Input Guidelines for Motor Vehicle Emissions Simulator Model, Volume 3: Final Report (2015)

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C. Survey Responses This section presents charts showing the distribution of responses to each question, along with free-response comments to each question or on each page of the survey. The free-response comments have not been edited. C.1 SURVEY RESPONDENTS Out of 79 responses, 59 agencies identified themselves in the survey, as listed below. In a few cases, two different people at a single agency responded. • MPOs (up to 38 agencies represented): - Anchorage Metropolitan Transportation Solutions; - Atlanta Regional Commission; - Boston Region MPO; - Capital District Transportation Committee (Albany, New York); - Chattanooga (Tennessee) Transportation Planning Organization; - Delaware-Muncie Metropolitan Plan Commission (Muncie, Indiana); - East-West Gateway Council of Governments (St. Louis, Missouri); - El Paso MPO; - Houston-Galveston Area Council; - Kentuckiana Regional Planning and Development Agency (Louisville, Kentucky); - KYOVA Interstate Planning Commission (Huntington, West Virginia); - Maricopa Association of Governments (Phoenix, Arizona); - Metropolitan Washington Council of Governments; - Mid Ohio Regional Planning Commission (Columbus, Ohio); - Mid-America Regional Council (Kansas City, Missouri); - Missoula (Montana) MPO; - Mountainland Association of Governments (Orem, Utah); - New York Metropolitan Transportation Council; - North Central Texas Council of Governments (Dallas-Fort Worth, Texas); - North Jersey Transportation Planning Authority; - Poughkeepsie-Dutchess County Transportation Council (New York); - Rogue Valley MPO (Medford, Oregon); C-1

- Southeast Michigan Council Of Governments (Detroit, Michigan); - Southeast Wisconsin Regional Planning Agency (Milwaukee, Wisconsin); - South Jersey Transportation Planning Organization; - Wasatch Front Regional Council (Salt Lake City, Utah); - Wood-Washington-Wirt Interstate Planning Commission (Parkersburg, West Virginia/Ohio); and - Eleven anonymous responses. • State DOTs (up to 14 agencies represented): - Connecticut DOT (two responses); - Delaware DOT; - Georgia DOT; - Kentucky Transportation Cabinet; - New Hampshire DOT; - New Jersey DOT; - New York State DOT; - North Carolina DOT; - Pennsylvania DOT; - South Carolina DOT; - Virginia DOT; - Washington State DOT; and - Two anonymous responses. • State Air Agencies (up to 22 agencies represented): - Alaska Department of Environmental Conservation; - Colorado Air Pollution Control Division; - Connecticut Department of Energy and Environmental Protection; - Georgia Department of Natural Resources – Environmental Protection Division; - Illinois Environmental Protection Agency; - Maine Department of Environmental Protection; - Maryland Department of the Environment; - Missouri Department of Natural Resources; - New Hampshire Department of Environmental Services; - New Jersey Department of Environmental Protection; - Pennsylvania Department of Environmental Protection; C-2

- South Carolina Department of Health and Environmental Control; - Tennessee Department of Environment and Conservation; - Utah Division of Air Quality; - Washington State Department of Ecology; - Wyoming Department of Environmental Quality – Air Quality Division; and - Six anonymous responses. • Other (five agencies represented): - Charlotte (North Carolina) Department of Transportation; - Louisville Metro Air Pollution Control District; - Mecklenburg County Air Quality; - Washoe County Health District, Air Quality Management Division; and - One anonymous response. C.2 AGENCY CHARACTERISTICS What type of agency do you represent? (N=77) C-3

What population is served by your agency? (N=77). Please indicate the nonattainment status of your region for CO (N=67), Ozone (N=72), PM2.5 (N=69), and PM10 (N=65). C-4

Please indicate the nonattainment status of your region for each of the following pollutants: Ozone Please indicate the nonattainment status of your region for each of the following pollutants: PM2.5 C-5

Please indicate the nonattainment status of your region for each of the following pollutants: PM10 What is your agency’s current status with respect to the use of MOVES? (N=77). C-6

C.3 EMISSION MODELING PRIORITIES For what regional planning purposes does your agency use, or plan to use, MOVES? For what project-level planning purposes does your agency use, or plan to use, MOVES? C-7

Does your agency interface emissions rates, or related data, from MOVES with the following types of models? Additional comments responses on emission modeling priorities: • (Note that question numbers refer to those found in the Word version of the survey.) On Q8, we haven’t used MOVES for an “official” conformity determination, but have used it for regional GHG analysis, for assistance with SIP development, and in work on transition from Mobile. On Q5, our agency doesn’t do the SIP development, but we do support the state air agency (NJDEP) by developing activity estimates. On Q7, we use the PPSuite travel demand model postprocessor, if this qualifies as an “emission processor.” PPSuite generates MOVES activity inputs based on travel model data and aggregates MOVES emission outputs for the purpose of regional air quality analysis. On Q9 (next section), we have decided to standardize on using MOVES in inventory mode for conformity analyses, but have used rate mode in GHG analyses. • At this time we are working on developing the in-house capability to perform MOVES runs. Up until now our MOVES work has been done by an outside contractor. • First question, this section  Items a and b are performed jointly by MPO staff and LAQA staff. Items c through e, for the most part, are performed by or will be performed by LAQA staff with MPO staff only providing input data. Responsibility for emission modeling for items f through i is not known at this time. Second question, this section  Efforts to interface MOVES w/travel demand model currently under development w/MPO staff attempting to implement postprocessor to accomplish this. Efforts to interface MOVES with traffic simulation models and other transportation models will probably be led by MPO staff to the extent that these things may occur. Efforts to interface MOVES with air quality dispersion models will be C-8

led by LAQA staff to the extent that these things may occur. Efforts to interface MOVES with emission processors may be led by either staff, if they occur. • For the items marked “Don’t know” above – We have not done this in the past, but recognize we may end up doing it at some point. • MOVES has been integrated with MPO regional travel models and with MD_SHA-based HPMS traffic/roadway data without travel models. MOVES will be used for future project-level analysis as warranted. • MOVES has been integrated with MPO regional travel models and with PennDOT HPMS and RMS traffic data in areas without travel models. MOVES will be used for future project-level analyses as warranted. • Our plans are to use MOVES output to input into SMOKE, but we haven’t done that ourselves yet. • Regarding unanswered questions, we anticipate GHG analysis under state direction in the future and would probably use MOVES but guidance uncertain now. • Responses to what portions of the area are in attainment, nonattainment, etc., are hard to answer. For example, only two out of eight counties in CT were ever in nonattainment for PM2.5. However, are DEEP has submitted a redesignation of that area to maintenance, but has not as yet received approval. Only three areas in Ct were in nonattainment for CO but have been in maintenance for over 12 years. It might have been better to have an option for a portion of the state or a combination. • This interface is not a direct interface but one where we put data from MOVES and put into other formats manually. • Though we don’t need emission rates for the overall emission calculations, we do need to use them for CMAQ processes and sometimes local projects assistance. C.4 MOVES-RELATED EXPERIENCE MOVES can be used in emissions inventory mode to calculate emissions entirely within MOVES, or in rate mode to output emission rates (or factors) to apply to travel activity data from a travel demand forecasting model (TDFM). C-9

