State of the Practice in Freight Modeling at State DOT s

Transcription

1 State of the Practice in Freight Modeling at State DOT s Presented to the Freight Demand Modeling: Tools for Public-Sector Decision Making Conference Washington DC September 25, 2006 By Gregory Giaimo, PE Ohio Department of Transportation

2 Context of Freight Modeling at State DOT s Almost invariably part of a larger statewide passenger and freight model Models developed primarily to study intercity highway corridors Prior to 1990 s, very few such models due to lack of computers and methods Since then about half the states have developed or are developing models

3 Statewide Model Status Source: NCHRP Synthesis 358, Statewide Travel Forecasting Models

4 Freight Model Types Freight modeling is handled in one of four ways by state DOT models 1. None 2. Traditional 3. Commodity Based 4. Integrated Land Use/ Economic/ Commodity Based

5 Freight Model Type by Peer Exchange Participants and NonParticipants Not 4 Peer Exchange None Traditional Commodity Econometric Unknown Source: TRB Electronic Circular E-C075: Statewide Travel Demand Modeling A Peer Exchange

6 Statewide Freight Model Components Summary VI. Freight/ Commercial FL IN KY LA MA MO Modes Included Modes Excluded Commodity Based Matrix Estimation HWY (net based), RR, Intermodal All others excluded because no sig. impact and difficult to estimate Yes New model may HW Y (commercial & heavy trk) HWY HWY HWY None Model built in response to All others due to needs which All others by budget & focus were hwy based design of project Data & project focus No No (but see below) Yes Yes No No Yes, original seed based on commodity flow Yes, to estimate trucks not covered by Transearch Yes, to estimate trucks not covered by Transearch No No For portion not covered by Transearch Yes, QRM methods No QRM for non-freight Fratar/QRM for Traditional Methods trucks forecasts No Transearch/CFS Yes/Transearch No Yes/Transearch Yes/Transearch No No NH NJ OH OR VA WI Modes Included None HWY HWY, RR, H2O, AIR emphases is trucks Gen1:HWY Gen2: Multimodal HWY HWY, RR, H2O, AIR Modes Excluded No Pipeline Gen1 for time/cost RR, H2O, AIR not explicitly model due to budget but forecasts from Transearch are Pipeline Commodity Based No No Yes Yes Yes Yes Matrix Estimation No No Interim model only No Yes, for portion not covered by Transearch No Traditional Methods No QRM Techniques Interim model only No Yes, estimates of non-freight components not included in Transearch are made Yes, 4 step techniques used at commodity level, VIUS and AADT factors Transearch/CFS No No Yes/Transearch Yes/CFS Yes/Transearch Yes/Transearch Source: TRB Electronic Circular E-C075: Statewide Travel Demand Modeling A Peer Exchange

7 Statewide Models with No Freight Component Some small states have models exactly analogous to urban passenger travel demand models Example: New Hampshire

8 Traditional Models Smaller states and states with more mature statewide models sometimes employ traditional methods from passenger demand forecasting This involves direct generation of trucks, generally using regression equations or trip rates Distribution via gravity model or Fratar methods Often employs Quick Response methods from the Quick Response Freight Manual (Report DOT-T-97-10) Example: New Jersey

9 Traditional Truck Modeling Input Data 1 Trip Generation 2 Daily. Economic Activity Data. # of Employees and Households by Traffic Analysis Zone (TAZ) and by Land Use (SIC). Trip Generation Rate by Truck Type and Land Use Classification Trips at External Stations 3 Daily. Internal-to-external trips. External-to-internal trips. External-to-external (through) trips j Trip Distribution 4 Daily i. Gravity Model (Travel Time t ij ). Zone to Zone / Station to Station Peak Off-Peak tij Trip Assignment 5 Daily Load Truck VMT onto Network Peak Off-Peak Control VMT 6 Compare Control VMT with Estimated VMT 7 Daily. Regional VMT Estimates. Vehicle Classification Data Convergence Criteria Met? Yes No Peak Off-Peak OUTPUT Daily Balanced Truck Trip Table and Truck Flows Peak Off-Peak Source: Quick Response Freight Manual, Report DOT-T-97-10

