4.5 Agency-submitted MOVES inputs
Many state and local agencies provided county-level MOVES inputs in the form of CDBs. This established format requirement enables EPA to more efficiently scan for errors and manage input datasets. EPA screened all submitted data using several quality assurance scripts that analyze the individual tables in each CDB to look for missing or unrealistic data values. EPA also reviewed submitted age distributions, road type VMT distributions, and monthly VMT distributions in consideration of whether to accept these data vs. county-specific EPA defaults.
4.5.1 Overview of MOVES input submissions
State and local agencies prepare complete sets of MOVES input data in the form of one CDB per county. One way agencies can ensure a correctly formatted CDB is to use the MOVES graphical user interface (GUI) county data manger (CDM) importer. With a proper template created for a single county, a larger set of counties (e.g., statewide) can be updated systematically with county-specific information if the preparer has well-organized county data and familiarity with MariaDB queries. However, there is no requirement of MariaDB experience to prepare the NEI submittal because the user can instead rely on the CDM to help build the individual CDBs one at a time. Table 4.8 lists the tables in each CDB and describes its content or purpose. Note that several of the tables are optional, which means that they may be left blank without consequence to a MOVES run’s completeness of results. If an optional CDB table is populated, the data override MOVES internal calculations and produce a different result that may better represent local conditions.
| Table Name | Description of Content |
|---|---|
| avft | Fuel type fractions |
| avgspeeddistribution | Average speed distributions |
| county | Description of the county |
| countyyear | Optional table - percent reduction of total potential vapor losses and refueling spillage losses by state or local programs |
| dayvmtfraction | Fractions to distribute VMT between day types |
| fuelformulation | Fuel properties |
| fuelsupply | Fuel differences by month of year |
| fuelusagefraction | Fraction of the time that E85 vs. gasoline is used in flex-fuel engine vehicles |
| hotellingactivitydistribution | Optional table – fraction of hoteling hours in which the power source is the main engine, diesel APU, electric APU, or engine-off |
| hotellingagefraction | Optional table – fraction of hoteling hours by age (e.g., to account for newer trucks having more hoteling activity). Fractions should sum to 1.0. |
| hotellinghourfraction | Optional table – fraction of hoteling in hours of the day. Fractions should sum to 1.0 for each day type. |
| hotellinghoursperday | Optional table – total hours of hoteling per day, including total time spent in all of the four operating modes defined in the hotellingactivitydistribution table. |
| hotellingmonthadjust | Optional table – adjustment factors to vary hoteling activity between different months. A factor of 1.0 for each month will model a situation where annual hoteling hours are evenly divided among months. A value of 1.1 for month ID 1 will increase the hoteling hours per day in January by 10%. |
| hourvmtfraction | Fractions to distribute VMT across hours in a day |
| hpmsvtypeday | VMT input by HPMS vehicle group, month, and day type (1 of 4 options) |
| hpmsvtypeyear | VMT input by HPMS vehicle group, as annual total (2 of 4 options) |
| idledayadjust | Optional table – adjustment factors used to vary idle activity provided in the idlemodelyeargrouping table by day type (weekday or weekend day). |
| idlemodelyeargrouping | Optional table – fraction of vehicle time operating when the speed is zero. This table is an alternative input to the totalidlefraction table. If used, idlemonthadjust and idledayadjust should also be supplied. |
| idlemonthadjust | Optional table – adjustment factors used to vary idle activity provided in the idlemodelyeargrouping table between different months. An adjustment factor of 1.0 for each month will model the situation where the total idle fraction does not change between months. |
| imcoverage | Description of the inspection and maintenance program |
