdistance database table.92 

### For the off

- network emissions rates (i.e., grams per SHP, grams per start, and grams per SHI)

the utility calculates the emissions rates based on the pollutant process. For processID = 12

(evaporative fuel vapor venting), the utility extracts the emissions data (by hour, pollutant,

pollutant process, source type, and fuel type) from the movesoutput database table for the

specified pollutants pollutants (or all of the pollutants if not specified) where roadTypeID = 1

(“off

- network” emissions) and the corresponding SHP activity (activityTypeID = 5) from the

movesactivityoutput database table

. The utility divides the emissions data by the corresponding

activity da ta, applies the appropriate units conversion factor and saves the emissions rates in the

ttirateperactivity database table.

Using the same calculation process, the utility also calcutes 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 SH P activity

(activityTypeID = 5), and for processID = 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 data (by hour, SUT, and fuel type) from the movesactivityoutput database

table. The utility divides the SHP activity by the population and saves the SHP per vehicle in the

ttiactpervehicle database table. Using t he same calculation process, the utility also calculates the

starts per vehicle and SHI per vehicle using the starts and SHI activity data.

MOVESratesAdj

The MOVESratesAdj utility applies emissions rate adjustments to an emissions rate database

table produ ced by MOVES (rateperdistance, ratepervehicle, rateperprofile), the

MOVESratesCalc utility (ttirateperdistance, ttirateperactivity) or by this utility to produce a new

emissions rate database table in the same format as the input emissions rate database ta ble. 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 o ption 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 combin ing 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 r ates for the

specified pollutants (or all the pollutants if not specified) from the input database emissions rate

table, applies the emissions rate adjustments (if necessary) and saves these adjusted emissions

rates. If more than one emissions rate databa se 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

adj usted 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 table93 

### and writes the adjusted emissions rates to this new emissions rate database table. A tab

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 tha t are included in the

database tables.

ShpExtIdleStartActBld

The ShpExtIdleStartActBld utility calculates the SHP and starts activity by hour, SUT, and fuel

type used to estimate emissions using the MOVESratesCalc emissions rates. The SHP is

calculated us ing the either the TDM or virtual link based link VMT and speeds (same as used in

the distance

- based emissions estimation), the VMT mix (by roadway type), and the SUT/fuel

type population (from the MOVESpopulationBuild). The starts activity is calculated using the

SUT/fuel type population and the starts per vehicle (typically from the ttiactpervehicle database

table created by the MOVESratesCalc utility). The utility also has the option of calculating the

SHI activity used to estimate emissions using the MOVESratesCalc emissions rates. However,

this method of estimating the extended idle hours is a direct function of the SHO and does not

consider the availability of locations where extended idling may occur. The suggested method

for estimating the SHI is discussed in the “ExtIdleHrsCalc” section.

For each link in the first VMT and speeds input with the desired county code, the utility

applies the appropriate VMT mix to distribute the link VMT to each SUT/fuel type, which is

added to the hourly SUT/fuel type VMT. The link VMT by SUT/fuel type is divided by the link

speed to calculate the link VHT (or SHO) by SUT/fuel type, which is added to the SUT fuel/type

VHT. This calculation process is repeated for each VMT and speeds input; therefore producing

24

- hourly values for VMT by SUT/fuel type and for VHT by SUT/fuel type.

The hourly SUT/fuel type speed, total hours (or source hours), and SHP are then calclulaed.

For each hour and SUT/fuel type, the hourly SUT/fuel type VMT is divided by the hourly

SUT/fuel type VHT to calculate the hourly SUT/fuel type speed. The hourly SUT/fuel type total

hours are set equal to the SUT/fuel type population. The hourly SUT/fuel type SHP by are

calculated subtracting the hourly SUT/fuel type VHT (or SHO) from the h ourly 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 vehi cle 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 calcul ated 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 emissions using the MOVESratesCalc emissions rates. This94 

### hourly SHI is calculated using a 24

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

VMT and speeds, base VMT mix, future link VMT and speeds, fut ure VMT mix, and the future

tab

- delimited hourly SHP by SUT/fuel type. All of the base data should be from the same year

and day

- type. Although the term future data is used, the future data can be a year previous to the

base data (i.e., historical year) and should be from the same year and day type. The 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

a pplies the appropriate base VMT mix for CLhT_Diesel to the link VMT to calculate the link

CLhT_Diesel VMT, which is added to the hourly CLhT_Diesel VMT. The link CLhT_Diesel

VMT is divided by the link speed to calculate the link CLhT_Diesel VHT, which is added to the

hourly CLhT_Diesel VHT. This calculation process is repeated for each base VMT and speeds

input; therefore producing 24

- hourly values for base CLhT_Diesel VMT and for base

CLhT_Diesel VHT by SUT/fuel type. The same calculation process is per formed for the future

data (future VMT and speeds, future VMT mix) to calculate the hourly future CLhT_Diesel

VMT and the hourly future CLhT_Diesel VHT.

The 24

- hour future SHI is calculated using the 24

- hour base CLhT_Diesel VMT, the 24

- hour

future CLhT_ Diesel VMT, and the 24

- hour base SHI. The scaling factor is calculated by the

dividing the 24

- hour future CLhT_Diesel VMT by the 24

- hour base CLhT_Diesel VMT. The

scaling factor is multiplied by the 24

- hour base SHI to estimate the 24

- hour future SHI.

The 24

- hour future SHI is distributed to each hour using SHI hourly factors. The SHI hourly

factors are calculated using the hourly CLhT_Diesel VMT. The hourly CLhT_Diesel VMT is

converted hourly CLhT_Diesel VMT fractions. The SHI hourly factors are cal culated by taking

the inverse of the hourly CLhT_Diesel VMT fractions (i.e., more VMT implies less SHI). The

SHI hourly factors are applied to the 24

- hour future SHI to calculate the initial hourly future

SHI, therefore distributing the 24

- hour future SHI to each hour of the day.

To form the final SHI activity by hour, the initial hourly future SHI is compared to the hourly

CLhT_Diesel SHP. If the initial hourly future SHI is greater than the hourly CLhT_Diesel SHP,

then the final SHI activity for that hour is set to the hourly CLhT_Diesel SHP. Otherwise, the

final SHI activity for that hour is set to the initial hourly future SHI. This comparison is

performed for each hour.

MOVESemscalc

The MOVESemscalc utili ty estimates the hourly link emissions for one user

- specified county

using the emissions factors (either from MOVES, MOVES ratesCalc or MOVESratesAdj), the

VMT mix, the hourly link VMT and speeds activity estimates (either from TRANSVMT or

VirtualLinkVMT), and the off

- network activity (either vehicle population or SHP, starts, and

SHI). This utility produces a tab

- delimited output summary (including hourly and 24

- hour

totals) and hourly link emissions output files (optional). The primary inputs to MOVESem scalc

are:

• Emissions factors from MOVES, MOVES ratesCalc or MOVESratesAdj;95 

### • L ink

- based hourly VMT and speeds developed with the TRANSVMT or

VirtualLinkVMT utility

. For each link, the following information is input to

MOVESemscalc : link start node, link e nd 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 for mat, vehicle

population by SUT/fuel type is required. If the emissions factors are in the

MOVESratesCalc format, the SHP, starts, and SHI by hour and SUT/fuel type are

required; and

• Link/MOVES roadway type designations, which associates the link roadway/a rea type

combination to the appropriate MOVES roadway type.

The emissions estimation can be categorized by two basic types based on the type of

emissions factors: the VMT

- based emissions and the off

- network emissions. For the VMT

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

is distributed to each of the SUT/fuel type combinations listed in the VMT mix by MOVES

roadway type (as designated in the link/MOVES roadway type designations). The emissions

facto rs are selected based on the MOVES roadway type and the link speed for each SUT/fuel

type combinations listed in the VMT mix. For link speeds greater than 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, the emissions factors

are interpolated using the emissions factor interpolation methodology in the following section.

These SUT/fuel type combination

- specific emissions factors are multiplied by the SUT/fuel type

combination

- specific VMT to estimate the mobile source emissions for that link by SUT/fuel

type combination.

The off

- network emissions calculation depends on the format of the input emissions factors.

If the emissions factors are in the MOVES format (rateperprofile and ratepervehicle), the

emissions factors by SUT/fuel type are multiplied by their associated vehicle population to

estimate emissions. If the emissions factors are in the MOVESratesCalc format, the emissions

factors by SUT/fuel type are multiplied by the appropriate activity, which is determined by the

pollutant process (see Table 42 ).

The emissions estimates are output in a tab

- delimited file (including all of the SUT/fuel type

combinations listed in the VMT mix on a single line, separated by a tab character) for the

specified county by pollutant, roadway type, and SUT/fuel type combination for each of the

specified episode time periods. A 24

- hour (or total if all 24 hours are not specified) output is

also included in the tab

- delimited file. This tab

- delimited file also includes hourly and 24

- hour

summaries of the off

- network activity and VMT, VHT, and speed by roadway. Link emissions

may also be output by county, pollutant, process, and each SUT/fuel type combination.

Emissions Factor Interpolation Methodology

To calculate emissions factors for link speeds that fall between two of the 16 MOVES spe ed bin

speeds, an interpolation methodology similar to the methodology used with MOBILE6 is used.

This methodology interpolates each emissions factor using a factor developed from the inverse96 

### link speed and the inverse high and low bounding speed bin spee ds. 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 (0.7413 g/mi

- 0.7274 g/mi);

= 0.7377 g/mi.



### MOVEStabcomb

The MOVEStabcomb utility combines the tab

- delimited output from multiple runs of the

MOVESemscac utility to produce a regional summary of the VMT, VHT, speed (VMT/VHT),

off

- network activity, and emissions for each hour along with a 24

- hour summary

. A maximum

of 1

, 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 regional tab

- delimited file is in the same form as the individual county files taken as

input, except that each time period includes the individual county data summaries plus the

regional summary. The individual county summaries are taken directly from th e 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.97 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 MOVESratesCalc98 

