1. Development of a Statewide Freight Trip Forecasting Model for Utah 14 th TRB Applications Conference May 06, 2013 Chad Worthen RSG Kaveh Shabani RSG Maren Outwater RSG Prepared by: Walt Steinvorth UDOT

2. Freight Model Components 2 Long-Haul Commodity Flow Freight Model Short-Haul Commercial Vehicle & Truck Model  Long-haul uses Transearch & socioeconomic data, short-haul uses socioeconomic data  Long-haul includes national component, short-haul is just statewide  Replaces the commercial and truck component in existing statewide model ClosingIssuesModel StepsIntroduction Details

3. Geographic Scope 3 Part 1: National Zone StructurePart 2: Statewide Zone Structure ~3,500 zones 284 zones ClosingIssuesModel StepsIntroduction

4.  Creates sub-area networks MPO Integration 4  Stand alone application added to USTM  To merge MPO model inputs to USTM inputs —Highway networks —TAZ shapefiles —SE data files —Trip tables ClosingIssuesModel StepsIntroduction USTM Connection to Cache USTM Connection to Dixie USTM Connection to Wasatch USTM External Node USTM Internal Node

5. GENERATION 5

6. Short Haul Generation 6 ClosingIssuesModel StepsIntroduction

7. Long Haul Generation  Multivariate and multi-tier regression analyses  Using some advanced outlier-detection methods Overall measures of influence ( Cook’s Distance and DFBETA ) Unusual observations ( questionable employment or tonnages or ratio ) Regression both with and without outliers ( and all reasonable combination of variables ) More than one trip generation equation for a commodity group Better measures of fitness (RMSE, R 2, t-stat, p-value) Grouping counties based on reasonable characteristics (rural, urban, etc.) Long Haul Trip End Model Estimation 7 ClosingIssuesModel StepsIntroduction Details

8. Long Haul Generation 8 ClosingIssuesModel StepsIntroduction Too high? Produced by commercial operators and by state and county agencies in most counties in Utah More than 200 active pits and quarries across the state! About 35 million tons of gravel, sand and crushed stone produced in 2009 Sand and Gravel Metallic Ores Nonmetallic Minerals MNRL

9. DISTRIBUTION 9

10. Friction Factors Long-Haul 10 ClosingIssuesModel StepsIntroduction Short-Haul Based on QRFM II and other area freight model Exponential function form Unique curve for light, medium and heavy Calibrated using Transearch and national skims Exponential, Gamma and Step function forms Unique curve for each commodity Unique set for internal-external movements (II, IX and XI) Details Note: internal-internal (II), internal-external (IX), external- internal (XI), and external-external (XX)

11. Trip Length Frequency Validation (Example) 11 ClosingIssuesModel StepsIntroduction Used step function to get the best match (MNRLs very important because of high total tons) Got a perfect match with a simple exponential function (several related friction factors also worked) One of the worst cases, ended up using a step function to get the best match

12. MODE SHARE 12

13. Modes and Mode Share Source: http://people.hoftsra.edu 13 ClosingIssuesModel StepsIntroduction  Mode share not mode choice model  Long haul only  Modes Truck – primary mode & purpose of model Intermodal (IMX) – to identify truck element —Goods moved by combination of TRUCK and RAIL —Connections happen at railroad terminals —No ports and airports terminals Other – modes not assigned —Pipeline and air —These modes are not assigned  Mode Share Mode shares determined by Transearch Exceptions: —Coal —Oil and gas Details

14. Mode Share by Commodity Group 14 ClosingIssuesModel StepsIntroduction Most II goods moved by truck IX & XI goods have larger share moved by modes other than truck Mineral, which had very high tonnage, is dominated by truck mode

