Paper Author (s)
Jose Holguín-Veras, Rensselaer Polytechnic Institute (jhv@rpi.edu)
Ivan Sanchez-Diaz (corresponding), Rensselaer Polytechnic Institute (sanchi2@rpi.edu)
Miguel Jaller, Rensselaer Polytechnic Institute (jallem@rpi.edu)
Catherine Theresa Lawson, State University of New York, Albany (lawsonc@albany.edu)
Shama Campbell, Rensselaer Polytechnic Institute (campbs4@rpi.edu)
Jeffrey Wojtowicz, Rensselaer Polytechnic Institute (wojtoj@rpi.edu)
Xiaokun (Cara) Wang, Rensselaer Polytechnic Institute (wangx18@rpi.edu)
Paper Title & Number
Freight Generation And Freight Trip Generation Modeling [ITM # 43]
Abstract
The current transportation planning process does not effectively estimate freight activity necessary to
assist decision makers when making infrastructure choices. This research seeks to provide improved
freight generation (FG) and freight trip generation (FTG) models for different land use characteristics
related to freight facilities and commercial operations to better inform state and local decision-making.
Freight generation models are estimated using the largest and most complete establishment-based
freight survey in the world (with 100,000 establishments samples): the Commodity Flow Survey. Freight
Trip Generation models are estimated using data from more than 600 establishments in New York City.
Statement of Financial Interest
This research was developed as part of NCFRP 25 Project: “Freight Trip Generation and Land Use”. There
is no further financial interest.
Statement of Innovation
This is the first time that freight generation models are developed using the Commodity Flow Survey
micro-data.This is the largest sample ever used to estimate freight generation models (100,000
establishments for the US dataset).Freight generation (cargo) and freight trip generation (vehicle trips)
are differentiated and the modeling methodology is adapted accordingly. The freight trip generation
models use different specifications that take into account the role of logistic decisions in different
industry sectors.
1
FREIGHT GENERATION AND FREIGHT TRIP GENERATION MODELING
José Holguín-Veras, Ph.D., P.E.
William H. Hart Professor
Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth St., Troy, New York 12180, USA
Fax.: +518-276-4833, Email: jhv@rpi.edu
Iván Sánchez-Díaz, M.S.
Graduate Research Assistant, Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth St., Troy, New York 12180, USA.
Email: sanchi2@rpi.edu
Miguel Jaller, Ph.D.
Senior Researcher, Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth St., Troy, New York 12180, USA.
Email: jallem@rpi.edu
Catherine T. Lawson, Ph.D.
Associate Professor and Director of the Masters in Urban and Regional Planning Program,
Department of Geography and Planning
University at Albany, 1400 Washington Ave. Albany, NY, 12222
Phone: 518-442-4775. Fax: 518-442-4742, Email: lawsonc@albany.edu
Shama Campbell, M.S.
Graduate Research Assistant, Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth St., Troy, New York 12180, USA.
Email: campbs4@rpi.edu
Jeff Wojtowicz, M.S.
Senior Research Engineer, Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth St., Troy, NY 12180, USA.
Email: wojtoj@rpi.edu
Cara Wang, Ph.D.
Assistant Professor, Department of Civil and Environmental Engineering
Rensselaer Polytechnic Institute, 110 Eighth St., Troy, NY 12180, USA.
Email: wangx18@rpi.edu
1. STATEMENT OF FINANCIAL INTEREST
This research was developed as part of NCFRP 25 Project: “Freight Trip Generation and Land Use”. There is
no further financial interest.
2
2. STATEMENT OF INNOVATIONS




This is the first time that freight generation models are developed using the Commodity Flow
Survey micro-data.
This is the largest sample ever used to estimate freight generation models (100,000
establishments for the US dataset).
Freight generation (cargo) and freight trip generation (vehicle trips) are differentiated and the
modeling methodology is adapted accordingly.
The freight trip generation models use different specifications that take into account the role
of logistic decisions in different industry sectors.
3
3. SHORT PAPER: FREIGHT GENERATION AND FREIGHT TRIP
GENERATION MODELING
3.1
Introduction
The lack of research and freight data affects all facets of transportation modeling: generation of cargo,
distribution to intermediate and end users where the cargo is processed, stored, and ultimately consumed;
mode choice, and traffic assignment. In particular, there is a great need for research to enhance the state of the
quantitative aspects of freight generation. A better understanding of the variables driving the generation of
freight demand would enable more accurate demand forecasts, and better quantification of the traffic impacts
of freight activity. This knowledge can support a more effective decision-making for state transportation
agencies and Metropolitan Planning Organizations which are constantly facing pressure to balance the
conflicting objectives of the stakeholders involved and impacted by freight activity.
