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Los Angeles County

Los Angeles County. Transportation leadership you can trust. Cargo Forecasting and Simulation Model. presented to TRB Planning Applications Conference presented by Vamsee Modugula and Maren Outwater Cambridge Systematics, Inc. May 2007. Overview. Background and Objectives

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Los Angeles County

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  1. Los Angeles County Transportation leadership you can trust. Cargo Forecasting and Simulation Model presented to TRB Planning Applications Conference presented by Vamsee Modugula and Maren Outwater Cambridge Systematics, Inc. May 2007

  2. Overview • Background and Objectives • Modeling Process • 2003 Model Calibration and Validation • Summary

  3. Background • Significant growth in goods movement in the Los Angeles region required improved models to evaluate impacts • Models needed to address different potential improvements • Higher capacity intermodal rail terminals • Truck-only lanes • Truckways • Extended working hours at the ports • Short-haul shuttles from ports to inland freight facilities

  4. Objectives • Components of the freight model should include • Long-haul freight from commodity flows • Short-haul freight from socioeconomic data in the region and warehouse and distribution centers • Service truck movements • Recognize trends in labor productivity, imports, and exports • Integrate with passenger models

  5. Study Area • Within 5 county SCAG region – zip codes • Remainder of California – counties • Remainder of USA – states • 4 external zones; 2 each for Canada and Mexico

  6. Modeling Process Coarse Zone Level {State/County/Zip} Generation Productions and Attractions by Commodity Class Long-Haul Flows by Commodity Class Distribution Direct Short-Haul Flows by Commodity Class by Truck Mode Choice Long Haul Flows by Mode and Commodity Class TLN Direct Long-Haul Flows by Mode and Commodity Class Long-Haul Flows to TLN by Mode and Commodity Class Short-Haul Flows to TLN by Truck and Commodity Class Fine Distribution Fine Zone Level Direct Short-Haul Flows by Commodity Class by Truck Direct Long-Haul Flows by Mode and Commodity Class Long-Haul Flows to TLN by Mode and Commodity Class Short-Haul Flows to TLN by Truck and Commodity Class Vehicle {Annual PA>Period OD} Assignment {6 Class}

  7. Model Descriptions • Trip Generation • Implemented at the Coarse Zone Level • Based on tonnage rate per employee • I-E and E-I trips allocated based on factors derived from ITMS • Port trips added from the Port’s models • Trip Distribution • Trips split into short-haul and Long Haul • Short trip distribution based on a gravity model • Long trips are distributed using a joint distribution and mode choice model

  8. Model Descriptions Mode Choice • Estimates Truck and Rail Trips • Based on a multinomial logit model • Applied for 3 distance classes • Service Model • Estimates safety, utility, public / personal vehicles • Fine Distribution Model • Disaggregates trips from coarse zone level to the fine-zone system

  9. Transport Logistics Node Model • Estimates direct and TLN movements

  10. Vehicle Model • Converts tons to trucks • Parameters to influence empty trucks • Standard Vehicle Model to generate direct O-D flows • Touring vehicle model that simulates multi-point pick-up and drop off

  11. Touring Vehicle Model • Performed on TLN’s and user-specified zones

  12. Model Validation • Model outputs compared to ITMS data by commodity group and distance class • Truck volumes compared to truck counts

  13. Outbound Tonnage Produced by Commodity Group Wholesale Trade 3% Agriculture 8% Cement and Concrete Manufacturing 11% Petroleum 8% Paper and Wood Products Manufacturing 4% Chemical Manufacturing 5% Other Transportation 9% Equipment Manufacturing 3% Nonmetallic Minerals 17% Food Manufacturing 11% Manufacturing 5% Motor Freight Transportation 11% Metals Manufacturing 5% Mining and Fuels 0%

  14. Production ValidationDifference in Observed and Model Commodity ShareOutbound Tonnage Share (in Percent) 20 ITMS Share of Commodity 18 Model Share of Commodity 16 14 12 10 8 6 4 2 0 Agriculture Chemical Manufacturing Food Manufacturing Other Transportation Petroleum Metals Manufacturing Motor Freight Transportation Cement and Concrete Manufacturing Paper and Wood Products Manufacturing Equipment Manufacturing Manufacturing Mining and Fuels Nonmetallic Minerals Wholesale Trade Commodity Group

  15. Inbound Tonnage Consumed by Commodity Group Wholesale Trade 2% Agriculture 13% Petroleum 6% Cement and Concrete Manufacturing 13% Paper and Wood Products Manufacturing 6% Other Transportation 7% Chemical Manufacturing 6% Nonmetallic Minerals 11% Equipment Manufacturing 3% Motor Freight Transportation 9% Food Manufacturing 13% Mining and Fuels 2% Manufacturing 5% Metals Manufacturing 4%

  16. Consumption ValidationDifference in Observed and Model Commodity ShareInbound Tonnage Share (in Percent) 20 ITMS Share of Commodity 18 Model Share of Commodity 16 14 12 10 8 6 4 2 0 Agriculture Chemical Manufacturing Food Manufacturing Other Transportation Petroleum Metals Manufacturing Motor Freight Transportation Cement and Concrete Manufacturing Equipment Manufacturing Manufacturing Mining and Fuels Nonmetallic Minerals Paper and Wood Products Manufacturing Wholesale Trade Commodity Group

  17. Import and Export Tonnage Validation Tonnage (in Millions) 30 ITMS Data Model Data 25 20 15 10 5 0 Agriculture Equipment Manufacturing Manufacturing Mining and Fuels Nonmetallic Minerals/Cement Concrete Paper and Wood Products Manufacturing Wholesale Trade Chemical Manufacturing Food Manufacturing Metals Manufacturing Motor Freight Transportation Other Transportation Petroleum Commodity Group

  18. Trip Distribution Validation for Short-Haul Trips Average Trip Length (in Miles) 80 ITMS Short-Haul Model Short-Haul 70 60 50 40 30 20 10 0 Agriculture Chemical Manufacturing Food Manufacturing Other Transportation Petroleum Metals Manufacturing Motor Freight Transportation Cement and Concrete Manufacturing Equipment Manufacturing Manufacturing Mining and Fuels Nonmetallic Minerals Paper and Wood Products Manufacturing Wholesale Trade Commodity Group

  19. Mode Choice Validation • Mode shares by commodity group

  20. Overall Assignment Validation

  21. Cordon Validation • Trucks at external stations

  22. Summary • Different levels of detail (zip codes and TAZs) useful for freight forecasting • TLN and service models provide more accurate accounting of truck trips • Detailed calibration provides more accurate results • Use of changes in labor productivity and trends in the future model • Cargo model can evaluate a wider range of alternatives

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