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Case Studies of Truck Activity Data for Emissions Modeling (2019)

Chapter: Appendix B. Case Study #2: Age Distributions from Inspection Station Data

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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
Page 47
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Suggested Citation:"Appendix B. Case Study #2: Age Distributions from Inspection Station Data." National Academies of Sciences, Engineering, and Medicine. 2019. Case Studies of Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25485.
×
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Guide to Truck Activity Data for Emissions Modeling B-1 Appendix B. Case Study #2: Age Distributions from Inspection Station Data B.1 Emissions Model Inputs Supported • Age distribution (primarily applicable for long-haul trucks on restricted access highways). B.2 Level of Effort for Local Application This data source requires a moderate level of effort for local application. B.3 Overview This case study focused on investigating the potential for using the Federal Motor Carrier Safety Administration’s (FMCSA) Motor Carrier Management Information System6 (MCMIS) for obtaining vehicle age distributions. The purpose of this case study is to showcase the viability of using MCMIS data to obtain age distributions for single-unit trucks and combination trucks for use with the U.S. EPA”s MOVES or another emissions model. MCMIS is FMCSA’s source for inspection, crash, compliance review, safety audit, and registration data. Each commercial motor vehicle operating in the United Sates is required to undergo a safety inspection on at least an annual basis to determine its roadworthiness. The data collected from each safety inspection are stored in MCMIS. MCMIS essentially serves as a national truck database. MCMIS data can be made available to the general public by making a DART request through the FMCSA. DART is a system to record and track system requests for FMCSA data. The Volpe Center manages the MCMIS database as well as all DART requests on behalf of FMCSA. B.4 Data Sources Data source Source Agency/ Organization Availability and Cost MCMIS FMCSA Publicly available. Was provided to the project team at no cost. These requests are not typical to FMCSA and there may be some cost associated with data requests. This case study focuses on analyzing vehicle age distributions from MCMIS for locations within the State of Texas and Commonwealth of Virginia. The Volpe team made an official DART request for MCMIS data that could be used for obtaining vehicle age distributions. The Volpe team requested all inspection data for the entire United States for the years 2013, 2014, and 2015. Table B.1 includes the data fields that were requested and provided by the DART request team. The total file size was approximately 2.3 gigabytes and the data were analyzed using Microsoft SQL Server, R scripts, and Microsoft Excel. 6 U.S. Department of Transportation, Federal Motor Carrier Safety Administration, MCMIS Catalog and Documentation, https://ask.fmcsa.dot.gov/app/mcmiscatalog/mcmishome.

Guide to Truck Activity Data for Emissions Modeling B-2 Table B.1 MCMIS Data Fields MCMIS Fields Field Description INSPECTION_ID Unique identifying number assigned to an inspection record INSP_DATE Date the inspection was conducted. (YYYYMMDD) INSP_START_TIME Time the inspection started. Format: HHMM (military time) where HH is the hour and MM is the minutes. Midnight is recorded as 2400. 9999 = Time unknown INSP_END_TIME Time the inspection ended. Format: HHMM (military time) where HH is the hour and MM is the minutes. Midnight is recorded as 2400. 9999 = Time unknown COUNTY_CODE_STATE State/district/province of the county where the inspection took place COUNTY_CODE FIPS code identifying the county in which the inspection occurred COUNTY_NAME County name LOCATION State assigned code representing the physical location of the inspection LOCATION_DESC Physical location where the inspection took place INSP_UNIT_ID Unique identifying number assigned to an inspected unit INSP_UNIT_TYPE_ID Unique identifying number assigned to an inspection record INSP_UNIT_TYPE_DESC A code identifying the type of vehicle unit: 1. BU – Bus 2. DC – Dolly Converter 3. FT – Full Trailer 4. LM – Limousine 5. MC – Motor Carrier 6. OT – Other 7. PT – Pole Trailer 8. SB – School Bus 9. ST – Semi Trailer 10. TR – Straight Truck 11. TT – Truck Tractor 12. VN – Van 13. ZZ – Unknown 14. Intermodal Chassis INSP_UNIT_LICENSE_STATE State/district/province issuing the license tags on the vehicle unit INSP_UNIT_LICENSE License tag number on the vehicle unit INSP_UNIT_VEHICLE_ID_NUM BER Vehicle identification number of the unit INSP_UNIT_YEAR Model year of the vehicle unit INSP_UNIT_GVWR Gross vehicle weight rating (GVWR) of the vehicle unit B.5 Data Processing and Analysis The steps of data processing undertaken by the project team were as follows: 1. Made the data processing request and obtained data. 2. Identified inspection station(s) corresponding with the study area geography. Safety inspection locations are commonly paired with weigh station facilities. CMVs will sometimes be flagged after completing the

Guide to Truck Activity Data for Emissions Modeling B-3 weighing process to proceed to the inspection area for a safety inspection. It is important to note that only a small number of CMVs are inspected relative to all the vehicles that pass through a weigh station facility. 3. Used Microsoft SQL Server, R scripts, and Microsoft Excel to conduct the analysis. MCMIS data from the DART request was loaded into a relational database (Microsoft SQL Server) to facilitate querying and analysis. The data was filtered by inspection year and State. The analysis team chose Texas and Virginia due to the moderate-to-large sample sizes of inspections and selected analyzed calendar years of 2013-2015 in order to compare against the 2014 National Emissions Inventory, State truck registrations, and other truck age data sources. 4. Analyzed truck ages separately for two different vehicle types: 1) tractor-tractors, and 2) straight trucks. The MCMIS tractor-trailer type description (INSP_UNIT_TYPE_DESC) is analogous to the MOVES combination truck class and MCMIS straight truck type description is analogous to the MOVES single- unit truck class. Note that this MCMIS analysis did not distinguish between short-haul and long-haul trucks due to limited information on average daily mileage of the trucks inspected. Instead MCMIS tractor-trailer ages were compared against the aggregate ages of MOVES short-haul combination trucks (sourceTypeID 61) and long-haul combination trucks (sourceTypeID 62). Likewise, MCMIS straight truck ages were compared against the aggregate ages of MOVES short-haul single-unit trucks (sourceTypeID 52) and long-haul single-unit trucks (sourceTypeID 53). 5. After appropriate formatting, imported MCMIS data from the SQL Server database into R (The R Project for Statistical Computing) for analysis and plotting. For each inspection record in the MCMIS data analyzed, the calendar year was queried from the inspection date field (INSP_DATE). Similarly for each MCMIS record, the truck model year was taken directly from the year of the inspection unit field (INSP_UNIT_YEAR). The truck age was determined by subtracting the inspection year (calendar year) by the inspection unit year (model year) of the vehicle. Any trucks greater than 30 years old were placed in the 30-year age bin and any early sale trucks with a model year newer than calendar year were placed in the zero-year age bin. 6. Once all the trucks were assigned ages, calculated the number of trucks inspected by county (COUNTY_NAME) in Texas and Virginia for each calendar year. To maintain sufficient inspection sample sizes, the three Texas counties and the three Virginia counties with the most tractor-trailer inspections by year were chosen to be analyzed. For county-level analysis of MCMIS truck ages, El Paso, Webb, and Hidalgo were chosen in Texas and Botetourt, Prince William, and Frederick were chosen in Virginia. To determine the county truck age distributions, the number of trucks for each year of age was divided by the total number of trucks for each county, each calendar year, and each vehicle type. This analysis contained 36 unique age distributions for six counties (El Paso, Webb, Hidalgo in TX and Botetourt, Prince William, and Frederick in VA), 3 calendar years (2013, 2014, and 2015), and 2 vehicle types (tractor-trailers and straight trucks). In addition, the State (COUNTY_CODE_STATE) was also reported to include State-level age distributions for Texas and Virginia using all truck inspections for each calendar year and each vehicle type. Note that the resulting age distributions represent the ages of trucks passing the inspection station, no matter where they are registered, rather than the ages of trucks registered in the county of interest. 7. Plotted the MCMIS truck age distributions with default MOVES age distributions, and analyzed MCMIS and MOVES average truck ages.

