ATTACHMENT E 

DEVELOPMENT OF HGB ON-ROAD EMISSIONS INVENTORIES FOR THE YEARS 

2012, 2014, 2017, 2020, 2023, 2026, AND 2028
ON-ROAD MOBILE SOURCE EMISSIONS INVENTORIES

FOR THE HGB 1997 EIGHT-HOUR OZONE 

NONATTAINMENT COUNTIES: 2012, 2014, 2017, 2020, 

2023, 2026, AND 2028. 

This appendix documents the development of the on-road mobile emissions inventory (EI) for 

the Houston-Galveston-Brazoria (HGB) 1997 Eight Hour Ozone Nonattainment Area Redesignation Substitution (RS) State Implementation Plan Revision. 

The development of the RS EIs was done by the Texas Transportation Institute (TTI) at the 

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

The on-road mobile source EIs and control strategy reduction estimates reflect the most recent 

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

development and resulting EI is provided in the attached document, On-Road Mobile Source 

Emissions Inventories for the HGB 1997 Eight-Hour Ozone Nonattainment Counties: 2012, 

2014, 2017, 2020, 2023, 2026, and 2028. The final emissions estimates are summarized in the 

“Summary of Results” section, Table 1, HGB Eight-County Area Re-Designation Inventories–

Summer Weekday (Tons per Day). The supporting electronic documents for the EI 

development, including MOVES2014 input and output files and the post processing 

spreadsheets used to summarize the inventories are available upon request in electronic format. 

Please contact the TCEQ, Air Quality Division, Area and Mobile Source Inventory and Data 

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

The report also documents the development of year-to-year control strategy reduction estimates 

for each of the RS milestone years between 2012 and 2028. Control strategy emission reduction 

estimates include the effects of the Federal Motor Vehicle Control Program, the HGB vehicle 

inspection and maintenance program, federal reformulated gasoline Phase 1 and Phase 2, and 

the Texas Low Emission Diesel Program. The control scenarios are the basis for quantifying the 

year-to-year reductions.
March 2015

Prepared by the

On-Road Mobile Source

Emissions Inventories for the 

HGB 1997 Eight-Hour

Ozone Nonattainment Counties:

2012, 2014, 2017, 2020,

2023, 2026, and 2028 

T C EXAS OMMISSION

ON NVIRONMENTAL UALITY E Q

T C EXAS OMMISSION

ON NVIRONMENTAL UALITY E Q

Transportation

Institute

Texas A&M
PRODUCTION OF 2011, 2012, 2014, 2017, 2020, 2023, 2026, AND 2028 ONROAD MOBILE SOURCE EMISSIONS INVENTORIES FOR THE EIGHT 

HOUSTON-GALVESTON AREA COUNTIES PREVIOUSLY 

DESINGATED AS NONATTAINMENT FOR THE ONE-HOUR AND THE 

1997 EIGHT-HOUR OZONE STANDARD 

1997 Eight-Hour Re-Designation and Maintenance: 2012, 2014, 2017, 

2020, 2023, 2026, and 2028 

TECHNICAL REPORT 

FINAL 

Prepared for the 

Texas Commission on Environmental Quality 

Air Quality Planning and Implementation Division 

Prepared by the 

Transportation Modeling Program 

Texas A&M Transportation Institute 

TTI Study No.: 604561-0001 

Study Title: Air Quality Technical Support 

(Umbrella Contract: 582-11-11226 – PGA: 582-15-51346-16) 

March 2015 

TEXAS A&M TRANSPORTATION INSTITUTE 

The Texas A&M University System 

College Station, Texas 77843-3135 
iii 

EXECUTIVE SUMMARY 

This project, performed by the Texas A&M Transportation Institute (TTI) for the Texas 

Commission on Environmental Quality (TCEQ), used the U.S. Environmental Protection 

Agency’s (EPA) most recent Motor Vehicle Emission Simulator (MOVES) model version, 

MOVES2014 October Release,1

 to develop on-road mobile emissions inventories for the eight 

Houston-Galveston-Brazoria (HGB) counties designated nonattainment for the 1997 eight-hour 

ozone standard. TTI produced average summer weekday inventory estimates for 2012, 2014, 

2017, 2020, 2023, 2026, and 2028 needed for the TCEQ’s development of the HGB 1997 EightHour Ozone Re-Designation Substitute report and/or State Implementation Plan (SIP) revision. 

(Additionally under this task, but documented separately, TTI produced inventories for TCEQ’s 

one-hour ozone standard re-designation analysis.) 

 These MOVES2014-based inventories, developed by TTI using its detailed link-based onroad inventory development utilities (recently adapted specifically for use with MOVES2014),2

are very similar to the other set of MOVES2014-based HGB inventories developed as part of this 

project (i.e., for TCEQ’s one-hour ozone standard re-designation analysis).3

 MOVES2014 

October Release (referred to in this report as “MOVES,” unless otherwise stated) incorporates 

significant changes to the emissions rates for each vehicle type. For the 2017-and-later calendar 

years, a substantial difference with MOVES2014 is the incorporation of new federal standards 

for Tier 3 light-duty vehicles and gasoline with a sulfur content of 10 parts per million (ppm). 

The currently available MOVES-based inventories incorporate 30 ppm sulfur gasoline and Tier 2 

light-duty vehicle standards that began with the 2004 model year. 

 To support the HGB area 1997 eight-hour ozone re-designation analysis, base and horizon 

year inventories were required with four inventory years in between. These years provide a basis 

for establishing the inventory when attainment is achieved, the inventory for a horizon year at 

least 10 years after the re-designation, and the inventory trend between attainment and the 

horizon year. Inventory development used the latest planning assumptions and the most recent 

version of the EPA’s emissions factor model for on-road sources. Emissions were estimated for 

volatile organic compounds (VOC), carbon monoxide (CO), oxides of nitrogen (NOx), 

particulate matter (PM) with diameter of 10 microns or less (PM10), PM with diameter of 2.5 

microns or less (PM2.5), atmospheric carbon dioxide (CO2), sulfur dioxide (SO2), and ammonia 

(NH3). The following table presents the HGB area aggregate inventory summary for particular 

pollutants, VMT and average speed. 

 

1

 MOVES2014 October Release is the latest version of MOVES, released in October, 2014. It follows the initial 

July, 2014 release of MOVES2014. 2

 The TTI’s on-road inventory development tools newly updated for use with MOVES2014, and used in this 

analysis, are documented in Update of On-road Inventory Development Methodologies for Compatibility with the 

2014 Version of the Motor Vehicle Emission Simulator Model, TTI, December 2014. 3

 The main difference between these two sets of inventories are analysis years (2011 versus 2012 base years and the 

additional 2028 future year) and the meteorological inputs to MOVES (based on local weather station data from 

the respective base years). 
iv 

HGB Eight-County Area Re-Designation Inventories – 

Summer Weekday (Tons per Day). 

Year VMT Speed1 VOC CO NOx PM10

2 PM2.52 CO2

2012 151,333,456 38.7 74.51 852.69 159.08 12.03 5.59 81,856.99

2014 144,546,685 39.2 61.48 728.00 124.64 10.43 4.48 76,652.90

2017 156,018,423 39.1 47.36 635.74 82.96 9.76 3.44 79,269.97

2020 167,051,752 38.9 40.38 587.43 61.06 9.71 2.93 79,752.34

2023 178,917,689 38.8 36.12 542.46 48.94 9.76 2.53 79,002.36

2026 190,707,272 38.5 31.71 474.28 40.24 10.13 2.37 77,660.33

2028 197,675,678 38.4 28.99 430.65 37.04 10.38 2.29 76,817.46

1

 System speed in miles-per-hour. 

2

 Within each size category, the PM estimates are the aggregate of the MOVES direct vehicle PM emissions 

(primary PM total exhaust plus brake wear and tire wear), i.e., re-suspended dust is not included. 

 The hourly, MOVES rates-per-activity, detailed traffic demand model (TDM) link-based 

inventory method was used (consistent with previously-developed HGB area MOVES2010bbased inventories4

), with the latest available data, models, and procedures. In general, MOVES 

emissions rates reflecting local conditions were produced and combined with local activity 

factors outside of the MOVES model to produce the emissions estimates externally. The 

inventory results were estimated by MOVES source use type (SUT) and fuel type combination 

(i.e., vehicle type) and TDM roadway class, for the gasoline and diesel-powered vehicle fleet 

(with alternative power sources treated as de minimis). Data sets extracted from the latest, 

regional HGB travel models (extracted and provided by the Houston-Galveston Area Council), 

were used to estimate hourly, directional, link (roadway segment)-level vehicle miles of travel 

(VMT) and operational speeds for the roadway-based emissions calculations. Using vehicle 

operating hours (VHT) estimates derived from the link data, local vehicle registrations, MOVES 

defaults, and other data, the hourly off-network activity factors were estimated for the offnetwork emissions calculations. The off-network activity factors are: source-hours-parked 

(SHP); starts; and for combination long-haul trucks only, hotelling hours, comprised of extended 

idling hours (SHI) and auxiliary power unit (APU) hours. The emissions rates were produced 

reflecting local conditions and multiplied by the associated local activity factors in the external 

emissions calculations. The emissions rates (e.g., grams/mile, grams/shp, grams/start, grams/shi, 

and grams/APU hour) were all available from the MOVES output, except grams/shp, estimated 

by post-processing particular MOVES input and output data. Texas Low Emissions Diesel 

(TxLED) fuel effects were incorporated in the MOVES emissions rates via post-processing. 

 

4 MOVES2010b-Based On-Road Inventories in Support of the HGB Ozone Nonattainment Area Re-Designation 

Substitute Analysis, TTI, June 2014. 
v 

 The inventories were produced using utilities5 developed by TTI to process on-road vehicle 

activity, off-network vehicle activity, and MOVES emissions rate data into spatially and 

temporally detailed emissions estimates. The EPA’s Technical Guidance6

 is the primary 

technical reference used for guidance on appropriate inputs and use of MOVES. 

 

5

 Updated for MOVES2014 from the MOVES2010b-based utilities as documented in TTI Emissions Inventory 

Estimation Utilities Using MOVES: MOVES2010bUtl User’s Guide, TTI, August 2013. 6 Using MOVES to Prepare Emission Inventories in State Implementation Plans and Transportation Conformity: 

Technical Guidance for MOVES2010, 2010a, and 2010b, EPA, April 2012. (Note that EPA released new 

guidance in January 2015, which was too late for use in this analysis.) 

vii 

TABLE OF CONTENTS 

List of Tables ................................................................................................................................. ix

Purpose .............................................................................................................................................1

Background ......................................................................................................................................1

Production of 2011, 2012, 2014, 2017, 2020, 2023, 2026 and 2028 On-Road Mobile Source 

Emissions Inventories for the Eight Houston-Galveston Area Counties Previously 

Designated as Nonattainment for the One-Hour and the 1997 Eight-Hour Ozone Standard ....1

Acknowledgments .......................................................................................................................4

Summary of Results .........................................................................................................................5

Overview of Methodology ...............................................................................................................6

Major Components ......................................................................................................................9

VMT Mix ...................................................................................................................................10

On-Road Fleet Link-VMT and Speeds ......................................................................................10

Vehicle Population and Off-Network Vehicle Activity Estimates ............................................10

MOVES Emissions Factors .......................................................................................................11

Emissions Calculations ..............................................................................................................12

Development of Vehicle Type VMT Mix .....................................................................................13

Estimation of VMT ........................................................................................................................15

Data Sources ..............................................................................................................................15

VMT Adjustments .....................................................................................................................15

2012 Historical Year Analysis – VMT Control Totals and VMT Adjustments ....................16

Future Year Analyses – HPMS Adjustment Factor ...............................................................17

Future Year Analyses – Seasonal Adjustment Factors ..........................................................18

Future Year Analyses – Intermediate Year Adjustment Factors ...........................................18

Future Year Analyses – VMT Summary ...............................................................................19

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

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

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

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

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

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

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

Vehicle Type Total Available Hours .....................................................................................26

Vehicle Type SHO .................................................................................................................26

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

Estimation of SHI and APU Hours ............................................................................................27

Hotelling Activity Scaling Factors.........................................................................................27

Hotelling Activity Hourly Factors .........................................................................................28

County-Level CLhT_Diesel Hotelling Activity by Hour Estimation ....................................28

County-Level CLhT_Diesel SHI and APU Hours Estimation ..............................................28

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

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

Summary of Control Programs Modeled ...................................................................................30

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

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

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

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

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

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

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

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

User Inputs – Locality Specific Inputs and Defaults Used ....................................................37

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

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

Activity and Vehicle Population Inputs to MOVES ..........................................................37

Age Distributions and Fuel Engine Fractions Inputs to MOVES ......................................37

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

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

Local I/M Inputs to MOVES .............................................................................................45

Checks and Runs ........................................................................................................................49

Post-Processing Runs .................................................................................................................49

NOx Adjustment for TxLED Effects .........................................................................................49

Emissions Calculations ..................................................................................................................50

Hourly Link-Based Emissions Calculations ..............................................................................51

Conversion of Emissions Inventories to XML Format ..............................................................54

Additional CDBs for MOVES Inventory Mode ........................................................................55

Quality Assurance ..........................................................................................................................57

A. Project Management .............................................................................................................57

B. Data Generation and Acquisition ..........................................................................................58

C. Assessment and Oversight ....................................................................................................59

D. Data Validation and Usability ...............................................................................................60

References ......................................................................................................................................62

Appendix A: Electronic Data Submittal ........................................................................................65

Appendix B: Emissions Estimation Utilities for MOVES-Based Emissions Inventories .............73

TTI Emissions Estimation Utilities for MOVES2014-Based Emissions Inventories ...................75

TRANSVMT .............................................................................................................................75

VirtualLinkVMT ........................................................................................................................75

VehPopulationBuild ...................................................................................................................76

OffNetActCalc ...........................................................................................................................78

MOVESactivityInputBuild ........................................................................................................80

RatesCalc ...................................................................................................................................85

RatesAdj .....................................................................................................................................87

EmsCalc .....................................................................................................................................88

Emissions Factor Interpolation Methodology ........................................................................89

Appendix C TxDOT District VMT Mix by Day of Week .............................................................93

Appendix D: TxDOT District Aggregate Weekday VMT Mix ...................................................107

Appendix E: Annually Compounded Growth Rates and Intermediate Year Adjustment 

Factors ....................................................................................................................................113

Appendix F: Capacity Factors, Speed Factors, and Speed Reduction Factors ............................125

Appendix G: Vehicle Population Estimates and 24-Hour SHP, Starts, and SHI Summaries .....137

Appendix H: Source Type Age and Fuel Engine Fractions Inputs to MOVES ...........................163

Appendix I: MOVES Run Summaries .........................................................................................195
ix 

LIST OF TABLES 

Table 1. HGB Eight-County Area Re-Designation Inventories – Summer Weekday (Tons 

per Day). ....................................................................................................................................5

Table 2. MOVES Source Use Type/Fuel Types (Vehicle Types). ..................................................7

Table 3. MOVES Model On-Road Fleet Emissions Processes. ......................................................8

Table 4. Emissions Rates by Process and Activity Factor. ..............................................................9

Table 5. VMT Mix Year/Analysis Year Correlations. ..................................................................14

Table 6. Analysis Year/TDM Year Designation. ...........................................................................16

Table 7. HGB AADT-to-Summer Weekday Factors for Control Total Development. .................16

Table 8. HGB 2012 Weekday VMT Control Totals and VMT Adjustment Factors. ....................17

Table 9. HGB Weekday Seasonal Adjustment Factors for Future Year Analyses. .......................18

Table 10. HGB 2014, 2017, and 2020 VMT Summary. ................................................................19

Table 11. HGB 2023 and 2026 VMT Summary. ...........................................................................20

Table 12. Weekday Time Period Hourly Travel Factors. ..............................................................21

Table 13. Registration Data Categories. ........................................................................................24

Table 14. TxDMV Vehicle Registration Aggregations and Associated Vehicle Types for 

Estimating Vehicle Populations. ..............................................................................................25

Table 15. Emissions Rates by Process and Activity Factor. ..........................................................29

Table 16. Emissions Control Strategies and Modeling Approaches. .............................................31

Table 17. RunSpec Selections by MOVES GUI Navigation Panel. ..............................................33

Table 18. CDB Input Tables. .........................................................................................................36

Table 19. Sources and Aggregations for SUT Age Distributions and Fuel Engine 

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

Table 20. Meteorological Inputs to MOVES. ................................................................................40

Table 21. MOVES (RFG) Gasoline Inputs – HGB Summer Emissions Rates Analysis. .............44

Table 22. MOVES IMCoverage Table Input Descriptions for HGB I/M Counties. .....................48

Table 23. Analysis Year TxLED Adjustment Factors Summary. .................................................50

Table 24. H-GAC TDM Road Type/Area Type to MOVES Road Type Designations. ...............53

Table 25. MOVES Inventory Mode CDB Data Sources. ..............................................................56

Table 26. Registration Categories. .................................................................................................77

Table 27. SUT/Registration Category Correlation. .......................................................................78

Table 28. SUTs/Registration Categories Correlation for SUT Age Distribution. .........................84

Table 29. SUTs/Registration Categories Correlation for Fuel/Engine Fractions. .........................84

Table 30. MOVES2014 Emissions Process and Corresponding Activity for Rate-perActivity Emissions Rates. ........................................................................................................86

1 

PURPOSE 

This project developed and produced Motor Vehicle Emission Simulator (MOVES) 2014-based, 

on-road mobile, link-based emissions inventories for the eight Houston-Galveston-Brazoria 

(HGB) counties, for analysis years 2011, 2012, 2014, 2017, 2020, 2023, 2026 and 2028. These 

emissions inventories were needed to support re-designation and maintenance analyses for the 

eight HGB ozone nonattainment area counties required for the development of the one-hour and 

1997 eight-hour HGB Re-Designation Substitute reports and/or state implementation plans 

(SIPs) revisions. 

 As a two-part project, two individual sets of inventories were developed and documented 

separately, one set for the one-hour re-designation substitute (RS) SIP analysis and one for the 

1997 eight-hour ozone SIP analysis. This document provides the details on development of the 

inventories to support the 1997 eight-hour ozone SIP revision. The inventory years for the 1997-

eight-hour SIP revision are 2012, 2014, 2017, 2020, 2023, 2026, and 2028. 

BACKGROUND 

The Texas Commission on Environmental Quality (TCEQ) works with local planning districts, 

the Texas Department of Transportation (TxDOT) and the Texas A&M Transportation Institute 

(TTI) to provide on-road mobile source emissions inventories of air pollutants. TxDOT typically 

funds transportation conformity determinations required under 40 Code of Federal Regulations 

Part 93. TCEQ funds mobile source inventory work in support of Federal Clean Air Act (CAA) 

requirements, such as attainment of the National Ambient Air Quality Standards (NAAQS), and 

the study and control of hazardous air pollutants, including those from motor vehicles and/or 

motor vehicle fuels (as mandated under CAA sections 202 and 211). 

 To support the development of the one-hour and 1997 eight-hour ozone re-designation 

substitution reports and/or SIP revisions for the HGB area, inventories needed to be developed 

for the one-hour and 1997 eight-hour base years, the horizon years, and four years between the 

base and horizon years. These inventories provide a basis for establishing: the inventory when 

attainment is achieved; the inventory for a horizon year at least 10 years after the re-designation; 

and the inventory trend between attainment and the horizon year. The inventory development 

included use of: the latest available fleet characterization data; the latest available transportation 

planning information; and the most recent version of the U.S. Environmental Protection 

Agency’s (EPA) emissions factor model for on-road sources. 

PRODUCTION OF 2011, 2012, 2014, 2017, 2020, 2023, 2026 AND 2028 ON-ROAD 

MOBILE SOURCE EMISSIONS INVENTORIES FOR THE EIGHT HOUSTONGALVESTON AREA COUNTIES PREVIOUSLY DESIGNATED AS 

NONATTAINMENT FOR THE ONE-HOUR AND THE 1997 EIGHT-HOUR OZONE 

STANDARD 

TTI produced hourly, link-based on-road mobile emissions inventory estimates for the eight 

Houston-Galveston counties previously designated as nonattainment for the one-hour ozone 

standard, and designated as nonattainment for the 1997 eight-hour ozone standard, which 

included the combined analysis years 2011, 2012, 2014, 2017, 2020, 2023, 2026, and 2028. For 
2 

the inventories to be consistent with inventory development for other SIP analyses, TTI used the 

most recent activity information, based on current travel demand modeling, and the newest 

version of the EPA’s on-road emissions model. The inventories were produced based on 

methods agreed upon in consultation with the TCEQ Project Representative. The methods used 

to produce the inventories were consistent with EPA guidance on the production of emissions 

inventories. The various inventory production parameters described below were used directly, or 

estimated as noted. TTI adhered to the following. 

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

recent version of EPA’s on-road emissions model, MOVES2014 October Release. 

 Emissions inventories were developed for the eight HGB counties previously designated 

as nonattainment for the one-hour and 1997 eight-hour ozone standards. The counties 

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

counties. 

 The analysis years for the one-hour re-designation and maintenance included: 2011, 

2014, 2017, 2020, 2023, and 2026. 

 The analysis years for the 1997 eight-hour re-designation and maintenance included: 

2012, 2014, 2017, 2020, 2023, 2026, and 2028. 

 The pollutants included to complete this task were volatile organic compounds (VOC), 

carbon monoxide (CO), oxides of nitrogen (NOx), carbon dioxide (CO2), sulfur dioxide 

(SO2), ammonia (NH3), particulate matter (PM) with aerodynamic diameters equal to or 

less than 10 microns (PM10), and PM with aerodynamic diameters equal to or less than 

2.5 microns (PM2.5). 

 The day type for all the inventories was average summer weekday. Activity levels were 

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

 The temperatures were average summer 2011 temperatures for the one-hour analysis 

years and average summer 2012 temperatures for the 1997 eight-hour analysis years. The 

temperature information was provided by TCEQ. 

 The humidity was average summer 2011 humidity for the one-hour analysis years and 

average summer 2012 humidity for the 1997 eight-hour analysis years. The humidity 

information was provided by TCEQ. 

 The barometric pressure was average summer 2011 barometric pressure for the one-hour 

analysis years and average summer 2012 barometric pressure for the 1997 eight-hour 

analysis years. The barometric pressure information was provided by TCEQ. 

 The vehicle miles traveled (VMT) mixes was consistent with the EPA MOVES source 

use types (SUTs). The most current VMT mix for the HGB area was used. 

 TTI used vehicle registration distributions developed from the Texas Department of 

Motor Vehicles (TxDMV) registration data as input for locality specific MOVES age 

distributions. For historical years, TTI used registration data for each historical year. For 

future analysis years, TTI used the most recent year vehicle registration distributions. 

 TTI used a link-based, time-of-day emissions analysis methodology for all of the 

referenced counties. 
3 

 For the off-network activity development process, TTI used historical year data for 

historical years. For future years the most recent data available adjusted as appropriate 

for future years was used. The development of the off-network activity inputs was based 

on current Texas on-road inventory development processes and was documented in the 

pre-analysis plan. 

 TTI developed control program parameters, including Reid Vapor Pressure (RVP) and 

fuel settings based upon HGB control strategies in effect for each analysis year. 

 TTI used year-specific Texas Low Emissions Diesel (TxLED) adjustment factors 

developed using the benefit information described in the EPA Memorandum on Texas 

Low Emission Diesel Fuel Benefits, and the method as documented in previous Texas onroad inventory development reports. 

 Two reports were written and provided to TCEQ: this report which documents development 

of the 2012, 2014, 2017, 2020, 2023, 2026, and 2028 emissions estimates for the eight HGB 

counties designated as nonattainment for the 1997 eight-hour ozone standard; and a second 

report documenting development of the 2011, 2014, 2017, 2020, 2023, and 2026 emissions 

estimates for the eight HGB counties previously designated as nonattainment for the one-hour 

ozone standard. 

 The following 1997 eight-hour ozone standard inventory analysis activities were completed. 

 Prepared county-level hourly and 24-hour day tables that provide SUT and fuel type 

combination (i.e., vehicle type) and roadway summaries of VMT, vehicle hours traveled 

(VHT), average speed, source hours parked (SHP), vehicle starts, extended idle hours 

(SHI), auxiliary power unit (APU) hours, and totals for the pollutants of VOC, CO, NOx, 

CO2, SO2, NH3, Primary Exhaust PM10 – Total, Primary Exhaust PM2.5 – Total (plus 

PM2.5 primary exhaust species: composite non-elemental carbon PM [NonECPM], 

elemental carbon [EC], organic carbon [OC], and sulfate [SO4]), and PM10 and PM2.5

brakewear and tirewear, by associated emissions processes. These files are tab-delimited 

for ease of loading into spreadsheet software such as Microsoft® Excel. 

 Prepared the inventory summaries in the format for uploading to TCEQ’s Texas Air 

Emissions Repository (TexAER), which is consistent with the EPA’s National Emissions 

Inventory (NEI) Format (the Consolidated Emissions Reporting Schema [CERS] written 

in Extensible Markup Language [XML]). In consultation with TCEQ, TTI used the 

EPA’s new source classification codes (SCCs) that are compatible with MOVES fuel 

types, SUTs, road types and emissions processes, and are available in the MOVES 

database. Two formats of the SCC-based inventory summaries were prepared, the NEI 

CERS XML format, and the tab-delimited text file format. 

 Prepared a set of MOVES county database files (CDBs) with county-specific tables 

sufficient for the CDBs to be used in MOVES inventory mode to produce results 

consistent with, though not necessarily identical to, the results produced using MOVES 

rates output and the TTI post-processing inventory development utility. 

 Prepared this documentation, complete and self-contained, including electronic data files 

and detailed descriptions thereof. 
4 

 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, and includes: a document listing all the files submitted and the file 

naming conventions; MOVES CDB files, MOVES run spec files, and MySQL files used to 

process data files for MOVES runs; all pertinent data relating to task activities; two standard sets 

of activity and inventory summary files: one with the typical labeling (e.g., MOVES SUT, fuel 

type, TDM road type), and one using the EPA’s new SCCs (which are now consistent with the 

MOVES SUTs); and TexAER-ready formatted inventory files. 

Acknowledgments 

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

contributed to the development of the MOVES link-based emissions estimates. HoustonGalveston Area Council (H-GAC) staff provided data sets extracted from the latest available 

HGB regional Travel Demand Models (TDMs). Daniel Perry, of TCEQ, provided 

meteorological input data. Dr. Perkinson produced the VMT mixes used to divide fleet VMT 

activity into MOVES SUT by fuel type categories, county VMT control totals, and hourly VMT 

factors. White processed roadway based activity (VMT and speeds) and produced the vehicle 

population estimates. Boardman produced MOVES model and MOVES output post-processor 

set-ups, and the MOVES-based emissions factors with adjustments for TxLED fuel. Schrank 

produced the off-network activity estimates and the emissions run set-ups and performed the 

emissions runs. White produced the extra set of MOVES CDBs that may be used with MOVES 

in inventory mode. Gary Lobaugh, of TTI, was responsible for editing, design, and production 

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

assurance of the inventory elements. Dr. Perkinson was the principle investigator for this 

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

the TCEQ project technical manager. 

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

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

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

Quality Assurance, and References. 
5 

SUMMARY OF RESULTS 

Table 1 summarizes the resulting emissions inventories estimated for the HGB region. 

Table 1. HGB Eight-County Area Re-Designation Inventories – 

Summer Weekday (Tons per Day). 

Year VMT Speed1 VOC CO NOx PM10

2 PM2.52 CO2

2012 151,333,456 38.7 74.51 852.69 159.08 12.03 5.59 81,856.99

2014 144,546,685 39.2 61.48 728.00 124.64 10.43 4.48 76,652.90

2017 156,018,423 39.1 47.36 635.74 82.96 9.76 3.44 79,269.97

2020 167,051,752 38.9 40.38 587.43 61.06 9.71 2.93 79,752.34

2023 178,917,689 38.8 36.12 542.46 48.94 9.76 2.53 79,002.36

2026 190,707,272 38.5 31.71 474.28 40.24 10.13 2.37 77,660.33

2028 197,675,678 38.4 28.99 430.65 37.04 10.38 2.29 76,817.46

1

 System speed in miles-per-hour. 

2

 Within each size category, the PM estimates are the aggregate MOVES direct vehicle PM emissions (primary PM 

total exhaust plus brake wear and tire wear), i.e., re-suspended dust is not included. 

 Two methods are used to establish VMT, a federal system — the Highway Performance 

Monitoring System (HPMS) — and locality-specific TDMs. The HPMS is a federally-mandated 

program used by the Federal Highway Administration (FHWA) to provide data to Congress 

concerning the nation’s streets and highways. Congress uses the data to allocate funds to the 

states. Every state collects, maintains, and reports specified data to the FHWA each year 

according to the methods prescribed in the Highway Performance Monitoring System Field 

Manual. For historical years, the HPMS data constitutes the official measurement of highway 

performance, including VMT. The TDM estimate of VMT distributes VMT to each roadway 

link within the local travel network, and, for future years, includes planned changes to the 

network. The TDM represents the best method for distributing VMT to the roadway links within 

the local travel network and for predicting future year VMT. To provide consistency between 

the estimates for the two systems, the TDM VMT estimates are adjusted to HMPS levels using 

an historical year for which the TDM was validated and for which the HPMS data has been made 

available. The TDM forecasts VMT growth from the TDM validation year. 

 As noted in the following, for 2012, 2014, 2017, 2020, 2023, 2026, and 2028, TDM VMT 

was developed, and adjusted for HPMS consistency, seasonality (i.e., summer), and day type 

(i.e., weekday). However, HPMS data exist for 2012, making it by definition a “historical” year. 

For years where HPMS data exist, the use of “historical” (i.e., actual) VMT data is mandated and 

supersedes the use of travel model estimated VMT. As a result of this procedural requirement, 

any difference between a series of TDM VMT forecasts provided by the travel modeling process 

and actual observed HPMS VMT is evident in the VMT for the historically-based years (in this 

case, 2012). In other words, the “historical” year is the intersection of observed conditions and 

the travel demand modeling process. The HPMS VMT for historical years may be inconsistent 
6 

with projected TDM VMT trends. Despite inconsistency, 2012 HPMS VMT is mandated and 

TDM forecast VMT is mandated for all the future years. 

 Inventory estimates with more detail (e.g., by county, SUT/fuel type, road type) may be 

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

OVERVIEW OF METHODOLOGY 

TTI used the established MOVES-based, detailed emissions inventory methodology, used in 

fiscal years 2012 through 2014 to produce on-road mobile inventories in support of the HGB 

one-hour ozone standard RS report analysis,7 the HGB reasonable further progress SIP8 and its 

VMT offset analysis component,9 and as originally used to develop baseline and base case 

modeling inventories in support of HGB attainment demonstration analyses.10

 The method is the detailed, hourly, MOVES rates-per-activity, TDM link-based method, 

which produces hourly emissions estimates by vehicle type (Table 2), pollutant, and emissions 

process (Table 3) for each county inventory. It is an adaptation of the previous TDM link-based 

emissions inventory method used with MOBILE6, which applied emissions rates for all 

emissions processes in terms of miles-traveled activity (e.g., grams/mile). 

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

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

and APU hours). Associated emissions rates must be produced in these terms for the offnetwork emissions process calculations. Previous versions of MOVES provided the off-network 

start, evaporative and extended idling rates only in “per vehicle” units, not applicable to the TTI 

activity-based inventory process; TTI post-processing utilities were used to produce the MOVES 

off-network rates in the needed activity units. One of the changes with MOVES2014, however, 

was the addition of several new types of emissions rates (i.e., off-network process rates in terms 

of mass per unit of activity). In fact, all the activity-based rates required in the TTI inventory 

process are now directly available from MOVES, except for the SHP-based rates; these are 

produced using TTI inventory utilities, recently updated for use with MOVES2014 (see 

Appendix B for more information on inventory utilities). 

 

7 MOVES2010b-Based On-Road Inventories in Support of the HGB Ozone Nonattainment Area Re-Designation 

Substitute Analysis, TTI, June 2014. 8 HGB MOVES-Based RFP On-Road Inventories and Control Strategy Reductions, TTI, March 2012. 9 VMT-Offset Emissions Inventories for the HGB Eight-Hour Ozone Nonattainment Counties, TTI, January 2013. 10 Development and Production of 2006 Base Case and 2008 Baseline On-Road Mobile Source Emissions 

Inventories for the HGB Nonattainment Area, TTI, July 2011. 
7 

Table 2. MOVES Source Use Type/Fuel Types (Vehicle Types). 

Source Use 

Type ID Source Use Type Description Source Use Type Abbreviation1

11 Motorcycle MC 

21 Passenger Car PC 

31 Passenger Truck PT 

32 Light Commercial Truck LCT 

41 Intercity Bus IBus 

42 Transit Bus TBus 

43 School Bus SBus 

51 Refuse Truck RT 

52 Single Unit Short-Haul Truck SUShT 

53 Single Unit Long-Haul Truck SULhT 

54 Motor Home MH 

61 Combination Short-Haul Truck CShT 

62 Combination Long-Haul Truck CLhT 

1

 The SUT/fuel type, or vehicle type, labels are the combined SUT abbreviation and fuel type names separated by an 

underscore (e.g., MC_Gas, RT_Diesel, and SBus_Gas are motorcycles, diesel-powered refuse trucks, and gasolinepowered school buses, respectively).
8 

Table 3. MOVES Model On-Road Fleet Emissions Processes. 

Process ID Process Name 

1 Running Exhaust 

2 Start Exhaust 

9 Brake Wear 

10 Tire Wear 

11 Evaporative Permeation 

12 Evaporative Fuel Vapor Venting 

13 Evaporative Fuel Leaks 

15 Crankcase Running Exhaust 

16 Crankcase Start Exhaust 

17 Crankcase Extended Idle Exhaust 

181 Refueling Displacement Vapor Loss 

191 Refueling Spillage Loss 

902 Extended Idle Exhaust 

912 Auxiliary Power Unit Exhaust 

1

 These emissions processes apply only to refueling emissions. 

2 These emissions processes are associated only with the diesel long-haul 

combination trucks and are directly related to use of the main engine (i.e., 

extended idling) or a trucks APU during hotelling periods. APU exhaust is new in 

MOVES2014. 
9 

 Table 4 shows emissions factors with associated processes and activity factors used. 

Table 4. Emissions Rates by Process and Activity Factor. 

Emissions Processes Activity1 Emissions Factor2

Running Exhaust 

Crankcase Running Exhaust 

Brake Wear 

Tire Wear 

VMT mass/mile 

Evaporative Permeation 

Evaporative Fuel Vapor Venting 

Evaporative Fuel Leaks 

VMT mass/mile 

SHP mass/shp 

Start Exhaust 

Crankcase Start Exhaust starts mass/start 

Auxiliary Power Exhaust APU hours mass/APU hour 

Extended Idle Exhaust 

Crankcase Extended Idle Exhaust SHI mass/shi 

1

 SHI and APU hours are for Combination Long-Haul Trucks only. 

2

 All of the rates are directly available from MOVES, except mass/shp, which is produced by the TTI 

RatesCalc utility using MOVES rates mode input and output data. 

Major Components 

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

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

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

 District, four-period, time-of-day, vehicle type VMT mix; 

 County, hourly, on-road fleet link VMT and average speeds; 

 County vehicle type populations; 

 County, hourly vehicle type SHP; 

 County, hourly vehicle type starts; 

 County, hourly combination long-haul truck SHI and APU hours; 

 County, hourly vehicle type emissions rates: mass/mile, mass/SHP, mass/start, mass/SHI 

and mass/APU hour; and 

 On-road SCCs from MOVES. 

 The TTI utilities used to develop or process these inventory components are outlined and 

described in Appendix B, which also includes an inventory production process flow diagram. 
10 

VMT Mix 

The VMT mix designates the vehicle types included in the analysis, and specifies the fraction of 

on-road fleet VMT attributable to each vehicle type by MOVES road type. 

 The VMT mixes were estimated based on TTI’s 24-hour average VMT mix method,11

expanded to produce the four-period, time-of-day estimates. The VMT mix method sets Texas 

vehicle registration category aggregations for MOVES SUT categories to be used in the VMT 

mix estimates, as well as for developing other fleet parameter inputs needed in the process (e.g., 

vehicle age distributions). The current VMT mix method produced a set of four time-of-day 

period average vehicle type VMT allocations by MOVES road type, estimated for each TxDOT 

district associated with the eight-county HGB area (i.e., Houston and Beaumont districts). The 

data sources used were recent, multi-year TxDOT vehicle classification counts, year-end 

TxDOT/DMV registration data, and MOVES default data, where needed. 

On-Road Fleet Link-VMT and Speeds 

Summer weekday fleet VMT and average operational speed inputs to the roadway-based 

emissions calculations (product of “mass per mile” emissions factors and VMT) were required. 

 TTI used data sets extracted from the latest, four-period, time-of-day, directional, regional 

HGB travel models (data sets extracted and provided by the H-GAC), along with growth (for 

inventories between TDM years), seasonal adjustment, and hourly allocation factors estimated 

by TTI to estimate the summer weekday hourly, directional, link-VMT (consistent with HPMS 

VMT) and associated average fleet speed inputs to the emissions calculations. The seasonal 

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

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

fleet speeds were estimated corresponding to the link VMT estimates using the Houston speed 

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

congestion-related speed reduction. 

Vehicle Population and Off-Network Vehicle Activity Estimates 

The non-roadway, travel-related, on-road mobile source emissions estimates (e.g., from vehicle 

starts, parked vehicle evaporative processes, hotelling activity consisting of extended idling and 

APU usage) were calculated as the product of the amount of associated activity and the pollutant 

mass per unit of activity (emissions factor terms as shown in Table 4). To estimate the SHP and 

vehicle starts activity, SUT/fuel type category population estimates were needed. Hotelling 

activity estimates (comprising the SHI and APU hours) were based on HGB county-specific 

actual estimates.12

 Vehicle Type Populations: TTI based the vehicle population estimates on vehicle registration 

data, vehicle population factors derived from VMT mix, and VMT-based growth estimates (for 

future years). For historical years, the vehicle type vehicle population estimates were based 

solely on mid-year TxDOT (or TxDMV) county registrations data and regional, all roadsweekday VMT mix-based population factors for the analysis year. For the future years, vehicle 

type populations were estimated as a function of base (e.g., latest available, mid-year) 

 

11 Methodologies for Conversion of Data Sets for MOVES Model Compatibility, TTI, August 2009. 12 Heavy-Duty Vehicle Idle Activity and Emissions Characterization Study, ERG, August 2004. 
11 

registrations, grown to the future value (growth as a function of base and future VMT), and all 

roads-weekday VMT mix-based vehicle type population factors applicable to the analysis year. 

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

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

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

estimates. The VMT mix was applied to the link VMT to produce vehicle type-specific VMT 

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

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

county by year and hour. For the future years, the vehicle 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 vehicle type population estimates. MOVES default weekday starts per vehicle 

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

county vehicle type population estimates. This was performed for each analysis year by county 

and hour. 

 SHI and APU Hours: The SHI and APU hours comprise the diesel combination long-haul 

truck hotelling hours, estimated based on information from the TCEQ Extended Idling Study

(ERG, August 2004), and additional factors developed by TTI. Hotelling activity for a 2004 

base year was derived from the 2004 idle activity study summer weekday extended idling hours 

estimates by Texas county. TTI used summer weekday 24-hour 2004 base hotelling estimates 

derived from this study in combination with 2004 base year and inventory analysis year link 

VMT and VMT mixes to produce county, hourly hotelling activity estimates for each analysis 

year. Hotelling hourly factors (estimated by inverting hourly VMT factors) were then applied to 

allocate the 24-hour hotelling hours estimates for each year to each hour of the day. Estimated 

proportions of SHI and APU hours were used to divide hourly hotelling hours into SHI and APU 

hours activity. 

MOVES Emissions Factors 

TTI produced the emissions rate look-up table inputs to the emissions calculations in three basic 

steps: set up and execute the MOVES emissions rate mode runs; perform the initial postprocessing, which calculates particular rates in the form needed that are not directly available 

from MOVES; and perform the final post-processing to screen out unneeded pollutants and make 

needed adjustments. 

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

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

characteristics, fuel properties, and inspection and maintenance (I/M) program). MOVES county 

scale, emissions rate mode rates were produced by pollutant, process, speed (for roadway-based 

processes), hour, road type, and average SUT/fuel type. MOVES data were post-processed: first 

to produce the emissions rates from emissions and activity input/output (i.e., the mass/SHP form 

of off-network evaporative rates not available from MOVES) and tabulated with the other 

MOVES-produced activity-based rates needed (mass/mile, mass/hour, mass/start) into databases 

of emissions rate look-up tables; and finally to extract the rates from the previous step for only 
12 

those pollutants needed in the emissions calculations, and to make required adjustments (i.e., 

apply estimated TxLED effects on diesel vehicle NOx rates). 

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

each year. Actual, local, scenario-specific activity estimates for each county were then combined 

with the associated emissions rates in the emissions calculations outside of, or external to the 

MOVES model. 

Emissions Calculations 

Average summer weekday emissions were calculated for each county using the previously 

described major inputs: TxDOT district-level time-of-day VMT mix by MOVES road type; 

county, hourly on-road fleet link VMT and speed estimates; county hourly off-network activity 

estimates by vehicle type of SHP, starts, SHI and APU hours (for CLHT diesel only); and 

county-level look-up tables of hourly emissions rates by MOVES road type, speed bin, vehicle 

type (SUT/fuel type) and emissions process. 

 For the VMT-based calculations, county-to-TxDOT district, TDM road type/area type-toMOVES road type, and hour-of-day to time-of-day period designations were used to match the 

appropriate VMT mixes with the link VMT. The VMT mixes by MOVES road type were 

multiplied by the link fleet VMT to distribute each link’s VMT to the different vehicle type 

categories. Emissions rates for each link’s average speed were interpolated (see procedure in 

Appendix B) from the appropriate set of look-up table emissions factors and corresponding index 

speeds (i.e., the average bin speeds of 2.5, 5.0, 10.0, 15.0, ... 75.0 mph), bounding the link’s 

average speed. For link speeds below or above the minimum and maximum average bin speeds 

of 2.5 and 75 mph, the rates for those bounding speeds were used. The estimated vehicle type 

and 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 estimates. This process 

was performed for each county, hour and analysis year. 

 For the off-network emissions calculations, which are county level, the vehicle type 

emissions factors were multiplied by the associated county total activity estimate, as determined 

by the pollutant process. This process was performed for each county, hour, and analysis year. 

 There are two types of tab-delimited output: a standard output and an SCC-based output. 

The standard tab-delimited output file for each county organizes the emissions estimated by 

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

hour period. This tab-delimited file also includes hourly and 24-hour summaries of the offnetwork activity, VMT, VHT, and speed by roadway. The SCC-based output organizes the 24-

hour activity and emissions by the SCCs in MOVES retaining fuel type, source type, road type, 

and emissions process level of detail. The SCC-based inventory summaries were converted to 

NEI CERS XML-type format for uploading to TCEQ’s TexAER. Appendix A contains more 

detailed output definitions and specifications. 

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

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

this analysis. Appendix B describes these applications. 
13 

DEVELOPMENT OF VEHICLE TYPE VMT MIX 

The vehicle type VMT mix is a major input to the MOVES link-based emissions estimation 

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

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

These hourly VMT estimates by vehicle type are combined with the appropriate emissions 

factors in the link-emissions calculations. 

 On-road mobile emissions are dependent upon the VMT assigned to each vehicle category. 

The VMT mix is used to distribute link VMT values to each vehicle category. Since the VMT 

mix can vary by time-of-day (and thus have an effect of the emissions totals), the TTI VMT mix 

procedure allows the option to develop VMT mix by time period. Time period VMT mix (by 

MOVES roadway type and vehicle type) consists of four time periods: morning rush hour (AM 

peak), mid-day, evening rush hour (PM peak), and overnight. 

