ATTACHMENT E

DEV ELO PMENT OF HGB ON

- ROAD EMISSION S INVENT O RIES FOR THE YEARS
20 12, 2014, 2017, 20 20, 2023, 2026 , AND 2028ON

- ROAD MOBILE SOURCE E MISSIONS INVENTORIES FOR THE HGB 1997 EIG HT- HOUR OZONE NONATTAINMENT COUNTI ES: 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 Re

designation 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 -r oad 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 -R oad Mobile Source Emissions Inventories for the HGB 1997 Eight

- Hour Ozone Non a ttainment 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

- Designa tion Inventories

– Summer Weekday (Tons per Day)

. The supporting electronic documents for the EI development, including MOVES 2014 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 201 2 and 20 28. Control strategy emission reduction estimates include the effe cts 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, 2 017, 2020, 2023, 2026, AND 2028 ONROAD MOBILE SOURCE EMISSIONS INVENTORIES FOR THE EIGHT HOUSTON-GALVESTON AREA COUNTIES PREVIOUSLY DESINGATED AS NONATTAINMEN T 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 Qual ity 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 77843-3135iii EXECUTIVE SUMMARY This project, performed by the Texas A&M Tr ansportation 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 emi ssions inventories for the eight Houston-Galveston-Brazoria (HGB) counties de signated nonattainment for the 1997 eight-hour ozone standard. TTI produced average summ er 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 Subst itute report and/or State Impl ementation Plan (SIP) revision. (Additionally under this task, but documented se parately, TTI produced inventories for TCEQ’s one-hour ozone standard re -designation analysis.)

These MOVES2014-based inventories, develo ped 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 se t of MOVES2014-based HGB inventorie s 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 eac h vehicle type. For the 2017-and-later calendar years, a substantial difference with MOVES2014 is the incorporat ion 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-ba sed 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 analys is, base and horizon year inventories were required wi th four inventory years in betw een. 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 th e 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 f actor model for on-road sources. Emissions were estimated for volatile organic compounds (VOC), carbon monoxide (CO), oxides of nitrogen (NO x ), particulate matter (PM) with diam eter of 10 microns or less (PM 10 ), PM with diameter of 2.5 microns or less (PM 2.5 ), atmospheric carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), and ammonia (NH 3 ). 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 MOVE S, 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 inventorie s are analysis years (2011 vers us 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-Des ignation Inventories – Summer Weekday (Tons per Day).

Year VMT Speed 1 VOC CO NO x PM 10 2 PM 2.5 2 CO 2 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 dir ect 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, detaile d traffic demand model (TDM) link-based inventory method was used (consistent w ith previously-developed HGB area MOVES2010bbased inventories 4 ), with the latest availa ble data, models, and proce dures. In general, MOVES emissions rates reflecting local conditions were produced and combined with local activity factors outside of the MOVES model to produ ce the emissions estimates externally. The inventory results were estimated by MOVES sour ce 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 (r oadway segment)-level vehicle miles of travel (VMT) and operational speeds for the roadway-ba sed emissions calculations. Using vehicle operating hours (VHT) estimates derived from th e link data, local vehicl e registrations, MOVES defaults, and other data, the hourly off-networ k activity factors were estimated for the offnetwork emissions calculations. The off-netw ork activity factors are: source-hours-parked (SHP); starts; and for combination long-haul tr ucks only, hotelling hours, comprised of extended idling hours (SHI) and a uxiliary power unit (APU) hours. Th e emissions rates were produced reflecting local conditions and multiplied by the asso ciated 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 utilities 5 developed by TTI to process on-road vehicle activity, off-network vehicle act ivity, and MOVES emissions ra te data into spatially and temporally detailed emissions estimates. The EPA’s Technical Guidance 6 is the primary technical reference used fo r guidance on appropriate i nputs 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-H our 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-Networ k Vehicle Activity Estimates ............................................10 MOVES Emissions Factors .......................................................................................................11 Emissions Calculations ........................................................................................................ ......12 Development of Vehi cle Type VMT Mix .....................................................................................13 Estimation of VMT ............................................................................................................. ...........15 Data Sources ..............................................................................................................................15 VMT Adjustments .....................................................................................................................15 2012 Historical Year Analysis – VMT C ontrol Totals and VMT Adjustments ....................16 Future Year Analyses – HP MS Adjustment Factor ...............................................................17 Future Year Analyses – S easonal Adjustment Factors ..........................................................18 Future Year Analyses – Intermed iate Year Adjustment Factors ...........................................18 Future Year Analys es – 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 Hotellin g 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 .........................................................................34viii 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 Zonem onthhour 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 NO x Adjustment for TxLED Effects .........................................................................................49 Emissions Calculations ........................................................................................................ ..........50 Hourly Link-Based Emissions Calculations ..............................................................................51 Conversion of Emissions Inve ntories 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 ........................................................................................6 5 Appendix B: Emissions Estimation Utilities for MOVES-Based Emissions Inventories .............73 TTI Emissions Estimation Utilities for M OVES2014-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 Rate s 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 .........................................................................................195ix 

### LIST OF TABLES

Table 1. HGB Eight-County Area Re-Designation Inventories – Summer Weekday (Tons per Day). ....................................................................................................................................5 Table 2. MOVES Source Use Type/F uel 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/Analy sis Year Correlations. ..................................................................14 Table 6. Analysis Year/TDM Year Designation. ...........................................................................16 Table 7. HGB AADT-to-Summer Weekday Fact ors for Control Total Development. .................16 Table 8. HGB 2012 Weekday VMT Control Tota ls 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 Aggrega tions and Associated Vehicle Types for Estimating Vehicle Populations. ..............................................................................................2 5 Table 15. Emissions Rates by Pr ocess and Activity Factor. ..........................................................29 Table 16. Emissions Control Strate gies 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 SU T 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 M ode CDB Data Sources. ..............................................................56 Table 26. Registration Categories. ............................................................................................ .....77 Table 27. SUT/Registration Category Correlation. .......................................................................78 Table 28. SUTs/Registration Categories Co rrelation for SUT Age Distribution. .........................84 Table 29. SUTs/Registration Categories Co rrelation for Fuel/Engine Fractions. .........................84 Table 30. MOVES2014 Emissions Process a nd Corresponding Activity for Rate-perActivity Emissions Rates. ........................................................................................................861 PURPOSE This project developed and produced Motor Vehicle Emission Simulator (MOVES) 2014-based, on-road mobile, link-based emissions inventorie s for the eight Houston-Galveston-Brazoria (HGB) counties, for analysis years 2011, 2012, 2014, 2017, 2020, 2023, 2026 and 2028. These emissions inventories were need ed to support re-desi gnation and maintenan ce analyses for the eight HGB ozone nonattainment area counties requir ed 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 inventorie s 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 ei ght-hour ozone SIP revision. Th e 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 (T CEQ) 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 ai r pollutants. TxDOT typically funds transportation conformity determinations required under 40 Code of Federal Regulations Part 93. TCEQ funds mobile so urce inventory work in support of Federal Clean Air Act (CAA) requirements, such as attainment of the Nati onal 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-hou r base years, the horizon years, and four years between the base and horizon years. These i nventories provide a basis for es tablishing: 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 a nd the horizon year. The inventory development included use of: the latest availa ble fleet characterization data; the latest available transportation planning information; and the most recent ve rsion 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 INVENT ORIES FOR THE EIGHT HOUSTONGALVESTON AREA COUNTIES PREVIOUSLY DESIGNATED AS NONATTAINMENT FOR THE ONE-HO UR 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 design ated 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. For2 the inventories to be consistent with inventory development for other SIP analyses, TTI used the most recent activity information, based on curr ent 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 invent ory production parameters described below were used directly, or estimated as noted. TTI adhered to the following.

 The emissions factor model used in developi ng inventories for this task was the most recent version of EPA’s on-road emissions model, MOVES2014 October Release.

 Emissions inventories were developed for th e eight HGB counties previously designated as nonattainment for the one-hour and 1997 ei ght-hour ozone standards. The counties are: Brazoria, Chambers, Fort Bend, Galves ton, Harris, Liberty, M ontgomery, and Waller counties.

 The analysis years for the one-hour re-d esignation and maintenance included: 2011, 2014, 2017, 2020, 2023, and 2026.

 The analysis years for the 1997 eight-hour re-designation and ma intenance included: 2012, 2014, 2017, 2020, 2023, 2026, and 2028.

 The pollutants included to complete this ta sk were volatile organic compounds (VOC), carbon monoxide (CO), oxi des of nitrogen (NO x ), carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), ammonia (NH 3 ), particulate matter (PM) with aerodynamic diameters equal to or less than 10 microns (PM 10 ), and PM with aerodynamic diameters equal to or less than 2.5 microns (PM 2.5 ).

 The day type for all the inventories was aver age 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 fo r the 1997 eight-hour analysis years. The temperature information was provided by TCEQ.

 The humidity was average summer 2011 humid ity for the one-hour analysis years and average summer 2012 humidity for the 1997 ei ght-hour analysis years. The humidity information was provided by TCEQ.

 The barometric pressure was average summe r 2011 barometric pressure for the one-hour analysis years and average summer 2012 baro metric pressure for the 1997 eight-hour analysis years. The barometric pressure information was provided by TCEQ.

 The vehicle miles traveled (VMT) mixes wa s 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 emissi ons analysis methodology for all of the referenced counties.3  For the off-network activity development pro cess, 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 developm ent 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 strate gies in effect for each analysis year.

 TTI used year-specific Texas Low Emissi ons 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 a nd 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 nonattain ment 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 inve ntory analysis activiti es 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 (A PU) hours, and totals for the pollutants of VOC, CO, NO x , CO 2 , SO 2 , NH 3 , Primary Exhaust PM 10 – Total, Primary Exhaust PM 2.5 – Total (plus PM2.5 primary exhaust speci es: composite non-elemental carbon PM [NonECPM], elemental carbon [EC], organic carbon [OC], and sulfate [SO 4 ]), and PM 10 and PM 2.5 brakewear and tirewear, by associated emissions processes. These files are tab-delimited for ease of loading into spreadsh eet software such as Microsoft ® Excel.

 Prepared the inventory summaries in the fo rmat for uploading to TCEQ’s Texas Air Emissions Repository (TexAER), which is cons istent 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 pr ocesses, and are available in the MOVES database. Two formats of the SCC-based i nventory summaries were prepared, the NEI CERS XML format, and the tab-delimited text file format.

 Prepared a set of MOVES count y 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 iden tical to, the results produced using MOVES rates output and the TTI post-proces sing inventory development utility.

 Prepared this documentation, complete and se lf-contained, including el ectronic data files and detailed descriptions thereof.4 TTI will maintain a record of all electronic fi les 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 re port. The electronic file submission is described in Appendix A – Electronic Data Submittal, and includes: a documen t 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 da ta relating to task activ ities; two standard sets of activity and inventory summary files: one wi th 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 MOVE S link-based emissions estimates. HoustonGalveston Area Council (H-GAC) st aff provided data sets extracted from the latest available HGB regional Travel Demand Models (TDMs) . Daniel Perry, of TCEQ, provided meteorological input data. Dr. Perkinson produc ed the VMT mixes used to divide fleet VMT activity into MOVES SUT by fuel type categorie s, county VMT control totals, and hourly VMT factors. White processed roadway based activ ity (VMT and speeds) and produced the vehicle population estimates. Boardman produced MOVE S model and MOVES output post-processor set-ups, and the MOVES-based em issions 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, wa s responsible for editing, design, and production of this Technical Report. Each member of the assigned TTI st aff contributed to the quality assurance of the inventory elements. Dr. Perk inson was the principle investigator for this project. This work was performed by TTI unde r 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, Estima tion 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 Speed 1 VOC CO NO x PM 10 2 PM 2.5 2 CO 2 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 aggr egate 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-specifi c TDMs. The HPMS is a federally-mandated program used by the Federal Highway Administ ration (FHWA) to provi de data to Congress concerning the nation’s streets and highways. Congr ess uses the data to allocate funds to the states. Every state collects, maintains, and re ports 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 co nstitutes the official measurement of highway performance, including VMT. The TDM estimat e of VMT distributes VMT to each roadway link within the local tr avel network, and, for future years, includes planned changes to the network. The TDM represents the best method for distributing VMT to th e roadway links within the local travel network and for predicting future year VMT. To provi de 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 validat ed and for which the HPMS data has been made available. The TDM forecasts VMT gr owth 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 consis tency, seasonality (i.e., summer), and day type (i.e., weekday). However, HPMS data exist fo r 2012, making it by definition a “historical” year. For years where HPMS data exist, the use of “his torical” (i.e., actual) VM T 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 for ecasts 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 obs erved conditions and the travel demand modeling process. The HPMS VMT for historical year s may be inconsistent6 with projected TDM VMT trends. Despite in consistency, 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/fu el type, road type) may be found in the electronic data submitta l (see description in Appendix A).

OVERVIEW OF METHODOLOGY TTI used the established MOVES-based, deta iled 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 SIP 8 and its VMT offset analysis component, 9 and as originally used to de velop 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 es timates 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 MOBI LE6, which applied emissions rates for all emissions processes in terms of mile s-traveled activity (e.g., grams/mile).

In addition to the VMT-based calculations of roadway-based emissions estimates, the TTI MOVES emissions inventory proces s 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 calcula tions. Previous versions of MOVES provided the off-network start, evaporative and extended idli ng rates only in “per vehicle” units, not applicable to the TTI activity-based inventory process; TTI post-processing utilities we re used to produce the MOVES off-network rates in the need ed 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 rate s required in the TTI inventory process are now directly avai lable from MOVES, except for th e SHP-based rates; these are produced using TTI inventory utilities, r ecently 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 HG B Eight-Hour Ozone Nonattainment Counties, TTI, January 2013.
10 Development and Production of 2006 Base Case and 2008 Baseline On-Road Mob ile 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 Abbreviation 1

11 Motorcycle MC 21 Passenger Car PC 31 Passenger Truck PT 32 Light Commercial Truck LCT 41 Intercity Bus IBus
42 Transit Bus TBus
43 School Bus SBus
51 Refuse Truck RT 52 Single Unit Short-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
18 1 Refueling Displacement Vapor Loss
19 1 Refueling Spillage Loss
90 2 Extended Idle Exhaust
91 2 Auxiliary Power Unit Exhaust

1 These emissions processes apply only to refueling emissions.
2 These emissions processes are associat ed 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 asso ciated processes and activity factors used.

Table 4. Emissions Rates by Process and Activity Factor.

Emissions Processes Activity 1 Emissions Factor 2 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 MOVE S, 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 proce ss requires development of the following major inventory components. All are i nputs to the emissions calculati ons, 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 li nk VMT and average speeds;

 County vehicle type populations;

 County, hourly vehicle type SHP;

 County, hourly vehicle type starts;

 County, hourly combination long-ha ul 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 pro cess these inventory com ponents are outlined and described in Appendix B, which also includes an inventory pr oduction process flow diagram.10 VMT Mix The VMT mix designates the vehicl e types included in the analysis , and specifies the fraction of on-road fleet VMT attributable to eac h 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-per iod, 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 fl eet parameter inputs needed in the process (e.g., vehicle age distributions). The current VMT mi x 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 ei ght-county HGB area (i.e., Houston and Beaumont districts). The data sources used were recent, multi-year TxDOT vehicle classifi cation counts, year-end TxDOT/DMV registration data, and MOVES default data, where needed.

On-Road Fleet Link-VMT and Speeds Summer weekday fleet VMT a nd average operational speed inputs to the roadway-based emissions calculations (product of “mass per mile” emissions f actors and VMT) were required.

TTI used data sets extracted from the latest , four-period, time-of-da y, directional, regional HGB travel models (data sets extracted and provided by the H-GAC), along with growth (for inventories between TDM years), seasonal adjust ment, and hourly allocation factors estimated by TTI to estimate the summer weekday hourly, di rectional, link-VMT (consistent with HPMS VMT) and associated average fleet speed input s 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 th e Houston area. The hourly average operational fleet speeds were estimated corresponding to th e link VMT estimates us ing the Houston speed model, which estimates operational speeds base d 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 evaporativ e 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 Tabl e 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 es timates on vehicle registration data, vehicle population factors derived from VMT mix, and VMT- based growth estimates (for future years). For historical years, the vehi cle type vehicle populati on estimates were based solely on mid-year TxDOT (or TxDMV) county registrations data a nd regional, all roadsweekday VMT mix-based population factors for the an alysis 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 populat ion factors applicable to the analysis year.

SHP : The SHP is estimated as a function of to tal hours (hours a vehicl e exists) minus its hours of operation on roads (SHO, which is the same as VHT). For histori cal years, the vehicle type SHP estimates were based on VMT mix, lin k VMT and speeds, and the vehicle population estimates. The VMT mix was applied to the link VMT to produce vehi cle type-specific VMT estimates. Link VMT was divided by the asso ciated 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 th e future year link VMT and speeds, VMT mix, and the vehicle population estimates.

Starts : Engine starts were based on the MOVES na tional 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 additi onal factors developed by TTI. Hotelling activity for a 2004 base year was derived from the 2004 idle ac tivity study summer weekda y extended idling hours estimates by Texas county. TTI used summer weekday 24-hour 2004 base hotelling estimates derived from this study in combination with 200 4 base year and invent ory analysis year link VMT and VMT mixes to produce county, hourly hotel ling activity estimates for each analysis year. Hotelling hourly factors (estimated by inve rting 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 input s to the emissions calculations in three basic steps: set up and execute the MOVES emissions rate mode runs; perform the initial postprocessing, which calculates particul ar rates in the form needed th at are not directly available from MOVES; and perform the final post-processi ng to screen out unneeded pollutants and make needed adjustments.

Local emissions factor mode ling input parameters were de veloped and used to produce emissions factors reflective of the local scenario conditions (e .g., weather and fleet characteristics, fuel properti es, and inspection and maintenan ce (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 a nd activity input/output (i .e., the mass/SHP form of off-network evaporative rate s not available from MOVES) and tabulated with the other MOVES-produced activity-based rates needed (ma ss/mile, mass/hour, mass/start) into databases of emissions rate look-up tables; and finally to extract the rates from the previous step for only12 those pollutants needed in the emissions calcula tions, and to make required adjustments (i.e., apply estimated TxLED effects on diesel vehicle NO x rates).

County-level hourly emissions factors were de veloped for the MOVES weekday day type for each year. Actual, local, scenario-specific activit y estimates for each county were then combined with the associated emissions rates in the emissi ons calculations outside of , or external to the MOVES model.

Emissions Calculations Average summer weekday emissions were calcul ated for each county using the previously described major inputs: TxDOT district-level time-of-day VM T 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, SH I and APU hours (for CLHT diesel only); and county-level look-up tables of hour ly 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 aver age speed were interpolated (see procedure in Appendix B) from the appropriate set of look-up table emissions factor s 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 speed s were used. The estimated vehicle type and MOVES road type link speed-s pecific 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 leve l, the vehicle type emissions factors were multiplied by the associat ed county total activity estimate, as determined by the pollutant process. This process was pe rformed 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 in cludes hourly and 24-hour summaries of the offnetwork activity, VMT, VHT, and speed by road way. 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 S CC-based inventory summar ies were converted to NEI CERS XML-type format for uploading to TCEQ’s TexAER. Appendix A contains more detailed output definiti ons and specifications.