Guide to Truck Activity Data for Emissions Modeling B-4 8. MCMIS data represents a sample of trucks operating near an inspection station instead of an entire truck population in a given county or State or even nationally. In order to make a direct comparison between MCMIS and MOVES, the MOVES national default truck age distributions were adjusted by the national default mileage accumulation rate (MAR), which is discussed further below. Similarly, all other truck age data that was population based had a MAR adjustment applied. This ensured that all the truck age distribution comparisons were on a relatively common basis. Converting VMT-Based Age Distributions to Population-Based Distributions (and vice-versa) One aspect of the case study was to compare observed age distributions with embedded or “default” age distributions in the MOVES model. Either the MOVES or MCMIS age distributions need to be adjusted to make these distributions comparable. When conducting a national scale or county/regional scale analyses using MOVES, the age distribution entered into the input database is based upon vehicle populations typically obtained by State registration data provided by IHS, Inc. as described in the MOVES technical report on vehicle populations and activity (U.S. EPA, 2016). When MOVES is executed, an adjustment is applied to the age distribution to account for vehicle activity. That adjustment is the MAR. Essentially, the MAR will apply adjustments to the age fraction due to newer vehicles having more activity than older vehicles. The MCMIS age distributions observed at a specific facility represent the age distribution of the vehicles traveling on the facility (i.e., vehicle-miles of travel or VMT age distributions). Newer vehicles travel more miles per year, and will therefore be disproportionately represented in observed vehicle travel compared to older vehicles. If observed MCMIS age distributions are to be compared with MOVES distributions, a MAR adjustment must be applied to either the MCMIS distributions (to “age” the fleet) or to the MOVES registration-based distribution (to make the fleet “younger”). This report uses the terms “VMT-based” and “registration-based” to distinguish between these two types of age distributions. Registration-based MOVES age distributions are entered as inputs into MOVES. Adjusted VMT-based MOVES age distributions are discussed frequently in this report. The MCMIS age distributions are VMT-based. Adjusted registration-based MOVES age distributions are also discussed. An example of how the MAR adjustment can be applied is shown in Table B.2. In this table, columns (B) and (C) are MOVES-output VMT and vehicle counts for source type 62 (long-haul combination trucks) for the State of Virginia for 2014. Column (D) is the computed VMT per vehicle = (B) / (C). Column (E) is the relative MAR, or the ratio of VMT per vehicle of age x to VMT per vehicle of age 0. Column (F) is the user input observed VMT age fraction from inspection station data or another point sampling on-road vehicles. Column (G) is the VMT age fraction divided by MAR for each age vehicle. Column (H) is the normalized age distribution equivalent to a registration-based distribution, equal to the column (G) value divided by the sum of the column (G) values (2.1037).

Guide to Truck Activity Data for Emissions Modeling B-5 Table B.2 Sample Age Distribution Adjustment for MAR (A) MOVES Age ID (B) MOVES VMT (C) MOVES Vehicle Count (D) MOVES VMT/ Vehicle (E) MOVES Relative MAR (F) VMT Age Fraction (Road Observation) (G) Age Fraction/ MAR (H) Pop. Age Fraction (Normalized) 0 370,547,260 3,614 102,524 1.0000 0.0992 0.0992 0.0472 1 328,909,600 3,210 102,460 0.9994 0.0763 0.0763 0.0363 2 319,174,154 3,128 102,052 0.9954 0.0691 0.0694 0.0330 3 281,995,558 2,483 113,587 1.1079 0.0330 0.0298 0.0142 4 209,731,756 1,930 108,672 1.0600 0.0232 0.0219 0.0104 5 261,317,216 2,529 103,310 1.0077 0.0294 0.0292 0.0139 6 196,817,110 2,051 95,946 0.9358 0.0188 0.0201 0.0096 7 634,587,774 6,962 91,150 0.8891 0.1162 0.1307 0.0621 8 439,604,766 5,133 85,637 0.8353 0.0830 0.0994 0.0473 9 389,547,616 4,887 79,704 0.7774 0.0730 0.0939 0.0446 10 217,752,611 2,960 73,575 0.7176 0.0338 0.0471 0.0224 11 192,678,202 2,795 68,930 0.6723 0.0373 0.0555 0.0264 12 129,868,595 2,072 62,666 0.6112 0.0280 0.0458 0.0218 13 148,876,619 2,884 51,618 0.5035 0.0452 0.0898 0.0427 14 205,834,533 4,305 47,814 0.4664 0.0629 0.1350 0.0642 15 139,551,618 3,446 40,499 0.3950 0.0428 0.1084 0.0516 16 87,372,355 2,816 31,023 0.3026 0.0299 0.0988 0.0470 17 51,112,860 1,971 25,930 0.2529 0.0187 0.0740 0.0352 18 55,890,108 2,501 22,349 0.2180 0.0187 0.0859 0.0408 19 50,860,993 2,637 19,287 0.1881 0.0167 0.0888 0.0422 20 32,636,851 2,017 16,184 0.1579 0.0112 0.0708 0.0337 21 21,019,328 1,440 14,596 0.1424 0.0075 0.0529 0.0252 22 13,114,383 1,051 12,472 0.1217 0.0039 0.0320 0.0152 23 10,180,850 945 10,773 0.1051 0.0024 0.0227 0.0108 24 9,746,189 1,113 8,759 0.0854 0.0026 0.0309 0.0147 25 8,679,282 1,122 7,736 0.0755 0.0034 0.0450 0.0214 26 6,335,502 1,002 6,320 0.0616 0.0029 0.0469 0.0223 27 5,164,676 841 6,141 0.0599 0.0030 0.0503 0.0239 28 2,334,920 538 4,338 0.0423 0.0019 0.0445 0.0212 29 1,866,927 545 3,424 0.0334 0.0021 0.0639 0.0304 30 1,967,575 788 2,497 0.0244 0.0035 0.1444 0.0687 Sum: 1.0000 2.1037 1.0000 Average Age: 8.5 14.8 Source: MOVES VMT and vehicle counts for source type 62 vehicles in Virginia in 2014, from MOVES2014 analysis by Cambridge Systematics, Inc. using inputs supplied by Virginia Department of Environmental Quality. Age fraction (road observation) estimated from Virginia inspection station data from MCMIS by Volpe National Transportation Systems Center.