15. Payload Factor 15 Average tons/truck Appeared unreasonably high Almost double the national average State Payload Factor (tons/truck) Colorado27 Montana24 Utah48 Wyoming33 USA26 Note: Data is for medium and heavy trucks Source: Vehicle Inventory and Use Survey (VIUS, 2002) Utah allows very large bulk carrier trucks (doubles) that are not allowed by most states Commodity Average Payload (Tons) 1Agricultural/meat/fish23.5 2Prepared foodstuff23.1 3Metal & Nonmetal Ores26.3 4Coal48.4 5Crude Petroleum & Gas30.9 6Petroleum or Coal Products32.3 7Chemicals18.7 8Textile & Paper13.5 9Building material & machinery22.6 10Manufactured equipment16.5 11Lumber & Retail19.5 12Intermodal & Mail25.9 ClosingIssuesModel StepsIntroduction Source: Vehicle Inventory and Use Survey (VIUS, 2002)

16. Annual Factor 16 ClosingIssuesModel StepsIntroduction Days/YearDescription 3657 Days a week (No Holidays) 3597 Days a week (Less 6 Major Holidays) 3126 Days a week (No Holidays) 3066 Days a week (Less 6 Major Holidays) 2605 Days a week (No Holidays) 2545 Days a week (Less 6 Major Holidays) Average Working Days per Year Medium + Heavy Truck Counts Distribution in truck counts shows stronger weekday trend More important, validation suggests that goods are distributed 5 days/week regardless if goods shipped weekdays or weekends Details

17. Percent Empty Source: 2002 VIUS database (note: some values interpolated) % Driven Empty with Utah Home Base by commodity group (for heavy trucks) Commodity % Empty (Input to the model) <= 50 Miles 51-100 Miles 101-200 Miles 201-500 Miles >500 Miles 1Agricultural/meat/fish 35%30%39%25%21% 2Prepared foodstuff 50%34%50%15%8% 3Metal & Nonmetal Ores 37%47%45%27%13% 4Coal 50% 32%33%8% 5Crude Petroleum & Gas 48%35%51%45%13% 6Petroleum or Coal Products 49%48%49%50%30% 7Chemicals 33%24% 43%6% 8Textile & Paper 39%40% 27%10% 9Building material & machinery 39%38%34% 21% 10Manufactured equipment 36%23%50%5%27% 11Lumber & Retail 18%28% 12%7% 12Intermodal & Mail 48%49%50%17%6% 17 ClosingIssuesModel StepsIntroduction The % empty return trips were calculated using the following formula, applied to the transposed truck trip matrices. Details

18. ASSIGNMENT 18

19. Truck Trip Summary SHORT HAUL 19 ClosingIssuesModel StepsIntroduction Short-Haul Truck Trips (per day) Trips proportional to socioeconomic activity, most of which occurs in MPO areas Internal short-haul trips inside MPO areas are replaced by data from MPO models Gray text indicates data to be replaced by MPO models

20. Truck Trip Summary LONG HAUL 20 ClosingIssuesModel StepsIntroduction Long-Haul Truck Trips (per day) All long-haul trips used by MPO models Utah has a high percentage of external through trips (nearly half of all long-haul trips) Mineral commodity type dominate the internal truck trips

21. Traffic Count Validation Locations  154 Truck Counts in Validation — 110 Arterial — 44 Freeway  58 Truck Counts on Primary Freight Corridor — 28 Arterial — 30 Freeway 21 ClosingIssuesModel StepsIntroduction

22. Truck Classification LTLightFHWA Class 1-3 MTMediumFHWA Class 4-7 HTHeavyFHWA Class 8-13 FHWA Vehicle Classification 22 ClosingIssuesModel StepsIntroduction Commercial Vehicle and Truck Classification

23. Volume Validation 23 Primary Freight Corridor in Non-MPO Area Only ClosingIssuesModel StepsIntroduction Corridor level validation still needed Details