## APPEN DIX C:

TXDOT DISTRICT VMT M IX BY DAY OF WEEK100 

### TxDOT District/HGB Counties

TxDOT District HGB County

Beaumont Liberty

Chambers

Houston Harris

Galveston

Fort Bend

Brazoria

Montgomery

Waller

VMT Mix Year/Analysis Year Correlations

VMT Mix Year Analysis Years

2000 1998 through 2002

2005 2003 through 2007

2010 2008 through 2012

2015 2013 through 2017

2020 2018 through 2022101 

### 2000 Weekday VMT Mix by TxDOT District (2002 Analysis Year)

SUT/F

T Beaumont Houston

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5

21_D 0.00218 0.0022

8 0.00232 0.00258 0.00199 0.00191 0.0022

3 0.00216

21_G 0.54312 0.5670

9 0.57688 0.64189 0.66078 0.6360

5 0.7426

4 0.71796

31_D 0.00248 0.0032

8 0.00263 0.00333 0.00244 0.0029

9 0.0021

6 0.00257

31_G 0.15223 0.2014

1 0.16175 0.20477 0.14991 0.18374 0.13311 0.15782

32_D 0.00449 0.0059

4 0.00477 0.00603 0.00442 0.00541 0.0039

2 0.00465

32_G 0.04708 0.0622

9 0.05003 0.06333 0.04637 0.0568

3 0.04117 0.04881

51_D 0.00131 0.0015

7 0.00117 0.00111 0.0008

0 0.0010

9 0.0006

9 0.0008

2

51_G 0.00033 0.0004

0 0.00030 0.00028 0.00041 0.0005

6 0.0003

5 0.0004

2

52_D 0.03591 0.0430

2 0.03215 0.03053 0.02090 0.0282

2 0.01797 0.02124

52_G 0.00920 0.0110

3 0.00824 0.00783 0.01067 0.01441 0.0091

7 0.01085

53_D 0.00173 0.0020

7 0.00155 0.00147 0.00230 0.0031

0 0.0019

7 0.00233

53_G 0.00044 0.0005

3 0.00040 0.00038 0.00117 0.0015

8 0.0010

1 0.00119

54_D 0.00188 0.0022

5 0.00168 0.00160 0.00116 0.00156 0.0009

9 0.00118

54_G 0.00048 0.0005

8 0.00043 0.00041 0.00059 0.0008

0 0.0005

1 0.0006

0

41_D 0.00188 0.0017

6 0.00164 0.00128 0.00090 0.0009

9 0.0012

1 0.00127

42_D 0.00072 0.0006

7 0.00062 0.00049 0.00034 0.0003

8 0.0004

6 0.0004

8

42_G 0.00000 0.0000

0 0.0000

0 0.00000 0.0000

0 0.0000

0 0.0000

0 0.0000

0

43_D 0.00215 0.0020

2 0.00188 0.00147 0.00103 0.00114 0.0013

8 0.00145

43_G 0.00002 0.0000

2 0.00002 0.00001 0.00001 0.0000

1 0.0000

1 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 0.0035

1 0.00581 0.00118 0.00362 0.0022

7 0.0014

9 0.00091

62_D 0.11873 0.0564

7 0.09345 0.01891 0.04295 0.02701 0.0176

5 0.01082

62_G 0.00000 0.0000

0 0.0000

0 0.00000 0.0000

0 0.0000

0 0.0000

0 0.0000

0102 

### 11_G 0.00055 0.0005

7 0.00058 0.00065 0.00066 0.0006

4 0.0007

5 0.00072103 

### 2010 Weekday VMT Mix by TxDOT District (2008 and 2011 Analysis Years)

SUT/F

T Beaumont Houston

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5

21_D 0.00055 0.0005

7 0.00058 0.00064 0.00066 0.0006

4 0.0007

4 0.00072

21_G 0.54475 0.5688

0 0.57862 0.64382 0.66211 0.63733 0.74413 0.71940

31_D 0.00495 0.0065

5 0.00526 0.00666 0.00503 0.0061

6 0.0044

6 0.00529

31_G 0.14976 0.1981

4 0.15912 0.20144 0.14732 0.18056 0.13081 0.15509

32_D 0.00340 0.0045

0 0.00362 0.00458 0.00335 0.00411 0.0029

8 0.00353

32_G 0.04817 0.0637

3 0.05118 0.06479 0.04743 0.05813 0.0421

2 0.04993

51_D 0.00065 0.0007

8 0.00058 0.00056 0.00040 0.0005

4 0.0003

5 0.00041

51_G 0.00017 0.0002

0 0.00015 0.00014 0.00021 0.0002

8 0.0001

8 0.00021

52_D 0.03650 0.0437

2 0.03267 0.03103 0.02124 0.0286

8 0.0182

6 0.02159

52_G 0.00935 0.01121 0.00837 0.00795 0.01084 0.01465 0.0093

2 0.01102

53_D 0.00176 0.0021

1 0.00158 0.00150 0.00233 0.00315 0.0020

1 0.00237

53_G 0.00045 0.0005

4 0.00040 0.00038 0.00119 0.00161 0.0010

2 0.00121

54_D 0.00192 0.0023

0 0.00172 0.00163 0.00118 0.0016

0 0.0010

2 0.00120

54_G 0.00049 0.0005

9 0.00044 0.00042 0.00060 0.0008

2 0.0005

2 0.00061

41_D 0.00196 0.0018

3 0.00170 0.00133 0.00093 0.0010

3 0.0012

5 0.00132

42_D 0.00061 0.0005

7 0.00053 0.00042 0.00029 0.0003

2 0.0003

9 0.00041

42_G 0.00000 0.0000

0 0.0000

0 0.00000 0.0000

0 0.0000

0 0.0000

0 0.0000

0

43_D 0.00218 0.0020

4 0.00190 0.00149 0.00104 0.00115 0.0014

0 0.00147

43_G 0.00002 0.0000

2 0.00002 0.00002 0.00001 0.0000

1 0.0000

1 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 0.0035

1 0.00581 0.00118 0.00362 0.0022

7 0.0014

9 0.00091

62_D 0.11873 0.0564

7 0.09345 0.01891 0.04295 0.02701 0.0176

5 0.01082

62_G 0.00000 0.0000

0 0.0000

0 0.00000 0.0000

0 0.0000

0 0.0000

0 0.0000

01 04 

### 11_G 0.00055 0.0005

7 0.00058 0.00065 0.00066 0.0006

4 0.0007

5 0.00072105 

### 2015 Weekday VMT Mix by TxDOT District (2014 and 2017 Analysis Years)

SUT/F

T Beaumont Houston

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5

21_D 0.00055 0.0005

7 0.00058 0.00064 0.00066 0.00064 0.0007

4 0.0007

2

21_G 0.54475 0.5688

0 0.57862 0.64382 0.66211 0.63733 0.74413 0.7194

0

31_D 0.00696 0.0092

1 0.00740 0.00936 0.00701 0.00859 0.0062

2 0.0073

8

31_G 0.14775 0.1954

8 0.15698 0.19874 0.14534 0.17813 0.1290

5 0.1530

0

32_D 0.00315 0.0041

6 0.00334 0.00423 0.00305 0.00373 0.0027

1 0.0032

1

32_G 0.04842 0.0640

7 0.05145 0.06514 0.04774 0.05851 0.0423

9 0.0502

5

51_D 0.00049 0.0005

9 0.00044 0.00042 0.00030 0.00041 0.0002

6 0.0003

1

51_G 0.00013 0.0001

5 0.00011 0.00011 0.00015 0.00021 0.0001

3 0.0001

6

52_D 0.03665 0.0439

1 0.03281 0.03116 0.02133 0.02881 0.0183

4 0.0216

8

52_G 0.00939 0.0112

5 0.00841 0.00799 0.01089 0.01471 0.0093

6 0.0110

7

53_D 0.00177 0.0021

2 0.00158 0.00150 0.00234 0.00317 0.0020

1 0.0023

8

53_G 0.00045 0.0005

4 0.00041 0.00039 0.00120 0.00162 0.0010

3 0.0012

2

54_D 0.00192 0.0023

0 0.00172 0.00163 0.00118 0.00160 0.0010

2 0.0012

0

54_G 0.00049 0.0005

9 0.00044 0.00042 0.00060 0.00082 0.0005

2 0.0006

1

41_D 0.00198 0.0018

6 0.00172 0.00135 0.00094 0.00104 0.0012

7 0.0013

4

42_D 0.00059 0.0005

5 0.00051 0.00040 0.00028 0.00031 0.0003

8 0.0004

0

42_G 0.00000 0.0000

0 0.0000

0 0.00000 0.0000

0 0.00000 0.0000

0 0.0000

0

43_D 0.00218 0.0020

4 0.00190 0.00149 0.00104 0.00115 0.0014

0 0.0014

7

43_G 0.00002 0.0000

2 0.00002 0.00002 0.00001 0.00001 0.0000

1 0.0000

1

61_D 0.06570 0.0312

5 0.05171 0.01047 0.04660 0.02931 0.01915 0.0117

4

61_G 0.00738 0.0035

1 0.00581 0.00118 0.00362 0.00227 0.0014

9 0.0009

1

62_D 0.11873 0.0564

7 0.09345 0.01891 0.04295 0.02701 0.0176

5 0.0108

2

62_G 0.00000 0.0000

0 0.0000

0 0.00000 0.0000

0 0.00000 0.0000

0 0.0000

0106 

### 11_G 0.00055 0.0005

7 0.00058 0.00065 0.00066 0.00064 0.0007

5 0.0007

2107 

### 2020 Weekday VMT Mix by TxDOT District (2018 and 2019 Analysis Years)

SUT/F

T Beaumont Houston

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5

21_D 0.0016

4 0.00171 0.00174 0.00193 0.00199 0.00191 0.00223 0.0021

6

21_G 0.5436

6 0.56766 0.57746 0.64254 0.66078 0.6360

5 0.74264 0.7179

6

31_D 0.0086

6 0.01146 0.00921 0.01165 0.00868 0.0106

4 0.00771 0.0091

4

31_G 0.14604 0.19323 0.15517 0.19645 0.14367 0.17608 0.12756 0.1512

4

32_D 0.0034

6 0.00457 0.00367 0.00465 0.00340 0.00417 0.00302 0.0035

8

32_G 0.04811 0.06366 0.05112 0.06472 0.04738 0.0580

7 0.04207 0.0498

8

51_D 0.0003

7 0.0004

4 0.00033 0.00031 0.00023 0.0003

1 0.00019 0.0002

3

51_G 0.0000

9 0.00011 0.0000

8 0.00008 0.00012 0.0001

6 0.00010 0.0001

2

52_D 0.03681 0.04410 0.03295 0.03130 0.02142 0.0289

3 0.01841 0.0217

7

52_G 0.0094

3 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 0.0031

8 0.00202 0.0023

9

53_G 0.0004

5 0.0005

4 0.00041 0.00039 0.00120 0.0016

2 0.00103 0.0012

2

54_D 0.0018

8 0.00225 0.00168 0.00160 0.00116 0.00156 0.00099 0.0011

8

54_G 0.0004

8 0.0005

8 0.00043 0.00041 0.00059 0.0008

0 0.00051 0.0006

0

41_D 0.0019

9 0.00186 0.00173 0.00136 0.00095 0.0010

5 0.00128 0.0013

4

42_D 0.0005

7 0.0005

4 0.00050 0.00039 0.00027 0.0003

0 0.00037 0.0003

9

42_G 0.0000

0 0.0000

0 0.0000

0 0.00000 0.0000

0 0.0000

0 0.0000

0 0.0000

0

43_D 0.0021

9 0.0020

5 0.00191 0.00149 0.00104 0.00115 0.00140 0.0014

8

43_G 0.0000

2 0.0000

2 0.00002 0.00002 0.00001 0.0000

1 0.00001 0.0000

1

61_D 0.0657

0 0.03125 0.05171 0.01047 0.04660 0.02931 0.01915 0.0117

4

61_G 0.0073

8 0.00351 0.00581 0.00118 0.00362 0.0022

7 0.00149 0.0009

1

62_D 0.11873 0.05647 0.09345 0.01891 0.04295 0.02701 0.01765 0.0108

2

62_G 0.0000

0 0.0000

0 0.0000

0 0.00000 0.0000

0 0.0000

0 0.0000

0 0.0000

0108 

### 11_G 0.0005

5 0.00057 0.00058 0.00065 0.00066 0.0006

4 0.00075 0.0007

2110 

## APPENDIX D:

TXDOT DISTRICT AGGRE GATE WEEKDAY VMT MIX112 

### TxDOT District/HGB Counties

TxDOT District HGB County

Beaumont Liberty

Chambers

Houston Harris

Galveston

Fort Bend

Brazoria

Montgomery

Waller

VMT Mix Year/Analysis Year Correlations

VMT Mix Year Analysis Years

2000 1998 through 2002

2005 2003 through 2007

2010 2008 through 2012

2015 2013 through 2017

2020 2018 through 2022113 

### 2000 Aggregate Weekday VMT Mix by TxDOT District (2002 Analysis Year)

SUT/FT Beaumont Houston

21_D 0.002280 0.002200

21_G 0.568760 0.732370

31_D 0.002820 0.002240

31_G 0.173240 0.137890

32_D 0.005110 0.004060

32_G 0.053580 0.042650

51_D 0.001320 0.000720

51_G 0.000340 0.000370

52_D 0.036230 0.018810

52_G 0.009280 0.009610

53_D 0.001750 0.002070

53_G 0.000450 0.001060

54_D 0.001890 0.001040

54_G 0.000490 0.000530

41_D 0.001720 0.001190

42_D 0.000660 0.000450

42_G 0.000000 0.000000

43_D 0.001970 0.001360

43_G 0.000020 0.000010

61_D 0.047110 0.020320

61_G 0.005290 0.001580

62_D 0.085140 0.018730

62_G 0.000000 0.000000

11_G 0.000570 0.000740114 

### 2010 Aggregate Weekday VMT Mix by TxDOT District (2008 and 2011 Analysis

Years)

SUT/FT Beaumont Houston

21_D 0.000570 0.000730

21_G 0.570470 0.733840

31_D 0.005630 0.004620

31_G 0.170420 0.135500

32_D 0.003870 0.003080

32_G 0.054810 0.043630

51_D 0.000660 0.000360

51_G 0.000170 0.000190

52_D 0.036820 0.019120

52_G 0.009440 0.009760

53_D 0.001780 0.002100

53_G 0.000450 0.001070

54_D 0.001940 0.001060

54_G 0.000500 0.000540

41_D 0.001790 0.001230

42_D 0.000560 0.000390

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 0.000740115 

### 2015 Aggregate Weekday VMT Mix by TxDOT District (2014 and 2017 Analysis

Years)

SUT/FT Beaumont Houston

21_D 0.000570 0.000730

21_G 0.570470 0.733840

31_D 0.007920 0.006450

31_G 0.168130 0.133680

32_D 0.003580 0.002800

32_G 0.055100 0.043910

51_D 0.000490 0.000270

51_G 0.000130 0.000140

52_D 0.036970 0.019200

52_G 0.009480 0.009800

53_D 0.001780 0.002110

53_G 0.000460 0.001080

54_D 0.001940 0.001060

54_G 0.000500 0.000540

41_D 0.001810 0.001250

42_D 0.000540 0.000370

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 0.000740116 

### 2020 Aggregate Weekday VMT Mix by TxDOT District (2018 and 2019 Analysis

Years)

SUT/FT Beaumont Houston

21_D 0.001710 0.002200

21_G 0.569330 0.732370

31_D 0.009860 0.007990

31_G 0.166190 0.132140

32_D 0.003930 0.003130

32_G 0.054750 0.043580

51_D 0.000370 0.000200

51_G 0.000090 0.000100

52_D 0.037130 0.019280

52_G 0.009520 0.009850

53_D 0.001790 0.002120

53_G 0.000460 0.001080

54_D 0.001890 0.001040

54_G 0.000490 0.000530

41_D 0.001820 0.001260

42_D 0.000520 0.000360

42_G 0.000000 0.000000

43_D 0.002000 0.001380

43_G 0.000020 0.000010

61_D 0.047110 0.020320

61_G 0.005290 0.001580

62_D 0.085140 0.018730

62_G 0.000000 0.000000

11_G 0.000570 0.000740118 

## APPENDIX E:

ANNUALLY COMPOUNDED GROWTH RATES AND INT ERMEDIATE

YEAR ADJUSTMENT FACT ORS120 

### AM Peak Annually Compounded Growth Rates

County 2008 TDM VMT 1 

### 2018 TDM VMT 1 

### Growth Rate

Harris 24,137,211.21 29,515,078.89 1.020318323

Brazoria 1,504,752.74 1,988,154.87 1.028249500

Fort Bend 2,207,433.11 3,081,077.38 1.033907071

Waller 359,846.35 471,071.38 1.027299227

Montgomery 2,427,161.45 3,269,963.78 1.030254281

Liberty 517,099.36 655,835.29 1.024052160

Chambers 484,153.97 608,412.83 1.023108013

Galveston 1,262,864.80 1,559,277.90 1.021307841

1 Includes the estimated intrazonal VMT.



### Mid

- Day Annually Compounded Growth Rates

County 2008 TDM VMT 1 

### 2018 TDM VMT 1 

### Growth Rate

Harris 30,851,904.42 37,562,123.16 1.019874693

Brazoria 1,896,517.55 2,479,277.45 1.027156991

Fort Bend 2,978,232.52 4,105,286.25 1.03261513

Waller 705,159.90 895,385.68 1.02417048

Montgomery 3,318,067.86 4,417,901.72 1.029041948

Liberty 809,261.88 1,017,182.34 1.023130362

Chambers 977,163.57 1,212,901.34 1.021846875

Galveston 1,667,384.19 2,042,911.27 1.020519676

1 Includes the estimated intrazonal VMT.121 

### PM Peak Annually Compounded Growth Rates

County 2008 TDM VMT 1 

### 2018 TDM VMT 1 

### Growth Rate

Harris 36,060,381.95 44,118,397.16 1.020372958

Brazoria 2,222,295.88 2,938,380.74 1.028325532

Fort Bend 3,384,832.85 4,718,385.09 1.033774025

Waller 630,829.33 818,256.13 1.026355324

Montgomery 3,740,962.99 5,061,203.06 1.030687562

Liberty 831,790.88 1,048,664.31 1.023439639

Chambers 846,945.89 1,059,706.50 1.022664059

Galveston 1,955,842.62 2,407,231.47 1.020982727

1 Includes the estimated intrazonal VMT.



### Overnight Annually Compounded Growth Rates

County 2008 TDM VMT 1 

### 2018 TDM VMT 1 

### Growth Rate

Harris 17,219,249.68 21,096,620.00 1.020516112

Brazoria 1,090,172.36 1,435,327.50 1.027887498

Fort Bend 1,797,320.33 2,506,902.15 1.033834902

Waller 446,574.17 568,279.76 1.024393604

Montgomery 2,068,521.90 2,763,404.19 1.029386407

Liberty 521,237.32 655,479.32 1.023180724

Chambers 601,331.08 742,728.09 1.021343008

Galveston 968,034.86 1,184,996.23 1.020428541

1 Includes the estimated intrazonal VMT.122 

### 2011 Intermediate Year Adjustment Factors

County AM Peak Mid

- D ay PM Peak Overnight

Harris 1.062201859 1.060816941 1.062372502 1.062819703

Brazoria 1.087165146 1.083703509 1.087406329 1.086017320

Fort Bend 1.105209263 1.101071326 1.104782654 1.104977842

Waller 1.084153769 1.074278198 1.081168086 1.074980471

Montgomery 1.093536499 1.089680642 1.094916764 1.090775280

Liberty 1.073905913 1.071008503 1.071980044 1.071166665

Chambers 1.070938319 1.066982909 1.069544799 1.065405317

Galveston 1.065295270 1.062830839 1.064278244 1.062546125

1 Applied to 2008 TDM VMT (including intrazonal VMT) to estimate analysis year VMT.



### 2014 Intermediate Year Adjustment Factors

County AM Peak Mid

- D ay PM Peak Overnight

Harris 0.922693066 0.924299542 0.922495462 0.921977956

Brazoria 0.894552786 0.898364732 0.894288251 0.895813627

Fort Bend 0.875132834 0.879520695 0.875583437 0.875377221

Waller 0.897867299 0.908889286 0.901174808 0.908097682

Montgomery 0.887610210 0.891800447 0.886118614 0.890607369

Liberty 0.909309416 0.912590847 0.911488232 0.912411187

Chambers 0.912670589 0.917184519 0.914256437 0.918995786

Galveston 0.919122367 0.921965075 0.920293641 0.922294484

1 Applied to 20 1 8 TDM VMT (including intrazonal VMT) to estimate analysis year VMT.123 

### 2017 Intermediate Year Adjustment Factors

County AM Peak Mid

- D ay PM Peak Overnight

Harris 0.980086290 0.980512613 0.980033812 0.979896337

Brazoria 0.972526610 0.973561012 0.972454704 0.972869115

Fort Bend 0.967204915 0.968415018 0.967329393 0.967272432

Waller 0.973426217 0.976399944 0.974321443 0.976187274

Montgomery 0.970634162 0.971777683 0.970226126 0.971452502

Liberty 0.976512759 0.977392556 0.977097195 0.977344448

Chambers 0.977413907 0.978620207 0.977838217 0.979102997

Galveston 0.979136710 0.979892915 0.979448500 0.979980429

1 Applied to 20 1 8 TDM VMT (including intrazonal VMT) to estimate analysis year VMT.



### 2019 Intermediate Year Adjustment Factors

County AM Peak Mid

- D ay PM Peak Overnight

Harris 1.020318323 1.019874693 1.020372958 1.020516112

Brazoria 1.028249500 1.027156991 1.028325532 1.027887498

Fort Bend 1.033907071 1.032615130 1.033774025 1.033834902

Waller 1.027299227 1.024170480 1.026355324 1.024393604

Montgomery 1.030254281 1.029041948 1.030687562 1.029386407

Liberty 1.024052160 1.023130362 1.023439639 1.023180724

Chambers 1.023108013 1.021846875 1.022664059 1.021343008

Galveston 1.021307841 1.020519676 1.020982727 1.020428541

1 Calculated as one year increment between 2008 and 2018 TDMs and applied to 2018 TDM VMT

(including intrazonal VMT) to estimate analysis year VMT.124 

## APPENDIX F:

CAPACITY FACTORS, SP EED FACTORS, AND SPE ED REDUCTION

FACTORS126 

### Capacity Factors

Time of Day Assignment Capacity Factor 1



### AM Peak 0.3333333

Mid

- Day 0.1666667

PM Peak 0.2500000

Overnight 0.0909091

Freeflow (Volume = 1) Speed Factors for Houston/Galveston Speed Model

Functional Group Area Type

CBD Urban Urban

Fringe Suburban Rural

Freeways, Interstates 1.147400 1.177508 1.157272 1.197842 1.137903

Principal Arterials 1.136702 0.860413 0.887801 1.002511 1.018546

Other Arterials, Major

Collectors 1.145946 0.811981 0.813370 0.813370 1.112661

Collectors 1.235367 0.857297 0.889344 0.974734 1.033400

Toll Roads 1.054545 1.100000 1.081379 1.057097 1.077846

Ramps 1.235367 0.857297 0.889344 0.974734 1.033400

Locals 1.000000 1.000000 1.000000 1.000000 1.000000

1 To obtain hourly capacities, a single capacity factor for each time

- of

- day assignment is used for all area

types and function classes.127 

### LOS E (V/C = 1.0) Speed Factors for Houston/Galveston Speed Model

Functional Group Area Type

CBD Urban Urban

Fringe Suburban Rural

Freeways, Interstates 0.700000 0.768691 0.737693 0.715108 0.645161

Principal Arterials 0.703191 0.578709 0.633464 0.750628 0.458973

Other Arterials, Major

Collectors 0.677477 0.560596 0.613733 0.870193 0.517909

Collectors 0.727049 0.567228 0.642497 0.761987 0.554081

Toll Roads 0.636364 0.636364 0.689705 0.645161 0.615385

Ramps 0.727049 0.567228 0.642497 0.761987 0.554081

Locals 1.000000 1.000000 1.000000 1.000000 1.000000128 

### Functional Classification to Functional Group Relationship

for the Application of and Speed Factors

Functional Group Corresponding Network

Functional Classifications

Freeways, Interstates 1. Urban Interstate Freeways

2. Urban Other Freeways

10. Rural Interstate Freeways

11. Rural Other Freeways

Principal Arterials 5. Urban Principal Arterials

12. Rural Principal Arterials

Other Arterials, Major

Collectors 6. Urban Other Arterials

13. Rural Other Arterials

14. Rural Major Collectors

Collectors 7. Urban Collectors

15. Rural Collectors

Toll Roads 3. Toll Roads

Ramps 4. Ramps

Locals 8. Locals (Centroid Connectors)

