Development of a Statewide Freight Trip Forecasting Model for Utah

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1 Development of a Statewide Freight Trip Forecasting Model for Utah Kaveh Shabani (Corresponding Author) Resource Systems Group Salt Lake City, Utah Phone: kaveh.shabani@rsginc.com Chad Worthen Resource Systems Group Salt Lake City, Utah Phone: chad.worthen@rsginc.com Maren Outwater Resource Systems Group Phone: maren.outwater@rsginc.com Walt Steinvorth Utah Department of Transportation Phone: wsteinvorth@utah.gov Submitted for publication and presentation to the rd Annual Transportation Research Board Meeting January -, 0 August, 0, words, Figures, Tables = Total, words

2 Shabani, Worthen, Outwater, Steinvorth 0 Abstract Trucking is the primary source of goods movement in Utah. The Utah Department of Transportation (UDOT) commissioned the development of a statewide freight model to assist in statewide long-range planning and to better assess roadway impacts from trucks. A long-haul commodity-based model combined with a short-haul commercial vehicle model was developed and integrated with the Utah Statewide Travel Model (USTM), which forecasts passenger travel for the state. This paper includes a discussion of the source data used in the freight model development and long-haul and short-haul forecasting methods and calibration results. This paper also discusses data needs and how alternate sources of data were used to overcome initial data deficiencies. Moreover, lessons learned to assist other regions seeking to develop freight demand modeling approaches are discussed. Keywords: Statewide Freight Modeling, State of Utah, Commodity Flow, Truck

3 Shabani, Worthen, Outwater, Steinvorth INTRODUCTION The Utah statewide freight model, developed for the Utah Department of Transportation (UDOT), uses an inter-regional commodity flow model for longer trips and an intra-regional commercial vehicle model for shorter trips. This paper presents (a) a brief contextual overview of the data sources used in developing the model; (b) model approach details on trip production and attraction, trip distribution, mode share, and assignment; (c) model validation; (d) an evaluation and discussion of the data needs and how additional data were used; and (e) lessons learned to assist other regions seeking to develop similar freight modeling approaches. UTAH STATEWIDE FREIGHT MODEL The Utah statewide freight model was based on a hybrid approach of estimating commodity flows and commercial vehicle movements. Commodity flow models were estimated using TRANSEARCH data. Many short-haul truck movements are not captured in the TRANSEARCH dataset. A separate commercial vehicle model based on FHWA research is used to capture these short-haul trips. This hybrid approach is more comprehensive than either approach by itself and accounts for all truck types. Data Sources UDOT purchased the TRANSEARCH commodity data from Global Insight, Inc., which is a commercially produced database of tonnages of goods moved. It uses several mode-specific data sources to create a picture of the nation s freight flows on an origin to destination basis. TRANSEARCH reports tonnages for base and forecast years, of which 00 and 00 were available for this project. The 0 TRANSEARCH Standard Transportation Commodity Code Version (STCC) commodity groups were collapsed into the following categories:. AGRI agriculture. FOOD prepared foodstuff. MNRL metal & nonmetal ores. COAL coal. OLGA crude petroleum & natural gas. PETR petroleum or coal products. CHEM chemicals. TEXT textile & paper. BULD building materials & machinery 0. MANU manufactured equipment. LRET lumber & retail. IMDL intermodal & mail The internal-internal (II) tonnage moved in Utah is dominated by the MNRL (metal and nonmetal ores including sand and gravel) with more than 0 million tons moved in 00. The next highest II tons moved was less than million. The high MNRL tons was thought to be erroneous. However, other data source revealed that Utah has more than 0 active pits and quarries across the state and in 00 about million tons of gravel, sand and crushed stone were produced ().

