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2020 N A T I O N A L C O O P E R A T I V E H I G H W A Y R E S E A R C H P R O G R A M NCHRP RESEARCH REPORT 934 Traffic Forecasting Accuracy Assessment Research Greg Erhardt Jawad Hoque Mei Chen Reginald Souleyrette University of KentUcKy Lexington, KY David Schmitt Ankita Chaudhary Sujith Rapolu Kyeongsu Kim connetics transportation GroUp Orlando, FL Steve Weller Jacobs Alexandria, VA Elizabeth Sall UrbanLabs Seattle, WA Martin Wachs University of caLifornia, Los anGeLes Los Angeles, CA Subscriber Categories Highways â¢ Operations and Traffic Management â¢ Planning and Forecasting Research sponsored by the American Association of State Highway and Transportation Officials in cooperation with the Federal Highway Administration
NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM Systematic, well-designed, and implementable research is the most effective way to solve many problems facing state departments of transportation (DOTs) administrators and engineers. Often, highway problems are of local or regional interest and can best be studied by state DOTs individually or in cooperation with their state universities and others. However, the accelerating growth of highway transporta- tion results in increasingly complex problems of wide interest to high- way authorities. These problems are best studied through a coordinated program of cooperative research. Recognizing this need, the leadership of the American Association of State Highway and Transportation Officials (AASHTO) in 1962 ini- tiated an objective national highway research program using modern scientific techniquesâthe National Cooperative Highway Research Program (NCHRP). NCHRP is supported on a continuing basis by funds from participating member states of AASHTO and receives the full cooperation and support of the Federal Highway Administration (FHWA), United States Department of Transportation, under Agree- ment No. 693JJ31950003. The Transportation Research Board (TRB) of the National Academies of Sciences, Engineering, and Medicine was requested by AASHTO to administer the research program because of TRBâs recognized objectivity and understanding of modern research practices. TRB is uniquely suited for this purpose for many reasons: TRB maintains an extensive com- mittee structure from which authorities on any highway transportation subject may be drawn; TRB possesses avenues of communications and cooperation with federal, state, and local governmental agencies, univer- sities, and industry; TRBâs relationship to the National Academies is an insurance of objectivity; and TRB maintains a full-time staff of special- ists in highway transportation matters to bring the findings of research directly to those in a position to use them. The program is developed on the basis of research needs iden- tified by chief administrators and other staff of the highway and transportation departments, by committees of AASHTO, and by the FHWA. Topics of the highest merit are selected by the AASHTO Special Committee on Research and Innovation (R&I), and each year R&Iâs recommendations are proposed to the AASHTO Board of Direc- tors and the National Academies. Research projects to address these top- ics are defined by NCHRP, and qualified research agencies are selected from submitted proposals. Administration and surveillance of research contracts are the responsibilities of the National Academies and TRB. The needs for highway research are many, and NCHRP can make significant contributions to solving highway transportation problems of mutual concern to many responsible groups. The program, however, is intended to complement, rather than to substitute for or duplicate, other highway research programs. Published research reports of the NATIONAL COOPERATIVE HIGHWAY 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 NCHRP RESEARCH REPORT 934 Project 08-110 ISSN 2572-3766 (Print) ISSN 2572-3774 (Online) ISBN 978-0-309-48143-4 Library of Congress Control Number 2020936428 Â© 2020 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, FTA, GHSA, NHTSA, 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; the FHWA; or the program sponsors. The Transportation Research Board; the National Academies of Sciences, Engineering, and Medicine; and the sponsors of the National Cooperative Highway Research Program do not endorse products or manufacturers. Trade or manufacturersâ 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. John L. Anderson 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 National 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 provide leadership in transportation improvements and innovation