Do you use MOVES in emission rate or inventory mode? (N=74). C.5 EXPERIENCE RELATED TO VEHICLE FLEET INPUTS What is your agency’s source of light-duty age distributions? (N=73). C-10

Responder comments on the source of light-duty age distributions: • Other agency provides data. • We use local data to get totals in 3 MOVES vehicle type groups: 11, 21, 31+32. We use MOVES’ technical guidance Table A.1 to split these into their individual vehicle types. • Attempting to use registration data, but not available for HD vehicles – must use defaults for this. • NC DMV via NC DAQ. • DMV data disaggregated using MOVES defaults. • CT DEEP prepares file. See their response. Based on DMV registrations. • Not sure. • It depends on quality of the data • Most of the data is locally collected, but MOVES defaults are used for a few items. • State DOT. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • We used Mobile 6 and MOVES fractions to determine light-duty fleet mix. • DMV, DOT, IM records, and MPO data collection efforts. • Working on this using local/state sources and MOVES data. • Combination of vehicle registration database from State’s DMV and MOVES defaults. • Still being determined. • R.L. Polk. C-11

What is your agency’s source of heavy-duty age distributions? (N=70). Responder comments source of heavy-duty age distributions: • Other agency provides data. • We use local data to get totals in 6 MOVES vehicle type groups: 41+42, 42, 51, 52+53, 54, 61+62. We use MOVES’ technical guidance table A.1 to split these into their individual vehicle types. • Fill in with MOVES proportional data where not available or complete. • For long-haul combination trucks we use national defaults but for other heavy-duty we use local data. • Moves default because there is no guarantee that the heavy-duty registered in the state is actually operating in the state. • NC DMV via NC DAQ. • CT DEEP prepares file. See their response. Based on DMV registrations. • Not sure. • Mostly locally collected, except for HDV8B where we use defaults. • Most of the data is from the MOVES defaults, but locally collected data is used to the extent possible (a few items). • State DOT. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. C-12

• Defaults for long-haul trucks and refuse trucks. • Source Types 11, 21, 31, 32, 41, 42, and 43 are based on local data. Others are based on MOVES defaults. • Same as previous … working on now. • We get available data from state DOT. • Still being determined. • R.L. Polk. Source type data: sources of light-duty population data (N=73). Source type data: sources of light-duty population data additional comments: • Other agency provides data. • Method described in section 3.3 of the technical guidance manual for MOVES 2010a. • Right now we use a combination of SCDMV population numbers for all source types, and apply percentages from MOVES default data to split into each source type category. We are working on generating MOVES input by source type using only DMV data. C-13

• Private company processed, QA/QC’d local vehicle registration data grown based on registration or human population trends. • The local reg for light cars and trucks but they have mixed both categories up so we use the total vehicle count and then use moves to make the splits between vehicle types 21, 31, and 32. • NC DMV via NC DAQ • CT DEEP prepares file. See their response. Based on DMV registrations. • State DOT. • Checks against veh population calculated from VMT using MOVES default (miles/vehicle). • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • MC, PC, from MVA data – rest based on MOVES defaults. • We used 2005 University study. • State DMV database. • Still being determined. • R.L. Polk and grown to current year using census data. Source type data: sources of heavy-duty population data (N=69). C-14

Source type data: sources of heavy-duty population data additional comments: • Other agency provides data. • Method described in section 3.3 of the technical guidance manual for MOVES 2010a. • Right now we use a combination of SCDMV population numbers for all source types, and apply percentages from MOVES default data to split into each source type category. We are working on generating MOVES input by source type using only DMV data. • Combination of what we did for light-duty plus for long-haul combination trucks we did a special calculation since they are not originating from the state but are interstate travel. • We use a combo of local data for MOVES light-duty vehicles. Like I said above we take the total cars and truck number from DMV and then use MOVES to split it out by 21, 31, and 32 vehicle types. Then if the new local population is higher than the moves default then we use that percent difference and adjust the heavy-duty end accordingly. • NC DMV via NC DAQ. • CT DEEP prepares file. See their response. Based on DMV registrations. • Some factoring with VMT and default data on source type 62 to account for more pass-through traffic. Future populations are grown based on ARC population forecasts. • State DOT. • Checks against veh population calculated from VMT using MOVES default (miles/vehicle). • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • Default and local when correct. • Buses and Motor Homes from MVA data – rest based on MOVES defaults. • Larger of local analysis of local data or a VMT-based approach for interstate HPMSVtypeIDs. • See below. • Not sure. • Still being determined. • R.L. Polk and grown to current year using census data MOVES defaults. C-15

C.6 EXPERIENCE RELATED TO REGIONAL ACTIVITY DATA What is your agency’s source of total VMT data for use in MOVES for regional analysis? (N=68). What is your agency’s source of total VMT data for use in MOVES for regional analysis? (Other comments.) • Other agency provides data. • Local data from WY DOT. • Illinois Department of Transportation. • For our marginal nonattainment area we depend on travel demand model; for all other areas of the state we have county-level VMT from our DOT. • HPMS for rural areas and TDFM for urbanized areas. C-16

Do you use locally derived data for any of the following inputs: VMT mix by source type, annual VMT by vehicle type, month fraction VMT, day fraction VMT, hour fraction VMT, ramp fractions, road type distribution, or average speed distribution? (N=67). How do you collect VMT data for the 13 MOVES source types? (N=59). C-17

What is your surrogate source used to derive VMT at the MOVES source type level (13 vehicle types)? (N=59). Further explanation of “Other” response: • Based on Vehicle registration and HPMS data, the VMT is mapped to 13 MOVES vehicle type using MOVES’ MOBILE6.2 to MOVES mapping tool. • Combination. • Combination of a) and (b). • Combination of registration data and MOVES defaults. • From MDOT. • HPMS data. • Local traffic counts, Vehicle registration data. • MOVES source type map from 6 HPMS to 13 MOVES types was generated using combination of vehicle registration, MOVES defaults, and travel model information. Custom software was developed to produce MOVES source type map for each county and road type combination. • MOVES source type maps from 6 to 13 types were developed using a combination of vehicle registration data, MOVES defaults, and TDFM information. Custom software was developed to generate a MOVES source type map for each county and road type combination. C-18

• Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • On street survey is used to characterize vehicle types in winter CO season. • Permanent Traffic Counters (ATR). • See below. • Self made converter. • State vehicle type counts (LD and HD based on axle spacing) and MOVES default VMT mix. • Use State of Connecticut data. • Vehicle count/fractions for motorcycle, HD, and LD vehicles from DOT. What is your source of month fraction VMT data for use in MOVES for regional analysis? (N=59). Further explanation of “Other” response: • Actual traffic count station data broken down by month. • Countywide ATR summaries. • Data from New York State Department of Environmental Conservation. • EPA Calculators with some local data. • From MDOT. C-19