4 Shabani, Worthen, Outwater, Steinvorth For internal-external (IX) movements, MNRL, COAL, CHEM and BULD had the highest tonnages with between and million tons. External-internal (XI) movements were fairly evenly distributed between the commodity groups. The vast majority of tonnage moved in Utah are external-external (XX), which reinforces the idea of Utah as the crossroads of the West. This pattern is even more pronounced in the future years. For the COAL & OLGA commodities, the TRANSEARCH data was found to have suspect or erroneous data and other data sources were used to replace the suspect data. This effort is described in more detail in the Data Issues section of this paper. Goods are moved in Utah predominately by truck. This is particularly true for II tonnage. Rail also plays an important role in goods movement through Utah (including truck-rail intermodal mode); however TRANSEARCH has this mode becoming less important in future years in favor of trucking. Air and other transportation modes are not shown to be a significant carrier of tonnage in Utah. Although these modes carry rather significant value of shipments, the primary goal of this version of the model is to focus on modes that are the most important for UDOT planning needs. The TRANSEARCH modes were aggregated into the following modes for the freight model: Truck Rail Truck-Rail Intermodal Air Other TRANSEARCH aggregated data at the county level within Utah and for select neighboring metropolitan areas and West Coast ports and at the Bureau of Economic Analysis (BEA) level for the rest of the U.S. It also included Canada provinces and Mexican states, resulting in zones in the freight national zone structure. At the state level, the freight model uses the zones from the passenger model, which has, internal and external zones. The Freight Analysis Framework version (FAF) () network was used to generate the national highway network. It contains travel speeds and distances on all major roads in U.S. The FAF network did not include Canada or Mexico. Canadian zones were directly linked to the nearest U.S. highways. A basic Mexican freeway system was coded into the network to help assign commodity flows to/from zones in Mexico. The national network was merged with the statewide network for use in the freight model. Trip Generation Long-haul trip generation production and attraction rates were estimated through linear regression using the TRANSEARCH commodity tonnages and county-level employment data. The selection of proper employment variables and their coefficients were generated using SPSS and Excel. A different set of employment variables were used for production and attraction equations depending on industry type and statistical measures. The analysis included removing outlier county data and grouping counties into tiers based on reasonable characteristics (e.g. rural, urban). The resulting generation rates were calculated for II productions and attractions, IX productions and XI attractions (see TABLE ). The external trip end of IX and XI were based on TRANSEARCH data. COAL, OLGA and PETR were estimated differently as discussed in the Data Issues section.

5 Shabani, Worthen, Outwater, Steinvorth TABLE Long-Haul Generation Equation Rates Commodity II Production II Attraction AGRI Tier :.0 * (frm) Tier :. * (frm). * (whl+mnf) FOOD. * ( mnf). * (rtl+whl+mnf) MNRL. * (mnr+mnf). * (cnst+mnf) COAL Produced at mines Attracted to power plants OLGA Produced at oil wells Attracted to oil refineries PETR Produced at refineries. * (rtl+whl) CHEM.0 * (mnf). * (whl+mnf) TEXT 0. * (mnf+whl) 0. * (rtl+whl) BULD. * (mnf). * (cnst) +. * (mnf) 0 MANU. * (mnf) * (mnf+whl+rtl). * (whl) + 0. * (trns+wrhs) * (rtl) LRET. * (whl+mnf+frst) 0. * (rtl+whl+mnf+trns) IMDL.0 * (frm). * (other+mnf+trns) Commodity IX Production XI Attraction AGRI FOOD Tier :. * (frm) Tier :. * (frm) Tier :. * (mnf) Tier :.0 * (mnf) MNRL. * (mnrl). * (whl+mnf). * (rtl+whl+mnf) Tier : 0.0 * (cnst+mnf) Tier :. * (cnst+mnf) COAL Produced at mines Attracted to power plants OLGA Produced at oil wells Attracted to oil refineries PETR Produced at refineries. * ( rtl+whl) CHEM. * (mnf) TEXT. * (mnf+whl) BULD. * (mnf) 0 MANU Tier :. * (mnf) Tier :. * (mnf) Tier :. * (whl+mnf) Tier :. * (whl+mnf) Tier :. * (whl+rtl) Tier :. * ( whl+rtl) Tier :. * ( cnst+mnf) Tier :. * ( cnst+mnf). * (rtl+whl+mnf+trns) LRET. * (whl+mnf+rtl). * (rtl+whl+mnf+trns) IMDL.0 * (mnf) + 0. * (whl). * (other+mnf+trns) Note: Employment variable acronyms are as follow. frm=farm, mnf=manufacturing, whl=wholesale, rtl=retail, cnst=construction, mnr=mineral Mining, trns=transportation and frst=forest. Short-haul trip generation rates were based on FHWA commercial vehicle research (). Rates were estimated from average per capita fleet size and vehicle trip rates from several U.S. urban areas. These averages were used to calculate total commercial vehicle trips which were then divided by household and employment variables to estimate commercial vehicle trip rates. Trip rates were estimated for nine commercial vehicle categories and three vehicle types as shown in

