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Commodity Flow Survey Microdata to Estimate the Generation of Freight, Freight Trips, and Service
Trips
Tuesday, September 26, 2017 1:00-2:30 PM ET
TRANSPORTATION RESEARCH BOARD
The Transportation Research Board has met the standards and
requirements of the Registered Continuing Education Providers Program.
Credit earned on completion of this program will be reported to RCEP. A
certificate of completion will be issued to participants that have registered
and attended the entire session. As such, it does not include content that
may be deemed or construed to be an approval or endorsement by RCEP.
Purpose Discuss research from National Cooperative Freight Research Program (NCFRP) Research Report 37: Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook.
Learning Objectives At the end of this webinar, you will be able to: • Describe the reasoning behind the Freight Trip Generation Models. • Discuss the main outcomes of Freight Trip Generation to improve space
management in urban areas. • Identify the uses that Freight Trip Generation Techniques could provide
to planning organizations.
Use of the Commodity Flow Survey and Establishment-Level Data to
Estimate the Generation of Freight, Freight Trips, and Service Trips
1
Moderator: Jeffrey M. Wojtowicz
Presenters: Catherine T. Lawson, University at Albany, SUNY
José Holguín-Veras, Rensselaer Polytechnic Institute Eulois Cleckley, Houston-Galveston Area Council
Overview
Part I: Basic Concepts and Rationale, Dr. Catherine T. Lawson
Part II: Modeling Process and Applications Dr. José Holguín-Veras
Part III: Using FTG Models in Planning Decisions Mr. Eulois Cleckley
Part IV: Additional Resources Available
Questions and Answers
2
Part I: Basic Concepts and Rationale
Dr. Catherine T. Lawson, Associate Professor
University at Albany [email protected]
Overview
Basic concepts Advantages of Using Economic Based Models,
(Formal) Industrial Classification Systems, and Establishment-Level Models
Mapping economic models into land use classes
4
Basic Concepts
Objective of FG/FTG/STG analyses
To produce quantitative estimates of the amount of freight, freight vehicle-trips, and service vehicle-trips, being generated (produced and attracted) as a function of economic variables (e.g., employment, total sales, industry types) Important for long term planning, analyses of parking
demand, traffic impact analyses, and land use planning at a fine level of detail
6
Key concepts
Generation of demand/cargo (FG): A manifestation of the production/consumption processes Implication: FG will increase with (economic) inputs
Generation of traffic (FTG): Result of logistical decisions Implication: FTG do not necessarily increase with (economic)
inputs (shippers can increase shipment size instead…)
Service Trip Generation (STG) A manifestation of the amount of services received or
produced by an establishment
7
Key concepts
Generation of vehicle-trips depends on mode choice Demand
Mode Choice Vehicle trips (either transit or car)
In freight, the situation is similar Demand (Freight generation)
Freight mode choice/shipment size Number of deliveries and shipments out Vehicle-trips (Freight trip generation)
8
Practical Uses of the FG/FTG/STG Models
FTA FTP FTG STA STP STG FA FP FGTraffic Impact Analysis Number of parking spaces needed by freight vehicles Number of parking spaces needed by service vehicles Number of parking spaces needed by commercial vehicles Analysis of trends in freight activity Analysis of trends in FSA Estimation of freight trip generation Estimation of service traffic generation Estimation of freight generation
Freight Trip Generation
Service Trip Generation
Freight Generation Description
Disaggregate vs. Aggregate Models
Aggregate models area-wide FG/FTG Advantages:
Experience in data collection Disadvantages:
Depend on the zoning system, require large samples Frequently ignore small freight vehicles (80% of total)
Disaggregate models establishment-level FG/FTG Advantages:
Can be used at all levels of geography: building, block, neighborhood, area, city, etc.