| monthvmtfraction | Fractions to distribute VMT across 12 months of the year |
| onroadretrofit | Optional table – data for heavy-duty diesel retrofit and/or replacement program data that apply adjustments to vehicle emission rates. |
| roadtypedistribution | Fractions to distribute VMT across the road types |
| sourcetypeagedistribution | Distribution of vehicle population by age |
| sourcetypedayvmt | VMT input by source use type, month, and day type (3 of 4 options) |
| sourcetypeyear | Vehicle populations |
| sourcetypeyearvmt | VMT input by source use type, as annual total (4 of 4 options) |
| starts | Optional table – starts activity, replacing the MOVES-generated starts table |
| startsageadjustment | Optional table – numbers reflecting relative differences in the number of vehicle starts by age. |
| startshourfraction | Optional table – fractions to distribute starts across hours in a day |
| startsmonthadjust | Optional table – fractions to vary the vehicle starts by month of year |
| startsopmodedistribution | Optional table – fractions to distribute the percent of engine soak-times by source type, day type, hour, and vehicle age. |
| startsperday | Optional table – total number of starts in a day |
| startsperdaypervehicle | Optional table – total number of starts per vehicle in a day by source type |
| startssourcetypefraction | Optional table – fractions to distribute starts among MOVES source types |
| State | Description of the state |
| totalidlefraction | Optional table – Fraction of vehicle operating time when speed is zero |
| year | Year of the database |
| zone | Allocations of starts, extended idle and vehicle hours parked to the county |
| zonemonthhour | Temperature and relative humidity values |
| zoneroadtype | Allocation of source hours operating to the county |
S/L/T agencies submitted a total of 1,425 CDBs for the 2023 NEI. Previously, agencies submitted 1,565 CDBs for the 2020 NEI, 1,693 for the 2017 NEI, 1,816 CDBs for the 2014 NEI, and 1,426 CDBs for the 2011 NEI. Agencies submitted data through the EPA Emissions Inventory System (EIS) and provided complete CDBs (i.e., each required table populated) along with documentation and a submission checklist indicating which of the CDB tables contained local data. Table 4.9 summarizes these submission checklists, showing the number of counties within each submittal for which the information was local data, as opposed to a default. Empty slots in the table indicate that the state or county did not provide local data for that particular CDB table. The grand totals of counties across all states show that VMT, population, road type distribution, and month VMT fractions were the most commonly provided local data types. Note that Table 4.9 is a select subsection of the list of CDB tables in Table 4.8. Tables not included below are tables that do not contain state specific data. For example, Year, Zone, and ZoneRoadType just list the year and geographic entity (state in this case) for the run.
Figure 4.1 shows the geographic coverage of CDB submissions where the state or local agency submitted data that was used for at least one table (dark blue). The light blue areas are counties for which the NEI uses EPA default 2023 CDBs.
Figure 4.1: Counties for which agencies submitted local data for at least one CDB table (Submitting areas are shown in dark blue)
| Variable | AK | AZ (Maricopa) | CT | DE | GA | ID | MA | MD | ME | NC | NH | NJ | NV (Clark) | NV (Washoe) | NY | OH | OR | PA | RI | SC | TN | TN (Knox) | TN (Davidson) | TX | UT | VA | VT | WA | WI | WV | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| avft | 30 | 1* | 8* | 3 | 159 | 44 | 24 | 16 | 1 | 1 | 62 | 67* | 91 | 1 | 133 | 39* | 72* | 752 | |||||||||||||
| avgspeeddistribution | 30 | 1 | 44 | 24 | 16 | 21 | 1 | 88 | 67 | 91 | 1 | 1 | 385 | ||||||||||||||||||
| countyyear | 24 | 21 | 45 | ||||||||||||||||||||||||||||
| dayvmtfraction | 30 | 1 | 8 | 44 | 24 | 16 | 21 | 1 | 1 | 88 | 67 | 5 | 91 | 1 | 254 | 133 | 39 | 824 | |||||||||||||
| fuelusagefraction | 8 | 24 | 16 | 1 | 14 | 63 | |||||||||||||||||||||||||
| hotellingactivitydistribution | 0 | ||||||||||||||||||||||||||||||
