Implementing and Leveraging Machine Learning at State Departments of Transportation (2024)
Chapter: Front Matter
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NCHRP Web-Only Document 404 |
Implementing and
Leveraging Machine
Learning at State
Departments of
Transportation
Mecit Cetin
Sherif Ishak
Old Dominion University
Norfolk, VA
Matthew Samach
Haley Townsend
Noblis
Washington, DC
Kaan Ozbay
Cognium LLC
Princeton, NJ
Conduct of Research Report for NCHRP Project 23-16
Submitted April 2024
NCHRP
Web-Only Document 404
Implementing and Leveraging Machine Learning at State Departments of Transportation
Mecit Cetin
Sherif Ishak
Old Dominion University
Norfolk, VA
Matthew Samach
Haley Townsend
Noblis
Washington, DC
Kaan Ozbay
Cognium LLC
Princeton, NJ
Conduct of Research Report for NCHRP Project 23-16
Submitted April 2024
© 2024 by the National Academy of Sciences. National Academies of Sciences, Engineering, and Medicine and the graphical logo are trademarks of the National Academy of Sciences. All rights reserved.
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 transportation results in increasingly complex problems of wide interest to highway 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 initiated an objective national highway research program using modern scientific techniques—the 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 Agreement No. 693JJ31950003.
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, APTA, FAA, FHWA, FTA, GHSA, or NHTSA 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.
Digital Object Identifier: 10.17226/27902
Epub ISBN: 978-0-309-72422-7
DISCLAIMER
The opinions and conclusions expressed or implied in this report are those of the researchers who performed the research. They 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 does not develop, issue, or publish standards or specifications. The Transportation Research Board manages applied research projects which provide the scientific foundation that may be used by Transportation Research Board sponsors, industry associations, or other organizations as the basis for revised practices, procedures, or specifications.
The Transportation Research Board, the National Academies, and the sponsors of the National Cooperative Highway 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 information contained in this document was taken directly from the submission of the author(s). This material has not been edited by TRB.
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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.
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Learn more about the Transportation Research Board at www.TRB.org.
COOPERATIVE RESEARCH PROGRAMS
CRP STAFF FOR NCHRP WEB-ONLY DOCUMENT 404
Waseem Dekelbab, Deputy Director, Cooperative Research Programs, and Manager, National Cooperative Highway Research Program
Sid Mohan, Associate Program Manager for Implementation and Technology Transfer
Mireya Kuskie, Senior Program Assistant
Natalie Barnes, Director of Publications
Heather DiAngelis, Associate Director of Publications
Jennifer J. Weeks, Publishing Projects Manager
NCHRP PROJECT 23-16 PANEL
Field of Agency—Area of Agency Administration
Frances D. Harrison, Spy Pond Partners, LLC, Arlington, MA (Chair)
Majed N. Al-Ghandour, North Carolina Department of Transportation, Raleigh, NC
Daniela Bremmer, Washington State Department of Transportation, Lacey, WA
Ross Cutts, Geosetta, Columbia, MD
Kira Marina Glover-Cutter, Oregon Department of Transportation, Salem, OR
Shashi S. Nambisan, University of Nevada, Las Vegas, Las Vegas, NV
Daniel Tran, University of Kansas, Lawrence, KS
Walter Yu, California Department of Transportation, Sacramento, CA
Mo Zhao, Virginia Transportation Research Council, Charlottesville, VA
Faisal Saleem, FHWA Liaison
Penelope Z. Weinberger, AASHTO Liaison
AUTHOR ACKNOWLEDGMENTS
The authors would like to acknowledge individuals who contributed to this research and its final products. From Noblis, Meenakshy Vasudevan and Karl Wunderlich served as advisors and provided critical input on the guide. From Old Dominion University, Tancy Vandecar-Burdin and Wendy Wilson-John helped with the coding and implementation of the state DOT survey, and Behrouz Salahshour and Mahta Zamanizadeh helped with coding the sample ML applications reported in Chapter 6. Behrouz Salahshour also helped with the literature review and compiling some of the ML tools listed in Chapter 5. Wendy Wilson-John also helped transcribe the interviews conducted with state DOT representatives for the case studies. The authors would also like to thank the survey respondents and case study interviewees for providing critical inputs to the guide and the final research report. Their insights and lessons learned will help future machine learning deployers at transportation agencies.
CONTENTS
Chapter 3 - Results of Surveys with State Departments of Transportation
Chapter 5 - Machine Learning Tools
Chapter 6 - Sample Machine Learning Applications
Chapter 7 - Machine Learning Guide for State DOTs
NCHRP Web-Only Document 404 contains the Conduct of Research Report for NCHRP Project 23-16 and accompanies NCHRP Research Report 1122: Implementing Machine Learning at State Departments of Transportation: A Guide. Readers can read or purchase NCHRP Research Report 1122 on the National Academies Press website (nap.nationalacademies.org).
List of Figures
Figure 1. The overall research approach followed in this NCHRP project.
Figure 2. Deep learning is a type of ML consisting of neural networks with many layers.
Figure 3. Number and percentage of ML-related papers published in TRR over the years.
Figure 4. Trends in TRR papers by the identified ML methods.
Figure 5. Distribution of ML-related papers in TRR by application area and travel mode.
Figure 6. Survey responses received from different states.
Figure 7. Sample image from the guardrail detection method [Source: NDOT].
Figure 8. Sample image from the ML method for detecting marked pedestrian crossings [Source: NDOT].
Figure 9. DelDOT framework for integrating AI applications into TMC processes (Gettman, 2019).
Figure 10. Responses from DALL-E about road conditions depicted in two images.
Figure 11. Sample output frame.
Figure 12. Sample output image from YOLO8.
Figure 13 Example Bayesian network.
Figure 15. Roadmap to building agency ML capabilities.
List of Tables
Table 2 List of key phrases used in identifying ML-related papers.
Table 3 Responses to the survey by agency/organization type.
Table 4 Responses to the survey by the primary function of the department unit.
Table 5 Familiarity of respondents with ML tools and methods.
Table 6 Data science competencies within the agency.
Table 7 Currently deployed or under development ML applications.
Table 8 ML Methods adopted or being developed for applications.
Table 9 Application areas with ML solutions adopted by the agency.
Table 10 Level of satisfaction with ML applications in use.
Table 11 Level of maturity of the ML applications.
Table 12 Type of input data for ML applications.
Table 13 Development/implementation period for ML application.
Table 14 Years ML application has been used in practice.
Table 17 Estimated annual operating cost of ML application.
Table 18 Motivation for the adoption of ML methods/solutions.
Table 19 Current and future challenges in the development and adoption of ML applications.
Table 20 Agencies interviewed for the case studies.
Table 21. Data sources and types.
Table 22. Incident identification algorithm performance for fatal and serious injury crashes.
Table 23. Video analytics performance.
Table 24. Estimated costs for predictive analytics supplemental agreement.
Table 25 Sample output table from the program.
Table 26 Categories for incident duration.
Table 27 Sample incident data.
