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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 - PowerPoint PPT Presentation
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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
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)
15
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%
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
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
ITMS Share of CommodityModel Share of Commodity
17
Import and Export Tonnage Validation
Commodity Group
0
5
10
15
20
25
30
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
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
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
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