40. Locals (Intrazonals)129



### 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 0SpeedReductionFactors130



### 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 l131



### Figure 3. Other 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 l132



### 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 l133 

### Functional Classification to Functional Group Relationship for the

Application of Speed Reduction Factors

Functional Group Corresponding Network

Functional Classifications

1. Freeways, Interstates 1. Urban Interstate Freeways

2. Urban Other Freeways

3. Toll Roads

10. Rural Interstate Freeways

11. Rural Other Freeways

2. Principal Arterials 5. Urban Principal Arterials

12. Rural Principal Arterials

3. Other Arterials, Major

Collectors 6. Urban Other Arterials

13. Rural Other Arterials

14. Rural Major Collectors

4. Collectors 4. Ramps

7. Urban Collectors

15. Rural Collectors134 

## APPENDIX G:

VEHICLE POPULATION E STIMATES AND 24

- HOUR SHP, STARTS,

AND SHI SUMMARIES136 

### 2002 Vehicle Population Estimates

SUT/F

T Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvest

on

21_D 4,763 309 510 35 395 93 39 342

21_G 1,585,418 102,754 169,88

9 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 3,940137 

### 2008 Vehicle Population Estimates

SUT/F

T Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvest

on

21_D 1,745 125 256 14 178 26 12 126

21_G 1,753,810 125,864 257,49

2 14,088 179,043 25,656 12,490 126,955

31_D 19,567 1,978 2,324 311 2,629 602 300 1,777

31_G 573,871 58,015 68,174 9,124 77,117 18,231 9,095 52,119

32_D 13,044 1,319 1,550 207 1,753 414 207 1,185

32_G 184,782 18,680 21,951 2,938 24,831 5,863 2,925 16,782

51_D 465 49 40 13 66 22 10 33

51_G 246 26 21 7 35 6 3 17

52_D 24,713 2,629 2,145 677 3,479 1,215 583 1,759

52_G 12,615 1,342 1,095 345 1,776 312 149 898

53_D 2,714 289 236 74 382 59 28 193

53_G 1,383 147 120 38 195 15 7 98

54_D 1,370 146 119 38 193 64 31 98

54_G 698 74 61 19 98 17 8 50

41_D 1,590 169 138 44 224 59 28 113

42_D 504 54 44 14 71 18 9 36

42_G 0 0 0 0 0 0 0 0

43_D 1,784 190 155 49 251 66 32 127

43_G 13 1 1 0 2 1 0 1

61_D 16,454 952 1,260 215 1,349 541 138 531

61_G 1,279 74 98 17 105 61 15 41

62_D 15,166 877 1,161 198 1,244 978 249 489

62_G 0 0 0 0 0 0 0 0

11_G 48,344 7,324 6,738 848 10,178 1,932 1,021 8,214138 

### 2011 Vehicle Population Estimates

SUT/F

T Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvest

on

21_D 1,825 156 332 16 227 33 17 129

21_G 1,834,66

7 156,418 334,153 16,108 228,540 33,415 16,932 129,801

31_D 19,708 2,370 2,833 344 3,204 775 392 1,828

31_G 578,006 69,504 83,102 10,093 93,957 23,448 11,855 53,621

32_D 13,138 1,580 1,889 229 2,136 532 269 1,219

32_G 186,114 22,380 26,758 3,250 30,253 7,541 3,813 17,266

51_D 507 66 56 15 85 31 15 40

51_G 267 35 29 8 45 8 4 21

52_D 26,910 3,525 2,954 804 4,536 1,720 860 2,136

52_G 13,736 1,800 1,508 410 2,315 441 221 1,090

53_D 2,956 387 324 88 498 83 42 235

53_G 1,506 197 165 45 254 21 11 120

54_D 1,492 195 164 45 251 91 45 118

54_G 760 100 83 23 128 23 12 60

41_D 1,731 227 190 52 292 84 42 137

42_D 549 72 60 16 93 26 13 44

42_G 0 0 0 0 0 0 0 0

43_D 1,942 254 213 58 327 93 47 154

43_G 14 2 2 0 2 1 0 1

61_D 16,172 1,034 1,369 208 1,412 497 184 557

61_G 1,257 80 106 16 110 56 21 43

62_D 14,907 953 1,262 192 1,302 898 332 514

62_G 0 0 0 0 0 0 0 0

11_G 48,384 8,573 8,913 933 12,280 2,367 1,223 8,095139 

### 2014 Vehicle Population Estimates

SUT/F

T Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvest

on

21_D 1,938 169 367 17 248 36 18 137

21_G 1,948,34

8 169,883 368,85

3 17,367 249,654 35,818 18,084 138,082

31_D 29,217 3,593 4,366 518 4,885 1,168 588 2,715

31_G 605,544 74,470 90,494 10,734 101,253 24,796 12,492 56,273

32_D 12,683 1,560 1,895 225 2,121 528 266 1,179

32_G 198,904 24,461 29,725 3,526 33,259 8,126 4,094 18,484

51_D 404 54 46 12 70 25 12 32

51_G 209 28 24 6 36 7 3 17

52_D 28,714 3,847 3,276 871 4,979 1,852 923 2,283

52_G 14,656 1,964 1,672 444 2,541 475 237 1,165

53_D 3,156 423 360 96 547 89 44 251

53_G 1,615 216 184 49 280 23 11 128

54_D 1,585 212 181 48 275 97 48 126

54_G 808 108 92 24 140 25 12 64

41_D 1,869 250 213 57 324 91 45 149

42_D 553 74 63 17 96 27 13 44

42_G 0 0 0 0 0 0 0 0

43_D 2,064 277 235 63 358 100 50 164

43_G 15 2 2 0 3 1 0 1

61_D 17,174 1,123 1,511 224 1,543 533 196 593

61_G 1,335 87 117 17 120 60 22 46

62_D 15,830 1,035 1,393 207 1,422 963 355 547

62_G 0 0 0 0 0 0 0 0

11_G 51,382 9,311 9,839 1,006 13,414 2,537 1,306 8,612140 

### 2017 Vehicle Population Estimates

SUT/F

T Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvest

on

21_D 2,058 184 405 19 271 38 19 146

21_G 2,069,07

5 184,508 407,157 18,724 272,720 38,393 19,315 146,892

31_D 31,028 3,902 4,820 558 5,337 1,252 629 2,888

31_G 643,066 80,880 99,892 11,573 110,608 26,579 13,342 59,863

32_D 13,469 1,694 2,092 242 2,317 566 284 1,254

32_G 211,228 26,567 32,812 3,801 36,332 8,711 4,373 19,663

51_D 429 59 51 13 76 26 13 34

51_G 222 30 26 7 40 7 3 18

52_D 30,493 4,178 3,617 939 5,439 1,985 986 2,429

52_G 15,564 2,133 1,846 479 2,776 509 253 1,240

53_D 3,351 459 397 103 598 96 47 267

53_G 1,715 235 203 53 306 25 12 137

54_D 1,683 231 200 52 300 104 52 134

54_G 858 118 102 26 153 27 13 68

41_D 1,985 272 235 61 354 97 48 158

42_D 588 81 70 18 105 29 14 47

42_G 0 0 0 0 0 0 0 0

43_D 2,192 300 260 67 391 107 53 175

43_G 16 2 2 0 3 1 1 1

61_D 18,238 1,220 1,668 242 1,685 571 210 631

61_G 1,418 95 130 19 131 64 24 49

62_D 16,811 1,124 1,537 223 1,554 1,032 379 581

62_G 0 0 0 0 0 0 0 0

11_G 54,566 10,113 10,860 1,085 14,654 2,720 1,395 9,161141 

### 2018 Vehicle Population Estimates

SUT/F

T Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvest

on

21_D 6,328 569 1,261 58 842 118 59 450

21_G 2,106,72

8 189,277 419,947 19,161 280,310 39,214 19,704 149,652

31_D 39,214 4,969 6,170 709 6,809 1,595 800 3,653

31_G 648,524 82,180 102,04

7 11,730 112,602 26,888 13,481 60,406

32_D 15,362 1,947 2,417 278 2,667 636 319 1,431

32_G 213,884 27,103 33,655 3,869 37,136 8,858 4,441 19,922

51_D 324 45 39 10 58 20 10 26

51_G 162 22 19 5 29 5 2 13

52_D 31,249 4,314 3,754 967 5,626 2,042 1,013 2,490

52_G 15,965 2,204 1,918 494 2,874 523 260 1,272

53_D 3,436 474 413 106 619 98 49 274

53_G 1,750 242 210 54 315 25 13 140

54_D 1,686 233 203 52 303 104 52 134

54_G 859 119 103 27 155 27 13 68

41_D 2,042 282 245 63 368 100 50 163

42_D 583 81 70 18 105 29 14 47

42_G 0 0 0 0 0 0 0 0

43_D 2,237 309 269 69 403 110 55 178

43_G 16 2 2 1 3 1 1 1

61_D 18,607 1,254 1,723 248 1,736 584 214 644

61_G 1,447 97 134 19 135 66 24 50

62_D 17,151 1,156 1,589 228 1,600 1,056 387 594

62_G 0 0 0 0 0 0 0 0

11_G 55,670 10,395 11,224 1,113 15,092 2,783 1,426 9,352142 

### 2019 Vehicle Population Estimates

SUT/F

T Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvest

on

21_D 6,457 584 1,304 59 867 121 60 459

21_G 2,149,37

2 194,560 434,00

8 19,647 288,690 40,132 20,142 152,769

31_D 40,008 5,108 6,377 727 7,012 1,633 818 3,729

31_G 661,652 84,473 105,46

4 12,028 115,969 27,517 13,780 61,665

32_D 15,673 2,001 2,498 285 2,747 651 326 1,461

32_G 218,214 27,859 34,782 3,967 38,247 9,065 4,540 20,337

51_D 331 46 40 10 60 21 10 26

51_G 165 23 20 5 30 5 3 13

52_D 31,882 4,435 3,880 991 5,794 2,090 1,035 2,542

52_G 16,288 2,266 1,982 506 2,960 536 265 1,299

53_D 3,506 488 427 109 637 101 50 280

53_G 1,786 248 217 56 325 26 13 142

54_D 1,720 239 209 53 313 106 53 137

54_G 876 122 107 27 159 28 14 70

41_D 2,084 290 254 65 379 102 51 166

42_D 595 83 72 19 108 29 14 47

42_G 0 0 0 0 0 0 0 0

43_D 2,282 317 278 71 415 113 56 182

43_G 17 2 2 1 3 1 1 1

61_D 18,984 1,289 1,781 254 1,788 598 219 657

61_G 1,476 100 138 20 139 67 25 51

62_D 17,499 1,188 1,642 234 1,648 1,081 396 606

62_G 0 0 0 0 0 0 0 0

11_G 56,797 10,685 11,600 1,141 15,543 2,849 1,458 9,547143 

### 2002 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 108,459 7,131 11,809 764 9,041 2,124 845 7,874

21_G 36,107,47

7 2,373,99

5 3,931,179 254,51

3 3,009,715 529,94

9 210,844 2,621,262

31_D 190,653 20,016 19,773 2,848 23,985 6,321 2,781 17,595

31_G 11,736,84

9 1,232,212 1,217,235 175,310 1,476,533 388,33

7 170,851 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 54,224 456,699 120,10

