1. B UILDING A C OMMODITY B ASED F REIGHT M ODEL IN C ARGO : L OS A NGELES E XAMPLE

2. Develops software for the modeling of transportation systems Offices FloridaUSA Paris, MilanEurope Beijing, MumbaiAsia 3000 cities on 6 continents in more than 70 countries

3. North America:  Los Angeles, Houston, Miami, Orlando, Washington. Atlanta, San Francisco, Minneapolis, St. Louis, Tampa, Baltimore, Pittsburgh, Cincinnati, Sacramento Europe:  Dublin, London, Manchester, Glasgow, Liverpool, Oslo, Paris, Lyon, Nice, Strasbourg, Valencia, Seville, Milan, Venice Asia-Pacific:  Taipei, Melbourne, Adelaide, Perth, Brisbane, Seoul, Beijing, Bangkok, Hong Kong, Singapore, Kuala Lumpur, Manila, Jakarta, Delhi Major engineering firms:  AECOM, PB, Jacobs, Wilbur Smith, URS, PBSJ, Parsons

4. Educational Institutions:  IITs, NITs, SPA, Engineering Colleges Research:  CRRI, ISRO, CSTEP Government  DMRC, Dimts, MOUD, UMTC, PMC, RITES Major engineering firms:  AECOM, PB, Jacobs, Wilbur Smith, Systra MVA, GMR, L&T Ramboll, Halcrow, Feedback Ventures, Mott MacDonald,

5. Why Cube is the Best Transportation Modeling System Cube 6 The only system that covers all facets of transportation modeling people goods land use region-wide traffic simulation multi-modal microsimulation

6. 6 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 – Extended working hours at the ports – Short-haul shuttles from ports to inland freight facilities

7. 7 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

8. 8 Modeling Process

9. 9 Data Requirements Detailed Socio-economic data Reliable Commodity Flow Data Origin-Destination Surveys to calibrate Trip Distribution Port Data Data on TLNs (Intermodal Terminals, distribution Centers, Warehouses) Truck Classification Counts

10. 10 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

11. 11 Truck Networks

12. 12 Rail Networks

13. 13 Truck Time Functions LTL Time = Time+40 hrs for loading / unloading TL Times – Drive 11 hrs between rest periods of 10 hrs

14. 14 Model Descriptions – Tonnage Generation Commodities were grouped into 14 categories Productions based on tonnage rate per employee Consumptions based on input-output matrix Port trips added from the Port’s models Trends in production efficiencies, imports and exports

15. 15 Tonnage Rates by Commodity CategoryDescriptionTonnage Rate Agriculture Crops311.51 Livestock4,863.69 Forestry, fishing, hunting, and trapping7,329.10 Cement and Concrete ManufacturingStone, clay, glass products472.50 Concrete products7,502.27 Chemical ManufacturingChemicals and allied products488.26 Equipment Manufacturing Industrial machinery and equipment36.83 Electrical and electronic equipment36.60 Transportation Equipment72.96 Manufactuing Textile mill products200.58 Apparel and other textile products8.15 Furniture and fixtures49.60 Printing and publishing24.47 Rubber and miscellaneous plastics170.78 Leather and leather products412.91 Instruments and related products1.84 Miscellaneous manufacturing industries7.86

16. 16 Commodity Classes Agriculture Mining and Fuels Cement and Concrete Manufacturing Motor Freight Transportation Chemical Manufacturing Nonmetallic Minerals Equipment Manufacturing Other Transportation Food Manufacturing Paper and Wood Products Manufacturing ManufacturingPetroleum Metals Manufacturing Wholesale Trade

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

18. 18 Model Descriptions – Tonnage Distribution Trips split into short-haul and Long Haul All short-haul trips are assumed to be truck trips Short trip distribution based on a gravity model Long trips are distributed using a joint distribution and mode choice model.

