FHWA Safety Program

Indoor Simulator Study and Field Study 

Evaluation of Sequential Flashing 

Chevron Signs on Two-lane Rural Highways 

Notice 

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TECHNICAL DOCUMENTATION PAGE 

1.Report No. FHWA-SA-18-075 2. Government 

Accession No. 

3. Recipient's Catalog No. 

4. Title and Subtitle 

Indoor Simulator and Field Study Evaluation of Sequential Flashing Chevron Signs 

on Two-lane Rural Highways 

5. Report Date 

November 30, 2017 

6. Performing Organization 

Code 

7. Author(s) 

Donnell, E. T., Porter, R. J., Zlatkovic, M., Cooper, J., Li, L., Strayer, D., and Lin, M. 

8. Performing Organization 

Report No. LTI 2018-07 

9. Performing Organization Name and Address 

Thomas D. Larson Pennsylvania Transportation Institute 

The Pennsylvania State University 11. Contract or Grant No. 

201 Transportation Research Building 

University Park, PA 16802 

10. Work Unit No. 

12. Sponsoring Agency Name and Address 

U.S. Department of Transportation 

Federal Highway Administration 

Office of Safety 

1200 New Jersey Avenue, SE 

Washington, DC 20590 

13. Type of Report and Period 

Final Report 10/1/12-9/30/18 

14. Sponsoring Agency Code: 

FHWA - HSA

15. Supplementary Notes. 

The Contract Officer’s Technical Representative was Joseph Cheung (joseph.cheung@dot.gov). 

16. Abstract Past research has found that the average crash rate along horizontal curves of two-lane rural highways is 

three times higher than on tangent road segments. Single-vehicle run-off-road crashes are four times more likely on 

horizontal curves relative to tangent roadway sections. One sign application that offers potential safety improvements 

along horizontal curves on two-lane rural highways is the Sequential Dynamic Curve Warning System (SDCWS), which 

uses solar powered flashing lights embedded in the sign to delineate the curve. The flashing lights can be simultaneous 

(i.e., each sign is flashing at the same time as the other signs) or a sequence of lights moving toward or away from the 

driver. The purpose of this study was to identify the optimal flash rate, speed-activation threshold, and flashing sequence 

when deploying the SDCWS along horizontal curves on two-lane rural highways. This was accomplished using two 

independent evaluations. A driver simulator study was undertaken to identify several SDCWS settings that produced the 

lowest operating speeds along a horizontal curve on two-lane rural highways. The results of this effort found that a low 

flash rate and a flashing pattern moving away from the driver, as well as a high flash rate with a simultaneous flashing 

pattern, produced the desirable speed reduction effects on study participants. A field study was then undertaken to further 

assess the flash rate and flashing patterns identified in the simulator study. Four different conditions were studied in the 

field, including a speed-activation threshold that was either 5 or 10 mph above the curve advisory speed, two different 

flash rates, and two different flashing patterns. These conditions were compared to a baseline condition (static chevron 

signs) and to the settings used in a previous study. The findings, based on comparisons to a previous study at the same 

sites, indicate that a flashing pattern away from the driver, at a 1 Hz flashing rate, with a speed-activation threshold equal 

to the curve advisory speed, was the optimal setting for the SDCWS, based on several speed-based performance metrics. 

17. Key Words: Operating speed, driving simulator, sequential 

flashing chevrons, two-lane rural highways 

18. Distribution Statement 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages: 

115 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized 
SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2 

ft2 square feet 0.093 square meters m2 

yd2 square yard 0.836 square meters m2 

ac acres 0.405 hectares ha 

mi

2 

square miles 2.59 square kilometers km2 

VOLUME 

fl oz fluid ounces 29.57 milliliters ml 

gal gallons 3.785 liters l 

fl' cubic feet 0.028 cubic meters m' 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3 

MASS oz ounces 28.35 grams g 

lb pounds 0.454 kilograms kg 

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

OF Fahrenheit 

TEMPERATURE (exact degrees) 

5 (F-32)/9 Celsius oc or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux Ix 

fl foot-Lamberts 3.426 candela/m2 cdlm2 

FORCEandPRESSUREorSTRESS 

lbf poundforce 4 .45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 sc:iuare meters 1.195 square yards yd2 

ha hectares 2.47 acres ac 

km2 square kilometers 0.386 square miles m, ·2 

VOLUME 

ml milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m' cubic meters 35.314 cubic feet ft' m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz 

kg kilograms 2.202 pounds lb 

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) 

oc Celsius 1.8C+32 Fahrenheit OF 

ILLUMINATION 

Ix lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-lamberts fl 

FORCEandPRESSUREorSTRESS 

N newtons 0.225 pound force lbf 

kPa kilo pascals 0.145 poundforce per square inch lbflin2 

·s1 is the symbol lor the International System or Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003) 

ii 
TABLE OF CONTENTS 

EXECUTIVE SUMMARY .......................................................................................................... 1 

CHAPTER 1. INTRODUCTION ................................................................................................ 3 

CHAPTER 2. LITERATURE REVIEW.................................................................................... 7 

DYNAMIC CURVE WARNING SIGNS .............................................................................................. 7 

SPEED FEEDBACK SIGNS................................................................................................................ 12 

WORK ZONE SPEED-ACTIVATED SIGN ..................................................................................... 12 

CHAPTER 3. INDOOR SIMULATOR EVALUATION........................................................ 15 

INTRODUCTION AND BACKGROUND......................................................................................... 15 

Speed-Dependent Flashing .................................................................................................................. 16 

Speed-Activation Thresholds .............................................................................................................. 17 

METHODS AND DATA ...................................................................................................................... 17 

Testing Procedure................................................................................................................................ 21 

Data Reduction and Quality Control ................................................................................................... 22 

Datasets ............................................................................................................................................... 25 

ANALYSIS RESULTS ......................................................................................................................... 33 

Verification of Speed and Lateral Position Performance in Simulator ............................................... 33 

Aggregate Analysis of Speed and Lateral Position ............................................................................. 37 

Disaggregate Analysis of Lane Departure Probability ........................................................................ 37 

Regression Analysis to Detect Possible Speed-Activation Thresholds ............................................... 40 

SUMMARY OF SIMULATOR STUDY............................................................................................. 43 

CHAPTER 4. OUTDOOR FIELD STUDY EVALUATION ................................................. 45 

OBJECTIVES ....................................................................................................................................... 45 

STUDY SITES ....................................................................................................................................... 45 

SPEED LIMIT COMPLIANCE AND OPERATING SPEED FIELD ASSESSMENT ................. 47 

Variables.............................................................................................................................................. 48 

ANALYSIS RESULTS ......................................................................................................................... 48 

Speed Difference Results .................................................................................................................... 48 

Operating Speed Differences between Data Collection Locations ..................................................... 49 

iii 
Statistical Tests.................................................................................................................................... 67 

Comparison of Speed Measures .......................................................................................................... 67 

Speed Differential Comparisons between Study Periods .................................................................... 74 

Comparison of Proportion of Vehicles Exceeding the Advisory Curve Speed ................................... 82 

SUMMARY OF FIELD STUDY ......................................................................................................... 86 

CHAPTER 5. DISCUSSION AND RECOMMENDATIONS ................................................ 88 

APPENDIX A .............................................................................................................................. 90 

Intake Questionnaire ............................................................................................................................ 90 

APPENDIX B .............................................................................................................................. 92 

APPENDIX C .............................................................................................................................. 99 

Data Collection Protocols .................................................................................................................... 99 

Sample Size Determination ................................................................................................................. 99 

Data Reduction and Analysis ............................................................................................................ 102 

Summary ........................................................................................................................................... 104 

DESCRIPTIVE STATISTICS........................................................................................................... 106 

APPENDIX D ............................................................................................................................ 117 

APPENDIX E ............................................................................................................................ 133 

Statistical Tests.................................................................................................................................. 133 

ACKNOWLEDGEMENTS ..................................................................................................... 143 

REFERENCES.......................................................................................................................... 145 

iv 
LIST OF TABLES 

Table 1. Curve groups................................................................................................................... 19 

Table 2. Curve data....................................................................................................................... 20 

Table 3. Scenario and curve combinations with the hanging control group. ................................ 21 

Table 4. Recording locations removed from the database. ........................................................... 23 

Table 5. Outliers deleted from the database. ................................................................................. 25 

....................................................................................................................................................... 27 

Table 6. Descriptive statistics for dataset of nine critical horizontal curve and upstream locations. 