6 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 94,448144 

### 200 8 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 39,704 2,900 5,978 312 4,104 585 275 2,916

21_G 39,923,59

4 2,916,20

6 6,010,712 314,02

0 4,127,020 585,66

7 275,444 2,932,124

31_D 454,455 46,650 54,523 7,260 61,749 14,125 6,969 41,815

31_G 13,328,83

7 1,368,20

4 1,599,112 212,93

3 1,811,070 427,55

9 210,956 1,226,390

32_D 302,957 31,099 36,348 4,840 41,165 9,709 4,791 27,876

32_G 4,291,804 440,553 514,904 68,563 583,153 137,510 67,847 394,890

51_D 9,988 1,121 869 288 1,461 487 220 730

51_G 5,289 593 460 152 772 125 57 386

52_D 531,131 59,569 46,194 15,321 77,627 27,149 12,287 38,779

52_G 271,124 30,407 23,580 7,821 39,625 6,961 3,150 19,795

53_D 58,335 6,543 5,074 1,683 8,526 1,313 594 4,259

53_G 29,721 3,333 2,585 857 4,344 332 150 2,170

54_D 29,432 3,302 2,560 849 4,302 1,431 648 2,149

54_G 14,996 1,682 1,304 433 2,191 369 167 1,095

41_D 34,283 3,876 3,012 1,007 5,081 1,331 597 2,510

42_D 10,894 1,230 957 320 1,613 417 187 797

42_G 0 0 0 0 0 0 0 0

43_D 38,489 4,350 3,381 1,130 5,702 1,488 667 2,818

43_G 280 31 25 8 41 15 7 20

61_D 352,612 19,974 26,362 3,944 27,071 11,574 1,111 10,427

61_G 27,423 1,554 2,051 307 2,106 1,300 125 811

62_D 325,025 18,412 24,300 3,636 24,953 20,917 2,009 9,612

62_G 0 0 0 0 0 0 0 0

11_G 1,158,082 175,671 161,543 20,328 244,102 46,338 24,480 197,021145 

### 20 11 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 41,496 3,611 7,763 357 5,243 761 375 2,981

21_G 41,725,512 3,630,92

1 7,805,561 359,46

5 5,272,193 762,05

6 375,369 2,997,219

31_D 456,879 55,905 66,401 8,026 75,182 18,153 9,091 43,020

31_G 13,399,93

7 1,639,66

2 1,947,484 235,40

1 2,205,027 549,49

6 275,183 1,261,750

32_D 304,572 37,270 44,266 5,351 50,120 12,478 6,249 28,680

32_G 4,314,699 527,960 627,077 75,798 710,005 176,727 88,503 406,275

51_D 10,906 1,515 1,208 344 1,910 692 331 898

51_G 5,774 800 639 181 1,010 178 85 475

52_D 579,920 80,465 64,191 18,250 101,468 38,610 18,481 47,724

52_G 296,029 41,074 32,766 9,316 51,795 9,899 4,738 24,361

53_D 63,693 8,838 7,050 2,004 11,145 1,867 894 5,242

53_G 32,451 4,503 3,592 1,021 5,678 472 226 2,671

54_D 32,136 4,460 3,557 1,011 5,623 2,035 974 2,645

54_G 16,374 2,272 1,812 515 2,864 525 251 1,348

41_D 37,427 5,228 4,181 1,198 6,639 1,892 898 3,086

42_D 11,892 1,659 1,328 380 2,107 592 281 980

42_G 0 0 0 0 0 0 0 0

43_D 42,019 5,867 4,694 1,345 7,451 2,115 1,003 3,464

43_G 306 42 34 10 54 21 10 25

61_D 343,088 21,441 27,919 3,613 27,205 10,053 1,587 11,001

61_G 26,683 1,668 2,172 281 2,117 1,129 178 856

62_D 316,247 19,764 25,735 3,330 25,077 18,169 2,869 10,140

62_G 0 0 0 0 0 0 0 0

11_G 1,158,899 205,638 213,698 22,375 294,501 56,769 29,320 194,164146 

### 20 14 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 44,062 3,925 8,568 385 5,730 817 400 3,171

21_G 44,304,96

4 3,946,15

8 8,615,046 387,37

4 5,761,095 817,68

3 400,094 3,188,400

31_D 677,216 84,796 102,281 12,077 114,762 27,387 13,645 63,885

31_G 14,035,45

8 1,757,432 2,119,803 250,29

1 2,378,489 581,38

0 289,669 1,324,049

32_D 293,961 36,810 44,399 5,242 49,817 12,379 6,168 27,732

32_G 4,610,254 577,266 696,295 82,213 781,265 190,53

1 94,931 434,912

51_D 8,681 1,235 997 278 1,571 551 262 716

51_G 4,508 641 518 144 815 147 70 372

52_D 618,378 87,901 71,025 19,756 111,822 41,622 19,785 50,995

52_G 315,621 44,865 36,251 10,083 57,075 10,673 5,074 26,028

53_D 67,968 9,660 7,806 2,171 12,289 2,004 952 5,605

53_G 34,784 4,944 3,995 1,111 6,290 518 247 2,868

54_D 34,125 4,852 3,920 1,090 6,172 2,184 1,038 2,815

54_G 17,387 2,472 1,997 555 3,144 563 268 1,434

41_D 40,409 5,780 4,693 1,314 7,375 2,053 968 3,339

42_D 11,949 1,710 1,388 389 2,182 613 289 988

42_G 0 0 0 0 0 0 0 0

43_D 44,636 6,382 5,183 1,450 8,143 2,270 1,071 3,687

43_G 325 46 38 10 59 23 11 27

61_D 364,293 23,283 30,670 3,895 30,504 10,781 1,722 11,693

61_G 28,332 1,811 2,386 303 2,373 1,210 193 910

62_D 335,793 21,462 28,271 3,591 28,118 19,484 3,113 10,779

62_G 0 0 0 0 0 0 0 0

11_G 1,230,702 223,343 235,888 24,123 321,711 60,851 31,315 206,552147 

### 20 17 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 46,747 4,261 9,454 415 6,255 875 426 3,373

21_G 47,005,45

5 4,284,49

7 9,505,293 417,58

6 6,289,325 876,05

3 427,022 3,391,228

31_D 718,755 92,081 112,853 13,020 125,318 29,349 14,569 67,955

31_G 14,896,36

3 1,908,41

0 2,338,928 269,83

3 2,597,257 623,04

1 309,285 1,408,390

32_D 311,992 39,972 48,988 5,652 54,400 13,267 6,586 29,499

32_G 4,893,038 626,858 768,272 88,632 853,125 204,18

4 101,360 462,616

51_D 9,202 1,341 1,099 299 1,714 590 279 762

51_G 4,778 696 570 155 890 157 74 395

52_D 655,443 95,421 78,249 21,297 122,003 44,586 21,109 54,229

52_G 334,539 48,704 39,938 10,870 62,271 11,433 5,413 27,679

53_D 72,042 10,487 8,600 2,340 13,408 2,146 1,016 5,960

53_G 36,869 5,367 4,401 1,198 6,863 555 263 3,050

54_D 36,170 5,267 4,318 1,175 6,733 2,340 1,108 2,993

54_G 18,429 2,683 2,200 599 3,430 603 286 1,525

41_D 42,833 6,275 5,172 1,416 8,049 2,200 1,033 3,551

42_D 12,666 1,857 1,530 419 2,381 657 308 1,050

42_G 0 0 0 0 0 0 0 0

43_D 47,314 6,929 5,713 1,564 8,888 2,432 1,142 3,921

43_G 345 50 41 11 64 24 11 28

61_D 385,899 25,245 33,732 4,197 33,192 11,536 1,834 12,423

61_G 30,013 1,964 2,624 327 2,582 1,295 206 967

62_D 355,708 23,270 31,094 3,869 30,596 20,850 3,315 11,452

62_G 0 0 0 0 0 0 0 0

11_G 1,306,916 242,568 260,380 26,008 351,430 65,226 33,446 219,730148 

### 20 18 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 143,711 13,201 29,444 1,284 19,413 2,687 1,308 10,377

21_G 47,844,07

1 4,394,66

1 9,802,149 427,311 6,462,743 894,60

3 435,489 3,454,692

31_D 908,192 117,242 144,456 16,537 159,856 37,391 18,538 85,930

31_G 15,019,55

5 1,938,941 2,389,004 273,49

3 2,643,697 630,21

5 312,457 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 90,198 871,896 207,61

9 102,937 468,685

51_D 6,952 1,021 840 227 1,307 457 216 576

51_G 3,457 509 418 113 651 111 52 287

52_D 671,253 98,504 81,174 21,935 126,152 45,842 21,674 55,599

52_G 342,950 50,326 41,473 11,207 64,452 11,754 5,558 28,406

53_D 73,825 10,832 8,927 2,412 13,873 2,210 1,045 6,114

53_G 37,601 5,518 4,547 1,229 7,067 568 269 3,114

54_D 36,197 5,313 4,377 1,183 6,804 2,333 1,103 2,998

54_G 18,444 2,707 2,230 603 3,467 605 286 1,528

41_D 44,063 6,504 5,390 1,464 8,358 2,265 1,063 3,656

42_D 12,566 1,857 1,538 418 2,386 647 303 1,043

42_G 0 0 0 0 0 0 0 0

43_D 48,228 7,122 5,900 1,604 9,151 2,489 1,167 4,002

43_G 351 52 43 12 66 25 12 29

61_D 393,348 25,930 34,815 4,303 34,131 11,799 1,872 12,676

61_G 30,592 2,017 2,708 335 2,655 1,325 210 986

62_D 362,575 23,902 32,092 3,966 31,462 21,324 3,384 11,684

62_G 0 0 0 0 0 0 0 0

11_G 1,333,353 249,337 269,096 26,669 361,934 66,753 34,188 224,307149 

### 20 19 24

- H our Weekday SHP Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 146,566 13,567 30,424 1,316 19,988 2,749 1,336 10,592