19. 19 Trip Distribution Validation for Short-Haul Trips Commodity Group 0 10 20 30 40 50 60 70 80 Average Trip Length (in Miles) ITMS Short-Haul Model Short-Haul AgricultureChemical Manufacturing Equipment Manufacturing Food Manufacturing Manufacturing Metals Manufacturing Mining and Fuels Motor Freight Transportation Nonmetallic Minerals Other Transportation Paper and Wood Products Manufacturing Petroleum Wholesale Trade Cement and Concrete Manufacturing

20. 20 Model Descriptions – Mode Choice / Service Estimates Truck and Rail Trips Based on a multinomial logit model Three independent variables, time, distance and highway generalized cost Applied for 3 distance classes – 1500 miles Service Model – Estimates safety, utility, public / personal vehicles

21. 21 Model Descriptions – Transport Logistics Node Model Estimates direct and non-direct trips Includes intermodal terminals, warehouses, distribution centers etc. Model Outputs are – Direct flows from origin to destination – Flows from origin to the TLN – Flows from the TLN to destination

22. 22 Transport Logistics Node Model

23. 23 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

24. 24 Touring Vehicle Model Performed on TLN’s and user-specified zones

25. 25 Assignment Validation – External Cordons GatewayRoutes Count Volumes Truck Model Volumes % Difference San Diego / Mexico I-8, I-15, I-5 26,058 24,436-6% Rest of CAUS-101, I-5, CA-14, US- 395 29,698 31,8407% ArizonaI-8, I-15, I- 40, I-10 25,534 27,1336% Total 81,29183,4093%

26. 26 Assignment Validation – Screenlines ScreenlineDirNumber of Counts Truck Counts Model Volumes % Diff 1N-S18 51,277 54,7187% 2E-W28 96,480 91,096-6% 3N-S18 70,323 53,375-24% 4E-W12 71,266 56,140-21% 5E-W23 77,268 74,714-3% 6E-W13 78,972 86,75310% 7N-S20 47,733 25,909-46% 8E-W14 64,199 60,048-6% 10E-W8 19,356 20,3975% 11E-W8 16,278 18,38913% 12E-W5 19,064 18,617-2% 13N-S6 17,291 14,349-17% 18N-S4 29,958 31,3315% Total191 700,699 644,421-8%

27. 27 Assignment at Key Freight Corridors CorridorDirCounts Model Diff% Diff I-15 – S/O I-10N-S 17,000 13,272 (3,728) -22% I -15 – N/O Sr - 138N-S 14,855 13,877 (978) -7% I-15 San Diego / LA County N-S 5,388 11,503 6,115 113% I-15 San Bernardino / Nevada State Line N-S 7,780 13,093 5,313 68% TOTAL I-15N-S 45,023 51,744 (3,072) -7% I-215 - Betw I-10 & Wash' N-S 10,267 8,224 (2,043) -20%

28. 28 2030 Model – Tonnage Generation Change in Labor Productivity Commodity GroupGrowth Agriculture1.43% Cement and Concrete0.66% Chemical Manufacturing1.85% Equipment Manufacturing2.55% Food Manufacturing1.47% Manufacturing3.39% Metals Manufacturing2.12% Mining and Fuels0.93% Motor freight transportation1.18% Nonmetallic minerals1.88% Other transportation1.93% Paper and Wood Products1.71% Petroleum2.57% Wholesale Trade3.94%

29. 29 2030 Model – Tonnage Generation Change in Imports and Exports Region / StateExportsImports Remainder of CA-8%-1% Sacramento-1%0% San Francisco Bay Area-4%0% San Diego-2%4% Florida1%0% Illinois1%0% Iowa0%-1% Arkansas0%-1% Texas2%-2% Colorado0%2% Arizona1%7% Utah1%0% Nevada2%-3% Washington1%-2% Oregon1%0% Mexico0%2%

30. 30 2030 – Growth in Autos and Trucks Mode20032030Growth% Growth Drive Alone25,645,643 35,513,032 9,867,38938% Shared Ride 2 6,241,877 8,515,208 2,273,33136% Shared Ride 3 3,685,651 4,947,531 1,261,88034% Total Auto35,573,171 48,975,771 13,402,60038% Trucks 679,220 905,052 225,83233% All Vehicles36,252,391 49,880,823 13,628,43238%

31. 31 2030 Assignments – Growth ScreenlineDir 2003 Trucks 2030 Trucks % Growth 1N-S 54,67668,49125% 2E-W 83,465100,31520% 3N-S 52,02955,2346% 4E-W 59,10676,66730% 5E-W 77,04486,60812% 6E-W 88,740135,73553% 7N-S 31,93045,00941% 8E-W 61,16899,55763% 10E-W 23,02330,10531% 11E-W 18,05825,17339% 12E-W 18,22428,51556% 13N-S 16,29125,73858% 18N-S 31,03039,68028% Total 656,818867,42532%

32. 32 Summary Developed and tested for one of the most complex freight transportation system in the US Multimodal tool useful for freight investment decisions TLN and service models provide accurate accounting of truck trips Use of changes in labor productivity and trends Model can evaluate a wider range of alternatives