6 Shabani, Worthen, Outwater, Steinvorth 0 0 TABLE. Short haul rates assume that commercial vehicles do not include trips from outside the model region as the long-haul freight model captures the inter-regional commercial vehicle movements. Rates for the urban freight goods movement were adapted from QRFM II (). Vehicle types are based on the FHWA s vehicle classification as follows: Light FHWA class - (-axle, -tire vehicles) Medium FHWA class - (single-unit trucks) Heavy FHWA class - (multi-unit trucks) TABLE Short-Haul Commercial Vehicle Trips Rates Vehicle Type Variables Light Medium Heavy School Bus Households Moving Shuttle Service Households + Employment People Private Transport Employment Goods Services Trip Distribution Package/Product/ Mail Households + Employment Ag., Mining, and Construction Industrial Urban Freight Retail Other Households Construction HH + Emp. + * Construction Emp Safety Households + Employment Utility Vehicles Households Business/Personal Services Households + Employment Long-haul commodities are distributed nationally to Bureau of Economic Analysis (BEA) zones outside of Utah and to TAZs inside Utah, and then trimmed to the statewide model space using a sub-area extraction. Short-haul trips are distributed on the statewide scale only. Both short and long-haul data are distributed via a gravity model. Initial long-haul friction factor curves were derived from the QRFM II then calibrated and adjusted using TRANSEARCH data and trial and error. Exponential and gamma functions were used to calculate the friction factors. Step functions were also used if needed to get the best fit to observed average trip length frequencies. The breakpoints for friction factors were determined during the trial and error process and leaded to the best fit. Short haul friction factors were adapted from QRFM () and other states coefficients for light, medium and heavy vehicle types. The long and short haul friction factor equations are shown in TABLE.

7 Shabani, Worthen, Outwater, Steinvorth TABLE Long and Short-Haul Friction Factor Equations Long-Haul II IX XI Commodity AGRI FOOD < 00 min: >00 min: <0 min: >0 min: MNRL COAL <0 min: >0 min: <0 min: >0 min: OLGA PETR CHEM TEXT <00 min: >00 min: <0 min: >0 min: BULD 0 MANU <00 min: >00 min: 0 LRET IMDL <0 min: >0 min: Short-Haul Vehicle Type II IX XI Light NA NA Medium NA NA Heavy NA NA Long-Haul Mode Share and All Truck Assignment A mode share model based on TRANSEARCH data was used to determine the long-haul tonnage moved by truck, including the truck portion of the rail-truck intermodal mode. Tonnages moved by rail and other modes were not used by the freight model. To capture the truck traffic coming out of and going into intermodal facilities, rail/truck intermodal facilities in Utah were identified using CTA Intermodal Terminal Database (), IANA North American Intermodal Terminal Database (), BTS Intermodal Terminal Database () and local knowledge. The freight movement between intermodal locations and traffic zones internal to Utah were distributed based on the size of the intermodal site (e.g. storage area/tracks). An annual factor was used to convert from annual to daily tons. A wide range of factors is considered in current freight literature (e.g.,,, 0, 0, and ) based on a, or days/week delivery schedule and if major holidays are included. The Utah freight model used

8 Shabani, Worthen, Outwater, Steinvorth 0 0 an annual factor of 0 days/year, representing delivery days/week excluding holidays as this factor gave the best validation results. This suggests that even though count data have truck volumes more evenly distributed throughout the week, the number of days per week goods are moved are closer to a typical day workweek schedule. Utilizing the Vehicle Inventory and Use Survey (VIUS) data (), payload factors (average weight of carried cargo) were used to convert commodity tonnage to trucks. Payload factors differ by commodity as each commodity contains different average shipment size and densities. The number of empty return trucks was also estimated from VIUS data, which varied by commodity and distance driven. The following formula was applied to the transpose of the truck trip matrix to calculate the number of empty return trucks: Empty Truck Trips = A x / ( x) Where: A = transposed truck matrix x = percent driven empty from VIUS data Average payload factors and empty percentages are shown in TABLE. TABLE Average Payload Factor and Percent Empty by Commodity Group Commodity Average Payload Factor % Empty <= 0 Miles -00 Miles 0-00 Miles 0-00 Miles >00 Miles AGRI. % 0% % % % FOOD. 0% % 0% % % MNRL. % % % % % COAL. 0% 0% % % % OLGA 0. % % % % % PETR. % % % 0% 0% CHEM. % % % % % TEXT. % 0% 0% % 0% BULD. % % % % % 0 MANU. % % 0% % % LRET. % % % % % IMDL. % % 0% % % The payload factor for coal in Utah appeared unreasonably high. According to VIUS data, the average coal payload factor in Utah was tons/truck, almost double the national average. However, the high payload factor was confirmed as Utah allows very large bulk carrier trucks (doubles) that are not allowed by most states which form the mainstay of the coal-hauling truck fleet in Utah. Long-haul truck trips were combined with the short-haul commercial vehicle trips (short-haul trips were calculated as vehicles and so do not have a mode component) and assigned to the highway network along with the passenger trips. Both the passenger model and the freight model are daily models and do not have diurnal factors or period components.