 TxDOT district-level, time period, day-of-week vehicle type VMT mixes (for gasolinepowered and diesel-powered vehicles) were estimated by the four MOVES road-type categories 

following the TTI methodology.13 This methodology characterizes VMT by vehicle type for a 

region (or district) as follows. 

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

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

 Redefine Roadway Functional Classifications from FHWA/TxDOT to MOVES types — 

A straightforward transposition of FHWA/TxDOT roadway functional classifications in 

the classification count data into the five MOVES road types. 

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

to FHWA C minus .001 for MCs. 

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

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

vehicles using approximate (rounded) MOVES default values. 

 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). 

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

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

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

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

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

homes/recreational vehicles. These are currently assigned a MOVES default value taken 

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

 Define MOVES vehicle categories - Buses — These are assigned MOVES defaults, 

which vary by analysis year. 

 

13 Methodologies for Conversion of Data Sets for MOVES Model Compatibility, TTI, August 2009, and Update of 

On-Road Inventory Development Methodologies for MOVES2010b, TTI, August 2013. 
14 

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

(CSH61 and CLH62) — Separates combination trucks into short-haul and long-haul 

based on local (TxDOT district) registrations versus observed vehicles from the 

classification counts. District allocations verified against statewide allocation. 

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

cars (FHWA C). 

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

MOVES defaults (21, 31, and 32) — 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 

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

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

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

 TxDOT district-level weekday vehicle type VMT mixes by MOVES road-type category 

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

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

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

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

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

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

applied to the analysis years based on Table 5. 

Table 5. VMT Mix Year/Analysis Year Correlations. 

VMT Mix Year Analysis Years 

2005 2003 through 2007 

2010 2008 through 2012 

2015 2013 through 2017 

2020 2018 through 2022 

2025 2023 through 2027 

2030 2028 through 2032 

2035 2033 through 2037 

2040 2038 + 
15 

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 characteristic of a typical summer weekday. The 

TRANSVMT utility (see Appendix B for a description of the utility), the latest available data 

sets from the HGB 2012, 2015, 2017, 2025, and 2035 TDMs, and post-processing factors 

developed from several other data sources, were used to produce this hourly VMT by direction. 

The hourly and 24-hour VMT and VHT summaries by county and road type were provided 

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

Data Sources 

The latest available link data, trips data, and zonal radii data sets extracted from the HGB 2012, 

2015, 2017, 2025, and 2035 TDMs (as provided by H-GAC) 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 data sets. 

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

estimate the summer weekday VMT. The first data source is HPMS VMT estimates, which are 

based on traffic count data collected according to a statistical sampling procedure specified by 

the FHWA designed to estimate VMT. The county total HPMS Annual Average Daily Traffic 

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

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

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

that reported through HPMS.) 

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

locations throughout Texas on a continuous basis. These vehicle counts are available by season, 

month, and weekday, as well as on an annual average daily basis (i.e., AADT). The counts are 

very well suited for making seasonal, day-of-week, and time-of-day comparisons (e.g., seasonal 

adjustment and hourly allocation factors), even though there may be relatively few ATR data 

collection locations in any given area. 

 Multiple years (2004 through 2013) of data from the ATR stations were grouped for this 

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

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

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

counties and the ATR data within the Houston TxDOT District was combined from those 

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

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

the data from the ATR stations within the eight-county region were combined. 

VMT Adjustments 

For each analysis year, the designated TDM VMT (see Table 6) was adjusted for HPMS 

consistency and for seasonality (i.e., summer weekday). For 2012, which by definition is a 

historical year (i.e., HPMS VMT data exists for those years), county-level VMT control totals 

were used to develop VMT adjustment factors, which were applied to the 2012 TDM. For the 
16 

remaining analysis years (2014, 2017, 2020, 2023, 2026, and 2028), 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 2014, 2020, 2023, 2026, and 2028. For 

those analysis years, intermediate year factors were developed using the bounding TDMs (i.e., 

2012 and 2015) and applied to the analysis year’s respective TDM. Hourly travel factors were 

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

Table 6. Analysis Year/TDM Year Designation. 

Analysis Year TDM Year 

2012 2012 

2014 2015 

2017 2017 

2020 2017 

2023 2025 

2026 2025 

2028 2025 

2012 Historical Year Analysis – VMT Control Totals and VMT Adjustments 

To estimate the 2012 HPMS-consistent summer weekday VMT, 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 2012 county-level HPMS AADT VMT and the AADTto-summer weekday adjustment factors. 

 The AADT-to-summer weekday adjustment factors were developed using aggregated ATR 

data for the years 2004 through 2013. Since the HGB area spans two TxDOT districts, two 

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. Table 7 shows the HGB AADT-tosummer weekday factors used in developing the VMT control totals. 

Table 7. HGB AADT-to-Summer Weekday Factors for Control Total Development. 

TxDOT District Weekday Adjustment Factor 

Beaumont1 1.07154 

Houston2 1.06045 

1

 Only used for Liberty and Chambers counties. 

2

 Only used for Harris, Galveston, Fort Bend, Brazoria, Montgomery, and Waller counties. 
17 

The VMT control totals were then developed by multiplying the 2012 HPMS AADT VMT 

for each county by the appropriate summer weekday adjustment factor to produce eight VMT 

control totals (one for each county). To develop the county-level VMT adjustment factors, each 

county’s respective control total was divided by the total VMT (TDM assignment VMT plus 

intrazonal VMT estimate) from the 2012 TDM to produce eight county-level VMT adjustment 

factors. For each link in the TDM, the volume was multiplied by the corresponding VMT 

adjustment factor (based on the county where the link is located). The adjusted link volumes 

were then multiplied by the associated link lengths to produce the 2012 link-level HPMS 

consistent, summer weekday VMT estimates. Table 8 shows the weekday VMT control totals, 

the total TDM VMT, and the VMT adjustment factors for 2012. 

Table 8. HGB 2012 Weekday VMT Control Totals and VMT Adjustment Factors. 

County 

VMT 

Control 

Total 

TDM VMT1

VMT 

Adjustment 

Factor 

Harris 110,390,055 108,085,538 1.021321236 

Brazoria 6,223,667 7,041,215 0.883891104 

Fort Bend 9,780,265 10,813,386 0.904459052 

Waller 2,030,133 1,717,709 1.181883969 

Montgomery 12,052,458 12,214,725 0.986715453 

Liberty 2,271,286 2,287,729 0.992812700 

Chambers 2,706,685 3,014,332 0.897938568 

Galveston 5,878,907 5,506,546 1.067621511 

1

 2012 TDM, including intrazonal VMT. 

Future Year Analyses – HPMS Adjustment Factor 

For the future year analyses, an HPMS adjustment factor is used to adjust the total VMT (TDM 

assignment VMT plus intrazonal VMT estimate) from each TDM for HPMS consistency. While 

TTI typically calculates this factor, the HPMS factor used in this analysis (0.905954) was 

provided directly by H-GAC. This factor was developed using the total TDM VMT from the 

2012 validation year TDM, the 2012 HGB HPMS VMT reported by TxDOT, and annual nonsummer weekday traffic (ANSWT) adjustment factor. The formula for the HPMS factor 

calculation is: 

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

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

 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: 

142,658,108.70 × 1.06796 = 152,353,153.77 (HPMS ANSWT VMT) 

152,353,153.77 / 168,168,737.87 = 0.905954 (HPMS Factor) 
18 

Future Year Analyses – Seasonal Adjustment Factors 

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

estimated intrazonal VMT to summer weekday VMT. The seasonal adjustment factors were 

developed using aggregated ATR data for the years 2004 through 2013. Since the HGB area 

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

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

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

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

These factors were calculated by dividing the average day-of-week (weekday) count by the 

annual non-summer weekday traffic (ANSWT) traffic count. Table 9 shows the seasonal 

adjustment factors by TxDOT district. 

Table 9. HGB Weekday Seasonal Adjustment Factors for Future Year Analyses. 

TxDOT District Weekday Seasonal Adjustment Factor 

Beaumont1 1.03609 

Houston2 0.99135 

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., 2014, 2020, 2023, 2026, and 

2028), 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 

(2012 and 2015 TDMs for 2014 analysis year, 2017 and 2025 TDMs for 2020 and 2023 analysis 

years, and 2025 and 2035 TDMs for 2026 and 2028 analysis years) and applied to the TDM as 

specified in Table 6. The intermediate year adjustment factors were based on the annually 

compounded growth rates between the bounding year TDMs. The annual growth rates were then 

converted into the intermediate year adjustment factors 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 consistency between counties and the four time periods in the TDM, these 

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

annually compounded growth rates and intermediate year adjustment factors for each analysis 

year without a TDM by county and time period. 
19 

Future Year Analyses – VMT Summary 

For each future year (i.e., 2014, 2017, 2020, 2023, 2026, and 2028), the final HPMS-consistent, 

day-type-specific VMT is comprised of two parts: the link-level VMT and the estimated 

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

adjustment factor, and the link’s respective length to estimate the link-level VMT (hourly factors 

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

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

previously) were also applied to the estimated intrazonal VMT. For those future years where 

TDMs do not exist (i.e., 2014, 2020, 2023, 2026, and 2028), 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 10 and Table 11 show the TDM and ozone season weekday 

VMT summaries. 

Table 10. HGB 2014, 2017, and 2020 VMT Summary. 

County 

20141 2017 20202

TDM3 Weekday TDM3 Weekday TDM3 Weekday 

Harris 117,759,558 102,731,928 122,411,193 109,939,634 122,411,193 116,845,148 

Brazoria 7,702,582 6,709,285 8,063,140 7,241,647 8,063,140 7,788,218 

Fort Bend 12,452,496 10,667,217 13,230,861 11,882,868 13,230,861 13,311,222 

Waller 2,037,695 1,723,040 2,013,525 1,808,382 2,013,525 1,894,337 

Montgomery 14,382,456 12,225,024 15,581,522 13,994,038 15,581,522 15,328,159 

Liberty 2,505,102 2,277,922 2,597,957 2,438,572 2,597,957 2,513,257 

Chambers 3,252,261 2,968,619 3,360,929 3,154,735 3,360,929 3,343,192 

Galveston 6,016,596 5,243,650 6,189,109 5,558,547 6,189,109 6,028,219 

1

 Based on 2015 TDM with intermediate year VMT factors. 

2

 Based on 2017 TDM with intermediate year VMT factors. 

3

 Includes intrazonal VMT. 
20 

Table 11. HGB 2023 and 2026 VMT Summary. 

County 

20231 20261 20281

TDM2 Weekday TDM2 Weekday TDM2 Weekday 

Harris 144,003,330 124,184,541 144,003,330 131,244,237 144,003,330 135,154,185

Brazoria 9,791,217 8,376,595 9,791,217 9,041,656 9,791,217 9,558,864 

Fort Bend 17,908,186 14,911,356 17,908,186 16,523,426 17,908,186 17,439,521 

Waller 2,279,178 1,984,465 2,279,178 2,088,555 2,279,178 2,174,317 

Montgomery 19,864,623 16,789,676 19,864,623 18,301,534 19,864,623 19,259,335 

Liberty 2,816,047 2,590,420 2,816,047 2,678,909 2,816,047 2,751,692 

Chambers 3,923,582 3,543,008 3,923,582 3,736,098 3,923,582 3,844,950 

Galveston 7,683,804 6,537,627 7,683,804 7,092,857 7,683,804 7,492,814 

1

 Based on 2025 TDM with intermediate year VMT factors. 

2

 Includes intrazonal VMT. 

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 (2004 through 2013) 

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

within each of the four assignment time periods, the hourly fractions were normalized within 

each time period to produce the time period hourly travel factors. Each factor (i.e., 24, or one for 

each hour of the day) was then multiplied by the link volume (in addition to the other VMT 

adjustment factors). These adjusted link volumes were then multiplied by their respective link 

lengths to estimate the link level, summer weekday VMT estimates for each analysis year. 

These factors were also multiplied by the estimated intrazonal VMT to produce the final hourlyadjusted VMT. Table 12 shows the weekday time period hourly travel factors. 
21 

Table 12. Weekday Time Period Hourly Travel Factors. 

Assignment Hour Base Factor Time Period Factor1

AM Peak 

6:00 a.m. 0.060573 0.323515 

7:00 a.m. 0.068299 0.364779 

8:00 a.m. 0.058362 0.311706 

Mid-Day 

9:00 a.m. 0.051748 0.160346 

10:00 a.m. 0.050344 0.155995 

11:00 a.m. 0.052612 0.163023 

12:00 p.m. 0.054542 0.169003 

1:00 p.m. 0.055429 0.171751 

2:00 p.m. 0.058053 0.179882 

PM Peak 

3:00 p.m. 0.063528 0.239907 

4:00 p.m. 0.068832 0.259937 

5:00 p.m. 0.072506 0.273807 

6:00 p.m. 0.059938 0.226349 

Overnight 

7:00 p.m. 0.045678 0.202803 

8:00 p.m. 0.035581 0.157973 

9:00 p.m. 0.031653 0.140533 

10:00 p.m. 0.024891 0.110511 

11:00 p.m. 0.016882 0.074953 

12:00 a.m. 0.009371 0.041605 

1:00 a.m. 0.006249 0.027744 

2:00 a.m. 0.005828 0.025875 

3:00 a.m. 0.005792 0.025715 

4:00 a.m. 0.010984 0.048767 

5:00 a.m. 0.032326 0.143521 

1

 Used in the VMT calculation process. 
22 

ESTIMATION OF LINK SPEEDS 

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 link-level travel model (input) speed to a freeflow speed and an LOS E speed (i.e., application of these factors results in two speeds). The 

free-flow speed factors (grouped by functional class and area type) were calculated by dividing 

the distance-weighted free-flow speed by the distance-weighted input speed for each functional 

class/area type combination. The distance-weighted free-flow speeds were calculated using 

output from the detailed speed model used by H-GAC in the travel model development process 

(as provided by H-GAC) with link volumes set to 0 (i.e., V/C = 0). The LOS E speed factors 

were calculated in a similar manner (distance-weighted LOS E speed divided by distanceweighted input speed) using the detailed speed model output with link volumes set equal to 

capacity (i.e., V/C = 1). Appendix F shows the speed factors and the network functional class 

and functional group relationship. 

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

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

v/c ratio = Vh / Ch 

 Where: 

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

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

Sunday); and 

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

Appendix 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 SRF functional class, and the SRFs. The SRFs 

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

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

ratios greater than 1.0, a SRF is not required. 

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

SV/C = S0.0-SRFV/C × (S0.0 - S1.0) 

 Where: 

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

given direction; 

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

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

 SRFV/C = SRF for the V/C ratio on the link. The V/C ratio can be 0.0 to 1.0. 
23 

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

used: 

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

))) 

 Where: 

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

direction; 

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

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

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

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

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

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

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

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

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

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

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

ESTIMATION OF OFF-NETWORK ACTIVITY 

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

rates (i.e., mass per SHP, mass per start, and mass per SHI), county-level estimates of the SHP, 

starts, SHI, and APU hours are required by hour and vehicle type for each inventory year (SHI 

and APU hours are for diesel combination long-haul trucks only). One of the main components 

of the SHP and starts off-network activity estimation is the analysis year county-level vehicle 

population. Summaries of the vehicle population and 24-hour SHP, starts, SHI, and APU hours 

off-network activity are included as Appendix G. Hourly SHP, starts, SHI, and APU hours 

activity estimates are included with the detailed inventory data provided (see inventory data file 

descriptions in Appendix A). 

 The county-level vehicle population estimates were developed using the 

MOVESpopulationBuild utility. The county-level SHP, starts, SHI, and APU hours of offnetwork activity were developed using the OffNetActCalc utility. Appendix B contains a 

description of the utilities. 

Estimation of Vehicle Population 

The vehicle type population estimates are needed to estimate the SHP and starts off-network 

activity. The vehicle population estimates (included as Appendix G) were produced for each 

county and analysis year. The vehicle population estimates are a function of vehicle registration 

data (TxDMV registration data sets), population scaling factors (where applicable), and VMT 

mix. 
24 

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

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

factors) exists. Therefore, the 2012 analysis year was considered a historical year and the 

vehicle population estimates were based on the TxDMV registration data for the analysis year. 

Since HPMS VMT data were not available for the 2014, 2017, 2020, 2023, 2026, and 2028 

analysis years, these years were considered future analysis years (i.e., TxDMV registration data 

and HPMS VMT data do not exist). For the future analysis years, the vehicle population 

estimates were based on the most recent year (2012) TxDMV registration data set for which 

HPMS VMT data exists and analysis year population scaling factors. 

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

categories) TxDOT district-level weekday VMT mix. The development of the VMT mix is 

described in more detail in the “Development of Vehicle Type VMT Mix” section and included 

as Appendix D. 

Historical Vehicle Population Estimates 

The county-level vehicle population estimates for the historical analysis year (2012) were 

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

assigned aggregate VMT mix (see Table 5 and Appendix D). The vehicle population estimation 

process assumes that all of the non-long-haul SUT category populations for a county are 

represented in the county vehicle registrations data. This process also estimates the long-haul 

category populations as an expansion of the county registrations. There are three main steps in 

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

the vehicle type population factors, and estimation of the county-level vehicle population by 

vehicle type. 

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

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

Table 13 shows these five categories. 

Table 13. 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 vehicle type population factors. Using the assigned 

aggregate VMT mix, population factors were calculated for each vehicle type. For the non-longhaul SUT categories, the population factors were calculated by dividing the vehicle type VMT 

mix by the summed total of the VMT mix fractions in its associated vehicle registration data 
25 

category. For example, the LCT_Diesel population factor using the VMT mix is 

LCT_Diesel/(PT_Gas + PT_Diesel + LCT_Gas + LCT_Diesel). For the long-haul SUTs, the 

vehicle type population factors were calculated by taking the ratio of the long-haul and shorthaul VMT mix values. For example, the SULhT_Gas population factor using SUT mix fractions 

is SULhT_Gas/SUShT_Gas. Table 14 shows the vehicle registration aggregations and their 

associated MOVES SUT/fuel types. 

Table 14. TxDMV Vehicle Registration Aggregations and Associated Vehicle Types for 

Estimating Vehicle Populations. 

Vehicle Registration1 Aggregation Associated Vehicle Type2

Motorcycles MC_Gas 

Passenger Cars (PC) PC_Gas; PC_Diesel 

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

LCT_Gas; LCT_Diesel 

Trucks > 8.5 and <= 19.5 K GVWR 

RT_Gas; RT_Diesel 

SUShT_Gas; SUShT_Diesel 

MH_Gas; MH_Diesel 

IBus_Diesel 

TBus_Gas; TBus_Diesel 

SBus_Gas; SBus_Diesel 

Trucks > 19.5 K GVWR CShT_Gas; CShT_Diesel 

NA1 SULhT_Gas; SULhT_Diesel 

CLhT_Gas; CLhT_Diesel 1

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

mix ratio applied to the short-haul SUT population estimate. 2

 The mid-year TxDMV county registrations data extracts were used (i.e., the three-file data set consisting of: 1 - 

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

estimating the vehicle populations. 

 The third step is the estimation of the county-level vehicle type population. The non-longhaul vehicle type populations were estimated by applying their vehicle type population factors to 

the appropriate registration data category. For the CLhT_Gas type, the vehicle population was 

set to 0. For the remaining three long-haul SUT/fuel types (SULhT_Gas, SULhT_Diesel, and 

CLhT_Diesel), the vehicle populations were calculated as the product of the corresponding 

short-haul category vehicle population and the associated long-haul population factor (e.g., 

SULhT_Gas vehicle population = SUShT_Gas vehicle population x [SULhT_Gas SUT mix 

fraction/ SUShT_Gas SUT mix fraction]). 

Future Vehicle Population Estimates 

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

years (2014, 2017, 2020, 2023, 2026, and 2028) is very similar to the historical vehicle 

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

recent (2012) mid-year TxDMV registration data sets for which HPMS data exists were used. 

Using these registration data sets and the assigned VMT mix, the base vehicle type population 

for 2012 was calculated. To estimate the future analysis year county-level vehicle populations, 
26 

future year county-level vehicle population scaling factors were applied to the base vehicle type 

population for 2012. 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-level 

weekday VMT for the year of the most recent (2012) mid-year TxDMV 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 mass per 

activity emissions rates are county-level weekday estimates of SHP by hour and vehicle type for 

each analysis year. For each hour, the county-level vehicle type SHP was calculated by taking 

the difference between the vehicle type total available hours minus the vehicle type SHO. Since 

this calculation was performed at the hourly level, the vehicle type total available hours was set 

equal to the vehicle type population. The SHO was calculated using the link VMT and speeds 

and the TxDOT district-level vehicle type VMT mixes by MOVES road-type category (see the 

“Development of Vehicle Type VMT Mix” section for more details). Appendix G includes the 

24-hour summaries of the county-level weekday estimates of SHP by hour and vehicle type for 

each analysis year (hourly summaries were provided electronically to TCEQ; see Appendix A 

for electronic data descriptions). 

Vehicle Type Total Available Hours 

The vehicle type total available hours is typically calculated as the vehicle type population times 

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

the vehicle type total available hours for each analysis year was set equal to the vehicle type 

vehicle population for the analysis year. 

Vehicle Type SHO 

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

using the TxDOT district-level vehicle type VMT mixes by MOVES road-type category, which 

was then divided by the link speed to calculate the link vehicle type SHO. These VMT mixes 

are the same VMT mixes used to estimate emissions in the emissions estimation process (see 

Table 5 and Appendix C). This SHO calculation was performed for each link in a given hour, 

aggregating the SHO to one value per vehicle type per hour. 

Estimation of Starts 

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

activity emissions rates are county-level weekday estimates of starts by hour and vehicle type for 

each analysis year. The vehicle type hourly default starts per vehicle were multiplied by the 

analysis year county-level vehicle type vehicle population to estimate the county-level vehicle 

type starts by hour. Appendix G includes the 24-hour summaries of the county-level vehicle 

type starts by hour 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 vehicle for a MOVES weekday run by 

dividing the MOVES start output by the MOVES vehicle population output. These MOVES 
27 

national default starts per vehicle do not vary by year, only by MOVES day type. For this 

summer weekday analysis, the MOVES national default “weekday” starts per vehicle were used. 

Estimation of SHI and APU Hours 

The remaining activity measures needed to estimate the off-network emissions using the mass 

per activity emissions rates are the hourly, county-level weekday heavy-duty diesel truck (SUT 

62, fuel type 2 [CLhT_Diesel]) hotelling activity (comprised of SHI and APU hours) for each 

analysis year. During hotelling, the truck’s main engine is assumed to be in idling mode or its 

auxiliary power unit is in use. For each analysis year, hotelling hours activity are first estimated, 

and then it is allocated to the SHI and APU hours components. 

 The hotelling activity was based on information from a TCEQ extended idling study, which 

produced 2004 summer weekday extended idling estimates for each Texas county, and hotelling 

activity data from MOVES. Hotelling scaling factors (by analysis year) were applied to the base 

2004 summer weekday hotelling values from the study to estimate the 24-hour hotelling by 

analysis year. Hotelling hourly factors were then applied to allocate the 24-hour hotelling by 

analysis year to each hour of the day. To ensure valid hourly hotelling values are used, the 

hourly hotelling activity was compared to the CLhT_Diesel hourly SHP (i.e., hourly hotelling 

values cannot exceed the hourly SHP values). SHI and APU hours factors were then applied to 

the hotelling hours to produce the hourly SHI and APU hours of activity for each analysis year. 

Appendix G incudes the 24-hour summaries of the county-level estimates of hotelling hours, 

SHI, and APU hours for each analysis year (hourly summaries were provided electronically to 

TCEQ; see Appendix A for electronic data descriptions). 

Hotelling Activity Scaling Factors 

To estimate the county-level 24-hour hotelling activity by analysis year, county-level hotelling 

activity scaling factors were developed using the county-level 2004 summer weekday link-level 

VMT and speeds, the TxDOT district-level base weekday vehicle 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 vehicle type VMT mix (by MOVES road type). The 2004 summer 

weekday link-level VMT and speeds were developed using a process similar to the historical 

analysis year summer weekday link-level VMT speed estimation, except using a 2004 summer 

weekday VMT control total. The vehicle type VMT mixes were the same VMT mixes used to 

estimate emissions in the emissions estimation process (see Table 5 and Appendix C). For the 

base weekday vehicle type VMT mix, the 2005 weekday vehicle 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 vehicle 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 year weekday linklevel VMT and speeds and the analysis year vehicle type VMT mix. The county-level 24-hour 

hotelling activity scaling factors by analysis year were calculated by dividing the analysis year 

and day type CLhT_Diesel 24-hour VMT by the CLhT_Diesel 24-hour 2004 summer weekday 

VMT. 
28 

Hotelling Activity Hourly Factors 

To allocate the analysis year weekday county-level 24-hour hotelling activity to each hour of the 

day, hotelling activity hourly factors were used. These hotelling activity 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 hourly 

analysis year weekday CLhT_Diesel VMT. The hourly analysis year weekday CLhT_Diesel 

VMT was converted to hourly fractions, therefore creating analysis year weekday CLhT_Diesel 

hourly VMT fractions. The inverse of these hourly VMT fractions were calculated and the 

inverse for each hour was divided by the sum of the inverse hourly VMT fractions across all 

hours to calculate the county-level analysis year weekday hotelling activity hourly factors. 

County-Level CLhT_Diesel Hotelling Activity by Hour Estimation 

The initial analysis year weekday CLhT_Diesel hotelling activity by hour was calculated by 

multiplying the 24-hour 2004 summer weekday hotelling hours by the analysis year hotelling 

activity scaling factor and by the analysis year hotelling activity hourly factors. For each hour, 

the initial analysis year weekday hotelling activity was then compared to the analysis year 

weekday CLhT_Diesel SHP to estimate the final analysis year weekday hotelling activity by 

hour. If the initial analysis year weekday hotelling activity value was greater than the analysis 

year weekday SHP value, then the final analysis year weekday hotelling activity for that hour 

was set to the analysis year weekday CLhT_Diesel SHP value. Otherwise, the final analysis year 

weekday hotelling activity for that hour was set to the base analysis year weekday hotelling 

activity value. All calculations (scaling factors, hotelling activity hourly factors, and hotelling 

activity by hour calculations) were performed by county and analysis year (i.e., eight hotelling 

activity scaling factors were calculated per analysis year). 

County-Level CLhT_Diesel SHI and APU Hours Estimation 

The hourly county-level hotelling activity for each analysis year was then allocated to SHI and 

APU hours activity components using aggregate extended idle mode and APU mode fractions. 

For each hour, the analysis year hotelling activity was multiplied by the SHI fraction to calculate 

the analysis year hourly SHI activity and by the APU fraction to calculate the analysis year 

hourly APU activity. 

 The aggregate SHI and the APU fractions were estimated using model year travel fractions 

(based on source type age distribution and relative mileage accumulation rates used in the 

MOVES runs) and the MOVES default hotelling activity distribution (i.e., a bi-modal 

distribution of 1.0 SHI prior to the 2010 model year and a 0.7/0.3 SHI/APU activity allocation 

for 2010 and later model years). The associated travel fractions were applied to the appropriate 

extended idle and APU operating mode fractions (of the hotelling operating mode distribution) 

by model year and summed within each mode to estimate the aggregate (across model years) 

individual SHI and APU fractions (which sum to 1.0). 

ESTIMATION OF EMISSIONS FACTORS 

TTI developed the summer weekday emissions factors consistent with TTI’s MOVES detailed 

link-based emissions estimation method that was established as an upgrade of the MOBILE6-
29 

based inventory process for producing SIP and conformity quality emissions estimates. TTI used 

EPA’s latest MOVES version, the MOVES2014 October Release.14

 The emissions factors were developed based on the TTI’s Updated Inventory Methods for 

MOVES15 and the EPA’s MOVES inventory development Technical Guidance16 and User’s 

Guide.17 (More information may found in these main references, if desired.) TTI’s MOVES 

data post-processing utilities, RatesCalc and RatesAdj used to produce databases of emissions 

rate look-up tables for input to the emissions calculations, are also summarized in Appendix B of 

this Technical Report. 

 The detailed link-based emissions estimation method of analysis requires emissions rates by 

speed in look-up table form. Emissions rate mode was therefore used to produce emissions rates 

indexed by 16 MOVES speed bin average speeds. Another requirement of the method is that all 

rates be in terms of mass per activity unit for the external emissions calculations, thus the 

appropriate alternatives to the MOVES off-network mass per vehicle rates were used. Table 15 

(which was included in a previous section, but is provided again here for convenience) shows the 

emissions rates types and associated activity factors. 

Table 15. Emissions Rates by Process and Activity Factor. 

Emissions Processes Activity1 Emissions Factor2

Running Exhaust 

Crankcase Running Exhaust 

Brake Wear 

Tire Wear 

VMT mass/mile 

Evaporative Permeation 

Evaporative Fuel Vapor Venting 

Evaporative Fuel Leaks 

VMT mass/mile 

SHP mass/shp 

Start Exhaust 

Crankcase Start Exhaust starts mass/start 

Auxiliary Power Exhaust APU hours mass/APU hour 

Extended Idle Exhaust 

Crankcase Extended Idle Exhaust SHI mass/shi 

1

 SHI and APU hours are for Combination Long-Haul Trucks only. 

2

 All of the rates are directly available from MOVES, except mass/shp, which is produced by the TTI 

RatesCalc utility using MOVES rates mode input and output data. 

 

14 Software and database (MOVESDB20141021) downloadable from 

http://www.epa.gov/otaq/models/moves/index.htm. 15 Update of On-Road Inventory Development Methodologies for MOVES2014, TTI, December 2014. 16 Using MOVES to Prepare Emission Inventories in State Implementation Plans and Transportation Conformity: 

Technical Guidance for MOVES2010, 2010a, and 2010b, EPA, April 2012, was used in this analysis. This 

document was superseded by a newer release by EPA (January 2015), which was late for use in this work, but 

will be used in future analyses. 17 Motor Vehicle Emission Simulator (MOVES) User Guide for MOVES1014, EPA, July 2014. 
30 

MOVES Inputs, Outputs and Post-Processing 

The MOVES model is equipped with default modeling values for the range of conditions that 

affect emissions. MOVES defaults may be replaced by alternate input data sets that better reflect 

local scenario conditions. Where local data were available and consistent with the methodology, 

MOVES defaults were replaced by local input values via the MOVES Run Specification input 

file (RunSpec or MRS) and MOVES CDB (county input database). (The MRS, CDB, and 

MOVES default database provide the data for each local scenario model run.) Inputs were 

developed and used to produce emissions factors reflecting local conditions including area June 

through August period average weather conditions, summer fuel properties, vehicle fleet 

characteristics (e.g., age), and emissions control programs. 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 (e.g., default activity is normalized in the emissions rates, and the emissions rates are 

later multiplied by the local activity estimates to calculate emissions external to MOVES). 

 There is one RunSpec required per county and analysis year, and a corresponding number of 

CDBs and output databases (i.e., one output database per run). Therefore, for eight counties and 

seven calendar years there are 56 each RunSpec input files, CDB inputs, MOVES output 

databases, RatesCalc output databases, and RatesAdj final rate output databases. RatesAdj 

produces the final rates by performing NOx TxLED effect adjustments to diesel vehicle NOx

rates and extracting and storing the rates for the inventoried pollutants in a separate, smaller 

database for input to the emissions runs. 

 The RatesCalc and RatesAdj utilities used to produce the emissions rates from the MOVES 

data are described in Appendix B. 

Summary of Control Programs Modeled 

Table 16 summarizes the control measures and modeling approaches (e.g., MOVES model 

defaults versus alternative local inputs, or post-processing of MOVES output). 
31 

Table 16. Emissions Control Strategies and Modeling Approaches. 

Individual Control Measures Approach 

Federal Motor Vehicle Control Program 

Standards MOVES model – defaults. 

Federal Heavy-Duty Diesel Engines Rebuild 

and 2004 Pull-Ahead Programs to Mitigate 

NOx Off-Cycle Effects 

MOVES model – defaults. 

Reformulated Gasoline (RFG) 

MOVES model – region-specific RFG fuel formulation 

inputs, based on the EPA’s 2012 summer season Houston 

retail outlet RFG survey data for 2012, and based on 

EPA’s latest available (2014) summer season Houston 

retail outlet RFG survey data for 2014 and later years, 

with average sulfur content for 2017 and later set for 

consistency with the Tier 3 sulfur standard. 

Texas Low-Emission Diesel 

MOVES model – 2012 and 2014 historical year sulfur 

content based on local summer season fuel survey data 

(TCEQ summer 2011 [no 2012 survey data available] 

and summer 2014 fuel survey); 2017 and later analysis 

years used MOVES default diesel sulfur content value, 

consistent with the federal (Ultra-Low Sulfur) rule. 

Post-processing of diesel vehicle NOx rates – diesel 

vehicle NOx emissions factors for all counties were 

adjusted using evaluation year and SUT-specific average 

NOx reduction factors produced following TCEQ’s 

procedure (which calculates the average NOx percent 

reduction based on 4.8% and 6.2% reductions for 2002 

and later, and 2001 and earlier model years, 

respectively). 

Inspection and Maintenance (I/M) Program 

MOVES model – locality specific inputs to model I/M 

effects for the five I/M Program counties (Brazoria, Fort 

Bend, Galveston, Harris, and Montgomery). Used 

available MOVES I/M parameters (in terms of MOVES 

I/M “teststandards” and associated “imfactors”) 

pertaining to the domain of I/M vehicles, consistent with 

current program descriptions and latest I/M modeling 

protocols, using MOVES I/M compliance factors input 

calculated per MOVES Technical Guidance (EPA, April 

2012) to include the regulatory class adjustments 

provided for Passenger Trucks and Light Commercial 

Trucks. 
32 

MOVES Emissions Factor Aggregation Levels 

The MOVES model produces results at different aggregation levels that may be specified in the 

MRS. The detailed, hourly, link-based inventory method required MOVES weekday day type 

emissions rates at the following MOVES output detail level: 

 Up to 13 source types (i.e., vehicle types); 

 Up to six fuel types; 

 Up to five road types (four actual MOVES road categories and “off-network”); 

 Each of the 24 hours in a day; 

 16 speed bins (only included in miles-based rate tables); 

 Up to 135 pollutants; and 

 Up to 14 emissions processes. 

 The vehicle fleet was modeled as powered only by the predominant on-road fuels of gasoline 

and diesel (alternate fuels considered de minimis). The five road type categories in MOVES are 

Off-Network (not actually a road type, this category is for parked vehicle activity), Rural 

Restricted Access, Rural Unrestricted Access, Urban Restricted Access, and Urban Unrestricted 

Access.18 The rates for each of the actual four MOVES road types are indexed by the 16 

MOVES speed bin average speeds: 2.5, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, and 

75 mph. 

MOVES Run Specifications 

 The MRS defines the place, time, road categories, vehicle and fuel types, pollutants and 

emissions processes, and the overall scale and level of output detail for the modeling 

scenario. TTI created an MRS for one county scenario using the MOVES graphical user 

interface (GUI), which was then converted to a template and used as a base MRS from 

which to build the MRSs for the analysis. 

 Table 17 describes the MRS selections TTI used, with further details on the selections 

provided after the table. 

 

18 The “separate ramps” feature intended for MOVES2014 October Release was not activated for emissions rates 

mode, but is expected to be available for emissions rates output in the next release of MOVES. 
33 

Table 17. RunSpec Selections by MOVES GUI Navigation Panel. 

Navigation Panel Detail Panel Selection 

Scale1 Model; Domain/Scale; 

Calculation Type 

On-Road; County; 

Emissions Rates 

Time Spans1 Time Aggregation Level; 

Years – Months – Days – Hours 

Hour; 

20121

 - July - Weekday - All 

Geographic 

Bounds1

Region; 

Selections; 

Domain Input Database 

Zone and Link; 

<COUNTY>;

1

<COUNTY INPUT DATABASE (CDB) NAME>1

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 

Processes2

Total Energy Consumption; VOC; 

CO; NOx; Atmospheric CO2; SO2; 

NH3; PM10 Total Exhaust, 

Brakewear, Tirewear; PM2.5 Total 

Exhaust, Brakewear, Tirewear, 

and PM2.5 exhaust species 

NonECPM, EC, OC, and SO4

Dependent on pollutant: 

Running Exhaust, Start Exhaust, Extended Idle Exhaust, Auxiliary 

Power Exhaust, Crankcase Running Exhaust, Crankcase Start 

Exhaust, Crankcase Extended Idle Exhaust, Evap Permeation, Fuel 

Vapor Venting, Fuel Leaks; Refueling Displacement Vapor Loss, 

Refueling Spillage Loss, Brakewear, Tirewear 

Manage Input Data 

Sets 

Additional Input Database 

Selections None 

Strategies Rate-of-Progress Not Applicable 

General Output 

Output Database; 

Units; 

Activity 

<MOVES OUTPUT DATABASE NAME>;

1

Pounds, KiloJoules, Miles; 

Hotelling Hours, Population, Starts (not adjustable, pre-selected) 

Output Emissions 

Detail 

Always; 

For All Vehicles/Equipment; 

On Road 

Time: Hour – Location: Link – Pollutant; 

Fuel Type, Emissions Process; 

Source Use Type 

Advanced 

Performance 

Measures 

Aggregation and Data Handling All check boxes are to be “un-checked” 

1

 County scale allows one county and year per run – the evaluation years and counties are 2012, 2014, 2017, 2020, 

2023, 2026, and 2028; Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, and Waller. 2

 Chained pollutants require other pollutants (not listed in the table) to be selected (e.g., VOC requires Total Gaseous 

Hydrocarbons [THC] and Non-Methane Hydrocarbons: CO2 requires Total Energy Consumption [TEC]). 
34 

Scale, Time Spans, and Geographic Bounds 

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

The MOVES Calculation Type “Emissions Rates” was selected for MOVES to produce the 

emissions rates with speed 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, for the selected year, month, and day type. 

One analysis year per run was selected, as MOVES allows only one “Years” selection for the 

County Domain Scale. For TTI’s MOVES-based link emissions estimation process, which is for 

a single day, one “Months” (July) and one “Days” (Weekdays) selection was made. 

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

The local CDB containing the calendar year scenario-specific input data for the county was 

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

as required for the emissions rates calculation type. With these required set-ups, one county, one 

year, one day type, and 16 (speed bin) average speeds were modeled per run. 

On-Road Vehicle Equipment and Road Type 

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

in the MOVES runs. The VMT mixes specify the vehicle fleet as the 22 gasoline and diesel 

SUTs designated as “on-road vehicle equipment” selections in Table 17. These SUT/fuel type 

combinations were chosen in all the MOVES RunSpecs. Note that, as required, the MOVES 

default fuel engine fractions (discussed later) were also replaced with local input data consistent 

with the SUT/fuel type selections shown in Table 17. 

 All five MOVES road type categories were selected (the “provide separate ramps output” 

box is not active when using emissions rates mode in the MOVES2014 October Release model). 

Pollutants and Processes 

In addition to the required pollutants within the scope of the inventory, MOVES requires that 

additional pollutants be selected for particular “chained” pollutants (i.e., pollutants that are 

calculated as a function of another MOVES pollutant). Of the pollutants listed for the inventory, 

the following additional pollutants were selected, as required by the model, due to chaining: nonmethane hydrocarbons and THC (for VOC), and TEC (for CO2 and SO4). All of the associated 

processes available by the selected pollutants were included, including the two refueling 

emissions processes (although emissions for these “area source” category processes were not 

estimated for this inventory). 

Manage Input Data Sets and Strategies 

The Manage Input Datasets feature allows alternate inputs other than those included in the CDB. 

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

 The Strategies feature is for modeling an alternate control program option, which was not 

applicable to this inventory analysis 
35 

Output 

The output units were pounds, kilojoules, and miles. The activity categories were pre-set by 

MOVES (and not adjustable) for inclusion in the output database. The output detail level was by 

hour, link (e.g., for MOVES rates mode, county/road type/speed bin combination), pollutant, 

process, SUT, and fuel type. 

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

the MRSs used. 

MOVES County Input Databases 

The locality-specific input data for the county scale runs were entered through the CDB. 

 TTI developed procedures to accommodate building and checking CDBs for large scale 

emissions inventory estimation projects. The basic procedure was to write a MySQL script to 

produce one county scenario CDB and convert it to a template from which all of the CDB scripts 

were built. The MySQL scripts were then run in batch mode to produce all CDBs for the 

analysis. 

 Data for populating the CDBs were first prepared in the form of text files and/or MySQL 

databases (e.g., for local fuels and weather data), and some values provided directly in the CDB 

builder MySQL script. Any default data used was selected from the latest MOVES default 

database, MOVESDB20141021 (e.g., for default activity data). After running the scripts to 

produce the CDBs, a CDB checker utility written by TTI was run to verify that all CDB tables 

were built and populated as intended. 

 Table 18 provides an outline and brief description of the CDBs, followed by discussion of the 

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

CDB table data applies to all counties and years. 
36 

Table 18. CDB Input Tables. 

MOVES Input Table Data Category Notes 

year Time Designates analysis year as a base year (base year means that local 

activity inputs are supplied rather than forecast by the model). 

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

county Geography/ 

Meteorology 

Identifies county of analysis, local altitude, and barometric pressure 

(base year 2012 summer period data were provided by TCEQ). 

zonemonthhour Meteorology Local, hourly temperature, and relative humidity for the county (base 

year 2012 summer period data were provided by TCEQ). 

roadtype1 Activity Lists the MOVES road types and associated ramp activity fractions. 

Road type ramp fractions were set to 0. 

hpmsvtypeyear2

Activity 

Used MOVES default national annual VMT by HPMS vehicle type. 

roadtypedistribution2 Used MOVES default road type VMT fractions. 

monthvmtfraction2 Used MOVES default month VMT fractions. 

dayvmtfraction2 Used MOVES default day VMT fractions. 

hourvmtfraction2 Used MOVES default hour VMT fractions. 

avgspeeddistribution2 Used MOVES default average speed distributions. 

sourcetypeyear2 Fleet Used MOVES default national SUT populations. 

sourcetypeagedistribution Fleet 

TTI estimated SUT age fractions using TxDOT/TxDMV vehicle 

registration data and MOVES defaults, as needed. TTI used the 

2012 mid-year registration data for 2012, and used the latest 

available (2014) mid-year registration data for 2014 and later years. 

avft Fleet 

TTI estimated SUT fuel fractions using TxDOT/TxDMV vehicle 

registration data and defaults where needed. Local data sets used by 

analysis year were consistent with sourcetypeagedistribution tables. 

zone Activity Start, idle, and SHP zone allocation factors. County = zone, and all 

factors were set to 1.0 (required for county scale analyses). 

zoneroadtype Activity SHO zone/roadtype allocation factors. County = zone, and all 

factors were set to 1.0 (required for county scale analyses). 

fuelsupply Fuel The fuel supply, or market shares, reflected one gasoline and one 

diesel fuel formulation for each county and year. 

fuelformulation Fuel 

For 2012 and 2014, based on local retail outlet data (i.e., summer 

2011/2014 TCEQ surveys [for diesel sulfur] and EPA summer 

2012/2014 RFG surveys). For future year diesel sulfur content, 

MOVES defaults were used. For future year RFG, used EPA 

summer 2014 RFG survey sample data, and (for 2017 and later) set 

average sulfur content to the Tier 3 Rule annual average standard (10 

parts per million [ppm]). 

imcoverage I/M 

TTI prepared locality-specific set-ups to represent I/M program 

design/description for each county and analysis year based on 

current I/M rules, prior modeling set-ups, and available MOVES I/M 

parameters (in terms of MOVES I/M “teststandards” and associated 

“imfactors”) for the I/M vehicles. Regulatory class adjustments 

were made per MOVES Technical Guidance (EPA, April 2012). 

countyyear Stage II Table included in CDB as standard procedure, but not applicable to 

this analysis, and with no effect on resulting emissions inventories. 