| hotellinghoursperday | 254 | 254 | |||||||||||||||||||||||||||||
| hotellingmonthadjust | 254 | 254 | |||||||||||||||||||||||||||||
| hourvmtfraction | 30 | 1 | 8 | 44 | 24 | 16 | 21 | 1 | 1 | 62 | 88 | 67 | 254 | 133 | 39 | 789 | |||||||||||||||
| hpmsvtypeyear | 30 | 1 | 8 | 159 | 44 | 14 | 24 | 16 | 100 | 10 | 21 | 88 | 36 | 67 | 5 | 46 | 91 | 1 | 1 | 29 | 133 | 14 | 39 | 72 | 55 | 1,104 | |||||
| imcoverage | 1 | 8 | 3 | 13 | 44 | 14 | 24 | 19 | 10 | 21 | 1 | 1 | 62 | 7 | 4 | 67 | 5 | 254 | 29 | 10 | 7 | 604 | |||||||||
| monthvmtfraction | 30 | 1 | 8 | 44 | 24 | 16 | 21 | 1 | 1 | 62 | 88 | 67 | 5 | 91 | 1 | 254 | 133 | 14 | 39 | 7 | 907 | ||||||||||
| roadtypedistribution | 30 | 1 | 8 | 159 | 44 | 14 | 24 | 16 | 100 | 10 | 21 | 1 | 62 | 88 | 67 | 5 | 91 | 1 | 1 | 254 | 29 | 133 | 14 | 39 | 72 | 1,284 | |||||
| sourcetypeagedistribution | 30 | 1* | 8* | 3 | 159 | 44 | 14 | 24 | 16 | 100 | 10 | 21 | 1 | 1 | 62 | 88* | 36 | 67* | 5* | 46 | 91 | 1 | 1 | 254 | 29 | 133 | 14* | 39* | 72* | 1,370 | |
| sourcetypedayvmt | 1 | 1 | |||||||||||||||||||||||||||||
| sourcetypeyear | 30 | 1 | 8 | 3 | 159 | 44 | 14 | 24 | 16 | 100 | 10 | 21 | 1 | 1 | 62 | 88* | 36 | 67 | 5 | 46 | 91 | 1 | 1 | 254 | 29 | 133 | 14* | 39* | 72 | 55 | 1,171 |
| sourcetypeyearvmt | 3 | 1 | 62 | 254 | 320 | ||||||||||||||||||||||||||
| startsperday | 21 | 21 | |||||||||||||||||||||||||||||
| zoneroadtype | 30 | 30 | |||||||||||||||||||||||||||||
| *Partial Table Submitted (i.e., partly MOVES Default) |
4.5.2 QA checks on MOVES CDB Tables
EPA reviewed lists of CDB data errors and warnings flagged by the NEI quality assurance script packaged with MOVES5. The quality assurance script reports the potential errors by compiling a list into a summary Excel file. The list of potential errors includes the CDB name, table name, a numeric error code, and in some cases the suspect data value or sum of values. EPA reviewed all potential errors, identified which ones needed to be addressed, and then coordinated with the responsible state/local agency to clarify whether the data were correct or needed revision.
The EPA MOVES team designed the NEI quality assurance script to identify errors that would cause MOVES to crash (e.g., missing or incorrectly formatted tables) or produce erroneous results. Aside from review of quality assurance script results, EPA prepared and reviewed graphs of submitted age distributions, month/day/hour VMT fractions, and speed distributions for consideration of where to override submittals with EPA default information specific to year 2023. Many of the 1,425 submitted CDBs required one or more updates due to missing or incorrect data or incorrect table formatting which was removed prior to use. The missing or incorrect data included the following problems:
- Age distribution represented a different data year than 2023 (i.e., LDV recession “dip” shifted by several years)
- Age distribution did not extend out to 40 years; some submittals had 30-year age distributions
- Age distribution with missing source types
- Expected “County” CDB table was missing
- Expected VMT tables (SourceTypeDayVMT, SourceTypeYearVMT, and HPMSVtypeDay) were missing
- Expected “AVFT” table required was missing
- Submittal documentation erroneously stated which data were local vs. default.
EPA resolved each of the above data problems by coordinating with state/local agencies individually. In some cases, the agency preferred to submit a corrected CDB, which EPA reviewed again to verify the intended correction. In other cases, the agency provided EPA with instructions for a spot correction to a table or simply accepted EPA’s proposed update. EPA also corrected minor formatting problems with the database tables. In some cases, tables had missing data fields and/or table keys; the missing fields did not house important content, but their presence is required for MOVES to run. EPA’s final decisions on the data source (submittal vs. EPA-developed information) for age distribution, speed distribution, and hourly VMT fractions can be found in the documentation spreadsheet 2023_Documentation_of_CDB_Input_Data_20251013.xlsx posted with the 2023 NEI supplemental data files.