Guide to Truck Activity Data for Emissions Modeling B-6 In Table B.2, columns (B), (C), and (F) are user input values. Other fields are calculated. The user could also start with column (D) or column (E) if they have VMT/vehicle by age or a relative MAR from a source other than MOVES outputs. The average age can be computed as the sumproduct (in Excel) of column (A) and column (F) or (H). The example shows that the average age of vehicles in the registered fleet is substantially older than the average age of vehicles observed at a random point on the road (14.8 versus 8.5 years in this example). This process can be reversed to convert a population-based age distribution to a road-based age distribution, for example, if the user desires to input source type age fraction for project-scale MOVES analysis when only a registration-based age distribution is available. In this procedure, column (H) is the user input; it is then multiplied by the MAR (column E) and then normalized to obtain column (F). Data Processing Scripts The scripts used to process the data are provided below. They are written in R with connections to Microsoft SQL Server, where the MCMIS database was stored. Users who wish to apply these scripts must install both SQL Server and R and then configure the database connection with the correct credentials on their machines. The scripts are specific to the MCMIS data request for the appropriate calendar years and other relevant fields as well as the license plate field studies conducted by UC-Riverside for FHWA. R script to join license plate fields from MCMIS and FHWA license plate reader data: #Load ggplot2 and RODBC libraries library(ggplot2) library(RODBC) library(plyr) library(data.table) library(xlsx) theme_set(theme_bw()) setwd("C:\\MCMIS") dbhandle = odbcDriverConnect("driver={SQL Server}; server=localhost; database=MCMIS; trusted_connection=true") truck_inspections_df = sqlQuery(dbhandle, "SELECT *, YEAR([INSP_DATE]) AS INSP_YEAR

Guide to Truck Activity Data for Emissions Modeling B-7 FROM [MCMIS].[dbo].[DHAO_FU20160728] WHERE [INSP_UNIT_TYPE_DESC] IN ('STRAIGHT TRUCK','TRUCK TRACTOR') AND [INSP_UNIT_LICENSE] NOT IN ('NEW','NO PLATE','NO PLATES')") truck_inspections_df$INSP_YEAR <- as.numeric(as.character(truck_inspections_df$INSP_YEAR)) truck_inspections_df$INSP_UNIT_YEAR <- as.numeric(as.character(truck_inspections_df$INSP_UNIT_YEAR)) truck_inspections_df$INSP_UNIT_GVWR <- as.numeric(as.character(truck_inspections_df$INSP_UNIT_GVWR)) truck_inspections_df$AGE_ID <- truck_inspections_df$INSP_YEAR - truck_inspections_df$INSP_UNIT_YEAR head(truck_inspections_df) join_license_data <- function(csv,xlsx) { license_plate_df <- read.csv(csv, sep=",", skip=2, col.names= c("license_no", "license_state","fhwa_weightid","moves_sourcetypeid")) head(license_plate_df) joined_license_plate_df <- merge(truck_inspections_df, license_plate_df, by.x="INSP_UNIT_LICENSE", by.y="license_no") count(joined_license_plate_df$INSP_UNIT_LICENSE_STATE) count(joined_license_plate_df$INSP_UNIT_LICENSE) joined_license_plate_df write.xlsx(joined_license_plate_df, xlsx) } join_license_data("Data\\Hesperia - Weekend.csv", "hesperia_weekend_joined.xlsx") join_license_data("Data\\East Las Vegas - Weekday.csv", "east_las_vegas_weekday_joined.xlsx") join_license_data("Data\\Banning - Weekday.csv", "banning_weekday_joined.xlsx")

Guide to Truck Activity Data for Emissions Modeling B-8 R Script to develop age distributions by county from MCMIS data: #Load ggplot2 and RODBC libraries library(ggplot2) library(RODBC) library(plyr) library(data.table) library(xlsx) theme_set(theme_bw()) setwd("C:\\MCMIS") dbhandle = odbcDriverConnect("driver={SQL Server}; server=localhost; database=MCMIS; trusted_connection=true") truck_inspections = sqlQuery(dbhandle, "SELECT *, YEAR([INSP_DATE]) AS INSP_YEAR FROM [MCMIS].[dbo].[DHAO_FU20160728] WHERE YEAR([INSP_DATE])=2015 AND [COUNTY_CODE_STATE] IN ('TX','VA') AND [INSP_UNIT_TYPE_DESC] IN ('STRAIGHT TRUCK','TRUCK TRACTOR') AND [COUNTY_NAME] IN ('EL PASO','WEBB','HIDALGO','BOTETOURT','PRINCE WILLIAM','FREDERICK')") truck_inspections$INSP_YEAR <- as.numeric(as.character(truck_inspections$INSP_YEAR)) truck_inspections$INSP_UNIT_YEAR <- as.numeric(as.character(truck_inspections$INSP_UNIT_YEAR)) truck_inspections$INSP_UNIT_GVWR <- as.numeric(as.character(truck_inspections$INSP_UNIT_GVWR)) truck_inspections$AGE_ID <- truck_inspections$INSP_YEAR - truck_inspections$INSP_UNIT_YEAR truck_stats <-data.table(truck_inspections)