TTI developed and maintains a series of co mputer utilities to calculate and summarize detailed on-road mobile source emissions inventorie s 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-base d 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 ar e 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 distri bute link VMT values to each vehicle category. Since the VMT mix can vary by time-of-day (and thus have an e ffect 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-w eek vehicle type VMT mixes (for gasolinepowered and diesel-powered vehicles) were es timated 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 Tx DOT District — This is the standard TxDOT classification data assembled and used for determining the in-use road fleet mix.

 Redefine Roadway Functional Classificati ons from FHWA/TxDOT to MOVES types — A straightforward transposition of FHWA/TxD OT roadway functional classifications in the classification count data into the five MOVES road types.

 Define MOVES vehicle categories. For exam ple, PV21 – Passenger vehicles equivalent to FHWA C minus .001 for MCs.

 Define MOVES vehicle cat egories - Passenger and Light Commercial Trucks — Separates FHWA light-truck category (P) in to passenger trucks and light commercial vehicles using approximate (rounded) MOVES default values.

 Define MOVES vehicle categ ories - Single-Unit Trucks RTF51 — These are refuse trucks. These are currently assigned a nom inal default value (.001) taken from the combined FHWA single-unit truck cate gory total (SU2, SU3, and SU4).

 Define MOVES vehicle categor ies - Single-Unit Trucks Shor t-Haul versus Long-Haul (SUSH52 and SULH53) per SUT_SSHX — Separate s 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 categ ories - Single-Unit Trucks MH54 — These are motor homes/recreational vehicles. These are curre ntly assigned a MOVES default value taken from the combined FHWA single-unit truc k category total (SU 2, SU3, and SU4).

 Define MOVES vehicle categ ories - 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 categor ies - Combination Trucks S hort-Haul versus Long-Haul (CSH61 and CLH62) — Separates combination trucks into short- haul and long-haul based on local (TxDOT district) registra tions versus observed vehicles from the classification counts. District allocations verified against st atewide allocation.

 Define MOVES vehicle categor ies - 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) — Othe r 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 databa se (though this latter option is not recommended). Note allocation of fuel type varies with analysis year.

 Fuel Type Allocation - Single Unit and Comb ination Trucks per Tx DOT registration data — As with PV and LDT, other fuel types currently treated as de minimus

.

 Aggregate and Calculate MOVES SUTs and a pply day-of-week factors from urban area classification count data (F riday, Saturday, and Sunday).

TxDOT district-level weekday vehicle t ype 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 registra tions data. Using the same data sets and a similar procedure, aggregate (i.e., all road-t ype categories) TxDOT district-level weekday vehicle type VMT mixes (used in the vehicle population estima tion) were also produced and included as Appendix D. To ensure general a pplicability 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 proc ess 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 char acteristic of a typica l summer weekday. The TRANSVMT utility (see Appendix B for a descripti on 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 zo nal radii data sets ex tracted 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 da ta 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 c ounty total HPMS Annual Average Daily Traffic (AADT) VMT was used to ensure the travel mo del VMT was consistent with the HPMS VMT estimates. (EPA and FHWA have endorsed HP MS 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 aver age daily basis (i.e., AADT). The counts are very well suited for making seasonal, day-of-wee k, 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 aggregati on levels, depending upon the purpose. This data source was used to produce the day-type-specific adjustment fact or, in which the data from the ATR stations within the Beaumont TxDOT District were co mbined for use with Chambers and Liberty counties and the ATR data within the Houst on 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 ti me-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 fo r those years), county-le vel VMT control totals were used to develop VMT adjustment factors, which were applied to the 2012 TDM. For the16 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, intermed iate year factors were deve loped using the bounding TDMs (i.e., 2012 and 2015) and applied to the an alysis year’s respec tive TDM. Hourly tr avel factors were also applied to distribute this adjusted VMT over each hour of the day.

Table 6. Analysis Year/T DM 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 w eekday VMT, county-level VMT control totals were used to develop county-level VMT adjust ment 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 fact ors were developed us ing 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 lo cated in the Beaumont TxDOT District) and one factor was developed for Harris, Galveston, Fort Bend, Braz oria, 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 tr affic count. Table 7 shows the HGB AADT-tosummer weekday factors used in de veloping the VMT control totals.

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

TxDOT District Weekday Adjustment Factor

Beaumont 1 1.07154

Houston 2 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 deve loped by multiplying the 2012 HPMS AADT VMT for each county by the appropriate summer wee kday adjustment factor to produce eight VMT control totals (one for each county). To devel op 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, th e volume was multiplied by the corresponding VMT adjustment factor (based on the county where th e link is located). Th e adjusted link volumes were then multiplied by the associated link lengths to produce the 2012 link-level HPMS consistent, summer weekday VMT estimates. Ta ble 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 To tals and VMT Adjustment Factors.

County VMT Control

Total TDM VMT 1 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 us ed in this analysis (0.905954) was provided directly by H-GAC. This factor was developed using the to tal 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 Adjustme nt 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 ac tual 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 – Se asonal Adjustment Factors For the future year analyses, seasonal adjustme nt 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 seas on summer weekday adjustment factors were developed. One factor was deve loped 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) tra ffic count. Table 9 shows the seasonal adjustment factors by TxDOT district.

Table 9. HGB Weekday Seasonal Adjustme nt Factors for Future Year Analyses.

TxDOT District Weekday Seasonal Adjustment Factor

Beaumont 1 1.03609

Houston 2 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 – Intermed iate 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 an alysis year VMT from an existing TDM. These adjustment factor s were developed usi ng 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 anal ysis 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 inte rmediate year adjustment factor s 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 fro m the range of TDM years encompassing the Target Year.

To maintain consistency between counties a nd 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 a nd 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 f actors (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 Tabl e 11 show the TDM and ozone season weekday VMT summaries.

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

County 2014 1 2017 2020 2

TDM 3 Weekday TDM 3 Weekday TDM 3 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 2023 1 2026 1 2028 1

TDM 2 Weekday TDM 2 Weekday TDM 2 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-c ounty HGB region. To maintain VMT proportions within each of the four assi gnment time periods, the hourly fractions were normalized within each time period to produce the time period hourly trav el 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 wee kday VMT estimates for each analysis year. These factors were also multiplied by the estim ated intrazonal VMT to produce the final hourlyadjusted VMT. Table 12 shows the wee kday time period hourly travel factors.21 Table 12. Weekday Time Period Hourly Travel Factors.
Assignment Hour Base Factor Time Period Factor 1

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 ce ntroid 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) a nd 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., applicatio n of these factors results in two speeds). The free-flow speed factors (grouped by functional cl ass and area type) were calculated by dividing the distance-weighted free-flow speed by the di stance-weighted input speed for each functional class/area type combination. The distance-wei ghted 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 (distan ce-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 ne twork 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 × HP MS factor × seasonal adjustment factor × hourly time period factor; Weekend profile factor is included for Saturday and Sunday); and Ch = the hourly link capacity (travel m odel capacity × hourly capacity factor). Appendix F shows the hour ly capacity factors.

After the V/C ratio was calculated, the link-spec ific 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-spec ific SRF was calculated using linear interpolation. For V/C ratios greater than 1.0, a SRF is not required.

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

S V/C = S 0.0 -SRF V/C × (S 0.0 - S 1.0 ) Where: S V/C = estimated directional speed for th e forecast V/C ratio on the link in the given direction; S 0.0 = estimated free-flow speed for the V/C ratio equal to 0.0; S 1.0 = estimated LOS E speed for the V/C ratio equal to 1.0; and SRF V/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:

S V/C = S 1.0 × (1.15/(1.0 + (0.15 × (v/c) 4 ))) Where: S v/c = estimated directional speed for the forecast V/C ratio on the link in the given direction; S 1.0 = estimated LOS E speed for the V/C ratio equal to 1.0; and v/c = the forecast V/C ratio on the link. The V/C ratio can be 1.0 to 1.5.

For V/C ratios greater than 1.5, the speed wa s calculated using the previous speed model extension, except the V/C ratio was set to 1.5.

These speed models were applied to all func tional classes excluding the centroid connector and intrazonal functional classes. For these functi onal 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 intr azonal functional class were estimated by zone as the average of the zone’s centroid connector speeds.

The hourly and 24-hour speed (VMT/VHT) su mmaries by county and road type were provided electronically to TCEQ (see Appe ndix 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 ma ss per SHI), county-level estimates of the SHP, starts, SHI, and APU hours are re quired by hour and vehicle type for each inventory year (SHI and APU hours are for diesel comb ination long-haul trucks only). One of the main components of the SHP and starts off-networ k activity estimation is the anal ysis year county-level vehicle population. Summaries of the ve hicle population and 24-hour SHP, starts, SHI, and APU hours off-network activity are include d as Appendix G. Hourly SHP, starts, SHI, and APU hours activity estimates are included with the detailed inventory data prov ided (see inventory data file descriptions in Appendix A).

The county-level vehicle population es timates were developed using the MOVESpopulationBuild utility. The county-le vel SHP, starts, SHI, and APU hours of offnetwork activity were develope d using the OffNetActCalc util ity. Appendix B contains a description of the utilities.

Estimation of Vehicle Population The vehicle type population estima tes are needed to estimate the SHP and starts off-network activity. The vehicle population estimates (inc luded 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), populati on 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 y ear was considered a hi storical year and the vehicle population estimates were based on the Tx DMV 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 futu re analysis years (i.e., TxDMV registration data and HPMS VMT data do not exist). For the fu ture 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 popul ation is the aggregat e (i.e., all road-type categories) TxDOT district-level weekday VM T 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 A ppendix 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 vehicl e registrations data. This process also estimates the long-haul category populations as an expans ion of the county registrations. There are three main steps in the vehicle population estimation process: registration data cate gory aggregation, calculation of the vehicle type population fact ors, and estimation of the county-level vehicle population by vehicle type.

The first step in the vehicle estimation proce ss is the registration data category aggregation. For each county, the analysis year vehicle registra tions 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 calculati ng the vehicle type population factors. Using the assigned aggregate VMT mix, population factors were calcul ated 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 data25 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 we re calculated by taking the rati o of the long-haul and shorthaul VMT mix values. For example, the SULh T_Gas population factor us ing 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 Aggre gations and Associated Vehicle Types for Estimating Vehicle Populations. Vehicle Registration 1 Aggregation Associated Vehicle Type 2
Motorcycles MC_Gas Passenger Cars (PC) PC_Gas; PC_Diesel
Trucks <= 8.5 K GVWR (pounds) PT_Gas; PT_Diesel; LCT_Gas; LCT_Diesel

Trucks > 8.5 and <= 19.5 K GVWR RT_Gas; RT_Diesel SUShT_Gas; SUShT_Diesel MH_Gas; MH_Diesel IBus_Diesel TBus_Gas; TBus_Diesel SBus_Gas; SBus_Diesel Trucks > 19.5 K GVWR CShT_Gas; CShT_Diesel

NA 1 SULhT_Gas; SULhT_Diesel CLhT_Gas; CLhT_Diesel

1 The four long-haul SUT/fuel type populations are estimated using a long-haul-to-short-haul 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 c ounty-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 ca lculated as the produc t 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 vehicl e 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 th e analysis year registra tion data sets, the most recent (2012) mid-year TxDMV registration data se ts for which HPMS data exists were used. Using these registration data se ts and the assigned VMT mix, th e 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 ap plied to the base vehicle type population for 2012. These future year county-le vel vehicle population scaling factors were calculated as the ratio of the county-level weekda y 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 increase s linearly with VMT).

Estimation of SHP The first activity measure needed to estimat e 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-le vel 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 leve l, the vehicle type tota l available hours was set equal to the vehicle type popul ation. 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 es timates of SHP by hour and vehicle type for each analysis year (hourly summaries were pr ovided electronically to TCEQ; see Appendix A for electronic data descriptions).

Vehicle Type Total Available Hours The vehicle type total available hours is typically calcu lated 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 av ailable 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 li nk, 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 emi ssions 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 va lue 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 star ts by hour and vehicle type for each analysis year. The vehicle type hourly defa ult starts per vehicle were multiplied by the analysis year county-level vehi cle 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 (hou rly summaries were provided electronically to TCEQ; see Appendix A for elect ronic data descriptions).

For the hourly default starts per vehicle, the MOVES defaults were used. The MOVES activity output was used to es timate the hourly starts per ve hicle for a MOVES weekday run by dividing the MOVES start outpu t by the MOVES vehicle populat ion output. These MOVES27 national default starts per vehicle do not vary by year, only by MOVES day type. For this summer weekday analysis, the MOVES national defa ult “weekday” starts per vehicle were used.

Estimation of SHI and APU Hours The remaining activity measures needed to esti mate the off-network emissions using the mass per activity emissions rates are the hourly, county-level weekda y 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, th e truck’s main engine is assumed to be in idling mode or its auxiliary power unit is in use. For each analys is year, hotelling hours activ ity are first estimated, and then it is allocated to the SHI and APU hours components.

The hotelling activity was base d on information from a TCEQ extended idling study, which produced 2004 summer weekday extended idling es timates for each Texas county, and hotelling activity data from MOVES. Hote lling scaling factors (by analysis year) were applie d to the base 2004 summer weekday hotelling values from th e study to estimate the 24-hour hotelling by analysis year. Hotelling hourly factors were th en applied to allocate the 24-hour hotelling by analysis year to each hour of the day. To ensure valid hourly hotelli ng values are used, the hourly hotelling activity was comp ared 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 th e county-level estimates of hotelling hours, SHI, and APU hours for each analysis year (hourl y summaries were provided electronically to TCEQ; see Appendix A for elect ronic data descriptions).

Hotelling Activity Scaling Factors To estimate the county-level 24-hour hotelling ac tivity by analysis year, county-level hotelling activity scaling factors were developed using the county-leve l 2004 summer weekday link-level VMT and speeds, the TxDOT district-level ba se weekday vehicle t ype VMT mix (by MOVES road type), the county-level analysis year w eekday 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 proce ss similar to the historical analysis year summer weekday link-level VM T 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 weekda y 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 ho ur to produce the CLhT_Diesel hourly and 24- hour 2004 summer weekday VMT. Using a simila r allocation process, the analysis year CLhT_Diesel hourly and 24-hour VMT was calcula ted using the analysis year weekday linklevel VMT and speeds and the analysis year ve hicle type VMT mix. The county-level 24-hour hotelling activity scaling factors by analysis year were calculate d by dividing the analysis year and day type CLhT_Diesel 24-hour VMT by th e CLhT_Diesel 24-hour 2004 summer weekday VMT.28 Hotelling Activity Hourly Factors To allocate the analysis year weekday county-level 24-hour hotelli ng activity to each hour of the day, hotelling activity hour ly factors were used. These hot elling 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. Th e hourly analysis year weekday CLhT_Diesel VMT was converted to hourly fractions, therefor e 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-lev el analysis year weekday hot elling activity hourly factors.

County-Level CLhT_Diesel Hotellin g Activity by Hour Estimation The initial analysis year wee kday CLhT_Diesel hotelling activity by hour was calculated by multiplying the 24-hour 2004 summer weekday hotel ling hours by the analysis year hotelling activity scaling factor an d by the analysis year hotelling ac tivity hourly factors. For each hour, the initial analysis year weekday hotelling activ ity 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 hotel ling activity value was grea ter than the analysis year weekday SHP value, then the final analys is year weekday hotelling activity for that hour was set to the analysis year wee kday CLhT_Diesel SHP value. Othe rwise, the final analysis year weekday hotelling activity for that hour was set to the base analysis year weekday hotelling activity value. All calculations (scaling factor s, hotelling activity hourly factors, and hotelling activity by hour calculati ons) were performed by county and anal ysis year (i.e., eight hotelling activity scaling factors were ca lculated per analysis year).

County-Level CLhT_Diesel SH I 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 ex tended idle mode and APU mode fractions. For each hour, the analysis year hotelling activit y was multiplied by the SHI fraction to calculate the analysis year hourly SHI ac tivity 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 dist ribution and relative mileage accumulation rates used in the MOVES runs) and the MOVES default hotelli ng 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 tr avel 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 fact ors 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 produc ing 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 MOVES 15 and the EPA’s MOVES inventory development Technical Guidance 16 and User’s Guide. 17 (More information may found in these ma in references, if desired.) TTI’s MOVES data post-processing utilities, RatesCalc and Rate sAdj used to produce databases of emissions rate look-up tables for input to the emissions calculations, are al so summarized in Appendix B of this Technical Report.

The detailed link-based emissi ons estimation method of analysis requires emissions rates by speed in look-up table form. Emissions rate m ode was therefore used to produce emissions rates indexed by 16 MOVES speed bin average speeds. A nother 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-netw ork mass per vehicle rates were used. Table 15 (which was included in a previous section, but is provided again here fo r convenience) shows the emissions rates types and associated activity factors.

Table 15. Emissions Rates by Process and Activity Factor.

Emissions Processes Activity 1 Emissions Factor 2 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 MOVE S, 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 Inven tory Development Methodo logies 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 mode ling 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 we re available and consis tent with the methodology, MOVES defaults were replaced by local input va lues via the MOVES Ru n Specification input file (RunSpec or MRS) and M OVES CDB (county input database ). (The MRS, CDB, and MOVES default database provide the data for ea ch 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 emi ssions control programs. In the case of the activity input data to MOVES, the MOVES defaults were in genera l used, which is basic to the emissions rates method (e.g., default activity is no rmalized 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 a nd analysis year, and a corresponding number of CDBs and output databases (i.e., one output database per run). Th erefore, for eight counties and seven calendar years there are 56 each RunS pec input files, CDB inputs, MOVES output databases, RatesCalc output databases, and Rate sAdj final rate output databases. RatesAdj produces the final rates by performing NO x TxLED effect adjustments to diesel vehicle NO x rates and extracting and storing the rates for th e 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 a nd modeling approaches (e.g., MOVES model defaults versus alternativ e local inputs, or post-pr ocessing 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 NO x 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 NO x rates – diesel vehicle NO x emissions factors for all counties were adjusted using evaluation year and SUT-specific average NO x reduction factors produced following TCEQ’s procedure (which calculates the average NO x 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 aggr egation levels that may be specified in the MRS. The detailed, hourly, link-based inve ntory method required MOVES weekday day type emissions rates at the follow ing 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 M OVES 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, Ur ban Restricted Access, and Urban Unrestricted Access. 18 The rates for each of the actual f our 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 categor ies, vehicle and fuel types, pollutants and emissions processes, and the overall scale a nd level of output detail for the modeling scenario. TTI created an MRS for one count y 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 us ed, with further details on the selections provided after the table.

18 The “separate ramps” feature intend ed for MOVES2014 October Release wa s 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

Scale 1 Model; Domain/Scale; Calculation Type On-Road; County; Emissions Rates Time Spans 1 Time Aggregation Level; Years – Months – Days – Hours Hour; 2012 1 - July - Weekday - All Geographic

Bounds 1 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

Processes 2 Total Energy Consumption; VOC; CO; NO x ; Atmospheric CO 2 ; SO 2 ; NH 3 ; PM 10 Total Exhaust, Brakewear, Tirewear; PM 2.5 Total

Exhaust, Brakewear, Tirewear, and PM 2.5 exhaust species NonECPM, EC, OC, and SO 4 Dependent on pollutant: Running Exhaust, Start Exhaust, Ex tended 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-Pr ogress 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 ch eck 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: CO 2 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 Rate s” was selected for MOVES to produce the emissions rates with speed bin indexing, as n eeded 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 a ll hours of the day, for the selected year, month, and day type. One analysis year per run was selected, as M OVES allows only one “Years” selection for the County Domain Scale. For TTI’s MOVES-based li nk 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 sc enario-specific input data for the county was specified as the County Domain Input Database, and under Regi on, “Zone & Link” was selected as required for the emissions rates calculation ty pe. With these require d 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 defi ne the SUT/fuel type combinations included in the MOVES runs. The VMT mixes specify th e 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 Ru nSpecs. Note that, as required, the MOVES default fuel engine fractions (dis cussed later) were also replaced w ith local input data consistent with the SUT/fuel type selections shown in Table 17.