9 Shabani, Worthen, Outwater, Steinvorth 0 0 Validation The freight model was validated based on link volumes, VMT (Vehicle Miles Traveled), VHT (Vehicle Hours Traveled) and average trip lengths. The overall validation approach can be summarized as follows: Perform reasonableness checks of the estimated model parameters to confirm they have the correct signs and reasonable magnitudes. Comparisons to model parameters in other models were part of these checks. Unreasonable model parameters were identified and modified. Perform reasonableness checks of the aggregate model results (e.g. total volumes by vehicle type and facility type) for the base year. Comparisons of observed and modeled flows along primary freight corridor in Utah. Comparisons of observed and modeled flows with count data on all facilities with truck counts. For validation, truck count locations (0 arterial and freeway locations) were used. These count locations covered the rural (non-mpo) parts of the state taken from UDOT s hours and ATR (Automatic Traffic Recorder) count data sets. It should be noted that for the second phase of this project more count locations will be used based on the availability of data. Of the locations, ( arterial and 0 freeway locations) were on Utah s primary freight corridors. FIGURE shows the traffic count validation locations.

10 Shabani, Worthen, Outwater, Steinvorth 0 FIGURE Validation Traffic Count Locations. Observed 00 truck volumes were adjusted based on trends in AADT (Average Annual Daily Traffic) to account for atypical counts in the recession years. Counts were classified as light, medium and heavy truck volumes bases on the FHWA s vehicle classification. Medium and heavy model volumes were compared to observed data for the total system and for the primary freight corridors. The results are shown in TABLE. The modeled volumes compared very well in aggregate for the primary freight corridors. The all facility validation was further off. Link level validation had a high degree of variability suggesting that more corridor level validation was warranted.

11 Shabani, Worthen, Outwater, Steinvorth 0 TABLE Link Level Validation by Facility Type for Primary Freight Corridor for 00 Estimated Counts Model Volumes % Difference Difference Link Class Light Medium Heavy Light Medium Heavy Light Medium Heavy Light Medium Heavy Freeways,00 0, 0,,, 0, -% -% 0% -0,0 - - Arterials,,, 0,, 0, % -% %,00 -,, Grand,,00,0, 0,, -% -% % -, -,, Total DATA CHALLENGES Significant data issues were discovered in the COAL and OLGA TRANSEARCH data. This section briefly discusses how the issues were approached and solved. Coal Movement Utah is one of the highest coal producing states, ranked th in 00, and is a neighbor to the highest coal producing state, Wyoming (0), making coal an important commodity for Utah. It was discovered that the TRANSEARCH data had incorrect distribution and mode shares for coal in Utah and the total coal tonnage was found to be too high in TRANSEARCH. The Annual Coal Distribution Report data from Energy Information Administration (EIA) () was obtained as an alternative source to the TRANSEARCH data. The EIA data is based on the U.S. Department of Labor s Mine Safety and Health Administration data and provides the total coal tonnage for Utah, the distribution by origin state and destination state, and mode of transportation. TABLE provides a comparison of the TRANSEARCH and the EIA total tons of coal by internal/external movements. EIA reports % fewer total tons of coal for Utah than TRANSEARCH. Both EIA and TRANSEARCH data have similar II share of the overall coal tonnage in Utah (% and %, respectively). However EIA reports more significant XI coal tonnage. TABLE Total Utah Coal Tons and Percentages by Direction of Movement (,000 tons) Coal II IX XI Total Transearch EIA Transearch EIA Transearch EIA Transearch EIA Tons,0 0,,,,,,0 % of Total % % % % 0.% 0% Source: () The internal county distribution of coal in the TRANSEARCH data also appeared erroneous, with % of the coal tonnage produced in counties with no active coal mines. Attraction totals were similarly off. To correct the distribution of coal in the freight model, it was assumed that coal is produced at coal mines and attracted to coal-fired power plants. The location and details (e.g. annual capacity and TAZ ID) of these facilities were found from the Utah Geological Survey and state mapping data. The distribution of coal productions was calculated as the ratio of total mine production in a zone to the total mine production for Utah multiplied by the total coal tonnage by movement from EIA. Similarly, the distribution of the coal attractions was calculated