Table 7. Descriptive statistics for dataset of five critical horizontal curve locations. .................. 29 

Table 8. Descriptive statistics for lane departure dataset. ............................................................. 31 

Table 9. Published 85th-percentile speed models. ......................................................................... 34 

Table 11. Distance between vehicle centroid and centerline for CRS sites (from Porter et al., 

Table 15. Difference in mean and 85th- percentile operating speeds between data collection 

Table 16. Difference in mean and 85th- percentile operating speeds between data collection - WI 

Table 17. Difference in mean and 85th- percentile operating speeds between data collection 

Table 10. Distance between the center of the simulator vehicle and the middle of the lane. ....... 36 

2004). ............................................................................................................................................ 37 

Table 12. Binary logit model of lane departure occurrence. ......................................................... 39 

Table 13. Comparison of mean speed models for various approach speeds. ............................... 42 

Table 14. Characteristics of Wisconsin study sites. ...................................................................... 46 

locations – WI 213 upstream-PC. ................................................................................................. 50 

213 upstream - midcurve. ............................................................................................................. 50 

locations – WI 213 PC-midcurve. ................................................................................................. 51 

Table 18. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 20 upstream-PC. ................................................................................................... 51 

Table 19. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 20 upstream-midcurve. ......................................................................................... 52 

Table 20. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 20 PC-midcurve. ................................................................................................... 52 

Table 21. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 67 upstream-PC. ................................................................................................... 53 

v 
Table 22. Difference in mean and 85th- percentile operating speeds between data collection 

locations - WI 67 (upstream-midcurve). ....................................................................................... 53 

Table 23. Difference in mean and 85th- percentile operating speeds between data collection 

locations - WI 67 (PC-midcurve). ................................................................................................. 54 

Table 24. Proportion of vehicles exceeding the advisory speed (daytime and nighttime combined 

Table 25. Proportion of vehicles exceeding the advisory speed (daytime and nighttime combined 

Table 26. Proportion of vehicles exceeding the advisory speed (daytime and nighttime combined 

Table 27. Proportions of vehicles exceeding the curve advisory speed during the daytime only – 

Table 28. Proportions of vehicles exceeding the curve advisory speed during the daytime only – 

Table 29. Proportions of vehicles exceeding the curve advisory speed during the daytime only – 

Table 30. Proportion of vehicles exceeding the curve advisory speed during the nighttime only - 

Table 31. Proportion of vehicles exceeding the curve advisory speed during the nighttime only - 

Table 32. Proportion of vehicles exceeding the curve advisory speed during the nighttime only - 

– WI 213). ..................................................................................................................................... 58 

– WI 20). ....................................................................................................................................... 58 

– WI 67). ....................................................................................................................................... 59 

WI 213. ......................................................................................................................................... 60 

WI 20. ........................................................................................................................................... 60 

WI 67. ........................................................................................................................................... 61 

WI 213. ......................................................................................................................................... 61 

WI 20. ........................................................................................................................................... 62 

WI 67. ........................................................................................................................................... 62 

Table 33. Operating speed for WI 213 – this study. ..................................................................... 69 

Table 34. Operating speed for WI 213 – Smadi et al., 2015. ........................................................ 70 

Table 35. Operating speed for WI 20 – this study. ....................................................................... 71 

Table 36. Operating speed for WI 20 – Smadi et al., 2015. .......................................................... 72 

Table 37. Operating speed for WI 67 – this study. ....................................................................... 73 

Table 38. Operating speed for WI 67 – Smadi et al., 2014. .......................................................... 74 

Table 39. Comparison of speed differentials between present and previous study – WI 213. ..... 75 

Table 40. Comparison of speed differentials between present and previous study – WI 20. ....... 76 

Table 41. Comparison of speed differentials between present and previous study – WI 67. ....... 77 

vi 
Table 42. Comparison of speed differentials across data collection locations between present and 

previous studies – WI 213............................................................................................................. 79 

Table 43. Comparison of speed differentials across data collection locations between present and 

previous studies – WI 20............................................................................................................... 80 

Table 44. Comparison of speed differentials across data collection locations between present and 

previous studies – WI 67............................................................................................................... 81 

Table 45. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 213 – this study.. ......................................................................................................... 82 

Table 46. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 213 – Smadi et al., 2015. ............................................................................................ 83 

Table 47. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 20 – this study. ............................................................................................................ 83 

Table 48. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 20 – Smadi et al., 2015. .............................................................................................. 83 

Table 49. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 67 – this study. ............................................................................................................ 83 

Table 50. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 67 – Smadi et al., 2015. .............................................................................................. 84 

Table 51. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 20 

mph at WI 213 – this study. .......................................................................................................... 84 

Table 52. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 20 

mph at WI 213 – Smadi et al., 2015. ............................................................................................ 84 

Table 53. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 20 

mph at WI 20 – this study. ............................................................................................................ 84 

Table 54. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 20 

mph at WI 20 – Smadi et al., 2015. .............................................................................................. 85 

Table 55. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 20 

mph at WI 67 – this study. ............................................................................................................ 85 

Table 56. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 20 

mph at WI 67 – Smadi et al., 2015. .............................................................................................. 85 

Table 57. Typical spacing of chevron alignment signs on horizontal curves ............................... 88 

Table 58. Values for speed sample size determination. .............................................................. 100 

Table 59. Sample size estimates for comparative research studies. ........................................... 101 

vii 
Table 60. Summary of data collection periods, measures of effectiveness, and statistical tests. 105 

Table 76. Difference in mean and 85th-percentile operating speeds between all conditions for all 

Table 77. Difference in mean and 85th-percentile operating speeds between all conditions for all 

Table 78. Difference in mean and 85th-percentile operating speeds between all conditions for all 

Table 85. Difference in mean and 85th-percentile operating speeds between all conditions for 

Table 61. Descriptive statistics for all operating speeds – WI 213. ............................................ 107 

Table 62. Descriptive statistics for all operating speeds – WI 20. .............................................. 108 

Table 63. Descriptive statistics for all operating speeds – WI 67. .............................................. 109 

Table 64. Descriptive statistics for daytime operating speeds – WI 213. ................................... 110 

Table 65. Descriptive statistics for daytime operating speeds – WI 20. ..................................... 111 

Table 66. Descriptive statistics for daytime operating speeds – WI 67. ..................................... 112 

Table 67. Descriptive statistics for nighttime operating speeds – WI 213. ................................ 113 

Table 68. Descriptive statistics for nighttime operating speeds – WI 20. .................................. 114 

Table 69. Descriptive statistics for nighttime operating speeds – WI 67. .................................. 115 

Table 70. Mean speed data for all time periods – WI 213. ......................................................... 118 

Table 71. 85th-percentile operating speed data for all time periods – WI 213 ........................... 118 

Table 72. Mean speed data speed for all time periods – WI 20. ................................................. 118 

Table 73. 85th-percentile operating speed data for all time periods – WI 20. ............................ 118 

Table 74. Mean speed data for all time periods – WI 67. ........................................................... 119 

Table 75. 85th-percentile operating speed data for all time periods – WI 67. ............................ 119 

time periods – WI 213. ................................................................................................................ 120 

time periods – WI 20. .................................................................................................................. 120 

time periods – WI 67. .................................................................................................................. 120 

Table 79. Mean speed data for daytime conditions – WI 213. ................................................... 123 

Table 80. 85th-percentile operating speed data for daytime conditions – WI 213 ..................... 123 

Table 81. Mean speed data for daytime conditions – WI 20. ..................................................... 123 

Table 82. 85th-percentile operating speed data for daytime conditions – WI 20. ...................... 123 

Table 83. Mean speed data for daytime conditions – WI 67. ..................................................... 123 

Table 84. 85th-percentile operating speed data for daytime conditions – WI 67. ...................... 124 

daytime conditions – WI 213. ..................................................................................................... 124 

viii 
Table 86. Difference in mean and 85th-percentile operating speeds between all conditions for 

daytime conditions – WI 20. ....................................................................................................... 125 

Table 87. Difference in mean and 85th-percentile operating speeds between all conditions for 

daytime conditions – WI 67. ....................................................................................................... 125 

Table 88. Mean operating speed data for nighttime conditions WI 213. ................................... 128 

Table 89. 85th - percentile operating speed data for nighttime conditions WI 213. .................... 128 

Table 90. Mean operating speed data for nighttime conditions WI 20. ...................................... 128 

Table 91. 85th - percentile operating speed data for nighttime conditions WI 20. ...................... 128 

Table 92. Mean operating speed data for nighttime conditions WI 67. ...................................... 128 

Table 93. 85th - percentile operating speed data for nighttime conditions WI 67. ...................... 129 

Table 94. Difference in mean and 85th-percentile operating speeds between all conditions for 

nighttime conditions – WI 213. .................................................................................................. 129 

Table 95. Difference in mean and 85th-percentile operating speeds between all conditions for 

Table 96. Difference in mean and 85th-percentile operating speeds between all conditions for 

Table 97. Statistical tests for mean speeds during daytime and nighttime periods combined - WI 

Table 98. Statistical tests for mean speeds during daytime and nighttime periods combined - WI 

Table 99. Statistical tests for mean speeds during daytime and nighttime periods combined - WI 

nighttime conditions – WI 20. .................................................................................................... 130 

nighttime conditions – WI 67. .................................................................................................... 130 

213............................................................................................................................................... 133 

20................................................................................................................................................. 134 

67................................................................................................................................................. 134 

Table 100. Statistical tests for mean speeds during daytime period – WI 213. .......................... 135 

Table 101. Statistical tests for mean speeds during daytime period – WI 20. ............................ 135 

Table 102. Statistical tests for mean speeds during daytime period – WI 67. ............................ 135 

Table 103. Statistical tests for mean speeds during nighttime period – WI 213. ....................... 136 

Table 104. Statistical tests for mean speeds during nighttime period – WI 20. ......................... 136 

Table 105. Statistical tests for mean speeds during nighttime period – WI 67. ......................... 136 

Table 106. Speed variance tests for daytime and nighttime periods combined - WI 213. ......... 137 

Table 107. Speed variance tests for daytime and nighttime periods combined - WI 20. ........... 137 

Table 108. Speed variance tests for daytime and nighttime periods combined - WI 67. ........... 137 

ix 
Table 109. Speed variance tests for daytime period – WI 213. .................................................. 138 

Table 115. Proportion of vehicles exceeding advisory speed (daytime and nighttime combined) - 

Table 116. Proportion of vehicles exceeding advisory speed (daytime and nighttime combined) - 

Table 117. Proportion of vehicles exceeding advisory speed (daytime and nighttime combined) - 

Table 110. Speed variance tests for daytime period – WI 20. .................................................... 138 

Table 111. Speed variance tests for daytime period – WI 67. .................................................... 138 

Table 112. Speed variance tests for nighttime period – WI 213. ................................................ 139 

Table 113. Speed variance tests for nighttime period – WI 20. .................................................. 139 

Table 114. Speed variance tests for nighttime period – WI 67. .................................................. 139 

WI 213. ....................................................................................................................................... 140 