All five MOVES road type categories were se lected (the “provide separate ramps output” box is not active when using emissions rates m ode in the MOVES2014 October Release model).

Pollutants and Processes In addition to the required pollutants within the scope of the invent ory, MOVES requires that additional pollutants be selected for particular “chained” pollut ants (i.e., pollutants that are calculated as a function of anothe r MOVES pollutant). Of the pollu tants 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 CO 2 and SO 4 ). All of the associated processes available by the selected pollutant s were included, includ ing the two refueling emissions processes (although emissions for th ese “area source” category processes were not estimated for this inventory).

Manage Input Data Sets and Strategies The Manage Input Datasets feature allows alternat e inputs other than thos e 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 analysis35 Output The output units were pounds, kilojoules, and mile s. The activity categories were pre-set by MOVES (and not adjustable) for inclusion in the output database. The out put 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 count y scale runs were en tered through the CDB.

TTI developed procedures to accommodate building and checking CDBs for large scale emissions inventory estimation projects. The ba sic procedure was to write a MySQL script to produce one county scenario CDB a nd convert it to a template from which all of the CDB scripts were built. The MySQL scripts were then r un in batch mode to produce all CDBs for the analysis.

Data for populating the CDBs were first prepar ed 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 activ ity data). After running the scripts to produce the CDBs, a CDB checker ut ility written by TTI was run to verify that all CDB tables were built and populated as intended.

Table 18 provides an outline and brief descrip tion 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 st ate (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). roadtype 1 Activity Lists the MOVES road types and asso ciated ramp activity fractions. Road type ramp fractions were set to 0. hpmsvtypeyear 2

Activity Used MOVES default national annual VMT by HPMS vehicle type. roadtypedistribution 2 Used MOVES default road type VMT fractions.

monthvmtfraction 2 Used MOVES default month VMT fractions.

dayvmtfraction 2 Used MOVES default day VMT fractions.
hourvmtfraction 2 Used MOVES default hour VMT fractions. avgspeeddistribution 2 Used MOVES default average speed distributions. sourcetypeyear 2 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 fracti ons using TxDOT/TxDMV vehicle registration data and defaults where needed. Local data sets used by analysis year were consistent w ith sourcetypeagedistribution tables. zone Activity Start, idle, and SHP zone allocation factors. County = zone, and all factors were set to 1.0 (require d for county scale analyses). zoneroadtype Activity SHO zone/roadtype allocation factors. County = zone, and all factors were set to 1.0 (require d for county scale analyses). fuelsupply Fuel The fuel supply, or market shar es, reflected one gasoline and one diesel fuel formulation fo r 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 se t-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 Unre stricted 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 Specifi c Inputs and Defaults Used All inputs discussed in this sec tion are input via the CDB. Un less 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 y ear, state, and county of the run.

The yearID field of the “year” table was populated with the analys is year value, and the year was set as a base year (to specify that particular user-input fleet a nd activity data were to be used, rather than forecast by MOVES during the model r uns). As part of designating the appropriate fuel supply for the modeling scenario, the fuelyearID in the year table was al so 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 co unty 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 ve hicle population input parameters under the methodology use the MOVES defaults. The tables are: hpmsvtypeyear, roadtypedistribution, mont hvmtfaction, dayvmtfraction, hourvmtfraction, avgspeeddistribution, and sourcetypeyear. Data for al l of these tables were selected and inserted from the MOVES default database.

The zone and zoneroadtype ta bles contain zonal sub-allocati on activity factors. For county scale analyses, county is equal to zone, therefore these alloca tion factors were set to 1.0.

Age Distributions and Fuel Engi ne Fractions Inputs to MOVES – The locality-specific fleet characteristics inputs to MOVES consist of county age distributi ons input datasets and statewide fuel engine fractions (formerly known as diesel sales fr actions) input data sets. The age distributions and fuel engine fractions inputs were calculate d and written to text files in preparation for loading the data into the appropriate CDB input tables: the sourcetypeagedistribu tion table for vehicle age distributions , and the AVFT table for fuel engine fractions. The MOVESfleetInputBu ild utility was used to produ ce these fleet inputs to MOVES in the required formats (see utility description in Appendix B), a nd MySQL scripts were used to populate the CDB input tables.38 The age distributions and fuel engine fr actions were based on TxDMV mid-year county registrations data and MOVES model defaults, wh ere needed. The fuel engine fractions were developed consistent with the local VMT mi x 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 ag e distributions were produced based on the TxDMV county vehicle registra tion category aggregations, c onsistent with the vehicle registration category aggregati ons of the VMT mix (see Appendi x B). Appendix H includes the age distributions and fuel e ngine fractions summaries.

Table 19 summarizes the data sources and aggr egation 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 Category 1 Aggregations for Age Distributions and Fuel Engine Fractions Geographic Aggregation for Age Distributions Geographic Aggregation for

Fuel Engine

Fractions 2

Motorcycle 11 Motorcycles County NA – 100 percent gas, no Fuel Engine Fractions Passenger Car 21 Passenger County MOVES default 2
Passenger Truck 31 Total Truc ks<=8500 County MOVES default 2 Light Commercial Truck 32 Total Trucks<=8500 County MOVES default 2
Single Unit ShortHaul Truck 52 >8500+ >10000+ >14000+>16000 HGB Region Texas StatewideSingle Unit LongHaul Truck 53 >8500+ >10000+ >14000+>16000 Texas Statewide Texas Statewide

Refuse Truck 51

MOVES default Motor Home 54 Intercity Bus 41

Transit Bus 2 42

School Bus 43
Combination Short-Haul Truck 61 >19500+ >26000+ >33000+ >60000 HGB Region Texas Statewide

Combination Long-Haul Truck 62 >19500+ >26000+ >33000+ >60000 Texas statewide NA – 100 percent diesel, no Fuel Engine Fractions

1 TxDOT mid-year 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 MOVE S2014 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 re lative humidity) tables. These input data were developed as June 1 through August 31, 20 12 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 Houst on, TX. Altitude, also an input of the county table, was set to “low” for all counties. Table 20 summarizes the temperatures, relative humidity, and barometr ic 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 summ er season fuel supply for each county and year consisted of one average RFG and one average di esel 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 tabl es (fuelformulation and fu elsupply), using IDs for the local fuel formulations outside the range ofMOVES default fuel formulation IDs. Countyfuel scenario data were sele cted from storage and inserted into the CDB fuelsupply and fuelformulation tables, and converted fr om 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 per tinent fuelRegionID [1370011000], a nd 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 fiel d values to zero. (This was to prevent MOVES from applying its default fu els 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 sh ares of the specified locally developed fuel formulations for the analysis), renaming countyID field as fu elRegionID (as newly required with MOVES2014), inserted the da ta into the CDB fuelsupply table, and updated fuelRegionID value (from the M OVES2010b-based FIPS county code to the required MOVES2014 fuelRegionID).

 Selected all of the local fuel formulation r ecords for the county-fuel scenario from the storage database, where stored fuelformula tionID corresponded with the CDB fuelsupply table’s fuelformulationID, and inserted the data into the CDB fuelformulation table.

The MOVES2014 fuel property fields and un its (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 we re 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, BioDiese lEsterVolume, 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 sa mples 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 fu el property inputs by y ear, 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 Hous ton summer 2014 data set (l atest 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 co ntent, data sources in clude 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 Formulati ons Inputs from RFG Survey Samples – On average, each summer period Houston survey data set incl uded 263 total samples taken during June 1 – September 15 (by grade: regular – 237, mid-grade – 6, and prem ium – 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 co mbining 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 th rough 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 wa s 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 i nput consists basically of average sulfur content. (The effects of TxLED were incorporated by emissions factor post-processing, discussed later.) The 2012 aver age sulfur content values we re 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 B eaumont District counties. The 2014 values, taken from the TCEQ summer 2014 survey summary, for the Houst on 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 2012 1 2014 2 2017 and Later Years 3

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 ad ministered to reduce gasoline vehicle emissions in five of the eight HGB c ounties. MOVES calculate s county emissions rates that reflect the emissions-reduci ng 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 M OVES 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 sin ce 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 su bject to the program);

 IMProgramID (arbitrary ID number);

 begModelYearID (first model year covered);

 endModelYearID (last model year covered);

 inspectFreq (inspection fr equency 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 se t of Texas counties imcoverage table input records by changing all IMprogramID values to exclude all MOVES default IMprogramIDs (the adjustment used was IMprogram ID = IMprogramID + 100). The new set of Texas imcoverage table records for all MOVES analysis years were st ored 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 differe nce of 0, i.e., no I/M effects.46 In addition to selecting the appropriate loca l 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 imc overage inputs, the follo wing two general steps were performed for all eight counties:

 Selected and inserted all MOVES default imc overage records for the scenario’s countyID and yearID into the CDB imcoverage tabl e, 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 c overage 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., fo r 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 c overage window (motorcycles, military tactical vehicles, diesel-powered vehicles, and antique vehicles are excluded). A gas cap integrity test is required on all these vehicl es, 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 a pplicable to Texas (see Table 22, column 5).

 Queried the MOVES default imfactor table (c ontains adjustments to emissions rates per various I/M scenarios by SUT/fuel type, ag e, 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 eff ects) 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 combinati on of test standard, test fr equency, begin model year, and end model year.

Table 22 and the associated table notes desc ribe the MOVES imcoverage records developed and used for the HGB analyses. Note that a re view of the pertinent MO VES 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 vehi cle categories (i.e., vehicles with GVWR > 8,500 pounds – see Table 22, note 4) except for evaporat ive tank vapor venting for a small fraction of the heavy-duty class that MOVES2014 attri butes to SUTs 31 and 32 (passenger and light commercial trucks), which are characterized main ly 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 standard s 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). Fo r this evaporative process, MOVES, however, includes non-zero I/M factors only for SUTs 21, 31, and 32, and attr ibutes 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, th e 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. Th is may result in an overall conservativ e I/M effects estimate, since excluding a heavy-duty exhaust I/M effect in SUTs 31 and 32 effec tively 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.

YearID 1 IMprogamID 2 begModel YearID 3 endModel YearID 3 testStandardsID SourcetypeID 4

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 adjust ments, 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 ra te, and the regulatory class coverage adjustment. The current Texas I/M program modeling protocol compliance an d 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, NO x , and tank vapor venting THC.
Note that the updated activity estimates in MOVES2014 affect the regulatory class activity proportions associated with each SUT. The regul atory class coverage ad justments (see Table 22, note 2) should be updated based on the new gui dance (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 pr eparation, 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 subseque nt 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-proces sed using a two-step process – for each county and year, an interim Rate sCalc rate database was produ ced, followed by the final rates RatesAdj database containing th e emissions rate tables for in put 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: Us ing 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 ra tepervehicle emissions rate output. The utility also copied the mass/mile, mass/star t, 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 proce sses. 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 f our emissions rate look-up tables produced for each scenario: ttirateperdistance, ttirateperst art, ttirateperhour (for extended idle and auxiliary power exhaust), and ttiratepershp.

 RatesAdj – Final Rate Databases: Using Rate sAdj, 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 pollu tants needed in the emissions calculations, and applied TxLED adjustments (see factor s in Table 23) to the diesel vehicle NO x 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, ttirateper hour, and ttiratepershp.

See Appendix B for more information on the TTI MOVES on-road rates development utilities.

NO x Adjustment for TxLED Effects TTI produced the TxLED NO x adjustment factors shown in Table 23 according to a TCEQ procedure that produces each dies el-powered SUT’s average adjust ment 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 NO x adjustment factors (across all model years). The resulting average NO x adjustment factors (by diesel SUT and calendar year of evaluation) are especially sensitive to the ag e distributions and calendar year inputs, which directly affect the relative NO x emissions contributions between the earlier and later model year groups. For 2012, Texas statewide vehicle age distri bution estimates were used that were based on mid-year 2012 statewide TxDM V 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 NO x Adjustment Factors 1

2012 2014 2017 2020 2023 2026 2028
Passenger Car 0.9413 0.94 39 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 elec tronic data submittal for th is inventory analysis, which includes the MRS files, CDBs, TxLED adjustment factor files used and calculation spreadsheets, and the final adjusted emissions rate look-up data base table inputs to the emissions calculations.

EMISSIONS CALCULATIONS TTI calculated hourly, summer weekday, link-ba sed emissions inventories by county for each year using the EmsCalc utility. Emissions calcul ations fall into two categories: VMT-based and off-network. The VMT-based emissions calculati ons 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 calc ulations 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 inve ntory 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 inpu t to TTI’s MOVESsccXMLformat utility, which converted them into an EPA CERS XML inventor y 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-l evel, 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 intrazona l 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 (functiona l class) code, link length, congested (operational) spee d, 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, st arts, SHI, and APU hours by vehicle type;

 Off-network (parked vehicl e) emissions factors – M OVES-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 ra tes road type codes) (see Table 24); and

 On-road SCCs – from the MOVES2014 defau lt database corresponding to MOVES SUT, fuel type, road type, and em issions process categories.

The VMT-based emissions were calculated for each hour using the TxDOT district-level vehicle type VMT mix with ti me 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 desi gnations. For each link, the link was assigned a MOVES road type (and VMT mix road type and rate s 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 Tx DOT district, and time-of-day period to hourof-day designation.

The emissions factors for each vehicle type fo r each hour were select ed based on the link’s designated rates road type code (same as MOVE S road type code) and the link speed. For link52 speeds falling between MOVES speed bin average speeds, emissions factor s were interpolated from bounding speeds. For link speeds falling outs ide of the MOVES speed range (less than 2.5 mph and greater than 75 mph), th e emissions factors for the a ssociated 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
2 – Rural Restricted Access 10 - Rural Interstate 5 – Rural 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 an alysis, are identical to th e 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 were54 multiplied by the appropriate off-network activ ity, as determined by the pollutant processactivity association (shown previously in Table 15).

The EmsCalc utility outputs for the analys is consist of the listing file (summarizing information regarding running the utility), the st andard 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 M OVES SCC tab-delimited 24-hour inventory report summary.

Conversion of Emissions In ventories to XML Format TTI converted the 24-hour emissions and activity resu lts 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 th at the new SCCs and the NEI pollutant codes available in the MOVES2014 default databa se (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 la bels (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 – specifi es 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-produce d, MOVES SCC-based, 24- hour emissions inventory summary file for each county to be include d in the XML file. Contains a county code record and the following fields: S CC, Activity Type, Activity, followed by an emissions field for each polluta nt using field header s 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 c oded inventories for all eight counties. Thus, seven XML files were produced, one for each invent ory year. The SCCs are 10 digits, composed of “22” (code for on-road mobile source) foll owed respectively by the four, two digit MOVES category IDs: fuelTypeID, sourceTypeID, roadType ID, processID. Thus, all these dimensions were retained in the XML inventory summar y, although off-network activity codes were not available. In the absence of EIS codes for o ff-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, NO x , SO 2 , NH 3 , PM 2.5 , PM 10 (exhaust, tirewear and brakewear), and CO 2 . Each run produced a LST file (execution information, input/output file listings, input summaries, and input and output totals summarie s with calculated differences for QA checks), the XML file, and one XML output summary of SCC-labeled inve ntory data in a tab-delimited55 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 MO VES Inventory Mode The MOVES CDBs used to produce emissions rate s for the link-based inve ntory analyses were designed only for use in MOVES rates mode ru ns. TTI developed an extra set of CDBs consistent with the link-based inventory data th at may be used with MOVES in the inventory mode. These inventory mode CD Bs were designed to input su mmer 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 analys is rates mode CDBs (see Tab l e 18), plus six additional tables (hotellingactivitydistribution, hotellinghours, sourcetypeage, starts, monthofanyyear, and dayofanyweek).

The inventory mode CDB da ta source categories are:

 Rates CDB (mainly local data directly from the link-based invent ory analysis rates CDBs);

 MOVESactivityInputBuild utility output (for ac tivity inputs built from the pertinent linkbased inventory data);

 OffNetActCalc utility output with adju stments (for hotelli nghours, starts);

 VehiclePopulationBuild u tility output (for vehi cle population estimates);

 MOVES defaults (only for hotellingactivitydistribution), and

 Adjusted MOVES defaults (for activity alloca tion factors modified as needed to produce daily output from daily ac tivity input, and for sour cetypeage table relativeMAR adjustments to produce VMT proportions between HPMS vehicle categories that more closely reflect the local VMT Mix).

The inventory development utilities descri ptions, 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 MOVESactiv ityInputBuild 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 hotellingactivitydistribut ion MOVES defaults

hotellinghours OffNetActCalc utility output with travel fractions (developed using the age distribu tion 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 MOVESactiv ityInputBuild utility output

sourcetypeage MOVES default with relativeMAR adjusted for VMT Mix (travel fractions calculated using relativ eMAR adjusted to match 24-hour VMT from link-level inventory) sourcetypeagedistri bution Rates CDB sourcetypeyear VehiclePopulat ionBuild utility output

starts OffNetActCalc utility output w ith age distribution applied to distribute to ageID
state Rates CDB year Rates CDB
zone Rates CDB
zonemonthhour Rates CDB zoneroadtype Rates CDB monthofanyyear MOVES defa ult with noOfDay = 7 dayofanyweek MOVES default with noOfRealDays = 157 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 emi ssions calculation process flow diagram.

QUALITY ASSURANCE Analyses and results were subjected to appropr iate internal review and QA/QC procedures, including independent verification and reasona bleness checks. All work was completed consistent with applicable el ements of ANSI/ASQ E4-2004: Specifications and Guidelines for Quality Systems for Environmental Data Collection and Technology Programs and the TCEQ Quality Management Plan.

Quality Assurance Project Plan (QAPP) Ca tegory II (Modeling for NAAQS Compliance) is the QAPP category that most closely matc hes 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 a nd 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 prev iously in the Acknow ledgements 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 certifica tions were required. The TTI project manager assured that the appropriate pr oject personnel had and used the most current, approved version of the QAPP.

After receiving the Notice to Commence (N TC) 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-analys is plan, the TTI project manager distributed the pre-analysis plan to the TTI inventory devel opers for use in both the invent ory 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 in ventory product in the quality suited to its purpose as specified (i.e., inventories needed for HGB re-designati on analyses purposes), informed by, and consistent w ith, 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 proce ss 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-desi gnation 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 a nd end-product quality ch ecks (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 measuremen ts) 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 fact ors, and for development of the activity inputs for external emissions calculations. These em issions 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 developm ent utilities used, which were lis ted 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 Planni ng Organization (MPO) or C ouncil of Governments (COG), TCEQ, and/or the EPA, and in most cases were QA’d by the providing agen cy. The data needed may include: HPMS data (from TxDOT’s Roadwa y Inventory Functional Classification Record [RIFCREC] report); regional trav el demand model data; speed m odel data; vehicle registration data; ATR data; vehicle classi fication count data; meteorolog ical data; fuels data; MOVES emissions model data; extended idling activity data; and vehicle I/M program design data.