12 Shabani, Worthen, Outwater, Steinvorth as the ratio of the capacity of the coal-fired power plants in a zone to the total capacity of all coal-fired power plants in Utah multiplied by the total statewide attracted coal tonnage. TRANSEARCH reported that % of the II coal tonnage was moved by truck and % by rail. EIA reported that more than half (%) of the II coal tonnage is moved by truck (% by rail). The FAF dataset also reported approximately 0% truck/rail mode share for coal movement by weight for Utah as an origin state (). The comparison of the TRANSEARCH and EIA mode shares by direction of movement can be found in TABLE. TABLE Utah Coal Mode Shares by Direction of Movement Mode II IX XI Transearch EIA Transearch EIA Transearch EIA Truck % % % % % % Rail % % % % % % Other 0% 0% 0% 0% 0% 0% Source: () The EIA total tons and mode share as well as the recalculated internal distribution was used, replacing the TRANSEARCH coal data in the freight model. Crude Oil Movement Utah is one of the highest crude oil producing states, ranked th in 0, and is neighbor with two high crude oil producing states, Wyoming and Colorado, ranked th and 0 th respectively (), making crude oil an important commodity moved in Utah. It was discovered that the TRANSEARCH data had incorrect total tons, distribution and mode shares for crude oil in Utah. Data sources including Crude Oil Data from Energy Information Administration (), the Utah Division of Oil, Gas & Mining () and the UGS (Utah Geological Survey, 00) were obtained as alternative sources to the TRANSEARCH data. EIA reported crude oil production by origin and destination district as well as by mode of transportation. EIA also reported state production totals but did not give state-to-state movements. The Utah Division of Oil, Gas & Mining and UGS reported Utah crude oil production by county and Utah refinery receipts of crude oil by state of origin. TRANSEARCH reported zero tons of crude oil moved internally in Utah and only a small fraction of tons either produced or attracted to Utah. EIA reported that Utah produced approximately million barrels of crude oil in 00. Using a conversion of US barrel = 0.0 US tons, this represents. million tons of crude oil produced in Utah. This information was also corroborated by data from the Utah Division of Oil, Gas & Mining. The UGS reported that in 00 Utah received. million barrels (about. million tons) of crude oil. Much of this came from Colorado and Wyoming and a smaller portion came from Canada. The remainder came from Utah or other states. The total crude oil total attractions and their sources had some variability over the past several years. The freight model used the 00 distribution of crude oil attractions. TABLE provides comparisons of the total tons of crude oil by movement in Utah from the TRANSEARCH data and the results of the analysis based on the EIA, Utah Division of Oil, Gas