• From TDFM. • Local data. • Monthly fractions are derived from Automatic Traffic Count Data and applied to VMT. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • Our state DOT compiles traffic count statistics from permanent traffic recorders as a special project for us (maybe it is HPMS-related) • Out of the TDM. • Permanent Traffic Counters (ATR). • Permanent traffic recorder data – we are using traffic count fractions instead of VMT fractions because that’s the best we have. • State departments of transportation (Kansas and Missouri). • State highway statistics (some from HPMS). • State traffic counts (not directly from HPMS). • Traffic study data. • Use State of Connecticut data. • We don’t use this crap because we do daily emissions analysis for SIP and conformity purposes. We have adjusted the model to run on a daily basis and the rest of the country should do the same. We make a monthly and weekday or weekend VMT adjustment to the VMT prior to it going into the model. For example we had to model a 25-day episode in January 2009. We had specific Monday-Friday VMT and Weekend Saturday-Sunday VMT adjusted for January. So at the summary level you could see for one county that you were putting in 13 million seasonally and daily adjusted VMT and you would get 13 million VMT getting out. The only real input that changed from day to day was the 24-hour temperature profile and HVMT profile that handles starts is different from weekdays to weekends. C-20

What is your source of day fraction VMT data for use in MOVES for regional analysis? (N=59). Further explanation of “Other” response: • 2006 statewide traffic count program. • Actual traffic count station data broken down by month. • Again we modified the MOVES DB to allow for the daily VMT. You can use the output from this model for inventory runs or emission factors. A majority of the modelers that I work with have always used Mobile 5, 5b, and 6 in this fashion. The only time we don’t is for the NEI. • Converter. • EPA Calculators with some local data. • Fractions are derived from Automatic Traffic Count Data and applied to VMT. • From MDOT. • Local data. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • Our state DOT compiles traffic count statistics from permanent traffic recorders as a special project for us (maybe it is HPMS-related). • Out of the TDM. C-21

• Permanent Traffic Counters (ATR). • Permanent traffic recorder data – we are using traffic count fractions instead of VMT fractions because that’s the best we have. • State highway statistics (some from HPMS). • State traffic counts. • Statewide traffic count program, data for year 2006. • Traffic study data. • Use State of Connecticut data. What is your source of hour fraction VMT data for use in MOVES for regional analysis? (N=59). Further explanation of “Other” response: • Actual traffic count station data broken down by month. • EPA Calculators with some local data. • Fractions are derived from Automatic Traffic Count Data and applied to VMT. • Hopefully this can come from TDMs. • Local Traffic Count Program. • MDOT. C-22

• MDSHA data. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • Our state DOT compiles traffic count statistics from permanent traffic recorders as a special project for us (maybe it is HPMS-related). • Out of the TDM. • Permanent Traffic Counters (ATR). • Permanent traffic recorder data – we are using traffic count fractions instead of VMT fractions because that’s the best we have. • Rural areas use MOVES default and the MPOs use data from the TDM like with mobile 6. • State highway statistics (some from HPMS). • TDFM postprocessor using hourly VMT distribution pattern developed from traffic counts. • Traffic study data. • Travel demand model. • Travel demand model. • Travel Demand Model for networks, rest it is defaults. • Travel Demand Model run 4 periods per day. • Travel model link hourly VMT in conjunction with the MOVES defaults. • Use State of Connecticut data. C-23

What is your source of ramp fractions? (N=59). Further explanation of “Other” response: • Adjusted VMT from Travel Demand Forecasting Model (TDFM) – See comments for explanation of VMT adjustment process. • Ask EPA what a “ramp” is … we have ramps that can be one-half-mile to 500 feet long. • Assume zero ramp fractions consistent with Travel Demand Model. • Do not have Freeways. • EPA Calculators with some local data. • EPA needs to address errors in its converters. VMT by geographic area is tabulated by four highway classifications: expressway, arterial/collector, local, and expressway ramp. Ramp VMT is estimated as a percentage of expressways’ VMT based on the ratio of ramp mileage versus expressway mileage in each county. Ramp Vehicle Hour Traveled (VHT) is estimated by dividing Ramp VMT by the average speed for the appropriate road types set forth in MOBILE6.2 guidance. • MDOT. • Moves Defaults and Travel Demand Model. • MOVES Defaults unless travel demand model has ramps coded – in those cases model data used. C-24

• MOVES Defaults unless travel model has ramps coded – in those cases model data used. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • Set to zero percent. • TDFM. • TDM. • Tested local data and found it equal to MOVES defaults. • Travel demand model. • Travel Demand Model for networks, rest it is defaults. • Travel Demand Model Outputs. • Travel Demand Model ramp traffic versus overall traffic for facility type. • Travel Demand Modeling. • Travel Model derived based on link VMT summarized for Interstates, freeways, and ramps. How has your agency estimated the proportion of VMT among MOVES road types? C-25

Further explanation of “Other” response: • EPA OTAQ MOBILE62 to MOVES Converters. • Haven’t figured this one out yet. Stay tuned. • MDOT. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • We also modified the model to separate local roads form arterial roads. The reason we did this is because there are more heavy-duty vehicles traveling on arterials than on local roads. This allows us to model arterials and their traffic for say an average speed of 35.6 mph and then we set the local roads to 12.9 mph. This is good since it utilizes the same type of road networks we have used in the past with Mobile 6. We also adjusted the output of MOVES so that it goes directly into the SCC road type classification. SO if we are modeling an arterial there are no local roads and when I look at my SCC output for arterials it is identical to the VMT I put into this classification scheme. This is another example of where EPA takes us in the wrong direction regarding scale. They have a model that has more vehicle classes but somehow they have combined local and arterial roads. It doesn’t make any sense. • WYDOT. How has your agency estimated the proportion of VMT among speed bins for the average speed distribution? C-26

Further explanation of “Other” response: • EPA methodology to estimate speed distribution from an average speed. • For rural areas we use the FHWA EMIT model which uses a HCM method and HMPS and capacity inputs to generate speeds. The MPOs generate speed profiles for urban freeways and arterials and urban local roads are set to 12.9. • In addition to TDFM and HPMS, Highway Economic Reporting System (HERS) methodology and Bureau of Public Roads formula were used. See comments for explanation of the procedure. • Local data. • MDOT. • MOVES converter tool. • Ohio DOT takes the lead in developing this data for consistency statewide in conjunction with Ohio BMV and Ohio EPA. • State has consultant help for this, provides results to MPOs • Used the USEPA spreadsheet converter with urban speed data the same as MOBILE6.2 data and MOVES default rural speed data. • WYDOT. If you indicated in the previous question that you use a travel demand forecasting model, have you postprocessed the speeds to account for impedance? C-27