21_G 48,794,62

4 4,516,64

9 10,128,391 438,13

8 6,654,119 915,361 445,009 3,526,393

31_D 926,365 120,505 149,266 16,957 164,609 38,263 18,947 87,717

31_G 15,320,10

4 1,992,90

5 2,468,553 280,43

2 2,722,294 644,90

9 319,345 1,450,655

32_D 362,896 47,206 58,473 6,643 64,484 15,250 7,551 34,362

32_G 5,052,594 657,264 814,133 92,487 897,817 212,46

0 105,206 478,428

51_D 7,088 1,049 868 233 1,345 467 221 588

51_G 3,525 523 432 116 671 113 54 293

52_D 684,337 101,230 83,824 22,490 129,856 46,902 22,144 56,748

52_G 349,635 51,719 42,827 11,491 66,344 12,026 5,678 28,993

53_D 75,264 11,132 9,219 2,473 14,280 2,261 1,068 6,241

53_G 38,334 5,671 4,696 1,260 7,274 581 275 3,179

54_D 36,902 5,460 4,520 1,213 7,004 2,387 1,127 3,060

54_G 18,803 2,782 2,303 618 3,569 619 293 1,560

41_D 44,923 6,685 5,567 1,501 8,604 2,318 1,086 3,732

42_D 12,811 1,909 1,588 429 2,457 662 310 1,065

42_G 0 0 0 0 0 0 0 0

43_D 49,169 7,320 6,094 1,644 9,421 2,547 1,193 4,085

43_G 358 53 44 12 68 25 12 30

61_D 400,925 26,633 35,928 4,411 35,093 12,066 1,910 12,932

61_G 31,182 2,072 2,795 343 2,730 1,355 214 1,006

62_D 369,559 24,550 33,117 4,066 32,348 21,808 3,453 11,921

62_G 0 0 0 0 0 0 0 0

11_G 1,360,324 256,295 278,105 27,346 372,753 68,316 34,947 228,979150 

### 2002 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 27,961 1,812 2,996 203 2,322 547 227 2,008

21_G 9,308,001 603,271 997,417 67,560 772,915 136,53

5 56,575 668,566

31_D 47,433 4,899 4,883 706 5,907 1,557 693 4,326

31_G 2,919,871 301,556 300,560 43,490 363,644 95,662 42,572 266,273

32_D 89,642 9,258 9,227 1,335 11,164 2,942 1,309 8,175

32_G 941,687 97,255 96,933 14,026 117,279 30,850 13,729 85,876

51_D 2,164 165 117 38 203 71 28 115

51_G 1,112 85 60 20 104 18 7 59

52_D 105,416 8,023 5,674 1,863 9,894 3,617 1,442 5,597

52_G 53,857 4,099 2,899 952 5,055 926 369 2,860

53_D 7,114 541 383 126 668 107 43 378

53_G 3,643 277 196 64 342 28 11 193

54_D 472 36 25 8 44 15 6 25

54_G 241 18 13 4 23 4 2 13

41_D 2,640 201 142 47 248 68 27 140

42_D 1,651 126 89 29 155 43 17 88

42_G 0 0 0 0 0 0 0 0

43_D 6,267 477 337 111 588 162 64 333

43_G 46 4 2 1 4 2 1 2

61_D 92,000 5,266 5,266 908 5,288 2,778 501 2,938

61_G 7,154 409 409 71 411 312 56 228

62_D 61,302 3,509 3,509 605 3,523 3,629 655 1,958

62_G 0 0 0 0 0 0 0 0

11_G 12,866 1,461 1,447 142 1,839 332 192 1,753151 

### 200 8 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 10,242 735 1,504 82 1,046 151 73 741

21_G 10,296,63

1 738,947 1,511,736 82,711 1,051,162 150,62

9 73,326 745,352

31_D 112,909 11,414 13,413 1,795 15,173 3,475 1,734 10,254

31_G 3,311,502 334,773 393,393 52,652 444,999 105,20

3 52,482 300,752

32_D 78,486 7,934 9,324 1,248 10,547 2,491 1,243 7,128

32_G 1,111,802 112,396 132,078 17,678 149,404 35,280 17,600 100,974

51_D 1,745 186 151 48 246 82 39 124

51_G 921 98 80 25 130 21 10 66

52_D 172,771 18,377 14,996 4,732 24,322 8,496 4,074 12,299

52_G 88,192 9,381 7,655 2,415 12,416 2,178 1,044 6,278

53_D 11,636 1,238 1,010 319 1,638 252 121 828

53_G 5,929 631 515 162 835 64 31 422

54_D 776 83 67 21 109 36 17 55

54_G 395 42 34 11 56 9 4 28

41_D 4,399 468 382 120 619 163 78 313

42_D 2,308 245 200 63 325 85 41 164

42_G 0 0 0 0 0 0 0 0

43_D 10,253 1,091 890 281 1,443 379 182 730

43_G 74 8 6 2 10 4 2 5

61_D 97,574 5,645 7,472 1,277 8,000 3,210 816 3,147

61_G 7,587 439 581 99 622 360 92 245

62_D 65,016 3,762 4,979 851 5,331 4,193 1,066 2,097

62_G 0 0 0 0 0 0 0 0

11_G 21,508 3,258 2,998 377 4,528 860 454 3,654152 

### 20 11 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 10,715 914 1,951 94 1,335 196 99 758

21_G 10,771,34

2 918,333 1,961,813 94,571 1,341,760 196,181 99,406 762,063

31_D 113,723 13,675 16,350 1,986 18,486 4,470 2,260 10,550

31_G 3,335,367 401,072 479,535 58,239 542,175 135,30

6 68,410 309,420

32_D 79,052 9,506 11,365 1,380 12,850 3,204 1,620 7,334

32_G 1,119,814 134,656 160,999 19,553 182,030 45,375 22,941 103,884

51_D 1,900 249 209 57 320 116 58 151

51_G 1,003 131 110 30 169 30 15 80

52_D 188,128 24,646 20,651 5,619 31,710 12,026 6,014 14,933

52_G 96,032 12,581 10,542 2,868 16,187 3,083 1,542 7,622

53_D 12,670 1,660 1,391 378 2,136 357 178 1,006

53_G 6,456 846 709 193 1,088 90 45 512

54_D 845 111 93 25 142 51 26 67

54_G 430 56 47 13 73 13 7 34

41_D 4,790 628 526 143 807 231 116 380

42_D 2,513 329 276 75 424 120 60 199

42_G 0 0 0 0 0 0 0 0

43_D 11,164 1,463 1,226 333 1,882 537 269 886

43_G 81 11 9 2 14 5 3 6

61_D 95,904 6,133 8,116 1,233 8,376 2,947 1,090 3,305

61_G 7,457 477 631 96 651 331 122 257

62_D 63,903 4,086 5,408 821 5,581 3,849 1,424 2,202

62_G 0 0 0 0 0 0 0 0

11_G 21,526 3,814 3,965 415 5,463 1,053 544 3,601153 

### 20 14 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 11,379 992 2,154 101 1,458 210 106 806

21_G 11,438,76

4 997,387 2,165,536 101,959 1,465,720 210,28

7 106,171 810,682

31_D 168,597 20,734 25,196 2,989 28,191 6,740 3,396 15,668

31_G 3,494,273 429,724 522,195 61,942 584,278 143,08

6 72,084 324,722

32_D 76,314 9,385 11,405 1,353 12,761 3,177 1,600 7,092

32_G 1,196,769 147,178 178,849 21,215 200,112 48,894 24,632 111,215

51_D 1,514 203 173 46 263 92 46 120

51_G 785 105 90 24 136 24 12 62

52_D 200,738 26,896 22,905 6,087 34,805 12,948 6,452 15,961

52_G 102,460 13,728 11,691 3,107 17,765 3,320 1,654 8,147

53_D 13,527 1,812 1,544 410 2,345 382 190 1,076

53_G 6,924 928 790 210 1,201 99 49 551

54_D 897 120 102 27 156 55 27 71

54_G 457 61 52 14 79 14 7 36

41_D 5,173 693 590 157 897 251 125 411

42_D 2,533 339 289 77 439 124 62 201

42_G 0 0 0 0 0 0 0 0

43_D 11,863 1,589 1,354 360 2,057 576 287 943

43_G 86 12 10 3 15 6 3 7

61_D 101,846 6,661 8,959 1,329 9,150 3,158 1,164 3,516

61_G 7,919 518 697 103 711 355 131 273

62_D 67,862 4,438 5,970 886 6,097 4,126 1,521 2,343

62_G 0 0 0 0 0 0 0 0

11_G 22,860 4,143 4,377 448 5,968 1,129 581 3,831154 

### 20 17 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 12,084 1,078 2,378 109 1,593 225 113 858

21_G 12,147,54

9 1,083,24

7 2,390,420 109,92

7 1,601,139 225,40

7 113,398 862,405

31_D 179,044 22,519 27,812 3,222 30,796 7,225 3,627 16,667

31_G 3,710,790 466,717 576,423 66,782 638,260 153,37

4 76,991 345,440

32_D 81,043 10,193 12,589 1,459 13,939 3,405 1,709 7,544

32_G 1,270,925 159,848 197,422 22,872 218,601 52,410 26,309 118,311

51_D 1,608 220 191 50 287 99 49 128

51_G 834 114 99 26 149 26 13 66

52_D 213,177 29,211 25,283 6,562 38,021 13,879 6,891 16,979

52_G 108,809 14,910 12,905 3,350 19,407 3,559 1,767 8,667

53_D 14,366 1,968 1,704 442 2,562 410 203 1,144

53_G 7,353 1,008 872 226 1,311 106 53 586

54_D 953 131 113 29 170 59 29 76

54_G 486 67 58 15 87 15 8 39

41_D 5,494 753 652 169 980 269 134 438

42_D 2,690 369 319 83 480 133 66 214

42_G 0 0 0 0 0 0 0 0

43_D 12,598 1,726 1,494 388 2,247 617 307 1,003

43_G 91 13 11 3 16 6 3 7

61_D 108,157 7,234 9,890 1,433 9,995 3,385 1,244 3,741

61_G 8,410 562 769 111 777 380 140 291

62_D 72,067 4,820 6,590 955 6,660 4,423 1,625 2,492

62_G 0 0 0 0 0 0 0 0

11_G 24,276 4,499 4,832 483 6,519 1,210 621 4,076155 

### 20 18 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 37,154 3,338 7,406 338 4,944 691 347 2,639

21_G 12,368,61

2 1,111,249 2,465,509 112,493 1,645,704 230,22

4 115,684 878,605

31_D 226,282 28,674 35,606 4,093 39,289 9,205 4,615 21,077

31_G 3,742,289 474,216 588,860 67,689 649,769 155,154 77,792 348,573

32_D 92,428 11,712 14,544 1,672 16,048 3,826 1,918 8,609

32_G 1,286,906 163,074 202,498 23,277 223,444 53,297 26,722 119,868

51_D 1,216 168 146 38 219 76 38 97

51_G 608 84 73 19 109 19 9 48

52_D 218,462 30,160 26,246 6,759 39,333 14,274 7,079 17,410

52_G 111,611 15,409 13,409 3,453 20,095 3,660 1,815 8,895

53_D 14,730 2,034 1,770 456 2,652 422 209 1,174

53_G 7,504 1,036 902 232 1,351 108 54 598

54_D 954 132 115 30 172 59 29 76

54_G 486 67 58 15 88 15 8 39

41_D 5,651 780 679 175 1,017 277 137 450

42_D 2,671 369 321 83 481 131 65 213

42_G 0 0 0 0 0 0 0 0

43_D 12,857 1,775 1,545 398 2,315 632 314 1,025

43_G 93 13 11 3 17 6 3 7

61_D 110,346 7,436 10,221 1,469 10,294 3,465 1,271 3,818

61_G 8,580 578 795 114 800 389 143 297

62_D 73,526 4,955 6,810 979 6,859 4,526 1,661 2,544

62_G 0 0 0 0 0 0 0 0

11_G 24,767 4,625 4,993 495 6,714 1,238 634 4,161156 

### 20 19 24

- H our Weekday Starts Summaries

SUT/FT Harris Brazori

a Fort

Bend Walle

r Montgome

ry Libert

y Chamber

s Galvesto

n

21_D 37,906 3,431 7,654 347 5,091 708 355 2,694

21_G 12,618,97

4 1,142,263 2,548,062 115,350 1,694,903 235,61

5 118,251 896,907

31_D 230,862 29,474 36,798 4,197 40,464 9,421 4,718 21,516

31_G 3,818,039 487,451 608,577 69,409 669,194 158,78

7 79,518 355,834

32_D 94,299 12,039 15,031 1,714 16,528 3,915 1,961 8,788

32_G 1,312,955 167,626 209,279 23,868 230,124 54,545 27,315 122,365

51_D 1,240 173 151 39 225 78 39 99

51_G 620 86 75 19 113 19 9 49

52_D 222,884 31,002 27,125 6,931 40,508 14,608 7,236 17,773

52_G 113,870 15,839 13,858 3,541 20,695 3,745 1,855 9,080

53_D 15,028 2,090 1,829 467 2,731 432 214 1,198

53_G 7,656 1,065 932 238 1,391 111 55 610

54_D 974 135 118 30 177 60 30 78

54_G 496 69 60 15 90 16 8 40

41_D 5,766 802 702 179 1,048 283 140 460

42_D 2,725 379 332 85 495 134 66 217

42_G 0 0 0 0 0 0 0 0

43_D 13,117 1,825 1,596 408 2,384 647 320 1,046

43_G 95 13 12 3 17 6 3 8

61_D 112,580 7,643 10,563 1,507 10,602 3,546 1,299 3,898

61_G 8,754 594 821 117 824 398 146 303

62_D 75,014 5,093 7,038 1,004 7,064 4,632 1,697 2,597

62_G 0 0 0 0 0 0 0 0

11_G 25,269 4,754 5,161 508 6,915 1,267 649 4,247157 

### 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 449 466 600 652 699 715 732

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

Galveston 481 503 517 550 585 597 609158 

## APPENDIX H:

SOURCE TYPE AGE AND FUEL/ENGINE FRACTION S INPUTS TO

MOVES160 

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

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.110231 0.056917 0.077390 0.077390 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.139160 0.083299 0.106849 0.106849 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.104446 0.097620 0.091753 0.091753 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.081912 0.086239 0.088335 0.088335 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.060901 0.078020 0.067279 0.067279 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.055116 0.074382 0.074891 0.074891 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.050244 0.065494 0.058156 0.058156 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.035932 0.070753 0.060415 0.060415 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.037454 0.058576 0.058029 0.058029 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.030146 0.055878 0.045078 0.045078 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.022838 0.044788 0.038469 0.038469 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.015834 0.042236 0.036803 0.036803 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.016748 0.034882 0.032086 0.032086 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.017052 0.033426 0.030857 0.030857 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.012485 0.023723 0.024615 0.024615 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.013398 0.018067 0.016565 0.016565 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.024665 0.015524 0.018232 0.018232 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.022838 0.013225 0.014814 0.014814 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.013398 0.010023 0.013572 0.013572 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.013398 0.005967 0.007513 0.007513 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.023752 0.004065 0.008050 0.008050 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.015225 0.003619 0.006044 0.006044 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.017966 0.002620 0.002994 0.002994 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.012485 0.002950 0.003658 0.003658 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.009744 0.002329 0.003305 0.003305 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.007308 0.001611 0.002528 0.002528 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.004066 0.001223 0.001447 0.001447 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.005082 0.000828 0.001345 0.001345 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.004193 0.000876 0.001289 0.001289 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.003304 0.000693 0.000684 0.000684 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.018678 0.010148 0.006956 0.006956 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715161 

### Brazoria County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.077826 0.056807 0.042579 0.042579 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.137493 0.089254 0.075420 0.075420 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.125341 0.086555 0.071932 0.071932 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.090251 0.082936 0.065669 0.065669 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.071682 0.074475 0.074120 0.074120 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.085336 0.070974 0.080433 0.080433 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.074413 0.070538 0.084096 0.084096 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.060896 0.066625 0.077908 0.077908 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.041917 0.068966 0.065919 0.065919 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.036729 0.059799 0.060831 0.060831 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.024031 0.050536 0.044867 0.044867 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.018979 0.041900 0.046267 0.046267 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.018433 0.032987 0.033691 0.033691 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.016794 0.032566 0.033141 0.033141 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.014746 0.024081 0.029440 0.029440 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.010104 0.019930 0.020552 0.020552 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.008329 0.015478 0.017052 0.017052 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.005461 0.012033 0.014126 0.014126 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.006144 0.008612 0.012226 0.012226 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.005735 0.007405 0.010814 0.010814 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.004642 0.005151 0.008626 0.008626 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.003960 0.003373 0.004400 0.004400 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.009421 0.002794 0.004638 0.004638 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.004096 0.002254 0.003775 0.003775 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.004506 0.001714 0.003188 0.003188 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.005188 0.001095 0.001863 0.001863 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.004322 0.000991 0.001541 0.001541 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.005403 0.000671 0.001433 0.001433 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.004457 0.000710 0.001364 0.001364 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.003512 0.000562 0.000729 0.000729 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.019854 0.008225 0.007360 0.007360 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502162 

### Brazoria County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.029489 0.041465 0.025332 0.025332 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.035165 0.059996 0.041857 0.041857 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.084868 0.058323 0.038005 0.038005 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.097743 0.088039 0.069630 0.069630 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.113526 0.091720 0.077310 0.077310 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.102174 0.081450 0.069196 0.069196 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.078222 0.075970 0.063213 0.063213 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.059532 0.065633 0.070261 0.070261 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.071577 0.061826 0.073680 0.073680 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.064655 0.060152 0.075947 0.075947 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.049702 0.054189 0.069147 0.069147 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.033504 0.053452 0.056586 0.056586 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028243 0.043257 0.050776 0.050776 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.022290 0.034683 0.035564 0.035564 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.015368 0.027046 0.036060 0.036060 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.015921 0.020837 0.025890 0.025890 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.013014 0.019007 0.025506 0.025506 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.011076 0.013489 0.021554 0.021554 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007615 0.010277 0.013849 0.013849 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.007061 0.007808 0.011582 0.011582 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.003323 0.006053 0.009291 0.009291 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.004015 0.004439 0.007457 0.007457 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003738 0.003324 0.006937 0.006937 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.003600 0.002372 0.005215 0.005215 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002769 0.001837 0.002564 0.002564 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004984 0.001398 0.002973 0.002973 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.003600 0.001450 0.002155 0.002155 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003600 0.001034 0.002329 0.002329 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003184 0.000647 0.001102 0.001102 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.004430 0.000565 0.001028 0.001028 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.022013 0.008262 0.008002 0.008002 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875163 

### Chambers 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 0.091127 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.078886 0.080517 0.076823 0.076823 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.044084 0.076176 0.083025 0.083025 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.062645 0.068320 0.059718 0.059718 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.041763 0.072351 0.063919 0.063919 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.027842 0.057778 0.061118 0.061118 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.034803 0.057364 0.046314 0.046314 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.020882 0.046305 0.041312 0.041312 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.009281 0.041240 0.037111 0.037111 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.011601 0.033592 0.032510 0.032510 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.013921 0.036072 0.026308 0.026308 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.011601 0.027390 0.023107 0.023107 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.000000 0.019018 0.014704 0.014704 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.009281 0.015917 0.016205 0.016205 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.020882 0.015090 0.015105 0.015105 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.013921 0.012610 0.014904 0.014904 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.011601 0.007235 0.008903 0.008903 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.023202 0.005478 0.008403 0.008403 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.013921 0.004651 0.007202 0.007202 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.009281 0.002791 0.003101 0.003101 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.016241 0.003824 0.004201 0.004201 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.004640 0.002687 0.003001 0.003001 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.004640 0.002481 0.002401 0.002401 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.000994 0.000986 0.001726 0.001726 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.001989 0.000807 0.001683 0.001683 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.001989 0.001435 0.001278 0.001278 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.000994 0.000717 0.000879 0.000879 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.014915 0.010939 0.005737 0.005737 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715164 

### Chambers County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.061704 0.051752 0.040313 0.040313 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.142018 0.090625 0.078471 0.078471 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.133203 0.099824 0.074958 0.074958 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.084231 0.089266 0.066496 0.066496 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.074437 0.074308 0.081025 0.081025 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.092067 0.065110 0.077193 0.077193 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.082272 0.068469 0.085575 0.085575 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.055828 0.060790 0.079987 0.079987 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.052889 0.064710 0.062744 0.062744 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.054848 0.056391 0.061946 0.061946 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.024486 0.046473 0.044145 0.044145 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.020568 0.040793 0.044384 0.044384 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.015671 0.033035 0.030893 0.030893 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.013712 0.033515 0.030494 0.030494 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.013712 0.020957 0.028419 0.028419 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.008815 0.018957 0.020356 0.020356 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.004897 0.014878 0.016444 0.016444 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.005877 0.013438 0.013571 0.013571 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.001959 0.011198 0.012453 0.012453 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.006856 0.009518 0.009899 0.009899 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.003918 0.005999 0.007663 0.007663 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.000979 0.004319 0.004311 0.004311 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.002938 0.003279 0.004470 0.004470 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.008815 0.002960 0.004550 0.004550 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.004897 0.002720 0.004071 0.004071 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.001959 0.001680 0.002634 0.002634 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.001259 0.000996 0.001938 0.001938 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.002519 0.000815 0.001864 0.001864 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.002519 0.001449 0.001373 0.001373 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.001259 0.000725 0.000944 0.000944 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.018889 0.011052 0.006415 0.006415 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502165 

### Chambers County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.016832 0.043599 0.026411 0.026411 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.028713 0.062691 0.040891 0.040891 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.086139 0.063048 0.046444 0.046444 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.094059 0.103227 0.077431 0.077431 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.130693 0.098597 0.083433 0.083433 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.109901 0.091757 0.075180 0.075180 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.080198 0.077652 0.066627 0.066627 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.068317 0.062122 0.075405 0.075405 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.075248 0.052148 0.073980 0.073980 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.067327 0.055639 0.069928 0.069928 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.045545 0.048372 0.067977 0.067977 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.039604 0.045166 0.051170 0.051170 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.040594 0.037900 0.045618 0.045618 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.023762 0.028924 0.031813 0.031813 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.010891 0.025219 0.031438 0.031438 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.011881 0.019377 0.022209 0.022209 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.006931 0.017953 0.024010 0.024010 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008911 0.011968 0.020408 0.020408 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007921 0.010401 0.013205 0.013205 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.003960 0.007053 0.010579 0.010579 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.004950 0.007124 0.008929 0.008929 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.001980 0.005486 0.007428 0.007428 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003960 0.004203 0.006227 0.006227 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.002970 0.002208 0.004727 0.004727 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.001980 0.002636 0.002776 0.002776 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.001980 0.001781 0.002701 0.002701 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.001980 0.001781 0.002176 0.002176 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003960 0.001282 0.002326 0.002326 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.000000 0.000427 0.001050 0.001050 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.002970 0.000712 0.000975 0.000975 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.015842 0.009546 0.006528 0.006528 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875166 

### Fort Bend County 200 2 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.151907 0.060388 0.093630 0.093630 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.141451 0.092442 0.123951 0.123951 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.107626 0.110740 0.111809 0.111809 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.091021 0.094678 0.100714 0.100714 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.069188 0.085928 0.076485 0.076485 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.046740 0.078293 0.075181 0.075181 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.045510 0.071256 0.053658 0.053658 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.037208 0.072659 0.056521 0.056521 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.036593 0.056825 0.051887 0.051887 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.031058 0.051550 0.040580 0.040580 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.019065 0.042365 0.032561 0.032561 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.014760 0.038568 0.028352 0.028352 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.011993 0.032265 0.024668 0.024668 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.013530 0.023938 0.022260 0.022260 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.010455 0.018644 0.018179 0.018179 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.011685 0.013797 0.012511 0.012511 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.017528 0.011080 0.013432 0.013432 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.013838 0.009930 0.012384 0.012384 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.011070 0.007435 0.010811 0.010811 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.016298 0.004378 0.005696 0.005696 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.018143 0.003421 0.006844 0.006844 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.012608 0.002430 0.005299 0.005299 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.010148 0.001831 0.002919 0.002919 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.008303 0.002171 0.004180 0.004180 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.009225 0.001755 0.003089 0.003089 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.004920 0.001291 0.002338 0.002338 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.005119 0.000776 0.001003 0.001003 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.006355 0.000784 0.001506 0.001506 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.003884 0.000542 0.001288 0.001288 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.004766 0.000537 0.000577 0.000577 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.018006 0.007302 0.005686 0.005686 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715167 

### Fort Bend County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.078955 0.058895 0.043554 0.043554 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.150193 0.088738 0.086139 0.086139 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.132977 0.088532 0.075756 0.075756 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.097952 0.082682 0.070267 0.070267 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.072573 0.076672 0.087097 0.087097 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.085040 0.074197 0.088320 0.088320 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.076877 0.073211 0.090320 0.090320 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.059068 0.069033 0.077746 0.077746 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.044969 0.070445 0.063820 0.063820 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.037103 0.057649 0.056341 0.056341 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.025082 0.050363 0.046352 0.046352 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.017067 0.041851 0.042256 0.042256 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.017513 0.033812 0.028181 0.028181 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.014248 0.031054 0.028202 0.028202 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.008459 0.022681 0.024479 0.024479 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.009795 0.018464 0.017064 0.017064 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.005936 0.014417 0.013341 0.013341 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.003265 0.011709 0.010266 0.010266 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.003265 0.008664 0.009458 0.009458 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.003710 0.005653 0.008011 0.008011 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.003562 0.003926 0.006064 0.006064 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.004156 0.002844 0.003830 0.003830 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.004452 0.001948 0.003883 0.003883 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.004452 0.001835 0.003138 0.003138 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.003413 0.001501 0.003043 0.003043 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.004601 0.000791 0.001500 0.001500 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.004204 0.000658 0.001155 0.001155 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.005219 0.000666 0.001733 0.001733 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.003189 0.000460 0.001482 0.001482 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.003914 0.000456 0.000664 0.000664 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.014788 0.006195 0.006541 0.006541 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502168 

### Fort Bend County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.043366 0.046518 0.029637 0.029637 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.048141 0.065960 0.053790 0.053790 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.102478 0.064906 0.044450 0.044450 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.101058 0.094485 0.079934 0.079934 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.120289 0.096165 0.088972 0.088972 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.103898 0.085778 0.072731 0.072731 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.079375 0.075541 0.066153 0.066153 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.059241 0.067667 0.078277 0.078277 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.067759 0.063112 0.078099 0.078099 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.056402 0.059721 0.078037 0.078037 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.043753 0.053351 0.065736 0.065736 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.032137 0.050441 0.050913 0.050913 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028653 0.038721 0.042448 0.042448 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.019489 0.031594 0.032045 0.032045 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.011745 0.024350 0.029011 0.029011 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.011874 0.017987 0.019202 0.019202 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.008776 0.015595 0.018680 0.018680 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.006324 0.010355 0.015689 0.015689 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007486 0.007780 0.010508 0.010508 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.003098 0.006066 0.008235 0.008235 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.002710 0.004559 0.006130 0.006130 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.001678 0.003633 0.005494 0.005494 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.002194 0.002202 0.004555 0.004555 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.002323 0.001756 0.003357 0.003357 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002710 0.001362 0.002450 0.002450 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.002323 0.001013 0.002085 0.002085 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.002581 0.000971 0.001939 0.001939 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.002452 0.000847 0.001887 0.001887 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.002839 0.000591 0.001136 0.001136 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.003872 0.000505 0.001220 0.001220 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.018973 0.006470 0.007203 0.007203 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875169 

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

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.118528 0.059071 0.086589 0.086589 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.127157 0.082439 0.107589 0.107589 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.102538 0.090599 0.088237 0.088237 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.091624 0.084523 0.087677 0.087677 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.061168 0.076398 0.068389 0.068389 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.041878 0.071276 0.072179 0.072179 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.052538 0.065498 0.053946 0.053946 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.039340 0.070488 0.059576 0.059576 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.034264 0.058099 0.056905 0.056905 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.027157 0.055543 0.045886 0.045886 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.015736 0.046219 0.039312 0.039312 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.013452 0.043049 0.034738 0.034738 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.019797 0.037236 0.031251 0.031251 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.013959 0.033200 0.030180 0.030180 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.013959 0.024593 0.023495 0.023495 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.014721 0.019270 0.016809 0.016809 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.024873 0.016652 0.017129 0.017129 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.021320 0.014577 0.015402 0.015402 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.016751 0.011846 0.015146 0.015146 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.020305 0.006260 0.008221 0.008221 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.019036 0.004614 0.008653 0.008653 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.013706 0.003380 0.006190 0.006190 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.018020 0.002863 0.003071 0.003071 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.011675 0.003450 0.004558 0.004558 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.010660 0.002793 0.003870 0.003870 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.006345 0.001821 0.002879 0.002879 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.004645 0.001285 0.001664 0.001664 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.007047 0.001049 0.001511 0.001511 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.005286 0.000865 0.001510 0.001510 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.005606 0.000821 0.000654 0.000654 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.026908 0.010225 0.006783 0.006783 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715170 