WI 20. ......................................................................................................................................... 140 

WI 67. ......................................................................................................................................... 141 

Table 118. Proportion of vehicles exceeding advisory speed during daytime – WI 213. .......... 141 

Table 119. Proportion of vehicles exceeding advisory speed during daytime – WI 20. ............ 141 

Table 120. Proportion of vehicles exceeding advisory speed during daytime – WI 67. ............ 141 

Table 121. Proportion of vehicles exceeding advisory speed during nighttime – WI 213. ........ 142 

Table 122. Proportion of vehicles exceeding advisory speed during nighttime – WI 20. .......... 142 

Table 123. Proportion of vehicles exceeding advisory speed during nighttime – WI 67. .......... 142 

x 
LIST OF FIGURES 

Figure 1. Photo. Sequential dynamic curve warning system. ......................................................... 4 

Figure 2. Photo. Representation of PA Route 851 in Utah Traffic Lab driving simulator. .......... 16 

Figure 3. Photo. Example treatment-flashing in unison. .............................................................. 18 

Figure 4. Graph. Box-and-whisker plots for lateral position. ....................................................... 23 

Figure 5. Graph. Histograms of lateral position. .......................................................................... 24 

Figure 6. Graph. Lateral position coordinate profile through curve 13: scenario 1, participant 1 

vehicle position test....................................................................................................................... 24 

Figure 7. Equation. Expected mean speed at horizontal curve midpoint. ..................................... 34 

Figure 8. Graph. Modeled 85th- percentile speed versus radius of horizontal curve. ................... 35 

Figure 9. Equation. Linear function of binary logit model. .......................................................... 38 

Figure 19. Graph. Proportion of vehicles exceeding the advisory speed (daytime and nighttime 

Figure 20. Graph. Proportion of vehicles exceeding the advisory speed (daytime and nighttime 

Figure 21. Graph. Proportion of vehicles exceeding the advisory speed (daytime and nighttime 

Figure 22. Graph. Proportion of vehicles exceeding the advisory speed during the daytime – WI 

Figure 23. Graph. Proportion of vehicles exceeding the advisory speed during the daytime – WI 

Figure 10. Equation. Binary logit model for the probability of lane departures........................... 38 

Figure 11. Equation. Regression equation of horizontal curve speed. .......................................... 40 

Figure 12. Graphic. Data collection layout. .................................................................................. 47 

Figure 13. Graph. Mean operating speed difference at the WI 213 site. ...................................... 55 

Figure 14. Graph. 85th-percentile operating speed difference at the WI 213 site. ....................... 55 

Figure 15. Graph. Mean operating speed difference at the WI 20 site. ........................................ 56 

Figure 16. Graph. 85th-percentile operating speed difference at the WI 20 site. ......................... 56 

Figure 17. Graph. Mean operating speed difference at the WI 67 site. ........................................ 57 

Figure 18. Graph. 85th-percentile operating speed difference at the WI 67 site. ......................... 57 

combined) – WI 213. .................................................................................................................... 63 

combined) – WI 20. ...................................................................................................................... 63 

combined) – WI 67. ...................................................................................................................... 64 

213................................................................................................................................................. 64 

20................................................................................................................................................... 65 

xi 
Figure 24. Graph. Proportion of vehicles exceeding the advisory speed during the daytime – WI 

67................................................................................................................................................... 65 

Figure 25. Graph. Proportion of vehicles exceeding the advisory speed during the nighttime – WI 

Figure 26. Graph. Proportion of vehicles exceeding the advisory speed during the nighttime – WI 

Figure 27. Graph. Proportion of vehicles exceeding the advisory speed during the nighttime – WI 

Figure 43. Graph. Mean and 85th-percentile operating speeds at WI 213 site for all time periods. 

Figure 44. Graph. Mean and 85th-percentile operating speeds at WI 20 site for all time periods. 

Figure 45. Graph. Mean and 85th-percentile operating speeds at WI 67 site for all time periods. 

Figure 46. Graph. Mean and 85th-percentile operating speeds at WI 213 site for daytime period. 

213................................................................................................................................................. 66 

20................................................................................................................................................... 66 

67................................................................................................................................................... 67 

Figure 28. Graph. Mean speed ± two standard errors of mean in curve section 1. ...................... 93 

Figure 29. Graph. Mean speed ± two standard errors of mean in curve section 2. ...................... 94 

Figure 30. Graph. Mean speed ± two standard errors of mean in curve section 3. ...................... 95 

Figure 31. Graph. Mean lateral placement ± two standard errors of mean in curve section 1. .... 96 

Figure 32. Graph. Mean lateral placement ± two standard errors of mean in curve section 2. .... 97 

Figure 33. Graph. Mean lateral placement ± two standard errors of mean in curve section 3. .... 98 

Figure 34. Equation. Minimum number of measured free-flow operating speeds. .................... 100 

Figure 35. Equation. Speed sample in comparison groups. ........................................................ 100 

Figure 36. Equation. Formula of t-statistics................................................................................ 102 

Figure 37. Equation. Percentage of vehicles exceeding posted or advisory speed limit. ........... 103 

Figure 38. Equation. Percentage reduction of speeding vehicles between two periods. ............ 103 

Figure 39. Equation. Formula of Z-statistic. ............................................................................... 104 

Figure 40. Equation. Combined proportion of speeding vehicles in two samples. .................... 104 

Figure 41. Equation. Formula of F-statistic. ............................................................................... 104 

Figure 42. Equation. Speed difference between data conditions and periods. ........................... 117 

..................................................................................................................................................... 121 

..................................................................................................................................................... 122 

..................................................................................................................................................... 122 

..................................................................................................................................................... 126 

xii 
Figure 47. Graph. Mean and 85th-percentile operating speeds at WI 20 site for daytime period. 

..................................................................................................................................................... 126 

Figure 48. Graph. Mean and 85th-percentile operating speeds at WI 67 site for daytime period. 

..................................................................................................................................................... 127 

Figure 49. Graph. Mean and 85th-percentile operating speeds at WI 213 site for nighttime 

period. ......................................................................................................................................... 131 

Figure 50. Graph. Mean and 85th-percentile operating speeds at WI 20 site for nighttime period. 

..................................................................................................................................................... 131 

Figure 51. Graph. Mean and 85th-percentile operating speeds at WI 67 site for nighttime period. 

..................................................................................................................................................... 132 

xiii 
LIST OF ABBREVIATIONS 

AADT annual average daily traffic (vehicles per day) 

AS advisory speed along a horizontal curve (miles per hour) 

DCWS dynamic curve warning signs 

DSDS dynamic speed display signs 

FHWA Federal Highway Administration 

ITE Institute of Transportation Engineers 

MC midpoint of a simple horizontal curve 

MOE measures of effectiveness 

MUTCD Manual on Uniform Traffic Control Devices 

OBS number of observations in a sample of operating speeds 

OLS ordinary least squares 

PC point of curvature, or the beginning of a simple horizontal curve 

PSL posted speed limit 

PT point of tangency, or the end of a simple horizontal curve 

SD standard deviation of observed operating speed (miles per hour) 

SDWCS Sequential Dynamic Curve Warning System 

TAPCO Traffic Parking and Control Company 

TCDs traffic control devices 

xiv 
EXECUTIVE SUMMARY 

Past research has found that the average crash rate along horizontal curves of two-lane rural 

highways is three times higher than on tangent road segments. Single-vehicle run-off-road 

crashes are four times more likely on horizontal curves relative to tangent roadway sections. A 

recent FHWA publication, Low-cost Treatments for Horizontal Curve Safety, offers guidance 

concerning the safety effects of various countermeasures intended to mitigate roadway departure 

crashes along horizontal curves. One sign application that was shown to offer potential safety 

improvements along horizontal curves on two-lane rural highways was the Sequential Dynamic 

Curve Warning System (SDCWS). The SDCWS is a series of horizontal curve chevron signs 

with solar-powered flashing lights embedded in the sign. The flashing lights can be simultaneous 

(i.e., each sign is flashing at the same time as the other signs) or a sequence of lights moving 

toward or away from the driver. 

The purpose of this study was to identify the optimal flash rate, speed-activation threshold, and 

flashing sequence when deploying SDCWS along horizontal curves on two-lane rural highways. 

This was accomplished using two independent evaluations. A driver simulator study was 

undertaken to identify several SDCWS settings that produced the lowest operating speeds along 

a horizontal curve on two-lane rural highways. The results of this effort indicate that a low flash 

rate and a flashing pattern moving away from the driver, as well as a high flash rate with a 

simultaneous flashing pattern, produced the desirable speed reduction effects on study 

participants. 

A field study was then undertaken to further assess the flash rate and flashing patterns identified 

in the simulator study. Four different conditions were studied in the field, including a speedactivation threshold that was either 5 or 10 mph above the curve advisory speed, two different 

flash rates, and two different flashing patterns. These conditions were compared to a baseline 

condition (static chevron signs) and to the settings used in a previous study. The findings, based 

on comparisons to a previous study at the same sites, indicate that a flashing pattern away from 

the driver, at a 1-Hz flashing rate, with a speed-activation threshold equal to the curve advisory 

speed, was the optimal setting for the SDCWS. 

1 

CHAPTER 1. INTRODUCTION 

In 2015, there were nearly 6.3 million police-reported crashes, resulting in 35,092 fatalities and 

more than 2.4 million injuries, on highways and streets in the United States.(NHTSA 2015) More than 

30 percent of all fatal crashes involved a single vehicle and occurred off of the roadway. There 

are a variety of factors that contribute to a roadway departure, many of which are related to the 

driver. One plausible safety management approach aims to prevent vehicles from leaving the 

roadway via use of traffic control devices. 