24. Data Issues 24

25. Long Haul Commodity Database 25 ClosingIssuesModel StepsIntroduction  Long-haul freight highly reliant on commodity flow database (Transearch)  For certain commodities, Transearch data appeared suspect Commodities: —Coal —Crude oil —Refined petroleum Issue: —Total tons —Distribution —Mode share  Other data sources needed to validate/correct commodity flow data: National —Energy Information Administration (EIA) —United States Bureau of Transportation Statistics (BTS) —Commodity Flow Survey (CFS) —Freight Analysis Framework (FAF3) Local —Utah Geological Survey —Utah Division of Oil, Gas & Mining-Department of Natural Resources

26. Coal Movement 26 ClosingIssuesModel StepsIntroduction Mode Share Distribution Total Tons  Transearch had too much coal for Utah  Distributed to wrong counties  Mode share close for IX & XI, but off for II (in thousands)

27. Crude Oil Movement 27 ClosingIssuesModel StepsIntroduction Mode Share Distribution Total Tons  Transearch had zero crude oil for Utah  Distributed to wrong counties  Mode share very different for II, IX & XI Details (in thousands)

28. 28 Petroleum Products Movement Crude oil is produced at wells and attracted to refineries So Refined petroleum productions should be synced with the crude oil-refined petroleum products supply chain Total tonnage >>> Not changed from Transearch ClosingIssuesModel StepsIntroduction

29. Conclusions & Lessons Learned 29 Data Modeling Future Freight Be aware of the limitations of data sources —Use local knowledge/judgment —Use publicly available data (e.g. EIA, FAF) for an economical way to overcome data limitations —Trade off between the level of detail needed and available resources Trip-based freight method worked well for Utah —Not a lot of intricate modal details —Mostly interested in truck volumes on highways ClosingIssuesModel StepsIntroduction Utah Freight Model is still a work in progress —MPOs implementing freight component —Corridor-level calibration needed

30. Kaveh Shabani, RSG kaveh.shabani@rsginc.com 801-456-4904 Chad Worthen, RSG chad.worthen@rsginc.com 801-456-4901 Maren Outwater, RSG maren.outwater@rsginc.com 414-446-5402 Walt Steinvorth, UDOT msteinvorth@utah.gov 801-965-3864 San Diego Evansville

31. APPENDIX 31

32. 12 Long Haul Commodity Groups Long Haul  Forecast tons then convert tons to vehicles  National and Utah-based flows  Based on purchased commodity flow data (Transearch) and additional data (COAL, OLGA, PETR) 32 Return

33. Commodity Group Detail Commodity GroupSTCCCommodity Description 11Farm Products 18Forest Products 19Fresh Fish Or Marine Products 220Food or Kindred Products 221Tobacco Products 310Metallic Ores 314Nonmetallic Minerals 411Coal 513Crude Petroleum or Natural Gas 629Petroleum or Coal Products 728Chemicals or Allied Products 730Rubber or Misc. Plastics 822Textile Mill Products 823Apparel or Related Products 826Pulp, Paper or Allied Products 827Printed Matter 831Leather or Leather Products 932Clay, Concrete, Glass or Stone 933Primary Metal Products 934Fabricated Metal Products 935Machinery 1036Electrical Equipment 1037Transportation Equipment 1038Instrum, Photo Equip, Optical Eq 1119Ordnance or Accessories 1124Lumber or Wood Products 1125Furniture or Fixtures 1139Misc. Manufacturing Products 1140Waste or Scrap Materials 1141Misc. Freight Shipments 1146Misc. Mixed Shipments 1242Shipping Containers 1243Mail or Contract Traffic 1244Freight Forwarder Traffic 1245Shipper Association Traffic 1247Small Packaged Freight Shipments 1248Waste Hazardous Materials 1249Hazardous Materials Or Substances 1250Secondary Traffic 33 Return