Any significant problems f ound during data review, verifica tion, and/or validation (see QA criteria and methods discussion in section D) were to be co rrected, and the QA procedure was repeated until satisfied. No significant problems were found.

Data Management: TTI emissions inventory data devel opers work as a closely coordinated team. The assigned staff used the same electr onic project folder structure on their individual workstations. As various scripts, inputs, and outputs were developed in the emissions inventory59 development process, data were sh ared within the team for crossc hecking via an intra-net, flash drive, or external hard drive. To perform th e 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 activit ies 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 in put data and batch files (i.e., R un Files) were QA’d, they were either executed on an individual work station, 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 lo g text files were (for cluste r runs first copied to an individual workstation), archived and processed further in prepar ation for input to the emissions calculations.

After the final product was completed, all the pr oject data archives we re compiled on a set of optical data discs (CD-ROM or DVD, depending on size), or on an ex ternal drive for very large project data sets. A complete ar chive of the project data is ke pt by TTI (the computer models and emissions inventory developmen t utilities used in the proce ss are included). An electronic data submittal package (containing the projec t deliverables as listed in Appendix A) was produced along with data descriptions (on CD-R OM, 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 (c onsistent with the intended purpose, for specified temporal resolution and geographic coverage, for sp ecified 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 accord ing to the plan; and

 Checked that correct output data were pr oduced (includes interi m output [output that becomes input to a subsequent step in the i nventory development process], as well as the final product). Records were ke pt of the checks performed.

In the case that any inconsistencies or de ficiencies were found, th e issue was directly communicated to the responsible staff for correc tions (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 reco rd (process was performed until QA check was satisfied).

In addition, technical systems aud its were performed as appropriate . Audits of data quality at the requisite 25% level were perf ormed 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 pr ocess flow with performance of the integrated QA checks at each step of the process was of the ut most 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 emissi ons 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., anal ysis years, geographi c coverage, seasonal periods, days, sources, pollutants);

 Methods, models, and data used (e.g., defau lt 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 preparati on, model or utility execution instructions (e.g., run specifications, scripts, JCFs, command files) , and output, as appropria te to the component:

 Input data preparation checks:

o Verified the basis of input da ta sets against the pre-analysis plan: Actual historical or latest available data, validated mode l, expected values or regulated limits, regulatory program design, model defaults , surrogates, professional judgment; aggregation levels;

o Data development: Depending on the proced ure and particular input data set, calculations were verified (e.g., re-calculated independently and compared with originally prepared values – if spot-checki ng a series of results, included extremes and intermediate values);

o Completeness: Verified that input data sets were with in the required dimensions, and all required fields were populated and properly coded or labeled;

o Format: Verified that formats were w ithin required specifications (e.g., field positions, data types and formats, and file formats), if any;

o Reasonability checks: (discusse d in the next section); and

o Ensured that any inputs provided from exte rnal sources were quality assured, as listed previously.

 Checked the model or utili ty 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); and61 o Verified that each utility or model r un script included the correct modeling specifications (e.g., commands, input values , input and output file paths, output options) for the application pe r applicable user guide.

 Checked for the successful completio n 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 file s 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, co mpleteness, 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 with in a time period, proportion of travel by vehicle categories on a part icular 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 activ ity 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 origin al aggregate totals, activity total summaries be tween utility applications [e.g., link-VMT producer and emissions calculator], and input hourly distri butions versus hourly summaries from the link activity output data);

 Calculated county, 24-hour, aggregate em issions rates (from aggregate VMT and emissions output) and compared the rates between counties examining the results for outliers while assessing the reasonability of a ny relative and directional differences (e.g., qualify based on activity distribut ions 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 rate s 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, Tr ansitioning 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 Commissi on 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. Environmen tal Protection Agency. March 2009.

Emissions Inventory Guidance for Implementa tion of Ozone and Particulate Matter National Ambient Air Quality Standards (N AAQS) and Regional Haze Regulations . EPA-454/R-05- 001, issued by the Emissions Inventory Gr oup, 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 Qualit y. 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), Nationa l 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 Plan ning 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 Utili ties Using MOVES: MOVES2010bUTL User’s Guide

. Prepared for the Texas Commission on Enviro nmental Quality. Texas A&M Transportation Institute, The Texas A&M University Sy stem, College Station, TX. August 2013.

Revision to the State Implementation Pl an 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 Utili ties Using MOVES: MOVES2010bUTL User’s Guide

. Prepared for the Texas Commission on Enviro nmental Quality. Texas A&M Transportation Institute, The Texas A&M University Sy stem, College Station, TX. August 2014.

Update of On-road Inventory Development Me thodologies for Compatibility with the 2014 Version of the Motor Vehicl e 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 Invento ries 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. Envi ronmental Protection Agency. April 2012.

VMT-Offset Emissions Inventories for the HGB Eight-Hour Ozone Nonattainment Counties

. Prepared for the Texas Commission on Enviro nmental Quality. Texas A&M Transportation Institute, The Texas A&M University Sy stem, College Station, TX. January 2013.65 

### APPENDIX A: ELECTRONIC DATA SUBMITTAL67 Electronic Data Submittal — HGB MOVES2 014 Inventories for the 1997 Eight-Hour Ozone Re-Designation Analysis

Described herein is the electronic data submitta l for one of two HGB inventory analyses TTI submitted to TCEQ, per Proposal for Gran t 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, NO x , PM 10 , PM 2.5 , SO 2 , NH 3 , and CO 2 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 i nventory development); and

 Additional data files (MOVE S CDBs developed for use in MOVES model inventory mode runs, vehicle population summar ies, 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, Mont gomery, 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”) comp ressed in “hgb8hr97_mvs14_emscalc.zip:”

 “hgb8hr97_mvs14_ FFFF

_ YYYY swkd_ems.TAB” are tab-delimite d text files of county emissions inventory data summary reports including hourly an d 24-hour activity and emissions tables. By roadway and vehicle type (SUT/Fuel Type), the reports include VMT, VHT, average speed (VMT/VHT), and po llutant/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

_ YYYY swkd_sccoutput_ems.TAB” are tab-delimited 24-hour inventory summaries by on-road SCCs availa ble 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 head ers: 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 th e “*.TABs,” listing the run execution times, run script, file locations, data de scriptions, 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 aggrega tions (activity and emissions by pollutant and process) per year in tab-delimited text file form. Each extract (o r “tabtots”) file contains countylevel data for all eight counties. These “tabto ts” and the “*.LST” files (49) were provided in “hgb8hr97_mvs14_tabtots.zip:”

 “hgb8hr97_mvs14_ YYYY swkd_tabtots.lst” (tabfiletota ls execution record); and

 “hgb8hr97_mvs14_ YYYY swkd_ ???? .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 incl uding input/output comparisons); one XML file (inventory data coded for uploading to TCEQ’s TexAER); a nd eight county-level SCC-based inventory summary files (each tab-delimited text file in cludes a county summary by SCC from the XMLcoded results). The following 70 output files were provided in “hgb8hr97_mvs14_xmlformat.zip:”

 MOVESsccXMLformat_hgb8hr97_mvs14_ YYYY swkd.lst;

 MOVESsccXMLform at_hgb8hr97_mvs14_ YYYY swkd.xml; and

 MOVESsccXMLformat_hgb8hr97_mvs14_ YYYY swkd_ FFFF _summary.tab.69 Emissions Factors – MOVES Input Files an d 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 require d 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_ YYYY SWKD_ FFFF _ER.MRS,” (56 MRS files); and

 “mvs14_hgb8hr97_ YYYY s_ 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 CDB s – CDBs were built using MySQL scripts, data files, and da tabases provided in “hgb8hr97_m vs14_CDBscripts_data.zip” (144 files):

 “MVS14_HGB8HR97_ YYYY S_ FFFF _ER_CDB_IN.SQL” (56 te xt files containing MySQL scripts for building the 56 CDBs);

 “hgb_metinputs_summer2012tceq” (database with meteorological inputs – county and zonemonthhour tables – and de velopment files – 12 files);

 “_fuels_hgb2012s_mvs10b,” “_fuels_hgb2014s_mvs10b,” and “_fuels_hgb2014s_2017t3sulfur_mvs10b” (three fuels inputs databases with fuelsupply and fuelformulation tables, used respec tively for 2012, 2014, and 2017 and later years – survey data spreadsheets and ot her development files – 33 files);

 “_tx1990_19992050_mvs14_imcoverage_213132” (imc overage database table used and a readme file – 5 files);

 “mvs14_movesdb20141021_HGB FFFF

_ XXXXj_SUTage.tab” and “*.LST” (tabdelimited, county sourcetypeagedis tribution text files, where XXXX is 2012 and 2014, which are registration data years, and the a ssociated “*.LST” text files are from each MOVESfleetbuilder utility run – 32 files);

 “mvs14_movesdb20141021_TX_ XXXX j_SUTage.tab,” “mvs14_movesdb20141021_TX_ XXXX j_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 databa se, 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 performe d NO x TxLED effects adjustments and produc ed the final emissions rates inputs to the emissions calculations – 56 MySQL database s each containing a “ratesadjrun” table (a run log) and the four rate tables: “ttirateperdista nce” for roadway-based emissions processes, and “ttirateperhour,” “ttiratepershp,” a nd “ttirateperstart” for off-networ k processes. Each rate table70 contains the emissions rate field that it is named for: ratePerDistance, ratePerHour, ratePerSHP, or ratePerStart. Rate table fi elds in common are: pollutantID, processID, hourID, sourceTypeID, fuelTypeID, and Units_Per_Activity. The tti rateperdistance table also includes the avgSpeedBinID and roadTypeID fields. Emi ssions rates are in pounds per unit of activity.

 “mvs14_hgb8hr97_ YYYY swkd_ 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 us ed (plus one for 2011) were provided along with a readme file, the TxLED factor de velopment spreadsheets, and other files (e.g., MOVES runs input/output, CDB scripts, et c.). These files were provided in “hgb8hr97_mvs14_txledFactors.zip.” The TxLED f actor 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 ma in steps to calculate the final emissions rates used in th e 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”), pl us the evaporative “per SHP” rates not directly available from MOVE S output, but calculat ed 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 NO x 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 ot her data (e.g., vehicle registrations) to produce data summaries and some data (vehicle popul ation and VMT factors) in MOVES-specified formats. (These data were not used in the M OVES emissions rate mode runs, but include, or are based on, data that were used externally in activity factor and emi ssions calculations).

The VehPopulationBuild utility runs produced the vehi cle population estimates used externally in the emissions es timation 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” (MOVE S sourcetypeyear files);

 “ *FFFF _VEHPOP_ YYYY _regdata.tab” (registrations by type and model year);

 “ *FFFF _VEHPOP_ YYYY _StFtPop.tab” (SUT/fuel type population summaries); and71  “ *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_hp msvtypeyear.tab;”

 Roadtypedistribution: “ *FFFF

_ YYYY_ swk_MOVESactInputBld_rtdist.tab;”

 Hourvmtfraction: “ *FFFF

_ YYYY_ swk_MOVESactInputBld_hr vmtfract.tab;” and

 Avgspeeddistribution: “ *FFFF

_ YYYY_ swk_MOVESactInputBld_avgspddist.tab.”

Additional data summary output text files from the MOVESactivityinputbuild runs include:

 “ *FFFF

_ YYYY swkd_MOVESactinput.lst” (run list with various data summaries);

 “ *FFFF

_ YYYY swkd_MOVESactinput_linkVHTsumm.tab” (VHT by hour, road type, area type, and avgspeedbinID); and

 “ *FFFF

_ YYYY swkd_MOVESactinput_linkVMTsumm.tab” (VMT by hour, road type, area type).

Additional MOVES In ventory 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 we re designed to input summer weekday (daily) activity data, and with the appropriate MRSs, produce summer weekday (daily) output inventor y estimates. One inventory mode CDB was built corresponding to each link-based inventory, or one per county pe r year, for a total of 56. The CDBs and MySQL script file for build ing them (contained in hgb8hr97_mvs14_cdbs_eimode.zip) are:

 “mvs14_hgb8hr97_ YYYY swkd_ FFFF _ei_cdb_in” (56 CDBs each, containing 26 database tables, consisting of three files pe r table, plus one db.opt file per CDB); and

 “MVS14_HGB8HR97_ YYYY SWKD_ FFFF _EI_CDB_IN.SQL” (56 text files containing MySQL scripts for building the 56 MOVES inventory mode CDBs).73 

### APPENDIX B: EMISSIONS ESTIMATION UTILITIE S FOR MOVES-BASED EMISSIONS INVENTORIES75 TTI EMISSIONS ESTIMATION UTILIT IES 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 specialadjustments to the emissions factors (when required), and multiply them with travel model linkbased or Highway Performance Monitoring Syst em (HPMS)-based (virtual link) activity estimates to produce emissions at user-s pecified temporal and spatial scales.

The main utilities for calculating hourly and 24-hour emissions using MOVES are TRANSVMT, VirtualLinkVMT, VehP opulationBuild, 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 pop ulation used to calculate the off-network activity. The OffNetActCalc util ity 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 MOVE SactivityInputBuild and MOVESf leetInputBuild utilities build inputs used in MOVES. The RatesCalc utility assembles the emissions rates from the MOVES output in terms of rate-per-a ctivity, including rate-per-SHP for the evaporative emissions processes. The RatesAdj utilit y makes special adjustments to th e emissions rates when required. The EmsCalc utility calculates emissions by hour ly time periods, producing a tab-delimited summary file (including 24-hour to tals), hourly link emissions out put files (optional), and an optional tab-delimited summary file by M OVES 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 as signment by multiplying the link volumes by the appropriate HPMS, seasonal, or ot her VMT factors. Hourly factor s 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. Sinc e 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 inpu t to the EmsCalc utili ty 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 coun ty HPMS average annual daily traffic (AADT) VMT, centerline miles, and lane miles by functi onal classification and area type (from the Texas Department of Transportation’s [TxDOT’s] annu al Roadway Inventory F unctional Classification Record [RIFCREC]) to produce hourly, on-road ve hicle fleet, seasonal and day-of-week specific76 actual or projected VMT, and directional opera tional speed estimates. These estimated VMT and speeds are produced for up to 42 directiona l HPMS functional classification/area type combinations, or “links.” The VirtualLinkVMT u tility was developed for use in areas that do not have TDM networks, as well as for inventory app lications 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 count y HPMS AADT VMT at the link level to the appropriate VMT (e.g., uses a county-level VM T control total-to-AADT ratio to produce seasonal, day-of-week specific VMT). Hourly fact ors 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- a nd 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 (mi nutes/mile) applied to the estimated free-flow speeds for each link. The virtual link VMT a nd speeds produced using the VirtualLinkVMT utility are an input to the emissi ons calculation utility, EmsCalc (as well as with ot her utilities to develop off-network ac tivity estimates).

VehPopulationBuild The VehPopulationBuild util ity builds the sourcetypey ear 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 th e OffNetActCalc utility to estimate starts and SHP) using the VMT mix and the Texas Departme nt 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 dies el trucks registration data file) that list 31 years of regist ration data. The primary i nputs to this utility are:

 County ID file, which specifies the county for which the output will be calculated;

 Age registration data file, wh ich 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 lis ts 31 years of regist ration 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 lis ts 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, MOVE S SUT, and MOVES fuel type;

 TxDOT district name file, which sp ecifies the VMT mix TxDOT district;

 MOVES default database;

 Population factor file (optional); and

 Year ID file (optional, only used if population factors are us ed), 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, Truc ks >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 truc k registration data (for th e Diesel >8500, Diesel >10000, Diesel >14000, Diesel >16000, Diesel > 19500, Diesel >26000, Diesel >33000, and Diesel >60000 diesel truck categor ies) are saved in the diesel tr uck section of the diesel/gas registration data array. The age registration data array and the diesel/gas re gistration data array are combined to form the registration category da ta array (seven categories for 31 years of data and the total) using the co mbinations in Table 26.

Table 26. Registration Categories. Registration

Category Vehicle Category Data Location
1 Passenger Vehicle Age registration data array 2 Motorcycles
3 Total Trucks <=8500

4 Diesel >8500, Diesel >10000, Diesel >14000, Diesel >16000

Diesel/gas registration data array 5 Diesel >19500, Diesel >26000, Diesel >33000, Diesel >60000

6 Gas >8500, Gas >10000, Gas >14000, Gas >16000

7 Gas >19500, Gas >26000, Gas >33000, Gas >60000

The registration category data array is used to fill the SUT population array (by SUT and fuel type) for all vehicles except l ong-haul trucks. Each SUT/fuel type combination is assigned the total registrations from one or more of the regi stration categories in the registration category data array. Table 27 shows the SUTs and their associ ated registration catego ry 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 calc ulated by SUT/fuel type usin g 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 factor s 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 appl ies 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, th e SUT population factors by fuel t ype 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 th e 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 f actor (if desired) ar e applied to the SUT population array for all SUTs except SUT 53 a nd 62. For SUT 53, the SUT population factors for SUT 53 are applied to the SUT populati on 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 sour cetypeyear 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, salesGrowt hFactor, and migrationrate. For the yearID, the year of the registration data is used, unless a pop ulation factor is used, in which case the year from the year ID input is used. The salesGrowt hFactor and migrationrate for each SUT is set 1. A text format of this database table is writt en by the utility as well. The SUT/fuel type population input file is written using the SUT population array.

OffNetActCalc
The OffNetActCalc calculates th e analysis scenario (i.e., year , season, day type) SHP, starts, SHI, and APU hours by hour, SUT, and fuel type us ed to estimate emissions using the EmsCalc utility. The SHI and APU hours are only calculated for SUT 62, fu el 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 mix79 (by roadway type and SUT/fuel type), a nd the SUT/fuel type population (from the VehiclePopulationBuild utility). The starts activity is calculated using the SUT/fuel type population and the starts per ve hicle (typically the MOVES defa ult). 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 hour s operating (SHO). However, this method of estimating the hotelling hours as a direct function of the SHO does not cons ider 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 VM T and speeds input, th e utility applies the appropriate VMT mix (either the 24-hour VMT mi x or the appropriate time period VMT mix as assigned by the user) to each link that has the de sired 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 s cenario hourly values for VMT by SUT/fuel type a nd for VHT by SUT/fuel type.

The analysis scenario hourly SUT/fuel type speed, total hours (or so urce hours), and SHP are then calculated. For each hour and SUT/fuel t ype, the hourly SUT/fuel type VMT is divided by the hourly SUT/fuel type VHT to calculate th e 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 subt racting the hourly SUT/fuel type VHT (or SHO) from the hourly SUT/fuel type total hours. If the calculated SH P 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 functionof SHO), the utility multiplies the CLhT_Diesel analysis scenario 24-hour SHO by the usersupplied extended idle factor, whic h represents the amount of extended idle time that must occur per SHO. For option 2 ( as a function of base hot elling data), the utility calculates the base 24- hour CLhT_Diesel VMT using the base VMT and sp eeds inputs and the ba se 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 hotelli ng hours. The analysis scenario hourly CLhT_Diesel SHO (from the SHP calcula tion process) is conve rted to hourly VHT fractions. The hourly hotelling fractions are calculated as the inverse of the hourly VHT fractions. The hourly hotelling fractions are th en applied to the anal ysis scenario 24-hour hotelling hours to calculate the hourly hotelling hours. For ea ch 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 h ours 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 M OVES 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 di stribution 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 th e sum of the travel fractions. These travel fractions are then applied to the appropriate opera ting mode fractions from the hotelling activity distribution (operating mode 200) and summed to calculate th e SHI fraction. Using a similar process, the APU fraction is calculated usi ng 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 fr action to calculate the analysis scenario APU hours.