A comprehensive, four-state study by Glennon et al. found that the average crash rate for 

horizontal curves on two-lane rural highways is three times higher than on tangent road 

segments.(Glennon et al. 1985) The authors also found that the average single-vehicle, run-off-road 

(SVROR) crash rate was four times higher on horizontal curves than on tangent segments. The 

severity of roadway departure crashes on horizontal curves was also higher than roadway 

departure crashes on tangent segments. 

A recent FHWA publication, Low-cost Treatments for Horizontal Curve Safety offers guidance 

concerning the safety effects of various countermeasures intended to mitigate roadway departure 

crashes along horizontal curves.(Albin et al. 2016) The authors identified many strategies that could be 

implemented individually, or in combination, to reduce roadway departure crashes on horizontal 

curves. The strategies could generally be classified as pavement markings, signs, roadway 

surface countermeasures, roadside countermeasures, and intersection treatments. 

One sign application that was shown to offer potential safety improvements along horizontal 

curves on two-lane rural highways was the SDCWS. Figure 1 shows a sample application of the 

SDCWS along a horizontal curve. The SDCWS is a series of horizontal curve chevron signs with 

solar-powered flashing lights embedded in the signs. The flashing lights can be simultaneous (i.e., 

each sign is flashing at the same time as the other signs) or a sequence of lights moving toward 

or away from the driver. In the latter case, this is typically accomplished by having each sign 

flash at least once per second, with each flash lasting at least 100 milliseconds. Each sign begins 

flashing at a time that is offset relative to the adjacent sign, producing a sequential flashing effect. 

A recent study by Smadi et al. evaluated the speed and safety effects of the SDCWS at 12 

horizontal curve locations on two-lane rural highways.(Smadi et al. 2015) The signs were installed in 

Missouri, Texas, Washington, and Wisconsin. The Traffic Parking and Control Company 

(TAPCO) version of the sign was deployed at each study site location. All curves selected for 

treatment with SDCWS were on two-lane rural paved roads with a posted speed limit of 50 mph 

or higher, existing chevrons, a high crash history, and travel speeds that often exceeded the 

advisory or regulatory speed limit. All installations of SDCWS at the curves occurred between 

June and September of 2012. 

3 
Figure 1. Photo. Sequential dynamic curve warning system. 

(Source: http://www.fhwa.dot.gov/hfl/partnerships/safety_eval/brochure_tapco.cfm) 

Speed data were collected before installation as well as 1 month, 12 months, 18 months, and 24 

months after installation of the SDCWS. Crash data were also compiled for each of the SDCWS 

and control sites, including five years before and two years after implementation. The results 

showed that vehicle operating speeds were lower at the beginning and midpoint of horizontal 

curves for all periods after the SDCWS were installed. The mean and 85th-percentile speeds 

were 1.1 to 1.7 mph lower in the 1 month, 12 month, 18 month, and 24 month periods after 

installing the SDCWS. The results were generally consistent when comparing speeds at the 

beginning and the midpoint of horizontal curves. The percentage of vehicles exceeding the 

posted and advisory speed limits was also lower after installing the SDCWS, and the results were 

generally consistent across all time periods after implementation. The change in the fraction of 

vehicles exceeding the advisory speed by 20 mph or more decreased by an average of 32 percent 

at the beginning of the horizontal curve. Similarly, the change in the fraction of vehicles 

exceeding the advisory speed by 15 mph or more decreased by an average of 30 percent at the 

beginning of the horizontal curve. The fraction of vehicles exceeding the advisory speed by 20 

mph or more at the midpoint of the curve decreased by 26 percent, while the fraction of vehicles 

exceeding the advisory speed by 15 mph or more declined by 16 percent after SDCWS 

installation. The results of the study suggest that SDCWS have a long-term and consistent effect 

on vehicle operating speeds. While the magnitude of the effect was relatively small, there was a 

pronounced effect on those vehicles substantially exceeding the advisory speed. 

The Smadi et al. study considered a sequential flashing pattern that moved away from the 

driver.(Smadi et al. 2015) Each chevron sign was programmed to flash for at least one second with a 

flash “on” time of 100 milliseconds. If at least nine chevron signs existed in an array along a 

horizontal curve, the array was subdivided into two separate groups in which the separate groups 

flashed in harmony (e.g., the first and fifth signs of nine flashed at the same time, followed by 

the second and sixth signs, etc.). 

The purpose of the present study was to identify, using an indoor driving simulator study, a set of 

flashing patterns, rates, and sequences that produced speed reductions when traveling from an 

4 
approach tangent into a horizontal curve. The patterns were then assessed in a field study and 

compared to the pattern, sequence, and flash rates used in the Smadi et al. study.(Smadi et al. 2015) 

The intent of the effort was to identify the optimal combination of flash rates, speed-activation 

thresholds, and flash sequences that minimized operating speeds along horizontal curves along 

two-lane rural highways. By lowering operating speeds, it is inferred that traffic safety may also 

improve. 

The following acronyms and terms are used frequently throughout the remainder of this report 

and are defined as follows: 

 85th-percentile speed: The speed at which 85 percent of the motor vehicles are traveling 

at or below (mph). 

 Lane departure: An event in which a least one vehicle tire leaves the intended travel lane 

by crossing over a longitudinal pavement marking. 

 Operating speed: The speed at which motor vehicles are observed traveling on a given 

roadway during free-flow conditions (mph). 

 Posted speed limit (PSL): The maximum speed that can be legally driven on a given 

roadway. The speed limit is typically posted on regulatory signs (mph). 

 Vehicle lateral position: The position of the vehicle in the travel lane, which is measured 

from the center of the travel lane to the lateral center of the vehicle 

This report is organized into five subsequent chapters. Chapter 2 critically reviews the extant 

literature related to SDCWS. Chapter 3 describes the indoor driving simulator study, while 

Chapter 4 explains the field evaluation of SDCWS. Chapter 5 offers a discussion and 

conclusions from the studies. 

5 

CHAPTER 2. LITERATURE REVIEW 

The literature review is organized into three sections. The first section describes field studies of 

dynamic curve warning signs, which are similar to the sequential flashing chevron signs 

evaluated in the present study. The second section includes a review of speed feedback signs, 

which are another form of communication to drivers, warning of instances when operating 

speeds exceed the posted speed limit. The third section summarizes several studies related to 

speed-activated signs in work zones, which also communicate instances when driving speeds 

exceed work zone speed limits. 

DYNAMIC CURVE WARNING SIGNS 

The impact of dynamic curve warning signs (DCWS) on vehicle operating speeds has been 

investigated by many researchers in recent years. Most of the studies have demonstrated the 

effectiveness of DCWS using observational before-after field studies. The focus of this review is 

on the effectiveness of DCWS when applied along horizontal curves of two-lane rural highways. 

Preston and Schoenecker evaluated the effectiveness of DCWS in reducing vehicle operating 

speeds on County Highway 54 in Minnesota.(Preston and Schoenecker 1999) The site was a two-lane rural 

highway with a 55 mph posted speed limit. The AADT was approximately 3,250 vehicles per 

day. The advisory speed on the static curve warning sign was 40 mph. The DCWS system had a 

changeable message sign and a radar unit. During the morning, midday, and evening peak 

periods, the dynamic sign was activated and displayed the message “CURVE AHEAD.” If the 

radar detected vehicles traveling above 53 mph, the vehicle was recorded for 18 seconds. In 

addition, during the time when the sign was activated, the message “CURVE AHEAD, 

REDUCE SPEED” was randomly displayed to approaching vehicles that exceeded 53 mph. The 

operating speeds were measured when the vehicles were entering and navigating the curve. The 

data were collected during a two-week period with only the static signs in place and a two-week 

period when the DCWS were activated. The results indicated that the difference between the 

highest and lowest speeds of vehicles entering and navigating the curve was 12.3 mph, which 

was produced by the sequential message “CURVE AHEAD, REDUCE SPEED.” In the static 

sign condition, the difference between the highest and lowest operating speed was 11.5 mph. 

Among the sample of vehicles traveling faster than 60 mph, the dynamic sign was more effective 

in reducing operating speeds than the static sign. In addition, 35 percent of vehicles exposed to 

only the static sign were unable to successfully navigate the curve within the limits of the lane 

lines, while only 26 percent of vehicles encountering the “CURVE AHEAD, REDUCE SPEED” 

dynamic sign were unable to navigate the curve within the limits of the lane lines. 

Tribbett evaluated the effectiveness of five dynamic curve warning systems in the Sacramento 

River Canyon.(Tribbett 2000) The study area was located in mountainous terrain and experienced 

high heavy truck crash frequencies. The traffic volumes at the study sites ranged from 7,650 to 

9,300 vehicles per day. The posted speed limits at the five sites ranged from 55 to 65 mph. The 

DCWS included a radar detector and a changeable message sign to display the site-specific 

advisory speed (50 mph or 60 mph) and warning information. The message was displayed on a 

7 
light-emitting diode (LED) sign. Each DCWS had a specific message. The messages could be 

rotated every 3 to 4 seconds, managed by the controller. The study focused on determining the 

effectiveness of DCWS on reducing vehicle operating speeds, crashes, and erratic driving 

maneuvers. The speed data were collected at the DCWS location and at the beginning of the 

horizontal curve, before and after the DCWS system was installed. The data collection period 

included 9 months prior to DCWS installation and during three time periods after installation of 

DCWS. The first after-period condition was immediately after the DCWS was installed, while 

the next two after-period data collection periods occurred 3 and 8 months after DCWS 

installation, respectively. The analysis of operating speeds at the point of curvature (PC) showed 

that there was a statistically significant reduction in mean truck speed at site #1 (from 2.4 to 5.4 

mph), site #3 (from 1.9 to 3.7 mph), and site #5 (4.5 mph for one time period). For mean 

passenger car speeds, there was a statistically significant reduction at site #1 (from 3.0 to 4.5 

mph), site #3 (from 5.2 to 7.8 mph), site #4 (1.4 mph for one time period), and site #5 (from 2 to 

3 mph) after the DCWS were installed. 

