GEORGIA DOT RESEARCH PROJECT 17-32 Final Report VALIDATING CHANGE OF SIGN AND PAVEMENT CONDITIONS AND EVALUATING SIGN RETROREFLECTIVITY CONDITION ASSESSMENT ON GEORGIA’S INTERSTATE HIGHWAYS USING 3D SENSING TECHNOLOGY Office of Performance-based Management and Research 600 West Peachtree St. NW | Atlanta, GA 30308 1.Report No.: 2. Government Accession 3. Recipient's Catalog No.: FHWA-GA-20-1732 No.: N/A N/A 4. Title and Subtitle: 5. Report Date: November 2019 Validating Change of Sign and Pavement Conditions and Evaluating Sign Retroreflectivity Condition Assessment 6. Performing Organization Code: on Georgia’s Interstate Highways Using 3D Sensing N/A Technology 7. Author(s): Yichang (James) Tsai, Ph.D.; 8. Performing Organ. Report No.: 17-32 Zhaohua Wang, Ph.D. 9. Performing Organization Name and Address: 10. Work Unit No.: Georgia Institute of Technology 790 Atlantic Drive 11. Contract or Grant No.: PI# A180701 Atlanta, GA 30332-0355 12. Sponsoring Agency Name and Address: 13. Type of Report and Period Covered: Georgia Department of Transportation Final; December 2017 – November 2019 Office of Performance-based Management and Research 600 West Peachtree St. NW 14. Sponsoring Agency Code: Atlanta, GA, 30308 15. Supplementary Notes: 16. Abstract: Traffic signs are important for roadway safety and provide critical guidance to road users with traffic regulations, road hazard warnings, destination information, and other geographic information. Pavement surface distress data is critical for monitoring the statewide pavement conditions, identifying maintenance activities, and optimally allocating pavement funds. In 2015, the Georgia Department of Transportation (GDOT) implemented a comprehensive sign inventory and asphalt pavement condition assessment on Georgia’s interstate highways through a research project (RP 15-11). To track the existence and condition change of signs, a new procedure was developed by utilizing the existing sign inventory and condition data. Meanwhile, a sign retro-reflectivity condition assessment method using mobile LiDAR was explored, and a case study was developed to demonstrate the use of the proposed method. The asphalt pavement condition of interstate highways was updated using newly collected 3D pavement laser data. In addition, the condition data of jointed plain concrete pavement (JPCP) and international roughness index (IRI) on interstate highways were collected for this project using the 3D pavement laser data. 17. Key Words: 18. Distribution Statement: No Interstate; Sign Inventory; PACES; Image; Mobile LiDAR; 3D Laser Restriction 19. Security Classification (of this report): 20. Security classification (of 21. Number 22. Price: Unclassified this page): Unclassified of Pages: 141 Free Contract Research GDOT Research Project No. 17-32 Final Report VALIDATING CHANGE OF SIGN AND PAVEMENT CONDITIONS AND EVALUATING SIGN RETROREFLECTIVITY CONDITION ASSESSMENT ON GEORGIA’S INTERSTATE HIGHWAYS USING 3D SENSING TECHNOLOGY By Yichang (James) Tsai, Ph.D., P.E. Zhaohua Wang, Ph.D., P.E. Georgia Institute of Technology Contract with Georgia Department of Transportation In cooperation with U.S. Department of Transportation Federal Highway Administration November 2019 The contents of this report reflect the views of the author(s) who is (are) responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Georgia Department of Transportation or of the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. i TABLE OF CONTENTS TABLE OF CONTENTS ii LIST OF TABLES vi LIST OF FIGURES viii ACKNOWLEDGEMENTS xiii EXECUTIVE SUMMARY xv CHAPTER 1. INTRODUCTION 1 RESEARCH BACKGROUND AND RESEARCH NEED ································ 1 RESEARCH OBJECTIVES ···································································· 3 REPORT ORGANIZATION ··································································· 4 CHAPTER 2. OVERVIEW OF THE SENSING DATA COLLECTION 5 GEORGIA TECH SENSING VEHICLE (GTSV) ··········································· 5 INTERSTATE HIGHWAY SYSTEM IN GEORGIA ······································ 9 CHAPTER 3. SIGN INVENTORY BASED ON PREVIOUSLY COLLECTED DATA 11 KEY CHARACTERISTICS OF TRAFFIC SIGNS ······································· 11 PROCEDURE TO CREATE UPDATED SIGN INVENTORY USING PREVIOUSLY COLLECTED DATA ······················································ 16 RESULTS ······················································································· 17 ii COMPARISON OF 2015 AND 2017 SIGN INVENTORY ····························· 27 SUMMARY ····················································································· 30 CHAPTER 4. SIGN RETRO-REFLECTIVITY CONDITION ASSESSMENT USING MOBILE LIDAR 33 