MOVESactivityInputBuild The MOVESactivityInputBuild utility builds the roadtypedistribution, hourvmtfraction, avgspeeddistribution, roadtype, hpmsvtypeye ar, year, state, zone, zoneroadtype, monthvmtfraction, and dayvmtfracti on data files in a format consistent with the MOVES input database tables using the li nk-based hourly VMT and speeds de veloped with the TRANSVMT or VirtualLinkVMT utility, the VMT mix, and the M OVES defaults. The primary inputs to this utility are:

 Link-based hourly VMT and speeds de veloped with the TRANSVMT or VirtualLinkVMT utility;

 County ID file which specif ies the county number in the link-based hourly VMT and speeds for which the output will be calculated;

 VMT roadway type designations, which lists a ssociations of the li nk 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 roadwa y 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/ar ea 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 save d in a VMT summary array based on hour, link functional class, and link area type. The li nk VHT (link VMT/link speed ) is saved in a VHT summary array based on hour, link functional clas s, 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 li nk functional class and lin k area type, and, if the81 link is specified as ramp by the link/ramp designations specified by the user, the VHT is additionally saved in the ramp segm ent of the road type VHT array.

A MOVES roadway type array by MOVES roadwa y type (roadTypeID codes 2 through 5) is also created using the data in the VMT summar y 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 arra y. For the link road types designated a MOVES road type of 7 or 9, the VMT is added to M OVES 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, a nd MOVES speed bin) is created using the VHT summary array and the VMT mix. Usi ng the appropriate MySQL code, the MOVES roadtypedistribution, hourvmtfraction, and avgspee ddistribution default values are extracted and saved for later use.

The VMT in the MOVES roadway type arra y 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 fo r a SUT over the five MOVES roadway types should equal 1), with the distri bution value for MOVES roadway t ype 0 (Off-Network) equal to 0. Using the appropriate MySQL code, the roadtype distribution 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 ro adway types where the VMT for all hours is greater than 0, this VMT is converted to an hour ly 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 hour vmtfraction 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 a rray 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 appropr iate MySQL code, the avgspeeddistribution database table is written. For those SUTs wh ere the VMT mix is greater than 0, the average speed distribution array is used. Otherwise, th e MOVES avgspeeddistribu tion default values are used. A tab-delimited version is also written (optional).

The VHT in the road type VHT array is c onverted to a proportion of ramp VHT by dividing the ramp segment of the road type VHT array by th e total VHT for the road type in the road type VHT. Using the appropriate MySQL code, the ro adtype database table is written using the proportions from the road type VHT array. If the ramp fraction fo r 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 th e roadtype database table. If the ramp fraction for roadTypeID 4 is greater than 0, then roadT ypeID 7 (with rampFraction equal to 0) and roadTypeID 9 (with82 rampFraction equal to 1) are also added to the ro adtype database table. A tab-delimited version is also written (optional).

The VMT in the hourly VMT array is aggregat ed to create the HP MS 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 hpmsvtypey ear 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 ba se year Off-Network VMT (automatically set to 0). A tab-delimited version is also written (optional).

Using the appropriate MySQL code, the fuel ye ar 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 “i sbaseYear” data is written as well (automatically set to “Y”). A tab-deli mited version is also written (optional).

The utility also produces two tab-delimite d 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 tabl es (state, zone, zonero adtype, monthvmtfraction, and dayvmtfraction) using the appropriate MySQ L 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 cr eated and the data from the MOVE S 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 fa ctors, and SHP allocation factors are all then replaced with values of 1 in the new table.

For the zoneroadtype database table, a new z oneroadtype database ta ble 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 ta ble 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 m onth 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-suppli ed day ID and 0 for all other months.

MOVESfleetInputBuild The MOVESfleetInputBuild utility builds the so urcetypeagedistribution database table and fuel/engine fraction inputs to MOVES using th e TxDOT registration data sets and the MOVES default database tables. The TxDOT registration data sets are thr ee sets of registration data (an age registration data file, a gas tr ucks registration data file, and a diesel truc ks registration data file) that list 31 years of re gistration data. The primary inputs to this utility are:83  Age registration data file, wh ich lists 31 years of registration data for the Passenger Vehicles, Motorcycles, Trucks <=6000, Tr ucks >6000 <=8500, Total Trucks <=8500, Gas Trucks >8500, Diesel Tr ucks >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 > 6000 0, and Diesel Totals diesel truck categories;

 SUT data sources input, which specifies th e data source for each SUT to use when building the souretypeagedistr ibution 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 da ta 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 th is input is processed, the utility maintains a list of the input sources. The same applies to the fuel/engine fractions, excep t 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, Truc ks <=6000, Trucks >6000 <=8500, and Total Trucks <=8500 vehicle categories) are saved in an age registration data ar ray. 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) ar e saved in the gas truck section of the diesel/gas registration data ar ray. The diesel truck registra tion data (for the Diesel > 8500, Diesel > 10000, Diesel > 14000, Diesel > 1600 0, Diesel > 19500, Diesel > 26000, Diesel > 33000, and Diesel > 60000 diesel truck categories) ar e saved in the diesel truck section of the diesel/gas registra tion data array.

The age registration data arra y and the diesel/gas registrati on data array are combined to create the registration cate gory 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 s ources input. If the county is found for a given source type, then the 31 years of registration data from the sour ce type’s corresponding category in the registration category da ta array are added to the SUT age distribution array. Table 28 shows the source types and their co rresponding registration categories.84 Table 28. SUTs/Registration Categories Co rrelation 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/e ngine fractions array. However, only SUTs 52, 53, 61, and 62 are processed due to data limitati ons. 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 Co rrelation for Fuel/E ngine 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 sourcetyp eage distribution input are processed and the data for th e registration data year are save d in the default age distribution array. For each source type in which the registra tion 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 re gistration data in the fuel/engine fractions array are converted to fuel/engin e fractions by dividing the yearly source type dies el 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 th an the registration data85 year, then the first fuel/engine fr action is extended to match the y ear from the year ID input and the appropriate number of years is dropped from th e end of the fuel/engine fractions to maintain the appropriate distribution. If th e year from the year ID input is less than the registration data year, then the last fuel/engine fraction is extende d to match the year from the year ID input and the appropriate number of years is dropped from the beginning of the fu el/engine fractions to maintain the appropriate distribut ion. For source type 62, all of th e fuel/engine fractions in the fuel/engine fractions array are set to a value of 1.

Using the appropriate MySQL code, a new s ourcetypeagedistributi on database table is created and the data from the SU T 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 appropr iate year (either the re gistration data year or the year ID input) are used to f ill the new database table. A text format of this database table may be written as well.

RatesCalc The RatesCalc utility calculates emissions rate s in terms of rate/SHP for the evaporative emissions processes using the data in the CDB used in the MOVES emis sions rates run and the MOVES default database. The utility also creates copies of the ratepe rdistance, rateperhour, rateperstart, rateperprofile (opt ional), and ratepervehicle (opti onal) emissions rate tables to include the units for each pollutant. If not specified, emissions rates are assembled for each pollutant and process combination (excluding tota l energy and the refueling emissions processes) in the MOVES emissions rate tables. The utility also uses the movesr un 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 M OVES emissions rate run. The type of activity used for the emissions rate ca lculation 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 rate s) emissions rate table, the utility creates a copy of the emissions rates in the specifi ed output database with the table name ttirateperdistance. If specific pollutants ar e specified, only the emi ssions rates for those pollutants are copied to the ttirateperdistance table. Otherwise, the entire rateperdistance table is copied to the ttirateperdistance ta ble. The utility also adds a “U nits_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 “-mo le” (i.e., pound-mole or gram-mole) are added to the “Units_Per_Activ ity” field. For those pollutants designated as TEQ, the text “TEQ” is added to the “Units_P er_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 rate s. 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 crankcas e extended idle rates will not be included the emissions rates. In this case, the utility creates a copy of the crankcase extended idle emissions87 rates in the baserateoutput table (located in the output database of the MOVES run) and places them in the ttirateperhour table. If these rate s are included in the rate perhour emissions rate table, the flag should be set to “NO” and no other processi ng is performed by the utility.

For the evaporative emissions rates, the uti lity 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 rate perprofile and ratepervehicle emissions rate tables, the utility calculates the rate-per-SHP emissions rates by multiplying the emissions rate by the appropriate vehicle populatio n and dividing by the appropriate SHP value. These rateper-SHP emissions rates are then sa ved in the ttiratepershp emissions rate table. Similar to the previous RatesCalc emissions ra te tables, the “Units_Per_Activ ity” field is added to the ttiratepershp table and filled based on the pollutants energy or mass designation.

RatesAdj The RatesAdj utility applies emi ssions rate adjustments to an emissions rate database table produced by RatesCalc utility (tti rateperdistance, ttirateperstart , ttirateperhour, ttiratepershp, ttiratepervehicle or ttirateperprof ile) 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 ap plied to all emissions ra tes 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, al ong with the output units of the emissions rate s. 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 adjust ment factors. Unit conversions between unit types (i.e., gram-moles to grams or TEQ to gram s) are not performed inte rnally 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 tabl e, the utility extracts the emissions rates for the specified pollutants (or a ll the pollutants if not sp ecified) from the input da tabase 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 pr ocess 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 em issions rates are summed to produce one emissions
rate.

After processing all of the i nput emissions rate database ta bles, 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 ne w 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 emi ssions for one user-specified county using the emissions factors (either from RatesCalc or Ra tesAdj), the 24-hour or time period VMT mix, the hourly link VMT and speeds activity estimates (e ither from TRANSVMT or VirtualLinkVMT), and the off-network activity (eith er vehicle population or SHP, st arts, and SHI). This utility produces a tab-delimited output summary (inclu ding hourly and 24-hour to tals) 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 de veloped with the TRANSVMT or VirtualLinkVMT utility. For each link, the following information is input to MOVESemscalc: link start node , link end node, link county nu mber, link roadway type number, link area type numb er, link VMT, and link opera tional speed estimate;

 24-hour or time period VMT mix by roadwa y type, MOVES SUT, and MOVES fuel type;

 Off-network activity. If the emissions factors are in gr ams-per-vehicle (i.e., the ttiratepervehicle or ttirateper profile 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 rate s), then the SHP, starts, SHI, and APU hours by hour and SUT/fuel type are required;

 VMT roadway type designations, which lists a ssociations of the li nk roadway types/area type combination to the VMT mix, emi ssions 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, onl y if the activity and emissions by SCC are to be created).

The emissions estimation can be categorized by two basic types ba sed on the type of emissions factors: the roadway-based emissions and the off-network-based emissions. For the roadway-based emissions (ttirateperdistance em issions factors), the VMT for each link is distributed to each of the SUT/fuel type comb inations listed in the VMT mix by roadway type (as designated in the VMT roadway type designa tions). 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 pollutant s input file, the emissions factors are selected based on the emissions rate roadwa y 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 emission s factors for 75 mph are used. For link speeds less than 2.5 mph, the emissions factors for 2.5 mph are used. Fo r those link speeds that fall between the 16 MOVES speeds, the emissions factor s are interpolated using the emissions factor interpolation methodology in the following section. These SUT/fu el 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/fu el 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 tt iratepervehicle or ttirateperprofile emissions rates, the emissions factors by SUT/fuel type are multiplied by th eir associated vehicle population to estimate emissions. If the emissions factors are the tt irateperstart, ttiratep erhour, 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-delim ited 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, a nd 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 w ith tab-delimited output units other th an “NONE” will appear in the tabdelimited output file. Prior to output, any un it conversions between ma ss 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 gram s) are not performed in ternally by the utility (these type of unit conversions must be done usin g 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 al so 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, wh ich lists the activity and emissi ons for each pollutant by SCC.

Emissions Factor Inte rpolation Methodology To calculate emissions factors for link speeds th at 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 (EF Interp ) is expressed as:

EF Interp = EF LowSpeed - FAC Interp H (EF LowSpeed - EF HighSpeed )

Where: EF LowSpeed = emissions factor (EF) correspondi ng to the speed below the link speed;

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

FAC Interp =

Given that: EF LowSpeed = 0.7413 g/mi; EF HighSpeed = 0.7274 g/mi; Speed lnk = 41.2 mph; Speed low = 40 mph; and Speed high = 45 mph.

FAC Interp = 1
412 1
40 1 45 1
40

. mph mph mph mph       

       = 

 0 00073

0 00278

.

. = 0.26214;

EF Interp = 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 Emissions Factors MOVES Network roadtype Designations (to Rates, VMT Mix, and MOVES roadtypes) 

### Travel Demand Model Network Link-Base d 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

Builders 1 Off-Network Activity 2

MOVES Inputs EmsCalc

MOVES Emissions and

Activity RatesCalc93 

### APPENDIX C TXDOT DISTRICT VMT MI X BY DAY OF WEEK95 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 2032962010 Weekday VMT Mix - Beaumont TxDOT District (2012 Analysis Year)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00222 0.00203 0.0023721_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 0.55180 0.50530 0.5899031_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 0.00305 0.00247 0.0038331_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 0.21446 0.17408 0.2695232_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 0.00299 0.00243 0.0037632_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 0.05344 0.04338 0.0671651_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 0.00159 0.00145 0.0009651_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 0.00045 0.00041 0.0002752_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 0.03425 0.03124 0.0206352_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 0.00960 0.00876 0.0057853_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 0.00176 0.00161 0.0010653_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 0.00049 0.00045 0.0003054_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 0.00120 0.00110 0.0007254_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 0.00034 0.00031 0.0002041_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 0.00110 0.00170 0.0009742_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 0.00046 0.00070 0.0004042_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 0.00000 0.00000 0.0000043_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 0.00142 0.00218 0.0012443_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 0.00001 0.00002 0.0000161_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 0.04022 0.07443 0.0102761_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 0.00398 0.00736 0.0010262_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 0.07462 0.13808 0.0190462_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 0.00000 0.00000 0.0000011_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 0.00055 0.00051 0.00059972010 Weekday VMT Mix - Houston TxDOTDistrict (2012 Analysis Year)SUF/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00239 0.00295 0.0029921_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 0.59560 0.73334 0.7436231_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 0.00334 0.00202 0.0022431_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 0.23496 0.14209 0.1579232_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 0.00334 0.00202 0.0022432_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 0.05849 0.03537 0.0393151_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 0.00105 0.00061 0.0005751_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 0.00063 0.00036 0.0003452_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 0.01993 0.01157 0.0109152_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 0.01191 0.00691 0.0065253_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 0.00385 0.00224 0.0021153_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 0.00230 0.00134 0.0012654_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 0.00079 0.00046 0.0004354_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 0.00047 0.00028 0.0002641_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 0.00090 0.00098 0.0008642_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 0.00037 0.00040 0.0003542_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 0.00000 0.00000 0.0000043_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 0.00116 0.00126 0.0011143_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 0.00001 0.00001 0.0000161_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 0.02881 0.02741 0.0130461_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 0.00234 0.00222 0.0010662_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 0.02675 0.02544 0.0121062_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 0.00000 0.00000 0.0000011_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 0.00060 0.00074 0.00075982015 Weekday VMT Mix - Beaumont TxDOT District (2014 and 2017 Analysis Years)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00388 0.00355 0.0041521_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 0.55013 0.50378 0.5881331_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 0.00371 0.00301 0.0046631_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 0.21461 0.17421 0.2697132_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 0.00295 0.00239 0.0037032_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 0.05266 0.04275 0.0661851_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 0.00163 0.00149 0.0009851_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 0.00046 0.00042 0.0002852_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 0.03414 0.03114 0.0205652_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 0.00957 0.00873 0.0057753_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 0.00176 0.00160 0.0010653_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 0.00049 0.00045 0.0003054_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 0.00128 0.00117 0.0007754_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 0.00036 0.00033 0.0002241_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 0.00035 0.00054 0.0003142_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 0.00070 0.00108 0.0006142_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 0.00000 0.00000 0.0000043_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 0.00192 0.00296 0.0016843_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 0.00002 0.00003 0.0000261_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 0.04022 0.07443 0.0102761_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 0.00398 0.00736 0.0010262_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 0.07462 0.13808 0.0190462_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 0.00000 0.00000 0.0000011_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 0.00055 0.00051 0.00059992015 Weekday VMT Mix - Houston TxDOT District (2014 and 2017 Analysis Years)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00419 0.00515 0.0052321_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 0.59381 0.73113 0.7413831_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 0.00407 0.00246 0.0027331_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 0.23513 0.14219 0.1580432_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 0.00317 0.00192 0.0021332_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 0.05776 0.03493 0.0388251_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 0.00108 0.00062 0.0005951_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 0.00064 0.00037 0.0003552_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 0.01987 0.01153 0.0108752_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 0.01187 0.00689 0.0064953_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 0.00384 0.00223 0.0021053_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 0.00229 0.00133 0.0012654_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 0.00085 0.00049 0.0004654_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 0.00051 0.00029 0.0002841_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 0.00029 0.00031 0.0002742_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 0.00057 0.00062 0.0005542_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 0.00000 0.00000 0.0000043_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 0.00157 0.00171 0.0015043_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 0.00002 0.00002 0.0000261_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 0.02881 0.02741 0.0130461_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 0.00234 0.00222 0.0010662_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 0.02675 0.02544 0.0121062_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 0.00000 0.00000 0.0000011_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 0.00060 0.00074 0.000751002020 Weekday VMT Mix - Beaumont TxDOT District (2020 Analysis Year)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00499 0.00457 0.0053321_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 0.54903 0.50276 0.5869431_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 0.00393 0.00319 0.0049431_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 0.21440 0.17403 0.2694432_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 0.00289 0.00235 0.0036332_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 0.05272 0.04279 0.0662551_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 0.00159 0.00145 0.0009651_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 0.00045 0.00041 0.0002752_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 0.03425 0.03124 0.0206352_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 0.00960 0.00876 0.0057853_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 0.00176 0.00161 0.0010653_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 0.00049 0.00045 0.0003054_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 0.00120 0.00110 0.0007254_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 0.00034 0.00031 0.0002041_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 0.00035 0.00054 0.0003142_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 0.00070 0.00108 0.0006142_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 0.00000 0.00000 0.0000043_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 0.00192 0.00295 0.0016843_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 0.00002 0.00003 0.0000261_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 0.04022 0.07443 0.0102761_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 0.00398 0.00736 0.0010262_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 0.07462 0.13808 0.0190462_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 0.00000 0.00000 0.0000011_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 0.00055 0.00051 0.000591012020 Weekday VMT Mix - Houston TxDOTDistrict (2020 Analysis Year)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00538 0.00663 0.0067221_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 0.59261 0.72966 0.7398931_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 0.00454 0.00275 0.0030531_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 0.23465 0.14190 0.1577132_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 0.00317 0.00192 0.0021332_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 0.05776 0.03493 0.0388251_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 0.00105 0.00061 0.0005751_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 0.00063 0.00036 0.0003452_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 0.01993 0.01157 0.0109152_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 0.01191 0.00691 0.0065253_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 0.00385 0.00224 0.0021153_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 0.00230 0.00134 0.0012654_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 0.00079 0.00046 0.0004354_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 0.00047 0.00028 0.0002641_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 0.00029 0.00031 0.0002742_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 0.00058 0.00062 0.0005542_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 0.00000 0.00000 0.0000043_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 0.00157 0.00170 0.0015043_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 0.00002 0.00002 0.0000261_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 0.02881 0.02741 0.0130461_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 0.00234 0.00222 0.0010662_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 0.02675 0.02544 0.0121062_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 0.00000 0.00000 0.0000011_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 0.00060 0.00074 0.000751022025 Weekday VMT Mix - Beaumont TxDOT District (2023 and 2026 Analysis Years)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00609 0.00558 0.0065221_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 0.54792 0.50175 0.5857631_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 0.00415 0.00337 0.0052131_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 0.21418 0.17385 0.2691732_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 0.00295 0.00239 0.0037032_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 0.05266 0.04275 0.0661851_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 0.00159 0.00145 0.0009651_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 0.00045 0.00041 0.0002752_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 0.03432 0.03131 0.0206752_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 0.00962 0.00878 0.0058053_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 0.00177 0.00161 0.0010753_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 0.00050 0.00045 0.0003054_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 0.00113 0.00103 0.0006854_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 0.00032 0.00029 0.0001941_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 0.00035 0.00054 0.0003142_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 0.00071 0.00109 0.0006242_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 0.00000 0.00000 0.0000043_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 0.00192 0.00294 0.0016743_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 0.00002 0.00003 0.0000261_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 0.04022 0.07443 0.0102761_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 0.00398 0.00736 0.0010262_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 0.07462 0.13808 0.0190462_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 0.00000 0.00000 0.0000011_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 0.00055 0.00051 0.000591032025 Weekday VMT Mix - Houston TxDOT District (2023 and 2026 Analysis Years)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00658 0.00810 0.0082121_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 0.59142 0.72819 0.7383931_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 0.00454 0.00275 0.0030531_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 0.23465 0.14190 0.1577132_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 0.00323 0.00195 0.0021732_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 0.05769 0.03489 0.0387851_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 0.00105 0.00061 0.0005751_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 0.00063 0.00036 0.0003452_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 0.01997 0.01159 0.0109352_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 0.01193 0.00693 0.0065353_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 0.00386 0.00224 0.0021153_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 0.00231 0.00134 0.0012654_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 0.00074 0.00043 0.0004154_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 0.00044 0.00026 0.0002441_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 0.00029 0.00031 0.0002742_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 0.00058 0.00063 0.0005542_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 0.00000 0.00000 0.0000043_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 0.00157 0.00170 0.0014943_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 0.00002 0.00002 0.0000261_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 0.02881 0.02741 0.0130461_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 0.00234 0.00222 0.0010662_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 0.02675 0.02544 0.0121062_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 0.00000 0.00000 0.0000011_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 0.00060 0.00074 0.000751042025 Weekday VMT Mix - Beaumont TxDOT District (2028 Analysis Years)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00609 0.00558 0.0065221_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 0.54792 0.50175 0.5857631_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 0.00437 0.00354 0.0054931_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 0.21396 0.17368 0.2688932_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 0.00295 0.00239 0.0037032_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 0.05266 0.04275 0.0661851_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 0.00159 0.00145 0.0009651_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 0.00045 0.00041 0.0002752_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 0.03432 0.03131 0.0206752_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 0.00962 0.00878 0.0058053_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 0.00177 0.00161 0.0010753_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 0.00050 0.00045 0.0003054_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 0.00113 0.00103 0.0006854_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 0.00032 0.00029 0.0001941_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 0.00035 0.00054 0.0003142_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 0.00072 0.00110 0.0006342_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 0.00000 0.00000 0.0000043_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 0.00191 0.00293 0.0016743_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 0.00002 0.00003 0.0000261_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 0.04022 0.07443 0.0102761_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 0.00398 0.00736 0.0010262_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 0.07462 0.13808 0.0190462_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 0.00000 0.00000 0.0000011_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 0.00055 0.00051 0.000591052025 Weekday VMT Mix - Houston TxDOTDistrict (2028 Analysis Years)SUT/FTAM PeakMid-DayPM PeakOvernightRT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT5RT2RT3RT4RT521_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 0.00718 0.00884 0.0089621_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 0.59082 0.72745 0.7376531_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 0.00478 0.00289 0.0032231_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 0.23441 0.14176 0.1575532_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 0.00323 0.00195 0.0021732_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 0.05769 0.03489 0.0387851_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 0.00105 0.00061 0.0005751_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 0.00063 0.00036 0.0003452_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 0.01997 0.01159 0.0109352_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 0.01193 0.00693 0.0065353_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 0.00386 0.00224 0.0021153_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 0.00231 0.00134 0.0012654_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 0.00074 0.00043 0.0004154_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 0.00044 0.00026 0.0002441_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 0.00029 0.00031 0.0002742_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 0.00059 0.00064 0.0005642_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 0.00000 0.00000 0.0000043_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 0.00156 0.00169 0.0014943_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 0.00002 0.00002 0.0000261_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 0.02881 0.02741 0.0130461_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 0.00234 0.00222 0.0010662_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 0.02675 0.02544 0.0121062_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 0.00000 0.00000 0.0000011_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 0.00060 0.00074 0.00075107 