Smaller samples than the ones for aggregate models Disadvantages: None to date…
10
Land Use Concepts
ITE trip generation manual codes Local land use planning codes National-level universal coding strategies Standard Land Use Coding Manual (SLUCM) Land-based Classification Standards (LBCS)
Employment categories operating on a site Standard Industrial Codes (SIC) North American Industry Classification System (NAICS)
11
Local Land Use Planning Codes
Local land use codes Planning inventories Zoning maps Related land use planning processes and products
Codes adopted by ordinance to classify permitted activities on parcels and across zoned areas
Coding schemes can be unique to each jurisdiction This uniqueness prevents transferability
12
National-level universal coding strategies
Standard land use coding manual (SLUCM) Used in the 1960s to implement urban renewal programs Updated in 1990s
Land-based Classification Standards (LBCS) Developed by FHWA, American Planning Association (APA)
and others Five classification dimensions: activity; function; structural
character; site development character; and ownership Geographic Information Systems (GIS)/ACCESS-based Can be applied in any jurisdiction using telescoping coding
scheme (e.g., 2-digit, 4-digit)
13
14
Source: American Planning Association
ITE Trip Generation Manual
Single designated numerical code Associated with a descriptive title for the physical
characteristics of the site where the trips were counted
Assumes passenger and freight trips share the same behavioral mechanisms
Long tradition of use for passenger trip generation by transportation planners
Major challenge: Square footage and variables like it are poor predictors of
freight and service activity Reason they do not capture the size and intensity of the
economic activity being performed
15
Advantages of Using Economic Based Models, (Formal) Industrial Classification Systems, and
Establishment-Level Models
Economic Classification Systems
Economic Classification Systems (NAICS, SIC, etc.) cluster commercial establishments taking into account the nature of activity
17 NA
ICS
Freight-intensive Sectors (FIS) NA
ICS
Service-intensive Sectors
11 Agriculture, Forestry, Fishing, Hunting 51 Information21 Mining, Quarrying, Oil / Gas… 52 Finance and Insurance22 Utilities 53 Real Estate and Rental and Leasing23 Construction 54 Professional,Scientific,Tech. Services
31-33 Manufacturing 55 Management of Companies / 42 Wholesale Trade 56 Administrative,Support,Waste Manag.
44-45 Retail Trade 61 Educational Services48-49 Transportation and Warehousing 62 Health Care and Social Assistance
72 Accommodation and Food Services 71 Arts, Entertainment, and Recreation81 Other Services 92 Public Administration
45% of establishments and about half the employment are in FIS
Who produces what?
18
NAICS Description Freight
Generation (FG)
Freight Trip Generation
(FTG)
Service Trip Generation
(STG)
11 Agriculture, Forestry, Fishing and Hunting +++ + +21 Mining, Quarrying, and Oil and Gas Extraction +++ + +22 Utilities ++ + +23 Construction +++ + +
31-33 Manufacturing ++ ++ +42 Wholesale Trade ++ +++ ++
44-45 Retail Trade ++ +++ ++48-49 Transport and Warehousing ++ ++ ++
72 Accommodation and Food Services ++ +++ ++
51 Information + + ++52 Finance and Insurance + + ++53 Real Estate and Rental and Leasing + + ++54 Professional, Scientific, and Technical Services + + +++55 Management of Companies and Enterprises + + ++56 Administrative, Waste Management… + + ++61 Educational Services + + ++62 Health Care and Social Assistance + + ++71 Arts, Entertainment, and Recreation + + ++81 Other Services (except Public Administration) + + ++
non-Freight Intensive Sectors (non-FIS)
Freight Intensive Sectors (FIS)
Advantages of NAICS in FG/FTG/STG models
Model quality increases because employment by industry sector is a better predictor than square footage of broadly defined land use types
The resulting models can use employment data that are routinely collected for other purposes ZIP Code / County Business Patterns Commodity Flow Survey Microdata
National sample of freight-producing firms
If collecting data is desired small samples could work because NAICS creates homogeneous groups Sample survey instruments are available for use
19
Advantages of Using Establishment-Level Models
They capture fairly well the relations between FG, FTG, STG and employment level
Can be estimated with relatively small samples Can be aggregated to any level of geography (e.g.,
building, blocks, ZIP codes, counties) means of suitable processes of aggregation
