Landscapes of Motor Freight Transportation and
Warehousing: Analysis and Findings from Six
Metropolitan Areas in the U.S.A.
Shuai Tang, Ph.D. Student
Qian Wang, Ph.D., Assistant Professor
Department of Civil, Structural and Environmental Engineering
University at Buffalo, the State University of New York
2015 International Urban Freight (I-NUF)
October 22, 2015
Warehouse Location Problem
Freight transport: movement of goods over
space and time
Warehouse
Warehouse
Scenario 1: Being close to the
market
Scenario 2: Being far away
from the market
Modeling the Interactions
Establishments
by type
Location and Land Use Forecasting:
• Spatial patterns (density? clusters if any?
distance to customers?)
• Location choices
Land Use Forecasting
Traffic flow
and travel
time
Freight Demand Forecasting
Freight Demand Forecasting:
• Freight Trip Generation
• Freight pressure on network
• Estimations of truck miles and emission
level
Study Areas
• Six U.S. metropolitan statistical areas (MSAs)and rank
by population:
New York-1;
Los Angeles-2;
Chicago-3;
Houston-9;
Miami-10;
Seattle-13.
• Combined together, they have most of the major ports in the
U.S.
Data
• Establishment data collected by ReferenceUSA
for year 2010
• Standard Industrial Classification (SIC) codes: 42
– 421: motor freight transportation establishments
– 422: warehousing establishments
Objectives
• To examine the spatial distribution patterns
(landscapes) of the motor freight transportation and
warehousing industry in the six metropolitan area.
• To identify the factors that affect the patterns
More Trucking than Warehousing
100
90
422:
Warehousing
80
70
%
60
50
40
421: Motor
Freight
30
20
10
0
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Dominancy of Small Establishments
Trucking Establishments
100
Large
Medium
90
80
70
%
60
50
Small
40
30
20
10
0
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Dominancy of Small Establishments (Cont.)
Warehousing Establishments
100
Large
Medium
90
80
70
%
60
50
Small
40
30
20
10
0
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Analyses
a)
b)
c)
d)
Landscapes: kernel density maps
Landscape metrics
Proximity to transportation networks
Accessibility to intermodal freight terminals
Chicago, IL
Houston, TX
Los Angeles, CA
Miami, FL
New York, NY
Seattle, WA
Spatial Pattern Metrics
• Clustered or random pattern?
• Compactness of cluster: perimeter/area
• A circle is the most compact shape by definition
Metro Region
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Nearest
Neighbor Ratio
z-score
0.364
0.381
0.324
0.303
0.260
0.311
-82.307
-71.467
-96.402
-76.652
-121.562
-57.255
Number
of Patches
13
8
44
164
12
21
Shape Index
1.831
1.673
3.299
4.458
1.718
2.038
Proximity to Highway
Distance from Trucking Establishments to Nearest Highways
100
90
80
2km
70
1.5km
%
60
50
1km
40
30
20
0.5km
10
0
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Proximity to Highway (Cont.)
Distance from Warehousing Establishments to Nearest Highways
100
90
70
2km
1.5km
60
1km
%
80
50
40
30
0.5km
20
10
0
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Proximity to Railway
Distance from Trucking Establishments to Nearest Railways
80
70
60
%
50
2km
40
1.5km
30
1km
20
10
0.5km
0
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Proximity to Railway (Cont.)
Distance from Warehousing Establishments to Nearest Railways
100
90
80
70
%
60
50
2km
1.5km
1km
40
30
20
0.5km
10
0
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Accessibility to Intermodal Terminals
Accessibility Score(A) is defined as:
1
Ai   2
j d ij
• A is the accessibility index for an intermodal
terminal , i =1, 2, …;
• d is the network distance between intermodal
terminal i and establishment j.
Accessibility to Intermodal Terminals
After normalizing the accessibility score into range 0 – 1,
the average accessibility scores of six cities are:
Chicago
Houston
Los Angeles
Miami
New York
Seattle
Number of
terminals
141
49
64
33
90
59
Average accessibility
score
0.227506
0.306439
0.436083
0.166355
0.179277
0.216729
Standardized Accessibility Scores of Terminals
6
5
Chicago
Houston
LA
Miami
NY
Seattle
4
3
2
1
0
0
20
40
60
80
100
120
-1
-2
Air – Port
-3
Accessibility Scores by Terminal Type
1.0
0.9
0.8
0.44
0.7
0.4
0.18
0.48
0.11
0.07
0.07
0.29
0.04
Air
0.38
0.39
0.30
0.37
0.2
0.40
0.28
Truck
0.19
0.29
0.28
0.12
0.16
Miami
New York
0.0
Chicago
Houston Los Angeles
Port
Rail
0.21
0.3
0.1
0.27
0.34
0.6
0.5
0.14
0.20
Seattle
Conclusions and Implications
• Landscapes of motor freight transportation and
warehousing establishments vary by region but they
tend to be clustered in the six regions to take
advantage of the economies of scale
• Establishments in six regions tend to locate close to
transportation networks
• Accessibility to freight terminals vary by mode and
region
Future Work
• Temporal analysis of establishments’ spatial pattern
changes
• Modeling individual establishment’s relocation
behavior
Thank you!
Shuai Tang
Email: [email protected]
Phone: (716) 262-5601