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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2016. Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/24602.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

N A T I O N A L C O O P E R A T I V E F R E I G H T R E S E A R C H P R O G R A M NCFRP RESEARCH REPORT 37 Subscriber Categories Freight Transportation • Planning and Forecasting Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips Guidebook aRensselaeR Polytechnic institute bstate univeRsity of new yoRk at albany cuniveRsity of califoRnia, Davis José Holguín-Verasa Catherine Lawsonb Cara Wanga Miguel Jallerc Carlos González-Calderóna Shama Campbella Lokesh Kalahashtia Jeffrey Wojtowicza Diana Ramirez-Ríosa Research sponsored by the Office of the Assistant Secretary for Research and Technology 2017

NATIONAL COOPERATIVE FREIGHT RESEARCH PROGRAM America’s freight transportation system makes critical contributions to the nation’s economy, security, and quality of life. The freight trans- portation system in the United States is a complex, decentralized, and dynamic network of private and public entities, involving all modes of transportation—trucking, rail, waterways, air, and pipelines. In recent years, the demand for freight transportation service has been increas- ingly fueled by growth in international trade; however, bottlenecks or congestion points in the system are exposing the inadequacies of cur- rent infrastructure and operations to meet the growing demand for freight. Strategic operational and investment decisions by government at all levels will be necessary to maintain freight system performance and will, in turn, require sound technical guidance based on research. The National Cooperative Freight Research Program (NCFRP) is a cooperative research program sponsored by the Office of the Assistant Secretary for Research and Technology under Grant No. DTOS59- 06-G-00039 and administered by the Transportation Research Board (TRB). The program was authorized in 2005 with the passage of the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU). On September 6, 2006, a contract to begin work was executed between the Research and Innovative Technology Administration, which is now the Office of the Assistant Secretary for Research and Technology, and the National Academies of Sciences, Engineering, and Medicine. NCFRP carries out applied research on problems facing the freight industry that are not being adequately addressed by existing research programs. Program guidance is provided by an oversight committee composed of a representative cross section of freight stakeholders appointed by the Academies. The NCFRP Oversight Committee meets annually to formulate the research program by identifying the highest priority projects and defining funding levels and expected products. Research problem statements recommending research needs for consideration by the NCFRP Oversight Committee are solicited annually but may be submitted to TRB at any time. Each selected project is assigned to a panel, appointed by TRB, which provides technical guidance and counsel throughout the life of the project. Heavy emphasis is placed on including members representing the intended users of the research products. NCFRP produces a series of research reports and other products such as guidebooks for practitioners. Primary emphasis is placed on disseminating NCFRP results to the intended users of the research: freight shippers and carriers, service providers, suppliers, and public officials. Published research reports of the NATIONAL COOPERATIVE FREIGHT RESEARCH PROGRAM are available from Transportation Research Board Business Office 500 Fifth Street, NW Washington, DC 20001 and can be ordered through the Internet by going to http://www.national-academies.org and then searching for TRB Printed in the United States of America NCFRP RESEARCH REPORT 37 Project NCFRP-25(01) ISSN 1947-5659 ISBN 978-0-309-44619-8 Library of Congress Control Number 2017931442 © 2017 National Academy of Sciences. All rights reserved. COPYRIGHT INFORMATION Authors herein are responsible for the authenticity of their materials and for obtaining written permissions from publishers or persons who own the copyright to any previously published or copyrighted material used herein. Cooperative Research Programs (CRP) grants permission to reproduce material in this publication for classroom and not-for-profit purposes. Permission is given with the understanding that none of the material will be used to imply TRB, AASHTO, FAA, FHWA, FMCSA, FRA, FTA, Office of the Assistant Secretary for Research and Technology, PHMSA, or TDC endorsement of a particular product, method, or practice. It is expected that those reproducing the material in this document for educational and not-for-profit uses will give appropriate acknowledgment of the source of any reprinted or reproduced material. For other uses of the material, request permission from CRP. NOTICE The research report was reviewed by the technical panel and accepted for publication according to procedures established and overseen by the Transportation Research Board and approved by the National Academies of Sciences, Engineering, and Medicine. The opinions and conclusions expressed or implied in this report are those of the researchers who performed the research and are not necessarily those of the Transportation Research Board; the National Academies of Sciences, Engineering, and Medicine; or the program sponsors. The Transportation Research Board; the National Academies of Sciences, Engineering, and Medicine; and the sponsors of the National Cooperative Freight Research Program do not endorse products or manufacturers. Trade or manufacturers’ names appear herein solely because they are considered essential to the object of the report.