13 Shabani, Worthen, Outwater, Steinvorth & Mining, and UGS data sources. It should be noted that the analysis of the crude oil movements between Utah and other areas were based on simplifying assumptions but it was felt that the assumptions gave a reasonable estimate of external crude oil movements for the purposes of the freight model. TABLE Total Utah Crude Oil by Direction of Movement (,000 tons) Crude Oil II IX XI Total Transearch EIA Transearch EIA Transearch EIA Transearch EIA Tons 0,,,,0 % of Total 0% % % % % % Source: () TRANSEARCH reported % of the IX crude oil tonnage was produced in Utah County, however there are no active wells producing crude oil in Utah County. As well, active wells in other counties producing crude oil were missing in the TRANSEARCH data. For the distribution of crude oil inside Utah, UGS and Utah Division of Oil, Gas & Mining reported Utah crude oil production by county. The county productions were then disseminated into model zones based on the locations of oil wells and their capacity. The attractions were based on Utah s five refineries locations and capacities. The external distribution of crude oil was based on movements between PADD (Petroleum Administration for Defense Districts) districts (EIA Petroleum and Other Liquids Data, 0). For mode share, TRANSEARCH reported that % of the IX crude oil tonnage was moved by rail. However, it was understood that much of the crude oil going outside of Utah moves by pipeline. TRANSEARCH did not report mode share data for II movements. EIA was thought to have better mode data and was therefore used in the freight model. EIA s pipeline mode share of crude oil was also confirmed with FAF data. The comparison of TRANSEARCH and EIA mode shares by direction of movement can be found in TABLE. TABLE Crude Oil Movement by dataset, by direction of movement, by mode shares (%) II P II A IX XI Mode Transearch EIA Transearch EIA Transearch EIA Transearch EIA Truck N/A 00% N/A 00% % % 0% % Rail N/A 0% N/A 0% % 0% % 0% Other (Pipeline) N/A 0% N/A 0% 0% % % % Source: () Refined Petroleum Products Movement The refined petroleum production locations were modified from the TRANSEARCH data to be consistent with the methodology as described in the previous section. It was assumed that refined petroleum was produced at the same refinery locations used in the crude oil attraction calculations. The weighted average of the refinery capacity was used to redistribute the zonal production inside Utah. The total tonnage of refined petroleum was unchanged from that reported in the TRANSEARCH data.

14 Shabani, Worthen, Outwater, Steinvorth 0 LESSONS LEARNED The availability of quality data is key to developing and validating freight models. Users should be aware of the limitations of the data being used and review it in detail to ensure it is sufficient for the intended travel model uses. This is especially important if the data will be heavily relied upon. Suspicious data may be erroneous or may in fact be accurate based on a region s unique characteristics. When key data sources are insufficient for obtaining reasonable freight forecasting results, it was our experience on this project that searches of publically available data (e.g. EIA, UGS, FAF and VIUS) can be a low cost solution to overcome data limitations. Other data sources (e.g. rail waybill and economic input/output accounts) could also be used in various steps of the model development. However, the level of detail used was sufficient for the first version of this model. Consulting various data sources and using local knowledge can be vital to obtaining accurate data. Good judgment as well as corroborated data sources should be used in determining the reasonableness of data. The approach to overcoming the data limitations addressed in this project is one of several ways to help improve data quality in the model. Other approaches including communicating with refineries, mines and power plants officials directly to get data could also be advantageous. However, additional contacts would require spending some level of additional resources. The user must decide on the trade-off between the level of detail needed and the resources available.

15 Shabani, Worthen, Outwater, Steinvorth REFERENCES. Gwynn, M. L., Krahulec, K., & Berg, M. V. (0). Utah Mining 00 (Vol. ). Utah Geological Survey.. United States Department of Transportation (US DOT), Federal Highway Administration (FHWA), Office of Operations, Freight Analysis Framework, FAF Data and Documentation (00), online doc: (Accessed May, 0).. Chatterjee, A., & Cohen, H. (00), Accounting for Commercial Vehicles in Urban Transportation Models, (Accessed May, 0). Beagan, D. F., Fischer, M. J., & Kuppam, A. R. (00), Quick response freight manual II (No. FHWA-HOP-0-00).. Systematics, C. (). Inc., Quick Response Freight Manual, U.S. Department of Transportation.. Oak Ridge National Laboratory, Center for Transportation Analysis (CTA). Intermodal Terminals Database. (Accessed August, 0).. Intermodal Association of North America (IANA), North American Intermodal Facilities Directory. (Accessed August, 0).. United States Bureau of Transportation Statistics (BTS), North American Transportation Atlas Data, Trailer on Flatcar/Container on Flatcar Point Database. ion_atlas_data/index.html (Accessed August, 0).. United States Department of Commerce, Bureau of the Census, 00 Vehicle Inventory and Use Survey (VIUS), online doc: (Accessed July, 0). 0. Utah Geological Survey, Annual Review and Forecast of Utah Coal Production and Distribution (00), online doc: (Accessed Jan, 0).. Energy Information Administration (EIA), Annual Coal Distribution Report, 0.. Utah Geological Survey, Energy and Mineral Data, Utah Crude Oil Production by County (00), online doc: (Accessed Jan, 0).. Energy Information Administration (EIA), Petroleum and other liquids data (0), online docs: (Accessed July, 0).. Utah Division of Oil, Gas & Mining-Department of Natural Resources, Utah Crude Oil Production by County (00), online doc: (Accessed Jan, 0).

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