### Galveston County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.072924 0.057145 0.046797 0.046797 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.133431 0.085064 0.084091 0.084091 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.130022 0.085457 0.074642 0.074642 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.095569 0.081664 0.065360 0.065360 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.069028 0.073079 0.080097 0.080097 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.084612 0.073127 0.082950 0.082950 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.074142 0.072143 0.086804 0.086804 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.052958 0.066761 0.078747 0.078747 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.044680 0.066658 0.060977 0.060977 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.036888 0.058152 0.058542 0.058542 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.027149 0.048449 0.045141 0.045141 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.019722 0.041108 0.041830 0.041830 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.018505 0.033050 0.030906 0.030906 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.018140 0.033357 0.030349 0.030349 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.012053 0.024677 0.027497 0.027497 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.008766 0.020042 0.020191 0.020191 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.006696 0.015746 0.013999 0.013999 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.006452 0.012803 0.012120 0.012120 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.006087 0.010285 0.011480 0.011480 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.004870 0.008310 0.010103 0.010103 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.005722 0.005131 0.006248 0.006248 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.005357 0.004092 0.004467 0.004467 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.006331 0.003187 0.004731 0.004731 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.007305 0.003037 0.003493 0.003493 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.004626 0.002337 0.003646 0.003646 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.006696 0.001479 0.002157 0.002157 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.003873 0.001232 0.001730 0.001730 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.005877 0.001006 0.001580 0.001580 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.004408 0.000830 0.001552 0.001552 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.004675 0.000788 0.000670 0.000670 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.022439 0.009805 0.007103 0.007103 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502171 

### Galveston County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.025243 0.043574 0.026319 0.026319 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.037738 0.058903 0.049632 0.049632 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.100719 0.063386 0.042001 0.042001 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.098574 0.088516 0.079627 0.079627 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.108166 0.090634 0.082367 0.082367 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.097816 0.083909 0.072569 0.072569 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.080020 0.075971 0.063120 0.063120 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.058437 0.064917 0.073910 0.073910 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.073457 0.062605 0.074861 0.074861 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.059447 0.059629 0.076888 0.076888 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.044806 0.052487 0.068292 0.068292 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.034709 0.050206 0.051254 0.051254 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.030418 0.041696 0.045928 0.045928 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.020825 0.033502 0.032860 0.032860 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.015777 0.026784 0.031798 0.031798 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.016534 0.020083 0.022671 0.022671 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.009719 0.018745 0.021832 0.021832 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008709 0.012337 0.017681 0.017681 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.007952 0.010034 0.012957 0.012957 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.004670 0.007490 0.008889 0.008889 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.004039 0.005813 0.007785 0.007785 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.003786 0.005017 0.007548 0.007548 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.004670 0.003564 0.005996 0.005996 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.004670 0.002427 0.004025 0.004025 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.003155 0.002056 0.002376 0.002376 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004670 0.001778 0.002404 0.002404 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.003155 0.001662 0.002111 0.002111 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.002524 0.001376 0.002264 0.002264 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003282 0.000873 0.001384 0.001384 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.005301 0.000657 0.001132 0.001132 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.027010 0.009369 0.007520 0.007520 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875172 

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

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.133995 0.076273 0.100452 0.100452 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.145925 0.093193 0.114137 0.114137 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.114043 0.092654 0.096030 0.096030 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.093503 0.080164 0.090982 0.090982 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.066704 0.074951 0.072659 0.072659 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.051592 0.070560 0.074424 0.074424 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.049483 0.064047 0.052854 0.052854 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.043293 0.069245 0.058809 0.058809 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.033507 0.055981 0.054443 0.054443 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.031087 0.052927 0.044816 0.044816 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.018742 0.046193 0.036332 0.036332 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.012656 0.042864 0.030902 0.030902 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.013140 0.037605 0.025900 0.025900 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.012241 0.031161 0.025331 0.025331 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.012449 0.024683 0.020578 0.020578 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.010685 0.018216 0.014423 0.014423 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.020263 0.014785 0.014715 0.014715 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.015664 0.012829 0.013796 0.013796 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.012794 0.009622 0.011971 0.011971 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.014074 0.005572 0.006788 0.006788 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.017808 0.004139 0.008045 0.008045 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.011515 0.003006 0.005852 0.005852 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.011169 0.002237 0.003099 0.003099 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.009267 0.002831 0.004308 0.004308 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.007089 0.002124 0.003630 0.003630 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.005913 0.001577 0.002999 0.002999 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.003569 0.001136 0.001504 0.001504 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.004734 0.000845 0.001815 0.001815 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.002977 0.000729 0.001364 0.001364 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.002903 0.000559 0.000656 0.000656 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.017215 0.007291 0.006386 0.006386 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715173 

### Harris County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.081872 0.068454 0.053354 0.053354 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.153339 0.085830 0.082236 0.082236 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.125414 0.077150 0.069308 0.069308 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.097075 0.074145 0.066264 0.066264 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.070660 0.067001 0.077040 0.077040 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.085678 0.066449 0.081121 0.081121 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.070619 0.069134 0.085339 0.085339 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.055353 0.067788 0.077288 0.077288 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.043666 0.070361 0.065555 0.065555 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.035041 0.060104 0.061776 0.061776 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.026167 0.052937 0.048235 0.048235 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.018555 0.045220 0.045049 0.045049 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.018120 0.036434 0.029491 0.029491 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.015079 0.035331 0.030662 0.030662 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.012390 0.026143 0.025870 0.025870 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.009412 0.021635 0.018621 0.018621 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.006143 0.017066 0.014131 0.014131 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.004282 0.013708 0.011442 0.011442 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.004427 0.010557 0.009923 0.009923 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.004054 0.007363 0.008943 0.008943 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.004613 0.005087 0.006649 0.006649 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.003516 0.003605 0.004179 0.004179 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.006495 0.002820 0.004250 0.004250 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.005606 0.002522 0.003756 0.003756 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.004696 0.001931 0.003482 0.003482 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.004261 0.001288 0.002136 0.002136 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.003804 0.001069 0.001785 0.001785 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.005046 0.000795 0.002155 0.002155 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.003173 0.000686 0.001611 0.001611 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.003095 0.000526 0.000785 0.000785 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.018351 0.006859 0.007566 0.007566 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502174 

### Harris County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.035445 0.047178 0.027418 0.027418 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.042013 0.057736 0.040492 0.040492 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.102145 0.052988 0.035349 0.035349 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.098830 0.078010 0.069539 0.069539 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.117470 0.082164 0.077581 0.077581 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.098789 0.076988 0.066823 0.066823 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.077021 0.071964 0.064280 0.064280 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.055649 0.062982 0.073246 0.073246 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.068660 0.060872 0.076613 0.076613 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.057818 0.061481 0.079337 0.079337 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.044703 0.058503 0.071480 0.071480 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.035425 0.057776 0.057650 0.057650 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028273 0.046624 0.051219 0.051219 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.020850 0.039183 0.038065 0.038065 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.014449 0.031811 0.035592 0.035592 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.013490 0.024008 0.023130 0.023130 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.011238 0.021505 0.023112 0.023112 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008924 0.015239 0.019319 0.019319 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.006484 0.011823 0.013504 0.013504 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.004962 0.009001 0.010016 0.010016 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.003419 0.006878 0.007798 0.007798 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.003482 0.005110 0.006732 0.006732 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003336 0.003481 0.005890 0.005890 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.003607 0.002469 0.004190 0.004190 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002877 0.001852 0.002588 0.002588 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004253 0.001431 0.002746 0.002746 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.004024 0.001468 0.002465 0.002465 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003440 0.001175 0.002283 0.002283 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.002648 0.000789 0.001410 0.001410 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.003982 0.000576 0.001734 0.001734 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.022289 0.006934 0.008398 0.008398 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875175 

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

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.096386 0.042443 0.079282 0.079282 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.129853 0.073665 0.103321 0.103321 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.115127 0.087241 0.082888 0.082888 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.100402 0.078119 0.081063 0.081063 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.065596 0.070581 0.067530 0.067530 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.048193 0.065527 0.075231 0.075231 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.058902 0.059960 0.054220 0.054220 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.048193 0.067926 0.058894 0.058894 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.038822 0.055848 0.053953 0.053953 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.029451 0.056876 0.043269 0.043269 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.020080 0.047240 0.038105 0.038105 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.010710 0.049081 0.035256 0.035256 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.009371 0.042614 0.029870 0.029870 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.010710 0.040558 0.031116 0.031116 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.014726 0.030622 0.026309 0.026309 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.014726 0.025440 0.016248 0.016248 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.022758 0.021628 0.019275 0.019275 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.021419 0.020686 0.019053 0.019053 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.010710 0.015033 0.017673 0.017673 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.018742 0.009422 0.011574 0.011574 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.020080 0.006467 0.011619 0.011619 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.021419 0.004411 0.008859 0.008859 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.016064 0.003041 0.003962 0.003962 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.004016 0.005097 0.007434 0.007434 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.004016 0.004668 0.005253 0.005253 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.005355 0.002398 0.003828 0.003828 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.001893 0.001305 0.002325 0.002325 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.006311 0.001395 0.002582 0.002582 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.007573 0.001080 0.001763 0.001763 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.006311 0.000720 0.000819 0.000819 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.022088 0.008907 0.007424 0.007424 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715176 

### Liberty County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.083851 0.048322 0.042173 0.042173 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.138199 0.075306 0.074469 0.074469 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.127329 0.076435 0.067620 0.067620 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.101967 0.075384 0.057027 0.057027 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.067805 0.057550 0.067142 0.067142 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.071946 0.056070 0.073275 0.073275 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.072981 0.058446 0.072916 0.072916 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.053830 0.061132 0.074310 0.074310 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.044513 0.065727 0.060332 0.060332 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.038820 0.062106 0.054916 0.054916 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.034679 0.053617 0.049062 0.049062 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.025362 0.049451 0.050496 0.050496 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.018634 0.042286 0.036359 0.036359 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.014493 0.041586 0.038509 0.038509 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.013975 0.032747 0.034009 0.034009 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.011387 0.028970 0.024531 0.024531 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.008282 0.024531 0.020389 0.020389 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.003623 0.020559 0.017801 0.017801 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.003623 0.015653 0.014496 0.014496 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.005694 0.012032 0.014336 0.014336 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.006211 0.009423 0.010633 0.010633 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.005176 0.005529 0.007009 0.007009 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.004141 0.004945 0.007248 0.007248 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.006729 0.004244 0.006013 0.006013 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.005694 0.003543 0.006093 0.006093 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.003106 0.001635 0.003066 0.003066 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.001198 0.001243 0.002495 0.002495 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.003993 0.001329 0.002741 0.002741 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.004791 0.001029 0.001850 0.001850 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.003993 0.000686 0.000891 0.000891 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.013975 0.008486 0.007792 0.007792 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502177 

### Liberty County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.023510 0.031586 0.022295 0.022295 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.029524 0.046288 0.032190 0.032190 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.083652 0.045982 0.031827 0.031827 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.092947 0.079865 0.065471 0.065471 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.107709 0.080018 0.072378 0.072378 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.109896 0.074275 0.065269 0.065269 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.082559 0.070868 0.054768 0.054768 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.061236 0.056013 0.062038 0.062038 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.061782 0.054520 0.069147 0.069147 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.064516 0.052950 0.070883 0.070883 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.057408 0.057736 0.069712 0.069712 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.041006 0.056893 0.054606 0.054606 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.035539 0.051725 0.051133 0.051133 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.025150 0.042536 0.042974 0.042974 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.019683 0.038707 0.044428 0.044428 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.015309 0.030476 0.030373 0.030373 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.009841 0.028294 0.032635 0.032635 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.007654 0.021134 0.027546 0.027546 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.006014 0.017191 0.017812 0.017812 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.003827 0.013668 0.016156 0.016156 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.002734 0.011716 0.011753 0.011753 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.002734 0.008002 0.010663 0.010663 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.004921 0.006394 0.009088 0.009088 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.004374 0.005054 0.007634 0.007634 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.002187 0.002948 0.004241 0.004241 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.009841 0.002259 0.003918 0.003918 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.003827 0.002106 0.003958 0.003958 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.004374 0.001646 0.003110 0.003110 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003827 0.000536 0.001494 0.001494 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.001640 0.000689 0.001535 0.001535 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.020776 0.007925 0.008966 0.008966 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875178 

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

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.100411 0.058798 0.088606 0.088606 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.145899 0.091847 0.120742 0.120742 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.126301 0.102367 0.099240 0.099240 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.098476 0.088076 0.093396 0.093396 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.072103 0.079640 0.071707 0.071707 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.049117 0.074316 0.073276 0.073276 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.049601 0.065947 0.054375 0.054375 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.042342 0.070309 0.059036 0.059036 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.035325 0.056640 0.052114 0.052114 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.027583 0.052346 0.043073 0.043073 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.015485 0.042947 0.034196 0.034196 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.012098 0.039221 0.030835 0.030835 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.015727 0.033928 0.026268 0.026268 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.014759 0.029263 0.023856 0.023856 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.010888 0.023000 0.019675 0.019675 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.014275 0.017373 0.013550 0.013550 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.020082 0.013995 0.015377 0.015377 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.019114 0.012844 0.014627 0.014627 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.016211 0.009557 0.014053 0.014053 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.016453 0.005915 0.007718 0.007718 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.018872 0.004264 0.008514 0.008514 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.013549 0.003135 0.006324 0.006324 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.012098 0.002507 0.002939 0.002939 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.011130 0.003181 0.005446 0.005446 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.006049 0.002810 0.004345 0.004345 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.005081 0.001772 0.003115 0.003115 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.004757 0.001184 0.001721 0.001721 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.004272 0.000897 0.001642 0.001642 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.003592 0.000791 0.001323 0.001323 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.002815 0.000654 0.000714 0.000714 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.015534 0.010477 0.008197 0.008197 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715179 

### Montgomery County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.074573 0.064362 0.048791 0.048791 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.133229 0.094632 0.089232 0.089232 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.129102 0.091875 0.078125 0.078125 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.100511 0.085342 0.071626 0.071626 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.077029 0.076827 0.083382 0.083382 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.087542 0.071816 0.086617 0.086617 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.071625 0.071660 0.085479 0.085479 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.057379 0.067542 0.078144 0.078144 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.046178 0.066783 0.061563 0.061563 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.039497 0.056500 0.055055 0.055055 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.027609 0.046903 0.043083 0.043083 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.020829 0.040509 0.041503 0.041503 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.019257 0.030158 0.029314 0.029314 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.013166 0.029037 0.029042 0.029042 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.012380 0.021621 0.023784 0.023784 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.010808 0.017013 0.017718 0.017718 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.006583 0.013732 0.012678 0.012678 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.004618 0.010724 0.010938 0.010938 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.004421 0.008543 0.009630 0.009630 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.003930 0.006645 0.008163 0.008163 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.004225 0.004352 0.005850 0.005850 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.003242 0.003392 0.003668 0.003668 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.006976 0.002617 0.003564 0.003564 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.004421 0.002159 0.003546 0.003546 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.005306 0.001914 0.003470 0.003470 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.004618 0.001060 0.002003 0.002003 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.004754 0.001038 0.001781 0.001781 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.004269 0.000787 0.001699 0.001699 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.003590 0.000694 0.001371 0.001371 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.002814 0.000574 0.000730 0.000730 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.015523 0.009189 0.008451 0.008451 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502180 

### Montgomery County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.040452 0.047069 0.031806 0.031806 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.035805 0.066982 0.050435 0.050435 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.089468 0.060890 0.043266 0.043266 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.087281 0.093845 0.077591 0.077591 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.110787 0.092229 0.083330 0.083330 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.105047 0.083485 0.068758 0.068758 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.082088 0.074366 0.064179 0.064179 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.061771 0.066269 0.073435 0.073435 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.074708 0.060237 0.076143 0.076143 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.056669 0.058257 0.073597 0.073597 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.047103 0.052050 0.068659 0.068659 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.038539 0.049792 0.050687 0.050687 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.028972 0.040304 0.044102 0.044102 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.019679 0.032307 0.033524 0.033524 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.015853 0.025846 0.032391 0.032391 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.015306 0.018497 0.023396 0.023396 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.011935 0.017126 0.021777 0.021777 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.008746 0.012005 0.017801 0.017801 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.008200 0.009174 0.012323 0.012323 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.006013 0.007304 0.009139 0.009139 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.003462 0.005529 0.007088 0.007088 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.003371 0.004268 0.006494 0.006494 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.003644 0.003410 0.005325 0.005325 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.003735 0.002438 0.003562 0.003562 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.003098 0.001875 0.002276 0.002276 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.004191 0.001436 0.002546 0.002546 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.004282 0.001256 0.002393 0.002393 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.003735 0.001077 0.002357 0.002357 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.003735 0.000858 0.001520 0.001520 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.004191 0.000653 0.001250 0.001250 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.018130 0.009169 0.008851 0.008851 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875181 

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

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.081505 0.041587 0.074618 0.074618 0.057086 0.044226 0.054177 0.027706 0.101906 0.102260 0.051473 0.039361 0.033087

1 0.115987 0.068359 0.098903 0.098903 0.067160 0.052030 0.063738 0.029896 0.137869 0.143115 0.060557 0.067005 0.059279

2 0.094044 0.086294 0.084508 0.084508 0.074487 0.057706 0.070691 0.043169 0.139706 0.123935 0.067163 0.084079 0.078329

3 0.090909 0.070612 0.085879 0.085879 0.076013 0.058889 0.072140 0.050515 0.117981 0.115521 0.068539 0.091826 0.078564

4 0.056426 0.071391 0.066882 0.066882 0.060030 0.072023 0.059327 0.039893 0.054890 0.055213 0.041990 0.066096 0.066623

5 0.062696 0.067233 0.071582 0.071582 0.050385 0.067887 0.057010 0.033484 0.081717 0.073413 0.066692 0.050839 0.052283