The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, non- governmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research. Dr. Marcia McNutt is president. The National Academy of Engineering was established in 1964 under the charter of the National Academy of Sciences to bring the practices of engineering to advising the nation. Members are elected by their peers for extraordinary contributions to engineering. Dr. C. D. Mote, Jr., is president. The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on medical and health issues. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president. The three Academies work together as the National Academies of Sciences, Engineering, and Medicine to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine. Learn more about the National Academies of Sciences, Engineering, and Medicine at www.national-academies.org. The Transportation Research Board is one of seven major programs of the National Academies of Sciences, Engineering, and Medicine. The mission of the Transportation Research Board is to increase the benefits that transportation contributes to society by providing leadership in transportation innovation and progress through research and information exchange, conducted within a setting that is objective, interdisciplinary, and multimodal. The Board’s varied committees, task forces, and panels annually engage about 7,000 engineers, scientists, and other transportation researchers and practitioners from the public and private sectors and academia, all of whom contribute their expertise in the public interest. The program is supported by state transportation departments, federal agencies including the component administrations of the U.S. Department of Transportation, and other organizations and individuals interested in the development of transportation. Learn more about the Transportation Research Board at www.TRB.org.

C O O P E R A T I V E R E S E A R C H P R O G R A M S AUTHOR ACKNOWLEDGMENTS This research was supported by NCFRP Project 25(01), “Estimating Freight Generation Using Commod- ity Flow Survey (CFS) Microdata.” This unique project, which involved unprecedented use of confidential data files for freight demand modeling, presented challenges not typically found in trans portation research. The team had to secure approval to conduct the research, obtain security clearances for all researchers that would use the data, travel to secure research data centers to use the data, follow strict disclosure procedures to release the results, and strictly follow Internal Revenue Service and U.S. Bureau of the Census guidelines for data protection. In following these lengthy and delicate procedures the team was fortunate enough to enjoy full support from the staff of multiple agencies, who provided invaluable guidance to the team. The team gratefully acknowledges the contributions from: Bureau of Transportation Statistics: Ronald Duych, Rolf Schmitt, Joy Sharp, Michael Sprung, and Edward Strocko; United States Bureau of the Census: Lynn Riggs, James Hinckley, Chester Ford, and Scot Dahl; and Census Bureau’s Research Data Center: Jonathan Fisher and Shirley H. Liu. Without their support and guidance, the team would not have been able to navigate the complex process of securing access to the confidential data used in the project. The team also extends thanks to the staff at the Capital District Transportation Committee (CDTC), in particular Michael V. Franchini and Vidya Mysore at FHWA, for their forward thinking to allow and provide funding to conduct freight trip generation and service trip generation surveys as part of the SHRP2 Implementation Assistance Program: Innovative Local Freight Data grant to the Capital District of New York State. Additionally, the team offers sincere thanks to Dr. David Hartgen and Dr. Hyeon-Shic Shin for sharing valuable data that aided in the creation of the final deliverables. A report of this breadth is always a joint effort. The Rensselaer and SUNY Albany team would like to acknowledge and thank the individuals not explicitly mentioned here who also contributed to NCFRP Project 25(01) with feedback and support. CRP STAFF FOR NCFRP RESEARCH REPORT 37 Christopher J. Hedges, Director, Cooperative Research Programs Lori L. Sundstrom, Deputy Director, Cooperative Research Programs William C. Rogers, Senior Program Officer Charlotte Thomas, Senior Program Assistant Eileen P. Delaney, Director of Publications Sharon Lamberton, Editor NCFRP PROjECT 25(01) PANEL Freight Research Projects Anastassios N. Perakis, University of Michigan, Ann Arbor, MI (Chair) Thomas W. Brahms, Institute of Transportation Engineers, Washington, DC Carolyn Clevenger, Metropolitan Transportation Commission–Oakland, Oakland, CA Quiang “Wesley” Hong, Center for Automotive Research, Ann Arbor, MI Bruce Lambert, Institute for Trade and Transportation Studies, New Orleans, LA Douglas D. MacIvor, California DOT, Sacramento, CA Bruce Xiubin Wang, Texas A&M University, College Station, TX Spencer Stevens, FHWA Liaison Jienki Synn, FHWA Liaison Thomas Palmerlee, TRB Liaison Ronald J. Duych, Office of the Assistant Secretary for Research and Technology Liaison

