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

1 Overview Background and Objectives Modeling Process 2003 Model Calibration and Validation Summary

2 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

3 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

4 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

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

6 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

7 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

8 Transport Logistics Node Model Estimates direct and TLN movements

9 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

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

11 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 miles >1500 miles

12 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%

13 Production Validation Difference in Observed and Model Commodity Share Outbound Tonnage ITMS Share of Commodity Model Share of Commodity Commodity Group 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 Share (in Percent)

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

15 Consumption Validation Difference in Observed and Model Commodity Share Inbound Tonnage Share (in Percent) Commodity Group 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 ITMS Share of Commodity Model Share of Commodity

16 Import and Export Tonnage Validation Commodity Group Agriculture Chemical Manufacturing Equipment Manufacturing Food Manufacturing Manufacturing Metals Manufacturing Mining and Fuels Motor Freight Transportation Nonmetallic Minerals/ Cement Concrete Other Transportation Paper and Wood Products Manufacturing Petroleum Wholesale Trade Tonnage (in Millions) ITMS Data Model Data

17 Trip Distribution Validation for Short-Haul Trips Commodity Group 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

18 Mode Choice Validation Mode shares by commodity group ITMS DataModelDifference Final Commodity GroupTRUCKRAILTRUCKRAILTRUCKRAIL Agriculture82%18%96%4%14%-14% Cement and Concrete M87%13%44%56%-43%43% Chemical Manufacturing46%54%39%61%-7%7% Equipment Manufacturing68%32%77%23%8%-8% Food Manufacturing71%29%65%35%-6%6% Manufacturing85%15%82%18%-3%3% Metals Manufacturing62%38%56%44%-6%6% Mining and Fuels0%100%0%100%0% Motor freight Trans100%0%100%0% Nonmetallic minerals93%7%84%16%-10%10% Other transportation0%100%0%100%0% Paper and Wood Products68%32%84%16%17%-17% Petroleum56%44%64%36%8%-8% Wholesale Trade2%98%8%92%6%-6%

19 Overall Assignment Validation Functional Class Number of Counts Count Volumes Truck Model Volumes Difference% Difference Validation by Functional Class - Trucks Freeways , ,013 (53,787)-9% Arterials ,580 48,232 (11,348)-19% Total , ,442 (82,938)-12% Validation by Functional Class - Autos Freeways 158 9,346,147 10,854,856 1,508,70916% Arterials 557 6,814,000 6,164,222 (649,778)-10% Total ,160,147 17,019, ,9305% Validation by Functional Class - Total Daily Freeways 158 9,949,947 11,404,869 1,454,92215% Arterials 557 6,913,580 6,212,454 (701,126)-10% Total ,863,527 17,639, ,9935%

20 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-5US-101, I-5, CA-14, US-395 I-8, I-15, I-40, I- 10

21 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