1

 MOVES will not produce “ramp road type” rates in a single run with all road types. To calculate emissions for 

certain travel model links coded as ramps, MOVES Unrestricted Access road type emissions rates were used. 2

 Use of a default set of activity and population inputs for all MOVES runs is basic to the inventory method, e.g., 

MOVES default activity is normalized in the calculated emissions rates for pertinent emissions processes, and 

actual local activity estimates are used in the external emissions calculations. 
37 

User Inputs – Locality Specific Inputs and Defaults Used 

All inputs discussed in this section are input via the CDB. Unless otherwise stated, the inputs 

apply to all counties and years. 

 Year, State, and County Inputs to MOVES – The year, state, and county tables are 

populated with data identifying the year, state, and county of the run. 

 The yearID field of the “year” table was populated with the analysis year value, and the year 

was set as a base year (to specify that particular user-input fleet and activity data were to be used, 

rather than forecast by MOVES during the model runs). As part of designating the appropriate 

fuel supply for the modeling scenario, the fuelyearID in the year table was also set to the analysis 

year. 

 StateID “48” (Texas) was inserted in the state table. In addition to identifying the county of 

analysis, the county table contains barometric pressure and altitude information (discussed 

further with other meteorological inputs). The county data were selected from a prepared local 

“meteorology” database containing tables of weather data records (i.e., “county” and 

“zonemonthhour” tables) for the analysis. 

 Roadtype Inputs to MOVES – Currently the MOVES model contains “ramp” emissions 

rates, but not an (activated) individual road type for separate ramps output (when using MOVES 

rates mode). In the roadtype table, MOVES provides a field “rampFraction” for including a 

fraction of estimated ramp activity as a fraction of SHO on each of the MOVES road types. For 

this analysis, the MOVES default roadtype table data were used, except the ramp fractions were 

set to zero (i.e., 100 percent of activity on each MOVES road type was based on the road type 

drive cycles assigned to that road type by MOVES, exclusive of ramp activity). 

 Activity and Vehicle Population Inputs to MOVES – The activity and vehicle population 

input parameters under the methodology use the MOVES defaults. The tables are: 

hpmsvtypeyear, roadtypedistribution, monthvmtfaction, dayvmtfraction, hourvmtfraction, 

avgspeeddistribution, and sourcetypeyear. Data for all of these tables were selected and inserted 

from the MOVES default database. 

 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. 

 Age Distributions and Fuel Engine Fractions Inputs to MOVES – The locality-specific 

fleet characteristics inputs to MOVES consist of county age distributions input datasets and 

statewide fuel engine fractions (formerly known as diesel sales fractions) input data sets. The 

age distributions and fuel engine fractions inputs were calculated and written to text files in 

preparation for loading the data into the appropriate CDB input tables: the 

sourcetypeagedistribution table for vehicle age distributions, and the AVFT table for fuel engine 

fractions. The MOVESfleetInputBuild utility was used to produce these fleet inputs to MOVES 

in the required formats (see utility description in Appendix B), and MySQL scripts were used to 

populate the CDB input tables. 
38 

 The age distributions and fuel engine fractions were based on TxDMV mid-year county 

registrations data and MOVES model defaults, where needed. The fuel engine fractions were 

developed consistent with the local VMT mix estimate (e.g., no CNG vehicles, no E-85 fuel 

type, and no gasoline transit buses in the VMT mix was reflected in the local fuel engine 

fractions estimates). Locality-specific SUT age distributions were produced based on the 

TxDMV county vehicle registration category aggregations, consistent with the vehicle 

registration category aggregations of the VMT mix (see Appendix B). Appendix H includes the 

age distributions and fuel engine fractions summaries. 

 Table 19 summarizes the data sources and aggregation levels used to estimate the HGB 

county sourcetypeagedistributions and fuel engine fractions. 
39 

Table 19. Sources and Aggregations for SUT Age Distributions and Fuel Engine Fractions. 

SUT Name SUT ID 

TxDMV Category1

Aggregations for Age 

Distributions and Fuel 

Engine Fractions 

Geographic 

Aggregation for 

Age Distributions 

Geographic 

Aggregation for 

Fuel Engine 

Fractions2

Motorcycle 11 Motorcycles County 

NA – 100 percent 

gas, no Fuel Engine 

Fractions 

Passenger Car 21 Passenger County MOVES default2

Passenger Truck 31 Total Trucks<=8500 County MOVES default2

Light Commercial 

Truck 32 Total Trucks<=8500 County MOVES default2

Single Unit ShortHaul Truck 52 >8500+ >10000+ 

>14000+>16000 HGB Region Texas Statewide 

Single Unit LongHaul Truck 53 >8500+ >10000+ 

>14000+>16000 Texas Statewide Texas Statewide 

Refuse Truck 51 

MOVES default 

Motor Home 54 

Intercity Bus 41 

Transit Bus2 42 

School Bus 43 

Combination 

Short-Haul Truck 61 >19500+ >26000+ >33000+ >60000 HGB Region Texas Statewide 

Combination 

Long-Haul Truck 62 >19500+ >26000+ 

>33000+ >60000 Texas statewide 

NA – 100 percent 

diesel, no Fuel 

Engine Fractions 

1

 TxDOT mid-year 2012 and 2014 (latest available for 2014 and later years) county vehicle registrations data (i.e., 

three-file data set: composite fuel light-duty categories; heavy-duty gasoline by eight weight categories; and 

heavy-duty diesel by eight weight categories) were used for developing local inputs (weights are gross vehicle 

weight rating [GVWR] in units of lbs.). The MOVES2014 model default age distributions are from the 

MOVESDB20141021 database. 2

 MOVES default fuel engine fractions for transit buses were revised to exclude the CNG and gasoline-fueled 

components, and light duty categories were revised to exclude E-85 fuel type, consistent with the local vehicle 

type VMT mixes. MOVES2014 default fuel engine fractions were taken from the MOVESDB20141021 sample 

vehicle population table. 

Local Meteorological (County and Zonemonthhour Table) Inputs to MOVES – 

The meteorological inputs, provided by TCEQ, were input via the “county” (barometric 

pressure) and “zonemonthhour” (temperature and relative humidity) tables. These input data 

were developed as June 1 through August 31, 2012 hourly temperature and relative humidity, 

and 24-hour barometric pressure averages, using the hourly observations over this period from 

the George Bush Intercontinental Airport weather station in Houston, TX. Altitude, also an input 

of the county table, was set to “low” for all counties. Table 20 summarizes the temperatures, 

relative humidity, and barometric pressure input values. 
40 

Table 20. Meteorological Inputs to MOVES. 

Hour Temperature 

(Degrees Fahrenheit) 

Relative Humidity 

(Percent) 

Barometric Pressure 

(Inches of Mercury) 

1 78.85 82.26 

29.83 

2 78.27 83.88 

3 77.71 85.25 

4 77.19 86.86 

5 76.86 88.00 

6 76.57 88.50 

7 76.57 88.51 

8 78.76 84.92 

9 81.79 77.01 

10 84.02 70.29 

11 86.37 63.91 

12 88.05 58.71 

13 89.21 55.48 

14 89.73 54.18 

15 90.13 53.20 

16 90.21 53.07 

17 89.71 54.25 

18 88.72 56.19 

19 87.49 58.81 

20 85.01 64.92 

21 82.84 70.35 

22 81.63 73.74 

23 80.62 76.94 

24 79.74 79.42 

Source: Provided by TCEQ – developed from George Bush Intercontinental Airport, Houston, TX 

weather station hourly observations. Data are from the period June through August 2012. 
41 

Fuels Inputs to MOVES – The local fuels inputs to MOVES were input in the CDB 

fuelsupply and fuelformulation tables. The summer season fuel supply for each county and year 

consisted of one average RFG and one average diesel fuel formulation (and therefore gasoline 

and diesel fuel supply market share values were 1.0 for each). The data sources for these fuel 

formulations were largely local survey data, although for average sulfur content, default values 

reflecting regulatory standards were used as the expected values for certain future years. TTI 

prepared the HGB fuels input data in spreadsheets, saved them to text files, and imported these 

local HGB fuels inputs to storage database tables (fuelformulation and fuelsupply), using IDs for 

the local fuel formulations outside the range of MOVES default fuel formulation IDs. Countyfuel scenario data were selected from storage and inserted into the CDB fuelsupply and 

fuelformulation tables, and converted from MOVES2010b to MOVES2014 specification as 

needed for input to MOVES emissions rate runs.19

 The following describes the procedure used to populate the CDB fuels tables for each CDB 

(county and year). 

 Selected all MOVES default fuelsupply records associated with the county-fuel scenario 

(i.e., for the county’s pertinent fuelRegionID [1370011000], and scenario’s fuelyearID 

[analysis year] and monthgroupID [7 for July, or summer season fuel]), inserted the data 

into the CDB fuelsupply table, and set their market share field values to zero. (This was 

to prevent MOVES from applying its default fuels data in addition to the desired local 

fuels input data). 

 Selected the local fuel supply records for the county-fuel scenario from the local storage 

database (i.e., the fuel formulation market shares of the specified locally developed fuel 

formulations for the analysis), renaming countyID field as fuelRegionID (as newly 

required with MOVES2014), inserted the data into the CDB fuelsupply table, and 

updated fuelRegionID value (from the MOVES2010b-based FIPS county code to the 

required MOVES2014 fuelRegionID). 

 Selected all of the local fuel formulation records for the county-fuel scenario from the 

storage database, where stored fuelformulationID corresponded with the CDB fuelsupply 

table’s fuelformulationID, and inserted the data into the CDB fuelformulation table. 

 The MOVES2014 fuel property fields and units (of the fuelformulation table) are: 

 RVP (psi); 

 sulfurLevel (ppm); 

 ETOHVolume (volume percent); 

 MTBEVolume (volume percent); 

 ETBEVolume (volume percent); 

 

19 In previous MOVES versions (e.g., MOVES2010b) the fuelsupply fields were countyID, fuelyearID, 

monthgroupID, fuelformulationID, marketshare, and marketshareCV. MOVES2014 replaced countyID with 

fuelRegionID. The HGB county fuelsupply records used in the analysis were stored in MOVES2010b form, and 

were changed in each input CDB for use with MOVES2014 (i.e., fuelsupply table countyID field and values were 

replaced with fuelRegionID field and appropriate values). 
42 

 TAMEVolume (volume percent); 

 aromaticContent (volume percent); 

 olefinContent (volume percent); 

 benzeneContent (volume percent); 

 e200 (vapor percent at 200 degrees Fahrenheit); 

 e300 (vapor percent at 300 degrees Fahrenheit); 

 T50 (degrees Fahrenheit at 50 percent vapor); and 

 T90 (degrees Fahrenheit at 90 percent vapor). 

 Although not listed above the fields, BioDieselEsterVolume, CetaneIndex, and PAHContent 

are also included in the fuelformulation table, but were not used. Additionally, note that when 

the T50/T90 values are input to MOVES, as was the case in this analysis, they are used instead 

of E200/E300 values. 

Data Sources – The EPA Office of Transportation and Air Quality (OTAQ) provided TTI 

with the Houston RFG retail outlet survey samples by fuel grade (regular, mid-grade, and 

premium) collected by the RFG Survey Association.20 TTI processed the sample data to 

estimate the Houston summer season average RFG fuel property inputs by year, which were used 

for all counties. The Houston summer 2012 survey data set was the basis of the 2012 analysis 

year RFG input parameter values, and the Houston summer 2014 data set (latest available) was 

the basis of the 2014 and later analysis year RFG input parameter values. For 2017 and later 

analysis years, the sulfur content value of the survey-based fuel property estimates was set for 

consistency with the Tier 3 rule gasoline sulfur standard (sulfur standard implementation date is 

January 1, 2017). For average diesel sulfur content, data sources include the TCEQ summer 

2011/2014 retail fuel survey summaries for 2012/2014 analysis years (2011 data were used for 

2012 since no local 2012 diesel fuel survey data were readily available) and MOVES default for 

2017 and later analysis years. 

Development of Fuel Formulations Inputs from RFG Survey Samples – On average, each 

summer period Houston survey data set included 263 total samples taken during June 1 – 

September 15 (by grade: regular – 237, mid-grade – 6, and premium – 20). The RFG sample 

data used were already in the units specified for MOVES. 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 were estimated using 

annual average sales volumes per day through retail outlet statistics for Texas.21

 The fuel supply value for each fuel formulation used was 1.0, as previously stated, meaning 

that for each county modeling scenario there was only one diesel and one gasoline (RFG) fuel 

formulation. Table 21 shows the RFG fuel formulations used. 

 

20 For more information see: http://www.epa.gov/otaq/fuels/rfgsurvey.htm. 21 Sales volumes by grade were from the Energy Information Administration’s (EIA) Petroleum Marketing Annuals. 

2009 sales (latest available at the time of original analysis) were used to produce the 2012 average RFG 

formulation, and 2011 sales (current latest available) were used for 2014 average RFG. 
43 

Diesel Fuel Formulations – The diesel fuel formulation input consists basically of average 

sulfur content. (The effects of TxLED were incorporated by emissions factor post-processing, 

discussed later.) The 2012 average sulfur content values were based on the TCEQ summer 2011 

fuel survey summary. The averages were, by district: 6.04 ppm for the Houston District 

counties, and 6.36 ppm for the Beaumont District counties. The 2014 values, taken from the 

TCEQ summer 2014 survey summary, for the Houston and Beaumont Districts are, respectively, 

5.13 ppm and 7.06 ppm. For 2014 and later years, the MOVES default value of 11 ppm was 

used for all counties. 
44 

Table 21. MOVES (RFG) Gasoline Inputs – HGB Summer Emissions Rates Analysis. 

Fuel Formulation Field 20121 20142 2017 and Later 

Years3

fuelFormulationID 10707 10714 10717 

fuelSubtypeID 12 12 12 

RVP 7.06 7.11 7.11 

sulfurLevel 32.16 28.15 10.00 

ETOHVolume 9.737 9.699 9.699 

MTBEVolume 0 0 0 

ETBEVolume 0 0 0 

TAMEVolume 0 0 0 

aromaticContent 14.614 14.711 14.711 

olefinContent 13.098 13.558 13.558 

benzeneContent 0.469 0.457 0.457 

e200 49.283 49.138 49.138 

e300 84.450 84.209 84.209 

volToWtPercentOxy 0.3488 0.3488 0.3488 

BioDieselEsterVolume \N \N \N 

CetaneIndex \N \N \N 

PAHContent \N \N \N 

T50 202.351 203.079 203.079 

T90 329.348 330.028 330.028 

1

 2012 – based on EPA Houston Summer 2012 retail outlet RFG survey data. 

2 2014 – based on EPA Houston Summer 2014 retail outlet RFG survey data. 

3 2017 and later – based on EPA Houston Summer 2014 retail outlet RFG survey data except 

sulfur content was set to 10 ppm for Tier 3 gasoline sulfur standard consistency. 
45 

Local I/M Inputs to MOVES – The current I/M program is administered to reduce gasoline 

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 design reflected in parameters 

specified in the MOVES IMcoverage table. TTI previously produced a set of Texas I/M county 

MOVES imcoverage records (for all years in MOVES and I/M areas in Texas) to replace the 

MOVES default imcoverage table records for Texas. These inputs, used in previous 

MOVES2010b-based HGB inventories (TTI, June 2014), were also applied in this analysis 

except for one modification that does not affect results, but was made strictly to eliminate 

conflicts with the new MOVES2014 defaults. (A review of the MOVES defaults found that 

imcoverage table records were updated since MOVES2010b and use some IMProgramIDs that 

overlap with the previously developed local input records. Additionally, it was noted that the 

updated MOVES default records do not accurately reflect Texas I/M program model year 

coverages.22) 

 The imcoverage table data parameters are: 

 polProcessID (pollutant and emissions process affected by the program); 

 stateID (state subject to the I/M program); 

 countyID (FIPS county code); 

 yearID (year administered); 

 sourceTypeID (SUT covered); 

 fuelTypeID (fuel type subject to the program); 

 IMProgramID (arbitrary ID number); 

 begModelYearID (first model year covered); 

 endModelYearID (last model year covered); 

 inspectFreq (inspection frequency for the program); 

 testStandardsID (I/M test type); 

 useIMyn (a Y/N [yes/no] switch that specifies whether or not to use the record); and 

 complianceFactor (an adjustment factor reducing the effects for compliance rate, waiver 

rates, or other adjustments). 

 TTI adjusted the previously produced the set of Texas counties imcoverage table input 

records by changing all IMprogramID values to exclude all MOVES default IMprogramIDs (the 

adjustment used was IMprogramID = IMprogramID + 100). The new set of Texas imcoverage 

table records for all MOVES analysis years were stored in a new imcoverage database for use in 

building the CDBs for MOVES emissions modeling. 

 

22 Additionally in MOVES2014, evaporative tank vapor venting hot soak base rates with a standard I/M difference 

were added for regulatory class ID 40 (Class 2b, two-axle, four-wheel vehicles), previously (in MOVES2010b 

these rates had a standard I/M difference of 0, i.e., no I/M effects. 
46 

 In addition to selecting the appropriate local imcoverage records for the modeling scenario 

from the Texas imcoverage database, all MOVES defaults for the modeling scenario had to be 

excluded. To prepare the appropriate county imcoverage inputs, the following two general steps 

were performed for all eight counties: 

 Selected and inserted all MOVES default imcoverage records for the scenario’s countyID 

and yearID into the CDB imcoverage table, and flagged them for non-use (i.e., set 

useIMyn = N) in the modeling run; and 

 Selected the imcoverage records from the local Texas MOVES imcoverage database for 

the scenario’s yearID and countyID and inserted the data into the CDB imcoverage table 

(with useIMyn = Y). 

Data Sources – TTI produced the I/M coverage input parameters to best represent Texas I/M 

program designs as specified in the Texas I/M SIP and Texas rules (using current Texas I/M 

modeling protocol compliance and waiver rates), and, for the most part, where the I/M coverage 

modeling parameters existed in MOVES (e.g., for SUT and fuel type categories for which 

MOVES contained I/M effects). The HGB I/M program requires annual emissions testing of 

gasoline vehicles within a 2-through-24 year vehicle age coverage window (motorcycles, 

military tactical vehicles, diesel-powered vehicles, and antique vehicles are excluded). A gas cap 

integrity test is required on all these vehicles, and, depending on the vehicle class and model 

year, the vehicle emissions testing may utilize on-board diagnostics (OBD) or the Accelerated 

Simulated Mode (ASM-2) test. For additional Texas I/M program details, see the current I/M 

SIP revision.23

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 22, column 5). 

 Queried the MOVES default imfactor table (contains adjustments to emissions rates per 

various I/M scenarios by SUT/fuel type, age, etc.) on the Texas I/M test frequency and 

fuel type (i.e., annual and gasoline) and on the imteststandards applicable to Texas – from 

this query, listed the SUTs, test standards, pollutant, and emissions process combinations 

with non-zero MOVES imfactors and corresponding base rates with non-zero standard 

I/M difference (i.e., I/M effects) available in MOVES. 

 Categorized counties and years in groups under the same MOVES test standards. 

 Assigned MOVES improgramIDs such that: 1) all MOVES default improgramIDs were 

excluded, and 2) per MOVES User’s Guide, for each yearID, each IMprogramID 

represented a unique combination of test standard, test frequency, begin model year, and 

end model year. 

 Table 22 and the associated table notes describe the MOVES imcoverage records developed 

and used for the HGB analyses. Note that a review of the pertinent MOVES data (IMfactors and 

 

23 Revision to the State Implementation Plan Mobile Source Strategies, Inspection and Maintenance State 

Implementation Plan Revision, TCEQ, adopted February 12, 2014. 
47 

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 22, note 4) except for evaporative tank vapor venting for a small fraction of 

the heavy-duty class that MOVES2014 attributes to SUTs 31 and 32 (passenger and light 

commercial trucks), which are characterized mainly as light-duty class. Although the Texas I/M 

program design includes heavy-duty vehicles, the current version of MOVES has limited 

capability for modeling different I/M test standards attributed to multiple regulatory classes (e.g., 

a combination of light and heavy) in a single SUT.24

 

24 MOVES contains potential heavy-duty I/M effects only for evaporative tank vapor venting (i.e., non-zero standard 

I/M difference in associated heavy class base rates in combination with pertinent non-zero I/M factors for SUTs 

to which MOVES attributes any heavy-duty fraction). For this evaporative process, MOVES, however, includes 

non-zero I/M factors only for SUTs 21, 31, and 32, and attributes a small fraction of heavy-duty class to SUTs 31 

and 32. Note that since MOVES does not contain heavy-duty exhaust I/M effects, the regulatory class fraction 

part of the I/M compliance factor (see Table 22, note 2) was used to prevent a heavy-duty exhaust I/M benefit 

being applied to SUTs 31 and 32. This may result in an overall conservative I/M effects estimate, since excluding 

a heavy-duty exhaust I/M effect in SUTs 31 and 32 effectively excludes a potential evaporative I/M benefit for 

heavy-duty (e.g., from gas cap check) as well. 
48 

Table 22. MOVES IMCoverage Table Input Descriptions for HGB I/M Counties. 

YearID1 IMprogamID2 begModel

YearID3

endModel

YearID3 testStandardsID SourcetypeID4

Harris County

21 

(PC – Passenger 

Car) 

31 

(PT – Passenger 

Truck) 

32 

(LCT – Light 

Commercial 

Truck) 

1999 through 

4/2002 

120 X X 12 (2500 RPM/Idle) 

150 X X 41 (Evp Cap) 

Harris, Brazoria, Fort Bend, Galveston, Montgomery Counties

5/2003 (5/2002, 

for Harris) 

through 2019 

130 X 1995 23 (A2525/5015 

Phase) 

151 X 1995 41 (Evp Cap) 

140 1996 X 51 (Exh OBD) 

160 1996 X 45 (Evp Cap, OBD) 

2020 through 

2050 

141 X X 51 (Exh OBD) 

161 X X 45 (Evp Cap, OBD) 

1

 County I/M implementation dates: Harris – 1/1/1997 with transition to the new I/M test types on 5/1/2002; Brazoria, 

Fort Bend, Galveston, Montgomery – 5/1/2003. 2

 Common parameters for Texas MOVES imcoverage records not shown include: annual test cycle, gasoline fuel 

type, use IMyn = Y. Aside from any non-standard adjustments, the compliancefactor values are common across 

areas: PC – 93.12%; PT – 87.53%; and LCT – 81.95%. Using the MOVES inventory development Technical 

Guidance (EPA, 2012) compliance factor equation (Section 3.10.6), compliance factors were calculated as the 

product of the percent compliance rate, percent waiver rate, and the regulatory class coverage adjustment. The 

current Texas I/M program modeling protocol compliance and waiver rates are 96% and 3%. The regulatory class 

adjustments used were taken directly from the MOVES inventory Technical Guidance, and for PC, PT, and LCT, 

the adjustments 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, except for the evaporative tank venting process; for light-duty 

gasoline vehicles, MOVES includes both exhaust and evaporative I/M factors and mean base rates with I/M effects. 

Via the imcompliance factor (note 2) the heavy-duty class I/M effects were not included and only light-duty 

gasoline vehicles (SUTs 21, 31, and 32) were included in the user input imcoverage records. The 

processes/pollutants affected by I/M are exhaust running and exhaust start THC, CO, NOx, and tank vapor venting 

THC. 

 Note that the updated activity estimates in MOVES2014 affect the regulatory class activity 

proportions associated with each SUT. The regulatory class coverage adjustments (see Table 22, 

note 2) should be updated based on the new guidance (EPA, January 2015) for subsequent 

analyses. 

 The MOVES input files (MRSs and CDBs) were provided as a part of the electronic data 

submittal (Appendix A) of this Technical Note. 
49 

Checks and Runs 

After completing the input data preparation, the CDBs were checked to verify that all 20 tables 

were in the CDBs and the tables were populated with data as intended. The MOVES RunSpecs 

were run in batches using the MOVES command line tool. The batches were designed to write 

each MOVES run log to a text file for subsequent error/warning checking, of which none were 

found. The MOVES run summaries are included as Appendix I. 

Post-Processing Runs 

 Each MOVES output database was post-processed using a two-step process – for each 

county and year, an interim RatesCalc rate database was produced, followed by the final rates 

RatesAdj database containing the emissions rate tables for input to the emissions inventory 

calculations. The following post-processing steps were performed on each MOVES output 

database. See the utility descriptions in Appendix B for more information. 

 RatesCalc – Interim Rates Databases: Using RatesCalc, the mass/SHP off-network 

evaporative process rates were calculated using data from the CDB, the MOVES default 

database, and the MOVES rateperprofile and ratepervehicle emissions rate output. The 

utility also copied the mass/mile, mass/start, mass/hour rates along with the units into 

emissions rate tables. This utility does not perform any unit conversions, and excludes 

total energy and refueling processes. It created an output database containing the rates 

tables input to the RatesAdj utility. In addition to a table containing the run information 

and one with the activity data, there were four emissions rate look-up tables produced for 

each scenario: ttirateperdistance, ttirateperstart, ttirateperhour (for extended idle and 

auxiliary power exhaust), and ttiratepershp. 

 RatesAdj – Final Rate Databases: Using RatesAdj, TTI produced the final emissions rates 

for input to the EmsCalc emissions calculator. RatesAdj extracted emissions rates from 

the RatesCalc rate tables for only those pollutants needed in the emissions calculations, 

and applied TxLED adjustments (see factors in Table 23) to the diesel vehicle NOx

emissions rates for all counties and years. The ratesadj output databases, one for each 

county and year created for input to the emissions calculations, contain a ratesadjrun table 

of utility execution information, and the four emissions rate tables: ttirateperdistance, 

ttirateperstart, ttirateperhour, and ttiratepershp. 

 See Appendix B for more information on the TTI MOVES on-road rates development 

utilities. 

NOx Adjustment for TxLED Effects 

TTI produced the TxLED NOx adjustment factors shown in Table 23 according to a TCEQ 

procedure that produces each diesel-powered SUT’s average adjustment factor as a combination 

of a 4.8% reduction and a 6.2% reduction, for the 2002 and newer and for the 2001 and older 

model year vehicles, respectively.25

 

25 “Memorandum: Texas Low Emission Diesel (LED) Fuel Benefits.” To Karl Edlund, EPA, Region VI, from 

Robert Larson, EPA, Office of Transportation and Air Quality (OTAQ), National Vehicle and Fuel Emissions 

Laboratory at Ann Arbor, Michigan. September 27, 2001. 
50 

 The procedure involves MOVES runs that produce by-model year output data that are 

processed in a spreadsheet to calculate the aggregate, average NOx adjustment factors (across all 

model years). The resulting average NOx adjustment factors (by diesel SUT and calendar year of 

evaluation) are especially sensitive to the age distributions and calendar year inputs, which 

directly affect the relative NOx emissions contributions between the earlier and later model year 

groups. For 2012, Texas statewide vehicle age distribution estimates were used that were based 

on mid-year 2012 statewide TxDMV vehicle registrations data. For 2014 and later years, 

statewide age distribution estimates were based on the latest available mid-year (2014) statewide 

TxDMV vehicle registrations data. 

Table 23. Analysis Year TxLED Adjustment Factors Summary. 

Vehicle Type 

TxLED-Fueled Vehicle NOx Adjustment Factors1

2012 2014 2017 2020 2023 2026 2028 

Passenger Car 0.9413 0.9439 0.9483 0.9508 0.9514 0.9516 0.9515 

Passenger Truck 0.9466 0.9473 0.9492 0.9499 0.9505 0.9506 0.9513 

Light Commercial Truck 0.9434 0.9440 0.9465 0.9479 0.9493 0.9500 0.9503 

Intercity Bus 0.9416 0.9419 0.9431 0.9439 0.9449 0.9461 0.9477 

Transit Bus 0.9420 0.9423 0.9434 0.9449 0.9460 0.9477 0.9491 

School Bus 0.9420 0.9423 0.9433 0.9443 0.9455 0.9470 0.9485 

Refuse Truck 0.9438 0.9444 0.9462 0.9476 0.9497 0.9507 0.9515 

Single Unit Short-Haul Truck 0.9496 0.9500 0.9511 0.9515 0.9517 0.9518 0.9519 

Single Unit Long-Haul Truck 0.9497 0.9501 0.9510 0.9514 0.9516 0.9517 0.9518 

Motor Home 0.9443 0.9447 0.9462 0.9471 0.9479 0.9484 0.9495 

Combination Short-Haul Truck 0.9456 0.9465 0.9481 0.9489 0.9502 0.9510 0.9514 

Combination Long-Haul Truck 0.9445 0.9454 0.9474 0.9488 0.9503 0.9512 0.9516 

1

 Source: Developed by TTI using the TCEQ procedure and MOVES2014 October Release. 

 Appendix A describes the electronic data submittal for this inventory analysis, which 

includes the MRS files, CDBs, TxLED adjustment factor files used and calculation spreadsheets, 

and the final adjusted emissions rate look-up database table inputs to the emissions calculations. 

EMISSIONS CALCULATIONS 

TTI calculated hourly, summer weekday, link-based emissions inventories by county for each 

year using the EmsCalc utility. Emissions calculations fall into two categories: VMT-based and 

off-network. The VMT-based emissions calculations use the roadway-based rates and the TDMbased VMT and speeds to estimate emissions at the TDM network link (or roadway segment) 
51 

level. The off-network emissions process calculations use off-network rates and off-network 

activity (SHP, starts, SHI, and APU hours) to estimate emissions at the county level. 

 EmsCalc output for each county and year included three files: a listing file (of run execution 

information), a standard tab-delimited inventory summary (with both hourly and 24-hour 

summary tables), and a tab-delimited 24-hour inventory summary by SCC. The county SCC 

inventory summaries for each year were input to TTI’s MOVESsccXMLformat utility, which 

converted them into an EPA CERS XML inventory format for each year for loading in TCEQ’s 

TexAER. 

Hourly Link-Based Emissions Calculations 

The county, analysis year, summer weekday, hourly emissions were calculated with the EmsCalc 

utility using the following major inputs: 

 Vehicle type VMT mix – TxDOT district-level, weekday, by MOVES roadway type for 

four time-of-day periods; 

 Time period designation – the four VMT mix time periods to hour-of-day associations; 

 Roadway-based activity – link (and intrazonal link)-specific, hourly, directional, 

operational VMT and speed estimates as developed by the TRANSVMT utility to 

include: A-node, B-node, county number, TDM road type (functional class) code, link 

length, congested (operational) speed, VMT, and TDM area type code; 

 Roadway-based emissions factors – MOVES-based, county level by pollutant, process, 

hour, average speed, MOVES road type, SUT, and fuel type; 

 Off-network activity – county, hourly SHP, starts, SHI, and APU hours by vehicle type; 

 Off-network (parked vehicle) emissions factors – MOVES-based, county level by 

pollutant, process, hour, SUT, and fuel type; 

 TDM road type designations – TDM road type and area type codes to MOVES road type 

codes (and to VMT mix road type, and to rates road type codes) (see Table 24); and 

 On-road SCCs – from the MOVES2014 default database corresponding to MOVES SUT, 

fuel type, road type, and emissions process categories. 

 The VMT-based emissions were calculated for each hour using the TxDOT district-level 

vehicle type VMT mix with time period-to-hour designations, the TDM link and intrazonal link 

VMT and speeds estimates, the MOVES-based “on-network” emissions factors, and the TDM 

road type/area type to MOVES road types designations. For each link, the link was assigned a 

MOVES road type (and VMT mix road type and rates road type, which for this analysis were the 

same as the MOVES road type) based on the link’s road type and area type. The link VMT was 

then distributed to each vehicle type using the appropriate VMT mix, based on the link’s 

designated VMT mix road type, its associated TxDOT district, and time-of-day period to hourof-day designation. 

 The emissions factors for each vehicle type for each hour were selected based on the link’s 

designated rates road type code (same as MOVES road type code) and the link speed. For link 
52 

speeds falling between MOVES speed bin average speeds, emissions factors were interpolated 

from bounding speeds. For link speeds falling outside of the MOVES speed range (less than 2.5 

mph and greater than 75 mph), the emissions factors for the associated bounding speeds were 

used. For each pollutant and process, the mass/mile rates were multiplied by the link VMT 

producing the link-level emissions estimates for each vehicle type. 
53 

Table 24. H-GAC TDM Road Type/Area Type to MOVES Road Type Designations. 

TDM Road Type 

(Code - Name)1

TDM Area Type 

(Code - Name)1

MOVES Road Type 

(Code - Name)1, 2

3 - Toll Roads 5 – Rural 

10 - Rural Interstate 5 – Rural 2 – Rural Restricted Access 

11 - Rural Other Freeway 5 – Rural 

4 - Ramps (Fwy/Toll/Frnt) 5 – Rural 

3 – Rural Unrestricted Access 

8 - Local (Centroid Connector) 5 – Rural 

12 - Rural Principal Arterial 5 – Rural 

13 - Rural Other Arterial 5 – Rural 

14 - Rural Major Collector 5 – Rural 

15 - Rural Collector 5 – Rural 

1 - Urban Interstate 1 – CBD; 2 – Urban; 3 – Urban 

Fringe 

4 – Urban Restricted Access 

2 - Urban Other Freeway 2 – Urban; 3 – Urban Fringe 

3 - Toll Roads 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban 

10 - Rural Interstate 2 – Urban; 3 – Urban Fringe; 4 – 

Suburban 

11 - Rural Other Freeway 3 - Urban Fringe; 4 – Suburban 

4 - Ramps (Fwy/Toll/Frnt) 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban 

5 – Urban Unrestricted Access 

5 - Urban Principal Arterial 1 – CBD; 2 – Urban; 3 – Urban 

Fringe 

6 - Urban Other Arterial 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban 

7 - Urban Collector 1 – CBD; 2 – Urban; 3 – Urban 

Fringe 

8 - Local (Centroid Connector) 1 – CBD; 2 – Urban; 3 – Urban 

Fringe; 4 – Suburban 

12 - Rural Principal Arterial 3 – Urban Fringe; 4 – Suburban 

13 - Rural Other Arterial 3 – Urban Fringe; 4 – Suburban 

14 - Rural Major Collector 3 – Urban Fringe; 4 – Suburban 

15 - Rural Collector 3 – Urban Fringe; 4 – Suburban 

40 - Local (Intrazonal) 40 – Local (Intrazonal) 

1 The TDM road type and area type code combinations are also correlated to VMT mix road type codes and 

emissions rate road type codes, which, for this analysis, are identical to the MOVES road type codes. 2

 The four period, time-of-day VMT mix to hour-of-day designations are: AM peak – three hours of 6 a.m. to 9 a.m.; 

mid-day – six hours of 9 a.m. to 3 p.m.; PM peak – four hours of 3 p.m. to 7 p.m.; and overnight – 11 hours of 7 

p.m. to 6 a.m. 

 The off-network emissions were calculated for each hour using the hourly MOVES-based 

off-network emissions factors by vehicle type and the county-level hourly vehicle type offnetwork activity estimates (SHP, starts, SHI, and APU hours). The emissions factors were 
54 

multiplied by the appropriate off-network activity, as determined by the pollutant processactivity association (shown previously in Table 15). 

 The EmsCalc utility outputs for the analysis consist of the listing file (summarizing 

information regarding running the utility), the standard tab-delimited inventory report summary 

file including both hourly and 24-hour activity and pollutant process emissions estimates by 

vehicle type and TDM road type, and the MOVES SCC tab-delimited 24-hour inventory report 

summary. 

Conversion of Emissions Inventories to XML Format 

TTI converted the 24-hour emissions and activity results for each year to a format compatible for 

uploading to the TCEQ’s TexAER – based on the EPA’s EIS NEI CERS XML format, which 

uses EPA’s EIS inventory data codes, except that the new SCCs and the NEI pollutant codes 

available in the MOVES2014 default database (MOVESdb20141021) “scc” and “pollutant” 

tables were used. To make the conversion of the EmsCalc utility’s SCC-based inventory output 

to the specified XML format, TTI used its MOVESsccXMLformat utility and the following 

inputs: 

 Pollutants file – list of input pollutant labels (as written in the EmsCalc SCC-based 

inventory file to be processed, e.g., “Pounds of CO Emissions”), and corresponding EIS 

pollutant codes (e.g., “CO”) and units codes (e.g., “LB”) for each pollutant to be included 

in the XML output; 

 XML header input file – specifies the data used in the header portion of the XML output 

file, along with the reporting period and the VMT units; and 

 Inventory data files to be converted – one EmsCalc-produced, MOVES SCC-based, 24-

hour emissions inventory summary file for each county to be included in the XML file. 

Contains a county code record and the following fields: SCC, Activity Type, Activity, 

followed by an emissions field for each pollutant using field headers labeled consistent 

with the EmsCalc emissions summary tables (e.g., Pounds of CO Emissions, Pounds of 

PM10_Total_Exh Emissions, Pounds of PM10_Brakewear Emissions). 

 TTI performed one MOVESsccXMLformat utility run per analysis year producing one XML 

document file per year containing the EIS CERS coded inventories for all eight counties. Thus, 

seven XML files were produced, one for each inventory year. The SCCs are 10 digits, composed 

of “22” (code for on-road mobile source) followed respectively by the four, two digit MOVES 

category IDs: fuelTypeID, sourceTypeID, roadTypeID, processID. Thus, all these dimensions 

were retained in the XML inventory summary, although off-network activity codes were not 

available. In the absence of EIS codes for off-network activity, the practice of coding these 

activity types (SHP, starts, SHI, and APU hours) as “VMT” with their values set to zero was 

applied. The pollutants included in the XML files were: VOC, CO, NOx, SO2, NH3, PM2.5, PM10

(exhaust, tirewear and brakewear), and CO2. 

 Each run produced a LST file (execution information, input/output file listings, input 

summaries, and input and output totals summaries with calculated differences for QA checks), 

the XML file, and one XML output summary of SCC-labeled inventory data in a tab-delimited 
55 

text file form for each county included in the run. All these files were included in the electronic 

data submittal (Appendix A). 

Additional CDBs for MOVES Inventory Mode 

The MOVES CDBs used to produce emissions rates for the link-based inventory analyses were 

designed only for use in MOVES rates mode runs. TTI developed an extra set of CDBs 

consistent with the link-based inventory data that may be used with MOVES in the inventory 

mode. These inventory mode CDBs were designed to input summer weekday (daily) activity 

data, and with the appropriate MRSs, produce summer weekday (daily) output inventory 

estimates. One inventory mode CDB was built corresponding to each link-based inventory, or 

one per county per year, for a total of 56. 

 These inventory CDBs include the 26 input data tables shown in Table 25 with 

corresponding data sources. The inventory CDBs include the same 20 MOVES tables used in 

the link-based inventory analysis rates mode CDBs (see Table 18), plus six additional tables 

(hotellingactivitydistribution, hotellinghours, sourcetypeage, starts, monthofanyyear, and 

dayofanyweek). 

 The inventory mode CDB data source categories are: 

 Rates CDB (mainly local data directly from the link-based inventory analysis rates 

CDBs); 

 MOVESactivityInputBuild utility output (for activity inputs built from the pertinent linkbased inventory data); 

 OffNetActCalc utility output with adjustments (for hotellinghours, starts); 

 VehiclePopulationBuild utility output (for vehicle population estimates); 

 MOVES defaults (only for hotellingactivitydistribution), and 

 Adjusted MOVES defaults (for activity allocation factors modified as needed to produce 

daily output from daily activity input, and for sourcetypeage table relativeMAR 

adjustments to produce VMT proportions between HPMS vehicle categories that more 

closely reflect the local VMT Mix). 

 The inventory development utilities descriptions, to include MOVESactivityInputBuild, 

OffNetActCalc, and VehiclePopulationBuild utilities are included in Appendix B. 
56 

Table 25. MOVES Inventory Mode CDB Data Sources. 

Table Data Source 

avft Rates CDB 

avgspeeddistribution MOVESactivityInputBuild utility output 

county Rates CDB 

countyyear Rates CDB 

dayvmtfraction MOVES default with dayVMTFraction = 1 (dayID = 5) and 

dayVMTFraction = 0 (dayID = 2) 

fuelsupply Rates CDB 

fuelformulation Rates CDB 

hotellingactivitydistribution MOVES defaults 

hotellinghours 

OffNetActCalc utility output with travel fractions (developed 

using the age distribution and relative MAR) applied to distribute 

to ageID 

hourvmtfraction MOVESactivityInputBuild utility output 

hpmsvtypeyear MOVESactivityInputBuild utility output 

imcoverage Rates CDB 

monthvmtfraction Rates CDB with monthVMTFraction = 1 

roadtype MOVESactivityInputBuild utility output 

roadtypedistribution MOVESactivityInputBuild utility output 

sourcetypeage 

MOVES default with relativeMAR adjusted for VMT Mix (travel 

fractions calculated using relativeMAR adjusted to match 24-hour 

VMT from link-level inventory) 

sourcetypeagedistribution Rates CDB 

sourcetypeyear VehiclePopulationBuild utility output 

starts OffNetActCalc utility output with age distribution applied to 

distribute to ageID 

state Rates CDB 

year Rates CDB 

zone Rates CDB 

zonemonthhour Rates CDB 

zoneroadtype Rates CDB 

monthofanyyear MOVES default with noOfDay = 7 

dayofanyweek MOVES default with noOfRealDays = 1 
57 

 TTI built the MOVES inventory mode CDBs, checked that each one contained all the 

required tables, and that they were populated as intended. These MOVES inventory mode CDBs 

along with the MySQL scripts used to create them, were provided as a part of the electronic data 

submittal. 

 See Appendix A (Electronic Data Submittal) for the listing and descriptions of emissions 

inventory data files submitted as a part of this project report. See Appendix B for additional 

EmsCalc utility information, and an emissions calculation process flow diagram. 

QUALITY ASSURANCE 

Analyses and results were subjected to appropriate internal review and QA/QC procedures, 

including independent verification and reasonableness checks. All work was completed 

consistent with applicable elements of ANSI/ASQ E4-2004: Specifications and Guidelines for 

Quality Systems for Environmental Data Collection and Technology Programs and the TCEQ 

Quality Management Plan. 

 Quality Assurance Project Plan (QAPP) Category II (Modeling for NAAQS Compliance) is 

the QAPP category that most closely matches these objectives and establishes QAPP 

requirements for projects involving applied research or technology evaluations. Internal review 

and quality control measures consistent with applicable NRML QAPP requirements, along with 

appropriate audits or assessments of data and reporting of findings, were conducted. These 

included, but were not limited to, the elements outlined in the following description. 

A. Project Management 

The project management was as listed previously in the Acknowledgements section. 

 The definition and background of the problem addressed by this project, the project/task 

description, and project documents and records produced are as described previously in the 

Purpose and Background sections. No special training or certifications were required. The TTI 

project manager assured that the appropriate project personnel had and used the most current, 

approved version of the QAPP. 

 After receiving the Notice to Commence (NTC) from TCEQ, the TTI project manager 

provided a detailed pre-analysis plan to the TCEQ project manager for review and concurrence. 

Upon concurrence of the pre-analysis plan, the TTI project manager distributed the pre-analysis 

plan to the TTI inventory developers for use in both the inventory development and QA review 

process. TTI maintains records of the project QA checks as a part of the project archive, for at 

least five years. 

 The objective was to produce the emissions inventory product in the quality suited to its 

purpose as specified (i.e., inventories needed for HGB re-designation analyses purposes), 

informed by, and consistent with, the appropriate guidance and methods provided in the listed 

references, as detailed in the pre-analysis plan, and in consultation with the TCEQ project 

manager. 
58 

 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 (i.e., for use in the HGB eightcounty area re-designation analysis); 

 The full extent of the modeling domain (i.e., analysis years, geographic coverage, 

seasonal periods, days, sources, pollutants) was included; 

 Agreed methods, models, tools, and data were used (i.e., as listed in the detailed preanalysis plan); 

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

with the pre-analysis plan; 

 Any deficiencies found during development and end-product quality checks (as discussed 

in QAPP Section D) were corrected; and 

 Aggregate emissions estimate results were assessed for comparability with available, 

similarly produced emissions estimates. 

