Crash Modification Factors for Automated Traffic Signal Performance Measures (2026)
Chapter: Front Matter
Crash Modification Factors for Automated Traffic Signal Performance Measures
Burak Cesme
James Bonneson
Bastian J. Schroeder
Nemanja Dobrota
Laura Zhao
Shannon Warchol
Kittelson & Associates, Inc.
Boston, MA
Christopher Day
Jonathan Wood
Anuj Sharma
Iowa State University
Ames, IA
Tingting Huang
Etalyc
Ames, IA
Conduct of Research Report for NCHRP Project 17-109
Submitted September 2025
NCHRP Web-Only Document 442
Crash Modification Factors for Automated Traffic Signal Performance Measures
© 2026 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 department of transportation (DOT) 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.
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COOPERATIVE RESEARCH PROGRAMS
CRP STAFF FOR NCHRP WEB-ONLY DOCUMENT 442
Monique R. Evans, Director, Cooperative Research Programs
Waseem Dekelbab, Deputy Director, Cooperative Research Programs, and Manager, National Cooperative Highway Research Program
Patrick Zelinski, Senior Program Officer
Kevin Padilla, Senior Program Assistant
Natalie Barnes, Director of Publications
Brian Haefs, Associate Director of Publications
Jennifer Correro, Assistant Editor
NCHRP PROJECT 17-109 PANEL
Field of Traffic – Area of Safety
Mark Don Taylor, Utah Department of Transportation, Salt Lake City, UT (Chair)
Jay Grossman, Valparaiso University, Valparaiso, IN
Khalid Jamil, Texas Department of Transportation, Austin, TX
Venkat Nallamothu, Mead & Hunt, Inc., Columbia, MD
Stacie Phillips, Kimley-Horn and Associates, Inc., Raleigh, NC
Sunil Taori, Virginia Department of Transportation, Fairfax, VA
Di Zhu, Tennessee Department of Transportation, Nashville, TN
Woon Kim, FHWA Liaison
Kelly K. Hardy, AASHTO Liaison
Contents
Prioritization of Knowledge Gaps
Data Collection and Study Method Summary
Suggestions for Future Research
Overview of ATSPM Evaluation Methodology
CHAPTER 2: STATE OF THE PRACTICE REVIEW
Key Findings Related to Safety Effects of ATSPMs
Key Findings Related to Safety Effects of other Signal System Solutions
Key Findings Related to CMF Development
Targeted Agency Outreach and Phone Interviews
CHAPTER 3: PRIORITIZATION OF KNOWLEDGE GAPS
Assess Data Availability and Quality
Data Availability and Quality for Case A CMFs
Data Availability and Quality for Case B CMFs
Potential Safety Impact of ATSPM Reports
Practitioner Interest in ATSPM Reports
Case B CMF Data Structure and Site Selection Plan
Case A CMF Data Collection and Reduction Summary
Data Collection for Case A Evaluation Sites
Data Collection for Case A CMF Comparison Sites
Crash Data Collection Summary for Case A CMF Development
Case B CMF Data Collection and Reduction Summary
Intersection Variables Data Collection Summary
Crash Data Collection Summary for Case B CMF Development
CHAPTER 5: CASE A CMF DEVELOPMENT AND RESULTS
Case A CMF – Analysis Methodology and Findings
Case A CMF ‒ Database Assembly
Crash Assignment for Virginia Data
Crash Assignment for Utah Data
Crash Assignment for Georgia Data
Case A CMF ‒ Before-After Study Statistical Analysis Methods
Case A CMF – Findings from Before-After Study
CHAPTER 6: CASE B CMF DEVELOPMENT AND RESULTS
Case B CMF – Analysis Methodology and Findings
Case B CMF ‒ Database Assembly
Crash Assignment for North Carolina Data
Case B CMF ‒ Cross-Sectional Study Statistical Analysis Methods
Case B CMF – Findings from Cross-Sectional Study
CPM for Left-Turn-Opposed Crash Frequency
CPM for NON-Left-Turn-Opposed Crash Frequency
CHAPTER 7: BENEFIT-COST ANALYSIS CASE STUDY
Benefit-Cost Analysis Methodology
Results for the Virginia DOT Arterials
Results for the Utah DOT Arterials
CHAPTER 9: CONCLUSIONS AND FUTURE RESEARCH
Suggestions for Future Research
APPENDIX A: IMPLEMENTATION ROADMAP
APPENDIX B: IMPLEMENTATION OF RESEARCH FINDINGS AND PRODUCTS
NCHRP Web-Only Document 442 contains the conduct of research report for NCHRP Project 17-109 and accompanies NCHRP Research Report 1170: Evaluating the Safety Effects of Automated Traffic Signal Performance Measures. Readers can read or purchase NCHRP Research Report 1170 on the National Academies Press website (nationalacademies.org/publications).
List of Tables
Table 1. Overview of the ATSPM Evaluation Methodology based on Case A CMFs.
Table 2. Overview of ATSPM Evaluation Methodology based on Case B CMFs.
Table 4. Comparison of CMFs for adaptive traffic signal control (Source: Avelar et al., 2021).
Table 5. Summary of ATSPM deploying agencies who responded to the survey.
Table 6. Selected individuals for targeted outreach and a summary of key findings.
Table 7. List of performance measures identified in selected publications.
Table 9. Target crash reduction factor for two study design options.
Table 10. Variables describing the arterial street and ATSPM system.