Guide to Truck Activity Data for Emissions Modeling B-9 lapply(c("COUNTY_CODE_STATE","COUNTY_NAME","INSP_UNIT_TYPE_DESC","LOCATION"), function(name) truck_stats[, .N, by = name]) sub_truck_inspections <- subset(truck_inspections, INSP_UNIT_YEAR>0) sub_truck_inspections[sub_truck_inspections$AGE_ID>30, "AGE_ID"] = 30 sub_truck_inspections[sub_truck_inspections$AGE_ID<0, "AGE_ID"] = 0 head(sub_truck_inspections) truck_counts <- count(sub_truck_inspections, c("COUNTY_CODE_STATE","INSP_UNIT_TYPE_DESC","INSP_YEAR","COUNTY_NAME","AGE_ID")) totals <- ddply(truck_counts, c("COUNTY_CODE_STATE","INSP_UNIT_TYPE_DESC","INSP_YEAR","COUNTY_NAME"), function(f) total=sum(f$freq)) age_distributions <- merge(truck_counts,totals,by=c("COUNTY_CODE_STATE","INSP_YEAR","INSP_UNIT_TYPE_DESC","COUNTY_NAME")) age_distributions$age_fraction <- age_distributions$freq/age_distributions$V1 age_distributions write.xlsx(age_distributions, "2015_MCMIS_age_distributions_topcounties.xlsx") pdf(paste("2015 MCMIS Age Distributions By County.pdf")) for (year in c("2015")){ for (state in c("TX")){ for (vtype in c("STRAIGHT TRUCK","TRUCK TRACTOR")){ for(county in c("EL PASO","WEBB","HIDALGO")){ plot_agedists <- subset(age_distributions,COUNTY_CODE_STATE==state & INSP_UNIT_TYPE_DESC==vtype & INSP_YEAR==year & COUNTY_NAME==county) print( ggplot(data=plot_agedists, aes(x=AGE_ID, y=age_fraction))+ geom_line()+geom_ribbon(aes(ymin=0, ymax=age_fraction),fill="darkseagreen")+ #("deeppink",dodgerblue","darkseagreen")

Guide to Truck Activity Data for Emissions Modeling B-10 labs(x="Age (Years)",y="Fraction of Vehicles")+ labs(title=paste(year,"Age Distributions, State:",state,", County:", county," Vehicle Type:", vtype))+ scale_x_continuous(expand = c(0, 0), limits = c(0, 30))+ scale_y_continuous(expand = c(0, 0), limits = c(0, 0.13)) )}}}} for (year in c("2015")){ for (state in c("VA")){ for (vtype in c("STRAIGHT TRUCK","TRUCK TRACTOR")){ for(county in c("BOTETOURT","PRINCE WILLIAM","FREDERICK")){ plot_agedists <- subset(age_distributions,COUNTY_CODE_STATE==state & INSP_UNIT_TYPE_DESC==vtype & INSP_YEAR==year & COUNTY_NAME==county) print( ggplot(data=plot_agedists, aes(x=AGE_ID, y=age_fraction))+ geom_line()+geom_ribbon(aes(ymin=0, ymax=age_fraction),fill="darkseagreen")+ #("deeppink",dodgerblue","darkseagreen") labs(x="Age (Years)",y="Fraction of Vehicles")+ labs(title=paste(year,"Age Distributions, State:",state,", County:", county," Vehicle Type:", vtype))+ scale_x_continuous(expand = c(0, 0), limits = c(0, 30))+ scale_y_continuous(expand = c(0, 0), limits = c(0, 0.13)) )}}}} dev.off() close(dbhandle) B.6 Findings from Sample Data B.6.1 In-State versus Out-of-State Vehicles An initial finding of this analysis is the large proportion of trucks that are being operated and inspected outside of the State where they were registered. Table B.4 lists the percentage of inspections with out-of- State registrations for straight trucks and truck tractors for select states for the years 2013 through 2015. As previously discussed, the MCMIS straight truck designation represents the single-unit truck MOVES source type and the MCMIS truck tractor designation represents the combination truck MOVES source type. The ‘Total Trucks Inspected’ column in Table B.3 represents the total sample size analyzed for each State and year. The proportion of out-of-State registered trucks being inspected varies State by State. Of the four

Guide to Truck Activity Data for Emissions Modeling B-11 select states, California has the lowest proportion of out-of-State trucks being inspected (6 to 13 percent of straight trucks, about 30 percent of truck tractors) while Virginia has the largest proportion of out-of-State trucks being inspected (46 to 50 percent of straight trucks, about 70 percent of truck tractors). As expected, straight trucks (which are typically used for shorter hauls) consistently show lower percentages of out-of- State inspections than truck tractors. Table B.3 Out-of-State Truck Inspections Year State Truck Type Out-of-State Trucks Inspected Total Trucks Inspected Percent of Out-of-State Truck Inspections 2013 California Straight Truck 8,633 136,168 6.34% 2014 California Straight Truck 9,583 144,483 6.63% 2015 California Straight Truck 20,050 158,468 12.65% 2013 Michigan Straight Truck 4,216 12,197 34.57% 2014 Michigan Straight Truck 4,443 12,197 36.43% 2015 Michigan Straight Truck 4,466 12,673 35.24% 2013 Texas Straight Truck 27,063 96,139 28.15% 2014 Texas Straight Truck 26,242 92,528 28.36% 2015 Texas Straight Truck 26,446 95,613 27.66% 2013 Virginia Straight Truck 4,044 8,787 46.02% 2014 Virginia Straight Truck 3,809 8,315 45.81% 2015 Virginia Straight Truck 4,513 9,088 49.66% 2013 California Truck Tractor 109,874 357,143 30.76% 2014 California Truck Tractor 113,661 368,604 30.84% 2015 California Truck Tractor 107,487 358,462 29.99% 2013 Michigan Truck Tractor 22,980 38,455 59.76% 2014 Michigan Truck Tractor 21,876 37,638 58.12% 2015 Michigan Truck Tractor 21,927 38,338 57.19% 2013 Texas Truck Tractor 274,936 448,475 61.30% 2014 Texas Truck Tractor 253,779 404,538 62.73% 2015 Texas Truck Tractor 263,997 421,746 62.60% 2013 Virginia Truck Tractor 16,360 23,177 70.59% 2014 Virginia Truck Tractor 16,113 23,023 69.99% 2015 Virginia Truck Tractor 19,317 26,571 72.70% Source: Analysis of MCMIS data by Volpe National Transportation Systems Center. B.6.2 Age Distributions in Virginia and Texas Figure B.1 and Figure B.2 display the VMT-based MOVES age distributions for single-unit trucks and combination trucks, respectively. These age distributions were obtained from executing a national scale MOVES run and analyzing the VMT activity output by vehicle age; therefore these age distributions are