EPA used CDBs constructed with EPA-generated data for counties where agencies did not submit input data. EPA developed new 2023 estimates of VMT, vehicle population, and hoteling at the county- and SCC-level for use in the subsequent SMOKE-MOVES processing step and inserted these data into the CDBs where states did not provide data. The SMOKE files contain this information at the resolution of SCC, which includes the source type, fuel type, and road type. When inserted into the CDB table for source type VMT (sourceTypeYearVMT), we sum over the fuel and road type. Similarly, for population, we sum over the SCC fuel type to aggregate population to the source type level for the CDB table containing population (sourceTypeYear). In contrast, the hoteling activity detail is much more disaggregated in the two MOVES tables (hotellingHours and hotellingActivityDistribution) compared to the SMOKE FF10 hoteling file. The script that inserts these data into the set of “all CDBs” (ReverseFF10_Script_20251008.plx) is listed in or_scripts_2023.zip. States and counties with CDBs that included EPA-generated activity and projected CDBs are those indicated by light blue shading in Figure 4.1. Table 4.10 below lists the sources of default information by MOVES CDB table. The spreadsheet “2023 Documentation of CDB Input Data_20251013.xlsx” provides specific information about where state-supplied data were used versus default data. Additional detail on processing steps in the SPGM data to create “AVFT” and “SourceTypeAgeDistribution” is provided below in Section 4.5.3.
| CDB Table | Default content for 2023 NEI |
|---|---|
| Avft | 2023 SPGM registration data |
| Avgspeeddistribution | StreetLight telematics data |
| County | MOVES5 default altitude, barometric pressure, and urban/rural county type |
| Dayvmtfraction | StreetLight telematics data |
| Fuelformulation | MOVES5 default (retail fuel sample data) |
| Fuelsupply | MOVES5 default (retail fuel sample data) |
| Fuelusagefraction | MOVES5 default E85 usage |
| Hotellingactivitydistribution | MOVES5 default APU vs. Main Engine fractions |
| Hotellingagefraction | Empty by default |
| Hotellinghourfraction | Empty by default |
| Hotellinghoursperday | 2023 EPA estimates of hoteling based on 2023 VMT |
| Hotellingmonthadjust | Flat profile that only accounts for the number of days in each month |
| Hourvmtfraction | StreetLight telematics data |
| Hpmsvtypeday | Empty by default |
| Hpmsvtypeyear | Empty by default |
| Idledayadjust | Empty by default |
| Idlemodelyeargrouping | Empty by default |
| Idlemonthadjust | Same data as Monthvmtfraction |
| Imcoverage | MOVES5 |
| Monthvmtfraction | 2022 TMAS traffic volume data |
| Onroadretrofit | Empty by default |
| Roadtypedistribution | 2023 FHWA data |
| Sourcetypeagedistribution | 2023 SPGM registration data |
| Sourcetypedayvmt | Empty by default |
| Sourcetypeyear | 2023 SPGM registration data |
| Sourcetypeyearvmt | 2023 VMT based on FHWA data |
| Starts | Empty by default |
| Startsageadjustment | Empty by default |
| Startshourfraction | Empty by default |
| Startsmonthadjust | Same data as Monthvmtfraction |
| Startsopmodedistribution | Empty by default |
| Startsperday | Empty by default |
| Startsperdaypervehicle | Empty by default |
| State | MOVES5 default idle region ID |
| Zonemonthhour | 2023 meteorology data averaged by county |
| Emissionratebyage (provided via input database; not in the CDB) |
The emissionratebyage tables for some LEV states were populated using appropriate data described in the guidance for states adopting California emission standards. These were provided to MOVES as separate databases from the CDB.