Since business practices are relatively the same across the country, for the same industry sectors, the models are likely to be transferable
20
Mapping Economic Models into Land Use Classes
Models Applied with Weights to Zoning… 22
FG model 7 FTG model 7
FG model 8 FTG model 8
FG model 1 FTG model 1
FG model 2 FTG model 2
FG model 3 FTG model 3
FG model 4 FTG model 4
FG model 5 FTG model 5
FG model 6 FTG model 6
FG and FTG models estimated for industry sectors (NAICS)
Land use classes defined and used by a particular planning jurisdiction
FG model 9 FTG model 9
FG model 10 FTG model 10
Retail
Light Industry
Office
W1,R W2,R
W4,R
W3,LI
W5,LI
W7,O
W8,O W9,O
W6,LI
W10,O
Part II: Modeling Process and Potential Applications
José Holguín-Veras, Ph.D., P.E., F.ASCE William H. Hart Professor
Director of the Center for Infrastructure, Transportation, and the Environment, and the
VREF Center of Excellence for Sustainable Urban Freight Systems [email protected]
Overview
Motivation and Basic Concepts Freight and Service Trip Generation (FTG and STG) Freight Generation Models (FG) from the CFS Spatial Aggregation of FG, FTG, and STG Models Sample Applications
24
Motivation and Basic Concepts
Measures of Freight and Service Activity (FSA)
Freight Generation (FG)Amount of cargo in/out Freight Attraction (FA) Cargo in Freight Production (FP) Cargo out
Freight Order Generation (FOG) Orders in/out Freight Deliveries (FD) Orders in Freight Shipments (FP) Orders out
Freight Trip Generation (FTG) Veh.-trips in/out Freight Trip Attraction (FTA) Freight trips in Freight Trip Production (FTP) Freight trips out
Service Trip Generation (STG) Services in/out Service Trip Attraction (STA) To service the place Service Trip Production (STP) To service others
26
Rec
eive
r dr
iven
C
arrie
r dr
iven
R
ecei
ver
driv
en
Freight, Freight Deliveries, and Freight-Trips 27
Base
4
Loaded vehicle-trip
Commodity flow
Notation:
Producer or Receiver of cargo
Empty vehicle-trip
Commodity Flows
Vehicle-Trips
1
2 3
5
One delivery one freight trip
Three deliveries one freight trip
Types of Models Estimated 28
Non-Linear Models (NL)
Constant and Employment Rate (C-ER)
Constant (C)
Measure of freight or service activity (FSA): - FO: FD, FS - STG: STA, STP - FG: FA, FP
Establishment employment
Employment Rate (ER)
Overview of Models Estimated (Selected)
29
2-digit NAICS
3-digit NAICS
2-digit NAICS
3-digit NAICS
Freight Order Generation (FOG) - - - -Freight Deliveries (FD) 35 - 35 -Freight Shipments (FS) 31 - 31 -Service Trip Generation (STG) - - - -Service Trip Attraction (STA) 59 - 59 -Service Trip Production (STP) - - - -Freight Generation (FG) (CFS) - - - -Freight Attraction (FA) - - - -Freight Production (FP) 91 251 279 759Total 271 251 486 759
Model TypeLinear Non-LinearMetric of Freight and Service
Activity (FSA)
Freight and Service Trip Generation (FTG and STG)
Freight Orders vs. Freight Trips 31
Inputs (FA) Outputs (FP)
Inputs (FA) and outputs (FP) are transported using the combination of order frequency and shipment sizes that minimizes the logistic costs of the receiver (customer)
The orders are sent to carriers that consolidate
orders from multiple customers and schedule a delivery / pick-up tours
Modeling strategy To model Freight Deliveries (FD) and Freight
Shipments (FS) at the establishment-level Obtain FTA and FTP from FD and FS Delivery and Shipment patterns (and Services received) are
relatively stable across the countryand service FD and FS models can be used across the nation The Conversion Factors (CF) account for the role played by
the concentration of freight activity and logistical aspects
32
FTA = FD
CFFTA Deliveries Received
Avg. Deliveries per trip =
FTP = FS
CFFTP Shipments Sent Out
Avg. Shipments per trip =
What we know about conversion factors…
To convert FD (deliveries received) into FTA Isolated establishments,
and competitive markets with many vendors/carriers Conversion Factor (CFFTA)=1 Not possible to consolidate
Large multitenant buildings (CFFTA) >>1 Multiple carriers: CFFTA =3-15 Parcel carriers: CFFTA =3-80
To convert FS (shipments sent out) into FTP Isolated establishments
Conversion Factor (CFFTP)>1 It is possible to consolidate
Large multitenant buildings (CFFTP) >>1 Not much is known
33
Additional research to quantify Conversion Factors for multitenant buildings is needed…
(In the case of Service Trips to Establishments CF =1)
Modeling Results (Examples)
Freight Order Generation (per day) 35
NAICS/Description Inter-cept
Employment
Coeff.