10 Traditional Truck Modeling Gravity Model Functions Source: Quick Response Freight Manual, Report DOT-T-97-10

11 Commodity Based Models This approach models commodity flows These flows are then converted to trucks, trains etc., generally using static mode and payload factors by commodity (and sometimes distance) This is the most common approach

12 Commodity Based Models (cont.) Almost universally, these approaches obtain their commodity flow data from the Commodity Flow Survey or the Transearch database The Vehicle Inventory and Use survey is often used to develop payload factors IO tables are typically needed to relate production and consumption industries

13 Commodity Based Models (cont.) These methods suffer from 2 main data limitations: 1. Lack of geographic specificity Commodity flow data is typically at the county level requiring disaggregation of flows to TAZ level using employment factors by industry 2. Missing industries/commodities These sources do not have universal coverage, thus this approach is often combined with matrix estimation techniques to account for the missing movements

14 Data Sources and Modes in Statewide Freight Models Payload Factors for Statewide Models Modes Represented in Statewide Freight Models Source: NCHRP Synthesis 358, Statewide Travel Forecasting Models

15 Commodity Based Models (cont.) There are 2 general approaches employed in this category 1. Use growth factors to project base year commodity flows to the future 2. Develop a model relating base year commodity flows to socio-economic variables and apply in the future

16 Commodity Based Models (cont.) 1. Growth Factor Method Commodity flows converted to trucks and assigned Missing trucks accounted for using matrix estimation techniques (some states such as Florida and Indiana use QRFM techniques to account for the missing trucks) Fratar method used to adjust commodity and truck flows in the future based on growth in socio-economic indicators Example: Virginia

17 Virginia Statewide Model Freight Component Source: NCHRP Synthesis 358, Statewide Travel Forecasting Models

18 Virginia Statewide Model Zones Source: NCHRP Synthesis 358, Statewide Travel Forecasting Models

19 Commodity Types and Payload Factors for Virginia Source: NCHRP Synthesis 358, Statewide Travel Forecasting Models

20 Commodity Based Models (cont.) 2. Commodity Demand Model Method Relationships between commodity flows and industry specific socio-economic variables developed This results in traditional generation and distribution models at the commodity level Special generators and labor force productivity must be explicitly taken into account Commodities converted to mode using fixed shares Example: Wisconsin

21 Wisconsin Statewide Model Freight Component Source: NCHRP Synthesis 358, Statewide Travel Forecasting Models

22 Trip Generation, Commodity Types and Payload Factors for Wisconsin Source: NCHRP Synthesis 358, Statewide Travel Forecasting Models

23 Integrated Land Use/ Economic/ Commodity Models This approach also models commodity flows The main difference from the previous type is the explicit econometric and land use models which feed commodity flows to the transport models Thus base year commodity flows are used to estimate these economic models rather than as an exogenous input Examples: Ohio, Oregon

24 Comparison of Oregon and Ohio Implementations Both have land use and economic models feeding them There are two main differences: 1. Ohio s approach accounts for non-freight travel in its disaggregate commercial vehicle model This model, estimated from establishment surveys, explicitly accounts for the commercial vehicle travel (including much short distance goods movement) not covered in the national commodity flow sources This then also represents a major point of departure from the other commodity based models which typically use matrix estimation or quick response techniques to account for this travel (if at all) 2. Oregon s approach contains a more detailed representation of the multi-modal, inter-modal system and seeks a more explicit treatment of the routing/mode choice/shipment size behavior of the freight system Does not include the non-freight commercial vehicle travel

25 Ohio Model Elements of the Ohio Model related to freight will be demonstrated for illustrative purposes The four components directly related to the creation of freight flows will be hi-lighted: 1. Interregional Economic Model 2. Activity Allocation Model 3. Aggregate Commercial Vehicle Model 4. Disaggregate Commercial Vehicle Model

26 Ohio Integrated Land Use/Economic/Transport Model Interregional Economic Model Aggregate Demographic Model Land Development Model Activity Allocation Model Disaggregate Household Synthesis and Employment Spatial Disaggregation Models Short Distance Travel Model Long Distance Travel Model Visitor Model Aggregate Commercial Vehicle Model Disaggregate Commercial Vehicle Model Assignment Model