### APPENDIX D: TXDOT DISTRICT AGGREG ATE WEEKDAY VMT MIX109 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 2032110 Aggregate Weekday VMT Mix - Beaumont TxDOT District

SUT/FT 2010 1 2015 2 2020 3 2025 4 2030 5

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 2010 1 2015 2 2020 3 2025 4 2030 5

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 AD JUSTMENT FACTORS115 2014 AM Peak Intermediate Year Adjustment Factors

County 2012 TDM VMT 1 2015 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 Ye ar Adjustment Factors

County 2012 TDM VMT 1 2015 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2015 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2015 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 intraz onal VMT) to estimate analysis year VMT.117 2020 AM Peak Intermediate Year Adjustment Factors

County 2017 TDM VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 intraz onal VMT) to estimate analysis year VMT.

2020 Mid-Day Intermediate Ye ar Adjustment Factors

County 2017 TDM VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 intraz onal VMT) to estimate analysis year VMT.

2020 Overnight Intermediate Year Adjustment Factors

County 2017 TDM VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 Ye ar Adjustment Factors

County 2017 TDM VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2025 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 Ye ar Adjustment Factors

County 2025 TDM VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 Ye ar Adjustment Factors

County 2025 TDM VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 VMT 1 2035 TDM VMT 1 Annual Compounded Growth Rate Intermediate Year Factor 2

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 F ACTORS, AND SP EED REDUCTION FACTORS127 Capacity Factors

Time of Day Assignment Capacity Factor 1

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 Speeds 1 DistanceWeighted FreeFlow Speeds 2 Free-Flow Speed Factor 3 Code Description Code Description

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 1.00194129 Free-Flow (V/C=0) Speed Factors for Houston/Galveston Speed Model - Continued

Functional Class Area Type Distance Weighted Input Speeds 1 DistanceWeighted FreeFlow Speeds 2 Free-Flow Speed Factor 3 Code Description Code Description

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 w ith 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 Speeds 1 DistanceWeighted FreeFlow Speeds 2 Free-Flow Speed Factor 3 Code Description Code Description

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 0.71270131 LOS E (V/C=1) Speed Factors for Houston/Galveston Speed Model - Continued

Functional Class Area Type Distance Weighted Input Speeds 1 DistanceWeighted FreeFlow Speeds 2 Free-Flow Speed Factor 3 Code Description Code Description

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 w ith link volumes set to 0 (V/C=0). 3 When inputs speeds are not available, speed factors are taken from the nearest area type.132Figure 1. Freeway Speed Reduction Factors by V/C Ratio.Speed Reduction Factors by V/C RatioFunctional Group 1 (Freeways)CBDUrbanUrban FringeSuburbanRuralV/C Ratios0.000.100.200.300.400.500.600.700.800.901.000.000.200.400.600.801.00

Speed Reduction Factors133Figure 2. Principal Arterial SpeedReduction Factors by V/C Ratio.

Speed Reduction FactorsCBDUrbanUrban FringeSuburbanRural134Figure 3. Other Arterial Speed Reduction Factors by V/C Ratio.

Speed Reduction FactorsCBDUrbanUrban FringeSuburbanRural135Figure 4. Collector Speed Reduction Factors by V/C Ratio.Speed Reduction Factors by V/C RatioFunctional Group 4 (Collectors)V/C Ratios0.000.100.200.300.400.500.600.700.800.901.000.000.200.400.600.801.00

Speed Reduction FactorsCBDUrbanUrban FringeSuburbanRural136 Functional Classification to Func tional 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 Collectors137 

### APPENDIX G: VEHICLE POPULATION ESTI MATES AND 24-HOUR SHP, STARTS, AND SHI SUMMARIES139 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 7,906140 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 7,052141 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 7,475142 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 8,107143 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 8,792144 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 9,539145 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 10,076146 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 23,351 189,638147 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 25,611 169,146148 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 27,217 179,304149 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 28,842 194,454150 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 30,566 210,887151 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 32,232 228,797152 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 33,171 241,698153 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 3,582154 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 3,195155 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 3,386156 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 3,673157 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 3,983158 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 4,321159 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 4,565160 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 109161 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 Hour s 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 177163 