Inter-cept
Employment
Coeff.23 - Construction 2.160 0.06831 - 33 Manufacturing 2.831 2.214 31 - Light Manufacturing 2.400 2.846 32 - Medium Manufacturing 4.420 0.023 33 - Heavy Manufacturing 2.490 1.75042 - Wholesale Trade 2.272 0.069 1.755 0.03644 - 45 Retail Trade 3.070 0.063 0.161 44 - Furniture, food, beverage, tobacco… 2.458 0.132 0.993 0.021 45 - Wood, paper, printing, chemicals 2.72448 - 49 Transportation and Warehousing 2.718 0.038 48 - Transportation and Warehousing* 2.725 0.03872 - Accommodation and Food 1.307 0.081
Freight Deliveries Freight Shipments
n/a
n/a
n/a
Notes: * Except postal and courier services, and warehousing
FD and FS are
constant FD and FS have a constant and an
employment term
FD and FS depend on
employment
Service Calls = Service Trip Attraction (per day)
36
NAICS/Description Inter-cept
Employ-ment
Coeff.23 - Construction 0.00431 - 33 Manufacturing 0.236 31 - Light Manufacturing 0.197 32 - Medium Manufacturing 0.251 33 - Heavy Manufacturing 0.23042 - Wholesale Trade 0.30444 - 45 Retail Trade 0.010 44 - Furniture, food, beverage, tobacco… 0.012 45 - Wood, paper, printing, chemicals 0.17454 - Professional and Technical Services 0.391 0.00162 - Health Care and Social Assistance 1.17972 - Accommodation and Food 0.022
FOG for FIS, and STA for all sectors
37
0
20
40
60
80
100
120
1 10 100 1000
Frei
ght o
rder
s (sh
ipm
ents
(in+
out)
/day
) or
Se
rvic
e ca
ll/da
y
Establishment Employment in FTE
FTG-H FTG-RPI STA
STA Models NCFRP 37
Freight Order Models NCFRP 37
External Validation Data in FTG
Freight Generation Models (FG) from the Commodity Flow Survey
The Commodity Flow Survey
Shipper based survey-Part of the Economic Census Sample size~100,000 establishments Designed to quantify regional trade flows Not designed for detailed transportation analysis Key input for regional freight demand modeling
For each shipment, the CFS collects: Origin, destination, mode Commodity type, value, and weight of shipment Hazardous material information: (UN/NA) code, etc.
The CFS only collects data about FP, not FA…
39
The Longitudinal Business Database (LBD)
Contains characteristics of all establishments in the US Over 8 million records Variables (selected): Establishment size (Employment) Industry type (NAICS) Firm location Start (birth) year Death year Annual payroll
The combination of the CFS and LBD forms the dataset used for FP modeling
40
Modeling Results (Examples)
Examples: 3 digit NAICS, Linear, All Modes, t/yr 42
Sample β t-stat F-stat Obs.