27 Model Modules Interregional economic model of production & consumption by economic sector reflecting national forecasts Demographic model tied to economic activity reflecting migration and changes in population & household composition Activity allocation model to distribute model area economic and demographic forecasts to analysis zones with the related flows of goods, services & labor among zones from which travel demands are derived Land development model simulating developer behavior in response to demands & prices by type by zone consistent with zoning & other development constraints Personal & household travel model reflecting person & household characteristics, zonal characteristics, inter-zonal economic flows & transport system supply characteristics, 2 components: short distance which looks like an activity/tour based urban area model and long distance, also tour based with purposes: business, recreation, other

28 Model Modules Aggregate model of goods and services transport arising from economic and demographic activity by zone very similar to the typical DOT commodity based transport model Disaggregate model of business-related person travel related to management functions, sales & support activities, provision of services and some short distance goods delivery. Model of visitor travel within and into the model area made by nonresidents Transport system supply model incorporating air, intercity bus/rail, MPO transit & roadway networks with their corresponding level-ofservice characteristics

29 Interregional Economic Model Establishes forecast flows of goods, services and labor (in $) between 14 regions of North America Uses exogenous national economic conditions and production composite utilities from the previous time step of the lower level models An inter-regions social accounting matrix based primarily upon IMPLAN data

30 Industry Employee Proprietor Indirect Total Output* Employment Compensation* Income* Income* Business Tax* Value Added* Region1 $716,166 7,966,019 $228,234 $21,666 $97,448 $27,562 $374,910 Region2 $549,640 5,250,326 $175,241 $11,328 $71,412 $19,524 $277,505 Region3 $1,631,352 16,534,837 $580,641 $70,729 $272,386 $75,873 $999,630 Region4 $1,258,568 12,878,897 $444,828 $47,631 $217,457 $61,111 $771,027 Region5 $1,315,875 15,632,564 $464,473 $40,713 $211,292 $57,678 $774,155 Region6 $159,981 1,927,710 $48,863 $5,282 $21,446 $7,229 $82,820 Region7 $1,578,235 19,633,779 $533,452 $55,056 $245,220 $72,198 $905,927 Region8 $277,576 3,077,661 $85,579 $8,885 $36,968 $9,938 $141,370 Region9 $987,168 10,362,852 $323,315 $32,811 $149,933 $41,378 $547,438 Region10 $3,841,148 43,334,759 $1,251,396 $172,516 $613,615 $173,929 $2,211,456 Region11 $1,814,957 20,563,180 $554,248 $84,581 $305,219 $80,778 $1,024,826 *Millions of dollars

31 Exogenous Economic Indicators Nation Economic Forecast Variables National Economic Growth Billions $20,000 $19,000 $18,000 $17,000 $16,000 $15,000 $14,000 $13,000 $12,000 $11,000 $10, Slow Growth Moderate Growth High Growth

32 Industry Groups and Their Relation to IMPLAN Sectors Industry Code Industry IMPLAN Sectors Production Non- Production 1 Agriculture, Forestry and Fisheries 1-27 Agricultural land Office land 2 Primary Metal Products Heavy industrial land Office land 3 Light Industry 4 Heavy Industry , , , ,58-60, , , , Transportation Equipment Light industrial land Heavy industrial land Heavy industrial land Office land Office land Office land 6 Wholesale 447 Warehouse land Office land 7 Retail Retail land Office land 8 Hotel and Accommodation 463 Accommodation land 9 Construction Construction 10 Health Care Hospital and institutional land 11 Transportation Handling Depot land 12 Utilities Services Heavy industrial land 13 Other Services , , ,494, Retail land 14 Gradeschool Education Gradeschool land Post-Secondary 15 Education Institutional space 16 Government and Other , Government support space 17 Dummy NA NA NA 18 Inventory Valuation Adjustment NA NA NA

33 Activity Allocation Model Subdivides economic activities to ~700 activity model zones (AMZ) within the internal model area Logit Model with terms for: Size (amount of stuff in the zone) Inertia (previous period amount) Buying/selling utilities including cost of transport Additional taxes, subsidies etc.