INTRODUCTION ·············································································· 34 CURRENT PRACTICES TO MEET THE MINIMUM RETRO-REFLECTIVITY REQUIREMENT FOR THE TRAFFIC SIGNS ··········································· 35 A COST-EFFECTIVE MEANS TO ASSESS SIGN RETRO-RETROFLECTIVITY CONDITION ···················································································· 40 DEVELOPMENT OF SIGN RETRO-REFLECTIVITY CONDITION ASSESSMENT USING MOBILE LIDAR ····································································· 42 VALIDATION OF SIGN RETRO-REFLECTIVITY CONDITION ASSESSMENT 49 CASE STUDY ·················································································· 55 SIGN RETO-INTENSITY CHANGE ANALYSIS (2015 – 2018) ······················ 58 SUMMARY ····················································································· 61 CHAPTER 5. ASPHALT PAVEMENT CONDITION EVALUATION 65 DISTRESSES DEFINED IN PACES ······················································· 65 STREAMLINED PROCEDURE ····························································· 67 RESULTS ······················································································· 72 COMPARISON WITH 2015 PAVEMENT CONDITION DATA ······················ 75 iii SUMMARY ····················································································· 78 CHAPTER 6. PCC PAVEMENT (JPCP) CONDITION EVALUATION 79 OVERVIEW OF JPCP AND RATING SYSTEM ········································· 79 STREAMLINED PROCEDURE USING 3D PAVEMENT DATA ···················· 81 RESULTS ······················································································· 87 COMPARISON WITH HISTORICAL CPACES RATING ····························· 90 CHAPTER 7. SMOOTHNESS EVALUATION USING 3D PAVEMENT DATA 93 METHOD FOR IRI MEASUREMENT USING 3D LASER DATA ··················· 93 FIELD VALIDATION WITH GDOT’S PROFILER ····································· 94 COMPARISON WITH PATHWAY DATA ·············································· 100 PROCESSED RESULTS FOR INTERSTATE HIGHWAY ···························· 101 SUMMARY ···················································································· 102 CHAPTER 8. CONCLUSIONS AND RECOMMENDATIONS 104 CONCLUSIONS ·············································································· 104 RECOMMENDATIONS FOR IMPLEMENTATION ··································· 109 REFERENCES 113 iv v LIST OF TABLES Table 3-1. Detailed statistics of traffic signs MUTCD categories ······················· 18 Table 3-2. Detailed statistics of traffic signs in poor conditions on each interstate highway. ···························································································· 20 Table 3-3. Detailed statistics of overhead traffic signs on each interstate highway. 22 Table 3-4. Detailed numbers of traffic signs in each working district. ················ 25 Table 3-5. Detailed statistics of traffic signs in poor conditions in each working district. ····························································································· 26 Table 3-6. Detailed statistics of overhead traffic signs in each working district. ···· 27 Table 3-7. Comparison of the number of signs in different sign classification between 2015 and 2018 sign inventory. ······················································ 28 Table 3-8. Comaprison of the number of signs in different poor condition categories between 2015 and 2018 sign inventory. ······················································ 29 Table 4-1. Strength and weaknesses of assessment methods. ···························· 39 Table 4-2. An example of sign retro-reflectivity condition classification using yellow prismatic sheeting signs with MR = 0.76. ··················································· 48 Table 4-3. Number of signs tested with different colors and methods. ················ 52 vi Table 4-4. Comaprison of nighttime visual assessment result and the retro- reflectivity condition. ············································································ 53 Table 5-1. Asphalt pavement distresses defined in COPACES. ························ 66 Table 6-1. JPCPACES Survey Distresses. ··················································· 80 Table 6-2. Comparison between GT-2018 JPCPACES rating and GDOT historical rating. ······························································································· 91 Table 7-1. IRI from GDOT profiler and GTSV. ··········································· 99 vii LIST OF FIGURES Figure 2-1. Photo. Mobile imaging sub-system on GTSV. ································ 6 Figure 2-2. Photo. Illustration of the collected sensing data using