### APPENDIX H: SOURCE TYPE AGE AN D FUEL ENGINE FRAC TIONS INPUTS TO MOVES165Brazoria County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.035148 0.047410 0.024423 0.024423 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.042178 0.063593 0.048063 0.048063 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.036664 0.064298 0.043025 0.043025 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.083115 0.057973 0.037827 0.037827 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.093728 0.085697 0.068646 0.068646 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.105445 0.086589 0.074174 0.074174 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.101034 0.076487 0.066812 0.066812 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.075534 0.071170 0.060893 0.060893 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.055410 0.060945 0.067802 0.067802 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.066988 0.057103 0.069857 0.069857 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.060786 0.055253 0.072976 0.072976 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.043005 0.048583 0.066115 0.066115 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.029773 0.047993 0.053310 0.053310 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.027981 0.037898 0.047574 0.047574 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.020675 0.029918 0.032801 0.032801 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.014473 0.023594 0.032886 0.032886 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.015438 0.017398 0.023775 0.023775 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.013784 0.015420 0.023396 0.023396 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.010200 0.010944 0.019666 0.019666 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.006478 0.008383 0.012731 0.012731 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.004824 0.006274 0.010762 0.010762 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.003446 0.004663 0.007998 0.007998 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.004273 0.003691 0.006445 0.006445 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.003584 0.002821 0.005956 0.005956 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.002481 0.001828 0.004452 0.004452 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.002757 0.001605 0.002263 0.002263 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.005238 0.001122 0.002397 0.002397 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.002619 0.001295 0.001896 0.001896 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.002068 0.000986 0.001945 0.001945 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.003722 0.000597 0.000881 0.000881 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.027154 0.008469 0.008255 0.008255 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006166Brazoria County 2014 Age Distribution Inputs toMOVES (2014, 2017, 2020, 2023, 2006, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.034051 0.049888 0.026692 0.026692 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.058353 0.082092 0.050348 0.050348 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.051368 0.076969 0.045098 0.045098 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.037398 0.066351 0.048366 0.048366 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.033469 0.060897 0.042381 0.042381 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.072905 0.052089 0.034989 0.034989 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.082800 0.076409 0.063810 0.063810 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.093277 0.075794 0.068950 0.068950 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.086147 0.065460 0.063712 0.063712 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.066938 0.060303 0.056247 0.056247 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.053842 0.050644 0.062244 0.062244 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.062427 0.045919 0.064202 0.064202 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.054424 0.043597 0.066490 0.066490 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.038271 0.037030 0.057875 0.057875 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.026193 0.035876 0.046175 0.046175 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.024447 0.027141 0.040423 0.040423 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.018626 0.020587 0.028099 0.028099 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.013097 0.016206 0.028393 0.028393 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.013242 0.011191 0.020022 0.020022 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.011496 0.010260 0.018614 0.018614 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.009313 0.006635 0.015628 0.015628 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.005384 0.005164 0.009974 0.009974 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.004220 0.003598 0.007979 0.007979 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.003783 0.003078 0.005936 0.005936 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.003929 0.002369 0.005054 0.005054 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.003347 0.001822 0.004699 0.004699 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.002474 0.001390 0.003525 0.003525 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.001455 0.001188 0.001799 0.001799 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.002910 0.000769 0.001775 0.001775 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.002328 0.000918 0.001493 0.001493 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.028085 0.008363 0.009007 0.009007 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026167Chambers County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.032854 0.057461 0.025441 0.025441 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.030801 0.069076 0.052380 0.052380 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.036961 0.068396 0.043400 0.043400 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.081109 0.061604 0.043924 0.043924 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.091376 0.096380 0.074379 0.074379 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.111910 0.090946 0.083957 0.083957 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.110883 0.084562 0.072134 0.072134 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.066735 0.069619 0.062332 0.062332 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.068789 0.057189 0.071685 0.071685 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.072895 0.045915 0.068692 0.068692 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.054415 0.050397 0.068393 0.068393 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.042094 0.041432 0.063903 0.063903 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.044148 0.038919 0.048788 0.048788 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.037988 0.032534 0.042353 0.042353 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.021561 0.026082 0.028435 0.028435 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.008214 0.021803 0.028884 0.028884 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.012320 0.017252 0.019979 0.019979 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.006160 0.014467 0.021925 0.021925 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.009240 0.010256 0.019306 0.019306 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.005133 0.008830 0.012796 0.012796 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.003080 0.006452 0.008680 0.008680 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.005133 0.005298 0.007857 0.007857 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.002053 0.003871 0.006585 0.006585 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.004107 0.003804 0.004340 0.004340 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.004107 0.001970 0.003667 0.003667 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.002053 0.002038 0.001871 0.001871 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.001027 0.000815 0.001796 0.001796 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.004107 0.001562 0.002394 0.002394 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.002053 0.001019 0.001497 0.001497 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.001027 0.000611 0.001122 0.001122 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.025667 0.009441 0.007109 0.007109 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006168Chambers County 2014 Age Distribution Inputs toMOVES (2014, 2017, 2020, 2023, 2026, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.026205 0.056954 0.034963 0.034963 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.041929 0.098149 0.057648 0.057648 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.062893 0.095394 0.053231 0.053231 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.036688 0.071753 0.059444 0.059444 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.037736 0.063809 0.044546 0.044546 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.080713 0.054904 0.042674 0.042674 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.070231 0.079185 0.070150 0.070150 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.094340 0.072202 0.073070 0.073070 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.106918 0.068038 0.064910 0.064910 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.068134 0.054328 0.054503 0.054503 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.057652 0.043308 0.059370 0.059370 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.064990 0.034595 0.059744 0.059744 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.045073 0.037927 0.056300 0.056300 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.032495 0.031136 0.052931 0.052931 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.039832 0.028894 0.041102 0.041102 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.028302 0.022551 0.035487 0.035487 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.016771 0.016849 0.024931 0.024931 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.012579 0.013582 0.022161 0.022161 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.014675 0.010827 0.016096 0.016096 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.006289 0.008393 0.017219 0.017219 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.009434 0.007111 0.012802 0.012802 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.005241 0.005061 0.009882 0.009882 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.006289 0.003075 0.007112 0.007112 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.001048 0.002691 0.005465 0.005465 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.003145 0.002306 0.004567 0.004567 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.003145 0.002306 0.003743 0.003743 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.002096 0.001345 0.002770 0.002770 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.001048 0.001217 0.001348 0.001348 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.001048 0.000641 0.001872 0.001872 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.004193 0.001217 0.002171 0.002171 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.018868 0.010250 0.007786 0.007786 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026169Fort Bend County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.046851 0.053758 0.030330 0.030330 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.057431 0.071312 0.059501 0.059501 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.051763 0.070497 0.054767 0.054767 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.094584 0.064229 0.043843 0.043843 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.094458 0.092627 0.077315 0.077315 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.114987 0.091304 0.085057 0.085057 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.094081 0.080117 0.068916 0.068916 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.074811 0.070150 0.063454 0.063454 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.052771 0.061728 0.073516 0.073516 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.063980 0.056799 0.072222 0.072222 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.053149 0.052662 0.072551 0.072551 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.037028 0.046587 0.060076 0.060076 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.029849 0.043415 0.046697 0.046697 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.027078 0.032826 0.038770 0.038770 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.018136 0.026532 0.028667 0.028667 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.011209 0.020441 0.026747 0.026747 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.010076 0.014271 0.017250 0.017250 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.008564 0.012307 0.016941 0.016941 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.005164 0.008049 0.014087 0.014087 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.005542 0.006009 0.009354 0.009354 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.002897 0.004550 0.007177 0.007177 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.002645 0.003548 0.005031 0.005031 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.001763 0.002835 0.004980 0.004980 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.002519 0.001738 0.003840 0.003840 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.002393 0.001378 0.002967 0.002967 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.002645 0.001146 0.001930 0.001930 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.003275 0.000831 0.001797 0.001797 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.002015 0.000825 0.001612 0.001612 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.003023 0.000694 0.001591 0.001591 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.003401 0.000520 0.000852 0.000852 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.021914 0.006317 0.008163 0.008163 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006170Fort Bend County 2014 Age Distribution Inputs toMOVES (2014, 2017, 2020, 2023, 2026, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.050696 0.058337 0.032080 0.032080 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.073662 0.090001 0.064914 0.064914 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.068043 0.083425 0.056273 0.056273 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.050452 0.073275 0.058103 0.058103 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.041656 0.066488 0.051514 0.051514 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.080015 0.057682 0.038498 0.038498 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.074884 0.080274 0.070114 0.070114 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.096995 0.077799 0.074872 0.074872 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.080625 0.066166 0.061161 0.061161 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.062790 0.056728 0.056222 0.056222 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.046787 0.048539 0.063535 0.063535 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.053872 0.043165 0.062248 0.062248 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.043000 0.038870 0.062761 0.062761 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.032861 0.033461 0.051736 0.051736 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.026631 0.030643 0.040127 0.040127 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.021500 0.022460 0.031838 0.031838 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.016003 0.017884 0.023579 0.023579 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.009406 0.013139 0.021809 0.021809 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.008307 0.008985 0.014224 0.014224 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.006719 0.007194 0.013731 0.013731 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.004520 0.004944 0.011025 0.011025 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.005375 0.003593 0.007585 0.007585 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.003298 0.002656 0.005553 0.005553 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.001832 0.002039 0.003953 0.003953 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.001344 0.001697 0.003802 0.003802 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.002077 0.001082 0.003159 0.003159 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.002199 0.000933 0.002223 0.002223 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.002443 0.000837 0.001418 0.001418 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.003176 0.000641 0.001398 0.001398 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.001099 0.000664 0.001207 0.001207 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.027730 0.006398 0.009335 0.009335 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026171Galveston County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.032127 0.050060 0.023687 0.023687 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.043258 0.064476 0.049915 0.049915 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.039464 0.062459 0.049245 0.049245 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.096509 0.061257 0.041219 0.041219 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.091955 0.086048 0.076896 0.076896 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.102707 0.085570 0.079339 0.079339 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.095244 0.078593 0.070350 0.070350 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.074627 0.071102 0.060830 0.060830 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.054263 0.060256 0.071341 0.071341 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.067417 0.057380 0.071676 0.071676 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.057172 0.054370 0.072932 0.072932 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.041108 0.047095 0.064878 0.064878 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.033898 0.044413 0.047444 0.047444 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.028712 0.036862 0.043047 0.043047 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.019732 0.029108 0.030597 0.030597 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.014799 0.023327 0.029466 0.029466 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.015431 0.017329 0.020979 0.020979 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.011257 0.015798 0.020798 0.020798 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.008095 0.010644 0.016261 0.016261 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.006957 0.008097 0.011041 0.011041 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.005059 0.006244 0.008221 0.008221 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.003921 0.004631 0.006784 0.006784 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.003036 0.003951 0.006379 0.006379 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.003795 0.002831 0.005597 0.005597 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.004301 0.002002 0.003392 0.003392 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.003542 0.001696 0.001884 0.001884 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.003289 0.001471 0.002219 0.002219 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.003668 0.001292 0.001940 0.001940 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.002403 0.001218 0.002052 0.002052 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.003289 0.000807 0.001312 0.001312 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.028965 0.009613 0.008277 0.008277 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006172Galveston County 2014 Age Distribution Inputs toMOVES (2014, 2017, 2020, 2023, 2026, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.032675 0.050373 0.031659 0.031659 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.064163 0.084895 0.058659 0.058659 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.054941 0.076401 0.045384 0.045384 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.040580 0.065228 0.049650 0.049650 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.033597 0.059393 0.045749 0.045749 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.083267 0.054553 0.038002 0.038002 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.079842 0.074858 0.068974 0.068974 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.091304 0.073456 0.072187 0.072187 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.081686 0.066351 0.062813 0.062813 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.064032 0.059954 0.054126 0.054126 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.049407 0.049882 0.063950 0.063950 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.060079 0.045968 0.064146 0.064146 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.048090 0.042939 0.064708 0.064708 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.035046 0.036829 0.057621 0.057621 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.031489 0.034073 0.041553 0.041553 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.024506 0.027557 0.037301 0.037301 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.020158 0.020845 0.025190 0.025190 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.015020 0.016188 0.024643 0.024643 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.012516 0.011650 0.017359 0.017359 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.009486 0.010535 0.016980 0.016980 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.006192 0.006895 0.013360 0.013360 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.005929 0.005043 0.008869 0.008869 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.004480 0.003928 0.006217 0.006217 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.003162 0.003086 0.005080 0.005080 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.002767 0.002490 0.005094 0.005094 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.003557 0.002083 0.004182 0.004182 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.002635 0.001557 0.002624 0.002624 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.002240 0.001164 0.001431 0.001431 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.004216 0.001108 0.001628 0.001628 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.002899 0.000996 0.001473 0.001473 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.030040 0.009721 0.009388 0.009388 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026173Harris County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.045882 0.056954 0.027448 0.027448 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.053886 0.058891 0.048330 0.048330 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.039223 0.057738 0.039544 0.039544 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.096000 0.052304 0.034800 0.034800 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.091194 0.077445 0.068786 0.068786 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.110500 0.079320 0.075463 0.075463 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.092660 0.074111 0.065119 0.065119 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.072255 0.068847 0.062347 0.062347 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.053173 0.060142 0.071715 0.071715 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.064781 0.057433 0.073917 0.073917 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.054028 0.057165 0.075757 0.075757 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.040200 0.053090 0.067240 0.067240 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.031525 0.051822 0.053864 0.053864 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.025721 0.041288 0.047382 0.047382 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.018634 0.033807 0.034697 0.034697 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.013115 0.027118 0.032633 0.032633 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.013054 0.019911 0.021189 0.021189 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.010793 0.017480 0.021057 0.021057 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.008227 0.012141 0.017385 0.017385 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.006822 0.009349 0.012007 0.012007 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.004093 0.006942 0.008639 0.008639 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.003401 0.005354 0.006729 0.006729 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.003238 0.003949 0.005774 0.005774 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.002831 0.002701 0.005151 0.005151 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.003421 0.001982 0.003483 0.003483 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.002566 0.001486 0.002231 0.002231 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.004114 0.001196 0.002535 0.002535 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.003849 0.001219 0.002143 0.002143 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.003095 0.000985 0.002053 0.002053 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.002464 0.000755 0.001334 0.001334 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.025253 0.007077 0.009249 0.009249 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006174Harris County 2014 Age Distribution Inputs to MOVES (2014, 2017, 2020, 2023, 2026, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.041700 0.065600 0.031518 0.031518 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.074575 0.076227 0.050539 0.050539 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.064531 0.068965 0.043006 0.043006 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.043713 0.058501 0.045770 0.045770 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.033668 0.055270 0.037853 0.037853 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.080294 0.048562 0.033218 0.033218 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.076032 0.070340 0.064775 0.064775 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.094751 0.071820 0.071330 0.071330 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.080786 0.066148 0.061027 0.061027 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.063717 0.060102 0.058713 0.058713 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.045983 0.051641 0.066065 0.066065 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.057506 0.048348 0.067479 0.067479 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.048853 0.046294 0.068120 0.068120 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.035232 0.041390 0.060118 0.060118 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.027479 0.039102 0.047321 0.047321 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.022403 0.030110 0.040584 0.040584 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.015785 0.023947 0.029053 0.029053 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.011351 0.018569 0.027243 0.027243 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.010966 0.013123 0.017448 0.017448 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.009274 0.010889 0.017264 0.017264 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.007068 0.007378 0.013932 0.013932 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.005569 0.005564 0.009254 0.009254 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.003641 0.004043 0.006578 0.006578 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.002699 0.003098 0.005093 0.005093 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.002741 0.002350 0.004350 0.004350 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.002420 0.001633 0.003927 0.003927 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.002484 0.001253 0.002622 0.002622 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.001928 0.001002 0.001749 0.001749 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.003491 0.000833 0.001985 0.001985 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.002806 0.000826 0.001763 0.001763 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.026558 0.007070 0.010301 0.010301 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026175Liberty County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.023891 0.042696 0.024444 0.024444 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.039249 0.050351 0.043519 0.043519 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.034130 0.052283 0.032334 0.032334 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.080205 0.047453 0.031480 0.031480 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.081342 0.076363 0.066010 0.066010 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.102389 0.074691 0.068898 0.068898 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.097270 0.070083 0.062472 0.062472 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.080774 0.068931 0.053362 0.053362 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.059158 0.053658 0.061496 0.061496 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.065415 0.052172 0.067393 0.067393 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.069397 0.051132 0.069305 0.069305 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.051763 0.052729 0.068288 0.068288 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.034699 0.054067 0.051613 0.051613 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.033561 0.048345 0.050433 0.050433 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.024460 0.038051 0.039818 0.039818 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.018203 0.033444 0.041811 0.041811 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.015358 0.025937 0.028714 0.028714 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.011945 0.023671 0.029365 0.029365 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.008532 0.017539 0.024810 0.024810 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.005688 0.014418 0.015211 0.015211 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.002844 0.011631 0.013503 0.013503 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.002844 0.009513 0.010087 0.010087 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.002844 0.005983 0.008582 0.008582 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.004551 0.004905 0.008012 0.008012 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.004551 0.004013 0.006223 0.006223 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.004551 0.002453 0.003172 0.003172 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.007395 0.001895 0.003213 0.003213 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.003413 0.001858 0.003172 0.003172 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.001706 0.001524 0.002684 0.002684 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.003413 0.000520 0.001708 0.001708 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.024460 0.007692 0.008866 0.008866 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006176Liberty County 2014 Age Distribution Inputs toMOVES (2014, 2017, 2020, 2023, 2026, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.035294 0.045784 0.025960 0.025960 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.051393 0.072277 0.047689 0.047689 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.044582 0.065762 0.043050 0.043050 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.035913 0.055592 0.041626 0.041626 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.031579 0.051321 0.033407 0.033407 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.081115 0.041911 0.030843 0.030843 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.074303 0.068404 0.059774 0.059774 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.092879 0.068006 0.061849 0.061849 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.085449 0.065364 0.061646 0.061646 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.078638 0.059428 0.050781 0.050781 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.052012 0.048136 0.057292 0.057292 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.050774 0.046797 0.064860 0.064860 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.060681 0.045277 0.062785 0.062785 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.043344 0.044915 0.061727 0.061727 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.029721 0.043540 0.047933 0.047933 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.023529 0.036518 0.042074 0.042074 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.022291 0.028882 0.035360 0.035360 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.014861 0.022367 0.035726 0.035726 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.016718 0.018277 0.024414 0.024414 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.008669 0.015201 0.024618 0.024618 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.012384 0.012052 0.020955 0.020955 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.004954 0.008578 0.013916 0.013916 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.003715 0.007419 0.010295 0.010295 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.002477 0.005718 0.007446 0.007446 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.002477 0.003511 0.006266 0.006266 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.005573 0.003004 0.005656 0.005656 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.003096 0.002316 0.004232 0.004232 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.000619 0.001810 0.002523 0.002523 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.004334 0.001194 0.002319 0.002319 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.000619 0.001484 0.002075 0.002075 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.026006 0.009157 0.010905 0.010905 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026177Montgomery County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.046041 0.057957 0.029836 0.029836 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.050343 0.070848 0.058195 0.058195 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.036247 0.069773 0.050798 0.050798 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.088330 0.058850 0.041676 0.041676 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.084943 0.088791 0.073653 0.073653 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.107368 0.086174 0.079387 0.079387 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.094279 0.077066 0.066106 0.066106 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.073135 0.068928 0.061257 0.061257 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.058490 0.060971 0.070167 0.070167 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.070389 0.054432 0.071353 0.071353 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.054462 0.051801 0.069875 0.069875 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.042380 0.046953 0.064371 0.064371 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.034783 0.043572 0.047754 0.047754 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.028650 0.034769 0.041445 0.041445 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.020686 0.027505 0.030898 0.030898 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.013730 0.021970 0.029660 0.029660 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.012082 0.015756 0.020811 0.020811 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.010069 0.014089 0.019227 0.019227 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.007414 0.009805 0.015803 0.015803 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.007048 0.007126 0.011060 0.011060 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.005309 0.005636 0.008070 0.008070 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.002654 0.004509 0.006265 0.006265 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.003295 0.003424 0.005495 0.005495 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.003295 0.002718 0.004637 0.004637 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.003387 0.001958 0.003088 0.003088 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.003478 0.001595 0.002017 0.002017 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.003844 0.001261 0.002106 0.002106 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.003661 0.001122 0.002150 0.002150 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.003387 0.000879 0.002088 0.002088 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.003295 0.000683 0.001274 0.001274 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.023524 0.009079 0.009477 0.009477 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006178Montgomery County 2014 Age Distribution Inputs toMOVES (2014, 2017, 2020, 2023, 2026, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.039192 0.062061 0.031242 0.031242 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.073451 0.096070 0.059737 0.059737 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.062945 0.084156 0.054686 