34 Activity Allocation Model Inputs include: Regional flows from Interregional Economic Model Land by category by AMZ from Land Use Model People from Aggregate Demographic Model Transport costs from previous iteration of Transport Models

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36 Aggregate Commercial Vehicle Model With the commodity flows established, the aggregate commercial vehicle model itself is very similar to the commodity representation in other statewide models Input is dollars of flow of goods, services and labor at the AMZ level Output is flows of trucks between Traffic Analysis zones (TAZ) Note: the distinction between AMZ and TAZ was due to computation times only, there was no theoretical or data limitation reason why the AA model could not produce TAZ level flows directly)

37 Aggregate Commercial Vehicle Model Flow Chart Total Dollars flows by AMZ Determine Mode By commodity class, by distance with a variable to relate train to highway impedance, based on CFS Convert Goods Flows to Tons By commodity class and distance, from CFS Determine Truck Type By commodity class and distance, from VIUS Determine Number of Truck Loads By commodity class and truck type, from VIUS Determine TAZ Based on employment levels by industry class Determine Trucks by Hour of Day Trucks by type by hour by OD TAZ Based on traffic counts, conversion from annual to weekday assumes 300 equivalent week days per year. This value is obtained as follows: (52 * 5) weekdays plus (52 * 2 * 0.44) weekday equivalents for weekends minus 6 holidays.

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39 Rail Network & Intermodals

40 Highway Network

41 Truck Mode Shares by Distance and Commodity < 50 SCTG DESCRIPTION 01 Live animals and fish Cereal grains Other ag products Animal feed and products Meat/fish/seafood Milled grain and bakery products Prepared foodstuffs, n.e.c Alcoholic beverages Tobacco products Monumental or building stone Natural sands Gravel or crushed stone Non-metallic minerals, nec Metallic ores Coal Gasoline and aviation fuel Fuel oils Petroleum products, n.e.c Basic chemicals Pharmaceutical products Fertilizers Chemical products Plastics and rubber Forest products Wood products Pulp/newsprint/paper Converted paper products Printed products Textiles and leather products Non-metallic mineral products Base metals Metal articles Machinery Computer equipment and software Vehicles and parts Transport equipment, n.e.c Precision instruments Furniture and furnishings Misc manufactured products Waste or scrap Mixed freight ALL to to to to to Total

42 Dollar to Ton Conversion by Distance and Commodity < to 99 SCTG DESCRIPTION 01 Live animals and fish 850 1,000 1,040 1,190 1,380 1,630 1,590 1, Cereal grains , Other ag products , Animal feed and products , Meat/fish/seafood 2,410 2,130 2,040 2,090 2,300 2,720 2,760 2, Milled grain and bakery products 1,210 1,160 1,220 1,210 1,350 1,350 1,440 1, Prepared foodstuffs, n.e.c ,150 1,420 1,580 1, Alcoholic beverages 1,260 1, , ,350 1, Tobacco products 9,260 11,680 13,150 26,200 29,690 31,440 31,380 13, Monumental or building stone , Natural sands Gravel or crushed stone Non-metallic minerals, nec Metallic ores ,340 1,150 1,660 9, Coal Gasoline and aviation fuel Fuel oils Petroleum products, n.e.c Basic chemicals ,170 1,730 1,580 7, Pharmaceutical products 19,230 26,980 31,330 20,790 14,860 23,020 25,070 22, Fertilizers Chemical products 2,310 1,980 1,970 2,270 2,710 3,100 3,710 2, Plastics and rubber 2,360 1,750 1,870 2,670 2,900 3,010 3,630 2, Forest products , Wood products ,000 1, Pulp/newsprint/paper 1, , , Converted paper products 1,110 1,110 1,320 1,660 1,890 1,850 2,280 1, Printed products 2,010 3,880 5,280 4,390 4,520 6,760 5,900 3, Textiles and leather products 6,710 8,510 6,460 7,620 8,830 10,740 13,260 8, Non-metallic mineral products , Base metals ,110 1,350 1,790 2, Metal articles 2,330 1,940 1,950 2,140 2,190 2,170 4,050 2, Machinery 7,800 8,310 7,270 8,160 8,210 9,340 10,220 8, Computer equip. and software 20,990 16,760 17,150 15,310 15,140 23,000 28,350 20, Vehicles and parts 4,510 5,530 5,450 5,470 6,450 7,200 7,440 5, Transport equipment, n.e.c. 27,040 21,280 11,160 11,350 13,140 22,280 47,590 18, Precision instruments 54,400 27,600 39,850 50,200 37,240 64,300 68,480 50, Furniture and furnishings 5,330 4,440 4,590 4,680 4,610 4,780 5,120 4, Misc. manufactured products 1,620 3,850 3,970 5,080 6,080 6,860 9,800 3, Waste or scrap Mixed freight 1,960 1,960 2,270 2,640 1, ,620 3,600 2,110 TOTAL ,220 2,050 2,760 3,310 5, to to to to or more TOTAL