Please identify any key data sources, pre- or postprocessing steps, or provide other explanation: • [The MPO] appears to use impedances, but I do not know how they do it. We do not post-process the speeds to account for impedance. • Basically, we use the TDFM’s total VMT but it is adjusted by HPMS data in county and road type level. We did not post-process the speeds to account for impedance since our TDFM uses the modified BPR function already. • Custom postprocessing software (PPSUITE) is used to disaggregate daily traffic volumes and recalculate hourly speeds for each roadway segment. The post processing software includes assumptions for intersection delay and the effects of congestion. The software aggregates VHT by each speed bin across all links within each hour, road type group. • Custom postprocessing software (PPSuite) is used to disintegrate daily traffic volumes and recalculate hourly speeds for each roadway segment. The postprocessing software includes assumptions for intersection delay and the effects of congestion. The software aggregates VHT by each speed bin across all links within each hour, road type group. • Discussion below concerning adjustment of VMT and speeds used as inputs to MOVES is related to questions concerning: a) ramp fractions, b) VMT proportions among speed bins, and c) last question above. Adjustment of VMT 1) Adjustment factors for base year developed by comparing HPMS VMT to model output VMT for each county and functional class  Ramps combined w/ interstates and other freeways and expressways in C-28

developing factors /2) Factors applied to future year model results based on county and functional class of model link / /Adjustment of Speeds /1) Estimates of base year free-flow speeds determined using Highway Economic Reporting System (HERS) methodology and HPMS data (e.g., horizontal curvature, and surface roughness) /2) Adjustment factors for base year developed by comparing HERS speeds to model output average speeds for each functional class  Ramps NOT combined w/interstates and other freeways and expressways in developing factors; ramp speeds not adjusted /3) Factors applied to future year model results based on functional class of model link / / Calculation of VMT by speed bin / – For each link, / 1) Adjust volume of each link based on county and functional class. /2) Adjust free-flow speed of each link based on functional class. /3) Disaggregate adjusted link volume into 24 hourly volumes based on information from continuous counting stations (in the region) for the same functional class. / – For each hourly volume, /4) Calculate congested speed using the Bureau of Public Roads formula (a=0.15, b=4) with the volume from step 3), and the free-flow speed from step 2). / 5) Calculate the hourly VMT for the link using volume from step 3) and the length of the link from the TDFM and assign it to the appropriate speed bin based on the congested speed from step 4). • Done by MPOs. • For the Anchorage MOVES runs for conformity, speeds were adjusted. I don’t know the details of how and why. • I think so. The speeds are described as “congested speed.” However, these are only single link speeds by 5 time periods. They are NOT speed distributions, so they are not ideal for MOVES. • No need, Model already is calibrated to account for that. • Please check with TTI on the above questions. For these first MOVES runs, TCEQ (state environmental agency) contracted with TTI to run the models. H-GAC did run the travel demand model. • PPSuite travel model postprocessor to generate MOVES input speeds by source type and by road type. Using hourly VMT distribution patterns, PPSuite estimates hourly speeds on each road link and aggregates data onto MOVES input format. • Q15: Total VMT is derived from the TDFM, adjusted by HPMS counts as required by regulation. Q25: We use the PPSuite postprocessor to generate MOVES input speeds by source type and by road type based on our TDFM. Using hourly VMT distribution patterns, PPSuite estimates hourly speeds on each roadway link and aggregates data into MOVES input format. • Speed from Travel demand Model was Post Processed using formulas from the Highway capacity Manual. C-29

• Takes average speed by road type. • The outputs of the travel demand model are compared to estimates of speed based on: 1) the equations of the Highway Economic Reporting System (HERS) and 2) the use of data from Automatic Continuous Traffic Recorders (ATR). • The State DOT had postprocessing software and procedure for use by its MPOs developed using consultants. • The travel demand model forecast volumes. The speed model is a post process model to estimate a more accurate speed according to Highway Capacity Manual using volumes from the TDM. • To meet EPA’s requirements, link volumes within the PERFORM model were stratified by HPMS functional class based on link location and facility type code. All highway network links in the model are individually coded for HPMS functional class. All HPMS functional classes are represented in the highway network. Intra-Zonal trips (those too short to get on the model network – less than 2 percent of the total VMT) are assigned an average trip length based on the size of the traffic analysis zone and were considered local road trips. The PERFORM model was adjusted in this manner to produce data for these road classifications. / CT DOT calibrated the 2009 model year VMT to 2009 HPMS VMT. These adjustments are carried throughout the forecasted years and are reflected in the 2017 and 2025 VMT estimates. / Connecticut had an average of 86.0 million VMT per day in 2009. / Validations of the PERFORM model were accomplished by comparing model output to known base data. In particular, HPMS VMT was an important basis of model validation and calibration. / I – 4 / A link by link assignment versus Average Daily Traffic (ADT) tabulation was made to examine expressway assignments. Graphic plots were used as a visual review of model output of the highway network, with assignments and ADTs posted on a link basis. / CT DOT also used a self-consistent equilibrium assignment process in that the state of equilibrium within the PERFORM model was determined by the closure ratio criterion. This is the ratio of the summation of the loaded network travel time to the projected summation of loaded travel time after capacity-restrained adjustment for the current iteration. The suggested default of 0.10 was retained for all assignment runs. This closure ratio was always attained at a point before the maximum number of iterations specified. The equilibrium assignment module uses volume-to-capacity ratios to adjust link speeds between iterations so that links are not over assigned. / VMT by geographic area is tabulated by four highway classifications: expressway, arterial/collector, local, and expressway ramp. Ramp VMT is estimated as a percentage of expressways’ VMT based on the ratio of ramp mileage versus expressway mileage in each county. Ramp Vehicle Hour Traveled (VHT) is estimated by dividing Ramp VMT by the average speed for the appropriate road types set forth in MOBILE6.2 guidance.8 / Connecticut used “Travel Activity by C-30

Vehicle Type and Functional System” data reported by CT DOT to the Federal Highway Authority for the HPMS program (see Table I.1.1-4a and Table I.1.1-4b). This report lists 13 HPMS vehicle type percentages on the 12 road types outlined previously. These data don’t categorize vehicle types in the same manner as MOBILE6.2. / HPMS vehicle fractions were converted to MOBILE6 vehicle fractions for input into a MOVES VMT Preprocessor by doing the following: / The HPMS vehicle count percentages were summed into light-duty and heavy-duty totals multiplied by the MOBILE6.2 vehicle mix for each HPMS road type. This generated a VMT fraction for each of the 14 HPMS facility type by vehicle type for each MOBILE6.2 16 vehicle type on each road. / The 13 vehicle groups associated with HPMS observations were summed into three groups, i.e., Light-Duty Vehicle observations (LDVo), Heavy-Duty Vehicles observations (HDVo) and Motorcycle observations (MCo) for each of the 14 HPMS road types. “Passenger Car” and “Other 2-Axle, 4-Tire Vehicles” vehicle count fraction observations were summed to get LDVo count fraction observations. “Motorcycle” count fraction observations were summed for count fraction observations and the remaining vehicle categories were summed for the HDVo count fraction observations group. All of the sums were done by the 14 HPMS road types. / A Connecticut vehicle VMT fraction augmented default group totals of LDVt, HDVt, and MCt per road were calculated from the augmented MOBILE6 default vehicle fractions. LDVt was a summation of VMT mix fractions for the MOBILE6.2 LDV and LDT1, LDT2, LDT3, and LDT4 vehicle classes. HDVt was a summation of the VMT mix fractions for the MOBILE6.2 HDV2B, HDV3, HDV4, HDV5, HDV6, HDV7, HDV8A, and HDV8B vehicle classes. MCt was the MC / I – 5 / default. / EPA national default values from Table 4.1.2 (National Average Vehicle Miles Traveled Fractions by Vehicle Class Using MOBILE6.2) found in Section 4.1.4 of Reference 8 were augmented using an additional step to adjust the mix percentages. The LDV, LDT1, LDT2, LDT3, LDT4, HDV2B, HDV3, HDV4, and HDV5 vehicle class EPA national default mix values were localized using DMV registration data by age and national default mileage accumulation. This adjustment was based on the vehicle counts by vehicle class and by age from the vehicle age distribution analyses and EPA’s Reference 15 annual mileage accumulation by vehicle class and by age, which was normalized to replace the existing LDV through HDV5 values. The Connecticut fleet is comprised of more cars (LDV’s) and heavier trucks than the national default and the adjustment of the national default values better aligns the MOVES VMT to the appropriate MOVES Source Types. It also better aligns the VMT apportionment to the appropriate vehicle age distribution. The localization of LDV through HDV5 was based on the MOBILE6.2 to MOVES Source Type mapping and consideration of how the vehicles would operate within the state and be appropriately represented by local data. VMT mix calculations were normalized to the fractional value of the default values being replaced. The complete set of augmented default MOBILE6.2 VMT mix values includes a composite of original default values C-31