NCFRP Research Report 37 provides policy makers with improved establishment-level models that estimate the freight trip generation (FTG), the number of vehicle trips produced and attracted at a given establishment; the freight production (FP), the amount of cargo produced by the establishment; and the service trip attraction (STA), the number of vehicle trips that arrive at the establishment to perform a service activity. These models, estimated with the best data available, provide tools to assess the various facets of the overall freight and service activity (FSA) that takes place in urban and metropolitan areas. The models will allow transportation practitioners to conduct sound curb-management, properly size loading and unloading areas, support traffic impact analyses, and improve transportation planning and management efforts. Cities and metropolitan areas are large economic markets where goods and services of all kinds are traded. The resulting spatial concentration of human and economic activities provides the residents of these areas with access to myriad products and services they need without having to travel long distances. Undoubtedly, FSA is a major contributor to quality of life and economic prosperity; on the other hand, inefficiencies in the transport of freight or the provision of services will negatively impact a large portion of the metropolitan economy. Identifying the most appropriate ways to ensure that FSAs fulfill their economic roles, while minimizing the negative externalities produced, requires solid data about the magnitude of the activity, both current and future. Having solid estimates of the generation of freight trips and service trips is critically important. Without understanding why and how many FSA trips take place, it is impossible to make sound decisions concerning infra- structure needs, or to define policies aimed at mitigating the congestion generated by FSA. Compounding this challenge is the lack of research and data for FSA, and a lack of appropriate modeling methodologies. In NCFRP Project 25(01), Rensselaer Polytechnic Institute (RPI) was asked to build on NCHRP Report 739/NCFRP Report 19: Freight Trip Generation and Land Use, to (1) investigate the use of the Commodity Flow Survey (CFS) microdata to develop FP models, (2) conduct case studies, and (3) develop a guidebook. In carrying out these tasks, the research team estimated FP models using the CFS microdata, collected additional FTG and STA data using pooled funds from project SHRP2 C-20, estimated new FTG models that supersede those in NCHRP Report 739/NCFRP Report 19, and estimated an entirely new set of STA models, the first STA models in the research literature. The models in NCFRP Research Report 37 provide improved ways to quantify and manage FSA to ensure they fulfill their economic functions while generating a minimal amount of externalities. F O R E W O R D By William C. Rogers Staff Officer Transportation Research Board

1 Summary 8 Chapter 1 Overview 9 Chapter 2 Key Concepts 9 The Freight System 12 Relationship Between the Freight System and Land Use 14 Chapter 3 Background 14 Urban and Metropolitan Economies 17 Chapter 4 The Generation of Freight, Freight Trips, and Service Trips 18 Generation by Industry Sector 19 Chapter 5 Modeling Principles 20 Establishment-Level (Disaggregate) Models 20 Economic-Based Nature of the Models 20 Applicability to Various Land Use Configurations 22 Chapter 6 Modeling Methodology 22 Model Typology 22 Econometric Models 24 FTG Rates by Employment Bin 24 Aggregation Procedures 24 Case 1: Establishment-Level Data Are Available 26 Case 2: Only Aggregate Data Are Available 27 Data Used 28 CFS Microdata 28 2008 Data and Models 29 Establishment Survey Data 30 Limitations 31 Chapter 7 How to Apply the Models 31 Practical Uses (When to Use What) 31 Typical Applications 32 Data Inputs 33 Model Outputs 34 Separate Models Are Available for Attractions and Productions 35 Only a Generation Model Is Available (Attractions Plus Productions) 35 Step-by-Step Process 36 Scenario 1: Single Establishment (Establishment-Level Data Available, Linear/Non-Linear Models) 36 Scenario 2: Complete Enumeration (Multiple Establishments; Establishment-Level Data Available for All; Linear/Non-Linear Models) C O N T E N T S