A speed-activated curve warning sign was tested in the United Kingdom by Winnett and 

Wheeler.(Winnett and Wheeler 2002) The curve warning message “SLOW DOWN” was activated if the 

vehicle speed exceeded a preset threshold, which was equal to the 50th-percentile operating speed 

of traffic. The signs were placed on two-lane roads before a horizontal curve. The study sites 

were located in Norfolk (30 sites), West Sussex (4 sites), Wiltshire (4 sites), and Kent (5 sites). 

The posted speed limits on these roads ranged from 20 to 60 mph. The operating speeds were 

measured at downstream locations close to the vehicle-activated signs. Operating speed data 

were collected using loop or tube detectors before sign installation, and 1 month and 1 year after 

installation. The results showed that the DCWS reduced the mean speed by 2.1 mph at the West 

Sussex sites, 3.0 mph at the Wiltshire sites, and 6.9 mph at the Norfolk sites. The study 

concluded that the speed-activated signs are very effective in reducing vehicle operating speeds 

as well as the proportion of vehicles exceeding the posted speed limit. 

Monsere and Bertini evaluated the effectiveness of DCWS on Interstate 5 near Myrtle Creek in 

Oregon.(Monsere and Bertini 2005,2006) The sites were located along a section of roadway with a 50 mph 

posted speed limit, and an advisory speed of 45 mph. The system displayed a warning message 

such as “Caution, Sharp Curves Ahead” or “Slow Down, Your Speed Is xx mph” or “Slow Down, 

Your Speed Is Over 70 mph” on the dynamic signs based on the operating speed of approaching 

vehicles. Vehicle operating speed data were collected approximately 420 ft downstream of the 

sign in the northbound direction (some data were collected 114 ft upstream). For the southbound 

traffic, data were collected upstream of the sign. The data were collected before and after 

installation of the DCWS. The statistical analysis indicated that, in terms of the mean speed of 

vehicles traversing the curve, both passenger car and truck operating speeds were reduced by 3 

mph in the southbound and 2 mph in the northbound direction. In the zone following the sign 

location, the maximum mean speed reduction in the southbound was 3.3 mph for passenger cars 

and 3.0 mph for commercial vehicles. In the northbound direction, the mean speed reduction was 

2.6 mph for passenger cars and 1.9 mph for commercial vehicles. The distribution of vehicles in 

different speed bins differed, and the difference was statistically significant. A lower number of 

8 
vehicles fell into higher speed bins (> 55 mph), and a higher concentration of vehicles traveled 

near the mean speed of traffic. Diercksen et al. conducted a second evaluation of this system four 

years after the installation.(Diercksen et al. 2008) This evaluation found that the speed reduction 

associated with the DCWS increased. When considering the northbound direction, the mean 

speed of traffic three years after installing the DCWS was 2 mph lower than the mean operating 

speed four months after DCWS installation, and lower than the before period by 4 mph. 

Vest evaluated the effect of several types or combinations of curve warning signs on vehicle 

operating speeds when traversing a horizontal curve at three test sites in Kentucky.(Vest 2005) The 

site characteristics included a sharp horizontal curve on a rural highway, high history of speedrelated incidents, long tangent section approaching the horizontal curve, no substantial vertical 

grade, and no intersections, driveways, or commercial activity that could adversely affect 

operating speed data. All of the sites had a horizontal alignment sign with an advisory speed 

plaque in advance of the curve for the existing condition. Several curve warning signs were 

added to the existing condition to represent the treatment condition. These included a onedirection large arrow sign, chevron alignment sign, the combination of horizontal alignment sign 

and advisory speed sign, existing warning sign with and without flags, and a new combination 

sign with flashing lights, post delineators, and transverse lines. Speeds were measured at 

locations along the approach tangent, PC, and midpoint of the curve. The study estimated the 

effectiveness of each treatment. In addition, one year after these treatments were tested, 

additional treatments were added to the sites, including rumble strips and the combination of 

rumble strips, as well as treatments from the initial field implementation of speed-reduction 

countermeasures. The study indicated that most treatments have the potential to reduce vehicle 

operating speeds within the horizontal curve. The most effective treatments were the 

combination of the horizontal curve and advisory speed signs, flashing lights on both existing 

and new warning signs, transverse lines, rumble strips, and post delineators. Comparing the 

combination of all proposed treatments and the existing condition, the mean operating speed at 

the midpoint of the curve decreased by 0.6 to 7.1 percent. The 85th-percentile operating speed at 

the midpoint of the curve was reduced by 0.7 to 6.1 percent. In addition, the average speed of 

those vehicles exceeding the 85th-percentile speed at the center of the curve was reduced by -0.2 

to 7.8 percent when a combination of signs was used to delineate the horizontal curve. 

The City of Bellevue, Washington, installed and evaluated 31 dynamic speed feedback signs, 

including two curve advisory warning signs on urban arterials.(The City of Bellevue, Washington 2009) The 

posted speed limit on these roads was 35 mph and the advisory speed was 25 mph. Operating 

speeds were detected using radar, which was displayed on the sign if the vehicle traveled faster 

than a pre-set threshold of 31 mph. The operating speed was measured at the location of the sign 

in advance of the curve. Data were collected before and during a period of 18 months to 2 years 

after installation. The results showed that the 85th-percentile operating speed was reduced by 3.3 

mph at one site and by 3.5 mph at another site, four months after the speed feedback sign 

installation. 

Knapp and Robinson evaluated the vehicle speed impact of DCWS on low-volume rural local 

highways in Minnesota.(Knapp and Robinson 2012) The sites were two-lane rural highways with posted 

9 
speed limits of 40 to 55 mph and an advisory speed warning of 40 or 50 mph. The speeds were 

measured at three locations within each site, including a control point on the approach tangent, 

PC, and within the horizontal curve. The speed data were collected 1 month before DCWS 

installation and 1 month, 6 months, 1 year, and 18 months after installation at three sites. The 

before-after study showed that the unadjusted average speed reduction was 1.0 to 8.8 mph at the 

PC and 1.8 to 6.3 mph within the curve after installation of DCWS. The study also concluded 

that the signs had a larger proportional impact on higher-speed vehicles compared to vehicles 

traveling closer to the advisory speed. For the site that had the advisory speed of 40 mph, the 

percentage of vehicles traveling at least 5 mph above the advisory speed decreased by 26.8 

percent, while the percentage of vehicles traveling more than 20 mph above the advisory speed 

decreased by 87.4 percent. For the site that had an advisory speed of 50 mph, the percentage of 

vehicles traveling at least 5 mph above the advisory speed declined by 44.7 percent, while the 

percentage of vehicles traveling at least 20 mph higher than the advisory speed decreased by 61.2 

percent. 

Montella studied driver behavior along horizontal curves on rural two-lane highways in relation 

to different traffic control devices, including static curve warning signs, curve warning signs 

with flashing beacons, and curve warning signs with driver speed feedback displays.(Montella 2015) 

A dynamic driving simulator experiment was performed on a route with three horizontal curves 

and a 70 km/h (45 mph) posted speed limit. The curve warning signs were placed 150 m (500 ft) 

before the curve with transverse rumble strips 75 m (250 ft) before the curve. The mean speeds 

were recorded 500 m (1,640 ft), 350 m (1,150 ft), 200 m (660 ft), and 100 m (330 ft) before the 

curve, and at the PC, 1/4 curve, mid-curve, 3/4 curve, and PT locations, as well as 100 m, 200 m, 

350 m, and 500 m after the curve. The results showed that, when comparing the scenario with the 

curve warning sign alone, adding flashing beacons produced deceleration 120 m (390 ft) further 

ahead of the curve. Adding a driver feedback sign produced a deceleration 60 m (200 ft) further 

ahead of the curve. 

Hallmark evaluated the effectiveness of DCWS at 22 horizontal curve locations on rural twolane highways in Arizona, Florida, Iowa, Ohio, Oregon, Texas, and Washington.(Hallmark 2015) The 

posted speed limits on these roads ranged from 50 to 70 mph, and the advisory speeds ranged 

from 15 to 50 mph. The system consisted of a curve warning sign and a speed feedback sign. The 

curve warning sign was activated by a vehicle traveling above the 50th-percentile speed and 

displayed the warning message “SLOW DOWN.” The speed feedback sign was activated by a 

vehicle traveling above the 50th-percentile speed and displayed “YOUR SPEED XX” and the 

message changed to “SPEED LIMIT XX” if the vehicle traveled 20 mph or more over the posted 

speed limit. The speeds were measured at locations one-half mile prior to the curve, PC, and 

midpoint of the curve. The data were collected before the signs were installed and during time 

periods 1, 12, and 24 months after installation. The results showed that, for both trucks and 

passenger cars, the mean operating speeds were reduced by 1.8 mph 1 month after installation, 

2.6 mph 12 months after installation, and 2.0 mph 24 months after installation, when measured at 

the PC location. At the midpoint of the curve, the mean speed reductions were 2.1 mph, 1.7 mph, 

and 1.8 mph during periods 1 month, 12 months, and 24 months after DCWS installation. When 

10 
considering the 85th-percentile operating speed, the speed reductions were between two and three 

mph at the PC location during the periods 1 month, 12 months, and 24 months after DCWS 

installation. At the midpoint of the horizontal curve, the 85th-percentile speed reductions were 

2.5 mph 1 month after DCWS installation, 1.6 mph 12 months after DCWS installation, and 1.9 

mph 24 months after DCWS installation. The percentage of vehicles exceeding the posted speed 

limit decreased by approximately 70 percent at the PC and mid-curve locations after installing 

the DCWS. 