that were not modified from their original values (MC, HDV6, HDV7, HDBS, HDBT, HDV8A, and HDV8B) and localized default values that were modified as described in this paragraph (LDV, LDT1, LDT2, LDT3, LDT4, HDV2B, HDV3, HDV4, and HDV5). / The net result of the additional localization of the data includes an emission reduction due to a greater percentage of vehicle VMT being assigned to passenger cars (MOVES Source Type 21) and an emission increase due to a greater percentage of vehicle VMT being assigned to commercial trucks (MOVES Source Type 32). The VMT contribution from lighter trucks (MOVES Source Type 31) is reduced proportionally to the VMT contribution increases. / The preprocessing of the MOVES VMT converter HPMS input table used the methodology outlined in the MOBILE6.2 Technical Guidance8 section 4.1.4: “Disaggregation of Local Information.” Following the calculation of the complete set of augmented default VMT mix values and calculation of the VMT fraction augmented default group totals. A Connecticut-specific table with MOBILE6.2 VMT mix values for each of the 14 HPMS road types was developed. This table was developed by multiplying the HPMS fractional observation count (LDVo, HDVo, MCo) times the augmented MOBILE6.2 default value divided by the MOBILE6.2 VMT fraction augmented default group totals (LDVt, HDVt, and MCt) for each of the 16 MOBILE6.2 vehicle classes and each of the 14 HPMS road types. This table was formatted to obtain a Connecticut localized input table for the MOVES VMT converter. Table I.1.1-4.c presents the results of the above calculation in the form of the Connecticut MOVES converter input for fraction of VMT on HPMS Road Type by MOBILE6.2 16 Vehicle Type. / The state-specific vehicle mix data was entered into the MOBILE6 to MOVES converter for each road class, together with county-level VMT (see Tables I.1.1-6a and 6b) for each of the 14 2010+ FHWA HPMS road types discussed above, the MOBILE6.2 VMT by hour data shown in Table I.1.1-7, the percent of Vehicle Hours Traveled on Ramps and the MOBILE6.2 Registration / I – 6 / Age Distribution so that appropriate MOVES inputs could be obtained. The CT DOT’s updated EPA 16 vehicle type/14 road type converter supplied the following: A daily VMT value (HPMSvTypeYear) that was input to the EPA’s average annual weekday vehicle miles traveled (aadvmtcalculator_hpms.xls) converter to generate annual VMT by MOVES HPMSVTypeID; An hourly fraction (HourVMTFraction) for each MOVES Source Type for each hour and day type (weekday and weekend); A road type VMT fraction (RoadTypeDistribution) which indicates the fraction VMT that a MOVES Source Type travels on each MOVES road type. The sum of all road type fractions will be a value of one for each MOVES source type and a value for road type 1 is required, but it will always be zero; An hourly fraction (VHT fraction aka RoadType) of the time spent on ramps relative to the total time spent on each restricted MOVES road type ramp (restricted road types are also called limited access road types); And a SourceTypeAgeDistribution that could be used or compared to a more accurate directly calculated age distribution obtained directly from registration data. / In addition to C-32

producing annual VMT by MOVES HPMSVTypeID mentioned above, EPA’s average annual weekday vehicle miles traveled (aadvmtcalculator_hpms.xls) converter also produced the dayVMTFraction and monthVMTFraction inputs for input to the MOVES Model. In addition to inputting a daily VMT value, Connecticut entered seasonal VMT adjustments based on winter, summer and annual VMT estimates to localize monthly adjustment factors. Weekday versus weekend factors were not altered from the EPA default values provided. Tables I.1.1-8a and I.1.1- 8b show the total annual VMT by MOVES HPMSVTypeID. • To more fully answer the first couple of questions, we use VIN-decoded data to determine source type populations, although some EPA mapping is required primarily because we have no way of distinguishing between short- or long-haul use. Additional research in this areas would be helpful. / / As for the last question on speed impedance, we get link volumes, capacities, and posted speeds from the travel demand model and use HCM equations to calculate the congested speed for each link. • Travel Demand Model speeds are calibrated to observed speeds, but not separately postprocessed. • VMT from TDM is performed by our MPO, we just receive the end results. • We adjust model speeds using travel time survey data, a single adjustment factor is applied to restricted access roadways (all freeways and expressways) and a separate, single adjustment factor is applied to unrestricted access roads. C.7 EXPERIENCE RELATED TO PROJECT-LEVEL DATA What sources of project-level vehicle volume information has your agency used? (N=51). C-33

Further explanation of “Other” response: • Actual traffic counts and travel demand model. • Agency does not do project-level analysis. • Have not performed project-level analysis to date. • Haven’t done project-level analysis. • HPMS. • MOVES ready input data sets provided to us by the project managers and their contractors. • No MOVES project-level analyses conducted yet; have used combinations of all above sources for past analyses. • No MOVES-based project-level analysis conducted yet. The scope of work would be restricted to review of MDE-based MOVES inputs such a age distributions, fuel, I&M, VPOP, and met data. • None. • Other agency addresses this. • Travel Demand Forecasting Model, HPMS, and Traffic Counts. • We do not do “project-level” analyses; only “county-level” analyses. • We don’t have a source. C-34

What sources of project-level vehicle speed information has your agency used? (N=50). Further explanation of “Other” response: • Agency does not do project-level analysis. • Have not performed project-level analysis to date. • Haven’t done project-level analysis. • LOS of roadway link. • No MOVES project-level analyses conducted yet; Have used combinations of all above sources for past analyses. • No MOVES-based project-level analysis conducted yet. The scope of work would be restricted to review of MDE-based MOVES inputs such a age distributions, fuel, I&M, VPOP, and met data. • None. • Note: Speed information from TDFM may be postprocessed as described in previous section. • Other agency addresses this. • We do not do “project-level” analyses; only “county-level” analyses. • We do not do project-level AQ analysis with MOVES, still using MOBILE for this. C-35