Guide to Truck Activity Data for Emissions Modeling B-12 activity based. Spikes and troughs are apparent in these charts that correspond to changing economic conditions when more or fewer new trucks were sold. For example, a trough is evident in the three- to five- year age bracket of 2013 inspections (five- to seven-year bracket of 2015 inspections), corresponding to the recession that occurred in the 2008 – 2010 timeframe. Figure B.3 and Figure B.4 display the MCMIS age distributions for Virginia single-unit trucks and combination trucks, respectively. Figure B.5 and Figure B.6 display the MCMIS age distributions for Texas single-unit and combination trucks, respectively. The VMT-based MCMIS age distributions in Figure B.3 and Figure B.5 can be compared to VMT-based MOVES age distributions shown in Figure B.1 and the VMT-based MCMIS age distributions in Figure B.4 and Figure B.6 can compared to the VMT-based MOVES age distributions shown in Figure B.2. The age distributions from both sources have similar trends across multiple years (2013 through 2015). Figure B.7 displays the VMT-based MCMIS and MOVES age distributions of single-unit trucks for Texas and Virginia. It is important to note that the MOVES age distributions for Texas and Virginia are virtually identical to each other and the MOVES national age distribution for the year 2015. Figure B.8 displays the VMT-based MCMIS and MOVES age distributions of combination trucks for Texas and Virginia. Once again the MOVES age distributions for combination trucks for Texas and Virginia are virtually identical to each other. The MOVES data appear to represent the “newest” age distribution, with the most vehicles in the youngest categories and the fewest in the oldest categories relative to the MCMIS distributions. The MCMIS age distribution in Virginia appears to be younger than the MCMIS age distribution in Texas.

Guide to Truck Activity Data for Emissions Modeling B-13 Figure B.1 VMT-Based MOVES Age Distributions Single-Unit Trucks 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age 2013 2014 2015

Guide to Truck Activity Data for Emissions Modeling B-14 Figure B.2 VMT-Based MOVES Age Distributions Combination Trucks Figure B.3 VMT-Based MCMIS Age Distributions Single-Unit Trucks—Virginia 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age 2013 2014 2015 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age 2013 2014 2015

Guide to Truck Activity Data for Emissions Modeling B-15 Figure B.4 VMT-Based MCMIS Age Distributions Combination Trucks—Virginia Figure B.5 VMT-Based MCMIS Age Distributions Single-Unit Trucks—Texas 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age 2013 2014 2015 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age 2013 2014 2015

Guide to Truck Activity Data for Emissions Modeling B-16 Figure B.6 VMT-Based MCMIS Age Distributions Combination Trucks—Texas Figure B.7 VMT-Based MOVES versus MCMIS Single-Unit Trucks—Virginia and Texas (2015) 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age 2013 2014 2015 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age Texas - MOVES Texas - MCMIS Virginia - MOVES Virginia - MCMIS

Guide to Truck Activity Data for Emissions Modeling B-17 Figure B.8 VMT-Based MOVES versus MCMIS Combination Trucks—Virginia and Texas (2015) Three counties in Virginia and three counties in Texas with the greatest number of State-level truck inspections were selected for additional modeling. Figure B.9 and Figure B.10 show the locations of these counties for Virginia and Texas, respectively. Figure B.11 and Figure B.12 display the VMT-based 2015 MCMIS age distributions for Virginia (statewide and the three selected counties) representing single-unit trucks and combination trucks, respectively. Figure B.13 and Figure B.14 display the VMT-based 2015 MCMIS age distributions for Texas (statewide and the three selected counties) representing single-unit trucks and combination trucks, respectively. The age distribution patterns are generally similar across the Virginia counties, although combination trucks appear a bit younger in Botetourt County (along I-81 near Roanoke) than in the two other counties. The age distributions appear very different across the Texas counties, with the newest vehicles in Webb County (along I-35 near Laredo) and the oldest in El Paso County. For both the State age distributions and the county age distributions shown in Figure B.11 through Figure B.14, the age distributions may be representative of truck traffic through the corridor where the inspection facility is located rather than the State or county as a whole. Since the stations are typically located on an Interstate or other major highway, it might be hypothesized that the distributions are newer than average for all roads, if these facilities see a higher proportion of long-haul vehicles (compared to, for example, drayage trucks). To compare the representativeness of the age distributions against the distributions of all vehicles operating in the State or county, however, it would be necessary to establish a random sampling framework on a representative set of roads, collecting age distribution information through a license plate survey or other observational means. Table B.4 and Table B.5 list the average ages of single-unit trucks and combination trucks for Virginia and Texas, respectively. The tables show VMT- and registration-based age distributions for both MOVES and 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age Texas - MOVES Texas - MCMIS Virginia - MOVES Virginia - MCMIS

Guide to Truck Activity Data for Emissions Modeling B-18 MCMIS. For testing the emission impacts, runs with the default registration-based MOVES age distributions are compared against runs with the registration-based MCMIS age distributions. There are very minor variations in the MOVES distributions across counties (typically less than 0.04 years), which are not shown in the table. Figure B.9 Selected Virginia Counties Map source: Google maps.

Guide to Truck Activity Data for Emissions Modeling B-19 Figure B.10 Selected Texas Counties Map source: Google maps.

Guide to Truck Activity Data for Emissions Modeling B-20 Figure B.11 VMT-Based MCMIS Age Distributions Single-Unit Trucks—Virginia and Counties, 2015 Figure B.12 VMT-Based MCMIS Age Distributions Combination Trucks—Virginia and Counties, 2015 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age BOTETOURT FREDERICK PRINCE WILLIAM Virginia 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age BOTETOURT FREDERICK PRINCE WILLIAM Virginia

Guide to Truck Activity Data for Emissions Modeling B-21 Figure B.13 VMT-Based MCMIS Age Distributions Single-Unit Trucks—Texas and Counties, 2015 Figure B.14 VMT-Based MCMIS Age Distributions Combination Trucks—Texas and Counties, 2015 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age EL PASO HIDALGO Texas WEBB 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Age Fraction Age EL PASO HIDALGO Texas WEBB