B. Data Generation and Acquisition 

Note that no sampling of data was involved in the emissions inventory development, thus only 

existing data (non-direct measurements) were used for this project. 

 The data needed for project implementation were in the categories needed for development 

of emissions rate model inputs and adjustment factors, and for development of the activity inputs 

for external emissions calculations. These emissions factor model inputs and activity inputs 

were developed using data sources as outlined previously and/or methods and procedures as 

detailed in the references listed, and as provided in the pre-analysis plan. 

 All data used either as direct input or to produce inputs (e.g., to the MOVES model or to 

TTI’s emissions inventory development utilities used, which were listed in the pre-analysis plan) 

were reviewed by TTI for suitability before use. The data sets for the project were provided by 

TxDOT, a Metropolitan Planning Organization (MPO) or Council of Governments (COG), 

TCEQ, and/or the EPA, and in most cases were QA’d by the providing agency. The data needed 

may include: HPMS data (from TxDOT’s Roadway Inventory Functional Classification Record 

[RIFCREC] report); regional travel demand model data; speed model data; vehicle registration 

data; ATR data; vehicle classification count data; meteorological data; fuels data; MOVES 

emissions model data; extended idling activity data; and vehicle I/M program design data. 

 Any significant problems found during data review, verification, and/or validation (see QA 

criteria and methods discussion in section D) were to be corrected, and the QA procedure was 

repeated until satisfied. No significant problems were found. 

 Data Management: TTI emissions inventory data developers work as a closely coordinated 

team. The assigned staff used the same electronic project folder structure on their individual 

workstations. As various scripts, inputs, and outputs were developed in the emissions inventory 
59 

development process, data were shared within the team for crosschecking via an intra-net, flash 

drive, or external hard drive. To perform the MOVES model runs, a computer cluster (multiple 

computer) configuration or individual workstation configuration was used. After input data were 

QA’d, depending on the size of the data set, the data sets were backed up and stored in 

compressed files. These activities were performed throughout the process until the final products 

were produced. 

 For MOVES model runs to produce emissions factor look-up tables for the emissions 

inventories, all run files (MOVES model inputs and batch files) were produced on an individual 

workstation. After the MOVES input data and batch files (i.e., Run Files) were QA’d, they were 

either executed on an individual workstation, or they were copied (via external hard drive) to the 

cluster’s master computer and executed. Upon execution, completion, and error checking, the 

MOVES output databases and run log text files were (for cluster runs first copied to an 

individual workstation), archived and processed further in preparation for input to the emissions 

calculations. 

 After the final product was completed, all the project data archives were compiled on a set of 

optical data discs (CD-ROM or DVD, depending on size), or on an external drive for very large 

project data sets. A complete archive of the project data is kept by TTI (the computer models 

and emissions inventory development utilities used in the process are included). An electronic 

data submittal package (containing the project deliverables as listed in Appendix A) was 

produced along with data descriptions (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 and geographic coverage, for specified sources, pollutants, 

and emissions processes). 

 Checked input data preparation, and model or utility execution instructions (e.g., run 

specifications, scripts, JCFs, command files) were prepared according to the plan; and 

 Checked that correct output data were produced (includes interim output [output that 

becomes input to a subsequent step in the inventory development process], as well as the 

final product). Records were kept of the checks performed. 

 In the case that any inconsistencies or deficiencies were found, the issue was directly 

communicated to the responsible staff for corrections (or the outside agency staff involved, if 

provided from outside of TTI, if needed). After a correction was made, the QA checks were 

performed again to ensure that the additional work resulted in the intended quality assured result, 

and the correction was noted in the QA record (process was performed until QA check was 

satisfied). 

 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 this draft report and will also be in the final reports. 
60 

D. Data Validation and Usability 

Erroneous or improper inputs at any point during the emissions inventory development process 

may produce resulting emissions estimates that are inaccurate and may not be suitable for their 

intended purpose. Adherence to the inventory process flow with performance of the integrated 

QA checks at each step of the process was of the utmost importance to ensure that the results met 

the project objectives. 

 Therefore, the QA checks listed were performed until satisfied to ensure that the resulting 

emissions inventories met the TCEQ’s requirements of intended use. 

 TTI 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 (i.e., needed for the HGB re-designation analysis); 

 Extent of the modeling domain (e.g., analysis years, geographic coverage, seasonal 

periods, days, sources, pollutants); 

 Methods, models, and data used (e.g., default versus local input data sources); and 

 Procedures and tools used and all required emissions output data sets were produced. 

 TTI performed checks on input data preparation, model or utility execution instructions (e.g., 

run specifications, scripts, JCFs, command files), and output, as appropriate to the component: 

 Input data preparation checks: 

o Verified the basis of input data sets against the pre-analysis plan: Actual historical 

or latest available data, validated model, expected values or regulated limits, 

regulatory program design, model defaults, surrogates, professional judgment; 

aggregation levels; 

o Data development: Depending on the procedure and particular input data set, 

calculations were verified (e.g., re-calculated independently and compared with 

originally prepared values – if spot-checking a series of results, included extremes 

and intermediate values); 

o Completeness: Verified that input data sets were within the required dimensions, 

and all required fields were populated and properly coded or labeled; 

o Format: Verified that formats were within required specifications (e.g., field 

positions, data types and formats, and file formats), if any; 

o Reasonability checks: (discussed in the next section); and 

o Ensured that any inputs provided from external sources were quality assured, as 

listed previously. 

 Checked the model or utility execution instructions: 

o Verified that the correct number of utility or model run specifications were 

prepared for each application (e.g., by year, county, season, day type); and 
61 

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; and 

o Checked the summary information provided in output listing files for any unusual 

results. 

 TTI performed further checks for consistency, completeness, and reasonability of data output 

from model or utility applications: 

 Verified that the data distributions and allocation factors produced or used sum to 1.0, as 

appropriate (e.g., hourly travel factors within a time period, proportion of travel by 

vehicle categories on a particular roadway category); 

 Verified that the required data fields were present, populated, and properly coded or 

labeled; verified that data and file formats were within specifications; 

 Verified that any activity, emissions rate, or emissions adjustments were performed as 

intended (e.g., seasonal activity factor, emissions control program adjustment); 

 For data sets prepared with temporal or geographic variation (e.g., activity distributions 

between weekends/weekdays, vehicle mix by day type, or average speeds between road 

types or time periods), compared and noted whether directional differences were as 

expected; 

 Checked for consistency between data sets (e.g., compared detailed spatially and 

temporally disaggregated activity estimates [e.g., link VMT] to original aggregate totals, 

activity total summaries between utility applications [e.g., link-VMT producer and 

emissions calculator], and input hourly distributions versus hourly summaries from the 

link activity output data); 

 Calculated county, 24-hour, aggregate emissions rates (from aggregate VMT and 

emissions output) and compared the rates between counties examining the results for 

outliers while assessing the reasonability of any relative and directional differences (e.g., 

qualify based on activity distributions by road type and speed, mix of vehicles by road 

type, meteorological variation, control program coverage). Compared the results to 

results from previous emissions analyses where available; and 

 Calculated county, 24-hour aggregate rates by vehicle class and compared between 

vehicle classes. Examined the results for consistent patterns. 
62 

 Any additional data products required for the emissions analysis were subjected to the 

appropriate QA checks previously listed. 

REFERENCES 

2008 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/. 

2011 On-Road Mobile Source Actual Annual and Weekday Emissions Inventories: Houston 

Area. Prepared for the Texas Commission on Environmental Quality. Texas A&M 

Transportation Institute, The Texas A&M University System, College Station, TX. August 

2012. 

Development and Production of 2006 Base Case and 2008 Baseline On-Road Mobile Source 

Emissions Inventories for the HGB Nonattainment Area, Prepared for the Texas 

Commission on Environmental Quality. Texas A&M Transportation Institute, The Texas 

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

Draft Motor Vehicle Emission Simulator (MOVES) 2009, Software Design and Reference 

Manual. EPA420-B-09-007, Assessment and Standards Division Office of Transportation 

and Air Quality, U.S. Environmental Protection Agency. March 2009. 

Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National 

Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations. EPA-454/R-05-

001, issued by the Emissions Inventory Group, Emissions, Monitoring and Analysis 

Division, Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 27711. August 2005. 

Guidance for Quality Assurance Project Plans for Modeling. EPA QA/G-5M, EPA/240/R02/007, Office of Environmental Information, U.S. Environmental Protection Agency. 

December 2002. 

Heavy-Duty Vehicle Idle Activity and Emissions Characterization Study. Eastern research 

Group, Inc. August 2004. 

HGB MOVES-Based RFP On-road Inventories and Control Strategy Reductions. Prepared for 

the Texas Commission on Environmental Quality. Texas A&M Transportation Institute, The 

Texas A&M University System, College Station, TX. March 2012. 
63 

“Memorandum: Texas Low Emission Diesel (LED) Fuel Benefits.” To Karl Edlund, EPA, 

Region VI, from Robert Larson, U.S. Environmental Protection Agency, Office of 

Transportation and Air Quality (OTAQ), National Vehicle and Fuel Emissions Laboratory at 

Ann Arbor, MI. September 27, 2001. 

Methodologies for Conversion of Data Sets for MOVES Model Compatibility. Prepared for the 

Texas Commission on Environmental Quality. Texas A&M Transportation Institute, The 

Texas A&M University System, College Station, TX. August 2009. 

Motor Vehicle Emissions Simulator (MOVES), User Guide for MOVES2010b. EPA420-B-12-

001b, Assessment and Standards Division, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency. June 2012. 

Motor Vehicle Emission Simulator (MOVES) User Guide for MOVES2014. EPA420-B-14-055, 

Assessment and Standards Division, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency. July 2014. 

MOVES2010b-Based On-Road Inventories in Support of the HGB Ozone Nonattainment Area 

Re-Designation Substitute Analysis. Prepared for the Texas Commission on Environmental 

Quality. Texas A&M Transportation Institute, The Texas A&M University System, College 

Station, TX. June 2014. 

Procedures for Emission Inventory Preparation, Volume IV: Mobile Sources. EPA420-R-92-

009, Emission Planning and Strategies Division, Office of Mobile Sources and Technical 

Support Division, Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency. December 1992. 

Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State 

Implementation Plans and Transportation Conformity. EPA420-B-10-023, Transportation 

and Regional Programs Division, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency. April 2010. 

TTI Emissions Inventory Estimation Utilities Using MOVES: MOVES2010bUTL User’s Guide. 

Prepared for the Texas Commission on Environmental Quality. Texas A&M Transportation 

Institute, The Texas A&M University System, College Station, TX. August 2013. 

Revision to the State Implementation Plan Mobile Source Strategies, Inspection and 

Maintenance State Implementation Plan Revision. Texas Commission on Environmental 

Quality, Austin, TX, adopted February 12, 2014. 

TTI Emissions Inventory Estimation Utilities Using MOVES: MOVES2010bUTL User’s Guide. 

Prepared for the Texas Commission on Environmental Quality. Texas A&M Transportation 

Institute, The Texas A&M University System, College Station, TX. August 2014. 

Update of On-road Inventory Development Methodologies for Compatibility with the 2014 

Version of the Motor Vehicle Emission Simulator Model. Prepared for the Texas 

Commission on Environmental Quality. Texas A&M Transportation Institute, The Texas 

A&M University System, College Station, TX. December 2014. 
64 

Using MOVES to Prepare Emission Inventories in State Implementation Plans and 

Transportation Conformity: Technical Guidance for MOVES2010, 2010a, and 2010b. EPA, 

April 2012. EPA420-B-10-028, Transportation and Regional Programs Division, Office of 

Transportation and Air Quality, U.S. Environmental Protection Agency. April 2012. 

VMT-Offset Emissions Inventories for the HGB Eight-Hour Ozone Nonattainment Counties. 

Prepared for the Texas Commission on Environmental Quality. Texas A&M Transportation 

Institute, The Texas A&M University System, College Station, TX. January 2013. 
65 

APPENDIX A: 

ELECTRONIC DATA SUBMITTAL 

67 

Electronic Data Submittal — HGB MOVES2014 Inventories for the 1997 Eight-Hour 

Ozone Re-Designation Analysis 

Described herein is the electronic data submittal for one of two HGB inventory analyses TTI 

submitted to TCEQ, per Proposal for Grant Activities No. 582-15-51346-16. (The other 

electronic data submittal was for the one-hour re-designation substitute inventories.) 

 The MOVES rates-per-activity, TDM link-based method produced HGB ozone season 

weekday inventories of VOC, CO, NOx, PM10, PM2.5, SO2, NH3, and CO2 for the seven years 

2012, 2014, 2017, 2020, 2023, 2026, and 2028, or 56 county-level inventories. 

Electronic Media 

Files and databases were compressed and submitted on one DVD, entitled: “HGB 1997 EightHour Ozone Analysis MOVES2014 On-Road Inventories – TTI FY2015.” In addition to this data 

description, the DVD includes: 

 Emissions inventory files (tab-delimited output and extracts, XML files); 

 Emissions factors (MOVES emissions rate mode input files and final TxLED-adjusted 

emissions rates used in inventory development); and 

 Additional data files (MOVES CDBs developed for use in MOVES model inventory 

mode runs, vehicle population summaries, VHT and VMT summaries, etc.). 

File-Naming Conventions 

 YYYY is the analysis year (i.e., 2012, 2014, 2017, 2020, 2023, 2026, and 2028), and 

 FFFF is FIPS county code (48039, 48071, 48157, 48167, 48201, 48339, and 48473 for 

Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, and Waller). 

 Note: MySQL database tables are composed of three files: “*.frm,” “*.MYD,” and “*.MYI.” 

Emissions Inventory Files 

The following lists the emissions inventory files contained in the electronic data submittal. 

 Emissions Inventory Output – Each EmsCalc utility run (56) produced three output files (two 

“*.TAB” files and an “*.LST”) compressed in “hgb8hr97_mvs14_emscalc.zip:” 

 “hgb8hr97_mvs14_FFFF _YYYYswkd_ems.TAB” are tab-delimited text files of county 

emissions inventory data summary reports including hourly and 24-hour activity and 

emissions tables. By roadway and vehicle type (SUT/Fuel Type), the reports include 

VMT, VHT, average speed (VMT/VHT), and pollutant/process emissions totals. For offnetwork-based processes by vehicle type, the reports include SHP, starts, SHI and APU 

hours, and pollutant/process emissions totals; 
68 

 “hgb8hr97_mvs14_FFFF _YYYYswkd_sccoutput_ems.TAB” are tab-delimited 24-hour 

inventory summaries by on-road SCCs available in MOVES (by fuel type, source type, 

road type, and process). The first row is descriptive text, the second row is the FIPS 

county code, the third row are column headers: SCC, Activity Type, Activity, then 

emissions headers for each pollutant, followed by the data records beginning in the fourth 

row; and 

 “*.LST” are utility execution listing text files corresponding to the above “*.TAB” files 

and have similar file name prefixes as the “*.TABs,” listing the run execution times, run 

script, file locations, data descriptions, and varied data summaries including hourly and 

24-hour activity, pollutant/process emissions totals, and average speed (VMT/VHT). 

 Six Inventory Extracts – Using the tabfiletotals utility, data were extracted from the EmsCalc 

output to produce six different inventory aggregations (activity and emissions by pollutant and 

process) per year in tab-delimited text file form. Each extract (or “tabtots”) file contains countylevel data for all eight counties. These “tabtots” and the “*.LST” files (49) were provided in 

“hgb8hr97_mvs14_tabtots.zip:” 

 “hgb8hr97_mvs14_YYYYswkd_tabtots.lst” (tabfiletotals execution record); and 

 “hgb8hr97_mvs14_YYYYswkd_????.tab” (six different extract files per year). 

 Where the “????” descriptor is: 

o “tabtots” (24-hr totals); 

o “tabtots_Hr” (hourly totals); 

o “tabtots_HrST” (hourly, SUT/fuel type totals); 

o “tabtots_RdType” (hourly, road type totals); 

o “tabtots_ST” (24-hr SUT/fuel type totals); and 

o “tabtots_24hourRdTypeST” (24 hour, road type, SUT/fuel type totals). 

 XML Files for TexAER and Tab-Delimited SCC-Based Inventory Summary Files – 

MOVESsccXMLformat utility 24-Hour output for each year: one LST text file (execution record 

with input/output summaries including input/output comparisons); one XML file (inventory data 

coded for uploading to TCEQ’s TexAER); and eight county-level SCC-based inventory 

summary files (each tab-delimited text file includes a county summary by SCC from the XMLcoded results). The following 70 output files were provided in 

“hgb8hr97_mvs14_xmlformat.zip:” 

 MOVESsccXMLformat_hgb8hr97_mvs14_YYYYswkd.lst; 

 MOVESsccXMLformat_hgb8hr97_mvs14_YYYYswkd.xml; and 

 MOVESsccXMLformat_hgb8hr97_mvs14_YYYYswkd_FFFF_summary.tab.
69 

Emissions Factors – MOVES Input Files and Final TxLED-Adjusted Emissions Rates 

The following emissions factor files are contained in the electronic data submittal. 

 MOVES Input Files – The 56 MOVES runs required 56 MRS XML files and 56 CDBs. The 

default database (MOVESDB20141021), also required but not included in this submittal, is 

available from the EPA’s MOVES website. The MRSs and CDBs were provided in: 

“hgb8hr97_mvs14_MRSs.zip” and “hgb8hr97_mvs14_CDBs_ermode.zip:” 

 “MVS14_HGB8HR97_YYYYSWKD_FFFF_ER.MRS,” (56 MRS files); and 

 “mvs14_hgb8hr97_YYYYs_FFFF_er_cdb_in” (56 CDBs each, containing 20 database 

tables, consisting of three files per table, plus one db.opt file per CDB). 

 MySQL Scripts, Databases and Files for Building CDBs – CDBs were built using MySQL 

scripts, data files, and databases provided in “hgb8hr97_mvs14_CDBscripts_data.zip” (144 

files): 

 “MVS14_HGB8HR97_YYYYS_FFFF_ER_CDB_IN.SQL” (56 text files containing 

MySQL scripts for building the 56 CDBs); 

 “hgb_metinputs_summer2012tceq” (database with meteorological inputs – county and 

zonemonthhour tables – and development files – 12 files); 

 “_fuels_hgb2012s_mvs10b,” “_fuels_hgb2014s_mvs10b,” and 

“_fuels_hgb2014s_2017t3sulfur_mvs10b” (three fuels inputs databases with fuelsupply 

and fuelformulation tables, used respectively for 2012, 2014, and 2017 and later years – 

survey data spreadsheets and other development files – 33 files); 

 “_tx1990_19992050_mvs14_imcoverage_213132” (imcoverage database table used and 

a readme file – 5 files); 

 “mvs14_movesdb20141021_HGBFFFF_XXXXj_SUTage.tab” and “*.LST” (tabdelimited, county sourcetypeagedistribution text files, where XXXX is 2012 and 2014, 

which are registration data years, and the associated “*.LST” text files are from each 

MOVESfleetbuilder utility run – 32 files); 

 “mvs14_movesdb20141021_TX_XXXXj_SUTage.tab,” 

“mvs14_movesdb20141021_TX_XXXXj_SUTfef.tab,” and “*.LST” (tab-delimited, 

statewide “sourcetypeagedistribution” files [SUTage] and “avft” files [SUTfef], where 

XXXX is 2012 and 2014, which are registration data years, and the associated “*.LST” 

files are from each MOVESfleetbuilder utility run – 6 text files); and 

 “MOVESDB20141021” (the MOVES default database, although not provided in this 

submittal, was also used in building the CDBs). 

 Final MOVES-Based, TxLED-Adjusted Emissions Factor Look-Up Tables – The RatesAdj 

utility performed NOx TxLED effects adjustments and produced the final emissions rates inputs 

to the emissions calculations – 56 MySQL databases each containing a “ratesadjrun” table (a run 

log) and the four rate tables: “ttirateperdistance” for roadway-based emissions processes, and 

“ttirateperhour,” “ttiratepershp,” and “ttirateperstart” for off-network processes. Each rate table 
70 

contains the emissions rate field that it is named for: ratePerDistance, ratePerHour, ratePerSHP, 

or ratePerStart. Rate table fields in common are: pollutantID, processID, hourID, sourceTypeID, 

fuelTypeID, and Units_Per_Activity. The ttirateperdistance table also includes the 

avgSpeedBinID and roadTypeID fields. Emissions rates are in pounds per unit of activity. 

 “mvs14_hgb8hr97_YYYYswkd_FFFF_er_outratesadj,” (56 MySQL database folders 

each containing five tables and one db.opt file). Each database contains the emissions 

rate look-up tables used in the emissions calculations. They are compressed in 

“hgb8hr97_mvs14_outratesadjDBs.zip.” 

 TxLED Factors – The seven TxLED factor files used (plus one for 2011) were provided 

along with a readme file, the TxLED factor development spreadsheets, and other files (e.g., 

MOVES runs input/output, CDB scripts, etc.). These files were provided in 

“hgb8hr97_mvs14_txledFactors.zip.” The TxLED factor input files used with MOVESRatesAdj 

are: 

 “YYYY_NOx_txledFactors*.tab” (TxLED factor input text files by analysis year). 

 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 RatesCalc utility, TTI produced “rates-per-activity” 

emissions rate look-up tables, including rates directly from the MOVES rate tables output 

(i.e., “per mile,” “per hour,” “per start”), plus the evaporative “per SHP” rates not directly 

available from MOVES output, but calculated by RatesCalc. 

2. Final Rates Adjustments: Using TTI’s RatesAdj utility, rates from RatesCalc output were 

extracted for only those pollutants needed in the emissions calculations. TxLED 

adjustments were applied to diesel vehicle NOx rates for all analysis years, and the 

extracted and adjusted rate tables were placed in a separate database (by county and year) 

for input to the emissions calculations. 

Additional Data Files 

TTI post-processed the inventory activity and other data (e.g., vehicle registrations) to produce 

data summaries and some data (vehicle population and VMT factors) in MOVES-specified 

formats. (These data were not used in the MOVES emissions rate mode runs, but include, or are 

based on, data that were used externally in activity factor and emissions calculations). 

 The VehPopulationBuild utility runs produced the vehicle population estimates used 

externally in the emissions estimation process. The following county-level text files were 

produced (contained in the zip file “hgb8hr97_mvs14 VehPop.zip,” with 224 files): 

 “*FFFF_VEHPOP_YYYY_sourcetypeyear.tab” (MOVES sourcetypeyear files); 

 “*FFFF_VEHPOP_ YYYY_regdata.tab” (registrations by type and model year); 

 “*FFFF_VEHPOP_ YYYY_StFtPop.tab” (SUT/fuel type population summaries); and 
71 

 “*FFFF_VEHPOP_ YYYY.lst” (utility run list files). 

 The MOVESactivityinputbuild utility runs produced the MOVES table data tab-delimited text 

files listed (contained in the zip file “hgb8hr97_mvs14 ActInput.zip,” with 504 files): 

 Year: “*FFFF_YYYY_swk_MOVESactInputBld_year.tab;” 

 Roadtype: “*FFFF_YYYY_swk_MOVESactInputBld_roadtypetable.tab;” 

 Hpmsvtypeyear: “*FFFF_YYYY_swk_MOVESactInputBld_hpmsvtypeyear.tab;” 

 Roadtypedistribution: “*FFFF_YYYY_swk_MOVESactInputBld_rtdist.tab;” 

 Hourvmtfraction: “*FFFF_YYYY_swk_MOVESactInputBld_hrvmtfract.tab;” and 

 Avgspeeddistribution: “*FFFF_YYYY_swk_MOVESactInputBld_avgspddist.tab.” 

 Additional data summary output text files from the MOVESactivityinputbuild runs include: 

 “*FFFF_YYYYswkd_MOVESactinput.lst” (run list with various data summaries); 

 “*FFFF_YYYYswkd_MOVESactinput_linkVHTsumm.tab” (VHT by hour, road type, 

area type, and avgspeedbinID); and 

 “*FFFF_YYYYswkd_MOVESactinput_linkVMTsumm.tab” (VMT by hour, road type, 

area type). 

Additional MOVES Inventory Mode CDBs 

TTI developed an extra set of CDBs consistent with the link-based inventory data that may be 

used with MOVES in inventory mode. These inventory mode CDBs were designed to input 

summer weekday (daily) activity data, and with the appropriate MRSs, produce summer 

weekday (daily) output inventory estimates. One inventory mode CDB was built corresponding 

to each link-based inventory, or one per county per year, for a total of 56. The CDBs and 

MySQL script file for building them (contained in hgb8hr97_mvs14_cdbs_eimode.zip) are: 

 “mvs14_hgb8hr97_YYYYswkd_FFFF_ei_cdb_in” (56 CDBs each, containing 26 

database tables, consisting of three files per table, plus one db.opt file per CDB); and 

 “MVS14_HGB8HR97_YYYYSWKD_FFFF_EI_CDB_IN.SQL” (56 text files 

containing MySQL scripts for building the 56 MOVES inventory mode CDBs). 

73 

APPENDIX B: 

EMISSIONS ESTIMATION UTILITIES FOR MOVES-BASED EMISSIONS 

INVENTORIES 

75 

TTI EMISSIONS ESTIMATION UTILITIES FOR MOVES2014-BASED EMISSIONS 

INVENTORIES 

The following is a summary of utilities developed by TTI (written in the Visual Basic 

programming language) for producing detailed, link-based, hourly, and 24-hour emissions 

estimates for on-road mobile sources using the latest version of EPA’s MOVES model 

(MOVES2014). These utilities produce inputs used with the MOVES model, make special 

adjustments to the emissions factors (when required), and multiply them with travel model linkbased or Highway Performance Monitoring System (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, VehPopulationBuild, OffNetActCalc, 

MOVESactivityInputBuild, MOVESfleetInputBuild, RatesCalc, RatesAdj, and EmsCalc. The 

TRANSVMT and VirtualLinkVMT prepare the link VMT and speeds activity input. The 

VehPopulationBuild utility builds the vehicle population used to calculate the off-network 

activity. The OffNetActCalc utility builds the SHP, starts, SHI, and APU hours required to 

estimate emissions using the rate-per-activity emissions rates produced by the RatesCalc or 

RatesAdj utilities. The MOVESactivityInputBuild and MOVESfleetInputBuild utilities build 

inputs used in MOVES. The RatesCalc utility assembles the emissions rates from the MOVES 

output in terms of rate-per-activity, including rate-per-SHP for the evaporative emissions 

processes. The RatesAdj utility makes special adjustments to the emissions rates when required. 

The EmsCalc utility calculates emissions by hourly time periods, producing a tab-delimited 

summary file (including 24-hour totals), hourly link emissions output files (optional), and an 

optional tab-delimited summary file by MOVES source classification code (SCC). 

 A process flow diagram follows the utility descriptions. 

TRANSVMT 

The TRANSVMT utility post-processes travel demand models (TDMs) to produce hourly, onroad vehicle, seasonal and day-of-week specific, directional link VMT, and speed estimates. The 

TRANSVMT utility processes a TDM traffic assignment by multiplying the link volumes by the 

appropriate HPMS, seasonal, or other VMT factors. Hourly factors are then used to distribute 

the link VMT to each hour in the day. The TTI speed model is used to estimate the operational 

time-of-day link speeds for each direction. Since intrazonal links are not included in the TDM, 

special intrazonal links are created and the VMT and speeds for these special links are estimated 

using the intrazonal trips from the trip matrix and the zonal radii. The link VMT and speeds 

produced by TRANSVMT are subsequently input to the EmsCalc utility for applying the 

MOVES-based emissions factors (as well as with other utilities to develop off-network activity 

estimates). 

VirtualLinkVMT 

The VirtualLinkVMT utility post-processes county HPMS average annual daily traffic (AADT) 

VMT, centerline miles, and lane miles by functional classification and area type (from the Texas 

Department of Transportation’s [TxDOT’s] annual Roadway Inventory Functional Classification 

Record [RIFCREC]) to produce hourly, on-road vehicle fleet, seasonal and day-of-week specific 
76 

actual or projected 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 inventory applications for which network link-based detail is 

not required. The main inputs to VirtualLinkVMT are: 

 County HPMS data sets, which include AADT VMT, centerline miles, and lane miles by 

HPMS area type and functional class; 

 County-level VMT control totals; 

 Hourly VMT distributions; and 

 Speed model inputs to include volume/delay equation parameters adapted for HPMS, and 

free-flow speeds and lane capacities by HPMS functional classification and area type. 

 VirtualLinkVMT initially scales the county HPMS AADT VMT at the link level to the 

appropriate VMT (e.g., uses a 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 adjusted VMT on each link to estimate the hourly, directional VMT (and volumes) by HPMS 

link. Congested speed models, each for the high- and low-capacity links, are used to estimate the 

hourly operational speeds by direction for each link. The operational speeds are based on 

volume/capacity (v/c)-derived directional delay (minutes/mile) applied to the estimated free-flow 

speeds for each link. The virtual link VMT and speeds produced using the VirtualLinkVMT 

utility are an input to the emissions calculation utility, EmsCalc (as well as with other utilities to 

develop off-network activity estimates). 

VehPopulationBuild 

The VehPopulationBuild utility builds the sourcetypeyear data files in a format consistent with 

the MOVES input database table and the SUT/fuel type population input file (can be used with 

the EmsCalc utility to estimate emissions or the OffNetActCalc utility to estimate starts and 

SHP) using the VMT mix and the Texas Department of Motor Vehicles (TxDMV) registration 

data sets. The TxDMV 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: 

 County ID file, which specifies the county for which the output will be calculated; 

 Age registration data file, which lists 31 years of registration data for the Passenger 

Vehicle, Motorcycles, Trucks <=6000, Trucks >6000 <=8500, Total Trucks <=8500, Gas 

Trucks >8500, Diesel Trucks >8500, Total Trucks >8500, and Total All Trucks vehicle 

categories; 

 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 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; 
77 

 VMT mix by TxDOT district, MOVES SUT, and MOVES fuel type; 

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

 MOVES default database; 

 Population factor file (optional); and 

 Year ID file (optional, only used if population factors are used), which specifies the year 

for calculating the output. 

 For the desired county (from the county ID file), the age registration data (for the Passenger 

Vehicle, Motorcycles, Trucks <=6000, Trucks >6000 <=8500, and Total Trucks <=8500 vehicle 

categories) are saved in an age registration data array. The 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 (seven categories for 31 years of data 

and the total) using the combinations in Table 26. 

Table 26. Registration Categories. 

Registration 

Category Vehicle Category Data Location 

1 Passenger Vehicle 

2 Motorcycles Age registration data array 

3 Total Trucks <=8500 

4 Diesel >8500, Diesel >10000, 

Diesel >14000, Diesel >16000 

Diesel/gas registration data 

array 

5 Diesel >19500, Diesel >26000, 

Diesel >33000, Diesel >60000 

6 Gas >8500, Gas >10000, 

Gas >14000, Gas >16000 

7 Gas >19500, Gas >26000, 

Gas >33000, Gas >60000 

 The registration category data array is used to fill the SUT population array (by SUT and fuel 

type) for all vehicles except long-haul trucks. Each SUT/fuel type combination is assigned the 

total registrations from one or more of the registration categories in the registration category data 

array. Table 27 shows the SUTs and their associated registration category in the registration 

category data array. 
78 

Table 27. SUT/Registration Category Correlation. 

SUT Registration Category 

11 2 

21 1 

31, 32 3 

41, 42, 43, 51, 52, 54 4 + 6 

61 5 + 7 

 SUT population factors are calculated by SUT/fuel type using the data from the VMT mix 

input for all SUTs except motorcycles (SUT 11) and the long-haul trucks (SUTs 53 and 62) and 

saved in the SUT population factors array. For SUT 21, the fuel type VMT mix is divided by the 

total VMT mix for SUT 21. For SUT 31, the fuel type VMT mix is divided by the total VMT 

mix for SUTs 31 and 32. The same process applies to SUT 32. For SUT 41, the fuel type VMT 

mix is divided by the total VMT mix for SUTs 41, 42, 43, 51, 52, and 54. The same process 

applies to SUTs 42, 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 SUT 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 

the fuel type VMT mix for SUT 53 by the fuel type VMT mix for SUT 52. For SUT 62, the 

SUT population factors by fuel type are calculated by dividing the fuel type VMT mix for SUT 

62 by the fuel type VMT mix for SUT 61, therefore creating a ratio of long-haul and short-haul 

trucks. 

 The SUT population factors and the population factor (if desired) are applied to the SUT 

population array for all SUTs except SUT 53 and 62. For SUT 53, the SUT population factors 

for SUT 53 are applied to the SUT population array for SUT 52. For SUT 62, the SUT 

population factors for SUT 62 are applied to the SUT population array for SUT 61. 

 Using the appropriate MySQL code, a new sourcetypeyear database table is created. The 

data in the SUT population array is aggregated by fuel type and used to fill the sourcetypeyear 

database table, along with the yearID, salesGrowthFactor, and migrationrate. For the yearID, the 

year of the registration data is used, unless a population factor is used, in which case the year 

from the year ID input is used. The salesGrowthFactor and migrationrate for each SUT is set 1. 

A text format of this database table is written by the utility as well. The SUT/fuel type 

population input file is written using the SUT population array. 

OffNetActCalc 

The OffNetActCalc calculates the analysis scenario (i.e., year, season, day type) SHP, starts, 

SHI, and APU hours by hour, SUT, and fuel type used to estimate emissions using the EmsCalc 

utility. The SHI and APU hours are only calculated for SUT 62, fuel type 2 (CLhT_Diesel). 

The SHP is calculated using either the TDM or the virtual link-based link VMT and speeds 

(same as used in the distance-based emissions estimation), the 24-hour or time period VMT mix 
79 

(by roadway type and SUT/fuel type), and the SUT/fuel type population (from the 

VehiclePopulationBuild utility). The starts activity is calculated using the SUT/fuel type 

population and the starts per vehicle (typically the MOVES default). The SHI and APU hours 

are a function of hotelling hours. This utility has two options for calculating the hotelling hours. 

Using the first option, the analysis scenario 24-hour hotelling hours is calculated using a usersupplied extended idle factor to the source hours operating (SHO). However, this method of 

estimating the hotelling hours as a direct function of the SHO does not consider the availability 

of locations where extended idling may occur. The second option (and suggested method) uses 

base data (24-hour hotelling, link VMT and speeds, and VMT mix), the analysis scenario data 

used to calculate the SHP, and the analysis scenario SHP to calculate the analysis scenario 24-

hour hotelling hours. 

 For the analysis scenario first hourly VMT and speeds input, the utility applies the 

appropriate VMT mix (either the 24-hour VMT mix or the appropriate time period VMT mix as 

assigned by the user) to each link that has the desired county code; thus distributing 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 

analysis scenario VMT and speeds input; therefore producing the analysis scenario hourly values 

for VMT by SUT/fuel type and for VHT by SUT/fuel type. 

 The analysis scenario hourly SUT/fuel type speed, total hours (or source hours), and SHP are 

then calculated. For each hour and SUT/fuel type, the hourly SUT/fuel type VMT is divided by 

the hourly SUT/fuel type VHT to calculate the hourly SUT/fuel type speed. The hourly 

SUT/fuel type total hours are set equal to the SUT/fuel type population. The hourly SUT/fuel 

type SHP is calculated by subtracting the hourly SUT/fuel type VHT (or SHO) from the hourly 

SUT/fuel type total hours. If the calculated SHP is negative (i.e., SHO is greater than the total 

hours), the SHP is set to 0. 

 To calculate the analysis scenario 24-hour hotelling hours under option 1 (as a direct function 

of SHO), the utility multiplies the CLhT_Diesel analysis scenario 24-hour SHO by the usersupplied extended idle factor, which represents the amount of extended idle time that must occur 

per SHO. For option 2 ( as a function of base hotelling data), the utility calculates the base 24-

hour CLhT_Diesel VMT using the base VMT and speeds inputs and the base VMT mix with the 

same procedure used in the analysis scenario SHP calculations. The 24-hour analysis scenario 

CLhT_Diesel VMT is then divided by the 24-hour base CLhT_Diesel VMT to create a scaling 

factor, which is then applied to the base 24-hour hotelling hours to calculate the analysis scenario 

24-hour hotelling hours. 

 The utility then calculates the analysis scenario hourly hotelling hours. The analysis scenario 

hourly CLhT_Diesel SHO (from the SHP calculation process) is converted to hourly VHT 

fractions. The hourly hotelling fractions are calculated as the inverse of the hourly VHT 

fractions. The hourly hotelling fractions are then applied to the analysis scenario 24-hour 

hotelling hours to calculate the hourly hotelling hours. For each hour, the hourly hotelling hours 

are then compared to the hourly CLhT_Diesel SHP. For those hours where the hotelling hours 

are greater than the SHP, hotelling hours are set to the SHP for that hour. 
80 

 The utility then calculates the SHI fraction and the APU fraction using the source type age 

distribution (same distribution used in the MOVES runs), the relative mileage accumulation 

rates, and the hotelling activity distribution. Travel fractions for SUT 62 (CLhT) by ageID (0 

through 30) are calculated by multiplying the age distribution by the appropriate relative mileage 

accumulation rate, which is then converted into a distribution by dividing the individual travel 

fraction (ageID 0 through 30) by the sum of the travel fractions. These travel fractions are then 

applied to the appropriate operating mode fractions from the hotelling activity distribution 

(operating mode 200) and summed to calculate the SHI fraction. Using a similar process, the 

APU fraction is calculated using the operating mode fractions for operating mode 201. For each 

hour the analysis scenario hotelling hours are multiplied by the SHI fraction to calculate the 

analysis scenario SHI activity and by the APU fraction to calculate the analysis scenario APU 

hours. 

MOVESactivityInputBuild 

The MOVESactivityInputBuild utility builds the roadtypedistribution, hourvmtfraction, 

avgspeeddistribution, roadtype, hpmsvtypeyear, year, state, zone, zoneroadtype, 

monthvmtfraction, and dayvmtfraction data files in a format consistent with the MOVES input 

database tables using the link-based hourly VMT and speeds developed with the TRANSVMT or 

VirtualLinkVMT utility, the VMT mix, and the MOVES defaults. The primary inputs to this 

utility are: 

 Link-based hourly VMT and speeds developed with the TRANSVMT or 

VirtualLinkVMT utility; 

 County ID file which specifies the county number in the link-based hourly VMT and 

speeds for which the output will be calculated; 

 VMT roadway type designations, which lists associations of the link roadway types/area 

type combination to the VMT mix, emissions rate, and MOVES roadway types (same as 

used with the EmsCalc utility); 

 24-hour or time period VMT mix by roadway type, MOVES source type, and MOVES 

fuel type (same as used with the EmsCalc utility); 

 Day ID, which specifies the MOVES day ID for calculating the output; 

 Year ID, which specifies the year for calculating the output; 

 Link/Ramp designations, which designates each link roadway type/area type combination 

to either ramp or non-ramp; and 

 MOVES default database. 

 For each link in the link-based hourly VMT and speeds in which the county number matches 

the desired county ID, the link VMT is saved in a VMT summary array based on hour, link 

functional class, and link area type. The link VHT (link VMT/link speed) is saved in a VHT 

summary array based on hour, link functional class, link area type, and MOVES average speed 

bin ID (determined using the MOVES average speed bins and the link speed). The link VHT is 

also saved in a road type VHT array based on link functional class and link area type, and, if the 
81 

link is specified as ramp by the link/ramp designations specified by the user, the VHT is 

additionally saved in the ramp segment of the road type VHT array. 

 A MOVES roadway type array by MOVES roadway type (roadTypeID codes 2 through 5) is 

also created using the data in the VMT summary array and VMT roadway type designations. 

For the link road types designated a MOVES road type of 6 or 8, the VMT is added to MOVES 

road type 2 in the MOVES roadway type array. For the link road types designated a MOVES 

road type of 7 or 9, the VMT is added to MOVES road type 4 in the MOVES roadway type 

array. An hourly VMT array (by MOVES SUT, MOVES roadway type, and hour) is formed 

using the data in the VMT summary array, the VMT roadway type designations, and the VMT 

mix. If the time period VMT mix is used, each hour is assigned a time period by the user. 

Otherwise, the same 24-hour VMT mix is used for all hours. An average speed distribution array 

(by MOVES SUT, MOVES roadway type, hour, and MOVES speed bin) is created using the 

VHT summary array and the VMT mix. Using the appropriate MySQL code, the MOVES 

roadtypedistribution, hourvmtfraction, and avgspeeddistribution default values are extracted and 

saved for later use. 

 The VMT in the MOVES roadway type array is used to produce the roadway type 

distribution array by MOVES SUT and MOVES roadway type. This VMT is converted to a 

distribution by MOVES SUT (i.e., the total for a SUT over the five MOVES roadway types 

should equal 1), with the distribution value for MOVES roadway type 0 (Off-Network) equal to 

0. Using the appropriate MySQL code, the roadtypedistribution database table is written. A tabdelimited version is also written (optional). 

 The VMT in the hourly VMT array is added to the hourly VMT fraction array (by SUT, 

MOVES roadway type, and hour) and for those roadway types where the VMT for all hours is 

greater than 0, this VMT is converted to an hourly distribution. For those roadway types where 

the VMT is equal to 0, a value of 1 is placed in the first hour, followed by 0 in the remaining 

hours. Using the appropriate MySQL code, the hourvmtfraction database table is written. For 

those SUTs where the VMT mix is greater than 0, the hourly VMT fraction array is used. 

Otherwise, the MOVES hourvmtfraction default values are used. A tab-delimited version is also 

written (optional). 

 The VHT in the average speed distribution array is converted to a distribution by SUT, 

MOVES roadway type, hour/day (combination of hour and the day ID specified by the user), and 

MOVES average speed bin. Using the appropriate MySQL code, the avgspeeddistribution 

database table is written. For those SUTs where the VMT mix is greater than 0, the average 

speed distribution array is used. Otherwise, the MOVES avgspeeddistribution default values are 

used. A tab-delimited version is also written (optional). 

 The VHT in the road type VHT array is converted to a proportion of ramp VHT by dividing 

the ramp segment of the road type VHT array by the total VHT for the road type in the road type 

VHT. Using the appropriate MySQL code, the roadtype database table is written using the 

proportions from the road type VHT array. If the ramp fraction for roadTypeID 2 is greater than 

0, then roadTypeID 6 (with rampFraction equal to 0) and roadTypeID 8 (with rampFraction 

equal to 1) are also added to the roadtype database table. If the ramp fraction for roadTypeID 4 

is greater than 0, then roadTypeID 7 (with rampFraction equal to 0) and roadTypeID 9 (with 
82 

rampFraction equal to 1) are also added to the roadtype database table. A tab-delimited version 

is also written (optional). 

 The VMT in the hourly VMT array is aggregated to create the HPMS vehicle type VMT 

array. Each SUT is assigned an HPMS vehicle type (SUT 11 is HPMS vehicle type 10; SUTs 

21, 31 and 32 are HPMS vehicle type 25; SUTs 41, 42, and 43 are HPMS vehicle type 40; SUTs 

51, 52, 53, and 54 are HPMS vehicle type 50; and SUTs 61 and 62 are HPMS vehicle type 60). 

Using the appropriate MySQL code, the hpmsvtypeyear database table is written using the VMT 

from the HPMS vehicle type VMT array, along with the user supplied year ID, the VMT growth 

factor (automatically set to “Null”), and the base year Off-Network VMT (automatically set to 

0). A tab-delimited version is also written (optional). 

