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

VAMSEE MODUGULA [email protected]

BUILDING A COMMODITY BASED FREIGHT MODEL IN CARGO : LOS ANGELES EXAMPLE

CITILABS – THE COMPANYDevelops software for the modeling of

transportation systems

OfficesFlorida USA

Paris, Milan EuropeBeijing, Mumbai Asia

3000 cities on 6 continents in more than 70 countries

WHO USES OUR

PRODUCTS

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

WHO USES OUR

PRODUCTS

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,

Comprehensive and Integrated

Wh

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the B

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Mod

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The only system that covers all facets of transportation modeling• people• goods• land use• region-wide traffic

simulation• multi-modal

microsimulation

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

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

Modeling Process

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

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

Truck Networks

12

Rail Networks

13

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

hrs

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

Tonnage Rates by CommodityCategory Description Tonnage

Rate

Agriculture

Crops 311.51

Livestock 4,863.69

Forestry, fishing, hunting, and trapping 7,329.10

Cement and Concrete Manufacturing Stone, clay, glass products 472.50

Concrete products 7,502.27

Chemical Manufacturing Chemicals and allied products 488.26

Equipment Manufacturing

Industrial machinery and equipment 36.83

Electrical and electronic equipment 36.60

Transportation Equipment 72.96

Manufactuing

Textile mill products 200.58

Apparel and other textile products 8.15

Furniture and fixtures 49.60

Printing and publishing 24.47

Rubber and miscellaneous plastics 170.78

Leather and leather products 412.91

Instruments and related products 1.84

Miscellaneous manufacturing industries 7.86

16

Commodity ClassesAgriculture 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

Manufacturing Petroleum

Metals Manufacturing Wholesale Trade

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Outbound Tonnage Produced by Commodity Group

Agriculture8%

Cement and Concrete Manufacturing11%

Chemical Manufacturing5%

Equipment Manufacturing3%

Food Manufacturing11%

Manufacturing5%

Metals Manufacturing5%Mining and Fuels

0%

Motor Freight Transportation11%

Nonmetallic Minerals17%

Other Transportation9%

Paper and Wood Products Manufacturing4%

Petroleum8%

Wholesale Trade3%

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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.

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Trip Distribution Validation for Short-Haul Trips

Commodity Group

0

10

20

30

40

50

60

70

80Average Trip Length (in Miles)

ITMS Short-HaulModel Short-Haul

Agriculture Chemical 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

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

– <500, 500-1500 ; >1500 miles

• Service Model– Estimates safety, utility, public / personal vehicles

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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

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Transport Logistics Node Model

Internal Area External Area External Zone TLNStudy Area

Define location of TLN

Define service area of TLN

Partitions into Long-Haul Direct Flows by mode

Partitions into Long-Haul TLN Flows and Short-Haul TLN Flows by mode

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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

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Touring Vehicle Model

• Performed on TLN’s and user-specified zones

Internal Area External Area External Zone TLNStudy Area

Generated tour from a TLN and back doing pickups and drop-offs

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Assignment Validation – External CordonsGateway Routes Count

Volumes Truck Model Volumes

% Difference

San Diego / Mexico

I-8, I-15, I-5 26,058 24,436 -6%

Rest of CA US-101, I-5, CA-14, US-395

29,698 31,840 7%

Arizona I-8, I-15, I-40, I-10

25,534 27,133 6%

Total 81,291 83,409 3%

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Assignment Validation – ScreenlinesScreenline Dir Number of

Counts Truck Counts

Model Volumes

% Diff

1 N-S 18 51,277 54,718 7%

2 E-W 28 96,480 91,096 -6%

3 N-S 18 70,323 53,375 -24%

4 E-W 12 71,266 56,140 -21%

5 E-W 23 77,268 74,714 -3%

6 E-W 13 78,972 86,753 10%

7 N-S 20 47,733 25,909 -46%

8 E-W 14 64,199 60,048 -6%

10 E-W 8 19,356 20,397 5%

11 E-W 8 16,278 18,389 13%

12 E-W 5 19,064 18,617 -2%

13 N-S 6 17,291 14,349 -17%

18 N-S 4 29,958 31,331 5%

Total 191 700,699 644,421 -8%

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Assignment at Key Freight CorridorsCorridor Dir Counts Model Diff % Diff

I-15 – S/O I-10 N-S 17,000 13,272

(3,728)

-22%

I -15 – N/O Sr - 138 N-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-15 N-S 45,023 51,744

(3,072)

-7%

I-215 - Betw I-10 & Wash'

N-S 10,267 8,224

(2,043)

-20%

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2030 Model – Tonnage Generation Change in Labor Productivity

Commodity Group GrowthAgriculture 1.43%

Cement and Concrete 0.66%

Chemical Manufacturing 1.85%

Equipment Manufacturing 2.55%

Food Manufacturing 1.47%

Manufacturing 3.39%

Metals Manufacturing 2.12%

Mining and Fuels 0.93%

Motor freight transportation 1.18%

Nonmetallic minerals 1.88%

Other transportation 1.93%

Paper and Wood Products 1.71%

Petroleum 2.57%

Wholesale Trade 3.94%

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2030 Model – Tonnage Generation Change in Imports and Exports

Region / State Exports Imports

Remainder of CA -8% -1%

Sacramento -1% 0%

San Francisco Bay Area -4% 0%

San Diego -2% 4%

Florida 1% 0%

Illinois 1% 0%

Iowa 0% -1%

Arkansas 0% -1%

Texas 2% -2%

Colorado 0% 2%

Arizona 1% 7%

Utah 1% 0%

Nevada 2% -3%

Washington 1% -2%

Oregon 1% 0%

Mexico 0% 2%

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2030 – Growth in Autos and Trucks

Mode 2003 2030 Growth % Growth

Drive Alone 25,645,643 35,513,032 9,867,389 38%

Shared Ride 2 6,241,877 8,515,208 2,273,331 36%

Shared Ride 3 3,685,651 4,947,531 1,261,880 34%

Total Auto 35,573,171 48,975,771 13,402,600 38%

Trucks 679,220 905,052 225,832 33%

All Vehicles 36,252,391 49,880,823 13,628,432 38%

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2030 Assignments – GrowthScreenline Dir 2003

Trucks2030 Trucks

% Growth

1 N-S 54,676 68,491 25%

2 E-W 83,465 100,315 20%

3 N-S 52,029 55,234 6%

4 E-W 59,106 76,667 30%

5 E-W 77,044 86,608 12%

6 E-W 88,740 135,735 53%

7 N-S 31,930 45,009 41%

8 E-W 61,168 99,557 63%

10 E-W 23,023 30,105 31%

11 E-W 18,058 25,173 39%

12 E-W 18,224 28,515 56%

13 N-S 16,291 25,738 58%

18 N-S 31,030 39,680 28%

Total 656,818 867,425 32%

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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