N C H R P 08-101: E nhanced Truck D ata C ollection and A nalysis for E m issions M odeling A -2-B -22 Table B.4 National, Virginia, and County Average Age Source Type Year National State County Virginia Botetourt Frederick Prince William VMT- Based MOVES1 Registration- Based MOVES1 VMT- Based MCMIS1 Registration- Based MCMIS1 VMT- Based MCMIS Registration -Based MCMIS VMT- Based MCMIS Registration- Based MCMIS VMT- Based MCMIS Registration -Based MCMIS Single-Unit Trucks 2013 7.54 11.68 9.19 15.04 8.52 14.75 8.70 13.40 6.75 9.77 2014 7.28 11.64 9.65 16.13 8.38 14.59 8.92 14.82 7.83 12.21 2015 7.01 11.59 9.23 15.88 8.14 13.98 9.05 17.21 7.36 11.01 Combination Trucks 2013 7.33 11.15 8.47 13.99 6.55 9.15 7.70 12.49 8.02 11.80 2014 7.49 11.43 8.54 14.83 6.54 10.13 8.15 13.00 7.99 12.14 2015 7.61 11.72 8.21 14.59 6.21 9.28 7.70 12.34 7.97 12.68 Table B.5 National, Texas, and County Average Age Source Type Year National State County Texas El Paso Hidalgo Webb VMT Based MOVES1 Registration -Based MOVES1 VMT- Based MCMIS1 Registration -Based MCMIS1s VMT- Based MCMIS Registration- Based MCMIS VMT- Based MCMIS Registration- Based MCMIS VMT- Based MCMIS Registration -Based MCMIS Single-Unit Trucks 2013 7.54 11.68 9.49 15.80 13.86 18.97 11.42 18.63 6.29 11.52 2014 7.28 11.64 9.70 16.13 13.60 18.79 11.70 18.73 6.52 11.67 2015 7.01 11.59 10.07 16.86 13.82 19.46 13.03 20.15 6.73 11.87 Combination Trucks 2013 7.33 11.15 9.69 15.92 12.32 17.23 10.64 19.25 8.98 13.17 2014 7.49 11.43 10.07 16.65 12.84 18.11 10.98 19.38 9.42 13.70 2015 7.61 11.72 10.29 16.87 12.44 18.28 11.52 20.06 10.31 14.49 1 The terms “VMT-based” and “registration-based” are used to minimize confusion when comparing MOVES and MCMIS data. “Registration-based MOVES” age distributions are unadjusted age distributions from MOVES inputs, while “VMT-based MOVES” distributions are adjusted by MAR. “VMT-based MCMIS” age distributions are unadjusted age distributions from MCMIS observations, while “registration-based MCMIS” distributions are adjusted by MAR.

Guide to Truck Activity Data for Emissions Modeling B-23 The MCMIS age distributions and the baseline MOVES national age distribution (Texas and Virginia age distributions are virtually the same as the national age distribution) were modeled with MOVES2014a to compare the emissions differences. The MCMIS age distributions were adjusted to reflect a population- based input into MOVES. B.6.3 Comparison of MCMIS, MOVES, and State Registration-Based Age Distributions for Virginia The project team had access to multiple sources of age distribution data from Virginia, and compared these various sources for the common year of 2014. The sources included: • MOVES default data. • MCMIS-based age distributions for vehicles observed at inspection stations. • Registration-based data obtained from IHS for the 2014 National Emissions Inventory developed by U.S. EPA. • MOVES input files created by the Virginia Department of Environmental Quality (DEQ), and based on State registration data. The project team had access to the NEI files based on their work with U.S. EPA supporting the NEI. The Virginia MOVES files were created by the Virginia DEQ and obtained from the Virginia Department of Transportation (DOT) in February 2017 for a separate project undertaken by the contractor for VDOT. The data for single-unit trucks were under review by DEQ at the time of the analysis, and therefore only data for combination trucks are presented for this dataset. Summary charts are included as Figure B.15 and Figure B.16 below, and the average age from each source is summarized in Table B.6. Note that the MCMIS data are “corrected” to be comparable to population-based distributions based on MARs by age, as described elsewhere in this case study. The MARs are computed from truck populations and VMT extracted from MOVES for Virginia and therefore may not reflect actual mileage accumulation rates for trucks operating in Virginia. Note that the choice of MAR can affect the resulting adjusted age distribution, possibly significantly. For example, the use of the MAR provided in the U.S. EPA’s report on vehicle population and activity inputs for MOVES (U.S. EPA, 2014) results in an average age of 12.2 years for source type 62 trucks, compared to 14.9 years using the MAR calculated from source type 62 VMT divided by population for Virginia embedded in MOVES. The MAR adjustments are illustrated in Table B.4. Table B.6 Average Age of Trucks in Virginia, 2014 Source Basis Single-Unit (52/53) Combination (61/62) Virginia MOVES inputs State motor vehicle registration data N/A 12.5 U.S. EPA 2014 National Emissions Inventory Registration data from IHS 12.0 13.0 MCMIS MCMIS database, trucks stopped at inspection stations 16.0 14.9 MOVES2014 defaults 2011 national registrations from R.L. Polk 11.6 11.4

Guide to Truck Activity Data for Emissions Modeling B-24 The following observations are noted: • For single-unit trucks (source type 52/53), the age distributions from NEI and MCMIS both track fairly closely with MOVES defaults, although their average ages are slightly older than MOVES. • For combination trucks (source type 61/62), all four data sources agree on general trends (year-to-year increases or decreases in vehicle population). The impact of both economic trends (recession in 2008/2009, followed by slow recovery) and possibly the NOx standards implemented beginning in 2007 is evident in all datasets as spike in vehicle purchases in the mid-2000s, falling off sharply in 2008 to 2011. • The average age of combination trucks observed in MCMIS is higher, at 14.9 years compared to 11.4 to 13 years for the other data sources. The project team hypothesized the opposite effect (i.e., that inspection station observations might be skewed towards the younger trucks found more predominantly in long-haul use (as compared to shorter-haul applications such as drayage where older trucks tend to be used)). While it is possible that inspection stations are over-sampling older trucks for some reason, it is also possible that the MARs used to adjust the MCMIS age distributions, which are based on MOVES inputs, are not representative of actual MARs for trucks operating in Virginia. • The NEI and State registration data for combination trucks track fairly closely, with an average age difference of only 0.5 year. This is older than the MOVES average age by about one to one and a half years. This could reflect general aging of the fleet (or the impact of economic cycles) compared to when the MOVES default data were collected. • The choice of the most appropriate age distribution may depend upon the project location. MCMIS data may be more representative of Interstate or major highways frequented by long-haul trucks, while State- based registration data may be more representative of areas frequented by short-haul trucks such as ports and warehousing/distribution areas. However, more localized data collection would be needed to confirm this hypothesis.