Smadi investigated the effectiveness of the SDWCS at 12 horizontal curve locations on two-lane 

rural highways in Iowa, Missouri, Texas, Washington, and Wisconsin.(Smadi et al. 2015) The posted 

speed limit on these roads ranged from 55 to 60 mph, and the advisory speeds ranged from 25 to 

40 mph. The SDCWS consisted of an advance curve warning sign ahead of the curve and an 

array of sequential flashing chevrons along the outside of the horizontal curve. When the 

approaching vehicle speeds exceeded the curve advisory speed, the chevrons would flash 

sequentially at a frequency of once per second. The data were collected at these sites before 

installation of the signs, and 1 month, 12 months, 18 months, and 24 months after installation of 

the SDCWS. The speeds were measured upstream approximately 500 to 1,000 ft prior to the 

curve, PC location, and at the center of the horizontal curve. Only passenger cars were included 

in the before-after analysis. The results showed that the change in mean speed was consistent 

between all data collection periods. The speed reduction at the PC was 1.7 mph 1 month after 

implementation and 1.3 mph during the periods 12 and 18 months after implementation. The 

85th-percentile speeds at the PC also decreased 1.7 mph 1 month after implementation of the 

SDCWS. The analysis also indicated that there were reductions in the percentage of vehicles 

exceeding the posted speed limit or curve advisory speed by 5, 10, 15, and 20 mph, particularly 

in the higher ranges. On average, the percentage of vehicles exceeding the curve advisory speed 

by 5 mph decreased by 11 percent. The percentage of vehicles exceeding the curve advisory 

speed by 10 mph, 15 mph, and 20 mph decreased by 22 percent, 30 percent, and 32 percent, 

respectively, after the SDCWS were installed. 

In summary, the speed reductions associated with DCWS occurred upstream prior to the curve, 

at the PC location, and at the midpoint of horizontal curves on two-lane rural highways. The 

reduction has been observed for both passenger cars and trucks. The metrics used to quantify the 

reduction were mean and 85th-percentile operating speeds and the percentage of vehicles 

exceeding the posted speed limit. For both passenger cars and trucks, the mean speed reduction 

associated with DCWS ranged from 1.4 to 7.8 mph before the beginning of a horizontal curve, 

1.3 to 2.9 mph at the PC location, and 1.7 to 2.1 mph at the midpoint of the horizontal curve. The 

85th-percentile speed reductions ranged from 1.7 to 2.9 mph at the PC location and 1.9 to 2.5 

mph at the midpoint of the curve. Furthermore, past studies found that DCWS are associated 

with a reduction in the percentage of vehicles exceeding the posted speed limit and curve 

advisory speeds on two-lane rural highways. 

11 
SPEED FEEDBACK SIGNS 

The 3M Company carried out a before-after observational study of speed feedback signs 

implemented along two single-lane, one-way streets in Durham, and 10 sites including two-lane 

rural roads, in Doncaster, United Kingdom.(3M Company 2006) The posted speed limits were 40 mph 

at all sites. Vehicle operating speeds were measured before and after the speed feedback signs 

were installed, at a location prior to the sign. The study showed that the 85th-percentile operating 

speed was reduced by up to 15 percent at the study sites. 

Cruzado and Donnell evaluated the effectiveness of dynamic speed display signs (DSDS) on 

rural two-lane highway transition zones in Pennsylvania.(Cruzado and Donell 2009) The posted speed 

limits were 45 to 55 mph at all study sites. Vehicle operating speed data were collected at 12 

transition zones during three periods: before implementation, during the first week that the 

DSDS were in use, and one week after the DSDS were removed. The speeds were measured 0.5 

miles upstream of the DSDS, adjacent to the DSDS, and 500 ft downstream of the DSDS. The 

results indicated that the dynamic speed display signs reduced mean operating speeds by an 

average of 6 mph. However, the speed reduction associated with the DSDS dissipated after the 

devices were removed from the sites. 

Santiago-Chaparro evaluated the effectiveness of speed feedback signs along a two-lane rural 

highway in Wisconsin.(Santiago-Chaparro 2012) Operating speed trajectories of vehicles approaching 

and passing the signs were collected. The study verified the effect of speed feedback signs on 

reducing the operating speed of vehicles. At the upstream location, more than 50 percent of 

vehicles lowered their operating speed by at least one mph. This proportion of drivers reduced 

their speed 1,200 to 1,400 ft upstream of the speed feedback signs. However, after passing the 

speed feedback sign location, more than 50 percent of the observed vehicles increased their 

operating speed by at least one mph, indicating that the presence of speed feedback signs has a 

positive effect on vehicles as they approach the sign, but a diminished effect immediately after 

the vehicle passes the sign. 

WORK ZONE SPEED-ACTIVATED SIGN 

Fontaine conducted a before-after study to assess the effectiveness of a portable speed display 

sign in Texas.(Fontaine, 2000) The work zone had a posted speed limit of 70 mph. The speed data 

were collected at two sites. The study found that mean passenger car speeds were reduced by 2 to 

9 mph, and mean truck speeds were reduced by 3 to 10 mph, when the portable speed display 

sign was present in a work zone, relative to no speed display sign. The percentage of cars 

exceeding the posted speed limit decreased by 15 percent after the portable speed display sign 

was activated. 

Mattox evaluated the effectiveness of a work zone speed-activated warning system in South 

Carolina.(Mattox, 2008) The system included a radar detector, fixed-message sign, speed-activated 

sign, and flashing beacon. The speed-activated sign and flashing beacon were activated if 

vehicles exceeded the speed threshold, which was 3 mph above the posted speed limit. The three 

sites were on secondary highways with a work zone speed limit of 45 mph. The speeds were 

12 
measured before, adjacent to, and after the speed-activated sign. The study found that the 

reduction in mean operating speed ranged from 2 to 6 mph with an average of 3.3 mph for all 

sites. At the sites where more than 50 percent of vehicles were speeding before implementation 

of the warning system, the mean speed reduction was 4.1 mph. 

Sun investigated the effect of sequential lights at nighttime work zones.(Sun 2012) The lights were 

expected to improve driver recognition of lane closures and work zone tapers. Vehicle lateral 

position, vehicle merge location data, and speed data were collected on three short-term work 

zones on Interstate 70 in Missouri. The three sites involved a right-lane closure, so traffic 

operated in the passing lane only. The speed limit in the work zone was 60 mph. Operating speed 

data were collected upstream of the tapers. The results showed that the sequential lights were 

associated with a decrease in mean operating speed of 2.2 mph for all vehicles, 2.2 mph for 

passenger cars, and 2.5 mph for trucks. The decrease in the 85th-percentile speed was 1.0 mph for 

all vehicles, 1.0 mph for passenger cars, and 1.0 mph for trucks. In addition, the driver speed 

compliance rate increased by 6.7 percent at night. 

13 

CHAPTER 3. INDOOR SIMULATOR EVALUATION 

INTRODUCTION AND BACKGROUND 

This section of the report describes the methodologies, data, and findings from a driving 

simulator evaluation of speed-activated traffic control devices (TCDs) on horizontal curves along 

rural, two-lane roads. A previous simulator study found that post delineators with lights that 

moved toward approaching drivers were the most effective treatment for reducing speeds on 

horizontal curves.(Molino et al. 2010) “Toward the driver” flashing patterns are intended to provide 

drivers with an exaggerated sense of their speed. Some “toward the driver” sequencing patterns 

are programed to flash at slower and slower rates as drivers reduce their speed.(Carson et al. 2008) The 

authors of the referenced simulator study recommended field validation; however, field study 

conclusions at this stage have been limited to the devices and flashing sequences installed at the 

treatment sites. Significant knowledge gaps still remain. Total effects and relative effects of 

varying flash rates, sequencing patterns, and speed thresholds are still unknown. Steps toward 

filling these knowledge gaps were explored in the present simulator study and were further field 

tested in a subsequent field study (see Chapter 4 of this report). The objective of this simulator 

study was to identify optimum flash rates, sequencing patterns, and thresholds for speedactivated TCDs on horizontal curves along rural, two-lane roads. The number of sequencing 

variables involved (e.g., flash rate, flash duration, sequencing direction) made a driving 

simulator study a strong methodological alternative to inform follow-on field-testing that is 

described in Chapter 4 of this report. 

A driving simulator located in the University of Utah’s Utah Traffic Lab was used to conduct the 

simulator study. The Utah Traffic Lab simulator was built by AAI, a contractor specializing in 

advanced human simulation systems and consultant to FHWA. The driving simulator was built 

with a custom VISSIM interface to pass traffic, car-following, lane changing, and signal data in 

real time. The driving simulator draws on metrics generated by VISSIM, scenarios coded in 

CREATOR, and representations generated by ARCHER. Calibrated VISSIM models can be 

replicated in the driving simulator to study driver behavior in traffic conditions observed in the 

field at different points in time (e.g., during morning peak, afternoon peak, evening peak, night). 

Most applicable to this study, the VISSIM connection was used to vary the flashing 

characteristics of the studied treatments. The VISSIM connection was also used to gather 

operational measures of interest (e.g., speed profile, lane position). The Utah Traffic Lab 

simulator has an “open” driver cockpit, three 55-inch full HD LCD based TVs, and three 7-inch 

LCD displays representing rear-view and side mirrors. 