This research provides an overview of the advances in the estimation of the freight generation (amount of
cargo generated) and the freight trip generation (number of freight vehicle trips generated) realized in the
context of NCFRP Project 25: Freight Trip Generation and Land Use.
3.2
Methodology
It is important to start by defining the two types of freight demand, freight generation (FG) and freight trip
generation (FTG). FG refers to the production and attraction of cargo, measured by tonnage or volume (e.g.
m3). FTG, in contrast, measures the number of freight vehicle trips that are generated by the transport of FG.
Treating FG and FTG as separate concepts is important because while FG is directly correlated with the size
of the establishments, FTG may not (1). This is the result of the role played by the shipment size, which
enables large business establishments to receive larger shipments minimally increasing the amount of vehicletrips produced. In consistency with the practices in passenger transport modelling, one could subdivide FG
and FTG in attractions and productions (2), leading to the concepts of freight attraction (FA), freight
production (FP), freight trip attraction (FTA), and freight trip production (FTP).
For the analysis, FG is quantified as the amount of cargo (in tons) and FTG is quantified using the number
of deliveries per establishment. This approach enables the estimation of FG and FTG as functions of land use
or industrial sector and employment. Knowing establishment characteristics, FTG can be readily estimated.
To estimate FG/FTG models, three different approaches were used: standard freight/freight trip generation
rates; linear regression; and Multiple Classification Analysis (MCA). (For a description of MCA, see (2). The
analyses were performed using the North American Industry Classification System (NAICS) to group
establishments with similar economic activity. For the case of linear regression models, the analyses used
total employees per establishment as the independent variable, after considering the data collection and
forecasting implications of different explanatory factors. While for FG models the function specification
always considers employment as an independent variable; for FTG models the estimates can be constant per
establishment.
The conceptual validity, the statistical significance and the Root Mean Square Error (RMSE) were the
criteria used to assess which functional form is more suitable to estimate FG and FTG.
4
For FTG, when the regression analyses found that FTG depends on business size, MCA models were
applied to estimate the trip rates for each stratum of employment and for each category of land use. (It does
not make sense to use MCA stratified by employment level if this variable is not statistically related to FTG).
The research explored different employee groupings to select the number and width of each interval class.
MCA models were then estimated, where appropriate, for the different groupings and combinations of
employee intervals.
For FG modeling, the authors used the micro-data at the Census Bureau’s Center for Economic Studies. The
Commodity Flow Survey (CFS) data is the largest and most complete establishment based freight survey in
the world (with 100,000 establishments samples for times a year). This is the first time the CFS micro-data
are used for freight generation modeling. These data contain information for selected warehousing, wholesale,
retail and manufacturing sectors.
For FTG modeling, the authors collected data from about 600 establishments including receivers and
carriers in New York City. The information was obtained through telephone interviews and supplemented
with records from the Dun and Bradstreet database. The resulting dataset includes FTG data (e.g., deliveries
received, freight-trips made from the establishment on a typical day), company attributes (e.g., employment,
and industry segment), and basic geographical information, see (1, 3, 4).
3.3
Results
This section provides a summary of the results for New York State. In the case of FTG the results
correspond to the NYC metropolitan area. In the tables, every row represents the model for an industry sector
defined by a NAICS code. The dependent variable for FP models corresponds to the amount of cargo
produced (in daily pounds). Table 1 shows the FP models estimated using the CFS micro-data.
5
Table 1: Freight Generation Regression Models for New York State
Description
NAICS Obs.