Guide to Truck Activity Data for Emissions Modeling B-25 Figure B.15 Comparison of Age Distributions Single-Unit Trucks, Virginia 2014 Figure B.16 Comparison of Age Distributions Combination Trucks, Virginia 2014 Note: MCMIS data adjusted based on MOVES VMT per vehicle. 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 MCMIS MOVES NEI 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 VA Reg Data MCMIS MOVES NEI

Guide to Truck Activity Data for Emissions Modeling B-26 B.6.4 Sensitivity Analysis—Impact on Emissions Figure B.17 through Figure B.22 display the percent differences in emissions for CO, CO2, NOx, PM2.5, and volatile organic compounds (VOCs). Figure B.17 and Figure B.18 show the emissions differences when using MCMIS versus MOVES age distributions in Virginia and Texas for years 2013, 2014, and 2015; they represent single-unit trucks and combination trucks, respectively. The overall trend observed in Figure B.17 and Figure B.18 is that using MCMIS-based age distributions result in significantly higher emissions for CO, NOx, PM2.5 and VOC compared to using the MOVES national age distributions. Conversely, the CO2 emissions for both single-unit trucks and combination trucks appear to slightly decrease when using MCMIS-based age distributions. Figure B.19 and Figure B.20 show the emissions differences when using MCMIS versus MOVES age distributions in Virginia and selected counties representing single-unit trucks and combination trucks, respectively. Figure B.21 and Figure B.22 show the emissions differences when using MCMIS versus MOVES age distributions in Texas and selected counties representing single-unit trucks and combination trucks, respectively. Figure B.17 Age Distribution Emissions Differences: MCMIS from MOVES Base Case Single-Unit Trucks -10% 0% 10% 20% 30% 40% 50% 60% 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 CO CO2 NOx PM2.5 VOC MCMIS Percent Difference vs. MOVES Virginia Texas

Guide to Truck Activity Data for Emissions Modeling B-27 Figure B.18 Age Distribution Emissions Differences: MCMIS from MOVES Base Case Combination Trucks Figure B.19 Age Distribution Emissions Differences: MCMIS from MOVES Base Case Virginia Single-Unit Trucks, 2015 -10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 CO CO2 NOx PM2.5 VOC MCMIS Percent Difference vs. MOVES Virginia Texas -10% 0% 10% 20% 30% 40% 50% CO CO2 NOx PM2.5 VOC MCMIS Percent Difference vs. MOVES Botetourt Frederick Prince William Virginia

Guide to Truck Activity Data for Emissions Modeling B-28 Figure B.20 Age Distribution Emissions Differences: MCMIS from MOVES Base Case Virginia Combination Trucks, 2015 Figure B.21 Age Distribution Emissions Differences: MCMIS from MOVES Base Case Texas Single-Unit Trucks, 2015 -30% -20% -10% 0% 10% 20% 30% 40% CO CO2 NOx PM2.5 VOC MCMIS Percent Difference vs. MOVES Botetourt Frederick Prince William Virginia -20% 0% 20% 40% 60% 80% 100% 120% 140% CO CO2 NOx PM2.5 VOC MCMIS Percent Difference vs. MOVES El Paso Hidalgo Webb Texas

Guide to Truck Activity Data for Emissions Modeling B-29 Figure B.22 Age Distribution Emissions Differences: MCMIS from MOVES Base Case Texas Combination Trucks, 2015 Using MCMIS age distributions rather than MOVES age distributions results in an increase of CO, NOx, PM2.5, and VOC emissions for both single-unit trucks and combination trucks for the majority of counties analyzed. A few select counties, Botetourt County in Virginia for combination trucks and Webb County in Texas for single-unit trucks, show lower emissions when using MCMIS age distributions rather than MOVES age distributions across all pollutants analyzed. B.6.5 License Plate and MCMIS Matching in Las Vegas and Los Angeles License plate surveys are described in the guidebook as a method to identify truck age distributions at user- defined sampling points. The license plate records must be matched with a registration or inspection database that includes both the license and age of the truck. To test the approach of matching license plate survey data with MCMIS data from inspected trucks, the project team made an effort to match field survey data with the MCMIS database. The project team obtained data from a series of FHWA-funded license plate surveys conducted in July 2010 in two metropolitan areas: Los Angeles and Las Vegas (Boriboonsomsin et al., 2011). The study included three freeway sites per metropolitan area: North Los Angeles, Hesperia, and Banning in Los Angeles, and North Las Vegas, South Las Vegas, and East Las Vegas, as shown in Figure B.23 and Figure B.24. Two video cameras were set up to record traffic in the outer lanes of each site, with a particular interest in truck traffic. Video recordings were made for five days (Tuesday, Wednesday, Thursday, Saturday, and Sunday) during morning and evening peak hours (6 – 10 AM and 3 – 7 PM). Survey data were grouped by weekday and weekend distinctions and then separated into 12 .csv files, one for each location-day type combination. -20% 0% 20% 40% 60% 80% 100% 120% 140% CO CO2 NOx PM2.5 VOC MCMIS Percent Difference vs. MOVES El Paso Hidalgo Webb Texas

Guide to Truck Activity Data for Emissions Modeling B-30 Figure B.23 Locations of License Plate Surveys in the Los Angeles Metropolitan Area Figure B.24 Locations of License Plate Surveys in the Las Vegas Metropolitan Area Source: Boriboonsomsin et al., 2011. Hesperia North Los Angeles Banning North Las Vegas South Las Vegas East Las Vegas