• We don’t have a source. Has your agency developed vehicle trajectory data or MOVES vehicle-specific power profiles (operating mode distributions) for link-level vehicle activity? (N=49). C-36

How has your agency determined project-level fleet mix (link source type) information? (N=48). Further explanation of “Other” response: • Agency does not do project-level analysis. • From county-level processed registration data with MOVES defaults for ‘fill-in’ data. • Have not performed project-level analysis to date. • Have not yet applied MOVES to project level. • Local vehicle registration and MOVES defaults. • Never. • No. • Other agency addresses this. • Project-level analysis not used so far. • We do not do “project-level” analyses; only “county-level” analyses. • We have not done this. C-37

How has your agency determined project-level age distribution information? (N=48). Further explanation of “Other” response: • How has your agency determined project-level age/distribution information?-TEXT. • Agency does not do project-level analysis. • From county-level processed registration data with MOVES defaults for ‘fill-in’ data. • Have not performed project-level analysis to date. • Have not yet applied MOVES to project level. • How would this be different than the county level we already have? • Local vehicle registration and MOVES defaults. • Other agency addresses this. • Project-level analysis not used so far. • We do not do “project-level” analyses; only “county-level” analyses. • We have not done this. C-38

How has your agency collected project-level “off-network” MOVES data, such as estimates of vehicle starts or extended idle fractions? (N=64). Further explanation of “Other” response: • How has your agency collected project-level “off-network” MOVES data, such as / estimates of vehicle s.– TEXT. • Have not performed project-level analysis to date. • Have not yet applied MOVES to project level. • Local data showed large differences in starts comparatively to MOVES defaults; since EPA requires use of MOVES defaults for starts, we use default values. • No. • Project-level analysis not used so far. • Who has money for this study. C-39

Please identify any key data sources, pre- or postprocessing steps, or provide other explanation: • Again TTI has used a combination of travel demand model data from H- GAC, HPMS, vehicle registration data, and in the future we intend to build in drayage truck inventory, speed and emissions data gained from study with EPA and TCEQ. • Have not done a project-level analysis. • Have not sorted this data element out yet. • Haven’t done any project-level analyses yet. • No MOVES-based project-level analysis conducted yet. The scope of work would be restricted to review of MDE-based MOVES inputs such a age distributions, fuel, I&M, VPOP, and met data. MD-SHA is responsible for hotspot analyses. However methods and procedures will be agreed upon through the interagency consultation process. • Note: No MOVES hotspot analyses completed to date. When one is completed an evaluation will be made of available data sources. Methods and procedures will be agreed on through the interagency consultation process. • Our agency doesn’t do project-level runs. • Project-level analysis is not conduct by MPO. C-40

C.8 EXPERIENCE WITH OTHER MOVES INPUTS How do you derive your temperature and humidity inputs to MOVES? (N=71). Further explanation of “Other” response: • 2008 NEI inventory tables. • data from department of energy and environmental protection. • Division of Air Quality responsibility. • From State DEEP. • Have use MOVES Defaults and output of MM5 met model. • MESO West. • NMIM defaults. • Typically set design temperatures per EPA guidance for SIP/conformity analyses. Local evaluations allowed for alternative look see analyses not requiring EPA approval. • We have used both b) and c) depending on the end use of the data. C-41

Does your area have an Inspection and Maintenance (I/M) program and, if so, what is your source of inputs? (N=72). What source of information does your agency use as inputs for fuel parameters such as Reid Vapor Pressure (RVP)? (N=72). C-42

Further explanation of “Other” response: • Data from department of energy and environmental protection. • DEQ. • Division of Air Quality responsibility. • EPA field test data from EPA web site. • EPA RFG survey data and MOVES defaults. • From EPA-collected field data on Internet web data. • From State DEEP. • MDOT. • Modify defaults based on local knowledge (ethanol waiver). • Provided by state air agency. • Results for analysis of RFG survey data. • State agency experts. • State air agency is developing this. • This information is provided by State DOT. • Use defaults modified with local data. • Use parameters per program regulations/EPA guidance/Past SIP parameters. • Use results from local fuel testing. • Use survey data and MOVES defaults. • Using what has been used in the past. Not sure of the root source … • Utah Refinery Association. • We are currently working with the fuel industry and they have given us a weighted average fuel inputs to deal with E10. We have E10 in every county in Utah beginning in 2010 and the EPA default does not reflect that and we are dealing with a winter time 24-hour PM2.5 issue centered on VOC. • We have statutory limitations on our RVP. • We sometimes use MOVES defaults and sometimes our regulatory RVP levels, depending on the purpose of the run. • We used the default profiles and county assignments as a starting point. We used local fuel data/regs to adjust Default RVP and E10 use, and also to assign counties to fuel profiles. C-43

If you do not use MOVES defaults for fuels, which fuel parameters do you change? (N=61). Has your agency performed any sensitivity testing on the use of alternative MOVES inputs (including fleet data, regional activity data, project-level activity data, or other inputs)? (N=68). C-44

Please identify any key data sources, pre- or postprocessing steps, or provide other explanation: • Comments: - Define IM program following MOVES guidelines. Each county program is described in a large Excel workbook with a separate tab for each year. All gasoline LD vehicle types are addressed, HD and diesel is ignored. - Other fuel parameters changed are sulfur content. • Comments: - As indicated above I/M and Fuel Parameter have been determined using a combination of sources, including local data surveys, I/M feedback, regulations, inputs from EPA, and parameters assumed in past SIP analyses. - MD updates much of the data on triennial basis. At the time of updates the sensitivity of key inputs have been evaluated. Typically vehicle age data has the largest impact on emissions. The fleet age in MD has been steadily aging since 2005, producing much higher emissions. • Comments: - As indicated above I/M and Fuel Parameters have been determined using a combination of sources, including local data surveys, regulations, input from EPA, and parameters assumed from past SIP analyses. - Pennsylvania updates much of their data inputs on a triennial basis. At time of updates the sensitivity of key inputs have been evaluated. Typically, vehicle age data has the largest impact on emissions. The fleet age in Pennsylvania has been steadily aging since 2005, producing much higher emissions results. • Comments - Fuel parameters changes are gasoline: sulfur, RVP, benzene, aromatic, olefin, E200, E300, ETOH, ETBE, MTBE, and TAME. - Sensitivity Testing: Fleet Mix changes, meteorological changes, 12- month run versus 1-month runs  Data available upon request by contacting Craig Butler of the Louisville Metro Air Pollution Control District at ___. - Due to lack of staffing, no sensitivity analysis is able to be performed. “Modeling” is seen as a black box where you just push a button. hard to convince of need for analysis. - Ethanol content. C-45