|
4.5.3 Preparation of “SourceTypeYear”, “SourceTypeAgeDistribution”, and “AVFT” CDB Tables
As mentioned above in Section 4.2.1, national vehicle population data from SPGM for 2023 were used to derive updated population by source type (MOVES “sourceTypeYear” table), age distributions by source type (MOVES “sourceTypeAgeDistribution” table), and fuel type splits by source type and model year (MOVES “AVFT” table) for the CDBs. These data were computed at the county level for the set of individual CDBs for all 3,224 counties, and they were averaged over county groups for the set of representative CDBs used in the MOVES runs for the NEI. Vehicle populations were summed (not averaged) over member counties for each representative county group. EPA performed the same averaging and summing for submitted data. As discussed in the data selection hierarchy, EPA preferred to use local data where they were found to be acceptable. Local data were used preferentially and supplemented with EPA-developed information where needed. In the EPA-developed data, the source registration data does not reliably distinguish between short-haul and long-haul activity, and so source types 52 and 53 (single unit trucks) have the same age distributions, as do source types 61 and 62 (combination unit trucks). In addition, all age distributions for long-haul trucks (source types 53 and 62) are a national average, because these vehicles are expected to travel long distances from the county where they are registered. Section 4.2.1 discussed procedures to split truck populations into short- vs. long-haul use types in the EPA Default data.
4.5.4 Transformation of StreetLight Telematics Data Summaries into Hour/Day Distributions of VMT and Speed Distribution inputs for MOVES and SMOKE
EPA purchased vehicle activity data for all months of 2022 and months January through May of 2023 from StreetLight and converted the information into MOVES and SMOKE model inputs. EPA leveraged prior work conducted for the 2017 and 2020 NEIs for the new data request to StreetLight and the data processing into NEI inputs.
Raw Data Format
Table 4.11 shows an example of five lines of data from StreetLight’s delivery to EPA. The table footnotes describe the scope of possible categories in each column. In total, StreetLight generated over 700 million rows of data in the format below.
| County FIPS (A) | Vehicle Category (B) | Road Type(C) | Year Month (D) | Day Type (E) | Hour of Day (F) | Speed Bin (G) | Total Segment Length (ft) (H) | Total Segment Time (sec) (I) | Counts in this Combination (J) |
|---|---|---|---|---|---|---|---|---|---|
| 1001 | PERS | Rural Restricted | 202201 | F | 0 | 15 | 84.76 | 4 | 1 |
| 1001 | PERS | Rural Restricted | 202201 | F | 0 | 25 | 111.93 | 3 | 1 |
| 1001 | PERS | Rural Restricted | 202201 | F | 0 | 35 | 145.98 | 3 | 1 |
| 1001 | PERS | Rural Restricted | 202201 | F | 0 | 40 | 353.62 | 6 | 2 |
| 1001 | PERS | Rural Restricted | 202201 | F | 0 | 45 | 682.94 | 10 | 3 |
- County FIPS: the numeric FIPS code for each county in the contiguous 48 states.
- Vehicle Category: personal (PERS) vehicles, commercial medium-duty trucks (COMM-MD), or commercial heavy-duty trucks (COMM-HD).
- Road Type: the four MOVES road types, combination of Urban/Rural and Restricted/Unrestricted access.
- Year-Month: Year and month of analysis in YYYYMM format.
- Day Type: M, Tu, W, Th, F, Sa, Su.
- Hour of Day: 0 to 23, representing the hour of the day.
- Speed Bin: 2.5, 5, 10, 15, …90, 95, 100+ miles per hour (mph). The first 16 bins correspond to MOVES speed bins, and the final 5 are for higher speeds above 75 mph up to 100+ mph.
- Cumulative travel distance for all vehicles traveling within the specific speed bin on roadway segments of the specified road type in the county, occurring in the month, day type of week, and hour. Units are in feet, rounded to 2 decimal places.
- Time corresponding to the cumulative travel distance defined above. Units are in seconds, and values are reported in whole seconds (no decimals).
- Counts in the Combination refers to the number of unique road segments included on the data row. It is an indicator of sample size but does not reflect vehicle volumes.
Data Coverage and Quality Assurance
Prior to transforming StreetLight data into model inputs, EPA reviewed the data to identify any unexpected data. The following data issues were found, and EPA’s solutions are listed for each:
- Sharp declines in peak afternoon hour VMT for commercial vehicles were observed in January–October 2022 data. These months were excluded from commercial vehicle hour/day VMT distribution calculations.
- Large dip in Wednesday day VMT for commercial heavy-duty observed in July 2022 data. July 2022 was substituted with August 2022 for commercial heavy-duty day VMT distributions.
- Higher average hourly speeds for personal vehicles were observed in January 2022 compared to all other months. January 2022 was excluded from personal vehicle speed distributions.
- Sparse data coverage for commercial medium-duty. Statewide averages were used for commercial medium-duty profiles.