Guide to Truck Activity Data for Emissions Modeling B-31 Each field survey .csv file contained the following fields: • License plate number. • Licensing state code. • Mapping to FHWA Highway Performance Monitoring System vehicle class.7 • Mapping to MOVES vehicle source use type (SourceTypeID) (U.S. EPA, 2016). Due to moderate video quality and upfront processing and development costs, the license plate numbers and states were extracted manually in the original FHWA study by student data technicians watching and pausing the video for each vehicle. The technicians, in some cases, were also able to assign the FHWA vehicle class and MOVES source type. However, the manual extraction process resulted in many unclear license plate numbers and/or states and those records could not be used in the matching process. The project team used the same MCMIS dataset from calendar years 2013, 2014, and 2015 as described above in this license plate analysis. To ensure the largest sample size possible, the relevant MCMIS data were imported into R, any truck inspection records without a license plate number were removed, and then merged with only the license plate number in the field survey data contained in each of the 12 .csv files. There was not enough agreement between datasets to also merge on licensing state or vehicle type. The matching process with these particular field surveys yielded modest results. For example, out of the 10,304 records in the Banning weekday .csv file, 253 records returned with matches to license plate numbers in MCMIS, as shown in Figure B.25. Many of the same license plate numbers appeared more than once, likely due the same truck being inspected in multiple locations from 2013 to 2015. In some sense, these duplicate records were useful; most of the recurring inspection records were linked to the same vehicle identification number and model year, which boosts the credibility of accurately determining the age for any given truck. Still, there were some vehicles supposedly built in model year 2011 and later, clearly after the field surveys were conducted. It is possible the older truck was retired and then the license plate number was reused on a newer vehicle, or perhaps that two trucks could be registered in two different states under the same license plate number since the datasets were not merged by State. Beyond the license plate number, VIN, and model year, the mappings to FHWA vehicle class and MOVES source type were unreliable. Often vehicles were classified as a straight truck or tractor in the MCMIS database but listed as a passenger car or light truck in the field survey data. Straight trucks in MCMIS did not always map to MOVES single-unit trucks and likewise tractors in MCMIS did not always to MOVES combination trucks. Similarly, licensing state occasionally agreed between MCMIS and the field survey, though more often did not. Given these results, the project team had much higher confidence in the MCMIS vehicle classifications and licensing state assignments than those in the field surveys. In the Banning weekday field survey data, for instance, there were 580 trucks listed as MOVES combination trucks. When the sample was merged with MCMIS, 115 records were classified as tractors and only 27 of those records were identified as MOVES combination trucks. Moreover, of the 27 records, there were eight unique license plates and it appears that vehicles in Utah, Nebraska, and Kansas had the same license plate number but different VINs—bringing the total up to 10 matching vehicles. None of those 27 records matched 7 FHWA, Office of Highway Policy Information, Traffic Monitoring Guide, Sept 2013, https://www.fhwa.dot.gov/policyinformation/tmguide/tmg_fhwa_pl_13_015.pdf.

Guide to Truck Activity Data for Emissions Modeling B-32 by licensing state and one vehicle was marked as having been built in model year 2012. Evidently, it is important not to over-constrain the matching process. This example underscores the need for researchers familiar with the datasets to perform quality control on the merged license plate data before calculating truck ages and age distributions. Researchers must comb through the matched records on a vehicle-by-vehicle basis to determine if it can be included in the sample used to create age distributions. Ultimately, whether to include certain vehicles or not comes down to the researchers’ best judgement. Still, in the example, the merged Banning weekday dataset had matches on more than 40 unique license plates of MCMIS-defined tractors and on more than 80 unique license plates of MCMIS-defined straight trucks. Not all of these vehicles could be used to develop age distributions but many can. Unfortunately these combined datasets did not yield large enough samples of well-classified trucks to confidently develop age distributions. None of these field surveys had more than roughly 600 single-unit trucks or more than 600 combination trucks. If these field surveys had thousands of definitive heavy-duty trucks, the license plate matching process could be a viable method for determining local age distributions. Pulling MCMIS data closer to the survey data could also increase the sample size. In the case of these 12 field surveys, it is likely that MCMIS data from calendar year 2009, 2010, and 2011 would have yielded more matches. In the future, field surveys with high-resolution video, reliable computer vision algorithms for license plate extraction, and/or just a longer sampling period merged with MCMIS data close to the survey date could provide enough expected matches for age distribution development.

N C H R P 08-101: E nhanced Truck D ata C ollection and A nalysis for E m issions M odeling B -33 Figure B.25 Screenshot Sample of Banning Weekday License Plate Field Survey Data Merged with the MCMIS Database

Guide to Truck Activity Data for Emissions Modeling B-34 B.6.6 Summary of Findings The following conclusions can be drawn from this comparison of MCMIS, MOVES, and registration-based age distributions in two states: • MCMIS is a publicly available data source for State DOTs and data inquiries can be initiated through a DART request. • Analysis of MCMIS data shows that a large portion or majority portion of heavy-duty trucks are not registered in the same State they were inspected. Therefore, it is possible that age distributions on specific roadway facilities within the State differ significantly compared to the distribution presented by vehicles registered in the State. • The MCMIS age distributions for the States and locations analyzed show consistency across different calendar years for most inspection locations. This will be influenced by inspection sample sizes, and not all inspection locations will have large sample sizes. • The MCMIS-based average age is generally higher than the MOVES default average age for both single-unit trucks and combination trucks for the States and counties analyzed. • The emissions were substantially higher in most cases when using MCMIS-based age distributions compared to MOVES national age distributions. • It cannot be determined with the available data whether the MCMIS observed age distributions are representative of all vehicles operating within the State. They are likely representative of traffic on Interstate and other major highways, especially in rural areas; comparability with urban Interstates and local roads would require additional field data collection through a method such as license plate surveys. • An attempt to match MCMIS data with an existing sample of vehicle license plates from six locations did not provide a large enough sample to reliably estimate an age distribution. Future efforts might yield a larger sample and more successful match through state-of-practice license plate capture and analysis methods that could bring costs down for larger samples; targeted sampling procedures; and matching of same-year license plate and MCMIS data. B.7 Transferability MCMIS is publicly available to State DOTs and access can be initiated through a DART request to FMCSA. To be sensitive of FMCSA resources associated with DART requests, a standard request format should be developed so the DART team can easily apply a standard query that can be reused with each request. This would reduce the burden on the DART request team and ensure consistency for age distribution data they are providing for various State agencies. This case study compared MCMIS data to MOVES base age distributions which represent national conditions. The project team suggests two opportunities for further validation of the representativeness of MCMIS data: • A comparison of MCMIS age distributions against State registry-based age distributions, especially focusing on single-unit trucks, which are more likely to be operated within the State of registration; and • Sampling on non-Interstate highways using license plate surveys (matched with data from MCMIS or another registration source) to compare distributions with those on corridors subject to MCMIS inspections.

Next: Appendix C. Case Study #3: Truck Starts and Soak Time from GPS Data »
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NCHRP Web-Only Document 261: Case Studies of Truck Activity Data for Emissions Modeling consists of seven case studies that are appendices A to G of NCHRP Report 909: Guide to Truck Activity Data for Emissions Modeling.

NCHRP Research Report 909 explores methods, procedures, and data sets needed to capture commercial vehicle activity, vehicle characteristics, and operations to assist in estimating and forecasting criteria pollutants, air toxics, and greenhouse gas emissions from goods and services movement.

NCHRP Research Report 909 is also supplemented by three MS Excel files that contain data from the case studies:

Case Studies #1 and #7

Case Study #2

Case Studies #3, #4, and #6

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