6 0.053292 0.061601 0.058167 0.058167 0.043430 0.065192 0.051257 0.073919 0.044630 0.040436 0.043052 0.048257 0.050489

7 0.053292 0.061428 0.061105 0.061105 0.058246 0.055131 0.067565 0.087457 0.062570 0.053893 0.052418 0.077861 0.070480

8 0.031348 0.055623 0.055817 0.055817 0.046777 0.050144 0.034008 0.056440 0.041229 0.037105 0.052131 0.050170 0.048179

9 0.043887 0.054757 0.045339 0.045339 0.040161 0.043380 0.042359 0.050280 0.034536 0.029449 0.037417 0.049692 0.046822

10 0.006270 0.045833 0.036428 0.036428 0.030868 0.039299 0.035652 0.022238 0.025730 0.021518 0.034250 0.038213 0.033984

11 0.018809 0.046959 0.033000 0.033000 0.036500 0.041479 0.047487 0.063049 0.021725 0.022132 0.026585 0.046726 0.041654

12 0.021944 0.046266 0.028692 0.028692 0.042298 0.062690 0.055385 0.055438 0.021122 0.020865 0.035957 0.047587 0.040577

13 0.025078 0.035609 0.027810 0.027810 0.043797 0.049789 0.032989 0.045171 0.016925 0.017194 0.048492 0.040891 0.033233

14 0.028213 0.036475 0.024089 0.024089 0.042540 0.040918 0.040951 0.063457 0.015937 0.015593 0.044900 0.032570 0.029600

15 0.015674 0.028331 0.018606 0.018606 0.045676 0.039103 0.042849 0.052492 0.010451 0.012053 0.045301 0.025348 0.027100

16 0.012539 0.020880 0.017235 0.017235 0.040017 0.034406 0.039061 0.067697 0.009848 0.016246 0.034884 0.020278 0.026394

17 0.031348 0.020447 0.016843 0.016843 0.036115 0.030509 0.034656 0.036794 0.009848 0.015724 0.038270 0.021952 0.029982

18 0.028213 0.017848 0.018410 0.018410 0.029365 0.024575 0.027654 0.039478 0.008449 0.011020 0.040114 0.019944 0.025329

19 0.018809 0.011090 0.011653 0.011653 0.011980 0.024091 0.010443 0.011694 0.004992 0.006199 0.026863 0.008417 0.011549

20 0.015674 0.007971 0.012241 0.012241 0.010437 0.013429 0.007905 0.013460 0.007105 0.010204 0.016211 0.012961 0.017771

21 0.012539 0.005372 0.009792 0.009792 0.009369 0.006026 0.009592 0.012328 0.006172 0.008081 0.009213 0.013248 0.018511

22 0.015674 0.005285 0.005092 0.005092 0.012480 0.016749 0.009645 0.003160 0.003566 0.005448 0.001241 0.010617 0.014015

23 0.015674 0.006585 0.007736 0.007736 0.007676 0.005071 0.008149 0.003332 0.004800 0.007735 0.009806 0.012052 0.017132

24 0.009404 0.003726 0.006365 0.006365 0.006146 0.002264 0.007108 0.003312 0.004142 0.005370 0.013354 0.007987 0.010966

25 0.006270 0.002253 0.004015 0.004015 0.006667 0.000458 0.007229 0.000000 0.002935 0.005128 0.018503 0.004591 0.007815

26 0.009512 0.002779 0.003055 0.003055 0.002553 0.000881 0.004887 0.006886 0.000953 0.003182 0.011604 0.001016 0.004190

27 0.005945 0.001902 0.002788 0.002788 0.003150 0.000601 0.006086 0.002022 0.001215 0.002707 0.003020 0.001656 0.003073

28 0.003567 0.001243 0.002874 0.002874 0.003099 0.000582 0.000000 0.005149 0.001045 0.002035 0.000000 0.001889 0.003322

29 0.002378 0.001170 0.001145 0.001145 0.002572 0.000150 0.000000 0.000084 0.000657 0.001589 0.000000 0.001389 0.002952

30 0.013080 0.013092 0.009332 0.009332 0.002922 0.000320 0.000000 0.000000 0.005346 0.015638 0.000000 0.005481 0.016715182 

### Waller County 2008 Age Distributions Inputs to MOVES

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.075472 0.044178 0.037914 0.037914 0.073317 0.062325 0.070930 0.028618 0.142370 0.136309 0.069827 0.030912 0.032667

1 0.133255 0.064246 0.067880 0.067880 0.075781 0.064419 0.073313 0.030575 0.113204 0.111540 0.072173 0.088500 0.087489

2 0.108491 0.073039 0.068596 0.068596 0.076500 0.065031 0.074009 0.051881 0.125541 0.126729 0.072858 0.069736 0.066595

3 0.095519 0.072968 0.058183 0.058183 0.077219 0.065643 0.074705 0.047316 0.115078 0.118261 0.073543 0.057804 0.057036

4 0.064858 0.059850 0.076385 0.076385 0.061728 0.052473 0.059718 0.038077 0.078008 0.081544 0.058789 0.048335 0.041313

5 0.091981 0.056942 0.079803 0.079803 0.049092 0.041732 0.047494 0.028053 0.067027 0.067105 0.046755 0.040164 0.036925

6 0.066038 0.061197 0.077657 0.077657 0.042638 0.036246 0.041250 0.028282 0.057280 0.053713 0.040608 0.039559 0.037541

7 0.055425 0.058573 0.073444 0.073444 0.048658 0.041363 0.047073 0.029602 0.059366 0.056490 0.046341 0.056636 0.053820

8 0.047170 0.067295 0.055560 0.055560 0.052347 0.044499 0.050642 0.041462 0.053000 0.048678 0.049855 0.076783 0.070148

9 0.043632 0.057793 0.056514 0.056514 0.051817 0.044049 0.050130 0.047062 0.045827 0.043835 0.049350 0.072849 0.065860

10 0.027123 0.050418 0.046419 0.046419 0.039682 0.052240 0.039977 0.036040 0.018276 0.021730 0.029318 0.057112 0.053164

11 0.017689 0.049638 0.048804 0.048804 0.032626 0.048235 0.037632 0.029632 0.025708 0.026547 0.045615 0.040597 0.040519

12 0.023585 0.041271 0.035291 0.035291 0.027262 0.044903 0.032799 0.063414 0.015108 0.014532 0.028545 0.038867 0.039695

13 0.012972 0.045029 0.037199 0.037199 0.035808 0.037190 0.042342 0.073481 0.019175 0.017464 0.034038 0.051102 0.049750

14 0.017689 0.034818 0.031873 0.031873 0.028164 0.033128 0.020873 0.046443 0.011956 0.011305 0.033154 0.035452 0.034067

15 0.015330 0.032903 0.024163 0.024163 0.023433 0.027774 0.025194 0.040094 0.009184 0.008309 0.023060 0.031820 0.031486

16 0.005896 0.025599 0.021540 0.021540 0.017635 0.024637 0.020763 0.017363 0.007402 0.006196 0.020669 0.023303 0.022304

17 0.010613 0.023188 0.016056 0.016056 0.020201 0.025191 0.026792 0.047690 0.005650 0.006110 0.015542 0.025032 0.026334

18 0.008255 0.017373 0.013512 0.013512 0.022918 0.037271 0.030590 0.041051 0.005787 0.006137 0.020578 0.023390 0.023247

19 0.005896 0.010708 0.012320 0.012320 0.022980 0.028666 0.017645 0.032392 0.004691 0.005234 0.026875 0.020406 0.019267

20 0.003538 0.010069 0.010730 0.010730 0.021616 0.022814 0.021212 0.044068 0.003701 0.004329 0.024099 0.015953 0.016249

21 0.009434 0.007446 0.007392 0.007392 0.022716 0.021339 0.021723 0.035678 0.002117 0.003009 0.023796 0.011760 0.013807

22 0.007075 0.003971 0.006120 0.006120 0.019266 0.018176 0.019171 0.044544 0.002741 0.003994 0.017740 0.007912 0.012199

23 0.005896 0.005815 0.007233 0.007233 0.017014 0.015771 0.016643 0.023689 0.002010 0.003636 0.019043 0.007004 0.013053

24 0.008255 0.003971 0.006438 0.006438 0.013536 0.012430 0.012995 0.024871 0.001630 0.002498 0.019531 0.006831 0.009886

25 0.004717 0.002766 0.003259 0.003259 0.005402 0.011921 0.004800 0.007207 0.001005 0.001297 0.012795 0.002335 0.004308

26 0.009434 0.002607 0.003069 0.003069 0.004604 0.006501 0.003555 0.008115 0.000735 0.001371 0.007554 0.001974 0.004519

27 0.005896 0.001784 0.003078 0.003078 0.004043 0.002854 0.004220 0.007271 0.001272 0.001835 0.004199 0.002960 0.006190

28 0.003538 0.001166 0.002854 0.002854 0.005267 0.007757 0.004150 0.001823 0.001026 0.001389 0.000553 0.004081 0.006416

29 0.002358 0.001098 0.001215 0.001215 0.003168 0.002297 0.003429 0.001880 0.000534 0.000994 0.004276 0.002509 0.004646

30 0.012972 0.012279 0.009496 0.009496 0.003562 0.001128 0.004233 0.002327 0.003591 0.007881 0.008922 0.008320 0.019502183 

### Waller County 2011 Age Distribution Inputs to MOVES (2011, 2014, 2017, 2018, and 2019 Analysis Years)

Age MC PC PT LCT IBus TBus SBus RT SUShT SULhT MH CShT CLhT

0 0.032184 0.023292 0.017334 0.017334 0.069073 0.061049 0.067376 0.028802 0.100184 0.104789 0.066877 0.022568 0.021517

1 0.031034 0.039236 0.032092 0.032092 0.063576 0.056190 0.062014 0.027620 0.051441 0.054592 0.061554 0.023917 0.018938

2 0.078161 0.041730 0.029125 0.029125 0.061072 0.053977 0.059571 0.027681 0.054203 0.051380 0.059130 0.035695 0.033960

3 0.072414 0.072239 0.061138 0.061138 0.067253 0.059440 0.065601 0.031074 0.152909 0.148094 0.065115 0.047339 0.047549

4 0.120690 0.071583 0.069415 0.069415 0.068818 0.060823 0.067127 0.032867 0.091936 0.091287 0.066630 0.108389 0.100800

5 0.101149 0.070009 0.063012 0.063012 0.068082 0.060172 0.066409 0.054654 0.098539 0.100234 0.065917 0.079662 0.075154

6 0.083908 0.068959 0.057469 0.057469 0.067348 0.059523 0.065693 0.048848 0.085962 0.091233 0.065206 0.068333 0.065321

7 0.058621 0.055902 0.071133 0.071133 0.052749 0.046621 0.051452 0.038516 0.059676 0.063794 0.051071 0.051025 0.044275

8 0.074713 0.054130 0.069806 0.069806 0.041523 0.036699 0.040503 0.028087 0.050670 0.051239 0.040203 0.042259 0.037609

9 0.050575 0.059051 0.073007 0.073007 0.035696 0.031549 0.034819 0.028027 0.043809 0.040653 0.034561 0.043562 0.037406

10 0.044828 0.059839 0.069962 0.069962 0.040316 0.035632 0.039325 0.029033 0.044015 0.042649 0.039034 0.056600 0.052107

11 0.042529 0.062594 0.050051 0.050051 0.042925 0.037938 0.041871 0.040246 0.038221 0.034783 0.041560 0.064916 0.064101

12 0.032184 0.054721 0.052393 0.052393 0.041615 0.036780 0.040592 0.044739 0.032016 0.031295 0.040292 0.057678 0.056536

13 0.025287 0.043435 0.042008 0.042008 0.031537 0.043166 0.032034 0.033904 0.013503 0.014618 0.023687 0.044327 0.046019

14 0.013793 0.038843 0.044741 0.044741 0.025659 0.039441 0.029841 0.027586 0.018346 0.016614 0.036470 0.036010 0.035234

15 0.016092 0.033134 0.031233 0.031233 0.021215 0.036330 0.025734 0.058414 0.011306 0.009689 0.022582 0.030435 0.032591

16 0.018391 0.031560 0.033497 0.033497 0.027572 0.029773 0.032872 0.066975 0.012102 0.010658 0.026644 0.040685 0.040969

17 0.009195 0.024014 0.026860 0.026860 0.021230 0.025963 0.015864 0.041439 0.007387 0.006977 0.025405 0.025310 0.026449

18 0.011494 0.020471 0.017569 0.017569 0.017475 0.021535 0.018944 0.035394 0.005165 0.005036 0.017483 0.022433 0.024074

19 0.004598 0.016141 0.016710 0.016710 0.012872 0.018696 0.015280 0.015002 0.004240 0.003477 0.015336 0.016634 0.017590

20 0.004598 0.012204 0.010541 0.010541 0.014586 0.018911 0.019505 0.040761 0.004278 0.003744 0.011408 0.016184 0.019591

21 0.005747 0.008792 0.009916 0.009916 0.016370 0.027679 0.022031 0.034709 0.004124 0.003655 0.014943 0.014431 0.017397

22 0.004598 0.007086 0.008745 0.008745 0.016236 0.021057 0.012570 0.027090 0.003726 0.003071 0.019303 0.012723 0.013278

23 0.006897 0.005118 0.007340 0.007340 0.015106 0.016577 0.014946 0.036454 0.002698 0.002499 0.017121 0.009710 0.010967

24 0.004598 0.004462 0.004919 0.004919 0.015702 0.015336 0.015140 0.029193 0.001747 0.001536 0.016722 0.007013 0.009105

25 0.008046 0.001968 0.004216 0.004216 0.013171 0.012919 0.013214 0.036047 0.001824 0.001835 0.012329 0.004046 0.008763

26 0.006897 0.002428 0.004216 0.004216 0.011504 0.011086 0.011346 0.018960 0.000951 0.001467 0.013089 0.003462 0.008281

27 0.004598 0.002493 0.003904 0.003904 0.009052 0.008642 0.008761 0.019687 0.000784 0.001153 0.013278 0.003731 0.006291

28 0.009195 0.001378 0.002342 0.002342 0.003572 0.008196 0.003201 0.005641 0.000450 0.000748 0.008601 0.001349 0.002814

29 0.004598 0.001115 0.002577 0.002577 0.003011 0.004420 0.002344 0.006282 0.000642 0.001073 0.005022 0.001708 0.004440

30 0.018391 0.012073 0.012727 0.012727 0.004084 0.003881 0.004023 0.006268 0.003148 0.006131 0.003427 0.007867 0.020875184 

### Texas Statewide 200 2 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 0.000 0.000 0.000

PT Gas 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010

PT Gas 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250 0.266

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750 0.734

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.202 0.265 0.346 0.330 0.413 0.450 0.456 0.703 0.492 0.493 0.504 0.517 0.570 0.606 0.723 0.775

SUShT Diesel 0.798 0.735 0.654 0.670 0.587 0.550 0.544 0.297 0.508 0.507 0.496 0.483 0.430 0.394 0.277 0.225

SULhT Gas 0.202 0.265 0.346 0.330 0.413 0.450 0.456 0.703 0.492 0.493 0.504 0.517 0.570 0.606 0.723 0.775

SULhT Diesel 0.798 0.735 0.654 0.670 0.587 0.550 0.544 0.297 0.508 0.507 0.496 0.483 0.430 0.394 0.277 0.225

MH Gas 0.600 0.630 0.660 0.680 0.710 0.740 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.400 0.370 0.340 0.320 0.290 0.260 0.230 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.062 0.102 0.094 0.100 0.131 0.202 0.156 0.403 0.125 0.135 0.213 0.175 0.159 0.155 0.199 0.207

CShT Diesel 0.938 0.898 0.906 0.900 0.869 0.798 0.844 0.597 0.875 0.865 0.787 0.825 0.841 0.845 0.801 0.793

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional i nternal c ombustion engine technology only.185 

### Texas Statewide 200 2 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 0.017 0.015 0.021 0.040 0.018 0.012 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014

LCT Gas 0.919 0.900 0.852 0.795 0.780 0.929 0.893 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

LCT Diesel 0.081 0.100 0.148 0.205 0.220 0.071 0.107 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

LCT Gas 0.919 0.900 0.852 0.795 0.780 0.929 0.893 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

LCT Diesel 0.081 0.100 0.148 0.205 0.220 0.071 0.107 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.327 0.485 0.615 0.676 0.674 0.736 0.941 0.954 0.971 0.976 0.991 0.991 0.991 0.991 0.991

SBus Diesel 0.673 0.515 0.385 0.324 0.326 0.264 0.059 0.046 0.029 0.024 0.009 0.009 0.009 0.009 0.009

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.775 0.814 0.791 0.806 0.839 0.932 0.968 0.969 0.984 0.977 0.969 0.993 0.987 0.994 0.999

SUShT Diesel 0.225 0.186 0.209 0.194 0.161 0.068 0.032 0.031 0.016 0.023 0.031 0.007 0.013 0.006 0.001

SULhT Gas 0.775 0.814 0.791 0.806 0.839 0.932 0.968 0.969 0.984 0.977 0.969 0.993 0.987 0.994 0.999

SULhT Diesel 0.225 0.186 0.209 0.194 0.161 0.068 0.032 0.031 0.016 0.023 0.031 0.007 0.013 0.006 0.001