 Using the appropriate MySQL code, the fuel year ID is extracted from the MOVES default 

year database table for the user-supplied year ID and the new year database table is written using 

the user-supplied year ID and the extracted fuel year ID. The “isbaseYear” data is written as 

well (automatically set to “Y”). A tab-delimited version is also written (optional). 

 The utility also produces two tab-delimited summary output files. A tab-delimited VMT 

summary is output by hour, link road type, and link area type for the user-specified county. A 

tab-delimited VHT summary is output by hour, link road type, link area type, and MOVES 

average speed bin for the user-specified county. 

 The utility outputs five other database tables (state, zone, zoneroadtype, monthvmtfraction, 

and dayvmtfraction) using the appropriate MySQL code and the user-supplied inputs. For the 

state database table, a new state database table is created and the data from the MOVES default 

state database table is copied to the new table for the state ID of 48. For the zone database table, 

a new zone database table is created and the data from the MOVES default zone data base table 

is copied to the new table for the county ID greater than 48000 and county ID less than 49000. 

The start allocation factors, idle allocation factors, and SHP allocation factors are all then 

replaced with values of 1 in the new table. 

 For the zoneroadtype database table, a new zoneroadtype database table is created and the 

data from the MOVES default zoneroadtype data base table is copied to the new table for the 

zone ID greater than 480000 and zone ID less than 490000. The SHO allocation factors are all 

then replaced with values of 1 in the new table. For the monthvmtfraction database table, a new 

monthvmtfraction database table is created and the data from the MOVES default 

monthvmtfraction database table is copied to the new database table and the month VMT 

fraction is set to 1 for the user-supplied month ID and 0 for all other months. For the 

dayvmtfraction database table, a new dayvmtfraction database table is created and the data from 

the MOVES default dayvmtfraction database table is copied to the new table and the day VMT 

fraction is set to 1 for the user-supplied day ID and 0 for all other months. 

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: 
83 

 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 trucks registration data file, which lists 31 years of registration data for the 

Diesel > 8500, Diesel > 10000, Diesel > 14000, Diesel > 16000, Diesel > 19500, 

Diesel > 26000, Diesel > 33000, Diesel > 60000, and Diesel Totals diesel truck 

categories; 

 SUT data sources input, which specifies the data source for each SUT to use when 

building the souretypeagedistribution database table; 

 Fuel/engine fractions data sources input, which specifies the data source for each 

SUT to use when building the fuel/engine fractions; 

 Default sourcetypeage distribution input; 

 MOVES default database; and 

 Year ID file (optional, only if year is not the registration data year as in a future year 

analysis), which specifies the year for calculating the output. 

 The SUT data sources input lists the data source for each SUT, either a single county, 

multiple counties, state, or MOVES default. As this input is processed, the utility maintains a list 

of the input sources. The same applies to the fuel/engine fractions, except data source inputs are 

only valid for source types 52, 53, and 61 (other are not valid due to data limitations and source 

type 62 are all considered diesel). 

 For each county (or state total) in the list of the input sources, the age registration data (for 

the Passenger Vehicle, Motorcycles, Trucks <=6000, Trucks >6000 <=8500, and Total Trucks 

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

create the registration category data array (a total of seven categories for 31 years of data and the 

total) using the combinations in Table 26 (Registration Categories). The county is compared to 

the data sources for each SUT in the SUT data sources input. If the county is found for a given 

source type, then the 31 years of registration data from the source type’s corresponding category 

in the registration category data array are added to the SUT age distribution array. Table 28 

shows the source types and their corresponding registration categories. 
84 

Table 28. 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 29 shows the SUTs and their corresponding 

registration categories. 

Table 29. 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 in the SUT age distribution array are converted to a distribution 

by dividing the source type yearly registration data by the source type total registration data. For 

each source type in which the defaults are to be used, the defaults values from the default age 

distribution array are copied to the SUT age distribution array. 

 The MOVES default fuel/engine fractions are extracted from the MOVES default database 

(using the appropriate code for MySQL) and saved in the default fuel/engine fractions array. For 

source types 52, 53, and 61, the source type yearly registration data in the fuel/engine fractions 

array are converted to fuel/engine fractions by dividing the yearly source type diesel registration 

data by the sum of the yearly source type diesel registration data and the yearly source type 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 
85 

year, then the first fuel/engine fraction is extended to match the year from the year ID input and 

the appropriate number of years is dropped from the end of the fuel/engine fractions to maintain 

the appropriate distribution. If the year from the year ID input is less than the registration data 

year, then the last fuel/engine fraction is extended to match the year from the year ID input and 

the appropriate number of years is dropped from the beginning of the fuel/engine fractions to 

maintain the appropriate distribution. For source type 62, all of the fuel/engine fractions in the 

fuel/engine fractions array are set to a value of 1. 

 Using the appropriate MySQL code, a new sourcetypeagedistribution database table is 

created and the data from the SUT age distribution array, along with the year ID (either from the 

registration data or the year ID input), are used to fill the new database table. A text format of 

this database table may be written as well. Using the appropriate MySQL code, a new 

AVFTfuelengfraction database table is created and the data from the fuel/engine fractions array 

are used to fill the new database table for SUTs 52, 53, 61, and 62. For all other SUTs, the 

default fuel/engine fraction array data for the appropriate year (either the registration data year or 

the year ID input) are used to fill the new database table. A text format of this database table 

may be written as well. 

RatesCalc 

The RatesCalc utility calculates emissions rates in terms of rate/SHP for the evaporative 

emissions processes using the data in the CDB used in the MOVES emissions rates run and the 

MOVES default database. The utility also creates copies of the rateperdistance, rateperhour, 

rateperstart, rateperprofile (optional), and ratepervehicle (optional) emissions rate tables to 

include the units for each pollutant. If not specified, emissions rates are assembled for each 

pollutant and process combination (excluding total energy and the refueling emissions processes) 

in the MOVES emissions rate tables. The utility also uses the movesrun database table, along 

with a pollutant energy or mass lookup table (mass, TEQ, or gmole), to determine the units of the 

emissions rates, which are added to the emissions rate tables, which will allow the user to specify 

any of the units available in MOVES for the MOVES emissions rate run. The type of activity 

used for the emissions rate calculation is determined by the process, as Table 30 shows. 
86 

Table 30. MOVES2014 Emissions Process and Corresponding Activity for Rate-perActivity Emissions Rates. 

MOVES2014 Emissions Process Activity Emissions Rate Units 

Running Exhaust Miles Traveled Rate/Mile 

Crankcase Running Exhaust Miles Traveled Rate/Mile 

Start Exhaust Starts Rate/Start 

Crankcase Start Exhaust Starts Rate/Start 

Extended Idle Exhaust Extended Idle Hours Rate/Extended Idle Hour 

Crankcase Extended Idle Exhaust Extended Idle Hours Rate/Extended Idle Hour 

Auxiliary Power Exhaust APU Hours Rate/APU Hour 

Evaporative Permeation Miles Traveled 

Source Hours Parked 

Rate/Mile 

Rate/SHP 

Evaporative Fuel Vapor Venting Miles Traveled 

Source Hours Parked 

Rate/Mile 

Rate/SHP 

Evaporative Fuel Leaks Miles Traveled 

Source Hours Parked 

Rate/Mile 

Rate/SHP 

Brake Wear Miles Traveled Rate/Mile 

Tire Wear Miles Traveled Rate/Mile 

 For the rateperdistance (rate/mile emissions rates) emissions rate table, the utility creates a 

copy of the emissions rates in the specified output database with the table name 

ttirateperdistance. If specific pollutants are specified, only the emissions rates for those 

pollutants are copied to the ttirateperdistance table. Otherwise, the entire rateperdistance table is 

copied to the ttirateperdistance table. The utility also adds a “Units_Per_Activity” field to the 

ttirateperdistance table and fills that field based on the pollutants energy or mass designation 

(mass, TEQ, or gmole). For those pollutants designated as mass, the mass units from the 

movesrun table are added to the “Units_Per_Activity” field. For those pollutants designated as 

gmole, the mass units from the movesrun table, along with the text “-mole” (i.e., pound-mole or 

gram-mole) are added to the “Units_Per_Activity” field. For those pollutants designated as 

TEQ, the text “TEQ” is added to the “Units_Per_Activity” field. No unit conversions are 

performed in this utility. The rateperstart, rateperhour, rateperprofile (optional), and 

ratepervehicle (optional) emissions rate tables are processed in a similar manner to produce the 

ttirateperstart, ttirateperhour, ttirateperprofile, and ttiratepervehicle emissions rate tables. 

 The utility also includes a crankcase extended idle flag, which signals the utility how to 

calculate the crankcase extended idle emissions rates. If these emissions rates are not included in 

the rateperhour emissions rate table (as was the case with the July 2014 release of MOVES2014), 

then the flag is set to “YES” or the crankcase extended idle rates will not be included the 

emissions rates. In this case, the utility creates a copy of the crankcase extended idle emissions 
87 

rates in the baserateoutput table (located in the output database of the MOVES run) and places 

them in the ttirateperhour table. If these rates are included in the rateperhour emissions rate 

table, the flag should be set to “NO” and no other processing is performed by the utility. 

For the evaporative emissions rates, the utility uses the CDB from the MOVES run and the 

MOVES default database to replicate the MOVES vehicle population and SHP calculation 

process. Using the emissions rates from the rateperprofile and ratepervehicle emissions rate 

tables, the utility calculates the rate-per-SHP emissions rates by multiplying the emissions rate 

by the appropriate vehicle population and dividing by the appropriate SHP value. These rateper-SHP emissions rates are then saved in the ttiratepershp emissions rate table. Similar to the 

previous RatesCalc emissions rate tables, the “Units_Per_Activity” field is added to the 

ttiratepershp table and filled based on the pollutants energy or mass designation. 

RatesAdj 

The RatesAdj utility applies emissions rate adjustments to an emissions rate database table 

produced by RatesCalc utility (ttirateperdistance, ttirateperstart, ttirateperhour, ttiratepershp, 

ttiratepervehicle or ttirateperprofile) or by this utility to produce a new emissions rate database 

table in the same format as the input emissions rate database table. The emissions rate 

adjustments can be linear adjustments that are applied to all emissions rates or can be applied by 

SUT, fuel type, pollutant, and process (adjustments may also include roadway type, average 

speed bin, and hour). The user has the option of selecting which pollutants will be in the new 

emissions rate database table, along with the output units of the emissions rates. This allows the 

user to perform any unit conversions between mass units (i.e., pounds to grams or pound-mole to 

gram-mole) without providing any addition adjustment factors. Unit conversions between unit 

types (i.e., gram-moles to grams or TEQ to grams) are not performed internally by the utility. 

These types of conversions must be made using the emissions rate adjustment factors. The 

utility also has the option for combining multiple emissions rate database tables into one new 

emissions rate database table, if the input emissions rate database tables are in the same format. 

 For the first input emissions rate database table, the utility extracts the emissions rates for the 

specified pollutants (or all the pollutants if not specified) from the input database emissions rate 

table, applies the emissions rate adjustments (if necessary) and any unit conversion adjustments, 

and saves these adjusted emissions rates. If more than one emissions rate database table is input, 

then the utility performs a similar calculation process to the first input emissions rate database 

table for each input emissions rate database table. If pollutants are found in more than one input 

emissions rate database table, the adjusted emissions rates are summed to produce one emissions 

rate. 

 After processing all of the input emissions rate database tables, the utility creates a new 

emissions rate database table in the same format as the first input emissions rate database table 

and writes the adjusted emissions rates to this new emissions rate database table. Using MySQL 

code, the utility also creates a minimum and maximum emissions rate summary for each input 

emissions rate table and the output emissions rate table by pollutant, process, and source 

type/fuel type, which is written to a tab-delimited file specified by the user. 
88 

EmsCalc 

The EmsCalc utility estimates the hourly link emissions for one user-specified county using the 

emissions factors (either from RatesCalc or RatesAdj), the 24-hour or time period VMT mix, the 

hourly link VMT and speeds activity estimates (either from TRANSVMT or VirtualLinkVMT), 

and the off-network activity (either vehicle population or SHP, starts, and SHI). This utility 

produces a tab-delimited output summary (including hourly and 24-hour totals) and hourly link 

emissions output files (optional). The primary inputs to MOVESemscalc are: 

 Emissions factors from RatesCalc or RatesAdj; 

 Link-based hourly VMT and speeds developed with the TRANSVMT or 

VirtualLinkVMT utility. For each link, the following information is input to 

MOVESemscalc: link start node, link end node, link county number, link roadway type 

number, link area type number, link VMT, and link operational speed estimate; 

 24-hour or time period VMT mix by roadway type, MOVES SUT, and MOVES fuel 

type; 

 Off-network activity. If the emissions factors are in grams-per-vehicle (i.e., the 

ttiratepervehicle or ttirateperprofile emissions rates), then vehicle population by SUT/fuel 

type is required. If the emissions factors are in grams-per-activity (i.e., the ttirateperstart, 

ttirateperhour, or ttiratepershp emissions rates), then the SHP, starts, SHI, and APU hours 

by hour and SUT/fuel type are required; 

 VMT roadway type designations, which lists associations of the link roadway types/area 

type combination to the VMT mix, emissions rate, and MOVES roadway types; 

 Pollutants input file, which specifies which pollutant/process combinations for which the 

emissions calculations will be performed and their respective units in the tab-delimited 

output; and 

 SCC input file (optional, only if the activity and emissions by SCC are to be created). 

 The emissions estimation can be categorized by two basic types based on the type of 

emissions factors: the roadway-based emissions and the off-network-based emissions. For the 

roadway-based emissions (ttirateperdistance emissions factors), the VMT for each link is 

distributed to each of the SUT/fuel type combinations listed in the VMT mix by roadway type 

(as designated in the VMT roadway type designations). If the time period VMT mix is input, 

each hour is assigned a time period by the user. Otherwise, the 24-hour VMT mix is used for all 

hours. For each pollutant/process combination in the pollutants input file, the emissions factors 

are selected based on the emissions rate roadway type (as designated in the VMT roadway type 

designations) and the link speed for each SUT/fuel type combinations listed in the VMT mix. 

For link speeds greater than 75 mph, the emissions factors for 75 mph are used. For link speeds 

less than 2.5 mph, the emissions factors for 2.5 mph are used. For those link speeds that fall 

between the 16 MOVES speeds, the emissions factors are interpolated using the emissions factor 

interpolation methodology in the following section. These SUT/fuel type combination-specific 

emissions factors are multiplied by the SUT/fuel type combination-specific VMT to estimate the 

mobile source emissions for that link by SUT/fuel type combination. If the activity and 

emissions by SCC are to be created, the activity and emissions are also aggregated by SCC using 

the SCC input file. 
89 

 The off-network emissions calculation depends on the format of the input emissions factors. 

If the emissions factors are the ttiratepervehicle or ttirateperprofile emissions rates, the emissions 

factors by SUT/fuel type are multiplied by their associated vehicle population to estimate 

emissions. If the emissions factors are the ttirateperstart, ttirateperhour, or ttiratepershp 

emissions rates, the emissions factors by SUT/fuel type are multiplied by the appropriate 

activity, which is determined by the emissions process (see Table 30). If the activity and 

emissions by SCC are to be created, the activity and emissions are also aggregated by SCC using 

the SCC input file. 

 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, link 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. Only those pollutant/process combinations in the 

pollutants input file with tab-delimited output units other than “NONE” will appear in the tabdelimited output file. Prior to output, any unit conversions between mass units (i.e., pounds to 

grams or pound-mole to gram-mole) are performed by the utility. Unit conversions between unit 

types (i.e., gram-moles to grams or TEQ to grams) are not performed internally by the utility 

(these type of unit conversions must be done using the RatesAdj utility). This tab-delimited file 

also includes hourly and 24-hour summaries of the off-network activity and VMT, VHT, and 

speed by link road type. Link emissions may also be output by county, pollutant, process, and 

each SUT/fuel type combination. If specified, the tab-delimited activity and emissions by SCC 

output file is also created, which lists the activity and emissions for each pollutant by SCC. 

Emissions Factor Interpolation Methodology 

To calculate emissions factors for link speeds that fall between two of the 16 MOVES speed bin 

speeds, an interpolation methodology similar to the methodology used with MOBILE6 is used. 

This methodology interpolates each emissions factor using a factor developed from the inverse 

link speed and the inverse high and low bounding speed bin speeds. The following is an 

example for a link speed of 41.2 mph. 
90 

 The interpolated emissions factor (EFInterp) is expressed as: 

EFInterp = EFLowSpeed - FACInterp H (EFLowSpeed - EFHighSpeed) 

 Where: 

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

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

 FACInterp = 

 Given that: 

 EFLowSpeed = 0.7413 g/mi; 

 EFHighSpeed = 0.7274 g/mi; 

 Speedlnk = 41.2 mph; 

 Speedlow = 40 mph; and 

 Speedhigh = 45 mph. 

 FACInterp = 

1

412

1

40

1

45

1

. mph mph mph mph 40  



 



  



 



 = 



0 00073

0 00278

.

. = 0.26214; 

 EFInterp = 0.7413 g/mi - (0.26214) H (0.7413 g/mi - 0.7274 g/mi); 

 = 0.7377 g/mi. 

11 11

Speed Speed Speed Speed link low high low

 



 



  



 




91 

START

Transportation

Network Data

Set

TRANSVMT

Time Period Directional VMT and Speed

for Each Link

VMT Mix

Adjusted Hourly

Emissions Factors

RatesAdj

VMT Factors,

Capacity Factors, and

Speed Model Parameters

Hourly

MOVES Emissions Factors

Network roadtype

Designations (to Rates, VMT

Mix, and MOVES roadtypes)

Travel Demand Model Network Link-Based

Hourly MOVES Emissions Estimates

Hourly Link

Emissions

Records

End

Hourly and Total

County Emissions

Summaries

1

 MOVESactivityInputBuild,

 MOVESfleetInputBuild,

 MOVESmetInputBuild, and

 VehPopulationBuild.

 VehPopulationBuild, and 2

 OffNetActCalc.

MOVES Input

Builders1

Off-Network

Activity2

MOVES

Inputs

EmsCalc

MOVES

Emissions and

Activity RatesCalc

93 

APPENDIX C 

TXDOT DISTRICT VMT MIX BY DAY OF WEEK 

95 

TxDOT District/HGB Counties 

TxDOT District HGB County 

Beaumont 

Liberty 

Chambers 

Houston 

Harris 

Galveston 

Fort Bend 

Brazoria 

Montgomery 

Waller 

VMT Mix Year/Analysis Year Correlations 

VMT Mix Year Analysis Years 

2000 1998 through 2002 

2005 2003 through 2007 

2010 2008 through 2012 

2015 2013 through 2017 

2020 2018 through 2022 

2025 2023 through 2027 

2030 2028 through 2032 
96 

2010 Weekday VMT Mix - Beaumont TxDOT District (2012 Analysis Year) 

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00228 0.00221 0.00228 0.00257 0.00224 0.00221 0.00226 0.00252 0.00236 0.00243 0.00241 0.00267 0.00197 21_G 0.56668 0.54935 0.56885 0.64101 0.55861 0.54988 0.56329 0.62854 0.58798 0.60397 0.60077 0.66436 0.48965 31_D 0.00246 0.00316 0.00268 0.00313 0.00226 0.00301 0.00247 0.00303 0.00229 0.00319 0.00236 0.00319 0.00186 31_G 0.17350 0.22225 0.18840 0.22025 0.15902 0.21223 0.17407 0.21368 0.16130 0.22451 0.16603 0.22442 0.13069 32_D 0.00242 0.00310 0.00263 0.00307 0.00222 0.00296 0.00243 0.00298 0.00225 0.00313 0.00232 0.00313 0.00182 32_G 0.04323 0.05538 0.04695 0.05488 0.03962 0.05288 0.04338 0.05325 0.04019 0.05594 0.04137 0.05592 0.03257 51_D 0.00174 0.00232 0.00175 0.00132 0.00191 0.00230 0.00176 0.00171 0.00122 0.00156 0.00110 0.00097 0.00123 51_G 0.00049 0.00065 0.00049 0.00037 0.00054 0.00064 0.00049 0.00048 0.00034 0.00044 0.00031 0.00027 0.00034 52_D 0.03754 0.04992 0.03773 0.02841 0.04112 0.04943 0.03787 0.03681 0.02635 0.03368 0.02358 0.02094 0.02644 52_G 0.01053 0.01400 0.01058 0.00797 0.01153 0.01386 0.01062 0.01032 0.00739 0.00944 0.00661 0.00587 0.00741 53_D 0.00193 0.00257 0.00194 0.00146 0.00212 0.00255 0.00195 0.00190 0.00136 0.00174 0.00121 0.00108 0.00136 53_G 0.00054 0.00072 0.00055 0.00041 0.00059 0.00071 0.00055 0.00053 0.00038 0.00049 0.00034 0.00030 0.00038 54_D 0.00132 0.00175 0.00133 0.00100 0.00144 0.00174 0.00133 0.00129 0.00093 0.00118 0.00083 0.00074 0.00093 54_G 0.00037 0.00049 0.00037 0.00028 0.00041 0.00049 0.00037 0.00036 0.00026 0.00033 0.00023 0.00021 0.00026 41_D 0.00150 0.00297 0.00154 0.00146 0.00173 0.00182 0.00153 0.00117 0.00153 0.00075 0.00129 0.00105 0.00313 42_D 0.00062 0.00122 0.00063 0.00060 0.00071 0.00075 0.00063 0.00048 0.00063 0.00031 0.00053 0.00043 0.00129 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00193 0.00382 0.00198 0.00187 0.00222 0.00233 0.00197 0.00151 0.00197 0.00096 0.00165 0.00135 0.00402 43_G 0.00002 0.00004 0.00002 0.00002 0.00002 0.00002 0.00002 0.00002 0.00002 0.00001 0.00002 0.00001 0.00004 61_D 0.05088 0.02827 0.04357 0.00991 0.05793 0.03373 0.05160 0.01312 0.05439 0.01873 0.04958 0.00421 0.09956 61_G 0.00503 0.00280 0.00431 0.00098 0.00573 0.00334 0.00510 0.00130 0.00538 0.00185 0.00490 0.00042 0.00985 62_D 0.09440 0.05245 0.08084 0.01838 0.10746 0.06257 0.09573 0.02435 0.10089 0.03475 0.09197 0.00780 0.18470 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00057 0.00055 0.00057 0.00064 0.00056 0.00055 0.00057 0.00063 0.00059 0.00061 0.00060 0.00067 0.00049 
97 

2010 Weekday VMT Mix - Houston TxDOT District (2012 Analysis Year) 

SUF/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00281 0.00259 0.00310 0.00296 0.00257 0.00238 0.00292 0.00278 0.00286 0.00262 0.00314 0.00300 0.00243 21_G 0.70085 0.64459 0.77230 0.73720 0.64013 0.59328 0.72744 0.69270 0.71156 0.65196 0.78070 0.74642 0.60611 31_D 0.00215 0.00277 0.00181 0.00228 0.00230 0.00289 0.00199 0.00241 0.00218 0.00307 0.00189 0.00232 0.00193 31_G 0.15174 0.19532 0.12758 0.16054 0.16232 0.20358 0.14035 0.16948 0.15341 0.21619 0.13313 0.16371 0.13581 32_D 0.00216 0.00278 0.00181 0.00228 0.00231 0.00289 0.00199 0.00241 0.00218 0.00307 0.00189 0.00233 0.00193 32_G 0.03777 0.04862 0.03176 0.03996 0.04040 0.05068 0.03494 0.04219 0.03819 0.05381 0.03314 0.04075 0.03381 51_D 0.00104 0.00135 0.00080 0.00084 0.00122 0.00171 0.00110 0.00141 0.00066 0.00092 0.00051 0.00063 0.00086 51_G 0.00062 0.00080 0.00048 0.00050 0.00073 0.00102 0.00066 0.00084 0.00040 0.00055 0.00030 0.00038 0.00051 52_D 0.01972 0.02555 0.01524 0.01595 0.02319 0.03242 0.02089 0.02669 0.01258 0.01750 0.00968 0.01199 0.01630 52_G 0.01178 0.01526 0.00910 0.00953 0.01386 0.01937 0.01248 0.01594 0.00752 0.01045 0.00578 0.00716 0.00974 53_D 0.00381 0.00494 0.00295 0.00308 0.00448 0.00627 0.00404 0.00516 0.00243 0.00338 0.00187 0.00232 0.00315 53_G 0.00228 0.00295 0.00176 0.00184 0.00268 0.00374 0.00241 0.00308 0.00145 0.00202 0.00112 0.00138 0.00188 54_D 0.00079 0.00102 0.00061 0.00064 0.00092 0.00129 0.00083 0.00106 0.00050 0.00070 0.00039 0.00048 0.00065 54_G 0.00047 0.00061 0.00036 0.00038 0.00055 0.00077 0.00050 0.00064 0.00030 0.00042 0.00023 0.00029 0.00039 41_D 0.00077 0.00163 0.00157 0.00201 0.00081 0.00100 0.00104 0.00125 0.00080 0.00054 0.00112 0.00101 0.00092 42_D 0.00032 0.00067 0.00065 0.00083 0.00033 0.00041 0.00043 0.00051 0.00033 0.00022 0.00046 0.00042 0.00038 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00099 0.00209 0.00202 0.00258 0.00104 0.00129 0.00134 0.00160 0.00103 0.00070 0.00144 0.00130 0.00118 43_G 0.00001 0.00002 0.00002 0.00003 0.00001 0.00001 0.00001 0.00002 0.00001 0.00001 0.00001 0.00001 0.00001 61_D 0.02946 0.02279 0.01259 0.00787 0.04951 0.03702 0.02185 0.01450 0.03031 0.01553 0.01115 0.00665 0.09028 61_G 0.00239 0.00185 0.00102 0.00064 0.00401 0.00300 0.00177 0.00118 0.00246 0.00126 0.00090 0.00054 0.00732 62_D 0.02735 0.02116 0.01169 0.00731 0.04596 0.03436 0.02028 0.01346 0.02814 0.01442 0.01035 0.00617 0.08381 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00070 0.00065 0.00078 0.00074 0.00064 0.00060 0.00073 0.00070 0.00072 0.00066 0.00078 0.00075 0.00061 
98 

2015 Weekday VMT Mix - Beaumont TxDOT District (2014 and 2017 Analysis Years) 

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00398 0.00386 0.00400 0.00451 0.00393 0.00386 0.00396 0.00442 0.00413 0.00424 0.00422 0.00467 0.00344 21_G 0.56497 0.54770 0.56714 0.63908 0.55693 0.54823 0.56160 0.62665 0.58621 0.60215 0.59896 0.66236 0.48817 31_D 0.00300 0.00385 0.00326 0.00381 0.00275 0.00367 0.00301 0.00370 0.00279 0.00389 0.00287 0.00388 0.00226 31_G 0.17363 0.22241 0.18854 0.22041 0.15913 0.21238 0.17420 0.21383 0.16142 0.22467 0.16615 0.22458 0.13078 32_D 0.00238 0.00305 0.00259 0.00303 0.00219 0.00292 0.00239 0.00294 0.00222 0.00309 0.00228 0.00308 0.00180 32_G 0.04260 0.05458 0.04626 0.05408 0.03905 0.05211 0.04274 0.05247 0.03961 0.05513 0.04077 0.05511 0.03209 51_D 0.00179 0.00238 0.00180 0.00135 0.00196 0.00235 0.00180 0.00175 0.00125 0.00160 0.00112 0.00100 0.00126 51_G 0.00050 0.00067 0.00050 0.00038 0.00055 0.00066 0.00051 0.00049 0.00035 0.00045 0.00031 0.00028 0.00035 52_D 0.03742 0.04976 0.03761 0.02831 0.04099 0.04927 0.03775 0.03670 0.02626 0.03357 0.02350 0.02087 0.02635 52_G 0.01049 0.01395 0.01055 0.00794 0.01149 0.01382 0.01059 0.01029 0.00736 0.00941 0.00659 0.00585 0.00739 53_D 0.00193 0.00256 0.00194 0.00146 0.00211 0.00254 0.00194 0.00189 0.00135 0.00173 0.00121 0.00108 0.00136 53_G 0.00054 0.00072 0.00054 0.00041 0.00059 0.00071 0.00055 0.00053 0.00038 0.00049 0.00034 0.00030 0.00038 54_D 0.00140 0.00187 0.00141 0.00106 0.00154 0.00185 0.00142 0.00138 0.00099 0.00126 0.00088 0.00078 0.00099 54_G 0.00039 0.00052 0.00040 0.00030 0.00043 0.00052 0.00040 0.00039 0.00028 0.00035 0.00025 0.00022 0.00028 41_D 0.00048 0.00094 0.00049 0.00046 0.00055 0.00058 0.00049 0.00037 0.00049 0.00024 0.00041 0.00033 0.00099 42_D 0.00095 0.00188 0.00098 0.00092 0.00110 0.00115 0.00097 0.00074 0.00097 0.00047 0.00082 0.00067 0.00199 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00261 0.00517 0.00268 0.00254 0.00301 0.00316 0.00267 0.00204 0.00266 0.00130 0.00224 0.00183 0.00545 43_G 0.00003 0.00005 0.00003 0.00003 0.00003 0.00003 0.00003 0.00002 0.00003 0.00001 0.00002 0.00002 0.00006 61_D 0.05088 0.02827 0.04357 0.00991 0.05793 0.03373 0.05160 0.01312 0.05439 0.01873 0.04958 0.00421 0.09956 61_G 0.00503 0.00280 0.00431 0.00098 0.00573 0.00334 0.00510 0.00130 0.00538 0.00185 0.00490 0.00042 0.00985 62_D 0.09440 0.05245 0.08084 0.01838 0.10746 0.06257 0.09573 0.02435 0.10089 0.03475 0.09197 0.00780 0.18470 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00057 0.00055 0.00057 0.00064 0.00056 0.00055 0.00057 0.00063 0.00059 0.00061 0.00060 0.00067 0.00049 
99 

2015 Weekday VMT Mix - Houston TxDOT District (2014 and 2017 Analysis Years) 

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00493 0.00453 0.00543 0.00518 0.00450 0.00417 0.00511 0.00487 0.00500 0.00458 0.00549 0.00525 0.00426 21_G 0.69874 0.64265 0.76997 0.73498 0.63820 0.59149 0.72525 0.69061 0.70941 0.65000 0.77835 0.74417 0.60428 31_D 0.00263 0.00338 0.00221 0.00278 0.00281 0.00352 0.00243 0.00293 0.00266 0.00374 0.00230 0.00283 0.00235 31_G 0.15185 0.19546 0.12767 0.16065 0.16243 0.20373 0.14045 0.16960 0.15352 0.21634 0.13323 0.16383 0.13591 32_D 0.00205 0.00263 0.00172 0.00216 0.00219 0.00274 0.00189 0.00229 0.00207 0.00291 0.00180 0.00221 0.00183 32_G 0.03730 0.04801 0.03136 0.03946 0.03990 0.05004 0.03450 0.04166 0.03771 0.05314 0.03273 0.04024 0.03338 51_D 0.00107 0.00138 0.00082 0.00086 0.00125 0.00175 0.00113 0.00144 0.00068 0.00095 0.00052 0.00065 0.00088 51_G 0.00064 0.00082 0.00049 0.00051 0.00075 0.00105 0.00067 0.00086 0.00041 0.00056 0.00031 0.00039 0.00053 52_D 0.01966 0.02546 0.01519 0.01590 0.02312 0.03232 0.02082 0.02660 0.01254 0.01744 0.00965 0.01195 0.01625 52_G 0.01174 0.01521 0.00907 0.00950 0.01381 0.01931 0.01244 0.01589 0.00749 0.01042 0.00576 0.00714 0.00971 53_D 0.00380 0.00492 0.00294 0.00307 0.00447 0.00625 0.00402 0.00514 0.00242 0.00337 0.00186 0.00231 0.00314 53_G 0.00227 0.00294 0.00175 0.00184 0.00267 0.00373 0.00240 0.00307 0.00145 0.00201 0.00111 0.00138 0.00188 54_D 0.00084 0.00108 0.00065 0.00068 0.00098 0.00138 0.00089 0.00113 0.00053 0.00074 0.00041 0.00051 0.00069 54_G 0.00050 0.00065 0.00039 0.00040 0.00059 0.00082 0.00053 0.00068 0.00032 0.00044 0.00025 0.00030 0.00041 41_D 0.00025 0.00052 0.00050 0.00064 0.00026 0.00032 0.00033 0.00040 0.00025 0.00017 0.00036 0.00032 0.00029 42_D 0.00049 0.00103 0.00100 0.00127 0.00051 0.00064 0.00066 0.00079 0.00051 0.00034 0.00071 0.00064 0.00058 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00135 0.00283 0.00274 0.00350 0.00141 0.00175 0.00181 0.00217 0.00139 0.00094 0.00195 0.00176 0.00160 43_G 0.00001 0.00003 0.00003 0.00004 0.00001 0.00002 0.00002 0.00002 0.00001 0.00001 0.00002 0.00002 0.00002 61_D 0.02946 0.02279 0.01259 0.00787 0.04951 0.03702 0.02185 0.01450 0.03031 0.01553 0.01115 0.00665 0.09028 61_G 0.00239 0.00185 0.00102 0.00064 0.00401 0.00300 0.00177 0.00118 0.00246 0.00126 0.00090 0.00054 0.00732 62_D 0.02735 0.02116 0.01169 0.00731 0.04596 0.03436 0.02028 0.01346 0.02814 0.01442 0.01035 0.00617 0.08381 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00070 0.00065 0.00078 0.00074 0.00064 0.00060 0.00073 0.00070 0.00072 0.00066 0.00078 0.00075 0.00061 
100 

2020 Weekday VMT Mix - Beaumont TxDOT District (2020 Analysis Year) 

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00512 0.00496 0.00514 0.00579 0.00505 0.00497 0.00509 0.00568 0.00531 0.00546 0.00543 0.00600 0.00442 21_G 0.56384 0.54660 0.56600 0.63780 0.55581 0.54712 0.56046 0.62539 0.58503 0.60094 0.59775 0.66103 0.48719 31_D 0.00318 0.00407 0.00345 0.00404 0.00291 0.00389 0.00319 0.00392 0.00296 0.00411 0.00304 0.00411 0.00239 31_G 0.17345 0.22219 0.18835 0.22019 0.15897 0.21216 0.17402 0.21362 0.16126 0.22445 0.16598 0.22435 0.13065 32_D 0.00234 0.00300 0.00254 0.00297 0.00214 0.00286 0.00235 0.00288 0.00217 0.00303 0.00224 0.00303 0.00176 32_G 0.04265 0.05463 0.04631 0.05414 0.03909 0.05217 0.04279 0.05253 0.03965 0.05519 0.04081 0.05516 0.03213 51_D 0.00174 0.00232 0.00175 0.00132 0.00191 0.00230 0.00176 0.00171 0.00122 0.00156 0.00110 0.00097 0.00123 51_G 0.00049 0.00065 0.00049 0.00037 0.00054 0.00064 0.00049 0.00048 0.00034 0.00044 0.00031 0.00027 0.00034 52_D 0.03754 0.04992 0.03773 0.02841 0.04112 0.04943 0.03787 0.03681 0.02635 0.03368 0.02358 0.02094 0.02644 52_G 0.01053 0.01400 0.01058 0.00797 0.01153 0.01386 0.01062 0.01032 0.00739 0.00944 0.00661 0.00587 0.00741 53_D 0.00193 0.00257 0.00194 0.00146 0.00212 0.00255 0.00195 0.00190 0.00136 0.00174 0.00121 0.00108 0.00136 53_G 0.00054 0.00072 0.00055 0.00041 0.00059 0.00071 0.00055 0.00053 0.00038 0.00049 0.00034 0.00030 0.00038 54_D 0.00132 0.00175 0.00133 0.00100 0.00144 0.00174 0.00133 0.00129 0.00093 0.00118 0.00083 0.00074 0.00093 54_G 0.00037 0.00049 0.00037 0.00028 0.00041 0.00049 0.00037 0.00036 0.00026 0.00033 0.00023 0.00021 0.00026 41_D 0.00048 0.00094 0.00049 0.00046 0.00055 0.00058 0.00049 0.00037 0.00049 0.00024 0.00041 0.00033 0.00099 42_D 0.00096 0.00189 0.00098 0.00093 0.00110 0.00116 0.00098 0.00075 0.00097 0.00047 0.00082 0.00067 0.00199 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00261 0.00516 0.00268 0.00253 0.00301 0.00316 0.00266 0.00204 0.00266 0.00130 0.00224 0.00183 0.00544 43_G 0.00003 0.00005 0.00003 0.00003 0.00003 0.00003 0.00003 0.00002 0.00003 0.00001 0.00002 0.00002 0.00005 61_D 0.05088 0.02827 0.04357 0.00991 0.05793 0.03373 0.05160 0.01312 0.05439 0.01873 0.04958 0.00421 0.09956 61_G 0.00503 0.00280 0.00431 0.00098 0.00573 0.00334 0.00510 0.00130 0.00538 0.00185 0.00490 0.00042 0.00985 62_D 0.09440 0.05245 0.08084 0.01838 0.10746 0.06257 0.09573 0.02435 0.10089 0.03475 0.09197 0.00780 0.18470 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00057 0.00055 0.00057 0.00064 0.00056 0.00055 0.00057 0.00063 0.00059 0.00061 0.00060 0.00067 0.00049 
101 

2020 Weekday VMT Mix - Houston TxDOT District (2020 Analysis Year) 

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00633 0.00582 0.00698 0.00666 0.00578 0.00536 0.00657 0.00626 0.00643 0.00589 0.00705 0.00674 0.00548 21_G 0.69733 0.64135 0.76842 0.73350 0.63692 0.59030 0.72379 0.68922 0.70799 0.64869 0.77678 0.74267 0.60306 31_D 0.00294 0.00378 0.00247 0.00311 0.00314 0.00394 0.00271 0.00328 0.00297 0.00418 0.00258 0.00317 0.00263 31_G 0.15154 0.19507 0.12741 0.16032 0.16210 0.20331 0.14017 0.16926 0.15321 0.21590 0.13296 0.16349 0.13563 32_D 0.00205 0.00263 0.00172 0.00216 0.00219 0.00274 0.00189 0.00229 0.00207 0.00291 0.00180 0.00221 0.00183 32_G 0.03730 0.04801 0.03136 0.03946 0.03990 0.05004 0.03450 0.04166 0.03771 0.05314 0.03273 0.04024 0.03338 51_D 0.00104 0.00135 0.00080 0.00084 0.00122 0.00171 0.00110 0.00141 0.00066 0.00092 0.00051 0.00063 0.00086 51_G 0.00062 0.00080 0.00048 0.00050 0.00073 0.00102 0.00066 0.00084 0.00040 0.00055 0.00030 0.00038 0.00051 52_D 0.01972 0.02555 0.01524 0.01595 0.02319 0.03242 0.02089 0.02669 0.01258 0.01750 0.00968 0.01199 0.01630 52_G 0.01178 0.01526 0.00910 0.00953 0.01386 0.01937 0.01248 0.01594 0.00752 0.01045 0.00578 0.00716 0.00974 53_D 0.00381 0.00494 0.00295 0.00308 0.00448 0.00627 0.00404 0.00516 0.00243 0.00338 0.00187 0.00232 0.00315 53_G 0.00228 0.00295 0.00176 0.00184 0.00268 0.00374 0.00241 0.00308 0.00145 0.00202 0.00112 0.00138 0.00188 54_D 0.00079 0.00102 0.00061 0.00064 0.00092 0.00129 0.00083 0.00106 0.00050 0.00070 0.00039 0.00048 0.00065 54_G 0.00047 0.00061 0.00036 0.00038 0.00055 0.00077 0.00050 0.00064 0.00030 0.00042 0.00023 0.00029 0.00039 41_D 0.00025 0.00052 0.00050 0.00064 0.00026 0.00032 0.00033 0.00040 0.00025 0.00017 0.00036 0.00032 0.00029 42_D 0.00049 0.00104 0.00100 0.00128 0.00052 0.00064 0.00066 0.00079 0.00051 0.00034 0.00071 0.00064 0.00058 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00134 0.00283 0.00273 0.00350 0.00141 0.00174 0.00181 0.00217 0.00139 0.00094 0.00195 0.00176 0.00160 43_G 0.00001 0.00003 0.00003 0.00004 0.00001 0.00002 0.00002 0.00002 0.00001 0.00001 0.00002 0.00002 0.00002 61_D 0.02946 0.02279 0.01259 0.00787 0.04951 0.03702 0.02185 0.01450 0.03031 0.01553 0.01115 0.00665 0.09028 61_G 0.00239 0.00185 0.00102 0.00064 0.00401 0.00300 0.00177 0.00118 0.00246 0.00126 0.00090 0.00054 0.00732 62_D 0.02735 0.02116 0.01169 0.00731 0.04596 0.03436 0.02028 0.01346 0.02814 0.01442 0.01035 0.00617 0.08381 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00070 0.00065 0.00078 0.00074 0.00064 0.00060 0.00073 0.00070 0.00072 0.00066 0.00078 0.00075 0.00061 
102 

2025 Weekday VMT Mix - Beaumont TxDOT District (2023 and 2026 Analysis Years) 

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00626 0.00607 0.00628 0.00708 0.00617 0.00607 0.00622 0.00694 0.00649 0.00667 0.00663 0.00734 0.00541 21_G 0.56270 0.54549 0.56486 0.63651 0.55469 0.54602 0.55933 0.62412 0.58385 0.59972 0.59655 0.65969 0.48621 31_D 0.00336 0.00430 0.00364 0.00426 0.00308 0.00410 0.00337 0.00413 0.00312 0.00434 0.00321 0.00434 0.00253 31_G 0.17328 0.22196 0.18816 0.21996 0.15881 0.21195 0.17384 0.21340 0.16109 0.22422 0.16581 0.22412 0.13052 32_D 0.00238 0.00305 0.00259 0.00303 0.00219 0.00292 0.00239 0.00294 0.00222 0.00309 0.00228 0.00308 0.00180 32_G 0.04260 0.05458 0.04626 0.05408 0.03905 0.05211 0.04274 0.05247 0.03961 0.05513 0.04077 0.05511 0.03209 51_D 0.00174 0.00232 0.00175 0.00132 0.00191 0.00230 0.00176 0.00171 0.00122 0.00156 0.00110 0.00097 0.00123 51_G 0.00049 0.00065 0.00049 0.00037 0.00054 0.00064 0.00049 0.00048 0.00034 0.00044 0.00031 0.00027 0.00034 52_D 0.03762 0.05003 0.03781 0.02847 0.04121 0.04954 0.03795 0.03689 0.02640 0.03375 0.02363 0.02098 0.02650 52_G 0.01055 0.01403 0.01060 0.00798 0.01156 0.01389 0.01064 0.01035 0.00740 0.00946 0.00663 0.00588 0.00743 53_D 0.00194 0.00258 0.00195 0.00147 0.00212 0.00255 0.00196 0.00190 0.00136 0.00174 0.00122 0.00108 0.00137 53_G 0.00054 0.00072 0.00055 0.00041 0.00060 0.00072 0.00055 0.00053 0.00038 0.00049 0.00034 0.00030 0.00038 54_D 0.00123 0.00164 0.00124 0.00093 0.00135 0.00162 0.00124 0.00121 0.00087 0.00111 0.00077 0.00069 0.00087 54_G 0.00035 0.00046 0.00035 0.00026 0.00038 0.00046 0.00035 0.00034 0.00024 0.00031 0.00022 0.00019 0.00024 41_D 0.00048 0.00094 0.00049 0.00046 0.00055 0.00058 0.00049 0.00037 0.00049 0.00024 0.00041 0.00033 0.00099 42_D 0.00096 0.00191 0.00099 0.00094 0.00111 0.00117 0.00098 0.00075 0.00098 0.00048 0.00083 0.00067 0.00201 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00260 0.00515 0.00267 0.00253 0.00300 0.00315 0.00265 0.00203 0.00265 0.00129 0.00223 0.00182 0.00543 43_G 0.00003 0.00005 0.00003 0.00003 0.00003 0.00003 0.00003 0.00002 0.00003 0.00001 0.00002 0.00002 0.00005 61_D 0.05088 0.02827 0.04357 0.00991 0.05793 0.03373 0.05160 0.01312 0.05439 0.01873 0.04958 0.00421 0.09956 61_G 0.00503 0.00280 0.00431 0.00098 0.00573 0.00334 0.00510 0.00130 0.00538 0.00185 0.00490 0.00042 0.00985 62_D 0.09440 0.05245 0.08084 0.01838 0.10746 0.06257 0.09573 0.02435 0.10089 0.03475 0.09197 0.00780 0.18470 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00057 0.00055 0.00057 0.00064 0.00056 0.00055 0.00057 0.00063 0.00059 0.00061 0.00060 0.00067 0.00049 
103 