California 15,647.25 3.52 12.36 65New York 13,063.22 5.39 29.05 65Ohio 18,717.00 5.11 26.15 85Texas 24,344.80 8.07 65.11 60Wyoming 76,615.03 3.92 15.40 15United 15,475.22 4.52 20.39 1550
California 58.93 6.15 37.87 140New York 72.32 6.89 47.50 90Ohio 75.02 7.88 62.14 165Texas 144.31 5.83 33.97 115Wyoming - - - -United 86.86 23.94 572.90 2145
California 9.47 2.94 8.67 105New York 20.42 1.65 2.72 95Ohio 124.55 2.32 5.39 140Texas 46.79 5.50 30.30 130Wyoming - - - -United 46.96 6.84 46.75 2075
Machinery Manufacturing (NAICS 333)
Plastics and Rubber Products Manufacturing
Mining, except Oil and Gas (NAICS 212)
Freight Production pattern is statistically the same
CFS sample design could exploit this …
Spatial Aggregation of FSA Models
43
Models can be aggregated… 44
…
Establishments
Buildings Census tracts … ZIP Codes
Long Beach
Spatial aggregation
The process by which zonal level estimates are produced from a disaggregate model
The aggregation process depends on type of model:
Case 1: fi is proportional to employment level
Case 2: fi is constant per establishment
45
*
11EEEF
n
ii
n
ii βββ === ∑∑
==
( )∑=
===n
iifentEstablishmperFSAFFSAAggregate
1
( )ii EentEstablishmatEmploymentf == β
α=if
αα nFn
i== ∑
=1
Spatial aggregation
Case 3: fi is a combination of a constant and a term that depends on employment level
The correct spatial aggregation procedure depends on the underlying disaggregate model
Different industry sectors have different FTG patterns, different aggregation procedures must be used if not, the estimation errors could be very large…
46
ii Ef βα +=
*
11)( i
n
ii
n
ii EnEnEF βαβαβα +=+=+= ∑∑
==
Sample Applications
FTG vs. Establishment Size 48
0%5%
10%15%20%25%30%35%40%45%50%
Perc
ent o
f tot
al F
TG
Establishment employment
NY-Northern NJ-Long IslandPalm Bay-Melbourne-Titusville, FLFargo, ND-MNLebanon, PA
Large Freight Traffic Generators 49
Freight Trip Generation by ZIP Code
50
Long Beach Portland
Houston DC-Baltimore
Freight Production by ZIP Code
Freight Production by ZIP Code 2007
52
Freight Production by ZIP Code 2011
53
Part III: Using FTG Models in Planning Decisions
Eulois Cleckley, Deputy MPO Director
Houston-Galveston Area Council Houston, TX
How to create a sustainable urban freight system?
55
Help to get the product to the consumer at the right time, place and price
Proper use of Public Space/Infrastructure
Implementation in Dense Urban Areas
The District (Dense Urban Area)
Approximately 640,000 residents Washington, DC Metro Region is the 7th largest
consumer market Land use pressure; increase in mixed-use
development projects Improvement to transportation system and modal
options (Bridges, Bikes, Boats, Streetcar) Transitioning demographics District residents are highly sensitive to freight uses
57
Challenges
Limited freight data at block level High demand for curb space Ambiguity within the process for allocating curbside
space for freight needs
58
Loading Zones Supporting Local Economy
$16.3 billion or 27% of city’s revenues are generated within 200 feet of existing loading zones (580 total loading zones in DC)
129,950 jobs directly or tangentially affected by truck represent 15.8% of the 823,000 jobs in the District (in 2011)
Business revenues citywide are $60.1 billion Generated by roughly 60,000 businesses
Average revenues per business is $1.2 million
59
System of Loading Zones in DC
Commercial Loading Zone Program Established new regulation requiring carriers to pay for
parking in loading zones Inventoried of over 580 loading zones in the city Conducted extensive industry outreach Developed data collection and evaluation procedures Provided detail information for each loading zone for carriers Developed new signage
60
Program Outcomes (products) 61
Interactive Freight Map Asset Management
New Signage
FTG Uses: Loading Zone Analysis
A sustainable, consistent, and repeatable process to uniformly evaluate curbside loading zone needs.
Freight Trip Generation (FTG) models are used to estimate the delivery needs for business establishments at a block face level.