43 Truck Type by Distance and Commodity LIGHT TRUCKS MILECLASS SCTGRP ALL ALL MEDIUM TRUCKS MILECLASS SCTGRP ALL ALL LT-HVY TRUCKS MILECLASS SCTGRP ALL ALL HEAVY TRUCKS MILECLASS SCTGRP ALL ALL

44 Payload Factors by Distance and Commodity VIUS2002 VEHICLE SIZE PROD# SCTG# SCTG DESCRIPTION LT MED LTHVY HVY ALL Live animals and fish Cereal grains Other ag products Animal feed and products Meat/fish/seafood Milled grain and bakery products Prepared foodstuffs, n.e.c Alcoholic beverages Tobacco products Monumental or building stone Natural sands Gravel or crushed stone Non-metallic minerals, nec Metallic ores Coal Gasoline and aviation fuel Fuel oils Petroleum products, n.e.c Basic chemicals Pharmaceutical products Fertilizers Chemical products Plastics and rubber Forest products Wood products Pulp/newsprint/paper Converted paper products Printed products Textiles and leather products Non-metallic mineral products Base metals Metal articles Machinery Computer equipment and software Vehicles and parts Transport equipment, n.e.c Precision instruments Furniture and furnishings , 23, Misc manufactured products , Waste or scrap Mixed freight X2 Hazardous Waste X3 Mail X4 Empty Containers X5 Passengers X6 Multiple Categories X7 Other ALL

45 Disaggregate Commercial Vehicle Model DCOM is designed to account for short distance commercial travel not related to the long distance shipping of freight (accounted for in ACOM) Long distance business travel is accounted for in the Long Distance Travel model of the personal transport model since these trips were obtained in the special long distance travel survey

46 Disaggregate Commercial Vehicle Model Employs a tour based microsimulation of employees Based on establishment surveys Analogous to HH based tour based model but based at the place of work Does not include route delivery vehicles

47 Disaggregate Commercial Vehicle Model Employment categorized as: Industrial Wholesale Retail Transportation Handling Service

48 Disaggregate Commercial Vehicle Trip purposes: Model Service Meeting Goods (delivery) Other (includes such things as stopping for lunch or fuel)

49 Disaggregate Commercial Vehicle Model Zonal Land Use Data Worker Traveler Generation Vehicle Assignment Starting Time Assignment Dynamic Activity Pattern Generation Next Stop Purpose Choice Model Next Stop Location Choice Model Post-Processor Commercial Vehicle Trip List

50 Disaggregate Commercial Vehicle Model Generation of a traveler done via logit Vehicle type by logit (light, medium, heavy) Start time from static distributions by industry and vehicle type Heart of the model is the next stop purpose choice model, decision to change to another purpose/location is made at 5 minute intervals based on activity state transitions Next stop location choice is a destination choice model

51 Possible Activity State Transitions with Intensity Rates, π(k k) Starting Activity States k' Stay at Current Activity New Goods Stop Destination Activity States k New Service New Other Stop Stop New Meeting Stop Return to Establishment At Establishment π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) n/a 1.00 At Goods Stop π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) 1.00 At Service Stop π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) 1.00 At Other Stop π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) 1.00 At Meeting Stop π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) π(k',k) 1.00 Sum

52 Top 10 Challenges Impeding Development of Freight Models by State DOT s 1. Lack of DATA 2. Lack of DATA 3. Lack of DATA 4. Lack of DATA 5. Lack of DATA 6. Lack of DATA 7. Lack of DATA 8. Lack of DATA 9. Lack of Money 10.Lack of better theoretical formulations (but see 1-8 for why this is the case)

53 Questions?