• Comments: - For our NEI tables we had to modify the IM defaults provided to us by EPA; they were incorrect. - For our NEI fuel tables – we will likely use MOVES defaults because EPA was unable to provide defaults. We may modify the tables based on local knowledge – we have a waiver for ethanol. - Fuel data are provided by New York State Department of Environmental Conservation. - Fuel formulations obtained from Utah Division of Air Quality. - Fuel parameters from EPA field test data: RVP, Ethanol, E200, E300, Benzene, etc. Did some sensitivity testing with earliest version of MOVES, including 12 mo. versus 1 repr. mo. runs, meteorology changes, and fleet age distribution changes (with MOVES2010). Results available upon request from Louisville Metro Air Pollution Control District, craig.butler@louisvilleky.gov. - Have local program; working with state air agency to develop MOVES inputs. • Comments: - I&M data extracted from I&M report. HDV I&M not credited due to considerations associated with VMT mix approach and inability of MOVES to directly model regulatory vehicle classes (i.e., Source Types would have undesirable bias if HDV I&M were included). - If you do not use MOVES defaults for fuels, which fuel parameters do you change? All. / - Sensitivity analyses – Speed, Ramp Fraction, VMT Mix and I&M. Analyses not saved, but some sent to MOBILE@EPA.GOV when results not as expected. - I/M inputs (percent compliance) as provided by NC DAQ. / NC DAQ defined local RVP input for Mecklenburg County only. • Comments: - I/M inputs developed using data supplied by agency I/M program office, such as compliance rate and waiver rate. - Fuel RVP and other parameters available at http://www.epa.gov/otaq/fuels/rfgsurvey.htm. - I/M program inputs are taken from state air agency. • Comments: - I/M program is run by our agency and the information regarding the I/M processes at our emission testing sites are provided by our Mobile C-46

and Area Source Program through William Cook, Manager of the Engine and Fuels Unit. - As for sensitivity tests, we have done emissions inventory test runs varying the fuel blend (all 10 percent ethanol versus no ethanol), RVP (varying it by 1 psi), and fuel type (CNG versus Diesel/Gasoline). The results of these tests are not finalized and are informal. If you are interested in our work in this regard, please contact me at gil.grodzinsky@dnr.state.ga.us - Met data typical comes from nearby airports. Our state air agency develops the fuel inputs for our use. - Our I/M program data comes from our state air agency. It differs from the MOVES defaults in our region. Feel free to contact about sensitivity testing. We mostly did some tests on VMT variations, age distributions, other simple things). - Oxygenated fuels - Q33: All I/M MOVES inputs are based on local data. Q34 and Q35: Fuel input was generated to include local data for RVP, sulfur level, and ethanol volume. Q36: Sensitivity testing was done for various VPOP inputs, VMT growth factors, and comparisons with MOVES default estimates. - Fuel input was generated to include local data from RVP, Sulfur Level, and Ethanol Volume. - Sensitivity testing was done for various source type population inputs. / - RVP and oxygen content. • Comments: – SEMCOG also changed the MarketShare in FuelSupply table based on local fuel survey data. – SEMCOG tested alternative inputs of vehicle population, age distribution, and VHT speed data during the processes of the local data development. - State develops IM data. For fuel, State is working on fuel formulations – has data to modify rvp and E200, E300. - The state provides the parameters for fuel and for I/M Program. TTI would have input this info into model - Use a combination of MOVES defaults and local factors - Use our I/M program’s vehicle coverage (different than defaults). - we are transitioning to MOVES - We create our own IM input table based on our regulations for the area. C-47

- We have done multiple statewide inventory runs for both SIP and sensitivity to determine possible impacts of I/M program changes. We are also working on off model credit for I/M programs. - We have found the MOVES default I/M coverage data does not accurately describe the Illinois program. For example, the ending model year is off by on year. In addition, MOVES does not allow modeling of the OBD and idle tests to be modeled for the same model year, but different GVWRs. In Illinois light-duty cars and trucks were required to use an OBD test while heavy-duty trucks (>8,500 lbs. GVWR) required an idle test. - We have updated RVP, Ethanol percent, and percent sulfur. • Comments: - We use our I/M program data to develop the inputs for the IMCoverage file. - For fuels, we changed RVP and the market share of E10. We also changed some county to profile assignments. - Sensitivity testing – compared each of the County Data Manager inputs for which we had local data to the default data for a single county. Some of the comparison will need to be redone, since some of the local data derivation processes have changed. - You cannot just change the RVP and expect a reduction in VOC emissions. We asked the refineries for their fuel and they gave us a fuel that had a lower RVP and guess what the VOC emissions are higher. I have too much data to give a link so contact me [contact information deleted] C-48

C.9 INPUT ON NCHRP 25-38 PROJECT OUTCOMES One objective of this NCHRP project is to provide methods and guidance for developing MOVES input data, and/or additional datasets that can be used. What datasets or methods/guidance would be the most important to your agency? Write-in responses: • VMT Fraction by Day/Month/Hour (Somewhat Important). • Alternative Fuel Vehicles (Somewhat Important). • Local fuel data, including actual RVP, etc. (Somewhat Important). • Accounting for latest engine technologies (Somewhat Important). • VMT Road Type split (Somewhat Important). • VMT (Very Important). • speed distribution (Very Important). • speed by source type (Very Important). • Source Type VMT Mix (Very Important). • Number of starts (Very Important). • default off-network inputs for various project types (Very Important). C-49

• MOVES Fleet Projections and Regulatory Class Distributions within MOVES Source Types 31 and 32 (Very Important). • Vehicle Starts (Very Important). • sourcetype by roadtype (not constant across roadtype) (Very Important). • Avg speed distribution by veh type (Very Important). • Alternative Fuel Emission Factors (i.e., CNG) (Somewhat Important). • Idle Fractions (Very Important). • While not really guidance – EPA had compiled a MOBILE62 Sensitivity Document that facilitated understanding both by the regulated community and regulators, by providing context and significance to Guidance. A MOVES Sensitivity Document could fill a similar role. (Somewhat Important). For any datasets developed as part of this project, how would you prefer they be made available? (N=73). C-50

Further explanation of “Other” response: • Both a and b. • Depends on the dataset. I would only put universally accepted and used datasets in MOVES. While others that are not so universally used, should be outside of MOVES. MySQL is not MOVES! • Incorporate into MOVES if this can be done in a timely manner; otherwise, make datasets available through organizations such as those listed in b. • Incorporate into MOVES, with the flexibility of minor editing for better local data if available. • Keep all the MOVES files distributed through one source – the EPA MOVES site. They don’t need to be built into MOVES, but it would be nice to only have one clearinghouse for MOVES info. • Option B when option A is not possible. • Please Be careful if you are going to be making additions to MOVES. We have created a MYSQL DB that holds all of the relevant data sets for VMT, vehicle population, age distribution, I/M programs files, and temperature profiles. Right now it is very easy to make a change in one of those data sets and do a sensitivity analysis. I only have to run a couple of scripts. If any of the CDM inputs change then I need to reformat my DB and that can be a pain. • Supplied by State DOT using local data. • Whatever method would provide most current and timely input data. C-51