2025 Weekday VMT Mix - Houston TxDOT District (2023 and 2026 Analysis Years) 

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00774 0.00712 0.00853 0.00814 0.00707 0.00655 0.00803 0.00765 0.00786 0.00720 0.00862 0.00824 0.00669 21_G 0.69592 0.64006 0.76687 0.73202 0.63563 0.58911 0.72233 0.68783 0.70656 0.64738 0.77521 0.74117 0.60185 31_D 0.00294 0.00378 0.00247 0.00311 0.00314 0.00394 0.00271 0.00328 0.00297 0.00418 0.00258 0.00317 0.00263 31_G 0.15154 0.19507 0.12741 0.16032 0.16210 0.20331 0.14017 0.16926 0.15321 0.21590 0.13296 0.16349 0.13563 32_D 0.00209 0.00268 0.00175 0.00221 0.00223 0.00280 0.00193 0.00233 0.00211 0.00297 0.00183 0.00225 0.00187 32_G 0.03726 0.04796 0.03133 0.03942 0.03986 0.04999 0.03446 0.04162 0.03767 0.05309 0.03269 0.04020 0.03335 51_D 0.00104 0.00135 0.00080 0.00084 0.00122 0.00171 0.00110 0.00141 0.00066 0.00092 0.00051 0.00063 0.00086 51_G 0.00062 0.00080 0.00048 0.00050 0.00073 0.00102 0.00066 0.00084 0.00040 0.00055 0.00030 0.00038 0.00051 52_D 0.01976 0.02560 0.01527 0.01599 0.02324 0.03249 0.02093 0.02674 0.01261 0.01754 0.00970 0.01202 0.01634 52_G 0.01181 0.01530 0.00912 0.00955 0.01389 0.01941 0.01251 0.01598 0.00753 0.01048 0.00579 0.00718 0.00976 53_D 0.00382 0.00495 0.00295 0.00309 0.00449 0.00628 0.00405 0.00517 0.00244 0.00339 0.00187 0.00232 0.00316 53_G 0.00228 0.00296 0.00176 0.00185 0.00268 0.00375 0.00242 0.00309 0.00146 0.00203 0.00112 0.00139 0.00189 54_D 0.00074 0.00095 0.00057 0.00059 0.00086 0.00121 0.00078 0.00100 0.00047 0.00065 0.00036 0.00045 0.00061 54_G 0.00044 0.00057 0.00034 0.00036 0.00052 0.00072 0.00047 0.00059 0.00028 0.00039 0.00022 0.00027 0.00036 41_D 0.00025 0.00052 0.00050 0.00064 0.00026 0.00032 0.00033 0.00040 0.00025 0.00017 0.00036 0.00032 0.00029 42_D 0.00050 0.00105 0.00101 0.00129 0.00052 0.00064 0.00067 0.00080 0.00051 0.00035 0.00072 0.00065 0.00059 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00134 0.00282 0.00273 0.00348 0.00141 0.00174 0.00180 0.00216 0.00139 0.00094 0.00194 0.00175 0.00159 43_G 0.00001 0.00003 0.00003 0.00004 0.00001 0.00002 0.00002 0.00002 0.00001 0.00001 0.00002 0.00002 0.00002 61_D 0.02946 0.02279 0.01259 0.00787 0.04951 0.03702 0.02185 0.01450 0.03031 0.01553 0.01115 0.00665 0.09028 61_G 0.00239 0.00185 0.00102 0.00064 0.00401 0.00300 0.00177 0.00118 0.00246 0.00126 0.00090 0.00054 0.00732 62_D 0.02735 0.02116 0.01169 0.00731 0.04596 0.03436 0.02028 0.01346 0.02814 0.01442 0.01035 0.00617 0.08381 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00070 0.00065 0.00078 0.00074 0.00064 0.00060 0.00073 0.00070 0.00072 0.00066 0.00078 0.00075 0.00061 
104 

2025 Weekday VMT Mix - Beaumont TxDOT District (2028 Analysis Years)

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00626 0.00607 0.00628 0.00708 0.00617 0.00607 0.00622 0.00694 0.00649 0.00667 0.00663 0.00734 0.00541 21_G 0.56270 0.54549 0.56486 0.63651 0.55469 0.54602 0.55933 0.62412 0.58385 0.59972 0.59655 0.65969 0.48621 31_D 0.00353 0.00453 0.00384 0.00448 0.00324 0.00432 0.00354 0.00435 0.00328 0.00457 0.00338 0.00457 0.00266 31_G 0.17310 0.22174 0.18797 0.21974 0.15865 0.21173 0.17367 0.21318 0.16093 0.22399 0.16564 0.22389 0.13039 32_D 0.00238 0.00305 0.00259 0.00303 0.00219 0.00292 0.00239 0.00294 0.00222 0.00309 0.00228 0.00308 0.00180 32_G 0.04260 0.05458 0.04626 0.05408 0.03905 0.05211 0.04274 0.05247 0.03961 0.05513 0.04077 0.05511 0.03209 51_D 0.00174 0.00232 0.00175 0.00132 0.00191 0.00230 0.00176 0.00171 0.00122 0.00156 0.00110 0.00097 0.00123 51_G 0.00049 0.00065 0.00049 0.00037 0.00054 0.00064 0.00049 0.00048 0.00034 0.00044 0.00031 0.00027 0.00034 52_D 0.03762 0.05003 0.03781 0.02847 0.04121 0.04954 0.03795 0.03689 0.02640 0.03375 0.02363 0.02098 0.02650 52_G 0.01055 0.01403 0.01060 0.00798 0.01156 0.01389 0.01064 0.01035 0.00740 0.00946 0.00663 0.00588 0.00743 53_D 0.00194 0.00258 0.00195 0.00147 0.00212 0.00255 0.00196 0.00190 0.00136 0.00174 0.00122 0.00108 0.00137 53_G 0.00054 0.00072 0.00055 0.00041 0.00060 0.00072 0.00055 0.00053 0.00038 0.00049 0.00034 0.00030 0.00038 54_D 0.00123 0.00164 0.00124 0.00093 0.00135 0.00162 0.00124 0.00121 0.00087 0.00111 0.00077 0.00069 0.00087 54_G 0.00035 0.00046 0.00035 0.00026 0.00038 0.00046 0.00035 0.00034 0.00024 0.00031 0.00022 0.00019 0.00024 41_D 0.00048 0.00094 0.00049 0.00046 0.00055 0.00058 0.00049 0.00037 0.00049 0.00024 0.00041 0.00033 0.00099 42_D 0.00098 0.00193 0.00100 0.00095 0.00112 0.00118 0.00100 0.00076 0.00100 0.00049 0.00084 0.00068 0.00204 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00259 0.00512 0.00266 0.00251 0.00298 0.00313 0.00264 0.00203 0.00264 0.00129 0.00222 0.00181 0.00540 43_G 0.00003 0.00005 0.00003 0.00003 0.00003 0.00003 0.00003 0.00002 0.00003 0.00001 0.00002 0.00002 0.00005 61_D 0.05088 0.02827 0.04357 0.00991 0.05793 0.03373 0.05160 0.01312 0.05439 0.01873 0.04958 0.00421 0.09956 61_G 0.00503 0.00280 0.00431 0.00098 0.00573 0.00334 0.00510 0.00130 0.00538 0.00185 0.00490 0.00042 0.00985 62_D 0.09440 0.05245 0.08084 0.01838 0.10746 0.06257 0.09573 0.02435 0.10089 0.03475 0.09197 0.00780 0.18470 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00057 0.00055 0.00057 0.00064 0.00056 0.00055 0.00057 0.00063 0.00059 0.00061 0.00060 0.00067 0.00049 
105 

2025 Weekday VMT Mix - Houston TxDOT District (2028 Analysis Years)

SUT/FT 

AM Peak Mid-Day PM Peak 

RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 RT3 RT4 RT5 RT2 

21_D 0.00844 0.00777 0.00930 0.00888 0.00771 0.00715 0.00876 0.00835 0.00857 0.00785 0.00941 0.00899 0.00730 21_G 0.69522 0.63941 0.76609 0.73128 0.63499 0.58852 0.72160 0.68713 0.70584 0.64673 0.77443 0.74042 0.60124 31_D 0.00309 0.00398 0.00260 0.00327 0.00330 0.00415 0.00286 0.00345 0.00312 0.00440 0.00271 0.00333 0.00277 31_G 0.15139 0.19487 0.12728 0.16016 0.16194 0.20311 0.14002 0.16908 0.15305 0.21568 0.13282 0.16333 0.13549 32_D 0.00209 0.00268 0.00175 0.00221 0.00223 0.00280 0.00193 0.00233 0.00211 0.00297 0.00183 0.00225 0.00187 32_G 0.03726 0.04796 0.03133 0.03942 0.03986 0.04999 0.03446 0.04162 0.03767 0.05309 0.03269 0.04020 0.03335 51_D 0.00104 0.00135 0.00080 0.00084 0.00122 0.00171 0.00110 0.00141 0.00066 0.00092 0.00051 0.00063 0.00086 51_G 0.00062 0.00080 0.00048 0.00050 0.00073 0.00102 0.00066 0.00084 0.00040 0.00055 0.00030 0.00038 0.00051 52_D 0.01976 0.02560 0.01527 0.01599 0.02324 0.03249 0.02093 0.02674 0.01261 0.01754 0.00970 0.01202 0.01634 52_G 0.01181 0.01530 0.00912 0.00955 0.01389 0.01941 0.01251 0.01598 0.00753 0.01048 0.00579 0.00718 0.00976 53_D 0.00382 0.00495 0.00295 0.00309 0.00449 0.00628 0.00405 0.00517 0.00244 0.00339 0.00187 0.00232 0.00316 53_G 0.00228 0.00296 0.00176 0.00185 0.00268 0.00375 0.00242 0.00309 0.00146 0.00203 0.00112 0.00139 0.00189 54_D 0.00074 0.00095 0.00057 0.00059 0.00086 0.00121 0.00078 0.00100 0.00047 0.00065 0.00036 0.00045 0.00061 54_G 0.00044 0.00057 0.00034 0.00036 0.00052 0.00072 0.00047 0.00059 0.00028 0.00039 0.00022 0.00027 0.00036 41_D 0.00025 0.00052 0.00050 0.00064 0.00026 0.00032 0.00033 0.00040 0.00025 0.00017 0.00036 0.00032 0.00029 42_D 0.00050 0.00106 0.00102 0.00131 0.00053 0.00065 0.00068 0.00081 0.00052 0.00035 0.00073 0.00066 0.00060 42_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 43_D 0.00133 0.00281 0.00271 0.00347 0.00140 0.00173 0.00179 0.00215 0.00138 0.00093 0.00194 0.00175 0.00158 43_G 0.00001 0.00003 0.00003 0.00004 0.00001 0.00002 0.00002 0.00002 0.00001 0.00001 0.00002 0.00002 0.00002 61_D 0.02946 0.02279 0.01259 0.00787 0.04951 0.03702 0.02185 0.01450 0.03031 0.01553 0.01115 0.00665 0.09028 61_G 0.00239 0.00185 0.00102 0.00064 0.00401 0.00300 0.00177 0.00118 0.00246 0.00126 0.00090 0.00054 0.00732 62_D 0.02735 0.02116 0.01169 0.00731 0.04596 0.03436 0.02028 0.01346 0.02814 0.01442 0.01035 0.00617 0.08381 62_G 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 11_G 0.00070 0.00065 0.00078 0.00074 0.00064 0.00060 0.00073 0.00070 0.00072 0.00066 0.00078 0.00075 0.00061 

107 

APPENDIX D: 

TXDOT DISTRICT AGGREGATE WEEKDAY VMT MIX

109 

TxDOT District/HGB Counties 

TxDOT District HGB County 

Beaumont 

Liberty 

Chambers 

Houston 

Harris 

Galveston 

Fort Bend 

Brazoria 

Montgomery 

Waller 

VMT Mix Year/Analysis Year Correlations 

VMT Mix Year Analysis Years 

2000 1998 through 2002 

2005 2003 through 2007 

2010 2008 through 2012 

2015 2013 through 2017 

2020 2018 through 2022 

2025 2023 through 2027 

2030 2028 through 2032 
110 

Aggregate Weekday VMT Mix - Beaumont TxDOT District 

SUT/FT 20101 20152 20203 20254 20305

21_D 0.00225 0.00395 0.00507 0.00620 0.00620 

21_G 0.56144 0.55975 0.55862 0.55749 0.55749 

31_D 0.00266 0.00324 0.00343 0.00362 0.00381 

31_G 0.18700 0.18714 0.18695 0.18676 0.18656 

32_D 0.00261 0.00257 0.00252 0.00257 0.00257 

32_G 0.04660 0.04592 0.04597 0.04592 0.04592 

51_D 0.00169 0.00173 0.00169 0.00169 0.00169 

51_G 0.00047 0.00048 0.00047 0.00047 0.00047 

52_D 0.03632 0.03620 0.03632 0.03640 0.03640 

52_G 0.01018 0.01015 0.01018 0.01021 0.01021 

53_D 0.00187 0.00187 0.00187 0.00188 0.00188 

53_G 0.00052 0.00052 0.00052 0.00053 0.00053 

54_D 0.00128 0.00136 0.00128 0.00119 0.00119 

54_G 0.00036 0.00038 0.00036 0.00033 0.00033 

41_D 0.00165 0.00052 0.00052 0.00052 0.00052 

42_D 0.00068 0.00104 0.00105 0.00106 0.00107 

42_G 0.00000 0.00000 0.00000 0.00000 0.00000 

43_D 0.00212 0.00287 0.00286 0.00285 0.00284 

43_G 0.00002 0.00003 0.00003 0.00003 0.00003 

61_D 0.04730 0.04730 0.04730 0.04730 0.04730 

61_G 0.00468 0.00468 0.00468 0.00468 0.00468 

62_D 0.08775 0.08775 0.08775 0.08775 0.08775 

62_G 0.00000 0.00000 0.00000 0.00000 0.00000 

11_G 0.00056 0.00056 0.00056 0.00056 0.00056 

1

 2012 analysis year. 

2

 2014 and 2017 analysis years. 

3

 2020 analysis year. 

4

 2023 and 2026 analysis years. 

5

 2028 analysis year. 
111 

Aggregate Weekday VMT Mix - Houston TxDOT District 

SUT/FT 20101 20152 20203 20254 20305

21_D 0.00295 0.00516 0.00663 0.00811 0.00884 

21_G 0.73387 0.73166 0.73019 0.72872 0.72798 

31_D 0.00204 0.00248 0.00278 0.00278 0.00292 

31_G 0.14351 0.14361 0.14332 0.14332 0.14317 

32_D 0.00204 0.00193 0.00193 0.00197 0.00197 

32_G 0.03572 0.03528 0.03528 0.03524 0.03524 

51_D 0.00090 0.00092 0.00090 0.00090 0.00090 

51_G 0.00054 0.00055 0.00054 0.00054 0.00054 

52_D 0.01709 0.01703 0.01709 0.01712 0.01712 

52_G 0.01021 0.01017 0.01021 0.01023 0.01023 

53_D 0.00330 0.00329 0.00330 0.00331 0.00331 

53_G 0.00197 0.00197 0.00197 0.00198 0.00198 

54_D 0.00068 0.00073 0.00068 0.00064 0.00064 

54_G 0.00041 0.00043 0.00041 0.00038 0.00038 

41_D 0.00114 0.00036 0.00036 0.00036 0.00036 

42_D 0.00047 0.00072 0.00072 0.00073 0.00074 

42_G 0.00000 0.00000 0.00000 0.00000 0.00000 

43_D 0.00146 0.00198 0.00198 0.00197 0.00196 

43_G 0.00001 0.00002 0.00002 0.00002 0.00002 

61_D 0.02038 0.02038 0.02038 0.02038 0.02038 

61_G 0.00165 0.00165 0.00165 0.00165 0.00165 

62_D 0.01892 0.01892 0.01892 0.01892 0.01892 

62_G 0.00000 0.00000 0.00000 0.00000 0.00000 

11_G 0.00074 0.00074 0.00074 0.00074 0.00074 

1

 2011 analysis year. 

2

 2014 and 2017 analysis years. 

3

 2020 analysis year. 

4

 2023 and 2026 analysis years. 

5

 2028 analysis year. 

113 

APPENDIX E: 

ANNUALLY COMPOUNDED GROWTH RATES AND 

INTERMEDIATE YEAR ADJUSTMENT FACTORS

115 

2014 AM Peak Intermediate Year Adjustment Factors 

County 2012 TDM VMT1 2015 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 21,870,586.66 21,138,868.57 0.988721023 1.011407644 

Brazoria 1,402,912.73 1,331,413.08 0.982714552 1.01758949 

Fort Bend 2,189,145.73 2,338,420.62 1.022231641 0.978251856 

Waller 330,014.65 352,663.70 1.022372555 0.978117023 

Montgomery 2,434,641.66 2,624,961.74 1.025406288 0.975223199 

Liberty 441,882.04 437,656.83 0.996802505 1.003207752 

Chambers 548,926.00 530,940.20 0.988956679 1.011166638 

Galveston 1,077,170.19 1,034,065.22 0.986479053 1.013706268 

1 Includes the estimated intrazonal VMT. 

2

 Applied to 2015 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2014 Mid-Day Intermediate Year Adjustment Factors 

County 2012 TDM VMT1 2015 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 34,459,569.08 39,574,751.65 1.047215637 0.954913166 

Brazoria 2,306,149.72 2,627,024.41 1.044380777 0.957505177 

Fort Bend 3,406,600.06 4,044,980.51 1.058924739 0.944354177 

Waller 623,496.46 701,153.15 1.039903321 0.961627855 

Montgomery 3,986,222.47 4,738,010.69 1.059281777 0.944035876 

Liberty 801,842.66 859,334.12 1.023350248 0.977182545 

Chambers 1,106,579.88 1,122,057.23 1.004640650 0.995380786 

Galveston 1,813,996.18 2,065,663.78 1.044257866 0.957617877 

1 Includes the estimated intrazonal VMT. 

2

 Applied to 2015 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
116 

2014 PM Peak Intermediate Year Adjustment Factors 

County 2012 TDM VMT1 2015 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 31,734,604.69 33,879,599.29 1.022041171 0.978434166 

Brazoria 2,050,019.33 2,199,943.75 1.023806479 0.976747091 

Fort Bend 3,247,054.36 3,659,500.05 1.040664483 0.960924502 

Waller 518,324.28 560,549.03 1.026448943 0.974232578 

Montgomery 3,598,311.14 4,061,822.00 1.041215740 0.960415754 

Liberty 668,153.49 687,455.45 1.009538236 0.990551883 

Chambers 830,110.32 829,885.09 0.999909553 1.000090455 

Galveston 1,591,439.81 1,714,381.26 1.025114551 0.975500737 

1 Includes the estimated intrazonal VMT. 

2

 Applied to 2015 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2014 Overnight Intermediate Year Adjustment Factors 

County 2012 TDM VMT1 2015 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 20,020,777.29 23,166,338.30 1.049845731 0.9525209 

Brazoria 1,282,132.88 1,544,201.22 1.063955877 0.9398886 

Fort Bend 1,970,586.02 2,409,594.33 1.069340902 0.935155476 

Waller 245,873.84 423,328.84 1.198547300 0.834343376 

Montgomery 2,195,549.81 2,957,661.99 1.104422082 0.905450929 

Liberty 375,850.40 520,655.42 1.114752512 0.8970601 

Chambers 528,715.85 769,378.75 1.133198398 0.882458007 

Galveston 1,023,939.85 1,202,485.55 1.055038904 0.947832347 

1 Includes the estimated intrazonal VMT. 

2

 Applied to 2015 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
117 

2020 AM Peak Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 22,024,435.65 25,846,694.12 1.020205186 1.061848555 

Brazoria 1,401,663.22 1,747,120.72 1.027921393 1.086124759 

Fort Bend 2,487,416.07 3,381,488.93 1.039130140 1.12204384 

Waller 354,095.94 393,205.96 1.013181833 1.040069072 

Montgomery 2,851,286.38 3,682,423.74 1.032491810 1.100676884 

Liberty 452,093.75 501,420.27 1.013028522 1.039597005 

Chambers 549,086.47 648,445.70 1.021007916 1.064357018 

Galveston 1,067,511.06 1,339,743.81 1.028800513 1.088913837 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2017 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2020 Mid-Day Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 41,098,864.26 48,244,708.31 1.020240320 1.061958265 

Brazoria 2,740,896.38 3,266,568.41 1.022174147 1.068008424 

Fort Bend 4,288,641.01 5,751,335.12 1.037363880 1.116331982 

Waller 698,444.18 782,800.42 1.014354870 1.043685754 

Montgomery 5,122,093.25 6,466,658.97 1.029565686 1.091345291 

Liberty 894,778.06 952,745.32 1.007877354 1.023818709 

Chambers 1,159,126.82 1,350,731.02 1.019306374 1.059044527 

Galveston 2,121,061.05 2,608,436.60 1.026191444 1.080650275 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2017 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
118 

2020 PM Peak Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 35,332,107.95 41,591,356.78 1.020596777 1.06307175 

Brazoria 2,313,185.57 2,869,368.83 1.027299289 1.084153967 

Fort Bend 3,891,909.07 5,282,843.49 1.038934415 1.12140993 

Waller 563,862.21 655,791.00 1.019058364 1.058271679 

Montgomery 4,407,544.92 5,662,097.01 1.031804862 1.098481405 

Liberty 710,301.63 790,388.39 1.013443912 1.040876381 

Chambers 859,128.92 1,018,671.02 1.021520183 1.065959869 

Galveston 1,766,947.09 2,201,050.32 1.027840683 1.085868939 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2017 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2020 Overnight Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 23,955,784.79 28,320,570.85 1.021142731 1.064778688 

Brazoria 1,607,394.69 1,908,159.20 1.021672044 1.066435342 

Fort Bend 2,562,895.17 3,492,518.31 1.039443723 1.123059959 

Waller 397,122.45 447,380.33 1.015007042 1.045700141 

Montgomery 3,200,597.68 4,053,443.67 1.029968949 1.092628177 

Liberty 540,783.79 571,492.71 1.006927911 1.020928053 

Chambers 793,586.36 905,733.97 1.016660165 1.050817802 

Galveston 1,233,589.86 1,534,573.14 1.027666264 1.085316236 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2017 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
119 

2023 AM Peak Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 22,024,435.65 25,846,694.12 1.020205186 0.960782195 

Brazoria 1,401,663.22 1,747,120.72 1.027921393 0.946411896 

Fort Bend 2,487,416.07 3,381,488.93 1.039130140 0.92610476 

Waller 354,095.94 393,205.96 1.013181833 0.974148603 

Montgomery 2,851,286.38 3,682,423.74 1.032491810 0.938051689 

Liberty 452,093.75 501,420.27 1.013028522 0.974443479 

Chambers 549,086.47 648,445.70 1.021007916 0.959272029 

Galveston 1,067,511.06 1,339,743.81 1.028800513 0.944795151 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2023 Mid-Day Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 41,098,864.26 48,244,708.31 1.020240320 0.960716023 

Brazoria 2,740,896.38 3,266,568.41 1.022174147 0.957084349 

Fort Bend 4,288,641.01 5,751,335.12 1.037363880 0.929261095 

Waller 698,444.18 782,800.42 1.014354870 0.971896824 

Montgomery 5,122,093.25 6,466,658.97 1.029565686 0.943391330 

Liberty 894,778.06 952,745.32 1.007877354 0.984429514 

Chambers 1,159,126.82 1,350,731.02 1.019306374 0.962477354 

Galveston 2,121,061.05 2,608,436.60 1.026191444 0.949605499 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
120 

2023 PM Peak Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 35,332,107.95 41,591,356.78 1.020596777 0.960045055 

Brazoria 2,313,185.57 2,869,368.83 1.027299289 0.947558484 

Fort Bend 3,891,909.07 5,282,843.49 1.038934415 0.926453732 

Waller 563,862.21 655,791.00 1.019058364 0.962945891 

Montgomery 4,407,544.92 5,662,097.01 1.031804862 0.939301164 

Liberty 710,301.63 790,388.39 1.013443912 0.973644834 

Chambers 859,128.92 1,018,671.02 1.021520183 0.958310169 

Galveston 1,766,947.09 2,201,050.32 1.027840683 0.946560534 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2023 Overnight Intermediate Year Adjustment Factors 

County 2017 TDM VMT1 2025 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 23,955,784.79 28,320,570.85 1.021142731 0.959018754 

Brazoria 1,607,394.69 1,908,159.20 1.021672044 0.958025305 

Fort Bend 2,562,895.17 3,492,518.31 1.039443723 0.925546063 

Waller 397,122.45 447,380.33 1.015007042 0.970648279 

Montgomery 3,200,597.68 4,053,443.67 1.029968949 0.942652744 

Liberty 540,783.79 571,492.71 1.006927911 0.986286848 

Chambers 793,586.36 905,733.97 1.016660165 0.967494234 

Galveston 1,233,589.86 1,534,573.14 1.027666264 0.946881867 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
121 

2026 AM Peak Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 25,846,694.12 29,839,938.85 1.014470196 1.014470196 

Brazoria 1,747,120.72 2,367,314.01 1.030844826 1.030844826 

Fort Bend 3,381,488.93 4,576,728.58 1.030729530 1.03072953 

Waller 393,205.96 498,875.24 1.024087783 1.024087783 

Montgomery 3,682,423.74 4,908,977.22 1.029166700 1.0291667 

Liberty 501,420.27 590,942.96 1.016563162 1.016563162 

Chambers 648,445.70 765,239.57 1.016698962 1.016698962 

Galveston 1,339,743.81 1,766,471.62 1.028036396 1.028036396 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2026 Mid-Day Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 48,244,708.31 55,773,030.06 1.014606068 1.014606068 

Brazoria 3,266,568.41 4,271,756.75 1.027191639 1.027191639 

Fort Bend 5,751,335.12 7,416,620.51 1.025755226 1.025755226 

Waller 782,800.42 941,170.57 1.018595443 1.018595443 

Montgomery 6,466,658.97 8,179,425.08 1.023774439 1.023774439 

Liberty 952,745.32 1,063,458.13 1.011054013 1.011054013 

Chambers 1,350,731.02 1,530,153.38 1.012550306 1.012550306 

Galveston 2,608,436.60 3,426,363.47 1.027650208 1.027650208 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
122 

2026 PM Peak Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 41,591,356.78 48,139,520.05 1.014728520 1.01472852 

Brazoria 2,869,368.83 3,788,285.14 1.028171659 1.028171659 

Fort Bend 5,282,843.49 6,890,720.98 1.026927283 1.026927283 

Waller 655,791.00 816,946.28 1.022216306 1.022216306 

Montgomery 5,662,097.01 7,410,468.19 1.027275272 1.027275272 

Liberty 790,388.39 936,417.88 1.017098268 1.017098268 

Chambers 1,018,671.02 1,216,749.90 1.017927241 1.017927241 

Galveston 2,201,050.32 2,909,145.37 1.028285105 1.028285105 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2026 Overnight Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 28,320,570.85 33,018,433.49 1.015466139 1.015466139 

Brazoria 1,908,159.20 2,504,146.30 1.027553657 1.027553657 

Fort Bend 3,492,518.31 4,572,335.55 1.027306274 1.027306274 

Waller 447,380.33 530,689.76 1.017223495 1.017223495 

Montgomery 4,053,443.67 5,140,509.47 1.024043021 1.024043021 

Liberty 571,492.71 630,080.13 1.009807306 1.009807306 

Chambers 905,733.97 1,018,214.50 1.011774809 1.011774809 

Galveston 1,534,573.14 2,006,758.64 1.027189934 1.027189934 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
123 

2028 AM Peak Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 25,846,694.12 29,839,938.85 1.014470196 1.044041778 

Brazoria 1,747,120.72 2,367,314.01 1.030844826 1.095418033 

Fort Bend 3,381,488.93 4,576,728.58 1.030729530 1.095050521 

Waller 393,205.96 498,875.24 1.024087783 1.07401799 

Montgomery 3,682,423.74 4,908,977.22 1.029166700 1.090077 

Liberty 501,420.27 590,942.96 1.016563162 1.050517045 

Chambers 648,445.70 765,239.57 1.016698962 1.050938108 

Galveston 1,339,743.81 1,766,471.62 1.028036396 1.086489345 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2028 Mid-Day Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 48,244,708.31 55,773,030.06 1.014606068 1.044461333 

Brazoria 3,266,568.41 4,271,756.75 1.027191639 1.083813177 

Fort Bend 5,751,335.12 7,416,620.51 1.025755226 1.079272758 

Waller 782,800.42 941,170.57 1.018595443 1.056830131 

Montgomery 6,466,658.97 8,179,425.08 1.023774439 1.073032426 

Liberty 952,745.32 1,063,458.13 1.011054013 1.033529962 

Chambers 1,350,731.02 1,530,153.38 1.012550306 1.038125426 

Galveston 2,608,436.60 3,426,363.47 1.027650208 1.085265366 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
124 

2028 PM Peak Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 41,591,356.78 48,139,520.05 1.014728520 1.044839543 

Brazoria 2,869,368.83 3,788,285.14 1.028171659 1.086918261 

Fort Bend 5,282,843.49 6,890,720.98 1.026927283 1.082976608 

Waller 655,791.00 816,946.28 1.022216306 1.068140575 

Montgomery 5,662,097.01 7,410,468.19 1.027275272 1.084077929 

Liberty 790,388.39 936,417.88 1.017098268 1.052176854 

Chambers 1,018,671.02 1,216,749.90 1.017927241 1.054751643 

Galveston 2,201,050.32 2,909,145.37 1.028285105 1.087278087 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 

2028 Overnight Intermediate Year Adjustment Factors 

County 2025 TDM VMT1 2035 TDM VMT1

Annual 

Compounded 

Growth Rate 

Intermediate 

Year Factor2

Harris 28,320,570.85 33,018,433.49 1.015466139 1.04711972 

Brazoria 1,908,159.20 2,504,146.30 1.027553657 1.084959502 

Fort Bend 3,492,518.31 4,572,335.55 1.027306274 1.08417608 

Waller 447,380.33 530,689.76 1.017223495 1.052565541 

Montgomery 4,053,443.67 5,140,509.47 1.024043021 1.073877162 

Liberty 571,492.71 630,080.13 1.009807306 1.029711412 

Chambers 905,733.97 1,018,214.50 1.011774809 1.035741999 

Galveston 1,534,573.14 2,006,758.64 1.027189934 1.083807781 

1

 Includes the estimated intrazonal VMT. 

2

 Applied to 2025 TDM VMT (including intrazonal VMT) to estimate analysis year VMT. 
125 

APPENDIX F: 

CAPACITY FACTORS, SPEED FACTORS, AND SPEED REDUCTION 

FACTORS

127 

Capacity Factors 

Time of Day Assignment Capacity Factor1

AM Peak 0.3333333 

Mid-Day 0.1666667 

PM Peak 0.2500000 

Overnight 0.0909091 

1

 To obtain hourly capacities, a single capacity factor for each timeof-day assignment is used for all. 
128 

Free-Flow (V/C=0) Speed Factors for Houston/Galveston Speed Model 

Functional Class Area Type Distance 

Weighted 

Input Speeds1

Distance 

Weighted FreeFlow Speeds2

Free-Flow 

Speed 

Factor Code Description Code Description 3

1 Urban Interstate 1 CBD 50.85 56.40 1.10906 

1 Urban Interstate 2 Urban 52.55 61.40 1.16842 

2 Urban Other Freeway 1 CBD N/A 58.00 1.21154 

2 Urban Other Freeway 2 Urban 52.00 63.00 1.21154 

3 Toll Road 1 CBD N/A 34.50 0.62652 

3 Toll Road 2 Urban 57.58 36.08 0.62652 

3 Toll Road 3 Urban Fringe 61.69 36.14 0.58577 

3 Toll Road 4 Suburban 64.34 37.99 0.59040 

3 Toll Road 5 Rural 59.13 38.43 0.64991 

4 Ramp 1 CBD 28.62 35.13 1.22734 

4 Ramp 2 Urban 40.06 36.26 0.90509 

4 Ramp 3 Urban Fringe 43.22 38.52 0.89119 

4 Ramp 4 Suburban 44.82 45.71 1.01987 

4 Ramp 5 Rural 55.16 52.11 0.94478 

5 Urban Principal Arterial 1 CBD 24.72 26.52 1.07262 

5 Urban Principal Arterial 2 Urban 35.78 29.69 0.82974 

6 Urban Other Arterial 1 CBD 22.00 24.64 1.11996 

6 Urban Other Arterial 2 Urban 34.57 27.31 0.79001 

7 Urban Collector 1 CBD 20.94 24.17 1.15413 

7 Urban Collector 2 Urban 35.36 25.78 0.72901 

10 Rural Interstate 3 Urban Fringe 57.84 61.40 1.06152 

10 Rural Interstate 4 Suburban 59.15 67.20 1.13613 

10 Rural Interstate 5 Rural 62.00 68.57 1.10599 

11 Rural Other Freeway 3 Urban Fringe 62.00 63.00 1.01613 

11 Rural Other Freeway 4 Suburban 62.00 69.00 1.11290 

11 Rural Other Freeway 5 Rural 64.00 71.00 1.10938 

12 Rural Principal Arterial 3 Urban Fringe 40.23 33.75 0.83890 

12 Rural Principal Arterial 4 Suburban 46.12 42.48 0.92125 

12 Rural Principal Arterial 5 Rural 60.00 55.53 0.92536 

13 Rural Other Arterial 3 Urban Fringe 39.05 30.51 0.78131 

13 Rural Other Arterial 4 Suburban 43.03 39.85 0.92612 

13 Rural Other Arterial 5 Rural 53.97 54.07 1.00194 
129 

Free-Flow (V/C=0) Speed Factors for Houston/Galveston Speed Model - Continued

Functional Class Area Type Distance 

Weighted 

Input Speeds1

Distance 

Weighted FreeFlow Speeds2

Free-Flow 

Speed 

Factor Code Description Code Description 3

14 Rural Major Collector 3 Urban Fringe 38.00 27.76 0.73061 

14 Rural Major Collector 4 Suburban 41.00 49.22 1.20059 

14 Rural Major Collector 5 Rural 53.00 54.06 1.02009 

15 Rural Collector 3 Urban Fringe 36.00 24.07 0.66864 

15 Rural Collector 4 Suburban 40.00 35.58 0.88938 

15 Rural Collector 5 Rural 49.00 49.86 1.01762 

1

 Based on 2012 TDM data. 

2

 Calculated from detailed speed model runs by HGAC with link volumes set to 0 (V/C=0). 3

 When inputs speeds are not available, speed factors are taken from the nearest area type. 
130 

LOS E (V/C=1) Speed Factors for Houston/Galveston Speed Model 

Functional Class Area Type Distance 

Weighted 

Input Speeds1

Distance 

Weighted FreeFlow Speeds2

Free-Flow 

Speed 

Factor Code Description Code Description 3

1 Urban Interstate 1 CBD 50.85 34.35 0.67549 

1 Urban Interstate 2 Urban 52.55 34.35 0.65370 

2 Urban Other Freeway 1 CBD N/A 35.00 0.67308 

2 Urban Other Freeway 2 Urban 52.00 35.00 0.67308 

3 Toll Road 1 CBD N/A 24.77 0.43011 

3 Toll Road 2 Urban 57.58 24.77 0.43011 

3 Toll Road 3 Urban Fringe 61.69 26.52 0.42983 

3 Toll Road 4 Suburban 64.34 29.54 0.45920 

3 Toll Road 5 Rural 59.13 29.70 0.50229 

4 Ramp 1 CBD 28.62 31.68 1.10692 

4 Ramp 2 Urban 40.06 30.03 0.74952 

4 Ramp 3 Urban Fringe 43.22 33.24 0.76908 

4 Ramp 4 Suburban 44.82 41.22 0.91979 

4 Ramp 5 Rural 55.16 49.01 0.88861 

5 Urban Principal Arterial 1 CBD 24.72 22.13 0.89529 

5 Urban Principal Arterial 2 Urban 35.78 24.44 0.68294 

6 Urban Other Arterial 1 CBD 22.00 20.80 0.94565 

6 Urban Other Arterial 2 Urban 34.57 22.76 0.65833 

7 Urban Collector 1 CBD 20.94 20.06 0.95782 

7 Urban Collector 2 Urban 35.36 21.23 0.60033 

10 Rural Interstate 3 Urban Fringe 57.84 39.25 0.67860 

10 Rural Interstate 4 Suburban 59.15 49.08 0.82973 

10 Rural Interstate 5 Rural 62.00 49.08 0.79157 

11 Rural Other Freeway 3 Urban Fringe 62.00 40.00 0.64516 

11 Rural Other Freeway 4 Suburban 62.00 50.00 0.80645 

11 Rural Other Freeway 5 Rural 64.00 50.00 0.78125 

12 Rural Principal Arterial 3 Urban Fringe 40.23 27.30 0.67871 

12 Rural Principal Arterial 4 Suburban 46.12 32.64 0.70784 

12 Rural Principal Arterial 5 Rural 60.00 38.32 0.63858 

13 Rural Other Arterial 3 Urban Fringe 39.05 24.81 0.63540 

13 Rural Other Arterial 4 Suburban 43.03 30.15 0.70070 

13 Rural Other Arterial 5 Rural 53.97 38.46 0.71270 
131 

LOS E (V/C=1) Speed Factors for Houston/Galveston Speed Model - Continued 

Functional Class Area Type Distance 

Weighted 

Input Speeds1

Distance 

Weighted FreeFlow Speeds2

Free-Flow 

Speed 

Factor Code Description Code Description 3

14 Rural Major Collector 3 Urban Fringe 38.00 22.22 0.58465 

14 Rural Major Collector 4 Suburban 41.00 34.09 0.83151 

14 Rural Major Collector 5 Rural 53.00 36.83 0.69499 

15 Rural Collector 3 Urban Fringe 36.00 19.74 0.54845 

15 Rural Collector 4 Suburban 40.00 26.40 0.65994 

15 Rural Collector 5 Rural 49.00 34.33 0.70057 

1

 Based on 2012 TDM data. 

2

 Calculated from detailed speed model runs by HGAC with link volumes set to 0 (V/C=0). 3

 When inputs speeds are not available, speed factors are taken from the nearest area type. 
132 

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

Speed Reduction Factors by V/C Ratio

Functional Group 1 (Freeways)

CBD Urban Urban FringeSuburban Rural

V/C Ratios

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.0

0.000.200.400.600.801.00

Speed Reduction Factors
133 

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

Speed Reduction Factors

CBD Urban Urban FringeSuburban Rural
134 

Figure 3. Other Arterial Speed Reduction Factors by V/C Ratio. 