through trusted, timely, impartial, and evidence-based information exchange, research, and advice regarding all modes of transportation. The Boardâs varied activities annually engage about 8,000 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 CRP STAFF FOR NCHRP RESEARCH REPORT 934 Christopher J. Hedges, Director, Cooperative Research Programs Lori L. Sundstrom, Deputy Director, Cooperative Research Programs Lawrence D. Goldstein, Senior Program Officer Anthony P. Avery, Senior Program Assistant Eileen P. Delaney, Director of Publications Natalie Barnes, Associate Director of Publications Sharon Lamberton, Editor NCHRP PROJECT 08-110 PANEL Field of Transportation PlanningâArea of Planning Methods and Processes Keith L. Killough, Arizona DOT, Phoenix, AZ (Chair) Christopher C. Chritton, Wisconsin DOT, Madison, WI Lori J. Duguid, Michael Baker International, Columbus, OH Derek L. Gunn, Maryland DOT, Baltimore, MD Mandar Khanal, Boise State University, Boise, ID Donald C. Mayle, Michigan DOT, Lansing, MI Jonathan W. Reynolds, Kentucky Transportation Cabinet, Frankfort, KY Ken Cervenka, FTA Liaison Brian Gardner, FHWA Liaison
AUTHOR ACKNOWLEDGMENTS Greg Erhardt at the University of Kentucky, Lexington, KY, and Dave Schmitt at the Connetics Transpor- tation Group, Orlando, FL, were principal investigators for this research, and were responsible for design- ing and directing all aspects of it. Jawad Hoque at the University of Kentucky was the primary research assistant throughout the project, conducting much of the Large-N analysis and the Cynthiana Deep Dive. Ankita Chaudhary at Connetics Transportation Group, Orlando, FL, developed the Large-N database and conducted the Indian Street Bridge Deep Dive. Kyeongsu Kim at Connetics Transportation Group, Fairfax, VA, conducted the Central Artery Tunnel and US-41 deep dives. Sujith Rapolu at Connetics Transportation Group, Fort Lauderdale, FL, conducted the Eastown Road Deep Dive. Marty Wachs at the University of California, Los Angeles, CA, facilitated the project workshop, helped to craft conclusions and recommendations, and wrote the initial summary. Mei Chen at the University of Kentucky oversaw the Large-N analysis and identified quantile regression as an appropriate tool. Reg Souleyrette, also at the University of Kentucky, directed the Cynthiana Bypass Deep Dive. Steve Weller at Jacobs, Alexandria, VA, conducted the South Bay Expressway Deep Dive. Elizabeth Sall at UrbanLabs, Seattle, WA, led the development of the forecast cards archiving system. The authors wish to thank the following individuals and organizations who provided data to support this research and who agreed to make relevant project data publicly available: Mark Byram and Greg Giaimo, Ohio DOT; Pavithra Parthasarathi, Puget Sound Regional Council (PSRC) and David Levinson, University of Sydney (Minnesota data); Don Mayle, Michigan DOT; Shi-Chiang Li and Hui Zhao, Florida DOT District 4; Jason Learned, Florida DOT District 5; Jonathan Reynolds, Kentucky Transportation Cabinet; John Miller, University of Virginia; Morten Skou Nicolaisen, City of Aarhus (European data); and Clint Daniels and Peter Stevens, San Diego Association of Governments (South Bay Expressway). We also thank those who organized and participated in two critical workshops: (1) the project workshop, which included Chris Hiebert, Southeast Wisconsin Regional Planning Commission; Brad Lane, Delaware Valley Regional Planning Commission; Nokil Park, Atlanta Regional Council; Thomas Hill, Florida DOT; Mark Byram, Ohio DOT; Amir Shahpar, Virginia DOT; Ed Azimi, Virginia DOT; Chowdhury Siddiqui, South Carolina DOT; Juan Robles, Colorado DOT; Chris Chritton, Wisconsin DOT; Don Mayle, Michigan DOT; Jonathan Reynolds, Kentucky Transportation Cabinet; Lori Duguid, Michael Baker International; Brian Gardner, Federal Highway Administration; Ken Cervenka, Federal Transit Administration; and (2) the TRB Workshop on Progress in Improving Travel Forecasting Accuracy at the 2019 Transportation Research Board Annual Meeting, which included David Hartgen, The Hartgen Group; Kay Axhuasen, ETH-Zurich; Ken Cervenka, Federal Transit Administration; Maren Outwater, RSG; Greg Giaimo, Ohio DOT; and Julie Dunbar, Dunbar Transportation Consulting. Finally, the authors wish to acknowledge the contributions of others who discussed this research with us, including Xu Zhang, University of Kentucky; David Hartgen, The Hartgen Group; Rob Bain, RB Consult; and John Miller, University of Virginia. We are indebted to all of the individuals listed above, as well as any we may have neglected to mention, for helping to craft this research and the resulting recommendations.