0.054686 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.044034 0.068771 0.055112 0.055112 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.033894 0.061796 0.046689 0.046689 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.072355 0.051148 0.037301 0.037301 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.076192 0.074088 0.066309 0.066309 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.092363 0.070885 0.069620 0.069620 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.080395 0.063575 0.059763 0.059763 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.065047 0.056213 0.055086 0.055086 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.050978 0.048773 0.062753 0.062753 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.061392 0.042868 0.062527 0.062527 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.047597 0.040514 0.061693 0.061693 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.037091 0.034744 0.055790 0.055790 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.027864 0.031977 0.041743 0.041743 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.023479 0.024845 0.034797 0.034797 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.015257 0.019141 0.025722 0.025722 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.011785 0.014755 0.025244 0.025244 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.010963 0.010269 0.017516 0.017516 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.009410 0.009168 0.015882 0.015882 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.007126 0.006100 0.012857 0.012857 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.006578 0.004421 0.008606 0.008606 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.004202 0.003329 0.006537 0.006537 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.002467 0.002654 0.004937 0.004937 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.002832 0.002324 0.004485 0.004485 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.003746 0.001832 0.003781 0.003781 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.003289 0.001392 0.002747 0.002747 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.002284 0.001157 0.001695 0.001695 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.002558 0.000918 0.001886 0.001886 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.003198 0.000814 0.001886 0.001886 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.026037 0.009242 0.011370 0.011370 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026179Waller County 2012 Age Distribution Inputs to MOVESAgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.029377 0.036244 0.017916 0.017916 0.049423 0.049425 0.049424 0.058824 0.097608 0.099113 0.058831 0.042515 0.0382641 0.045828 0.042338 0.038129 0.038129 0.045329 0.059714 0.035028 0.031880 0.126779 0.136008 0.043831 0.033445 0.0309802 0.043478 0.048175 0.037133 0.037133 0.040021 0.036554 0.038290 0.025261 0.045306 0.047248 0.038698 0.024642 0.0192163 0.075206 0.042273 0.028482 0.028482 0.033552 0.037391 0.045610 0.033512 0.047438 0.044614 0.032443 0.034463 0.0334394 0.084606 0.070755 0.059184 0.059184 0.043562 0.052738 0.050298 0.026019 0.132945 0.128003 0.042122 0.046983 0.0468055 0.108108 0.069985 0.067070 0.067070 0.057084 0.051227 0.052116 0.090902 0.079617 0.078341 0.055025 0.105601 0.0987386 0.082256 0.069408 0.062400 0.062400 0.058637 0.036948 0.061244 0.068069 0.086083 0.086493 0.056346 0.077818 0.0744987 0.075206 0.065495 0.055662 0.055662 0.060621 0.057742 0.054563 0.064173 0.074270 0.077526 0.058252 0.068306 0.0642948 0.052879 0.053756 0.069750 0.069750 0.058898 0.047342 0.053753 0.038437 0.051737 0.054670 0.056420 0.052380 0.0439689 0.069330 0.050741 0.067682 0.067682 0.054588 0.046391 0.046283 0.037755 0.044384 0.044263 0.052291 0.039152 0.03689310 0.050529 0.058374 0.072506 0.072506 0.051128 0.047023 0.048610 0.027369 0.038057 0.034819 0.048976 0.041099 0.03587811 0.048179 0.056386 0.067836 0.067836 0.049164 0.054162 0.044361 0.033663 0.037538 0.036367 0.046947 0.051584 0.04942112 0.035253 0.057990 0.049154 0.049154 0.046755 0.036642 0.048269 0.046006 0.032663 0.029381 0.044646 0.061272 0.06045313 0.035253 0.048881 0.050685 0.050685 0.045451 0.035523 0.044662 0.065891 0.027050 0.026411 0.043265 0.052513 0.05301114 0.028202 0.042658 0.038588 0.038588 0.034444 0.041691 0.035245 0.060566 0.011652 0.012393 0.025435 0.040701 0.04290115 0.014101 0.034383 0.041421 0.041421 0.028025 0.038094 0.032832 0.029272 0.015536 0.013865 0.039162 0.033313 0.03308416 0.014101 0.029380 0.029783 0.029783 0.023170 0.035089 0.028314 0.037973 0.009070 0.008000 0.024172 0.028181 0.03002717 0.014101 0.024055 0.030626 0.030626 0.030114 0.028755 0.036168 0.048730 0.010004 0.008690 0.028520 0.037073 0.03813918 0.015276 0.020720 0.022893 0.022893 0.023187 0.025076 0.017454 0.034209 0.005878 0.005700 0.027108 0.023182 0.02484719 0.012926 0.015973 0.015849 0.015849 0.019086 0.020799 0.020843 0.015577 0.004068 0.003981 0.018654 0.020837 0.02234520 0.002350 0.012124 0.014624 0.014624 0.014059 0.018057 0.016812 0.013865 0.003216 0.002807 0.016312 0.014688 0.01609721 0.003525 0.010135 0.010413 0.010413 0.015931 0.018265 0.021460 0.021702 0.003354 0.003026 0.012134 0.014113 0.01775122 0.004700 0.006928 0.009647 0.009647 0.017879 0.026733 0.024239 0.015397 0.003262 0.002931 0.015893 0.012387 0.01531223 0.005875 0.005453 0.007963 0.007963 0.017733 0.020338 0.013830 0.023721 0.002732 0.002358 0.020466 0.011370 0.01189024 0.007051 0.004811 0.006431 0.006431 0.016499 0.016010 0.016445 0.013653 0.002086 0.001936 0.018152 0.008760 0.00983825 0.003525 0.003464 0.003598 0.003598 0.017150 0.014812 0.016658 0.012243 0.001441 0.001212 0.017729 0.005795 0.00808026 0.004700 0.001283 0.003675 0.003675 0.014386 0.012478 0.014539 0.006260 0.001441 0.001462 0.013030 0.003451 0.00779727 0.003525 0.001860 0.003369 0.003369 0.012564 0.010708 0.012483 0.006290 0.000715 0.001166 0.013833 0.002876 0.00710728 0.003525 0.001668 0.003598 0.003598 0.009886 0.008347 0.009640 0.005160 0.000726 0.000970 0.014032 0.003274 0.00544229 0.003525 0.001283 0.001761 0.001761 0.003902 0.007916 0.003521 0.002322 0.000415 0.000591 0.009061 0.000929 0.00248030 0.023502 0.013022 0.012174 0.012174 0.007748 0.008017 0.007005 0.005296 0.002927 0.005654 0.008214 0.007300 0.021006180Waller County 2014 Age Distribution Inputs toMOVES (2014, 2017, 2020, 2023, 2026, and 2028 Analysis Years)AgeMCPCPTLCTIBusTbusSbusRTSUShTSULhTMHCShTCLhT0 0.032768 0.039187 0.020653 0.020653 0.055550 0.055650 0.055556 0.064759 0.084720 0.080736 0.064928 0.040776 0.0440071 0.051977 0.063437 0.044613 0.044613 0.049845 0.049946 0.049851 0.058272 0.099024 0.097975 0.058445 0.045307 0.0489092 0.054237 0.062910 0.038365 0.038365 0.046006 0.046100 0.046012 0.053507 0.139229 0.150497 0.053673 0.043397 0.0504863 0.038418 0.048559 0.039247 0.039247 0.042195 0.055697 0.032610 0.028999 0.095185 0.104892 0.039988 0.031182 0.0328744 0.032768 0.049379 0.035352 0.035352 0.037253 0.034095 0.035647 0.022977 0.034371 0.036093 0.035305 0.020122 0.0177425 0.070056 0.042701 0.027561 0.027561 0.031055 0.034678 0.042221 0.030317 0.035143 0.034013 0.029436 0.028828 0.0314156 0.072316 0.061856 0.057695 0.057695 0.039993 0.048515 0.046183 0.023359 0.101750 0.093698 0.037928 0.045973 0.0436407 0.092655 0.063730 0.059827 0.059827 0.052281 0.047012 0.047736 0.081433 0.059089 0.057695 0.049441 0.104962 0.0940868 0.067797 0.063730 0.059018 0.059018 0.053396 0.033713 0.055776 0.060642 0.066056 0.064010 0.050348 0.074224 0.0706799 0.074576 0.060918 0.050860 0.050860 0.055068 0.052558 0.049572 0.057049 0.057215 0.057569 0.051939 0.068094 0.06134210 0.049718 0.049965 0.066956 0.066956 0.053504 0.043092 0.048835 0.034170 0.040706 0.040882 0.050306 0.053036 0.04111911 0.056497 0.047680 0.065192 0.065192 0.049302 0.041983 0.041807 0.033378 0.033980 0.033007 0.046365 0.034735 0.03291312 0.055367 0.048676 0.064971 0.064971 0.046065 0.042452 0.043802 0.024143 0.029049 0.026419 0.043333 0.034069 0.03243713 0.041808 0.047856 0.062987 0.062987 0.044039 0.048613 0.039741 0.029530 0.028527 0.027473 0.041306 0.044819 0.04353014 0.035028 0.050199 0.049831 0.049831 0.041778 0.032808 0.043136 0.040271 0.025129 0.021921 0.039197 0.055168 0.05252015 0.036158 0.038425 0.045201 0.045201 0.040614 0.031806 0.039913 0.057676 0.020569 0.019697 0.037984 0.042464 0.04422516 0.023729 0.033271 0.033588 0.033588 0.030599 0.037111 0.031314 0.052718 0.008941 0.009478 0.022205 0.034913 0.03712017 0.011299 0.027413 0.036601 0.036601 0.024835 0.033826 0.029099 0.025424 0.010264 0.010221 0.034115 0.026429 0.02810118 0.015819 0.020326 0.025503 0.025503 0.020413 0.030975 0.024947 0.032795 0.005473 0.005665 0.020938 0.024519 0.02589819 0.007910 0.017690 0.025062 0.025062 0.026465 0.025322 0.031789 0.041994 0.006415 0.006044 0.024651 0.033048 0.03208020 0.012429 0.011949 0.018668 0.018668 0.020257 0.021951 0.015251 0.029313 0.003919 0.003876 0.023297 0.021543 0.02086721 0.006780 0.009489 0.012568 0.012568 0.016634 0.018163 0.018167 0.013318 0.002726 0.002748 0.015998 0.017856 0.01799022 0.003390 0.007146 0.011392 0.011392 0.012252 0.015769 0.014653 0.011855 0.002065 0.001875 0.013989 0.012970 0.01287023 0.005650 0.005858 0.007350 0.007350 0.013802 0.015855 0.018594 0.018450 0.001774 0.002168 0.010346 0.013059 0.01420924 0.004520 0.003925 0.007497 0.007497 0.015451 0.023149 0.020950 0.013061 0.001594 0.001735 0.013522 0.011060 0.01257225 0.004520 0.003222 0.005806 0.005806 0.015325 0.017611 0.011953 0.020123 0.001333 0.001442 0.017413 0.009550 0.00960526 0.003390 0.003163 0.004777 0.004777 0.014173 0.013781 0.014128 0.011515 0.001143 0.001180 0.015355 0.007596 0.00785927 0.003390 0.002402 0.003454 0.003454 0.014696 0.012718 0.014276 0.010304 0.000561 0.000678 0.014965 0.004842 0.00620228 0.004520 0.000879 0.002499 0.002499 0.012327 0.010714 0.012460 0.005268 0.000601 0.000875 0.010998 0.003243 0.00608329 0.002260 0.001289 0.002278 0.002278 0.010702 0.009139 0.010634 0.005263 0.000652 0.000762 0.011609 0.002710 0.00559630 0.028249 0.012769 0.014626 0.014626 0.014149 0.015191 0.013386 0.008114 0.002797 0.004677 0.020675 0.009506 0.021026181Texas Statewide 2012 Fuel Engine Fractions SummarySUTFuelTypeModel Year2012201120102009200820072006200520042003200220012000199919981997PC 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 0.997 0.998 0.998 0.999PC 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 0.003 0.002 0.002 0.001PT 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 0.992 0.981 0.993 0.992PT 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 0.008 0.019 0.007 0.008PT 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 0.992 0.981 0.993 0.992PT 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 0.008 0.019 0.007 0.008LCT 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 0.949 0.929 0.950 0.927LCT 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 0.051 0.071 0.050 0.073LCT 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 0.949 0.929 0.950 0.927LCT 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 0.051 0.071 0.050 0.073IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010 0.010SBus 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 0.990 0.990 0.990 0.990RT 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 0.002 0.169 0.404 0.019RT 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 0.998 0.831 0.596 0.981SUShT 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 0.344 0.353 0.421 0.421SUShT 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 0.656 0.647 0.579 0.579SULhT 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 0.344 0.353 0.421 0.421SULhT 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 0.656 0.647 0.579 0.579MH 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 0.660 0.680 0.710 0.740MH 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 0.340 0.320 0.290 0.260CShT 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 0.089 0.102 0.131 0.147CShT 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 0.911 0.898 0.869 0.853CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.182Texas Statewide 2012 Fuel EngineFractions Summary - ContinuedSUTFuelTypeModel Year199619951994199319921991199019891988198719861985198419831982PC 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 0.956 0.923 0.893PC 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 0.044 0.077 0.107PT 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 0.979 0.972 0.943PT 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 0.021 0.028 0.057PT 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 0.979 0.972 0.943PT 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 0.021 0.028 0.057LCT 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 0.948 0.933 0.892LCT 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 0.052 0.067 0.108LCT 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 0.948 0.933 0.892LCT 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 0.052 0.067 0.108IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.615 0.676 0.674SBus 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 0.385 0.324 0.326RT 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 0.054 0.099 0.090RT 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 0.946 0.901 0.910SUShT 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 0.811 0.746 0.955SUShT 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 0.189 0.254 0.045SULhT 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 0.811 0.746 0.955SULhT 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 0.189 0.254 0.045MH 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 0.850 0.850 0.850MH 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 0.150 0.150 0.150CShT 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 0.294 0.346 0.554CShT 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 0.706 0.654 0.446CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.183Texas Statewide 2014 Fuel Engine Fractions SummarySUTFuelTypeModel Year2014201320122011201020092008200720062005200420032002200120001999PC 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 0.996 0.997 0.997 0.998PC 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 0.004 0.003 0.003 0.002PT 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 0.983 0.989 0.992 0.981PT 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 0.017 0.011 0.008 0.019PT 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 0.983 0.989 0.992 0.981PT 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 0.017 0.011 0.008 0.019LCT 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 0.956 0.908 0.949 0.929LCT 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 0.044 0.092 0.051 0.071LCT 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 0.956 0.908 0.949 0.929LCT 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 0.044 0.092 0.051 0.071IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010 0.010SBus 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 0.990 0.990 0.990 0.990RT 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 0.005 0.006 0.002 0.169RT 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 0.995 0.994 0.998 0.831SUShT 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 0.268 0.311 0.350 0.348SUShT 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 0.732 0.689 0.650 0.652SULhT 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 0.268 0.311 0.350 0.348SULhT 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 0.732 0.689 0.650 0.652MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600 0.630 0.660 0.680MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.490 0.470 0.460 0.440 0.430 0.410 0.400 0.370 0.340 0.320CShT 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 0.077 0.084 0.090 0.107CShT 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 0.923 0.916 0.910 0.893CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.184Texas Statewide 2014 Fuel EngineFractions Summary - ContinuedSUTFuelTypeModel Year199819971996199519941993199219911990198919881987198619851984PC 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 0.991 0.966 0.956PC 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 0.009 0.034 0.044PT 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 0.986 0.984 0.979PT 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 0.014 0.016 0.021PT 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 0.986 0.984 0.979PT 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 0.014 0.016 0.021LCT 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 0.956 0.958 0.948LCT 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 0.044 0.042 0.052LCT 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 0.956 0.958 0.948LCT 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 0.044 0.042 0.052IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.327 0.484 0.615SBus 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 0.673 0.516 0.385RT 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 0.062 0.051 0.054RT 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 0.938 0.949 0.946SUShT 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 0.742 0.731 0.920SUShT 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 0.258 0.269 0.080SULhT 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 0.742 0.731 0.920SULhT 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 0.258 0.269 0.080MH Gas 0.710 0.740 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850 0.850MH Diesel 0.290 0.260 0.230 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150 0.150CShT 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 0.227 0.252 0.491CShT 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 0.773 0.748 0.509CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.185Texas Statewide 2017 Fuel Engine Fractions SummarySUTFuelTypeModel Year2017201620152014201320122011201020092008200720062005200420032002PC 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 0.995 0.997 0.996 0.996PC 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 0.005 0.003 0.004 0.004PT 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 0.979 0.982 0.982 0.983PT 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 0.021 0.018 0.018 0.017PT 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 0.979 0.982 0.982 0.983PT 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 0.021 0.018 0.018 0.017LCT 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 0.946 0.951 0.951 0.956LCT 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 0.054 0.049 0.049 0.044LCT 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 0.946 0.951 0.951 0.956LCT 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 0.054 0.049 0.049 0.044IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010 0.010SBus 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 0.990 0.990 0.990 0.990RT 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 0.003 0.005 0.004 0.005RT 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 0.997 0.995 0.996 0.995SUShT 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 0.232 0.245 0.260 0.268SUShT 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 0.768 0.755 0.740 0.732SULhT 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 0.232 0.245 0.260 0.268SULhT 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 0.768 0.755 0.740 0.732MH Gas 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.510 0.530 0.540 0.560 0.570 0.590 0.600MH Diesel 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.490 0.470 0.460 0.440 0.430 0.410 0.400CShT 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 0.051 0.052 0.055 0.077CShT 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 0.949 0.948 0.945 0.923CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.186Texas Statewide 2017 Fuel EngineFractions Summary - ContinuedSUTFuelTypeModel Year200120001999199819971996199519941993199219911990198919881987PC 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 0.999 1.000 0.987PC 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 0.001 0.000 0.013PT 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 0.992 0.997 0.996PT 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 0.008 0.003 0.004PT 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 0.992 0.997 0.996PT 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 0.008 0.003 0.004LCT 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 0.976 0.952 0.986LCT 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 0.024 0.048 0.014LCT 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 0.976 0.952 0.986LCT 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 0.024 0.048 0.014IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.229 0.250 0.265SBus 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 0.771 0.750 0.735RT 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 0.029 0.106 0.106RT 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 0.971 0.894 0.894SUShT 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 0.572 0.640 0.654SUShT 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 0.428 0.360 0.346SULhT 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 0.572 0.640 0.654SULhT 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 0.428 0.360 0.346MH Gas 0.630 0.660 0.680 0.710 0.740 0.770 0.790 0.820 0.850 0.850 0.850 0.850 0.850 0.850 0.850MH Diesel 0.370 0.340 0.320 0.290 0.260 0.230 0.210 0.180 0.150 0.150 0.150 0.150 0.150 0.150 0.150CShT 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 0.114 0.157 0.163CShT 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 0.886 0.843 0.837CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.187Texas Statewide 2020 Fuel Engine Fractions SummarySUTFuelTypeModel Year2020201920182017201620152014201320122011201020092008200720062005PC 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 0.999 1.000 0.993 0.995PC 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 0.001 0.000 0.007 0.005PT 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 0.977 0.981 0.975 0.979PT 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 0.023 0.019 0.025 0.021PT 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 0.977 0.981 0.975 0.979PT 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 0.023 0.019 0.025 0.021LCT 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 0.941 0.948 0.938 0.946LCT 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 0.059 0.052 0.062 0.054LCT 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 0.941 0.948 0.938 0.946LCT 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 0.059 0.052 0.062 0.054IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010 0.010SBus 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 0.990 0.990 0.990 0.990RT 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 0.005 0.001 0.003 0.003RT 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 0.995 0.999 0.997 0.997SUShT 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 0.287 0.256 0.238 0.232SUShT 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 0.713 0.744 0.762 0.768SULhT 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 0.287 0.256 0.238 0.232SULhT 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 0.713 0.744 0.762 0.768MH 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 0.510 0.530 0.540 0.560MH 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 0.490 0.470 0.460 0.440CShT 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 0.058 0.031 0.050 0.051CShT 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 0.942 0.969 0.950 0.949CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.188Texas Statewide 2020 Fuel EngineFractions Summary - ContinuedSUTFueltypeModel Year200420032002200120001999199819971996199519941993199219911990PC 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 0.999 0.997 0.999PC 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 0.001 0.003 0.001PT 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 0.985 0.994 0.989PT 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 0.015 0.006 0.011PT 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 0.985 0.994 0.989PT 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 0.015 0.006 0.011LCT 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 0.951 0.937 0.984LCT 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 0.049 0.063 0.016LCT 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 0.951 0.937 0.984LCT 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 0.049 0.063 0.016IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.090 0.124SBus 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 0.990 0.910 0.876RT 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 0.210 0.101 0.204RT 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 0.790 0.899 0.796SUShT 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 0.511 0.465 0.539SUShT 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 0.489 0.535 0.461SULhT 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 0.511 0.465 0.539SULhT 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 0.489 0.535 0.461MH 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 0.850 0.850 0.850MH 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 0.150 0.150 0.150CShT 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 0.160 0.161 0.144CShT 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 0.840 0.839 0.856CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.189Texas Statewide 2023 Fuel Engine Fractions SummarySUTFuelTypeModel Year2023202220212020201920182017201620152014201320122011201020092008PC 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 0.988 0.990 0.993 0.999PC 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 0.012 0.010 0.007 0.001PT 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 0.980 0.987 0.985 0.977PT 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 0.020 0.013 0.015 0.023PT 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 0.980 0.987 0.985 0.977PT 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 0.020 0.013 0.015 0.023LCT 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 0.947 0.962 0.955 0.941LCT 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 0.053 0.038 0.045 0.059LCT 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 0.947 0.962 0.955 0.941LCT 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 0.053 0.038 0.045 0.059IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010 0.010SBus 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 0.990 0.990 0.990 0.990RT 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 0.003 0.002 0.002 0.005RT 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 0.997 0.998 0.998 0.995SUShT 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 0.234 0.274 0.351 0.287SUShT 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 0.766 0.726 0.649 0.713SULhT 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 0.234 0.274 0.351 0.287SULhT 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 0.766 0.726 0.649 0.713MH 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 0.500 0.500 0.500 0.510MH 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 0.500 0.500 0.500 0.490CShT 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 0.057 0.081 0.052 0.058CShT 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 0.943 0.919 0.948 0.942CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.190Texas Statewide 2023 Fuel EngineFractions Summary - ContinuedSUTFuelTypeModel Year200720062005200420032002200120001999199819971996199519941993PC 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 0.999 1.000 0.999PC 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 0.001 0.000 0.001PT 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 0.995 0.991 0.986PT 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 0.005 0.009 0.014PT 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 0.995 0.991 0.986PT 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 0.005 0.009 0.014LCT 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 0.932 0.974 0.974LCT 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 0.068 0.026 0.026LCT 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 0.932 0.974 0.974LCT 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 0.068 0.026 0.026IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.114 0.147 0.121SBus 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 0.886 0.853 0.879RT 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 0.010 0.105 0.031RT 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 0.990 0.895 0.969SUShT 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 0.673 0.508 0.519SUShT 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 0.327 0.492 0.481SULhT 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 0.673 0.508 0.519SULhT 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 0.327 0.492 0.481MH 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 0.790 0.820 0.850MH 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 0.210 0.180 0.150CShT 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 0.275 0.117 0.117CShT 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 0.725 0.883 0.883CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.191Texas Statewide 2026 Fuel Engine Fractions SummarySUTFuelTypeModel Year2026202520242023202220212020201920182017201620152014201320122011PC 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 0.988 0.988 0.988 0.988PC 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 0.012 0.012 0.012 0.012PT 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 0.980 0.980 0.980 0.980PT 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 0.020 0.020 0.020 0.020PT 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 0.980 0.980 0.980 0.980PT 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 0.020 0.020 0.020 0.020LCT 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 0.947 0.947 0.947 0.947LCT 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 0.053 0.053 0.053 0.053LCT 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 0.947 0.947 0.947 0.947LCT 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 0.053 0.053 0.053 0.053IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010 0.010SBus 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 0.990 0.990 0.990 0.990RT 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 0.003 0.003 0.003 0.003RT 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 0.997 0.997 0.997 0.997SUShT 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 0.396 0.371 0.219 0.234SUShT 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 0.604 0.629 0.781 0.766SULhT 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 0.396 0.371 0.219 0.234SULhT 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 0.604 0.629 0.781 0.766MH 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 0.500 0.500 0.500 0.500MH 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 0.500 0.500 0.500 0.500CShT 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 0.094 0.199 0.110 0.057CShT 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 0.906 0.801 0.890 0.943CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.192Texas Statewide 2026 Fuel EngineFractions Summary - ContinuedSUTFuelTypeModel Year201020092008200720062005200420032002200120001999199819971996PC 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 0.998 0.999 0.999PC 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 0.002 0.001 0.001PT 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 0.993 0.992 0.981PT 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 0.007 0.008 0.019PT 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 0.993 0.992 0.981PT 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 0.007 0.008 0.019LCT 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 0.950 0.927 0.971LCT 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 0.050 0.073 0.029LCT 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 0.950 0.927 0.971LCT 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 0.050 0.073 0.029IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.042SBus 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 0.990 0.990 0.958RT 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 0.404 0.019 0.012RT 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 0.596 0.981 0.988SUShT 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 0.435 0.436 0.427SUShT 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 0.565 0.564 0.573SULhT 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 0.435 0.436 0.427SULhT 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 0.565 0.564 0.573MH 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 0.710 0.740 0.770MH 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 0.290 0.260 0.230CShT 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 0.134 0.147 0.146CShT 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 0.866 0.853 0.854CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.193Texas Statewide 2028 Fuel Engine Fractions SummarySUTFuelTypeModel Year2028202720262025202420232022202120202019201820172016201520142013PC 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 0.988 0.988 0.988 0.988PC 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 0.012 0.012 0.012 0.012PT 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 0.980 0.980 0.980 0.980PT 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 0.020 0.020 0.020 0.020PT 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 0.980 0.980 0.980 0.980PT 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 0.020 0.020 0.020 0.020LCT 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 0.947 0.947 0.947 0.947LCT 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 0.053 0.053 0.053 0.053LCT 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 0.947 0.947 0.947 0.947LCT 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 0.053 0.053 0.053 0.053IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010 0.010SBus 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 0.990 0.990 0.990 0.990RT 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 0.003 0.003 0.003 0.003RT 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 0.997 0.997 0.997 0.997SUShT 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 0.396 0.396 0.396 0.371SUShT 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 0.604 0.604 0.604 0.629SULhT 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 0.396 0.396 0.396 0.371SULhT 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 0.604 0.604 0.604 0.629MH 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 0.500 0.500 0.500 0.500MH 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 0.500 0.500 0.500 0.500CShT 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 0.094 0.094 0.094 0.199CShT 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 0.906 0.906 0.906 0.801CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.194Texas Statewide 2028 Fuel EngineFractions Summary - ContinuedSUTFuelTypeModel Year201220112010200920082007200620052004200320022001200019991998PC 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 0.997 0.998 0.998PC 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 0.003 0.002 0.002PT 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 0.992 0.981 0.993PT 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 0.008 0.019 0.007PT 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 0.992 0.981 0.993PT 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 0.008 0.019 0.007LCT 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 0.949 0.929 0.950LCT 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 0.051 0.071 0.050LCT 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 0.949 0.929 0.950LCT 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 0.051 0.071 0.050IBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000TBus Gas 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000TBus Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000SBus 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 0.010 0.010 0.010SBus 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 0.990 0.990 0.990RT 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 0.002 0.169 0.404RT 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 0.998 0.831 0.596SUShT 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 0.350 0.348 0.435SUShT 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 0.650 0.652 0.565SULhT 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 0.350 0.348 0.435SULhT 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 0.650 0.652 0.565MH 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 0.660 0.680 0.710MH 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 0.340 0.320 0.290CShT 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 0.090 0.107 0.134CShT 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 0.910 0.893 0.866CLhT Diesel 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0001Conventional internal combustion engine technology only.195 

### APPENDIX I: MOVES RUN SUMMARIES197 Appendix I is being transmitted electronically.