Speed Reduction Factors

CBD Urban Urban FringeSuburban Rural
135 

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 1.0

0.000.200.400.600.801.00

Speed Reduction Factors

CBD Urban Urban FringeSuburban Rural
136 

Functional Classification to Functional Group Relationship for the 

Application of Speed Reduction Factors

Functional Group Corresponding Network Functional 

Classifications

1. Freeways, Interstates 

1. Urban Interstate Freeways 

2. Urban Other Freeways 

3. Toll Roads 

10. Rural Interstate Freeways 

11. Rural Other Freeways 

2. Principal Arterials 5. Urban Principal Arterials 

12. Rural Principal Arterials 

3. Other Arterials, Major 

Collectors 

6. Urban Other Arterials 

13. Rural Other Arterials 

14. Rural Major Collectors 

4. Collectors 

4. Ramps 

7. Urban Collectors 

15. Rural Collectors 
137 

APPENDIX G: 

VEHICLE POPULATION ESTIMATES AND 24-HOUR SHP, 

STARTS, AND SHI SUMMARIES

139 

2012 Vehicle Population Estimates 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 7,554 556 1,223 62 838 107 59 536 

21_G 1,879,113 138,422 304,294 15,527 208,541 26,804 14,664 133,343 

31_D 8,715 910 1,084 145 1,258 274 149 797 

31_G 613,116 64,014 76,248 10,225 88,478 19,248 10,462 56,087 

32_D 8,715 910 1,084 145 1,258 269 146 797 

32_G 152,606 15,933 18,978 2,545 22,022 4,797 2,607 13,960 

51_D 1,564 178 142 41 227 74 38 122 

51_G 939 107 85 24 136 21 10 73 

52_D 29,706 3,386 2,697 772 4,309 1,585 810 2,311 

52_G 17,747 2,023 1,611 461 2,574 444 227 1,381 

53_D 5,736 654 521 149 832 82 42 446 

53_G 3,424 390 311 89 497 23 12 266 

54_D 1,182 135 107 31 171 56 29 92 

54_G 713 81 65 19 103 16 8 55 

41_D 1,982 226 180 51 287 72 37 154 

42_D 817 93 74 21 118 30 15 64 

42_G 0 0 0 0 0 0 0 0 

43_D 2,538 289 230 66 368 93 47 197 

43_G 17 2 2 0 3 1 0 1 

61_D 16,346 902 1,177 210 1,221 374 158 514 

61_G 1,323 73 95 17 99 37 16 42 

62_D 15,175 837 1,092 195 1,134 694 294 478 

62_G 0 0 0 0 0 0 0 0 

11_G 49,104 7,255 7,940 851 10,925 1,758 974 7,906 
140 

2014 Vehicle Population Estimates 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 12,296 1,049 2,334 93 1,487 189 113 836 

21_G 1,743,488 148,773 330,890 13,138 210,890 26,801 16,035 118,576 

31_D 9,861 1,193 1,437 150 1,551 334 199 865 

31_G 571,011 69,061 83,225 8,685 89,812 19,319 11,483 50,064 

32_D 7,674 928 1,118 117 1,207 265 158 673 

32_G 140,278 16,966 20,446 2,134 22,064 4,740 2,818 12,299 

51_D 1,488 196 158 35 235 76 42 111 

51_G 890 117 95 21 141 21 12 66 

52_D 27,548 3,637 2,931 653 4,355 1,585 886 2,054 

52_G 16,451 2,172 1,751 390 2,601 444 248 1,227 

53_D 5,322 703 566 126 841 82 46 397 

53_G 3,187 421 339 75 504 23 13 238 

54_D 1,181 156 126 28 187 60 33 88 

54_G 696 92 74 16 110 17 9 52 

41_D 582 77 62 14 92 23 13 43 

42_D 1,165 154 124 28 184 46 25 87 

42_G 0 0 0 0 0 0 0 0 

43_D 3,203 423 341 76 506 126 70 239 

43_G 32 4 3 1 5 1 1 2 

61_D 15,212 972 1,283 178 1,239 375 174 459 

61_G 1,232 79 104 14 100 37 17 37 

62_D 14,122 903 1,192 165 1,150 696 322 426 

62_G 0 0 0 0 0 0 0 0 

11_G 45,698 7,821 8,660 722 11,081 1,763 1,068 7,052 
141 

2017 Vehicle Population Estimates 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 13,158 1,132 2,599 97 1,702 202 120 886 

21_G 1,865,811 160,578 368,599 13,789 241,406 28,691 17,040 125,697 

31_D 10,553 1,287 1,601 157 1,775 358 211 916 

31_G 611,073 74,540 92,710 9,115 102,809 20,681 12,203 53,071 

32_D 8,212 1,002 1,246 123 1,382 284 168 713 

32_G 150,120 18,312 22,776 2,239 25,257 5,075 2,994 13,038 

51_D 1,593 212 176 37 269 81 45 118 

51_G 952 127 105 22 161 22 12 70 

52_D 29,481 3,926 3,266 685 4,985 1,696 942 2,178 

52_G 17,606 2,344 1,950 409 2,977 476 264 1,301 

53_D 5,695 758 631 132 963 88 49 421 

53_G 3,410 454 378 79 577 24 14 252 

54_D 1,264 168 140 29 214 64 35 93 

54_G 744 99 82 17 126 18 10 55 

41_D 623 83 69 14 105 24 14 46 

42_D 1,246 166 138 29 211 49 27 92 

42_G 0 0 0 0 0 0 0 0 

43_D 3,428 456 380 80 580 134 75 253 

43_G 35 5 4 1 6 1 1 3 

61_D 16,279 1,050 1,430 187 1,418 402 185 486 

61_G 1,318 85 116 15 115 40 18 39 

62_D 15,113 974 1,327 174 1,316 745 342 451 

62_G 0 0 0 0 0 0 0 0 

11_G 48,904 8,442 9,647 758 12,685 1,887 1,135 7,475 
142 

2020 Vehicle Population Estimates 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 17,969 1,565 3,742 131 2,396 268 164 1,235 

21_G 1,979,021 172,350 412,076 14,415 263,889 29,510 18,022 136,044 

31_D 12,571 1,552 2,010 185 2,180 391 237 1,114 

31_G 648,109 80,000 103,638 9,529 112,376 21,293 12,919 57,436 

32_D 8,728 1,077 1,396 128 1,513 287 174 773 

32_G 159,540 19,693 25,512 2,346 27,663 5,236 3,177 14,138 

51_D 1,656 223 193 38 289 82 47 125 

51_G 994 134 116 23 173 23 13 75 

52_D 31,443 4,237 3,671 720 5,480 1,754 1,001 2,370 

52_G 18,785 2,531 2,193 430 3,274 492 281 1,416 

53_D 6,072 818 709 139 1,058 90 52 458 

53_G 3,625 488 423 83 632 25 14 273 

54_D 1,251 169 146 29 218 62 35 94 

54_G 754 102 88 17 131 17 10 57 

41_D 662 89 77 15 115 25 14 50 

42_D 1,325 179 155 30 231 51 29 100 

42_G 0 0 0 0 0 0 0 0 

43_D 3,643 491 425 83 635 138 79 275 

43_G 37 5 4 1 6 1 1 3 

61_D 17,301 1,129 1,602 196 1,553 414 196 527 

61_G 1,401 91 130 16 126 41 19 43 

62_D 16,062 1,048 1,487 182 1,442 768 363 490 

62_G 0 0 0 0 0 0 0 0 

11_G 51,975 9,079 10,807 794 13,894 1,945 1,203 8,107 
143 

2023 Vehicle Population Estimates 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 23,361 2,059 5,127 168 3,210 338 212 1,639 

21_G 2,099,066 184,995 460,676 15,071 288,465 30,355 19,060 147,241 

31_D 13,361 1,669 2,252 194 2,388 425 265 1,208 

31_G 688,818 86,044 116,097 9,982 123,091 21,924 13,677 62,289 

32_D 9,468 1,183 1,596 137 1,692 302 188 856 

32_G 169,369 21,157 28,546 2,454 30,266 5,391 3,363 15,316 

51_D 1,761 240 217 40 316 84 49 135 

51_G 1,057 144 130 24 190 23 14 81 

52_D 33,497 4,568 4,122 756 6,016 1,812 1,063 2,576 

52_G 20,016 2,729 2,463 452 3,595 508 298 1,540 

53_D 6,476 883 797 146 1,163 94 55 498 

53_G 3,874 528 477 87 696 26 15 298 

54_D 1,252 171 154 28 225 59 35 96 

54_G 744 101 91 17 134 16 10 57 

41_D 704 96 87 16 127 26 15 54 

42_D 1,428 195 176 32 257 53 31 110 

42_G 0 0 0 0 0 0 0 0 

43_D 3,855 526 474 87 692 142 83 296 

43_G 39 5 5 1 7 1 1 3 

61_D 18,388 1,214 1,794 205 1,701 427 207 572 

61_G 1,489 98 145 17 138 42 21 46 

62_D 17,071 1,127 1,666 191 1,579 791 384 531 

62_G 0 0 0 0 0 0 0 0 

11_G 55,240 9,765 12,106 832 15,219 2,005 1,275 8,792 
144 

2026 Vehicle Population Estimates 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 24,689 2,222 5,681 177 3,499 349 224 1,778 

21_G 2,218,396 199,683 510,479 15,861 314,440 31,392 20,099 159,746 

31_D 14,121 1,802 2,495 204 2,603 439 280 1,311 

31_G 727,977 92,875 128,648 10,506 134,175 22,673 14,422 67,579 

32_D 10,006 1,277 1,768 144 1,844 312 198 929 

32_G 178,997 22,836 31,632 2,583 32,991 5,575 3,546 16,617 

51_D 1,861 259 240 42 345 87 52 147 

51_G 1,117 156 144 25 207 24 14 88 

52_D 35,402 4,930 4,568 796 6,558 1,874 1,121 2,795 

52_G 21,154 2,946 2,729 476 3,919 526 315 1,670 

53_D 6,845 953 883 154 1,268 97 58 540 

53_G 4,094 570 528 92 758 27 16 323 

54_D 1,323 184 171 30 245 61 37 104 

54_G 786 109 101 18 146 17 10 62 

41_D 744 104 96 17 138 27 16 59 

42_D 1,510 210 195 34 280 55 33 119 

42_G 0 0 0 0 0 0 0 0 

43_D 4,074 567 526 92 755 147 88 322 

43_G 41 6 5 1 8 2 1 3 

61_D 19,434 1,310 1,988 216 1,854 441 219 621 

61_G 1,573 106 161 17 150 44 22 50 

62_D 18,041 1,217 1,846 201 1,721 818 405 576 

62_G 0 0 0 0 0 0 0 0 

11_G 58,380 10,540 13,414 875 16,590 2,074 1,344 9,539 
145 

2028 Vehicle Population Estimates 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 27,713 2,561 6,536 200 4,014 359 230 2,047 

21_G 2,282,196 210,894 538,242 16,496 330,565 32,244 20,685 168,585 

31_D 15,274 2,001 2,767 223 2,877 475 303 1,455 

31_G 748,921 98,091 135,646 10,926 141,057 23,265 14,827 71,319 

32_D 10,305 1,350 1,866 150 1,941 320 204 981 

32_G 184,340 24,144 33,388 2,689 34,720 5,727 3,650 17,555 

51_D 1,917 274 253 44 363 89 54 155 

51_G 1,150 164 152 26 218 25 15 93 

52_D 36,456 5,213 4,821 828 6,901 1,925 1,154 2,953 

52_G 21,784 3,115 2,881 495 4,124 540 324 1,764 

53_D 7,049 1,008 932 160 1,334 99 60 571 

53_G 4,216 603 558 96 798 28 17 342 

54_D 1,363 195 180 31 258 63 38 110 

54_G 809 116 107 18 153 17 10 66 

41_D 767 110 101 17 145 28 16 62 

42_D 1,576 225 208 36 298 57 34 128 

42_G 0 0 0 0 0 0 0 0 

43_D 4,174 597 552 95 790 150 90 338 

43_G 43 6 6 1 8 2 1 3 

61_D 20,012 1,385 2,098 225 1,951 453 225 656 

61_G 1,620 112 170 18 158 45 22 53 

62_D 18,579 1,286 1,948 209 1,812 841 417 609 

62_G 0 0 0 0 0 0 0 0 

11_G 60,120 11,143 14,158 911 17,458 2,130 1,384 10,076 
146 

2012 24-Hour Weekday SHP Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 172,505 12,972 28,610 1,402 19,345 2,469 1,311 12,440 

21_G 42,912,570 3,226,916 7,117,261 348,683 4,812,369 616,171 327,064 3,094,689 

31_D 202,632 21,504 25,405 3,391 29,530 6,430 3,460 18,805 

31_G 14,254,085 1,512,697 1,787,164 238,536 2,077,288 451,994 243,222 1,322,845 

32_D 202,626 21,503 25,405 3,391 29,529 6,309 3,395 18,804 

32_G 3,547,866 376,514 444,829 59,372 517,041 112,636 60,611 329,259 

51_D 34,972 4,142 3,168 933 5,179 1,688 831 2,790 

51_G 20,991 2,486 1,901 560 3,108 469 231 1,675 

52_D 664,069 78,653 60,153 17,718 98,353 36,283 17,846 52,985 

52_G 396,738 46,989 35,938 10,585 58,759 10,169 5,002 31,655 

53_D 128,216 15,187 11,614 3,421 18,990 1,868 919 10,230 

53_G 76,543 9,066 6,934 2,042 11,337 519 255 6,107 

54_D 26,420 3,129 2,393 705 3,913 1,279 629 2,108 

54_G 15,934 1,887 1,443 425 2,360 360 177 1,271 

41_D 43,916 5,267 4,008 1,201 6,585 1,658 805 3,524 

42_D 18,108 2,172 1,653 495 2,715 683 332 1,453 

42_G 0 0 0 0 0 0 0 0 

43_D 56,228 6,745 5,131 1,537 8,432 2,130 1,034 4,512 

43_G 371 46 34 10 57 20 10 30 

61_D 353,507 18,952 24,542 3,895 24,387 7,684 1,529 10,423 

61_G 28,613 1,534 1,986 315 1,974 760 151 843 

62_D 328,188 17,595 22,785 3,616 22,640 14,256 2,838 9,677 

62_G 0 0 0 0 0 0 0 0 

11_G 1,176,296 174,024 190,373 20,400 262,006 42,165 23,351 189,638 
147 

2014 24-Hour Weekday SHP Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 281,032 24,454 54,585 2,085 34,314 4,349 2,524 19,410 

21_G 39,849,544 3,467,491 7,740,110 295,641 4,865,563 616,176 357,605 2,752,303 

31_D 229,358 28,179 33,687 3,505 36,409 7,855 4,622 20,391 

31_G 13,281,878 1,631,808 1,950,788 202,950 2,108,404 453,674 266,955 1,180,811 

32_D 178,479 21,929 26,215 2,727 28,333 6,230 3,666 15,868 

32_G 3,262,910 400,880 479,242 49,858 517,963 111,322 65,505 290,085 

51_D 33,278 4,563 3,530 812 5,367 1,735 933 2,543 

51_G 19,899 2,728 2,111 485 3,209 481 259 1,521 

52_D 616,121 84,470 65,359 15,025 99,369 36,296 19,524 47,083 

52_G 367,928 50,444 39,030 8,972 59,340 10,177 5,474 28,117 

53_D 119,027 16,319 12,627 2,903 19,197 1,875 1,009 9,096 

53_G 71,283 9,772 7,562 1,738 11,496 521 280 5,447 

54_D 26,423 3,622 2,803 644 4,261 1,364 734 2,019 

54_G 15,547 2,132 1,649 379 2,508 381 205 1,188 

41_D 12,926 1,792 1,380 322 2,108 524 277 993 

42_D 25,861 3,585 2,761 644 4,218 1,048 555 1,986 

42_G 0 0 0 0 0 0 0 0 

43_D 71,124 9,858 7,594 1,772 11,599 2,894 1,532 5,461 

43_G 714 99 76 18 117 30 16 55 

61_D 328,371 20,436 26,630 3,249 24,614 7,695 1,610 9,274 

61_G 26,579 1,654 2,155 263 1,992 761 159 750 

62_D 304,853 18,972 24,723 3,016 22,851 14,276 2,986 8,610 

62_G 0 0 0 0 0 0 0 0 

11_G 1,094,728 187,602 207,638 17,315 265,756 42,288 25,611 169,146 
148 

2017 24-Hour Weekday SHP Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 300,752 26,391 60,810 2,185 39,238 4,655 2,683 20,575 

21_G 42,645,808 3,742,209 8,622,767 309,861 5,563,818 659,647 380,108 2,917,502 

31_D 245,454 30,415 37,525 3,675 41,658 8,409 4,912 21,615 

31_G 14,213,966 1,761,266 2,173,035 212,790 2,412,344 485,658 283,724 1,251,679 

32_D 191,004 23,669 29,202 2,860 32,418 6,669 3,896 16,821 

32_G 3,491,892 432,683 533,841 52,275 592,631 119,170 69,620 307,495 

51_D 35,608 4,925 3,932 851 6,138 1,857 992 2,696 

51_G 21,292 2,944 2,351 509 3,670 515 275 1,612 

52_D 659,263 91,170 72,793 15,748 113,635 38,859 20,759 49,904 

52_G 393,691 54,444 43,470 9,404 67,860 10,895 5,821 29,801 

53_D 127,361 17,613 14,063 3,042 21,953 2,008 1,073 9,641 

53_G 76,274 10,547 8,422 1,822 13,147 558 298 5,773 

54_D 28,273 3,909 3,122 675 4,873 1,460 780 2,140 

54_G 16,636 2,301 1,837 397 2,868 408 218 1,260 

41_D 13,839 1,934 1,538 338 2,411 561 294 1,052 

42_D 27,687 3,869 3,078 675 4,823 1,122 589 2,105 

42_G 0 0 0 0 0 0 0 0 

43_D 76,147 10,641 8,464 1,858 13,263 3,098 1,626 5,790 

43_G 764 107 85 19 133 32 17 58 

61_D 351,062 22,038 29,600 3,416 28,057 8,227 1,685 9,819 

61_G 28,415 1,784 2,395 276 2,271 814 167 795 

62_D 325,918 20,459 27,480 3,172 26,047 15,262 3,127 9,116 

62_G 0 0 0 0 0 0 0 0 

11_G 1,171,534 202,487 231,302 18,172 304,207 45,270 27,217 179,304 
149 

2020 24-Hour Weekday SHP Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 410,572 36,467 87,509 2,941 55,198 6,157 3,648 28,669 

21_G 45,218,091 4,016,209 9,637,759 323,917 6,079,199 678,430 401,959 3,157,429 

31_D 292,372 36,665 47,111 4,315 51,136 9,174 5,511 26,275 

31_G 15,072,411 1,890,198 2,428,711 222,436 2,636,212 500,004 300,349 1,354,576 

32_D 202,949 25,453 32,704 2,995 35,498 6,740 4,049 18,240 

32_G 3,710,273 465,297 597,859 54,756 648,938 122,948 73,854 333,446 

51_D 37,015 5,182 4,307 872 6,575 1,870 1,027 2,860 

51_G 22,217 3,109 2,585 523 3,946 520 285 1,716 

52_D 702,868 98,394 81,789 16,555 124,848 40,180 22,069 54,308 

52_G 419,918 58,783 48,863 9,890 74,588 11,262 6,185 32,446 

53_D 135,707 18,999 15,792 3,197 24,106 2,069 1,136 10,486 

53_G 81,015 11,342 9,427 1,908 14,391 575 316 6,260 

54_D 27,964 3,915 3,254 659 4,967 1,416 778 2,161 

54_G 16,865 2,361 1,963 397 2,996 398 219 1,303 

41_D 14,700 2,080 1,722 354 2,639 578 312 1,141 

42_D 29,407 4,161 3,445 707 5,279 1,168 631 2,283 

42_G 0 0 0 0 0 0 0 0 

43_D 80,891 11,443 9,476 1,946 14,519 3,181 1,717 6,279 

43_G 812 115 95 20 146 33 18 63 

61_D 372,877 23,704 33,128 3,579 30,690 8,480 1,787 10,647 

61_G 30,181 1,919 2,681 290 2,484 839 177 862 

62_D 346,171 22,006 30,755 3,323 28,492 15,733 3,315 9,885 

62_G 0 0 0 0 0 0 0 0 

11_G 1,245,104 217,770 259,103 19,036 333,205 46,657 28,842 194,454 
150 

2023 24-Hour Weekday SHP Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 533,755 47,971 119,963 3,756 73,953 7,762 4,726 38,037 

21_G 47,957,578 4,310,269 10,778,868 337,516 6,644,690 697,958 424,948 3,417,618 

31_D 310,715 39,432 52,785 4,515 56,010 9,982 6,163 28,497 

31_G 16,018,018 2,032,825 2,721,199 232,776 2,887,474 514,968 317,939 1,469,125 

32_D 220,166 27,942 37,403 3,199 39,689 7,086 4,375 20,193 

32_G 3,938,564 499,838 669,097 57,236 709,982 126,619 78,174 361,233 

51_D 39,358 5,576 4,830 912 7,206 1,928 1,088 3,104 

51_G 23,623 3,346 2,899 548 4,325 536 302 1,863 

52_D 748,656 106,062 91,878 17,356 137,071 41,531 23,435 59,049 

52_G 447,361 63,377 54,902 10,371 81,907 11,649 6,574 35,285 

53_D 144,752 20,506 17,765 3,356 26,502 2,145 1,211 11,417 

53_G 86,591 12,267 10,627 2,007 15,854 605 341 6,830 

54_D 27,992 3,965 3,435 649 5,125 1,358 766 2,208 

54_G 16,612 2,354 2,039 385 3,042 376 212 1,311 

41_D 15,633 2,238 1,933 370 2,892 596 331 1,239 

42_D 31,712 4,540 3,922 751 5,866 1,215 675 2,512 

42_G 0 0 0 0 0 0 0 0 

43_D 85,583 12,251 10,584 2,027 15,830 3,267 1,813 6,780 

43_G 863 124 107 21 160 34 19 69 

61_D 395,898 25,505 36,927 3,654 33,654 8,683 1,869 11,562 

61_G 32,045 2,064 2,988 296 2,723 859 185 936 

62_D 367,544 23,678 34,282 3,392 31,244 16,109 3,468 10,734 

62_G 0 0 0 0 0 0 0 0 

11_G 1,323,310 234,221 290,254 19,940 364,975 48,089 30,566 210,887 
151 

2026 24-Hour Weekday SHP Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 563,887 51,772 132,892 3,953 80,570 8,027 4,983 41,264 

21_G 50,664,919 4,651,759 11,940,544 355,204 7,239,228 721,738 448,066 3,707,581 

31_D 328,306 42,559 58,476 4,752 61,038 10,323 6,498 30,916 

31_G 16,924,895 2,194,067 3,014,587 244,982 3,146,655 532,539 335,251 1,593,834 

32_D 232,631 30,158 41,435 3,367 43,251 7,328 4,613 21,907 

32_G 4,161,550 539,485 741,237 60,237 773,710 130,939 82,431 391,897 

51_D 41,577 6,018 5,348 960 7,851 1,994 1,147 3,368 

51_G 24,955 3,611 3,210 576 4,712 554 319 2,021 

52_D 790,866 114,471 101,737 18,267 149,336 42,948 24,711 64,060 

52_G 472,583 68,402 60,793 10,915 89,236 12,047 6,931 38,279 

53_D 152,913 22,132 19,671 3,532 28,873 2,218 1,277 12,386 

53_G 91,473 13,239 11,767 2,113 17,272 626 360 7,409 

54_D 29,570 4,280 3,804 683 5,584 1,404 808 2,395 

54_G 17,549 2,541 2,258 405 3,314 389 224 1,422 

41_D 16,511 2,416 2,140 390 3,150 616 349 1,344 

42_D 33,495 4,899 4,342 791 6,390 1,257 711 2,725 

42_G 0 0 0 0 0 0 0 0 

43_D 90,395 13,221 11,717 2,134 17,243 3,379 1,912 7,355 

43_G 912 134 118 22 174 36 20 74 

61_D 418,108 27,523 40,863 3,847 36,630 8,982 1,973 12,542 

61_G 33,842 2,228 3,307 311 2,964 889 195 1,015 

62_D 388,163 25,552 37,937 3,572 34,007 16,663 3,660 11,644 

62_G 0 0 0 0 0 0 0 0 

11_G 1,398,519 252,816 321,629 20,986 397,836 49,732 32,232 228,797 
152 

2028 24-Hour Weekday SHP Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 632,755 59,649 152,844 4,486 92,374 8,244 5,128 47,509 

21_G 52,109,173 4,912,250 12,587,099 369,405 7,607,272 741,299 461,091 3,912,459 

31_D 355,061 47,258 64,814 5,196 67,454 11,160 7,039 34,304 

31_G 17,409,313 2,317,123 3,177,953 254,786 3,307,367 546,428 344,653 1,681,981 

32_D 239,540 31,883 43,727 3,506 45,508 7,527 4,748 23,143 

32_G 4,285,149 570,339 782,225 62,713 814,079 134,498 84,833 414,005 

51_D 42,803 6,361 5,642 1,000 8,258 2,048 1,181 3,557 

51_G 25,691 3,817 3,386 600 4,956 569 328 2,135 

52_D 814,186 121,004 107,318 19,017 157,088 44,113 25,430 67,666 

52_G 486,518 72,306 64,128 11,363 93,868 12,374 7,133 40,434 

53_D 157,422 23,395 20,750 3,677 30,372 2,279 1,314 13,083 

53_G 94,170 13,995 12,413 2,199 18,169 642 370 7,826 

54_D 30,442 4,524 4,013 711 5,874 1,442 831 2,530 

54_G 18,066 2,686 2,382 422 3,486 400 230 1,502 

41_D 16,997 2,553 2,257 406 3,313 633 359 1,419 

42_D 34,950 5,250 4,642 834 6,812 1,303 739 2,918 

42_G 0 0 0 0 0 0 0 0 

43_D 92,564 13,904 12,293 2,210 18,042 3,458 1,961 7,728 

43_G 939 142 125 23 183 37 21 79 

61_D 430,356 29,084 43,086 4,007 38,503 9,227 2,032 13,246 

61_G 34,834 2,354 3,487 324 3,116 913 201 1,072 

62_D 399,533 27,001 40,000 3,720 35,746 17,118 3,769 12,297 

62_G 0 0 0 0 0 0 0 0 

11_G 1,440,170 267,277 339,458 21,848 418,654 51,083 33,171 241,698 
153 

2012 24-Hour Weekday Starts Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 40,620 2,992 6,578 336 4,508 578 316 2,882 

21_G 10,105,106 744,375 1,636,368 83,496 1,121,447 144,139 78,858 717,064 

31_D 48,604 5,075 6,044 811 7,014 1,527 830 4,446 

31_G 3,419,188 356,989 425,215 57,023 493,420 107,341 58,344 312,784 

32_D 52,439 5,475 6,521 875 7,567 1,616 879 4,797 

32_G 918,204 95,867 114,189 15,313 132,505 28,860 15,687 83,996 

51_D 6,021 686 547 156 873 284 145 468 

51_G 3,613 412 328 94 524 79 40 281 

52_D 212,188 24,184 19,266 5,512 30,776 11,321 5,788 16,510 

52_G 126,766 14,448 11,510 3,293 18,386 3,173 1,622 9,863 

53_D 25,510 2,907 2,316 663 3,700 363 186 1,985 

53_G 15,228 1,736 1,383 396 2,209 101 52 1,185 

54_D 674 77 61 18 98 32 16 52 

54_G 407 46 37 11 59 9 5 32 

41_D 5,705 650 518 148 827 207 106 444 

42_D 3,884 443 353 101 563 141 72 302 

42_G 0 0 0 0 0 0 0 0 

43_D 14,928 1,701 1,355 388 2,165 544 278 1,162 

43_G 102 12 9 3 15 5 3 8 

61_D 99,206 5,474 7,142 1,275 7,411 2,270 961 3,122 

61_G 8,032 443 578 103 600 225 95 253 

62_D 65,052 3,590 4,683 836 4,860 2,974 1,259 2,047 

62_G 0 0 0 0 0 0 0 0 

11_G 22,245 3,287 3,597 386 4,949 796 441 3,582 
154 

2014 24-Hour Weekday Starts Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 66,120 5,642 12,549 498 7,998 1,017 608 4,497 

21_G 9,375,766 800,040 1,779,393 70,652 1,134,079 144,122 86,228 637,654 

31_D 54,991 6,651 8,015 836 8,649 1,865 1,109 4,821 

31_G 3,184,380 385,133 464,125 48,434 500,860 107,735 64,038 279,195 

32_D 46,172 5,584 6,730 702 7,262 1,596 949 4,048 

32_G 844,026 102,080 123,017 12,837 132,754 28,522 16,954 74,001 

51_D 5,728 756 610 136 905 291 163 427 

51_G 3,424 452 364 81 541 81 45 255 

52_D 196,774 25,980 20,939 4,662 31,107 11,318 6,329 14,674 

52_G 117,510 15,515 12,505 2,784 18,576 3,174 1,774 8,763 

53_D 23,668 3,125 2,519 561 3,741 364 204 1,765 

53_G 14,172 1,871 1,508 336 2,240 101 57 1,057 

54_D 674 89 72 16 106 34 19 50 

54_G 397 52 42 9 63 9 5 30 

41_D 1,677 221 178 40 265 66 37 125 

42_D 5,538 731 589 131 875 216 121 413 

42_G 0 0 0 0 0 0 0 0 

43_D 18,841 2,488 2,005 446 2,978 739 413 1,405 

43_G 190 25 20 5 30 8 4 14 

61_D 92,323 5,901 7,790 1,082 7,517 2,277 1,054 2,784 

61_G 7,475 478 631 88 609 225 104 225 

62_D 60,539 3,870 5,108 709 4,929 2,983 1,381 1,826 

62_G 0 0 0 0 0 0 0 0 

11_G 20,702 3,543 3,923 327 5,020 799 484 3,195 
155 

2017 24-Hour Weekday Starts Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 70,759 6,090 13,979 523 9,155 1,089 647 4,767 

21_G 10,033,572 863,521 1,982,174 74,151 1,298,184 154,286 91,634 675,948 

31_D 58,849 7,179 8,928 878 9,901 1,997 1,178 5,111 

31_G 3,407,797 415,692 517,018 50,833 573,337 115,333 68,053 295,961 

32_D 49,412 6,027 7,497 737 8,313 1,709 1,008 4,291 

32_G 903,243 110,180 137,036 13,473 151,964 30,534 18,016 78,445 

51_D 6,130 816 679 142 1,037 312 173 453 

51_G 3,665 488 406 85 620 87 48 271 

52_D 210,580 28,041 23,326 4,893 35,608 12,117 6,725 15,555 

52_G 125,755 16,746 13,930 2,922 21,264 3,397 1,886 9,289 

53_D 25,328 3,373 2,806 588 4,283 390 216 1,871 

53_G 15,166 2,020 1,680 352 2,565 108 60 1,120 

54_D 721 96 80 17 122 36 20 53 

54_G 425 57 47 10 72 10 6 31 

41_D 1,794 239 199 42 303 70 39 133 

42_D 5,926 789 656 138 1,002 232 129 438 

42_G 0 0 0 0 0 0 0 0 

43_D 20,163 2,685 2,233 468 3,409 791 439 1,489 

43_G 204 27 23 5 34 8 5 15 

61_D 98,801 6,370 8,677 1,135 8,605 2,437 1,120 2,952 

61_G 7,999 516 703 92 697 241 111 239 

62_D 64,787 4,177 5,690 744 5,643 3,193 1,468 1,935 

62_G 0 0 0 0 0 0 0 0 

11_G 22,155 3,824 4,370 343 5,747 855 514 3,386 
156 

2020 24-Hour Weekday Starts Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 96,631 8,415 20,121 704 12,885 1,440 880 6,643 

21_G 10,642,366 926,830 2,215,976 77,520 1,419,090 158,693 96,913 731,589 

31_D 70,108 8,654 11,211 1,031 12,156 2,179 1,322 6,213 

31_G 3,614,334 446,140 577,964 53,139 626,694 118,745 72,045 320,303 

32_D 52,513 6,482 8,397 772 9,105 1,727 1,048 4,654 

32_G 959,924 118,489 153,500 14,113 166,442 31,503 19,114 85,069 

51_D 6,373 859 744 146 1,111 314 179 480 

51_G 3,824 515 446 88 666 87 50 288 

52_D 224,595 30,264 26,221 5,143 39,140 12,529 7,151 16,929 

52_G 134,179 18,080 15,665 3,073 23,383 3,512 2,004 10,114 

53_D 27,001 3,638 3,152 618 4,705 402 229 2,035 

53_G 16,119 2,172 1,882 369 2,809 112 64 1,215 

54_D 714 96 83 16 124 35 20 54 

54_G 430 58 50 10 75 10 6 32 

41_D 1,907 257 223 44 332 72 41 144 

42_D 6,298 849 735 144 1,098 241 138 475 

42_G 0 0 0 0 0 0 0 0 

43_D 21,429 2,888 2,502 491 3,734 812 464 1,615 

43_G 216 29 25 5 38 9 5 16 

61_D 105,007 6,850 9,720 1,189 9,426 2,512 1,187 3,201 

61_G 8,502 555 787 96 763 249 117 259 

62_D 68,856 4,492 6,374 780 6,181 3,291 1,555 2,099 

62_G 0 0 0 0 0 0 0 0 

11_G 23,546 4,113 4,896 360 6,294 881 545 3,673 
157 

2023 24-Hour Weekday Starts Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 125,626 11,072 27,571 902 17,264 1,815 1,140 8,812 

21_G 11,287,920 994,829 2,477,325 81,044 1,551,247 163,235 102,498 791,803 

31_D 74,511 9,308 12,559 1,080 13,315 2,370 1,478 6,738 

31_G 3,841,361 479,844 647,440 55,667 686,448 122,266 76,273 347,370 

32_D 56,968 7,116 9,602 826 10,180 1,815 1,132 5,152 

32_G 1,019,063 127,296 171,758 14,768 182,106 32,435 20,234 92,153 

51_D 6,778 924 834 153 1,217 324 190 521 

51_G 4,067 555 500 92 730 90 53 313 

52_D 239,267 32,627 29,443 5,401 42,973 12,945 7,596 18,403 

52_G 142,974 19,496 17,594 3,227 25,679 3,631 2,131 10,997 

53_D 28,802 3,927 3,544 650 5,173 416 244 2,215 

53_G 17,229 2,349 2,120 389 3,094 117 69 1,325 

54_D 714 97 88 16 128 34 20 55 

54_G 424 58 52 10 76 9 5 33 

41_D 2,028 277 250 46 364 75 44 156 

42_D 6,791 926 836 153 1,220 251 147 522 

42_G 0 0 0 0 0 0 0 0 

43_D 22,674 3,092 2,790 512 4,072 835 490 1,744 

43_G 230 31 28 5 41 9 5 18 

61_D 111,603 7,368 10,889 1,246 10,324 2,589 1,258 3,472 

61_G 9,036 597 882 101 836 256 124 281 

62_D 73,181 4,831 7,140 817 6,770 3,392 1,648 2,276 

62_G 0 0 0 0 0 0 0 0 

11_G 25,025 4,424 5,484 377 6,895 908 578 3,983 
158 

2026 24-Hour Weekday Starts Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 132,768 11,951 30,551 949 18,819 1,877 1,202 9,561 

21_G 11,929,628 1,073,814 2,745,150 85,294 1,690,932 168,811 108,084 859,049 

31_D 78,747 10,047 13,916 1,136 14,514 2,451 1,559 7,310 

31_G 4,059,739 517,942 717,435 58,587 748,261 126,443 80,430 376,871 

32_D 60,206 7,681 10,640 869 11,097 1,877 1,194 5,589 

32_G 1,076,996 137,403 190,326 15,542 198,504 33,543 21,337 99,979 

51_D 7,163 998 924 161 1,327 335 200 566 

51_G 4,298 599 555 97 796 93 56 339 

52_D 252,869 35,218 32,626 5,684 46,843 13,387 8,010 19,966 

52_G 151,101 21,044 19,496 3,396 27,991 3,755 2,247 11,931 

53_D 30,439 4,239 3,927 684 5,639 430 258 2,403 

53_G 18,208 2,536 2,349 409 3,373 121 73 1,438 

54_D 755 105 97 17 140 35 21 60 

54_G 448 62 58 10 83 10 6 35 

41_D 2,143 298 277 48 397 77 46 169 

42_D 7,177 1,000 926 161 1,330 259 155 567 

42_G 0 0 0 0 0 0 0 0 

43_D 23,963 3,337 3,092 539 4,439 863 516 1,892 

43_G 243 34 31 5 45 9 5 19 

61_D 117,947 7,953 12,066 1,311 11,254 2,677 1,326 3,766 

61_G 9,549 644 977 106 911 265 131 305 

62_D 77,341 5,215 7,912 860 7,380 3,508 1,738 2,470 

62_G 0 0 0 0 0 0 0 0 

11_G 26,448 4,775 6,077 397 7,515 939 609 4,321 
159 

2028 24-Hour Weekday Starts Summaries 

SUT/FT Harris Brazoria Fort Bend Waller Montgomery Liberty Chambers Galveston

21_D 149,028 13,771 35,147 1,077 21,586 1,928 1,237 11,009 

21_G 12,272,719 1,134,101 2,894,445 88,708 1,777,644 173,397 111,233 906,581 

31_D 85,182 11,157 15,428 1,243 16,044 2,650 1,689 8,112 

31_G 4,176,535 547,026 756,460 60,932 786,639 129,745 82,689 397,728 

32_D 62,003 8,121 11,230 905 11,678 1,928 1,229 5,905 

32_G 1,109,141 145,271 200,889 16,181 208,904 34,456 21,959 105,623 

51_D 7,376 1,055 975 168 1,396 344 206 597 

51_G 4,426 633 585 101 838 96 57 358 

52_D 260,403 37,232 34,435 5,917 49,295 13,750 8,244 21,092 

52_G 155,603 22,248 20,576 3,536 29,456 3,857 2,312 12,603 

53_D 31,346 4,482 4,145 712 5,934 442 265 2,539 

53_G 18,751 2,681 2,480 426 3,550 125 75 1,519 

54_D 777 111 103 18 147 36 22 63 

54_G 462 66 61 10 87 10 6 37 

41_D 2,207 316 292 50 418 79 47 179 

42_D 7,492 1,071 991 170 1,418 269 161 607 

42_G 0 0 0 0 0 0 0 0 

43_D 24,551 3,510 3,247 558 4,648 884 530 1,989 

43_G 251 36 33 6 47 9 6 20 

61_D 121,461 8,408 12,735 1,365 11,843 2,750 1,365 3,979 

61_G 9,834 681 1,031 111 959 272 135 322 

62_D 79,645 5,513 8,351 895 7,766 3,603 1,789 2,609 

62_G 0 0 0 0 0 0 0 0 

11_G 27,236 5,048 6,414 413 7,909 965 627 4,565 
160 

2011 and 2014 24-Hour Weekday SHI and APU Hours Summaries (CLhT_Diesel Only) 

County 

2011 2014 

Hotelling SHI APU Hotelling SHI APU 

Harris 19,766 19,405 362 18,755 17,059 1,695 

Brazoria 170 167 3 187 170 17 

Fort Bend 3,384 3,322 62 3,724 3,387 337 

Waller 1,687 1,657 31 1,634 1,486 148 

Montgomery 4,505 4,423 82 4,626 4,208 418 

Liberty 463 454 8 458 417 41 

Chambers 1,721 1,690 31 2,203 2,004 199 

Galveston 477 468 9 439 399 40 

2017 and 2020 24-Hour Weekday SHI and APU Hours Summaries (CLhT_Diesel Only) 

County 

2017 2020 

Hotelling SHI APU Hotelling SHI APU 

Harris 20,238 17,058 3,180 21,511 16,860 4,652 

Brazoria 202 170 32 216 170 47 

Fort Bend 4,204 3,543 661 4,708 3,690 1,018 

Waller 1,703 1,435 268 1,781 1,396 385 

Montgomery 5,286 4,455 831 5,784 4,533 1,251 

Liberty 494 417 78 508 398 110 

Chambers 2,379 2,005 374 2,516 1,972 544 

Galveston 467 393 73 506 397 109 
161 

2023 and 2026 24-Hour Weekday SHI and APU Hours Summaries (CLhT_Diesel Only) 

County 

2023 2026 

Hotelling SHI APU Hotelling SHI APU 

Harris 22,987 17,340 5,647 24,297 17,646 6,651 

Brazoria 228 172 56 246 179 67 

Fort Bend 5,467 4,124 1,343 6,056 4,398 1,658 

Waller 1,890 1,426 464 1,986 1,442 544 

Montgomery 6,233 4,702 1,531 6,785 4,928 1,857 

Liberty 543 409 133 560 407 153 

Chambers 2,658 2,005 653 2,799 2,033 766 

Galveston 545 411 134 591 429 162 

2028 24-Hour Weekday SHI and APU Hours Summaries (CLhT_Diesel Only) 

County 

2023 

Hotelling SHI APU 

Harris 25,024 17,946 7,078 

Brazoria 260 186 73 

Fort Bend 6,388 4,581 1,807 

Waller 2,063 1,479 583 

Montgomery 7,131 5,114 2,017 

Liberty 574 412 162 

Chambers 2,878 2,064 814 

Galveston 624 448 177 

163 

APPENDIX H: 

SOURCE TYPE AGE AND FUEL ENGINE FRACTIONS INPUTS TO 

MOVES

165 

Brazoria County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.035148 0.047410 0.024423 0.024423 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.042178 0.063593 0.048063 0.048063 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.036664 0.064298 0.043025 0.043025 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.083115 0.057973 0.037827 0.037827 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.093728 0.085697 0.068646 0.068646 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.105445 0.086589 0.074174 0.074174 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.101034 0.076487 0.066812 0.066812 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.075534 0.071170 0.060893 0.060893 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.055410 0.060945 0.067802 0.067802 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.066988 0.057103 0.069857 0.069857 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.060786 0.055253 0.072976 0.072976 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.043005 0.048583 0.066115 0.066115 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.029773 0.047993 0.053310 0.053310 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.027981 0.037898 0.047574 0.047574 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.020675 0.029918 0.032801 0.032801 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.014473 0.023594 0.032886 0.032886 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.015438 0.017398 0.023775 0.023775 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.013784 0.015420 0.023396 0.023396 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.010200 0.010944 0.019666 0.019666 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.006478 0.008383 0.012731 0.012731 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.004824 0.006274 0.010762 0.010762 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.003446 0.004663 0.007998 0.007998 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.004273 0.003691 0.006445 0.006445 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.003584 0.002821 0.005956 0.005956 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.002481 0.001828 0.004452 0.004452 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.002757 0.001605 0.002263 0.002263 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.005238 0.001122 0.002397 0.002397 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.002619 0.001295 0.001896 0.001896 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.002068 0.000986 0.001945 0.001945 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.003722 0.000597 0.000881 0.000881 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.027154 0.008469 0.008255 0.008255 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
166 

Brazoria County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2006, and 2028 An

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.034051 0.049888 0.026692 0.026692 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.058353 0.082092 0.050348 0.050348 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.051368 0.076969 0.045098 0.045098 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.037398 0.066351 0.048366 0.048366 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.033469 0.060897 0.042381 0.042381 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.072905 0.052089 0.034989 0.034989 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.082800 0.076409 0.063810 0.063810 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.093277 0.075794 0.068950 0.068950 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.086147 0.065460 0.063712 0.063712 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.066938 0.060303 0.056247 0.056247 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.053842 0.050644 0.062244 0.062244 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.062427 0.045919 0.064202 0.064202 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.054424 0.043597 0.066490 0.066490 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.038271 0.037030 0.057875 0.057875 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.026193 0.035876 0.046175 0.046175 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.024447 0.027141 0.040423 0.040423 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.018626 0.020587 0.028099 0.028099 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.013097 0.016206 0.028393 0.028393 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.013242 0.011191 0.020022 0.020022 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.011496 0.010260 0.018614 0.018614 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.009313 0.006635 0.015628 0.015628 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.005384 0.005164 0.009974 0.009974 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.004220 0.003598 0.007979 0.007979 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.003783 0.003078 0.005936 0.005936 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.003929 0.002369 0.005054 0.005054 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.003347 0.001822 0.004699 0.004699 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.002474 0.001390 0.003525 0.003525 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.001455 0.001188 0.001799 0.001799 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.002910 0.000769 0.001775 0.001775 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.002328 0.000918 0.001493 0.001493 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.028085 0.008363 0.009007 0.009007 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
167 

Chambers County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.032854 0.057461 0.025441 0.025441 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.030801 0.069076 0.052380 0.052380 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.036961 0.068396 0.043400 0.043400 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.081109 0.061604 0.043924 0.043924 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.091376 0.096380 0.074379 0.074379 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.111910 0.090946 0.083957 0.083957 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.110883 0.084562 0.072134 0.072134 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.066735 0.069619 0.062332 0.062332 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.068789 0.057189 0.071685 0.071685 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.072895 0.045915 0.068692 0.068692 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.054415 0.050397 0.068393 0.068393 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.042094 0.041432 0.063903 0.063903 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.044148 0.038919 0.048788 0.048788 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.037988 0.032534 0.042353 0.042353 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.021561 0.026082 0.028435 0.028435 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.008214 0.021803 0.028884 0.028884 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.012320 0.017252 0.019979 0.019979 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.006160 0.014467 0.021925 0.021925 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.009240 0.010256 0.019306 0.019306 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.005133 0.008830 0.012796 0.012796 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.003080 0.006452 0.008680 0.008680 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.005133 0.005298 0.007857 0.007857 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.002053 0.003871 0.006585 0.006585 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.004107 0.003804 0.004340 0.004340 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.004107 0.001970 0.003667 0.003667 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.002053 0.002038 0.001871 0.001871 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.001027 0.000815 0.001796 0.001796 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.004107 0.001562 0.002394 0.002394 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.002053 0.001019 0.001497 0.001497 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.001027 0.000611 0.001122 0.001122 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.025667 0.009441 0.007109 0.007109 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
168 

Chambers County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 A

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.026205 0.056954 0.034963 0.034963 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.041929 0.098149 0.057648 0.057648 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.062893 0.095394 0.053231 0.053231 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.036688 0.071753 0.059444 0.059444 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.037736 0.063809 0.044546 0.044546 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.080713 0.054904 0.042674 0.042674 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.070231 0.079185 0.070150 0.070150 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.094340 0.072202 0.073070 0.073070 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.106918 0.068038 0.064910 0.064910 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.068134 0.054328 0.054503 0.054503 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.057652 0.043308 0.059370 0.059370 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.064990 0.034595 0.059744 0.059744 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.045073 0.037927 0.056300 0.056300 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.032495 0.031136 0.052931 0.052931 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.039832 0.028894 0.041102 0.041102 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.028302 0.022551 0.035487 0.035487 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.016771 0.016849 0.024931 0.024931 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.012579 0.013582 0.022161 0.022161 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.014675 0.010827 0.016096 0.016096 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.006289 0.008393 0.017219 0.017219 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.009434 0.007111 0.012802 0.012802 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.005241 0.005061 0.009882 0.009882 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.006289 0.003075 0.007112 0.007112 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.001048 0.002691 0.005465 0.005465 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.003145 0.002306 0.004567 0.004567 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.003145 0.002306 0.003743 0.003743 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.002096 0.001345 0.002770 0.002770 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.001048 0.001217 0.001348 0.001348 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.001048 0.000641 0.001872 0.001872 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.004193 0.001217 0.002171 0.002171 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.018868 0.010250 0.007786 0.007786 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
169 