State departments of transportation and other transportation planning agencies use traffic forecasts to inform important decisions about transportation projects, including the selection of which projects to build and the design of particular elements as a function of demand. It is therefore important that transportation planners and policy makers base such decisions on the most accurate possible traffic forecasts. The evidence on forecast accuracy, however, remains subject to debate, with only a small set of empirical studies examining non-toll traffic forecast accuracy in the United States. A major barrier to such research has been the lack of data, largely because it is often difficult to compile information on forecasts made years earlier. NCHRP Research Report 934 aims to fill that gap, focusing specifically on project-level traffic forecasts for public roads in the United States. To accomplish this task, the research team assembled the largest known database of traffic forecast accuracy, composed of infor- mation about traffic forecasts and about measured outcomes after the projects open. The report examines the accuracy of these forecasts and factors related to accuracy, and presents a series of case studies aimed at providing better understanding of the sources of inaccuracy. Together, the analysis coupled with the case studies provide empirical evidence about the accuracy of past traffic forecasts. Under NCHRP Project 08-110, âTraffic Forecasting Accuracy Assessment Research,â a research team headed by the University of Kentucky was asked to develop a process to analyze and improve the accuracy, reliability, and utility of project-level traffic forecasts. The resulting guidebook and technical analysis document not only the historical accuracy of traffic forecasts, but also measure bias and spread. The accuracy of traffic forecasts is related to a number of factors, with higher-volume roads, shorter-term forecasts, and travel models producing more accurate forecasts than their respective alternatives. The research shows that, although certain known factors such as population, employment, and fuel price projections are important contributors to forecast inaccuracy, the specific contributors are diverse and do not lend themselves to easy solutions. The output of the research is presented in two parts. Part I, which includes a project summary, is the guidance document. Part II is the technical report, and Part III includes a set of appendices that presents the detailed case studies at the root of the analysis. The research products also include a PowerPoint presentation directed at decision makers and an Excel spreadsheet that demonstrates application of quantile regression models to help determine forecast accuracy. Recognizing that no forecasts will be perfectly accurate, it is important to quantify the expected inaccuracy around traffic forecasts and consider that F O R E W O R D By Lawrence D. Goldstein Staff Officer Transportation Research Board
uncertainty when making decisions. The quantile regression models provide a means of estimating that uncertainty, improving the decision-making process. Together, these products provide guidance to state departments of transportation, metropolitan planning organizations, and other agencies that produce project-level traffic forecasts, describing not only recommended methods for quantifying uncertainty but also how to implement a process for the continual improvement of forecast accuracy. This process involves compiling the relevant data, periodically testing and reporting forecast accuracy, and using past accuracy assessments to improve subsequent traffic forecasting methods. More accurate traffic forecasts, coupled with a better understanding of the uncertainty around these forecasts, can lead to a more efficient allocation of resources while helping to build public confidence in the agencies that produce them. All documents and supplemental resources are available for download. They can be accessed from the report webpage by going to www.trb.org and searching âNCHRP Research Report 934â.
S-1 Summary P A R T I Guidance Document I-3 Chapter 1 Introduction I-3 1.1 Purpose of the Guidance Document I-5 1.2 Research Summary I-16 1.3 Conclusions and Recommendations I-25 Chapter 2 Using Measured Accuracy to Communicate Uncertainty I-25 2.1 Quantifying Uncertainty I-27 2.2 Introduction to Quantile Regression I-28 2.3 Default Versus Local Quantile Regression I-29 2.4 Applying Quantile Regression Methods I-34 Chapter 3 Archiving Traffic Forecasts and Associated Data I-34 3.1 Archiving Levels I-37 3.2 Forecast Archive and Information System I-46 Chapter 4 Reporting Accuracy Results I-46 4.1 Segment- and Project-Level Observations I-47 4.2 Summary Reports I-49 4.3 Updating Quantile Regression Models I-51 4.4 Using the Forecast Archive and Information System I-52 4.5 Deep Dives I-54 Chapter 5 Improving Traffic Forecasting Methods I-54 5.1 Using Deep Dives to Guide Model Improvements I-55 5.2 Project-Level Testing and Validation I-57 5.3 Large-N Analysis for Method Selection I-59 Chapter 6 Implementation and Future Research I-61 References C O N T E N T S
P A R T I I Technical Report II-3 Chapter 1 Overview II-3 1.1 Research Objective II-3 1.2 Overall Approach II-6 1.3 Technical Report Contents II-7 Chapter 2 Large-N Analysis II-7 2.1 Introduction to Large-N Analysis II-8 2.2 Data and Methodology II-15 2.3 Results II-17 2.4 Quantile Regression Results II-20 Chapter 3 Deep Dives II-20 3.1 Introduction to the Deep Dives II-21 3.2 Methodology II-28 3.3 Results II-40 Chapter 4 Conclusions II-40 4.1 Research Questions II-40 4.2 Large-N Findings II-43 4.3 Deep Dive Findings II-45 4.4 Process Findings II-48 References P A R T I I I Appendices III-A-1 Appendix A Electronic Resources III-B-1 Appendix B Forecast Archive Annotated Outline (Silver Standard) III-C-1 Appendix C Deep Dive Annotated Outline III-D-1 Appendix D Forecast Card Data Assumptions III-E-1 Appendix E Implementation Plan III-F-1 Appendix F Literature Review III-G-1 Appendix G Large-N Analysis III-H-1 Appendix H Deep Dives 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.