What additional guidance for obtaining and preparing MOVES input data, at either the regional or project level, would you find helpful that is not already available in existing EPA guidance: • Comments: - 1. We have a method we use to determine long-haul combination truck VMT and population. It works for us but if you have additional guidance for vehicles traveling through a region (not originating there) it would be useful. - 2. We would like a way to better analyze the benefits of CNG emissions other than using just transit buses. We have fleets with CNG for other vehicle types. Any good way to deal with this other than AP-42, etc.? - A reference guide with examples from other areas/jurisdictions would be helpful in setting the model up. - Additional or updated guidance on mapping of 13 MOVES source types from 6 types of HPMS vehicle classification data. - Additional or updated guidance on mapping of 13 MOVES vehicle types from HPMS classification data (6 or 13 types). • Comments: - Descriptions and references for all the default data. - An on-line data/idea sharing forum for the MOVES community (maybe you already have this). - Once you get the results of this survey, publish some of the user methods for developing local data and using national data to refine local data. - Do not know. - Guidance on generating speed distribution from the Travel Demand Model. - How do we use MOVES emission rates in other processors such as CMAQ program? - I would personally like to be able to show people how to modify MOVES to accept Daily VMT and Local roads. Since I have made these changes it has been very easy to get some of my older Modeling colleagues to buy into the MOVES model. It is also very satisfying and rewarding to be able to see an input like VMT and see it go into and out of the model and have some reassurances that the model is working. I might add that since we have the Daily and local road setup that we have been able to identify input errors in minutes that could trip up users of the default model for days. Missing days matters for MOVES. I have setup batch runs that go on for a week and I can do that because I C-52

have the confidence that the model is going to work with my inputs. My method gives the modeler confidence in MOVES. I think that once you get a modeler use to running MOVES on the inventory side with daily inputs and local roads that the project-level side would be pretty easy … My method is about building confidence in the model because it allows the user to utilize their own inputs. The way that the model is setup now it is hard for the user to take ownership. - In addition of all the above, EPA needs to do a better job on documenting from where the Emission Factors for air toxics and HONO are coming from. - Methods for estimating vehicle population for regions with high levels of “through” traffic. - Methods for estimating vehicle populations for regions with high levels of ‘through’ or inter-county traffic. - More detailed guidance on development of I/M program inputs - MOVES is sensitive to the number of starts, which is sensitive to the vehicle population which is a very elusive number (interstate vehicles, registered vehicle that are inactive, etc.). We need better vehicle population data or a better surrogate for vehicle starts. I suggest the latter – and the Travel Demand Model may be the solution by estimating vehicle trips and relating that to vehicle starts. This would give credit to reducing vehicle starts by increasing transit usage – something the current MOVES model does NOT do. - N/A - Provide more details on how to develop and locate data for each of the MOVES model inputs. - Sample project-level analyses for a few different project types (intersection, interchange, a couple with off-network inputs, etc.) that lead you step-by-step through the preparation of the MOVES input file and Project Data Manager inputs that can be easily followed when preparing hotspot analyses. • Comments: - Since EPA does fuel surveys, the MOVES defaults should reflect the most recent EPA survey data available and not require states to alter it. - The one area that we believe many states struggle with the most is characterizing the fleet. MOVES requires a registration age distribution AND total vehicle counts (source type population) by newly defined MOVES vehicle classes. I know it would be a big effort, but it would be great if states could submit a list of active VINs by county and a VIN decoder could be used to create these default MOVES inputs. The big advantage to doing this is that there would be standardization of the C-53

data and it would hopefully be more accurate than what states are able to cobble together from their registration databases. • The current guidance has a degree of flexibility to accommodate unique circumstances and situations, where EPA regions provide input based on applicable data. This allows a more intelligent approach based on the model sensitivity and significance. Areas where some additional standardization could aid the process could include: − We would be interested in EPA information and guidance on the speciation of regulatory classes within MOVES source types. For example, a project-scale analysis of a fleet of Honda CRVs versus GMC Hummers was supported by MOBILE62, but is not directly supported by MOVES. The closest approximation that can be made based on my understanding is that one could model the CRVs with the regulatory class distribution of MOVES Source Type 31 and the Hummers with the regulatory class distribution of MOVES Source Type 32. The project scale provides the best example of this issue, but I am actually more interested in being able to adjust the regulatory class distribution within the source types within the county scale, because we have a heavier distribution than what is assumed in MOVES (because many LDT’s are LDV’s in Connecticut). − Additional fleet data (beyond that stated in item 1) is embedded in portions of the MOVES model. This data is often from 1990 era studies. This data should be updated to allow MOVES to accurately calculate emissions. − Ramp fraction plays a more significant role in emissions, than was expected. EPA should fix the converters to indicate Vehicle Hours Traveled (VHT) rather than the current incorrect entry of Vehicle Miles Traveled (VMT). − Current MOBILE62 to MOVES converters are aligned with the old FHWA format. We supplied an updated converter consistent with the FHWA 2010+ HPMS Classes. This CT FHWA 2010+ based converter used nonstandard numbering for new FHWA classes, because SCC codes do not yet exist for the new FHWA classes. − Many states have asked for additional source type population guidance and tools to support implementation of that guidance. We have developed a series of tools, including a VIN decoder/analysis tool that could support regional analyses. There is enough guidance in place to develop source type population estimates, however additional sensitivity analyses and guidelines for EPA regions would be helpful, since many of minor details are left to either the state or the EPA region. Many of the decisions critical to the analysis center not on what is right, but rather what is consistent with the prior analyses, such that C-54

comparisons are apples-to-apples. Sensitivity analyses could help EPA regions reviewing analyses focus on what is important. − Compromises in Speed-VMT were needed in regional analyses, because MOVES can only support one speed-VHT input for a run and because of limitations imposed by the SMOKE MOVES interface. This may be something that could be worth addressing. − We have not had the chance to use MOVES to estimate refueling emissions and have stuck with the MOVES defaults in the inventory mode for estimating these emissions. The old method of producing a controlled (Stage II) and an uncontrolled composite gram per gallon emission factor from MOBILE62 appeared to make good sense and work well. Is there any way that MOVES could support this type of approach, so that we could better estimate area source refueling emissions? − Additional SCCs are needed to properly reflect the FHWA 2010+ HPMS road types. Ideally the MOBILE62 to MOVES VMT Converter would use HPMS numbering consistent with the SCCs. EPA maintains the SCC list and controls the MOVES internal classifications to SCC associations. This is not critical to states because this issue is a matter of presentation based on road type and regulations are more focused on regulated vehicle classification and associated emission regulation and reductions that what type of road the vehicle is traveling. • The most helpful guidance would be how to develop local inputs. What kinds of data should we be asking for? What it the first choice isn’t available? What are options for putting together the various local data sets? • VMT for Maine registered vehicles to use as growth factors. • We are told that MOVES is very useful and provides detailed information, but yet we are having to resort to using defaults since the input information is just not there. We are not given any assistance or funding for collecting the input data either. How or why would anyone develop a model claimed to provide detailed information, where the input data is not available and approximations are used. The whole accuracy is thrown out the window. How was it envisioned that agencies would collect data such as: VMT by 13 vehicle types by 4 road types by month-hour by 16 speed bins, for input? / / • We would like to be able to use MOVES for credible forecasts of GHG, and fleet age distribution would be very helpful, especially with new CAFE standards coming on line. C-55