Table 11. Crash data variables.
Table 12. Variables describing the intersection approach.
Table 13. Summary of variables per segment, for Lee Highway (US 29), Virginia.
Table 14. Summary of variables per segment, for Gallows Road, Virginia.
Table 15. Summary of variables per segment, for SR 71, Utah.
Table 16. Summary of variables per segment, for SR 71, Utah – continued.
Table 17. Summary of variables per segment, for SR 266, Utah.
Table 18. Summary of variables per segment, for SR 8 (Harcourt Dr. to Montreal Rd.), Georgia.
Table 19. Summary of variables per segment, for SR 8 (Lakeshore Dr. to Orion Dr.), Georgia.
Table 21. Summary of variables for comparison sites in Virginia.
Table 22. Summary of variables for comparison sites in Utah.
Table 23. Summary of variables for comparison sites in Georgia.
Table 24. Crash data variables.
Table 25. Case B CMF data collection summary and ATSPM Reports that can be generated.
Table 26. Case B CMF ATSPM data collection summary.
Table 27. Study sites for case A CMF development.
Table 28. Data time periods for case A CMF development.
Table 29. Crash data variables ‒ Case A CMF.
Table 30. Crash types removed from the database ‒ Case A CMF.
Table 31. Segment traffic volume – Case A CMF.
Table 32. Segment crash characteristics – Case A CMF.
Table 33. Signalized intersection crash characteristics – Case A CMF.
Table 34. Before-after crash data; all severities, facility types, and hours – Case A CMF.
Table 35. Before-after crash data by crash severity; all facility types and hours – Case A CMF.
Table 36. Before-after crash data by facility type; all severities and hours – Case A CMF.
Table 37. Before-after crash data by time period; all severities and facility types – Case A CMF.
Table 38. Overall CMF values; all severities, facility types, and hours – Case A CMF.
Table 39. CMF values by crash severity; all facility types and hours – Case A CMF.
Table 40. CMF values by facility type; all severities and hours – Case A CMF.
Table 41. CMF values by time period; all severities and facility types – Case A CMF.
Table 42. Study-site count by state, year, and ATSPM report for case B CMF development.
Table 43. Database variables for case B CMF development.
Table 45. Characteristics of sites used for left-turn gap availability ‒ case B CMF.
Table 47. Summary ATSPMs at sites used for left-turn gap availability ‒ case B CMF.
Table 49. Crash characteristics at sites used for left-turn gap availability ‒ case B CMF.
Table 50. Predictive model estimation statistics ‒ FI crashes, platoon ratio and split failure.
Table 51. Predictive model estimation statistics ‒ PDO crashes, platoon ratio and split failures.
Table 52. Predictive model estimation statistics ‒ FI crashes, yellow actuations.
Table 53. Predictive model estimation statistics ‒ PDO crashes, yellow actuations.
Table 54. Predictive model estimation statistics ‒ Left-turn-opposed crashes, gap availability.
Table 55. Predictive model estimation statistics ‒ Non-left-turn opposed crashes, gap availability.
Table 56. Comprehensive crash unit cost by speed limit.
Table 57. Benefit-cost analysis results for Virginia ATSPM-operated arterials.
Table 58. Benefit-cost analysis results for Utah ATSPM-operated arterials.
Table 59. List of state DOTs and agencies that participated in a webinar.
List of Figures
Figure 2. ATSPM reports used for safety-based decision-making.
Figure 3. Example chart usage data from Utah DOT, showing the number of reports run in 2022.
Figure 4. Example chart usage data from Georgia DOT, showing the number of reports run in 2022.
Figure 5. Signal and segment numbering scheme.
Figure 6. Minimum segment length.
Figure 8. Illustration of segment boundaries for crash assignment in Utah ATSPM Corridor SR 71.
Figure 9. Percent arrivals on green for Phase 6 based on Georgia DOT’s Open-Source Platform.
Figure 10. Percent arrivals on green for Phase 6 after processing the raw high-resolution data.
Figure 14. Percent of large gaps for Phase 6 after processing the raw high-resolution data.
Figure 15. Relationship between platoon ratio and fatal-and-injury crash rate.
Figure 18. Relationships for proportion-of-cycles-with-through-phase-split-failure.
Figure 19. Relationship between permissive left-turn measures and crash rate.
Figure 20. Predicted vs. reported FI crash frequency based on platoon ratio and split failures.
Figure 21. Predicted vs. reported PDO crash frequency based on platoon ratio and split failures.
Figure 22. Predicted vs. reported FI crash frequency based on platoon ratio and yellow actuations.
Figure 23. Predicted vs. reported PDO crash frequency based on platoon ratio and yellow actuations.
Figure 24. Predicted vs. reported LT crash frequency based on left-turn gap availability.
Figure 25. Predicted vs. reported nonLT crash frequency based on left-turn gap availability.
Figure 27. Estimated platoon ratio AF – Platoon ratio, split failure, yellow actuations.
Figure 28. Estimated split failure AF – Platoon ratio, split failure, yellow actuations.
Figure 29. Estimated yellow actuations AF – Platoon ratio, split failure, yellow actuations.
Figure 30. Predicted crash frequency for base conditions – Left-turn gap availability.
Figure 31. Estimated left-turn-gap-availability AF – Left-turn gap availability.
Figure 32. Cost and benefit elements used for the benefit-cost analysis (Day et al., 2020).