62
FTG Benefits
Begins to better understand impact of deliveries on transportation network
Improved data will aid with right-sizing curb space Supports justification of procedures and policies Feeds into other program initiatives Project development Off-Hours Delivery Program
63
DC Deliveries
In DC:
64
FTG = Number of Curbside Deliveries
SIC Code # of Businesses % of Total FTG % of Total Industry58 2949 6.15% 11043.398 17.54% Eating and Drinking80 5829 12.16% 6339.238 10.07% Health Services59 1358 2.83% 5063.175 8.04% Miscellaneous Retail81 5186 10.82% 4571.491 7.26% Legal Service 54 706 1.47% 3578.336 5.68% Food Stores
Implementation in Less Dense Urban Areas
Houston’s Regional Freight System
Over 6 million residents 12,500 square miles 4 major deep water ports 315 million tons port cargo 25,000 miles of roadway 21,500 miles of pipeline 3 Class I Railroads 2 major airports Approximately half of region’s
employment is generated by the freight industry
66
Major Issue: Congestion
173 million miles driven daily
Truck traffic is forecasted to increase by 77% by 2040
45 of the top 100 most congested roads in the state
Over 4.9 billion in annual truck congestion cost
67
Source: Cambridge Systematics, Inc., based on IHS Global Insight TRANSEARCH data.
FTG Uses/Benefits: Regional Model Development
Improves data for travel demand model Refines trip rates for attractions
Aids with tour based truck model Primary truck route and critical urban freight corridor
designation Aids with analyzing projects for freight funding
eligibility
68
Greater Houston Freight System 69
Closing Remarks
Equitable allocation of curbside space requires an understanding of the number of freight and service trips that take place, and their parking durations
The increasing numbers of internet deliveries to households, together with traditional + internet deliveries to businesses stress the importance of proper curb management
The availability of FTG and STG models that use public data provides tremendous support to curb space management
See “Use of Freight Trip Generation Techniques to Manage Curb Space” at https://coe-sufs.org/wordpress/peer-to-peer-exchange-program/webinar16/
70
Part IV: Additional Resources Available
Jeffrey M. Wojtowicz Senior Research Engineer
Center for Infrastructure, Transportation, and the Environment, and the VREF Center of Excellence for Sustainable Urban Freight Systems
Entrepreneurial Ecosystem Atlas http://eea.availabs.org/
72
http://eea.availabs.org/
The Entrepreneurial Ecosystem Web Tool provides a regional view of NAICS clusters by ZIP code.
Entrepreneurial Ecosystem Web Tool
Albany-Schenectady-Troy MSA – ZIP Business Patterns
Albany-Schenectady-Troy MSA – ZIP Business Patterns
Albany-Schenectady-Troy MSA – ZIP Business Patterns
Commercial Establishment Freight and Service Activity Generation Software
https://coe-sufs.org/wordpress/software/
Overview Web application that generates zip code and business
level estimates of FSA at 2- and 3-digit NAICS codes The software currently uses two types of data: ZIP code level data Individual business-level data
The software estimates: FP: Freight Production (pounds/day) FA: Freight Attraction (pounds/day) CFS-FP: Commodity Flow Survey FP (pounds/yr) FS: Freight Shipments (shipments sent/day) FD: Freight Deliveries (deliveries received/day) STA: Service Trip Attraction (vehicle trips/day)
Screenshots
Screenshots
Acknowledgements
Acknowledgements
The financial support of the National Cooperative Freight Research Program (NCFRP) and National Cooperative Highway Research Program (NCHRP), and the guidance and support of Dr. William Rogers (NAS/TRB) are acknowledged and appreciated
Other contributors to the research were: Cara Wang, Jeff Wojtowicz, Shama Campbell, Lokesh Kalahasthi, Herbert Levinson (deceased), Diana Ramirez-Ríos, Miguel Jaller, Ivan Sanchez-Diaz, Carlos González-Calderón, and Erica Powers.
82
Questions? Thanks!
Today’s Participants • Jeffrey Wojtowicz, Rensselaer Polytechnic Institute,
[email protected] • Jose Holguin-Veras, Rensselaer Polytechnic Institute,
[email protected] • Catherine Lawson, University at Albany, SUNY,
[email protected] • Eulois Cleckley, Houston-Galveston Area Council,
Panelists Presentations
http://onlinepubs.trb.org/onlinepubs/webinars/170926.pdf
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