MH Gas 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.254 0.277 0.282 0.351 0.348 0.311 0.590 0.647 0.640 0.697 0.734 0.614 0.577 0.802 0.894

CShT Diesel 0.746 0.723 0.718 0.649 0.652 0.689 0.410 0.353 0.360 0.303 0.266 0.386 0.423 0.198 0.106

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

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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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

PC Diesel 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

PT Gas 0.977 0.977 0.977 0.987 0.987 0.987 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979

PT Diesel 0.023 0.023 0.023 0.013 0.013 0.013 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021

PT Gas 0.977 0.977 0.977 0.987 0.987 0.987 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979

PT Diesel 0.023 0.023 0.023 0.013 0.013 0.013 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021

LCT Gas 0.895 0.913 0.921 0.891 0.819 0.867 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915

LCT Diesel 0.105 0.087 0.079 0.109 0.181 0.133 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085

LCT Gas 0.895 0.913 0.921 0.891 0.819 0.867 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915

LCT Diesel 0.105 0.087 0.079 0.109 0.181 0.133 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085

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 0.114 0.147 0.121

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.886 0.853 0.879

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 0.284 0.211 0.212 0.193 0.202 0.248 0.281 0.309 0.373 0.369 0.472 0.479 0.469 0.688 0.513 0.521

SUShT Diesel 0.716 0.789 0.788 0.807 0.798 0.752 0.719 0.691 0.627 0.631 0.528 0.521 0.531 0.312 0.487 0.479

SULhT Gas 0.284 0.211 0.212 0.193 0.202 0.248 0.281 0.309 0.373 0.369 0.472 0.479 0.469 0.688 0.513 0.521

SULhT Diesel 0.716 0.789 0.788 0.807 0.798 0.752 0.719 0.691 0.627 0.631 0.528 0.521 0.531 0.312 0.487 0.479

MH Gas 0.510 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0.660 0.680 0.710 0.740 0.770 0.790 0.820 0.850

MH Diesel 0.490 0.470 0.460 0.440 0.430 0.410 0.400 0.370 0.340 0.320 0.290 0.260 0.230 0.210 0.180 0.150

CShT Gas 0.046 0.024 0.065 0.059 0.057 0.054 0.084 0.073 0.085 0.095 0.122 0.154 0.133 0.303 0.108 0.111

CShT Diesel 0.954 0.976 0.935 0.941 0.943 0.946 0.916 0.927 0.915 0.905 0.878 0.846 0.867 0.697 0.892 0.889

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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### ( 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 1.000 1.000 1.000 1.000 1.000 1.000 0.988 0.981 0.969 0.939 0.924 0.953 0.969 0.985

PC Diesel 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.012 0.019 0.031 0.061 0.076 0.047 0.031 0.015

PT Gas 0.979 0.980 0.987 0.992 0.995 0.990 0.978 0.983 0.985 0.979 0.960 0.982 0.988 0.986 0.986

PT Diesel 0.021 0.020 0.013 0.008 0.005 0.010 0.022 0.017 0.015 0.021 0.040 0.018 0.012 0.014 0.014

PT Gas 0.979 0.980 0.987 0.992 0.995 0.990 0.978 0.983 0.985 0.979 0.960 0.982 0.988 0.986 0.986

PT Diesel 0.021 0.020 0.013 0.008 0.005 0.010 0.022 0.017 0.015 0.021 0.040 0.018 0.012 0.014 0.014

LCT Gas 0.930 0.932 0.916 0.907 0.923 0.939 0.919 0.900 0.852 0.795 0.780 0.929 0.893 0.958 0.958

LCT Diesel 0.070 0.068 0.084 0.093 0.077 0.061 0.081 0.100 0.148 0.205 0.220 0.071 0.107 0.042 0.042

LCT Gas 0.930 0.932 0.916 0.907 0.923 0.939 0.919 0.900 0.852 0.795 0.780 0.929 0.893 0.958 0.958

LCT Diesel 0.070 0.068 0.084 0.093 0.077 0.061 0.081 0.100 0.148 0.205 0.220 0.071 0.107 0.042 0.042

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.010 0.090 0.124 0.229 0.250 0.266 0.327 0.485 0.615 0.676 0.674 0.736 0.941 0.954 0.971

SBus Diesel 0.990 0.910 0.876 0.771 0.750 0.734 0.673 0.515 0.385 0.324 0.326 0.264 0.059 0.046 0.029

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 0.526 0.505 0.517 0.554 0.702 0.757 0.808 0.817 0.809 0.808 0.882 0.889 0.926 0.974 0.993

SUShT Diesel 0.474 0.495 0.483 0.446 0.298 0.243 0.192 0.183 0.191 0.192 0.118 0.111 0.074 0.026 0.007

SULhT Gas 0.526 0.505 0.517 0.554 0.702 0.757 0.808 0.817 0.809 0.808 0.882 0.889 0.926 0.974 0.993

SULhT Diesel 0.474 0.495 0.483 0.446 0.298 0.243 0.192 0.183 0.191 0.192 0.118 0.111 0.074 0.026 0.007

MH Gas 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.167 0.161 0.140 0.121 0.178 0.145 0.261 0.260 0.276 0.329 0.478 0.308 0.292 0.592 0.747

CShT Diesel 0.833 0.839 0.860 0.879 0.822 0.855 0.739 0.740 0.724 0.671 0.522 0.692 0.708 0.408 0.253

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

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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 1.000 1.000 1.000 1.000 1.000

PC Diesel 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.000 0.000 0.000 0.000 0.000

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987 0.987 0.987 0.977 0.977 0.990 0.981 0.980

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013 0.013 0.013 0.023 0.023 0.010 0.019 0.020

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987 0.987 0.987 0.977 0.977 0.990 0.981 0.980

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013 0.013 0.013 0.023 0.023 0.010 0.019 0.020

LCT Gas 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867 0.843 0.827 0.877 0.833 0.962 0.864 0.911

LCT Diesel 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133 0.157 0.173 0.123 0.167 0.038 0.136 0.089

LCT Gas 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867 0.843 0.827 0.877 0.833 0.962 0.864 0.911

LCT Diesel 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133 0.157 0.173 0.123 0.167 0.038 0.136 0.089

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250 0.266 0.312 0.348 0.359 0.426 0.423 0.435

SUShT Diesel 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750 0.734 0.688 0.652 0.641 0.574 0.577 0.565

SULhT Gas 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250 0.266 0.312 0.348 0.359 0.426 0.423 0.435

SULhT Diesel 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750 0.734 0.688 0.652 0.641 0.574 0.577 0.565

MH Gas 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0.660 0.680 0.710 0.740 0.770

MH Diesel 0.500 0.500 0.500 0.490 0.470 0.460 0.440 0.430 0.410 0.400 0.370 0.340 0.320 0.290 0.260 0.230

CShT Gas 0.046 0.071 0.047 0.053 0.026 0.059 0.048 0.050 0.050 0.078 0.077 0.083 0.102 0.131 0.152 0.146

CShT Diesel 0.954 0.929 0.953 0.947 0.974 0.941 0.952 0.950 0.950 0.922 0.923 0.917 0.898 0.869 0.848 0.854

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 201 1 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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.988 0.981 0.969 0.939 0.924

PC Diesel 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.012 0.019 0.031 0.061 0.076

PT Gas 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990 0.978 0.983 0.985 0.979 0.960 0.982

PT Diesel 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010 0.022 0.017 0.015 0.021 0.040 0.018

PT Gas 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990 0.978 0.983 0.985 0.979 0.960 0.982

PT Diesel 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010 0.022 0.017 0.015 0.021 0.040 0.018

LCT Gas 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939 0.919 0.900 0.852 0.795 0.780 0.929

LCT Diesel 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061 0.081 0.100 0.148 0.205 0.220 0.071

LCT Gas 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939 0.919 0.900 0.852 0.795 0.780 0.929

LCT Diesel 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061 0.081 0.100 0.148 0.205 0.220 0.071

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250 0.266 0.327 0.485 0.615 0.676 0.674 0.736

SBus Diesel 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750 0.734 0.673 0.515 0.385 0.324 0.326 0.264

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 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658 0.719 0.768 0.767 0.825 0.773 0.847 0.976

SUShT Diesel 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342 0.281 0.232 0.233 0.175 0.227 0.153 0.024

SULhT Gas 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658 0.719 0.768 0.767 0.825 0.773 0.847 0.976

SULhT Diesel 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342 0.281 0.232 0.233 0.175 0.227 0.153 0.024

MH Gas 0.790 0.820 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 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.306 0.112 0.123 0.164 0.161 0.153 0.124 0.170 0.148 0.250 0.239 0.284 0.384 0.311 0.626

CShT Diesel 0.694 0.888 0.877 0.836 0.839 0.847 0.876 0.830 0.852 0.750 0.761 0.716 0.616 0.689 0.374

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

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### Texas Statewide 201 4 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 1.000 1.000

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 0.000 0.000

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987 0.987 0.987 0.977 0.977

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013 0.013 0.013 0.023 0.023

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987 0.987 0.987 0.977 0.977

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013 0.013 0.013 0.023 0.023

LCT Gas 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867 0.843 0.827 0.877 0.833

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133 0.157 0.173 0.123 0.167

LCT Gas 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867 0.843 0.827 0.877 0.833

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133 0.157 0.173 0.123 0.167

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250 0.266 0.312 0.348 0.359

SUShT Diesel 0.789 0.789 0.789 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750 0.734 0.688 0.652 0.641

SULhT Gas 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250 0.266 0.312 0.348 0.359

SULhT Diesel 0.789 0.789 0.789 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750 0.734 0.688 0.652 0.641

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0.660 0.680

MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.490 0.470 0.460 0.440 0.430 0.410 0.400 0.370 0.340 0.320

CShT Gas 0.046 0.046 0.046 0.046 0.071 0.047 0.053 0.026 0.059 0.048 0.050 0.050 0.078 0.077 0.083 0.102

CShT Diesel 0.954 0.954 0.954 0.954 0.929 0.953 0.947 0.974 0.941 0.952 0.950 0.950 0.922 0.923 0.917 0.898

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional i nternal c ombustion engine technology only.191 

### Texas Statewide 201 4 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 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.988 0.981

PC Diesel 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 0.012 0.019

PT Gas 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990 0.978 0.983 0.985

PT Diesel 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010 0.022 0.017 0.015

PT Gas 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995 0.990 0.978 0.983 0.985

PT Diesel 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005 0.010 0.022 0.017 0.015

LCT Gas 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939 0.919 0.900 0.852

LCT Diesel 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061 0.081 0.100 0.148

LCT Gas 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923 0.939 0.919 0.900 0.852

LCT Diesel 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077 0.061 0.081 0.100 0.148

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250 0.266 0.327 0.485 0.615

SBus Diesel 0.958 0.958 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750 0.734 0.673 0.515 0.385

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 0.426 0.423 0.435 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658 0.719 0.768 0.767 0.825

SUShT Diesel 0.574 0.577 0.565 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342 0.281 0.232 0.233 0.175

SULhT Gas 0.426 0.423 0.435 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658 0.719 0.768 0.767 0.825

SULhT Diesel 0.574 0.577 0.565 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342 0.281 0.232 0.233 0.175

MH Gas 0.710 0.740 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.290 0.260 0.230 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.131 0.152 0.146 0.306 0.112 0.123 0.164 0.161 0.153 0.124 0.170 0.148 0.250 0.239 0.284

CShT Diesel 0.869 0.848 0.854 0.694 0.888 0.877 0.836 0.839 0.847 0.876 0.830 0.852 0.750 0.761 0.716

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 i nternal c ombustion engine technology only.



### Texas Statewide 201 7 Fuel / Engine Fractions Summary 1192 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 0.987 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 0.013 0.013 0.013 0.013

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.987 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 0.013 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 0.913 0.921 0.891 0.819 0.867 0.843

LCT Diesel 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 0.157

LCT Gas 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 0.843

LCT Diesel 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 0.157

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250 0.266

SUShT Diesel 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 0.734

SULhT Gas 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 0.266

SULhT Diesel 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 0.734

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600

MH Diesel 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 0.400

CShT Gas 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 0.078

CShT Diesel 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 0.922

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional i nternal c ombustion engine technology only.193 

### Texas Statewide 201 7 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 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 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

PT Gas 0.987 0.977 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 0.023 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 0.977 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 0.023 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 0.827 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 0.173 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 0.827 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 0.173 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

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.114 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 0.886 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

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 0.312 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 0.719

SUShT Diesel 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 0.281

SULhT Gas 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 0.719

SULhT Diesel 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 0.281

MH Gas 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 0.370 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 0.077 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 0.148

CShT Diesel 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 0.852

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 i nternal c ombustion engine technology only.



### Texas Statewide 201 8 Fuel / Engine Fractions Summary 1194 SUT Fuel

Type Model Year

2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1900 1989 1988 1987

PC Gas 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996 0.996

PC Diesel 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.987 0.987 0.987

PT Diesel 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.013 0.013 0.013

LCT Gas 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133

LCT Gas 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819 0.867

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181 0.133

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250

SUShT Diesel 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750

SULhT Gas 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229 0.250

SULhT Diesel 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.779 0.693 0.744 0.762 0.781 0.789 0.771 0.750

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590

MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.490 0.470 0.460 0.440 0.430 0.410

CShT Gas 0.046 0.046 0.046 0.046 0.046 0.046 0.046 0.046 0.071 0.047 0.053 0.026 0.059 0.048 0.050 0.050

CShT Diesel 0.954 0.954 0.954 0.954 0.954 0.954 0.954 0.954 0.929 0.953 0.947 0.974 0.941 0.952 0.950 0.950

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional i nternal c ombustion engine technology only.195 

### Texas Statewide 201 8 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 0.995

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005

PT Gas 0.987 0.987 0.977 0.977 0.990 0.981 0.980 0.985 0.982 0.979 0.979 0.980 0.987 0.992 0.995

PT Diesel 0.013 0.013 0.023 0.023 0.010 0.019 0.020 0.015 0.018 0.021 0.021 0.020 0.013 0.008 0.005

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077

LCT Gas 0.843 0.827 0.877 0.833 0.962 0.864 0.911 0.881 0.898 0.915 0.930 0.932 0.916 0.907 0.923

LCT Diesel 0.157 0.173 0.123 0.167 0.038 0.136 0.089 0.119 0.102 0.085 0.070 0.068 0.084 0.093 0.077

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.266 0.312 0.348 0.359 0.426 0.423 0.435 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658

SUShT Diesel 0.734 0.688 0.652 0.641 0.574 0.577 0.565 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342

SULhT Gas 0.266 0.312 0.348 0.359 0.426 0.423 0.435 0.674 0.516 0.523 0.515 0.497 0.530 0.540 0.658

SULhT Diesel 0.734 0.688 0.652 0.641 0.574 0.577 0.565 0.326 0.484 0.477 0.485 0.503 0.470 0.460 0.342

MH Gas 0.600 0.630 0.660 0.680 0.710 0.740 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850

MH Diesel 0.400 0.370 0.340 0.320 0.290 0.260 0.230 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150

CShT Gas 0.078 0.077 0.083 0.102 0.131 0.152 0.146 0.306 0.112 0.123 0.164 0.161 0.153 0.124 0.170

CShT Diesel 0.922 0.923 0.917 0.898 0.869 0.848 0.854 0.694 0.888 0.877 0.836 0.839 0.847 0.876 0.830

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional i nternal c ombustion engine technology only.



### Texas Statewide 201 9 Fuel / Engine Fractions Summary 1196 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 0.977 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 0.023 0.013 0.013

PT Gas 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 0.977 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 0.023 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 0.895 0.913 0.921 0.891 0.819

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181

LCT Gas 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.895 0.913 0.921 0.891 0.819

LCT Diesel 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.087 0.079 0.109 0.181

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042

SBus Diesel 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958 0.958

RT Gas 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

RT Diesel 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960 0.960

SUShT Gas 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.211 0.221 0.307 0.256 0.238 0.219 0.211 0.229

SUShT Diesel 0.789 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

SULhT Gas 0.211 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

SULhT Diesel 0.789 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

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570

MH Diesel 0.500 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

CShT Gas 0.046 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

CShT Diesel 0.954 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

CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1 Conventional i nternal c ombustion engine technology only.197 

### Texas Statewide 201 9 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 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

PC Diesel 0.004 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

PT Gas 0.987 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

PT Diesel 0.013 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

PT Gas 0.987 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

PT Diesel 0.013 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

LCT Gas 0.867 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

LCT Diesel 0.133 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

LCT Gas 0.867 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

LCT Diesel 0.133 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

IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SBus Gas 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229

SBus Diesel 0.958 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

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 0.250 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

SUShT Diesel 0.750 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

SULhT Gas 0.250 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

SULhT Diesel 0.750 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

MH Gas 0.590 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

MH Diesel 0.410 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

CShT Gas 0.050 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

CShT Diesel 0.950 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

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 i nternal c ombustion engine technology only.198 

## APPENDIX I:

MOVES RUN SUMMARIES

### Appendix I Available Upon Request

Mary McGarry

- Barber

Emissions Assessment Section

mary.mcgarry

- barber@tceq.texas.gov

512.239.1987