Fort Bend County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.046851 0.053758 0.030330 0.030330 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.057431 0.071312 0.059501 0.059501 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.051763 0.070497 0.054767 0.054767 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.094584 0.064229 0.043843 0.043843 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.094458 0.092627 0.077315 0.077315 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.114987 0.091304 0.085057 0.085057 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.094081 0.080117 0.068916 0.068916 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.074811 0.070150 0.063454 0.063454 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.052771 0.061728 0.073516 0.073516 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.063980 0.056799 0.072222 0.072222 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.053149 0.052662 0.072551 0.072551 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.037028 0.046587 0.060076 0.060076 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.029849 0.043415 0.046697 0.046697 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.027078 0.032826 0.038770 0.038770 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.018136 0.026532 0.028667 0.028667 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.011209 0.020441 0.026747 0.026747 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.010076 0.014271 0.017250 0.017250 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.008564 0.012307 0.016941 0.016941 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.005164 0.008049 0.014087 0.014087 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.005542 0.006009 0.009354 0.009354 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.002897 0.004550 0.007177 0.007177 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.002645 0.003548 0.005031 0.005031 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.001763 0.002835 0.004980 0.004980 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.002519 0.001738 0.003840 0.003840 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.002393 0.001378 0.002967 0.002967 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.002645 0.001146 0.001930 0.001930 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.003275 0.000831 0.001797 0.001797 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.002015 0.000825 0.001612 0.001612 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.003023 0.000694 0.001591 0.001591 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.003401 0.000520 0.000852 0.000852 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.021914 0.006317 0.008163 0.008163 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
170 

Fort Bend County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 A

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.050696 0.058337 0.032080 0.032080 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.073662 0.090001 0.064914 0.064914 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.068043 0.083425 0.056273 0.056273 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.050452 0.073275 0.058103 0.058103 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.041656 0.066488 0.051514 0.051514 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.080015 0.057682 0.038498 0.038498 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.074884 0.080274 0.070114 0.070114 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.096995 0.077799 0.074872 0.074872 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.080625 0.066166 0.061161 0.061161 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.062790 0.056728 0.056222 0.056222 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.046787 0.048539 0.063535 0.063535 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.053872 0.043165 0.062248 0.062248 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.043000 0.038870 0.062761 0.062761 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.032861 0.033461 0.051736 0.051736 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.026631 0.030643 0.040127 0.040127 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.021500 0.022460 0.031838 0.031838 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.016003 0.017884 0.023579 0.023579 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.009406 0.013139 0.021809 0.021809 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.008307 0.008985 0.014224 0.014224 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.006719 0.007194 0.013731 0.013731 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.004520 0.004944 0.011025 0.011025 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.005375 0.003593 0.007585 0.007585 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.003298 0.002656 0.005553 0.005553 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.001832 0.002039 0.003953 0.003953 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.001344 0.001697 0.003802 0.003802 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.002077 0.001082 0.003159 0.003159 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.002199 0.000933 0.002223 0.002223 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.002443 0.000837 0.001418 0.001418 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.003176 0.000641 0.001398 0.001398 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.001099 0.000664 0.001207 0.001207 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.027730 0.006398 0.009335 0.009335 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
171 

Galveston County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.032127 0.050060 0.023687 0.023687 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.043258 0.064476 0.049915 0.049915 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.039464 0.062459 0.049245 0.049245 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.096509 0.061257 0.041219 0.041219 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.091955 0.086048 0.076896 0.076896 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.102707 0.085570 0.079339 0.079339 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.095244 0.078593 0.070350 0.070350 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.074627 0.071102 0.060830 0.060830 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.054263 0.060256 0.071341 0.071341 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.067417 0.057380 0.071676 0.071676 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.057172 0.054370 0.072932 0.072932 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.041108 0.047095 0.064878 0.064878 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.033898 0.044413 0.047444 0.047444 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.028712 0.036862 0.043047 0.043047 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.019732 0.029108 0.030597 0.030597 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.014799 0.023327 0.029466 0.029466 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.015431 0.017329 0.020979 0.020979 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.011257 0.015798 0.020798 0.020798 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.008095 0.010644 0.016261 0.016261 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.006957 0.008097 0.011041 0.011041 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.005059 0.006244 0.008221 0.008221 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.003921 0.004631 0.006784 0.006784 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.003036 0.003951 0.006379 0.006379 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.003795 0.002831 0.005597 0.005597 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.004301 0.002002 0.003392 0.003392 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.003542 0.001696 0.001884 0.001884 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.003289 0.001471 0.002219 0.002219 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.003668 0.001292 0.001940 0.001940 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.002403 0.001218 0.002052 0.002052 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.003289 0.000807 0.001312 0.001312 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.028965 0.009613 0.008277 0.008277 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
172 

Galveston County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 A

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.032675 0.050373 0.031659 0.031659 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.064163 0.084895 0.058659 0.058659 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.054941 0.076401 0.045384 0.045384 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.040580 0.065228 0.049650 0.049650 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.033597 0.059393 0.045749 0.045749 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.083267 0.054553 0.038002 0.038002 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.079842 0.074858 0.068974 0.068974 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.091304 0.073456 0.072187 0.072187 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.081686 0.066351 0.062813 0.062813 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.064032 0.059954 0.054126 0.054126 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.049407 0.049882 0.063950 0.063950 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.060079 0.045968 0.064146 0.064146 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.048090 0.042939 0.064708 0.064708 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.035046 0.036829 0.057621 0.057621 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.031489 0.034073 0.041553 0.041553 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.024506 0.027557 0.037301 0.037301 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.020158 0.020845 0.025190 0.025190 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.015020 0.016188 0.024643 0.024643 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.012516 0.011650 0.017359 0.017359 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.009486 0.010535 0.016980 0.016980 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.006192 0.006895 0.013360 0.013360 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.005929 0.005043 0.008869 0.008869 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.004480 0.003928 0.006217 0.006217 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.003162 0.003086 0.005080 0.005080 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.002767 0.002490 0.005094 0.005094 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.003557 0.002083 0.004182 0.004182 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.002635 0.001557 0.002624 0.002624 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.002240 0.001164 0.001431 0.001431 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.004216 0.001108 0.001628 0.001628 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.002899 0.000996 0.001473 0.001473 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.030040 0.009721 0.009388 0.009388 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
173 

Harris County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.045882 0.056954 0.027448 0.027448 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.053886 0.058891 0.048330 0.048330 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.039223 0.057738 0.039544 0.039544 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.096000 0.052304 0.034800 0.034800 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.091194 0.077445 0.068786 0.068786 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.110500 0.079320 0.075463 0.075463 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.092660 0.074111 0.065119 0.065119 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.072255 0.068847 0.062347 0.062347 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.053173 0.060142 0.071715 0.071715 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.064781 0.057433 0.073917 0.073917 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.054028 0.057165 0.075757 0.075757 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.040200 0.053090 0.067240 0.067240 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.031525 0.051822 0.053864 0.053864 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.025721 0.041288 0.047382 0.047382 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.018634 0.033807 0.034697 0.034697 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.013115 0.027118 0.032633 0.032633 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.013054 0.019911 0.021189 0.021189 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.010793 0.017480 0.021057 0.021057 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.008227 0.012141 0.017385 0.017385 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.006822 0.009349 0.012007 0.012007 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.004093 0.006942 0.008639 0.008639 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.003401 0.005354 0.006729 0.006729 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.003238 0.003949 0.005774 0.005774 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.002831 0.002701 0.005151 0.005151 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.003421 0.001982 0.003483 0.003483 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.002566 0.001486 0.002231 0.002231 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.004114 0.001196 0.002535 0.002535 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.003849 0.001219 0.002143 0.002143 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.003095 0.000985 0.002053 0.002053 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.002464 0.000755 0.001334 0.001334 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.025253 0.007077 0.009249 0.009249 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
174 

Harris County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 Ana

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.041700 0.065600 0.031518 0.031518 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.074575 0.076227 0.050539 0.050539 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.064531 0.068965 0.043006 0.043006 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.043713 0.058501 0.045770 0.045770 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.033668 0.055270 0.037853 0.037853 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.080294 0.048562 0.033218 0.033218 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.076032 0.070340 0.064775 0.064775 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.094751 0.071820 0.071330 0.071330 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.080786 0.066148 0.061027 0.061027 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.063717 0.060102 0.058713 0.058713 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.045983 0.051641 0.066065 0.066065 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.057506 0.048348 0.067479 0.067479 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.048853 0.046294 0.068120 0.068120 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.035232 0.041390 0.060118 0.060118 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.027479 0.039102 0.047321 0.047321 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.022403 0.030110 0.040584 0.040584 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.015785 0.023947 0.029053 0.029053 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.011351 0.018569 0.027243 0.027243 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.010966 0.013123 0.017448 0.017448 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.009274 0.010889 0.017264 0.017264 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.007068 0.007378 0.013932 0.013932 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.005569 0.005564 0.009254 0.009254 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.003641 0.004043 0.006578 0.006578 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.002699 0.003098 0.005093 0.005093 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.002741 0.002350 0.004350 0.004350 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.002420 0.001633 0.003927 0.003927 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.002484 0.001253 0.002622 0.002622 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.001928 0.001002 0.001749 0.001749 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.003491 0.000833 0.001985 0.001985 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.002806 0.000826 0.001763 0.001763 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.026558 0.007070 0.010301 0.010301 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
175 

Liberty County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.023891 0.042696 0.024444 0.024444 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.039249 0.050351 0.043519 0.043519 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.034130 0.052283 0.032334 0.032334 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.080205 0.047453 0.031480 0.031480 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.081342 0.076363 0.066010 0.066010 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.102389 0.074691 0.068898 0.068898 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.097270 0.070083 0.062472 0.062472 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.080774 0.068931 0.053362 0.053362 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.059158 0.053658 0.061496 0.061496 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.065415 0.052172 0.067393 0.067393 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.069397 0.051132 0.069305 0.069305 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.051763 0.052729 0.068288 0.068288 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.034699 0.054067 0.051613 0.051613 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.033561 0.048345 0.050433 0.050433 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.024460 0.038051 0.039818 0.039818 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.018203 0.033444 0.041811 0.041811 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.015358 0.025937 0.028714 0.028714 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.011945 0.023671 0.029365 0.029365 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.008532 0.017539 0.024810 0.024810 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.005688 0.014418 0.015211 0.015211 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.002844 0.011631 0.013503 0.013503 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.002844 0.009513 0.010087 0.010087 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.002844 0.005983 0.008582 0.008582 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.004551 0.004905 0.008012 0.008012 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.004551 0.004013 0.006223 0.006223 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.004551 0.002453 0.003172 0.003172 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.007395 0.001895 0.003213 0.003213 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.003413 0.001858 0.003172 0.003172 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.001706 0.001524 0.002684 0.002684 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.003413 0.000520 0.001708 0.001708 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.024460 0.007692 0.008866 0.008866 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
176 

Liberty County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 An

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.035294 0.045784 0.025960 0.025960 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.051393 0.072277 0.047689 0.047689 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.044582 0.065762 0.043050 0.043050 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.035913 0.055592 0.041626 0.041626 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.031579 0.051321 0.033407 0.033407 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.081115 0.041911 0.030843 0.030843 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.074303 0.068404 0.059774 0.059774 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.092879 0.068006 0.061849 0.061849 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.085449 0.065364 0.061646 0.061646 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.078638 0.059428 0.050781 0.050781 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.052012 0.048136 0.057292 0.057292 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.050774 0.046797 0.064860 0.064860 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.060681 0.045277 0.062785 0.062785 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.043344 0.044915 0.061727 0.061727 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.029721 0.043540 0.047933 0.047933 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.023529 0.036518 0.042074 0.042074 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.022291 0.028882 0.035360 0.035360 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.014861 0.022367 0.035726 0.035726 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.016718 0.018277 0.024414 0.024414 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.008669 0.015201 0.024618 0.024618 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.012384 0.012052 0.020955 0.020955 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.004954 0.008578 0.013916 0.013916 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.003715 0.007419 0.010295 0.010295 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.002477 0.005718 0.007446 0.007446 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.002477 0.003511 0.006266 0.006266 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.005573 0.003004 0.005656 0.005656 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.003096 0.002316 0.004232 0.004232 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.000619 0.001810 0.002523 0.002523 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.004334 0.001194 0.002319 0.002319 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.000619 0.001484 0.002075 0.002075 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.026006 0.009157 0.010905 0.010905 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
177 

Montgomery County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.046041 0.057957 0.029836 0.029836 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.050343 0.070848 0.058195 0.058195 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.036247 0.069773 0.050798 0.050798 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.088330 0.058850 0.041676 0.041676 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.084943 0.088791 0.073653 0.073653 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.107368 0.086174 0.079387 0.079387 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.094279 0.077066 0.066106 0.066106 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.073135 0.068928 0.061257 0.061257 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.058490 0.060971 0.070167 0.070167 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.070389 0.054432 0.071353 0.071353 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.054462 0.051801 0.069875 0.069875 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.042380 0.046953 0.064371 0.064371 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.034783 0.043572 0.047754 0.047754 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.028650 0.034769 0.041445 0.041445 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.020686 0.027505 0.030898 0.030898 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.013730 0.021970 0.029660 0.029660 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.012082 0.015756 0.020811 0.020811 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.010069 0.014089 0.019227 0.019227 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.007414 0.009805 0.015803 0.015803 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.007048 0.007126 0.011060 0.011060 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.005309 0.005636 0.008070 0.008070 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.002654 0.004509 0.006265 0.006265 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.003295 0.003424 0.005495 0.005495 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.003295 0.002718 0.004637 0.004637 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.003387 0.001958 0.003088 0.003088 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.003478 0.001595 0.002017 0.002017 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.003844 0.001261 0.002106 0.002106 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.003661 0.001122 0.002150 0.002150 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.003387 0.000879 0.002088 0.002088 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.003295 0.000683 0.001274 0.001274 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.023524 0.009079 0.009477 0.009477 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
178 

Montgomery County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 A

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.039192 0.062061 0.031242 0.031242 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.073451 0.096070 0.059737 0.059737 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.062945 0.084156 0.054686 0.054686 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.044034 0.068771 0.055112 0.055112 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.033894 0.061796 0.046689 0.046689 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.072355 0.051148 0.037301 0.037301 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.076192 0.074088 0.066309 0.066309 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.092363 0.070885 0.069620 0.069620 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.080395 0.063575 0.059763 0.059763 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.065047 0.056213 0.055086 0.055086 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.050978 0.048773 0.062753 0.062753 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.061392 0.042868 0.062527 0.062527 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.047597 0.040514 0.061693 0.061693 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.037091 0.034744 0.055790 0.055790 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.027864 0.031977 0.041743 0.041743 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.023479 0.024845 0.034797 0.034797 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.015257 0.019141 0.025722 0.025722 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.011785 0.014755 0.025244 0.025244 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.010963 0.010269 0.017516 0.017516 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.009410 0.009168 0.015882 0.015882 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.007126 0.006100 0.012857 0.012857 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.006578 0.004421 0.008606 0.008606 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.004202 0.003329 0.006537 0.006537 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.002467 0.002654 0.004937 0.004937 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.002832 0.002324 0.004485 0.004485 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.003746 0.001832 0.003781 0.003781 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.003289 0.001392 0.002747 0.002747 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.002284 0.001157 0.001695 0.001695 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.002558 0.000918 0.001886 0.001886 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.003198 0.000814 0.001886 0.001886 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.026037 0.009242 0.011370 0.011370 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
179 

Waller County 2012 Age Distribution Inputs to MOVES 

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.029377 0.036244 0.017916 0.017916 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 1 0.045828 0.042338 0.038129 0.038129 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 2 0.043478 0.048175 0.037133 0.037133 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 3 0.075206 0.042273 0.028482 0.028482 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 4 0.084606 0.070755 0.059184 0.059184 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 5 0.108108 0.069985 0.067070 0.067070 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 6 0.082256 0.069408 0.062400 0.062400 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 7 0.075206 0.065495 0.055662 0.055662 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 8 0.052879 0.053756 0.069750 0.069750 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 9 0.069330 0.050741 0.067682 0.067682 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 10 0.050529 0.058374 0.072506 0.072506 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 11 0.048179 0.056386 0.067836 0.067836 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 12 0.035253 0.057990 0.049154 0.049154 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 13 0.035253 0.048881 0.050685 0.050685 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 14 0.028202 0.042658 0.038588 0.038588 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 15 0.014101 0.034383 0.041421 0.041421 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 16 0.014101 0.029380 0.029783 0.029783 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 17 0.014101 0.024055 0.030626 0.030626 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 18 0.015276 0.020720 0.022893 0.022893 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 19 0.012926 0.015973 0.015849 0.015849 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 20 0.002350 0.012124 0.014624 0.014624 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 21 0.003525 0.010135 0.010413 0.010413 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 22 0.004700 0.006928 0.009647 0.009647 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 23 0.005875 0.005453 0.007963 0.007963 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 24 0.007051 0.004811 0.006431 0.006431 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 25 0.003525 0.003464 0.003598 0.003598 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 26 0.004700 0.001283 0.003675 0.003675 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 27 0.003525 0.001860 0.003369 0.003369 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 28 0.003525 0.001668 0.003598 0.003598 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 29 0.003525 0.001283 0.001761 0.001761 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 30 0.023502 0.013022 0.012174 0.012174 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 
180 

Waller County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 Ana

Age MC PC PT LCT IBus Tbus Sbus RT SUShT SULhT 0 0.032768 0.039187 0.020653 0.020653 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 1 0.051977 0.063437 0.044613 0.044613 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 2 0.054237 0.062910 0.038365 0.038365 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 3 0.038418 0.048559 0.039247 0.039247 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 4 0.032768 0.049379 0.035352 0.035352 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 5 0.070056 0.042701 0.027561 0.027561 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 6 0.072316 0.061856 0.057695 0.057695 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 7 0.092655 0.063730 0.059827 0.059827 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 8 0.067797 0.063730 0.059018 0.059018 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 9 0.074576 0.060918 0.050860 0.050860 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 10 0.049718 0.049965 0.066956 0.066956 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 11 0.056497 0.047680 0.065192 0.065192 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 12 0.055367 0.048676 0.064971 0.064971 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 13 0.041808 0.047856 0.062987 0.062987 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 14 0.035028 0.050199 0.049831 0.049831 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 15 0.036158 0.038425 0.045201 0.045201 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 16 0.023729 0.033271 0.033588 0.033588 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 17 0.011299 0.027413 0.036601 0.036601 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 18 0.015819 0.020326 0.025503 0.025503 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 19 0.007910 0.017690 0.025062 0.025062 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 20 0.012429 0.011949 0.018668 0.018668 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 21 0.006780 0.009489 0.012568 0.012568 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 22 0.003390 0.007146 0.011392 0.011392 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 23 0.005650 0.005858 0.007350 0.007350 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 24 0.004520 0.003925 0.007497 0.007497 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 25 0.004520 0.003222 0.005806 0.005806 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 26 0.003390 0.003163 0.004777 0.004777 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 27 0.003390 0.002402 0.003454 0.003454 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 28 0.004520 0.000879 0.002499 0.002499 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 29 0.002260 0.001289 0.002278 0.002278 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 30 0.028249 0.012769 0.014626 0.014626 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 
181 

Texas Statewide 2012 Fuel Engine Fractions Summary 

SUT Fuel Type Model Year 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2

PC Gas 0.988 0.988 0.990 0.993 0.999 1.000 0.993 0.995 0.997 0.996 0.996 0.997 0PC Diesel 0.012 0.012 0.010 0.007 0.001 0.000 0.007 0.005 0.003 0.004 0.004 0.003 0

PT Gas 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0PT Diesel 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0PT Gas 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0PT Diesel 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0

LCT Gas 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0LCT Diesel 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0LCT Gas 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0LCT Diesel 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.002 0.002 0.005 0.001 0.003 0.003 0.005 0.004 0.005 0.006 0RT Diesel 0.997 0.997 0.998 0.998 0.995 0.999 0.997 0.997 0.995 0.996 0.995 0.994 0

SUShT Gas 0.207 0.210 0.237 0.324 0.270 0.245 0.226 0.218 0.236 0.253 0.266 0.311 0SUShT Diesel 0.793 0.790 0.763 0.676 0.730 0.755 0.774 0.782 0.764 0.747 0.734 0.689 0

SULhT Gas 0.207 0.210 0.237 0.324 0.270 0.245 0.226 0.218 0.236 0.253 0.266 0.311 0SULhT Diesel 0.793 0.790 0.763 0.676 0.730 0.755 0.774 0.782 0.764 0.747 0.734 0.689 0

MH Gas 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0MH Diesel 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

CShT Gas 0.103 0.056 0.078 0.052 0.057 0.027 0.053 0.049 0.047 0.052 0.075 0.079 0CShT Diesel 0.897 0.944 0.922 0.948 0.943 0.973 0.947 0.951 0.953 0.948 0.925 0.921 0

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

1 Conventional internal combustion engine technology only. 
182 

Texas Statewide 2012 Fuel Engine Fractions Summary - Continued 

SUT Fuel Type Model Year 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 

PC Gas 0.999 0.999 1.000 0.999 0.999 0.997 0.999 0.999 1.000 0.987 0.991 0.966 0PC Diesel 0.001 0.001 0.000 0.001 0.001 0.003 0.001 0.001 0.000 0.013 0.009 0.034 0

PT Gas 0.981 0.995 0.991 0.986 0.985 0.994 0.989 0.992 0.997 0.996 0.986 0.984 0PT Diesel 0.019 0.005 0.009 0.014 0.015 0.006 0.011 0.008 0.003 0.004 0.014 0.016 0PT Gas 0.981 0.995 0.991 0.986 0.985 0.994 0.989 0.992 0.997 0.996 0.986 0.984 0PT Diesel 0.019 0.005 0.009 0.014 0.015 0.006 0.011 0.008 0.003 0.004 0.014 0.016 0

LCT Gas 0.971 0.932 0.974 0.974 0.951 0.937 0.984 0.976 0.952 0.986 0.956 0.958 0LCT Diesel 0.029 0.068 0.026 0.026 0.049 0.063 0.016 0.024 0.048 0.014 0.044 0.042 0LCT Gas 0.971 0.932 0.974 0.974 0.951 0.937 0.984 0.976 0.952 0.986 0.956 0.958 0LCT Diesel 0.029 0.068 0.026 0.026 0.049 0.063 0.016 0.024 0.048 0.014 0.044 0.042 0IBus 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

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 0TBus 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

SBus Gas 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250 0.265 0.327 0.484 0SBus Diesel 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750 0.735 0.673 0.516 0

RT Gas 0.012 0.010 0.105 0.031 0.210 0.101 0.204 0.029 0.106 0.106 0.062 0.051 0RT Diesel 0.988 0.990 0.895 0.969 0.790 0.899 0.796 0.971 0.894 0.894 0.938 0.949 0

SUShT Gas 0.437 0.675 0.506 0.510 0.518 0.487 0.532 0.550 0.658 0.725 0.774 0.755 0SUShT Diesel 0.563 0.325 0.494 0.490 0.482 0.513 0.468 0.450 0.342 0.275 0.226 0.245 0

SULhT Gas 0.437 0.675 0.506 0.510 0.518 0.487 0.532 0.550 0.658 0.725 0.774 0.755 0SULhT Diesel 0.563 0.325 0.494 0.490 0.482 0.513 0.468 0.450 0.342 0.275 0.226 0.245 0

MH Gas 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0MH Diesel 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

CShT Gas 0.143 0.293 0.120 0.122 0.159 0.166 0.140 0.122 0.159 0.152 0.247 0.239 0CShT Diesel 0.857 0.707 0.880 0.878 0.841 0.834 0.860 0.878 0.841 0.848 0.753 0.761 0

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

1 Conventional internal combustion engine technology only. 
183 

Texas Statewide 2014 Fuel Engine Fractions Summary 

SUT Fuel Type Model Year 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2

PC Gas 0.988 0.988 0.988 0.988 0.990 0.993 0.999 1.000 0.993 0.995 0.997 0.996 0PC Diesel 0.012 0.012 0.012 0.012 0.010 0.007 0.001 0.000 0.007 0.005 0.003 0.004 0

PT Gas 0.980 0.980 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0PT Diesel 0.020 0.020 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0PT Gas 0.980 0.980 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0PT Diesel 0.020 0.020 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0

LCT Gas 0.947 0.947 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0LCT Diesel 0.053 0.053 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0LCT Gas 0.947 0.947 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0LCT Diesel 0.053 0.053 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.003 0.003 0.002 0.002 0.005 0.001 0.003 0.003 0.005 0.004 0RT Diesel 0.997 0.997 0.997 0.997 0.998 0.998 0.995 0.999 0.997 0.997 0.995 0.996 0

SUShT Gas 0.396 0.371 0.219 0.234 0.274 0.351 0.287 0.256 0.238 0.232 0.245 0.260 0SUShT Diesel 0.604 0.629 0.781 0.766 0.726 0.649 0.713 0.744 0.762 0.768 0.755 0.740 0

SULhT Gas 0.396 0.371 0.219 0.234 0.274 0.351 0.287 0.256 0.238 0.232 0.245 0.260 0SULhT Diesel 0.604 0.629 0.781 0.766 0.726 0.649 0.713 0.744 0.762 0.768 0.755 0.740 0

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 0MH 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

CShT Gas 0.094 0.199 0.110 0.057 0.081 0.052 0.058 0.031 0.050 0.051 0.052 0.055 0CShT Diesel 0.906 0.801 0.890 0.943 0.919 0.948 0.942 0.969 0.950 0.949 0.948 0.945 0

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

1 Conventional internal combustion engine technology only. 
184 

Texas Statewide 2014 Fuel Engine Fractions Summary - Continued 

SUT Fuel Type Model Year 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1

PC Gas 0.998 0.999 0.999 0.999 1.000 0.999 0.999 0.997 0.999 0.999 1.000 0.987 0PC Diesel 0.002 0.001 0.001 0.001 0.000 0.001 0.001 0.003 0.001 0.001 0.000 0.013 0

PT Gas 0.993 0.992 0.981 0.995 0.991 0.986 0.985 0.994 0.989 0.992 0.997 0.996 0PT Diesel 0.007 0.008 0.019 0.005 0.009 0.014 0.015 0.006 0.011 0.008 0.003 0.004 0PT Gas 0.993 0.992 0.981 0.995 0.991 0.986 0.985 0.994 0.989 0.992 0.997 0.996 0PT Diesel 0.007 0.008 0.019 0.005 0.009 0.014 0.015 0.006 0.011 0.008 0.003 0.004 0

LCT Gas 0.950 0.927 0.971 0.932 0.974 0.974 0.951 0.937 0.984 0.976 0.952 0.986 0LCT Diesel 0.050 0.073 0.029 0.068 0.026 0.026 0.049 0.063 0.016 0.024 0.048 0.014 0LCT Gas 0.950 0.927 0.971 0.932 0.974 0.974 0.951 0.937 0.984 0.976 0.952 0.986 0LCT Diesel 0.050 0.073 0.029 0.068 0.026 0.026 0.049 0.063 0.016 0.024 0.048 0.014 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0.229 0.250 0.265 0SBus Diesel 0.990 0.990 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0.771 0.750 0.735 0

RT Gas 0.404 0.019 0.012 0.010 0.105 0.031 0.210 0.101 0.204 0.029 0.106 0.106 0RT Diesel 0.596 0.981 0.988 0.990 0.895 0.969 0.790 0.899 0.796 0.971 0.894 0.894 0

SUShT Gas 0.435 0.436 0.427 0.673 0.508 0.519 0.511 0.465 0.539 0.572 0.640 0.654 0SUShT Diesel 0.565 0.564 0.573 0.327 0.492 0.481 0.489 0.535 0.461 0.428 0.360 0.346 0

SULhT Gas 0.435 0.436 0.427 0.673 0.508 0.519 0.511 0.465 0.539 0.572 0.640 0.654 0SULhT Diesel 0.565 0.564 0.573 0.327 0.492 0.481 0.489 0.535 0.461 0.428 0.360 0.346 0

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 0MH 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

CShT Gas 0.134 0.147 0.146 0.275 0.117 0.117 0.160 0.161 0.144 0.114 0.157 0.163 0CShT Diesel 0.866 0.853 0.854 0.725 0.883 0.883 0.840 0.839 0.856 0.886 0.843 0.837 0

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

1 Conventional internal combustion engine technology only. 
185 

Texas Statewide 2017 Fuel Engine Fractions Summary 

SUT Fuel Type Model Year 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2

PC Gas 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.990 0.993 0.999 1.000 0.993 0PC Diesel 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.010 0.007 0.001 0.000 0.007 0

PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0

LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.002 0.002 0.005 0.001 0.003 0RT Diesel 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.998 0.998 0.995 0.999 0.997 0

SUShT Gas 0.396 0.396 0.396 0.396 0.371 0.219 0.234 0.274 0.351 0.287 0.256 0.238 0SUShT Diesel 0.604 0.604 0.604 0.604 0.629 0.781 0.766 0.726 0.649 0.713 0.744 0.762 0

SULhT Gas 0.396 0.396 0.396 0.396 0.371 0.219 0.234 0.274 0.351 0.287 0.256 0.238 0SULhT Diesel 0.604 0.604 0.604 0.604 0.629 0.781 0.766 0.726 0.649 0.713 0.744 0.762 0

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 0MH 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

CShT Gas 0.094 0.094 0.094 0.094 0.199 0.110 0.057 0.081 0.052 0.058 0.031 0.050 0CShT Diesel 0.906 0.906 0.906 0.906 0.801 0.890 0.943 0.919 0.948 0.942 0.969 0.950 0

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

1 Conventional internal combustion engine technology only. 
186 

Texas Statewide 2017 Fuel Engine Fractions Summary - Continued 

SUT Fuel Type Model Year 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1

PC Gas 0.997 0.997 0.998 0.998 0.999 0.999 0.999 1.000 0.999 0.999 0.997 0.999 0PC Diesel 0.003 0.003 0.002 0.002 0.001 0.001 0.001 0.000 0.001 0.001 0.003 0.001 0

PT Gas 0.989 0.992 0.981 0.993 0.992 0.981 0.995 0.991 0.986 0.985 0.994 0.989 0PT Diesel 0.011 0.008 0.019 0.007 0.008 0.019 0.005 0.009 0.014 0.015 0.006 0.011 0PT Gas 0.989 0.992 0.981 0.993 0.992 0.981 0.995 0.991 0.986 0.985 0.994 0.989 0PT Diesel 0.011 0.008 0.019 0.007 0.008 0.019 0.005 0.009 0.014 0.015 0.006 0.011 0

LCT Gas 0.908 0.949 0.929 0.950 0.927 0.971 0.932 0.974 0.974 0.951 0.937 0.984 0LCT Diesel 0.092 0.051 0.071 0.050 0.073 0.029 0.068 0.026 0.026 0.049 0.063 0.016 0LCT Gas 0.908 0.949 0.929 0.950 0.927 0.971 0.932 0.974 0.974 0.951 0.937 0.984 0LCT Diesel 0.092 0.051 0.071 0.050 0.073 0.029 0.068 0.026 0.026 0.049 0.063 0.016 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.042 0.114 0.147 0.121 0.010 0.090 0.124 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.958 0.886 0.853 0.879 0.990 0.910 0.876 0

RT Gas 0.006 0.002 0.169 0.404 0.019 0.012 0.010 0.105 0.031 0.210 0.101 0.204 0RT Diesel 0.994 0.998 0.831 0.596 0.981 0.988 0.990 0.895 0.969 0.790 0.899 0.796 0

SUShT Gas 0.311 0.350 0.348 0.435 0.436 0.427 0.673 0.508 0.519 0.511 0.465 0.539 0SUShT Diesel 0.689 0.650 0.652 0.565 0.564 0.573 0.327 0.492 0.481 0.489 0.535 0.461 0

SULhT Gas 0.311 0.350 0.348 0.435 0.436 0.427 0.673 0.508 0.519 0.511 0.465 0.539 0SULhT Diesel 0.689 0.650 0.652 0.565 0.564 0.573 0.327 0.492 0.481 0.489 0.535 0.461 0

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 0MH 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

CShT Gas 0.084 0.090 0.107 0.134 0.147 0.146 0.275 0.117 0.117 0.160 0.161 0.144 0CShT Diesel 0.916 0.910 0.893 0.866 0.853 0.854 0.725 0.883 0.883 0.840 0.839 0.856 0

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

1 Conventional internal combustion engine technology only. 
187 

Texas Statewide 2020 Fuel Engine Fractions Summary 

SUT Fuel Type Model Year 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2

PC Gas 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.990 0.993 0PC Diesel 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.010 0.007 0

PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.987 0.985 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.013 0.015 0PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.987 0.985 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.013 0.015 0

LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.962 0.955 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.038 0.045 0LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.962 0.955 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.038 0.045 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.002 0.002 0RT Diesel 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.998 0.998 0

SUShT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.371 0.219 0.234 0.274 0.351 0SUShT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.629 0.781 0.766 0.726 0.649 0

SULhT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.371 0.219 0.234 0.274 0.351 0SULhT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.629 0.781 0.766 0.726 0.649 0

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0

CShT Gas 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.199 0.110 0.057 0.081 0.052 0CShT Diesel 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.801 0.890 0.943 0.919 0.948 0

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

1 Conventional internal combustion engine technology only. 
188 

Texas Statewide 2020 Fuel Engine Fractions Summary - Continued 

SUT Fuel type Model Year 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1

PC Gas 0.997 0.996 0.996 0.997 0.997 0.998 0.998 0.999 0.999 0.999 1.000 0.999 0PC Diesel 0.003 0.004 0.004 0.003 0.003 0.002 0.002 0.001 0.001 0.001 0.000 0.001 0

PT Gas 0.982 0.982 0.983 0.989 0.992 0.981 0.993 0.992 0.981 0.995 0.991 0.986 0PT Diesel 0.018 0.018 0.017 0.011 0.008 0.019 0.007 0.008 0.019 0.005 0.009 0.014 0PT Gas 0.982 0.982 0.983 0.989 0.992 0.981 0.993 0.992 0.981 0.995 0.991 0.986 0PT Diesel 0.018 0.018 0.017 0.011 0.008 0.019 0.007 0.008 0.019 0.005 0.009 0.014 0

LCT Gas 0.951 0.951 0.956 0.908 0.949 0.929 0.950 0.927 0.971 0.932 0.974 0.974 0LCT Diesel 0.049 0.049 0.044 0.092 0.051 0.071 0.050 0.073 0.029 0.068 0.026 0.026 0LCT Gas 0.951 0.951 0.956 0.908 0.949 0.929 0.950 0.927 0.971 0.932 0.974 0.974 0LCT Diesel 0.049 0.049 0.044 0.092 0.051 0.071 0.050 0.073 0.029 0.068 0.026 0.026 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.042 0.114 0.147 0.121 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.958 0.886 0.853 0.879 0

RT Gas 0.005 0.004 0.005 0.006 0.002 0.169 0.404 0.019 0.012 0.010 0.105 0.031 0RT Diesel 0.995 0.996 0.995 0.994 0.998 0.831 0.596 0.981 0.988 0.990 0.895 0.969 0

SUShT Gas 0.245 0.260 0.268 0.311 0.350 0.348 0.435 0.436 0.427 0.673 0.508 0.519 0SUShT Diesel 0.755 0.740 0.732 0.689 0.650 0.652 0.565 0.564 0.573 0.327 0.492 0.481 0

SULhT Gas 0.245 0.260 0.268 0.311 0.350 0.348 0.435 0.436 0.427 0.673 0.508 0.519 0SULhT Diesel 0.755 0.740 0.732 0.689 0.650 0.652 0.565 0.564 0.573 0.327 0.492 0.481 0

MH Gas 0.570 0.590 0.600 0.630 0.660 0.680 0.710 0.740 0.770 0.790 0.820 0.850 0MH Diesel 0.430 0.410 0.400 0.370 0.340 0.320 0.290 0.260 0.230 0.210 0.180 0.150 0

CShT Gas 0.052 0.055 0.077 0.084 0.090 0.107 0.134 0.147 0.146 0.275 0.117 0.117 0CShT Diesel 0.948 0.945 0.923 0.916 0.910 0.893 0.866 0.853 0.854 0.725 0.883 0.883 0

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

1 Conventional internal combustion engine technology only. 
189 

Texas Statewide 2023 Fuel Engine Fractions Summary 

SUT Fuel Type Model Year 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2

PC Gas 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0PC Diesel 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0

PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0

LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0RT Diesel 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0

SUShT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.371 0.219 0SUShT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.629 0.781 0

SULhT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.371 0.219 0SULhT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.629 0.781 0

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0

CShT Gas 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.199 0.110 0CShT Diesel 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.801 0.890 0

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

1 Conventional internal combustion engine technology only. 
190 

Texas Statewide 2023 Fuel Engine Fractions Summary - Continued 

SUT Fuel Type Model Year 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1

PC Gas 1.000 0.993 0.995 0.997 0.996 0.996 0.997 0.997 0.998 0.998 0.999 0.999 0PC Diesel 0.000 0.007 0.005 0.003 0.004 0.004 0.003 0.003 0.002 0.002 0.001 0.001 0

PT Gas 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0.992 0.981 0.993 0.992 0.981 0PT Diesel 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0.008 0.019 0.007 0.008 0.019 0PT Gas 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0.992 0.981 0.993 0.992 0.981 0PT Diesel 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0.008 0.019 0.007 0.008 0.019 0

LCT Gas 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0.949 0.929 0.950 0.927 0.971 0LCT Diesel 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0.051 0.071 0.050 0.073 0.029 0LCT Gas 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0.949 0.929 0.950 0.927 0.971 0LCT Diesel 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0.051 0.071 0.050 0.073 0.029 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.042 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.958 0

RT Gas 0.001 0.003 0.003 0.005 0.004 0.005 0.006 0.002 0.169 0.404 0.019 0.012 0RT Diesel 0.999 0.997 0.997 0.995 0.996 0.995 0.994 0.998 0.831 0.596 0.981 0.988 0

SUShT Gas 0.256 0.238 0.232 0.245 0.260 0.268 0.311 0.350 0.348 0.435 0.436 0.427 0SUShT Diesel 0.744 0.762 0.768 0.755 0.740 0.732 0.689 0.650 0.652 0.565 0.564 0.573 0

SULhT Gas 0.256 0.238 0.232 0.245 0.260 0.268 0.311 0.350 0.348 0.435 0.436 0.427 0SULhT Diesel 0.744 0.762 0.768 0.755 0.740 0.732 0.689 0.650 0.652 0.565 0.564 0.573 0

MH Gas 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0.660 0.680 0.710 0.740 0.770 0MH Diesel 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

CShT Gas 0.031 0.050 0.051 0.052 0.055 0.077 0.084 0.090 0.107 0.134 0.147 0.146 0CShT Diesel 0.969 0.950 0.949 0.948 0.945 0.923 0.916 0.910 0.893 0.866 0.853 0.854 0

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

1 Conventional internal combustion engine technology only. 
191 

Texas Statewide 2026 Fuel Engine Fractions Summary 

SUT Fuel Type Model Year 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2

PC Gas 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0PC Diesel 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0

PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0

LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0RT Diesel 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0

SUShT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0SUShT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0

SULhT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0SULhT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0

CShT Gas 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0CShT Diesel 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0

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

1 Conventional internal combustion engine technology only. 
192 

Texas Statewide 2026 Fuel Engine Fractions Summary - Continued 

SUT Fuel Type Model Year 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1

PC Gas 0.990 0.993 0.999 1.000 0.993 0.995 0.997 0.996 0.996 0.997 0.997 0.998 0PC Diesel 0.010 0.007 0.001 0.000 0.007 0.005 0.003 0.004 0.004 0.003 0.003 0.002 0

PT Gas 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0.992 0.981 0PT Diesel 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0.008 0.019 0PT Gas 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0.992 0.981 0PT Diesel 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0.008 0.019 0

LCT Gas 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0.949 0.929 0LCT Diesel 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0.051 0.071 0LCT Gas 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0.949 0.929 0LCT Diesel 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0.051 0.071 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.002 0.002 0.005 0.001 0.003 0.003 0.005 0.004 0.005 0.006 0.002 0.169 0RT Diesel 0.998 0.998 0.995 0.999 0.997 0.997 0.995 0.996 0.995 0.994 0.998 0.831 0

SUShT Gas 0.274 0.351 0.287 0.256 0.238 0.232 0.245 0.260 0.268 0.311 0.350 0.348 0SUShT Diesel 0.726 0.649 0.713 0.744 0.762 0.768 0.755 0.740 0.732 0.689 0.650 0.652 0

SULhT Gas 0.274 0.351 0.287 0.256 0.238 0.232 0.245 0.260 0.268 0.311 0.350 0.348 0SULhT Diesel 0.726 0.649 0.713 0.744 0.762 0.768 0.755 0.740 0.732 0.689 0.650 0.652 0

MH Gas 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0.660 0.680 0MH Diesel 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

CShT Gas 0.081 0.052 0.058 0.031 0.050 0.051 0.052 0.055 0.077 0.084 0.090 0.107 0CShT Diesel 0.919 0.948 0.942 0.969 0.950 0.949 0.948 0.945 0.923 0.916 0.910 0.893 0

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

1 Conventional internal combustion engine technology only. 
193 

Texas Statewide 2028 Fuel Engine Fractions Summary 

SUT Fuel Type Model Year 2028 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2

PC Gas 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0.988 0PC Diesel 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0

PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0PT Gas 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0.980 0PT Diesel 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0

LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0LCT Gas 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0.947 0LCT Diesel 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0.053 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0RT Diesel 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0.997 0

SUShT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0SUShT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0

SULhT Gas 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0.396 0SULhT Diesel 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0.604 0

MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0

CShT Gas 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0CShT Diesel 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0.906 0

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

1 Conventional internal combustion engine technology only. 
194 

Texas Statewide 2028 Fuel Engine Fractions Summary - Continued 

SUT Fuel Type Model Year 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2

PC Gas 0.988 0.988 0.990 0.993 0.999 1.000 0.993 0.995 0.997 0.996 0.996 0.997 0PC Diesel 0.012 0.012 0.010 0.007 0.001 0.000 0.007 0.005 0.003 0.004 0.004 0.003 0

PT Gas 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0PT Diesel 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0PT Gas 0.980 0.980 0.987 0.985 0.977 0.981 0.975 0.979 0.982 0.982 0.983 0.989 0PT Diesel 0.020 0.020 0.013 0.015 0.023 0.019 0.025 0.021 0.018 0.018 0.017 0.011 0

LCT Gas 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0LCT Diesel 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0LCT Gas 0.947 0.947 0.962 0.955 0.941 0.948 0.938 0.946 0.951 0.951 0.956 0.908 0LCT Diesel 0.053 0.053 0.038 0.045 0.059 0.052 0.062 0.054 0.049 0.049 0.044 0.092 0IBus 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

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 0TBus 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

SBus Gas 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0SBus Diesel 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0.990 0

RT Gas 0.003 0.003 0.002 0.002 0.005 0.001 0.003 0.003 0.005 0.004 0.005 0.006 0RT Diesel 0.997 0.997 0.998 0.998 0.995 0.999 0.997 0.997 0.995 0.996 0.995 0.994 0

SUShT Gas 0.219 0.234 0.274 0.351 0.287 0.256 0.238 0.232 0.245 0.260 0.268 0.311 0SUShT Diesel 0.781 0.766 0.726 0.649 0.713 0.744 0.762 0.768 0.755 0.740 0.732 0.689 0

SULhT Gas 0.219 0.234 0.274 0.351 0.287 0.256 0.238 0.232 0.245 0.260 0.268 0.311 0SULhT Diesel 0.781 0.766 0.726 0.649 0.713 0.744 0.762 0.768 0.755 0.740 0.732 0.689 0

MH Gas 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0MH Diesel 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

CShT Gas 0.110 0.057 0.081 0.052 0.058 0.031 0.050 0.051 0.052 0.055 0.077 0.084 0CShT Diesel 0.890 0.943 0.919 0.948 0.942 0.969 0.950 0.949 0.948 0.945 0.923 0.916 0

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

1 Conventional internal combustion engine technology only. 
195 

APPENDIX I: 

MOVES RUN SUMMARIES

197 

Appendix I is being transmitted electronically.