The Utah Traffic Lab driving simulator includes a model of a six-mile-long stretch of 

Pennsylvania Route 851. This is a rural, two-lane road in Pennsylvania with significant 

horizontal curvature along the alignment. It has been used for previous FHWA research efforts 

and is shown in figure 2. The Utah Traffic Lab established a VISSIM connection to this scenario, 

which was then used to control the flashing sequences and rates on the TCDs. The PA Route 851 

scenario was ultimately used for the simulator experiments conducted during this research based 

15 
on input received during the kick-off meeting for this project, where FHWA expressed interest in 

using this scenario if possible, and after consideration of other possible scenarios. The advantage 

of this approach is the use of an actual roadway, serving as an intermediate step between Molino 

et al.’s recommendations and the field test described in Chapter 4 of this report.(Molino et al. 2010) 

The disadvantage is less direct control over the curve characteristics and curve directions 

presented to the driver. 

Figure 2. Photo. Representation of PA Route 851 in Utah Traffic Lab driving simulator. 

Source: Department of Civil and Environmental Engineering, University of Utah 

Speed-Dependent Flashing 

The possibility of including a speed-dependent flashing condition (i.e., flashing rates change as 

the detected vehicle speed changes) was explored during the early stages of this simulator study. 

The research team considered this alternative, but pilot testing recommended removing the 

speed-dependent flashing manipulation from the current experimental design. This 

recommendation was based on two factors: 

1) Precise speed-dependent behavior can only be applied to a global scenario such that a 

particular flashing rate would occur at a particular speed regardless of the curve design 

encountered by subjects. This is problematic because not all curves can be safely or 

comfortably navigated at the same speed. The requirement to convey over-speed urgency 

to drivers should be curve dependent. That is, speed-dependent flashing rates should be 

governed not only by absolute speed, but speed through a given curve with a given super 

elevation. This was not feasible with the current simulator technology and study size. 

16 
2) Implementing this type of dynamic experimental condition would necessarily result in a 

highly unbalanced sampling of speed modulation throughout the changes in flashing 

patterns and rates. That is, speed-dependent flashing would necessarily create a “messy” 

experimental design under the current sample size scope with results that would be 

difficult or impossible to cleanly interpret. 

Through pilot testing, the research team concluded that the experimental manipulation of 

carefully controlled speed-independent flashing rates and sequencing patterns would allow more 

precise statements about the effect of a given flashing rate and pattern on changes in vehicle 

speed approaching horizontal curves. Controlling flashing patterns and rates would increase the 

chances of providing unambiguous results related to these characteristics of the treatment, which 

could then be applied in future speed-dependent flashing TCDs to accurately attain the desired 

effect. 

Speed-Activation Thresholds 

While speed-dependent flashing was not addressed by this test plan, studying and recommending 

possible speed-activation thresholds for the follow-on field study described in Chapter 4 of this 

report was an important objective of the simulator effort. It is discussed both in the following 

“Methods and Data” section as well as the section titled, “Regression Analysis to Detect Possible 

Speed-Activation Thresholds.” The results of the simulator study are presented in the “Analysis 

Results” section of this chapter. The experimental methods and data compiled during the 

simulator study are described in the “Methods and Data” section below. 

METHODS AND DATA 

This study was approved by the University of Utah Institutional Review Board (IRB) on June 18, 

2014 (#00072404). 

Fifty-four subjects (27 male, 27 female) between the ages of 18 and 25 were initially recruited to 

participate in this research. The proposed age range was based on the research team’s previous 

experience with simulator studies (see the discussion on motion sickness at the conclusion of this 

section). This target sample size was based on the need to have an appropriately counterbalanced 

research design as well as on the anticipated effect sizes of the proposed treatment manipulations 

for investigation. Subjects were recruited from a local population of university students and the 

surrounding community and were compensated with course credit or $30 for participation. In the 

end, project resources allowed the research team to run a total of 68 subjects (35 male, 33 female) 

to improve the power of the statistical analysis and account for any possible missing data 

following data reduction and quality control. 

A 3 (flash pattern) x 2 (flashing rate) experimental design was used with a hanging control group. 

The three flashing patterns that were explored were: (1) flashing in sequence toward the driver, 

(2) flashing in sequence away from the driver, and (3) flashing in unison. The two flashing rates 

that were used were: (1) slow flashing, defined as one flash per second, and (2) fast flashing, 

17 
defined as five flashes per second. In order to remove any confounding effects, each of the 

flashing treatments was implemented as flashing post delineators. The hanging control group 

scenario consisted of the actual PA Route 851 TCDs without any flashing. An example of the 

flashing post-mounted delineators along one of the PA Route 851 horizontal curves is provided 

in figure 3. 

Figure 3. Photo. Example treatment-flashing in unison. 

Source: Department of Civil and Environmental Engineering, University of Utah 

In order to implement this experimental design, the previously described six-mile stretch of PA 

Route 851 was divided into three distinct groups of curves, each roughly two miles in length. 

One of the flashing treatments (with a treatment distinguished by the combination of flashing 

pattern and rate) was applied to each of these curve sections. The curve sections, curve 

identification numbers, and individual curve radii are summarized in table 1 and table 2. 

Overall, six distinct simulator scenarios were created, each scenario with three distinct flashing 

treatments, one on each of the three curve sections. As noted, a seventh scenario was created 

where each of the curves had the actual PA Route 851 TCDs without flashing. The seven 

scenarios and their relationships to the curve groups, flashing sequences, and flashing rates are 

shown in table 3. During experimentation, subjects drove one practice scenario, two of the six 

possible treatment scenarios shown in table 3, and the hanging control scenario (Scenario 7). As 

demonstrated in table 3, each subject drove either treatment scenarios 1 and 4, 2 and 5, or 3 and 6, 

in addition to the control scenario, which was driven by all subjects. This resulted in every 

subject seeing each of the six total treatments, with each treatment previously defined by the 

18 
flashing sequence (3 levels) and flashing rate (2 levels). The precise scenarios driven by each 

subject were assigned in a counterbalanced order so that all treatment-curve combinations were 

driven an equal number of times over the course of the study and to minimize the effect of 

ordering/learning. All scenarios represented nighttime, clear-weather conditions on the 6-mile 

stretch of PA Route 851. 

Table 1. Curve groups. 

Curve Sections PA Route 851 Curve Identification Number 

Curve section 1 3 4 6 7 8 --

Curve section 2 11 12 13 14 15 17 

Curve section 3 20 21 24 27 28 32 

-- no data available. 

19 
Table 2. Curve data. 

Curve No. Radius (ft) Average Vertical Grade Curve to Curve tangent (ft) Direction 

3 1165 6.4% 174.89 Right 

4 874 -1.2% 1348.13 Right 

6 292 -2.2% 1152.1 Right 

7 326 -5.9% 311.07 Left 

8 136 3.2% 1375.73 Right 

11 539 -5.7% 610.81 Right 

12 128 -13.3% 235.72 Left 

13 283 -11.3% 263.14 Right 

14 186 -2.2% 89.51 Left 

15 371 7.8% 81.16 Right 

17 315 9.9% 65.7 Right 

20 123 -8.4% 43.35 Right 

21 310 -9.8% 207.77 Left 

24 302 1.6% 28.2 Left 

27 84 -9.8% 168.19 Right 

28 698 2.8% 47.96 Left 

32 213 2.2% 157.85 Right 

20 
Table 3. Scenario and curve combinations with the hanging control group. 

Curve Group 

Number 

Scenario 

1 

Scenario 

2 

Scenario 

3 

Scenario 

4 

Scenario 

5 

Scenario 

6 

Scenario 

7 

1 1.1 3.1 2.1 1.2 3.2 2.2 control 

2 2.2 1.2 3.2 2.1 1.1 3.1 control 

3 3.1 2.1 1.1 3.2 2.2 1.2 control 

*Sequence X._ = 1– toward the driver; 2 – non-directional (simultaneous flashing); 3 – away from the driver. 

Flashing rate _.X = 1 – low (1Hz); 2 – high (5 Hz). 

Varying levels of speed-activation thresholds were not explicitly included in the experimental 

design shown in table 3 for two main reasons: 

 Speed-activation thresholds will likely be site-specific in the field, a function of operating 

speeds, curve radius, superelevation (and resulting side friction demand), and 

(potentially) sight obstructions; and 

 Since experimenters were not restricting the speeds at which subjects could drive to a 

certain range, there could be frequent “holes” in the data (when the flashing devices are 

not activated), resulting in an unbalanced design—the effective sample for studying the 

main flashing characteristics (sequence and rate) would then be a function of the actual 

sample minus instances where drivers did not trigger the devices at all. 

While not explicitly included in the experimental design, the richness of the data being collected 

allowed speed-activation threshold recommendations to be developed indirectly through the data 

analysis activities. The end goal of the speed-activation threshold analysis was insights and 

recommendations that inform the follow-on field study. Further advancement of the exploration 

of these threshold levels in an outdoor setting is described in Chapter 4. 

Testing Procedure 

During the course of participating in this experiment, research participants first filled out a brief 

driving survey that assessed driving habits, history, and demographics (see Appendix A). 

Following this survey, participants were introduced to the simulator through a practice scenario, 

which was the same PA Route 851 scenario driven with the actual TCDs in the opposite 

direction of travel as the experimental runs. Once fully accustomed to the dynamics of the 

simulator, participants drove the three experimental scenarios (two treatment scenarios and one 

control scenario) as described in the previous section. Following each scenario, participants were 

encouraged to take a brief break in order to reduce the potential for motion sickness. Data 

collection ran over a full 2 months. 

One concern related to the investigation of driver performance characteristics in a simulated 

environment is motion sickness. In order to minimize the incidence and severity of motion 

sickness, a number of precautionary measures were taken. First, only participants between the 

21 
ages of 18 and 25 were eligible to participate in this research. Experience from previous research 

suggests that this group is significantly less susceptible to motion sickness than older drivers. If 

incidence of motion sickness does occur, younger drivers tend to recover more quickly than 

older drivers. In addition to these age criteria, researchers were trained to look for early signs of 

motion sickness and they followed a detailed response plan if any signs were observed. This 

aggressive approach proved very effective at reducing the incidence of motion sickness and 

reducing its severity when it did occur. 

