Building a Commodity Based Freight Model in Cargo

Report
BUILDING A COMMODITY BASED FREIGHT MODEL IN
CARGO : LOS ANGELES EXAMPLE
Develops software for the modeling of
transportation systems
Florida
Paris, Milan
Beijing, Mumbai
Offices
USA
Europe
Asia
3000 cities on 6 continents
in more than 70 countries
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
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,
Cube 6
Why Cube is the Best Transportation Modeling System
The only system that covers
all facets of transportation
modeling
• people
• goods
• land use
• region-wide traffic
simulation
• multi-modal
microsimulation
Background
• Significant growth in goods movement in the Los
Angeles region required improved models to
evaluate impacts
• Models needed to address different potential
improvements
–
–
–
–
6
Higher capacity intermodal rail terminals
Truck-only lanes
Extended working hours at the ports
Short-haul shuttles from ports to inland freight
facilities
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
7
Modeling Process
8
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
9
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
10
Truck Networks
11
Rail Networks
12
Truck Time Functions
• LTL Time = Time+40 hrs for loading /
unloading
• TL Times – Drive 11 hrs between rest periods
of 10 hrs
13
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
14
Tonnage Rates by Commodity
Category
Agriculture
Cement and Concrete Manufacturing
Chemical Manufacturing
Equipment Manufacturing
Manufactuing
15
Description
Tonnage
Rate
Crops
311.51
Livestock
4,863.69
Forestry, fishing, hunting, and trapping
7,329.10
Stone, clay, glass products
472.50
Concrete products
7,502.27
Chemicals and allied products
488.26
Industrial machinery and equipment
36.83
Electrical and electronic equipment
36.60
Transportation Equipment
72.96
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
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
Manufacturing
Petroleum
Metals Manufacturing
Wholesale Trade
16
Outbound Tonnage
Produced by Commodity Group
Wholesale Trade
3%
Petroleum
8%
Paper and Wood
Products Manufacturing
4%
Other Transportation
9%
Nonmetallic Minerals
17%
Motor Freight Transportation
11%
Mining and Fuels
0%
17
Agriculture
8%
Cement and Concrete Manufacturing
11%
Chemical Manufacturing
5%
Equipment Manufacturing
3%
Food Manufacturing
11%
Manufacturing
5%
Metals Manufacturing
5%
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.
18
Trip Distribution Validation for
Short-Haul
Trips
Average Trip Length (in Miles)
80
ITMS Short-Haul
Model Short-Haul
70
60
50
40
30
20
10
0 Agriculture
Chemical
Manufacturing
Cement
and Concrete
Manufacturing
Equipment
Manufacturing
Commodity Group
19
Food
Manufacturing
Metals
Manufacturing
Manufacturing
Mining
and Fuels
Motor Freight
Transportation
Other Transportation
Petroleum
Nonmetallic Minerals Paper and
Wood Products
Manufacturing
Wholesale
Trade
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
20
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
21
Transport Logistics Node Model
Partitions into Long-Haul
Direct Flows by mode
Define location of TLN
Define service area of TLN
Partitions into Long-Haul TLN
Flows and Short-Haul TLN
Flows by mode
Study Area
22
Internal Area
External Area
External Zone
TLN
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
23
Touring Vehicle Model
• Performed on TLN’s and user-specified zones
Generated tour from a TLN and
back doing pickups and drop-offs
Study Area
24
Internal Area
External Area
External Zone
TLN
Assignment Validation – External
Cordons
Gateway
Routes
Count
Volumes
San Diego /
Mexico
I-8, I-15, I-5
26,058
Truck
Model
Volumes
24,436
Rest of CA
US-101, I-5,
CA-14, US395
I-8, I-15, I40, I-10
29,698
31,840
7%
25,534
27,133
6%
Arizona
Total
81,291
25
83,409
%
Difference
-6%
3%
Assignment Validation – Screenlines
Screenline
Dir
Number of
Counts
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%
700,699
644,421
-8%
Total
26
191
Truck
Counts
Model
Volumes
% Diff
Assignment at Key Freight Corridors
Corridor
Dir
I-15 – S/O I-10
N-S
Counts
17,000
Model
Diff
13,272
% Diff
-22%
(3,728)
I -15 – N/O Sr - 138
N-S
14,855
13,877
-7%
(978)
I-15 San Diego / LA
County
N-S
I-15 San Bernardino /
Nevada State Line
N-S
TOTAL I-15
N-S
5,388
11,503
113%
6,115
7,780
13,093
68%
5,313
45,023
51,744
-7%
(3,072)
I-215 - Betw I-10 &
Wash'
27
N-S
10,267
8,224
-20%
(2,043)
2030 Model – Tonnage Generation
Change in Labor Productivity
Commodity Group
Growth
Agriculture
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
28
3.94%
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%
0%
2%
Mexico
29
2030 – Growth in Autos and Trucks
Mode
2003
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%
35,573,171
48,975,771
13,402,600 38%
679,220
905,052
225,832 33%
36,252,391
49,880,823
13,628,432 38%
Total Auto
Trucks
All Vehicles
30
2030
Growth
% Growth
2030 Assignments – Growth
Screenline
Dir
1
N-S
2
2030
Trucks
% Growth
54,676
68,491
25%
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%
656,818
867,425
32%
Total
31
2003
Trucks
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
32

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