Los Angeles County

Transportation leadership you can trust.

presented topresented to

TRB Planning Applications ConferenceTRB Planning Applications Conference

presented bypresented by

Vamsee Modugula and Maren OutwaterVamsee Modugula and Maren Outwater

Cambridge Systematics, Inc.Cambridge Systematics, Inc.

May 2007May 2007

Los Angeles County Cargo Forecasting and Simulation Model

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

GenerationGeneration

Productions and Attractions Productions and Attractions by Commodity Class by Commodity Class

DistributionDistribution Long-Haul Flows by Long-Haul Flows by Commodity Class Commodity Class

Long Haul Flows by Mode Long Haul Flows by Mode and Commodity Class and Commodity Class

TLNTLN

Long-Haul Flows to TLN by Long-Haul Flows to TLN by Mode and Commodity Class Mode and Commodity Class

Direct Short-Haul Direct Short-Haul Flows by Commodity Flows by Commodity

Class by TruckClass by Truck

Direct Long-Haul Direct Long-Haul Flows by Mode and Flows by Mode and Commodity ClassCommodity Class

Fine Zone Level

Coarse Zone Level {State/County/Zip}

Vehicle {Annual PA>Period OD} Vehicle {Annual PA>Period OD} Assignment {6 Class}Assignment {6 Class}

Direct Short-Haul Flows by Direct Short-Haul Flows by Commodity Class by Truck Commodity Class by Truck

Short-Haul Flows to TLN by Short-Haul Flows to TLN by Truck and Commodity Class Truck and Commodity Class

Long-Haul Flows to Long-Haul Flows to TLN by Mode and TLN by Mode and Commodity Class Commodity Class

Short-Haul Flows to Short-Haul Flows to TLN by Truck and TLN by Truck and Commodity Class Commodity Class

Mode ChoiceMode Choice

Fine DistributionFine Distribution

Direct Long-Haul Flows by Direct Long-Haul Flows by Mode and Commodity ClassMode and Commodity 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

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

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

Internal Area External Area External Zone TLNStudy Area

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

12

Model outputs compared to ITMS data by commodity group and distance class

Truck volumes compared to truck counts

Model Validation

Agriculture Agriculture Mining and Fuels Mining and Fuels

Cement and Concrete Manufacturing Cement and Concrete Manufacturing Motor Freight Transportation Motor Freight Transportation

Chemical Manufacturing Chemical Manufacturing Nonmetallic Minerals Nonmetallic Minerals

Equipment Manufacturing Equipment Manufacturing Other Transportation Other Transportation

Food Manufacturing Food Manufacturing Paper and Wood Products Manufacturing Paper and Wood Products Manufacturing

Manufacturing Manufacturing Petroleum Petroleum

Metals Manufacturing Metals Manufacturing Wholesale Trade Wholesale Trade

<=500 miles<=500 miles 500-1500 miles500-1500 miles >1500 miles>1500 miles

13

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%

14

Production ValidationDifference in Observed and Model Commodity ShareOutbound Tonnage

0

2

4

6

8

10

12

14

16

18

20ITMS Share of CommodityModel Share of Commodity

Commodity Group

Agriculture Chemical Manufacturing


Food Manufacturing

Manufacturing


Mining and Fuels





Petroleum

Wholesale Trade


Share (in Percent)

15

Inbound Tonnage Consumed by Commodity Group

Agriculture

13%


13%


6%


3%Food Manufacturing

13%Manufacturing

5%Metals Manufacturing

4%

Mining and Fuels

2%


9%


11%


7%


6%

Petroleum

6%

Wholesale Trade

2%

16

Consumption ValidationDifference in Observed and Model Commodity ShareInbound Tonnage

Share (in Percent)

0

2

4

6

8

10

12

14

16

18

20

Commodity Group



Food Manufacturing

Manufacturing


Mining and Fuels





Petroleum

Wholesale Trade


ITMS Share of CommodityModel Share of Commodity

17

Import and Export Tonnage Validation

Commodity Group

0

5

10

15

20

25

30

Agriculture



Food Manufacturing

Manufacturing


Mining and Fuels


Nonmetallic Minerals/

Cement Concrete



Petroleum

Wholesale Trade

Tonnage (in Millions)

ITMS Data

Model Data

18

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



Food Manufacturing

Manufacturing


Mining and Fuels





Petroleum

Wholesale Trade


19

Mode Choice Validation

Mode shares by commodity group

ITMS Data Model Difference

Final Commodity Group TRUCK RAIL TRUCK RAIL TRUCK RAIL

Agriculture 82% 18% 96% 4% 14% -14%

Cement and Concrete M 87% 13% 44% 56% -43% 43%

Chemical Manufacturing 46% 54% 39% 61% -7% 7%

Equipment Manufacturing 68% 32% 77% 23% 8% -8%

Food Manufacturing 71% 29% 65% 35% -6% 6%

Manufacturing 85% 15% 82% 18% -3% 3%

Metals Manufacturing 62% 38% 56% 44% -6% 6%

Mining and Fuels 0% 100% 0% 100% 0% 0%

Motor freight Trans 100% 0% 100% 0% 0% 0%

Nonmetallic minerals 93% 7% 84% 16% -10% 10%

Other transportation 0% 100% 0% 100% 0% 0%

Paper and Wood Products 68% 32% 84% 16% 17% -17%

Petroleum 56% 44% 64% 36% 8% -8%

Wholesale Trade 2% 98% 8% 92% 6% -6%

20

Overall Assignment Validation

Functional Class

Number of Counts

Count Volumes

Truck Model Volumes

Difference % Difference

Validation by Functional Class - Trucks

Freeways 67 603,800 550,013 (53,787) -9%

Arterials 124 59,580 48,232 (11,348) -19%

Total 191 703,380 620,442 (82,938) -12%

Validation by Functional Class - Autos

Freeways 158 9,346,147 10,854,856 1,508,709 16%

Arterials 557 6,814,000 6,164,222 (649,778) -10%

Total 715 16,160,147 17,019,077 858,930 5%

Validation by Functional Class - Total Daily

Freeways 158 9,949,947 11,404,869 1,454,922 15%

Arterials 557 6,913,580 6,212,454 (701,126) -10%

Total 715 16,863,527 17,639,520 775,993 5%

21

Cordon Validation

Trucks at external stations

Total Annual Tons

35,461,096 53,633,179 54,059,268

Total Annual Trucks

6,514,167 8,851,110 7,767,139

Average Daily Trucks

26,057 35,404 31,069

Observed Daily Trucks

26,948 29,698 28,848

Truck Count Locations

I-8, I-15, I-5 US-101, I-5, CA-14, US-395

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

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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