The vehicle telematics summary datasets included the lower 48 states. EPA substituted finished model-ready profiles of Montana statewide averages to cover the Alaska boroughs and nationwide averages to cover Hawaii, Puerto Rico, and the U.S. Virgin Islands.
Gap Filling
The nature of the SMOKE-MOVES and representative county approach to the on-road NEI requires that activity profiles exist for all categories (i.e., all road types, vehicle classes, hours, day types), even including freeways in counties that have minimal to no freeways, or urban roads in a rural county (or vice versa – rural roads in an urban county).
EPA established multiple gap filling procedures to improve profiles for areas with low data coverage and fill profiles with missing data. Separate processes were established for day VMT distributions, hour VMT distributions and speed distributions for each vehicle category defined in StreetLight. With the exception of commercial medium-duty profiles which used statewide averages, EPA generally preferred to let the local data from each county stand on its own, representing only itself, even in areas that may have missing hours of data. Missing data often occurred during overnight hours, when the sample size of vehicles driving on roads is typically lower. In areas with very low data or missing data across many hours, county profiles were substituted with another county in the same state with a similar road type distribution. For the VMT distributions with missing hours, EPA set those hours’ values to zero (0), interpreting the missing coverage as no vehicle activity. In contrast for the speed distributions, EPA did not allow any missing hours of data in the modeling profiles due to the potential for data loss in the SMOKE-MOVES system and representative county approach. Below is a summary of gap-filling steps:
- Hour VMT Fraction:
- Group restricted and unrestricted road types (remove urban/rural distinction)
- Group day types (remove weekday/weekend distinction, commercial heavy-duty only)
- Group all road types
- Substitute similar county/counties
- Day VMT Fraction:
- Substitute another month
- Group all months (annual average)
- Group restricted and unrestricted road types (remove urban/rural distinction)
- Group day types (remove weekday/weekend distinction, commercial heavy-duty only)
- Group all road types
- Substitute similar county/counties
- Average Speed Distribution:
- Group restricted and unrestricted road types (remove urban/rural distinction)
- Group all vehicle types
- Substitute similar county/counties
- Substitute another hour (to fill any missing hours)
EPA developed decision tree flowcharts that describe the gap-filling procedure in full detail. The decision flowchart is named 2022-2023_StreetLight_Grouping_Decision_Charts.pdf and is included with the supporting data listed in Table 4.17.
4.5.5 Default California emission standards
EPA populated an alternative MOVES database table ‘EmissionRateByAge’ in the CDBs for states that have adopted emission standards from California’s Low Emission Vehicle (LEV) program. Table 4.12 shows states that adopted the California standards and the year the program began in each state. EPA developed these tables using built-in MOVES tools and guidance on LEV modeling. The LEV databases are included with MOVES_Input_DBs.zip that is available with the supporting data described in Table 4.17.
| FIPS State ID | State Name | LEV Program Start Year |
|---|---|---|
| 06 | California | 1994 |
| 08 (A) | Colorado (A) | 2022 (A) |
| 09 | Connecticut | 2008 |
| 10 | Delaware | 2014 |
| 23 | Maine | 2001 |
| 24 | Maryland | 2011 |
| 25 | Massachusetts | 1995 |
| 27 (B) | Minnesota (B) | 2025 (B) |
| 32 (B) | Nevada (B) | 2025 (B) |
| 35 (B) | New Mexico (B) | 2026 (B) |
| 34 | New Jersey | 2009 |
| 36 | New York | 1996 |
| 41 | Oregon | 2009 |
| 42 | Pennsylvania | 2008 |
| 44 | Rhode Island | 2008 |
| 50 | Vermont | 2000 |
| 53 | Washington | 2009 |
- Colorado LEV database for program start year 2022 was not created for 2023 NEI because 2017-2024 emission factors are the same between MOVES and LEV.
- Minnesota, Nevada, and New Mexico LEV databases were not created for 2023 NEI because the program start year happens in the future after year 2023.
In addition to California LEV standards adoption, several states participated in early adoption of the national low emission vehicle (NLEV) standards, including Connecticut, Delaware, Maryland, New Hampshire, New Jersey, Pennsylvania, Rhode Island, and Virginia.