34. Long Haul Generation Variables Long Haul Trip End Model Estimation Production VariablesAttraction Variables 1AGRIFarmWholesale Trade, Manufacturing 2FOODManufacturingManufacturing, Retail, Wholesale Trade 3MNRLMinerals, ManufacturingConstruction, Manufacturing 4COALMinesPower plants 5OLGAWellsRefineries 6PETRRefineriesWholesale Trade, Retail 7CHEMManufacturingWholesale Trade, Manufacturing 8TEXTManufacturing, Wholesale TradeWholesale Trade, Retail 9BULDManufacturingManufacturing, Construction 10MANUManufacturingWholesale Trade, Manufacturing, Retail, Transportation 11LRETWholesale Trade, Manufacturing, RetailWholesale Trade, Manufacturing, Retail, Transportation 12IMDLWholesale Trade, ManufacturingTransportation, Manufacturing, Other  Pivot off base-year Transearch data  Generation equations determine spatial location inside Utah & calculate "new" tonnage  Controls to interpolated Transearch data at state-level  Production & attraction variables differ slightly for internal & external movements 34 Return

35. Regression Equations (IIP) Commodity Description and codeR2R2 Number of Obs. Considered VariablesCoefficientt-statp Value 1Agricultural/meat/fish Tier 1-Main data points0.6818Frm22.046.060.000 Tier 2-Outlier data points0.733Frm49.732.330.145 2Prepared foodstuff0.6414Mnfct11.354.850.000 3Metal & Nonmetal Ores0.6910Mnrl+ Mnfct349.464.510.001 4Coal--Produced at mines--- 5Crude Petroleum & Gas------ 6Petroleum Products--Produced at Refineries--- 7Chemicals0.718Mnfct3.304.130.004 8Textile & Paper0.9711Mnfct + Whlsl0.7618.650.000 9Building materials & machinery0.7722Mnfct78.338.490.000 10Manufactured equipment0.929Mnfct1.269.670.000 11Lumber & Retail0.5220Mnfct + Whlsl + Rtl0.604.510.000 12Intermodal & Mail0.9325Mnfct + Whlsl59.8217.810.000 ″II″ Production 35 Return

36. Regression Equations (IIA) Commodity Description and codeR2R2 Number of Obs. Considered VariablesCoefficientt-statp Value 1Agricultural/meat/fish0.785Whlsl + Mnfct8.643.790.019 2Prepared foodstuff0.8829Rtl + Whlsl + Mnfct2.9514.590.000 3Metal & Nonmetal Ores0.9627Cnst + Mnfct168.9725.160.000 4Coal--Attracted to power plants--- 5Crude Petroleum & Gas--Attracted to refineries--- 6Petroleum or Coal Products0.9628Whlsl + Rtl4.3725.680.000 7Chemicals0.7028Whlsl + Mnfct1.697.950.000 8Textile & Paper0.9525Whlsl + Rtl0.5420.990.000 9Building material & machinery0.9729Cnst,35.232.310.029 Mnfct62.864.630.000 10Manufactured equipment1.0023Whlsl,1.225.810.000 Trns, Wrhs,0.633.130.005 Rtl0.205.300.000 11Lumber & Retail0.8129Rtl+Whlsl+Mnfct+ Trns0.7511.090.000 12Intermodal & Mail0.8227Other+Mnfct+ Trns5.6610.890.000 ″II″ Attraction 36 Return

37. Regression Equations (IXP) Commodity Description and codeR2R2 Number of Obs. Considered VariablesCoefficientt-statp Value 1Agricultural/meat/fish Tier 1-Main data points0.8425Frm141.3211.220.000 Tier 2-Outlier data points0.824Frm192.593.760.033 2Prepared foodstuff Tier 1-Main data points0.5123Mnfct16.414.780.000 Tier 2-Outlier data points0.883Mnfct26.053.790.063 3Metal & Nonmetal Ores Tier 1-Main data points0.8023Mnrls + Mnfct33.469.440.000 Tier 2-Outlier data points0.865Mnrls + Mnfct4136.175.020.007 4Coal--Produced at Mines--- 5Crude Petroleum & Gas--Produced at Wells--- 6Petroleum or Coal Products--Produced at Refineries--- 7Chemicals0.5023Mnfct55.774.650.000 8Textile & Paper0.6323Mnfct + Whlsl16.666.070.000 9Building materials & machinery0.7127Mnfct48.357.890.000 10Manufactured equipment Tier 1-Main data points0.7220Mnfct3.617.030.000 Tier 2-Outlier data points0.983Mnfct5.138.900.012 11Lumber & Retail0.8127Mnfct + Whlsl + Rtl8.6310.480.000 12Intermodal & Mail0.9827Mnfct + Whlsl11.3732.230.000 ″IX″ Production 37 Return