Mining
212
66
Manufacturing Group 1:
31
171
food,tobacco,textile, apparel
Manufacturing: food
311 98
Manufacturing: beverage and tobacco
312 26
Manufacturing: textile mills
313 13
Manufacturing: textile products mills
314 15
Manufacturing: apparel
315 19
Manufacturing Group 2: wood, paper,
chemicals, petroleum, plastics,
32
468
nonmetallic
Manufacturing: wood products
321 60
Manufacturing: paper
322 58
Manufacturing: printing and related
323 53
Manufacturing: petroleum and coal
324 17
Manufacturing: chemical
325 99
Manufacturing: plastics and rubber
326 90
Manufacturing: nonmetallic
327 91
Manufacturing Group 3: metal products,
machinery, computer, equipment,
33
540
miscellaneous
Manufacturing: primary metal
331 41
Manufacturing: fabricated metal
332 125
Manufacturing: machinery
333 94
Manufacturing: computer
334 72
Manufactruing: electrical equipment
335 31
Manufacturing: transportation equipment
336 48
Manufacturing: furniture products
337 51
339 78
Manufacturing: miscellaneous
Wholesale
42
980
Wholesale: durable goods
423 540
Wholesale: nondurable goods
424 440
454
82
Retail: electronic shop and fuel
493
28
Warehousing
511
39
Information: newspaper
551
32
Management: corporate offices (1)
Constant
Rate per employee Adj. R2
1,181,292.6**
51,427.2**
0.36
RMSE
3,067,508
-
2,968.43**
0.35
851,044
-
2,332.1**
7,139.2**
117.7
168.8*
10.2*
0.42
0.59
0.30
0.60
0.16
645,147
1,404,852
16,966
14,093
4,081
304,238.8**
128.2**
0.00
1,061,092
187,484.5**
-
2,760.5**
1,457.5**
412.1**
60,257.5
133.2**
555.3*
3,943.5**
0.41
0.44
0.75
0.31
0.06
0.35
0.18
180,306
376,299
48,834
4,053,051
640,043
165,197
1,081,073
-
172.2*
0.05
317,314
7,806.8*
4927.2*
62,225.2**
115,117.8**
-
2,674.9**
219.9**
132.5*
3.0*
65.1*
319.9*
76.1*
24.2*
1,400.0**
1,148.6*
1,460.8*
884.5
2,920.9*
130.7*
847.5
0.43
0.19
0.36
0.13
0.39
0.62
0.21
0.11
0.04
0.01
0.12
0.23
0.58
0.67
0.22
865,117
89,916
60,571
6,599
26,112
160,345
26,137
18,262
695,353
817,215
501,540
271,765
868,740
44,304
726,910
Notes: FG is estimated in pounds/day; * significant at the 5% level, ** significant at the 1% level; (1) the model
includes cargo sent from headquarters to branches of the firm.
As shown, the models’ adjusted R2 range from 0.005 (for Group 2 of manufactures) to 0.75 (for printing and
related products manufacturing). In terms of the specification, 7 out of the 31 models have an intercept and an
employment term, 20 of the 31 models are a statistically significant employment rate, and for the remaining 4
models the employment term is not statistically significant. For the cases where there is no intercept, the
largest coefficient was found for manufacturing of petroleum and coal products (60,257.5 daily
pounds/employee) and the smallest for computer manufacturing (3 daily pounds/employee).
Table 2 shows the disaggregate models for freight trip attraction (FTA) considering business size for
different industry segments. To gain further insight into the relation between FTG and business size, the
authors selected those industries that exhibited a combination of a constant per establishment and a term
6
based on employment to estimate MCA models. Table 3 shows the resulting MCA models for these
industries. These estimates indicate that, on average, those establishments in the retail trade industry (NAICS
44) receive one more delivery than those in the accommodation and food service (NAICS 72) and wholesale
trade (NAICS 42) industries.
Table 2: Freight Trip Attraction (deliveries/day) Models by NAICS
Description
NAICS
Construction*
Manufacturing*
Food, Beverage, Tobacco, Textile, Apparel,
Leather & Allied Product Manufacturing
Wood, paper, printing, petroleum and coal
products, chemical, plastics, nonmetallic and
mineral product manufacturing
23
31-33
Metal, machinery, computer, electronic, electrical,
transportation, furniture and misc. manufacturing
Wholesale Trade*
42
Retail Trade*
44-45
Motor vehicle, furniture, electronics, building
material, food and beverage, health, gasoline, and
clothing stores
Sporting goods, hobby, book, and music stores
Accommodation and Food*
72
25
51
Rate per
RMSE
employee
2.160
1.364
2.831
2.791
31
21
2.400
1.295
32
10
4.420
5.483
33
20
2.490
2.483
117
98
2.272
3.070
0.069
0.063
3.655
4.054
44
69
2.458
0.132
4.298
45
29
56
2.724
1.307
0.081
4.352
3.091
Obs.
Constant
* Group Models
Source: Adapted from Holguín Veras et al. (5)
Table 3: MCA Rates for Freight Trip Attraction (deliveries/day)
Employees*
Economic Classification System: NAICS
72: Accomodation
42: Wholesale trade
44: Retail trade
and food
1-10
2.443
3.543
1.902
11-20
3.341
4.442
2.801
21-30
5.685
6.785
5.144
3.658
4.197
3.355
RMSE
* For establishments with more than 30 employees results were not
statistically significant
Source: Adapted from Holguín Veras et al. (5)
Similarly for FTP, Table 4 shows the resulting disaggregate models for the different NAICS codes. For FTP,
30% of the models are a constant per establishment, another 30% are employment rates, and the remaining
40% have a constant and an employment term. These results show, yet again, that a large number of industry
sectors have a constant FTA that does not depend on business size.
Table 4: Freight Trip Production (trips/day) Models by NAICS
7
Description
NAICS
Obs.