37 Scenario 3: Sample Estimation (Multiple Establishments; Establishment-Level Data Available for a Sample; Linear/Non-Linear Models) 37 Scenario 4: Only Aggregate Data (Multiple Establishments; Only Aggregate Data Available; Linear Models) 38 Chapter 8 Freight, Freight Trip, and Service Trip Generation Models 38 Technical Note 1: Interpretation of R2 38 Technical Note 2: Bias Correction in Non-Linear Models 39 Technical Note 3: Statistical Significance and Conceptual Validity 39 FTG Models (RPI) 39 FTA (Deliveries/Day), Linear Models 41 FTA (Deliveries/Day), Non-Linear Models 42 FTP (Shipments/Day), Linear Models 43 FTP (Shipments/Day), Non-Linear Models 44 FTG Models (Hartgen) 44 FTG (Vehicles/Day) Rates 45 STG Models (RPI) 45 STA (Service Calls/Day), Linear Models 46 STA (Service Calls/Day), Non-Linear Models 48 FG Models (RPI) 48 FA (Pounds/Day), Linear Models 49 FA (Pounds/Day), Non-Linear Models 50 FP (Pounds/Day), Linear Models 50 FP (Pounds/Day), Non-Linear Models 51 FTG as a Function of FG (RPI) 51 FTA (Vehicle Trips/Day) as a Function of FA (Pounds/Day), Linear Models 52 FTA (Vehicle Trips/Day) as a Function of FA (Pounds/Day), Non-Linear Models 52 FTP (Vehicles/Day) as a Function of FP (Pounds/Day), Linear Models 53 FTP (Vehicles/Day) as a Function of FP (Pounds/Day), Non-Linear Models 53 FP Models, New York State (CFS) 53 FP (Pounds/Year) 2-Digit NAICS (All Modes), Linear Models 54 FP (Pounds/Year) 3-Digit NAICS (All Modes), Linear Models 55 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Linear Models 55 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Linear Models 56 FP (Pounds/Year) 2-Digit NAICS (All Modes), Non-Linear Models 58 FP (Pounds/Year) 3-Digit NAICS (All Modes), Non-Linear Models 61 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Non-Linear Models 62 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Non-Linear Models 64 FP Models, California (CFS) 64 FP (Pounds/Year) 2-Digit NAICS (All Modes), Linear Models 65 FP (Pounds/Year) 3-Digit NAICS (All Modes), Linear Models 66 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Linear Models 66 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Linear Models 67 FP (Pounds/Year) 2-Digit NAICS (All Modes), Non-Linear Models 69 FP (Pounds/Year) 3-Digit NAICS (All Modes), Non-Linear Models 72 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Non-Linear Models 74 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Non-Linear Models 77 FP Models, Texas (CFS) 77 FP (Pounds/Year) 2-Digit NAICS (All Modes), Linear Models 78 FP (Pounds/Year) 3-Digit NAICS (All Modes), Linear Models

79 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Linear Models 80 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Linear Models 81 FP (Pounds/Year) 2-Digit NAICS (All Modes), Non-Linear Models 83 FP (Pounds/Year) 3-Digit NAICS (All Modes), Non-Linear Models 86 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Non-Linear Models 88 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Non-Linear Models 90 FP Models, Wyoming (CFS) 90 FP (Pounds/Year) 2-Digit NAICS (All Modes), Linear Models 91 FP (Pounds/Year) 3-Digit NAICS (All Modes), Linear Models 91 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Linear Models 91 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Linear Models 92 FP (Pounds/Year) 2-Digit NAICS (All Modes), Non-Linear Models 93 FP (Pounds/Year) 3-Digit NAICS (All Modes), Non-Linear Models 94 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Non-Linear Models 95 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Non-Linear Models 96 FP Models, Ohio (CFS) 96 FP (Pounds/Year) 2-Digit NAICS (All Modes), Linear Models 97 FP (Pounds/Year) 3-Digit NAICS (All Modes), Linear Models 98 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Linear Models 99 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Linear Models 100 FP (Pounds/Year) 2-Digit NAICS (All Modes), Non-Linear Models 102 FP (Pounds/Year) 3-Digit NAICS (All Modes), Non-Linear Models 105 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Non-Linear Models 107 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Non-Linear Models 110 FP Models, United States (CFS) 110 FP (Pounds/Year) 2-Digit NAICS (All Modes), Linear Models 111 FP (Pounds/Year) 3-Digit NAICS (All Modes), Linear Models 112 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Linear Models 113 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Linear Models 114 FP (Pounds/Year) 2-Digit NAICS (All Modes), Non-Linear Models 116 FP (Pounds/Year) 3-Digit NAICS (All Modes), Non-Linear Models 119 FP (Pounds/Year) 2-Digit NAICS (Road Modes), Non-Linear Models 121 FP (Pounds/Year) 3-Digit NAICS (Road Modes), Non-Linear Models 124 Chapter 9 Illustrative Applications 124 Application 1: Quantification of Commercial Parking Needs for a Commercial Center 126 Application 2: FTG Trends at the County/Borough Level 127 Application 3: FTG Analyses to Support Development of Freight Models 129 Application 4: FG Analyses at the State/MPO Level 132 References 134 Appendix A Descriptive Statistics for the Sample Used to Estimate the RPI Models 146 Appendix B Final Models Including Statistics 236 Appendix C Survey Used to Collect FSA Data Note: Photographs, figures, and tables in this report may have been converted from color to grayscale for printing. The electronic version of the report (posted on the web at www.trb.org) retains the color versions.

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TRB's 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 provides policy makers with improved establishment-level models that estimate the Freight Trip Generation (FTG), the number of vehicle trips produced and attracted at a given establishment; the Freight Production (FP), the amount of cargo produced by the establishment; and the Service Trip Attraction (STA), and the number of vehicle trips that arrive at the establishment to perform a service activity. These models, estimated with the best data available, provide tools to assess the various facets of the overall Freight and Service Activity (FSA) that takes place in urban and metropolitan areas. The models will allow transportation practitioners to conduct sound curb-management, properly size loading and unloading areas, support traffic impact analyses, and improve transportation planning and management efforts.

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