38. Regression Equations (XIA) Commodity Description and codeR2R2 Number of Obs. Considered VariablesCoefficientt-statp Value 1Agricultural/meat/fish0.4429Whlsl + Mnfct38.892.590.015 2Prepared foodstuff0.8027Rtl + Whlsl + Mnfct11.4810.080.000 3Metal & Nonmetal Ores (Tier 1-Border Counties)0.716Cnst + Mnfct103.903.500.017 (Tier 2-Middle Counties)0.6223Cnst + Mnfct2.256.000.000 4Coal--Attracted to Power Plants -- - 5Crude Petroleum & Gas--Attracted to refineries - - - 6Petroleum or Coal Products0.8029Whlsl + Rtl7.1710.430.000 7Chemicals Tier 1-Main data points0.5926Whlsl + Mnfct22.415.990.000 Tier 2-Outlier data points0.953Whlsl + Mnfct33.276.400.024 8Textile & Paper Tier 1-Main data points0.7324Whlsl + Rtl3.457.980.000 Tier 2-Outlier data points0.985Whlsl + Rtl8.5214.510.000 9Building materials & machinery Tier 1-Main data points0.7626Cnst + Mnfct19.968.930.000 Tier 2-Outlier data points0.983Cnst + Mnfct35.959.780.010 10Manufactured equipment0.6327Rtl + Whlsl + Mnfct + Trns1.726.660.000 11Lumber & Retail0.8729Rtl + Whlsl + Mnfct + Trns14.8913.410.000 12Intermodal & Mail0.9329Other+ Mnfct + Trns2.8218.870.000 ″XI″ Attraction 38 Return

39. Friction Factor Equations (II) 39 Return

40. Friction Factor Equations (IX) 40 Return

41. Friction Factor Equations (XI) 41 Return

42. Intermodal Mode  Goods moved by combination of TRUCK and RAIL  Connections happen at railroad terminals ( no ports and airports terminals ) Locations in Utah 4 locations5 Locations Distributing freight between Intermodal locations Based on each location’s storage area/tracks percentage of total BTS Bureau of Transportation Statistics CTA by David Middendorf in 1998 IANA Intermodal Association of North America Google Map Different for “Coal” and “Oil and Gas” (IX) Source: http://people.hoftsra.edu 42 Return

43. Volume Validation 43 Primary Freight Corridor in Non-MPO Area Only Using Annual Factor = 306 (instead of 260) ClosingIssuesModel StepsIntroduction Return 2 Return 1

44. Derivation of % Empty Truck Equation In the VIUS survey, the driver was asked what % of the time did they drive empty. This question presupposes the % of total trip time that was driven empty. To calculate the number of truck trips driven empty, we apply the formulas outlined in this derivation. 44 ClosingIssuesModel StepsIntroduction Return

45. U.S. Crude Oil and Refined Products Pipelines Source: American Petroleum Institute (API) Pipelines from Wyoming and Colorado 45 Return

46. Crude Oil Movement PADD 46 PADD: Petroleum Administration for Defense Districts PAD District 4 (Rocky Mountain) Colorado, Idaho, Montana, Utah, Wyoming Source: U.S. Energy Information Administration (EIA) Generation & Mode Share : “Energy Information Administration” and “Utah Geological Survey” data Distribution: crude oil is produced at wells and attracted to refineries Return