Constant
Rate per
employee
Best
Model
23 - Construction*
23
9
E
1.586
31, 32 and 33 - Manufacturing*
31 - Food, beverage, tobacco, textile, apparel,
leather and allied product manufacturing
32 - Wood, paper, printing, petroleum and coal
products, chemical, plastics, nonmetalluc and
mineral manufacturing
33 - Metal, machinery, computer, electronic,
electrical, transportation, furniture and misc.
manufacturing
31-33
28
2.214
S
3.599
31
13
2.846
S
4.990
32
7
E
0.648
33
8
1.750
S
1.639
42 - Wholesale Trade*
42
124
1.755
0.036
C
5.094
0.161
E
6.485
44 and 45 - Retail Trade*
44-45
44 - Motor vehicle, furniture, electronics,
building material, food and beverage, health,
44
gasoline, and clothing stores
48 and 49 - Transportation and Warehousing* 48-49
48 - Air, rail, water, truck, transit, pipeline, scenic
48
and sightseeing, and support activities
* Group models
0.068
RMSE
0.023
9
5
0.993
0.021
C
0.237
157
2.718
0.038
C
4.811
153
2.725
0.038
C
4.005
Source: Adapted from Holguín Veras et al. (5)
Table 5 shows the MCA results for the different industries. These results indicate that establishments in the
construction (NAICS 23) industry averaged approximately one more trip than those in the manufacturing
(NAICS 32) industry. Establishments in the transport and warehousing (NAICS 42 and 48) industries both
averaged at least one more trip than establishments in the retail trade (NAICS 44) industry.
Table 5: MCA Models for Freight Trip Production (trips/day)
23 Construction
Economic Classification System: NAICS
32 - Wood,
42 - Wholesale
44 - Motor
paper,
Trade
vehicle, furniture,
petroleum,coal,
electronics, food
chemical,
and beverage
plastics
retail
1.303
2.946
1.685
48 and 49 Transportation
2.424
21-40
1.727
0.606
2.564
1.303
2.998
41-60
2.061
0.939
3.283
2.023
3.718
61-80
4.061
2.939
2.764
1.504
3.199
>80
5.121
4.000
7.609
6.348
8.043
RMSE
1.074
0.934
4.650
0.618
5.219
Employees
1-20
3.381
Source: Adapted from Holguín Veras et al. (5)
For a comparison between estimating methodologies and for a description of models estimated for other
industry classification systems and land use categories see Holguín-Veras et al. (1), Lawson et al. (6) and
Holguin-Veras et al. (7).
8
3.4
Conclusions and Implications
This paper provides an overview of the concepts of freight generation (FG) and freight trip generation
(FTG) and the corresponding modelling processes. The estimates obtained for FG are of major importance as
they are based on the largest and most complete establishment based freight survey in the world. The authors
are currently conducting similar analysis to obtain FP estimates for the whole country. These models are an
extraordinary contribution to the freight transportation modeling community and for practitioners, as they
provide accurate estimates of FG for a good number of industries at the establishment level.
Another important finding is the importance of treating FG and FTG as two different concepts. While FG is
the amount of cargo generated by an establishment, FTG is the number of freight vehicle trips required to
transport the cargo. In competitive markets, larger businesses are expected to produce proportionally more FG
than small ones, thus FG increases with business size. This is not necessarily the case for FTG, since logistics
decisions on shipment size, vehicle (and mode) choice and frequency of distribution come into play when
determining the number of truck trips generated.
Finally, while the FTG estimates were found to be transferable across different geographic contexts (7), this
is not the case for FG models.
3.5
1.
2.
3.
4.
5.
6.
7.
References
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Freight Trip Generation, and the Perils of Using Constant Trip Rates. Transportation Research
Record, Vol. 2224, No. 2011, pp. 68-81.
Ortúzar, J.D. and L.G. Willumsen Modelling Transport. New York, 2011.
Lawson, C., J. Holguín-Veras, I. Sanchez-Diaz, M. Jaller, S. Campbell, and E. Powers Estimation of
Freight Trip Generation Based on Land Use, (in press)
Campbell, S., M. Jaller, I. Sanchez-Diaz, J. Holguin-Veras, and C. Lawson. Comparison Between
Industrial Classification Systems in Freight Trip Generation (FTG) Modeling. In 91st Annual
Meeting of the Transportation Research Board. 2011: Washington, D.C
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Powers, and L. Tavasszy. NCHRP Report 739 / NCFRP Report 19: Freight Trip Generation and
Land Use. T.R.B.o.t.N. Academies, Washington D.C., 2012.
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2012, pp. 65-72.
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Transferability of Freight Trip Generation Models. Transport Research Record, Vol. (in print), No.
2013, pp.