Data Reduction and Quality Control 

As noted previously, the research team ran 68 subjects (35 male, 33 female) between the ages of 

18 and 25 through the experiment. There were some cases of simulator sickness symptoms, 

where some participants were not feeling well and could not complete the entire experiment. 

Additional technical challenges due to the complexity of the scenarios and simulator itself would 

sometimes prolong or postpone the experiment. These events resulted in eliminating data from 5 

of the 68 subjects. 

Prior to data analysis, a series of custom data cleaning and preparation scripts were written. The 

purpose of these scripts was to comb through each of the recorded performance files, identify 

curve steps at each desired distance or time interval, and record the nearest performance 

measurement for that curve step. This approach allowed the research team to generate very 

precise speed and location profiles for each driver at each curve step under each condition. These 

data were then aggregated to form the basis for the final analysis. This step was critical in order 

to transform the time series results (in the form of continuous profiles) into the type of 

geospatially synchronized format needed to appropriately analyze changes in different 

performance measures as participants enter and exit each curve. 

Each scenario run consisted of 17 curves that were negotiated by the participants. For each 

participant/scenario/curve combination, three performance measures were ultimately recorded, 

including vehicle lateral position, speed, and the occurrence of one or more lane departures. For 

lateral position and speed, recordings began about 200 ft upstream of the beginning of each curve 

(point of curvature, or PC) and continued through the entire curve. Table 2 shows the upstream 

tangent length for each tested curve and curve direction. The main locations where the 

parameters were recorded for analysis were selected relative to the PC point. With “L” as the 

length of the entire horizontal curve and PT representing the point of tangency, lateral position 

and speed were recorded for the following points inside and upstream of the curve: PC, PC+1/4L, 

PC+1/2L, PC+3/4L, PT, PC-50ft, PC-100ft, PC-150ft, and PC-200ft. 

The simulator output data were analyzed at these critical locations inside and upstream of each 

curve. If the tangent length between two curves was less than the predefined recording location 

(e.g., if the PC-200 was actually inside of a previous curve), than those data were not taken into 

account. For instance, if the tangent length between two consecutive curves was less than 200 ft, 

then the results recorded at 200 ft upstream of the PC were removed. Table 4 shows which 

locations were removed for this reason. 

22 
_I • 

• 

• 

I ! ! 

i • 

13, PC 13, PC+1/2L 13, PC+1/4L 

LP 

0 10 20 30 40 0 10 20 30 40 

13, PC+3/4L 13, PT 

Graphs by curve and Location 

Table 4. Recording locations removed from the database. 

Removal 

Location PC-200 PC-150 PC-100 PC-50 

Curve Number 

3,14,15,17,20,24, 

27,28,32 

14,15,17,20,24,28 14,15,17,20,24,28 20, 24, 28 

Additionally, an analysis of possible outliers was conducted using box-and-whisker plots of 

speed and lateral position. After visual inspection of the box-and-whisker plots, extreme outliers 

were eliminated from the dataset. In terms of process, the distributions of lateral positions and 

speeds for each identified location were observed using box-and-whisker and histograms 

generated with STATA. The extreme outliers were easily distinguished in the box-and-whisker 

plots for lateral position, and appeared to be due to the simulator measurement error or some 

random operator error. Figure 4 and figure 5 show examples of the box-and-whisker plots and 

histograms for curve 13, where two extreme outliers at locations PC and PC+1/4L are easily 

identified. After checking the trajectory for this particular case from the original dataset, it was 

observed that a severe run-off-road event happened at this location, as shown in figure 6. Table 5 

provides details on outliers that were removed from the database. 

Figure 4. Graph. Box-and-whisker plots for lateral position. 

23 
I\___ '-----

.fl " " " ' 

~ . A .. .. .. 

0 .1 .2 .3 0 .1 .2 .3 

-5 0 5 10 15 20 25 30 35 40 

13, PC 13, PC+1/2L 13, PC+1/4L 

13, PC+3/4L 13, PT

Density 

-5 0 5 10 15 20 25 30 35 40 -5 0 5 10 15 20 25 30 35 40 

LP 

Density 

normal LP 

Graphs by curve and Location 

 ■ 

• 

• 

• 

• 

Figure 5. Graph. Histograms of lateral position. 

‐2200

‐2190

‐2180

‐2170

‐2160

‐2150

‐2140

‐2130

‐2120

‐2110

‐2100

600 700 800 900 1000 1100 1200 1300 1400

y (ft)

x (ft)

Curve 13: Lateral Position Coordinates of Vehicle

pc

PT

PC+1/4

PC+1/2

PC+3/4

CL

Simulator

LE

RE

Figure 6. Graph. Lateral position coordinate profile through curve 13: scenario 1, 

participant 1 vehicle position test. 

24 
Table 5. Outliers deleted from the database. 

Subject ID Scenario Curve Location 

8 6 28 PC-50,PC,PC+1/4L 

37 3,6 28 PC-50, PC, PC+1/4L 

38 6 28 PC-50, PC 

44 5 28,32 All locations 

57 1 13 All locations 

In summary, 63 participants drove 3 scenarios, each with 17 curves, with each curve having 

between 5 and 9 data recording points (depending on the upstream tangent length). All the output 

data were checked using quality control procedures to eliminate outliers, leading to a total 

number of 23,450 successful observations. 

Datasets 

Three different datasets were prepared from the raw simulator data. Table 6 shows descriptive 

statistics for all variables extracted from the simulation runs, including speed and lateral position 

at the nine critical locations upstream and within the horizontal curves (i.e., PC, PC+1/4L, 

PC+1/2L, PC+3/4L, PT, PC-50ft, PC-100ft, PC-150ft, and PC-200ft). There were 23,450 

observations in this dataset. All roadway data were obtained from the roadway files of the 

simulator scenario, which were built from data collected on the actual PA Route 851. The 

research team used “x,y,z” coordinates to develop geometric variables such as radii and vertical 

grade. 

Speed was measured directly from the simulator-generated profiles. The unit of speed was miles 

per hour (mph). The mean speed across all data points in this first dataset was 32.04 mph. 

According to the Permanent International Association of Road Congresses (PIARC) Road Safety 

Manual, the ideal position of a vehicle along a horizontal curve is in the center of the lane. (PIARC 

2003,Babaee et al. 2014) Therefore, vehicle lateral position was calculated as the distance between the 

middle of the travel lane and the center of the simulator vehicle. Left of center was “negative” 

and right of center was “positive.” The mean lateral position across all data points in this first 

dataset was +1.37 ft (i.e., 1.37 ft to the right of the center of the travel lane). 

For illustration purposes, table 7 shows descriptive statistics for all variables extracted from the 

simulation runs, including speed and lateral position, but for only the five critical locations 

within the horizontal curves (i.e., PC, PC+1/4L, PC+1/2L, PC+3/4L, and PT). The observations 

in this dataset totaled 15,449. The average of mean speed across all data points inside of the 

horizontal curves was 29.98 mph which, as expected, is lower than the mean speed from table 6 

25 
The mean lateral position across all data points in this second dataset was +1.32 ft (i.e., 1.32 ft to 

the right of center). 

Table 8 shows descriptive statistics for the database used to analyze lane departures. The 

occurrence of one or more lane departures was determined by creating computer algorithm in 

MATLAB to track vehicle trajectory versus center and edge line locations in the simulator. For 

each pass through a horizontal curve, an indicator variable for “lane departure” was coded as “1” 

if at least one lane departure outside of the travel lane occurred and “0” if the vehicle stayed 

within the travel lane for the entire stretch through the curve. A total of 3,087 observations, with 

an observation defined as one vehicle pass through a horizontal curve from curve PC to PT, 

make up the dataset in table 8. 

26 
Table 6. Descriptive statistics for dataset of nine critical horizontal curve and upstream 

locations. 

Variable Obs. Mean Std. 

Dev. Min. Max. 

Speed (mph) 23450 32.04 8.39 7.98 63.93 

Lateral position (ft) 23450 1.37 2.15 -22.92 20.48 

Inverse of horizontal curve radius (ft) 23450 0.00283 0.00310 0 0.01195 

Vertical grade (%) 23450 -2.80 6.99 -15.94 12.43 

Indicator variable for flashing rate and 

sequence (1 = low rate toward the driver, 

0=otherwise) 

23450 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = low rate with flashing in unison, 

0=otherwise) 

23450 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = low rate away from the driver, 

0=otherwise) 

23450 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = high rate toward the driver, 

0=otherwise) 

23450 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = high rate with flashing in unison, 

0=otherwise) 

23450 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = high rate away from the driver, 

0=otherwise) 

23450 0.11 0.31 0 1 

27 
Indicator variable for control scenario (1 = 

control, 0=otherwise) 23450 0.34 0.47 0 1 

Indicator variable for location (1= upstream 

200 ft from PC (PC-200), 0 = otherwise) 23450 0.06 0.24 0 1 

Indicator variable for location (1 = upstream 

150 ft from PC (PC-150), 0 = otherwise) 23450 0.09 0.28 0 1 

Indicator variable for location (1 = upstream 

100 ft from PC (PC-100), 0 = otherwise) 23450 0.09 0.28 0 1 

Indicator variable for location (1 = upstream 50 

ft from PC (PC-50), 0 = otherwise) 23450 0.11 0.31 0 1 

Indicator variable for location (1 = point of 

curvature (PC), 0 = otherwise) 23450 0.13 0.34 0 1 

Indicator variable for location (1= middle point 

of curve (PC+1/2L), = 0 = otherwise) 23450 0.13 0.34 0 1 

Indicator variable for location (1 = quarter 

point inside curve (PC+1/4L) , 0 = otherwise) 23450 0.13 0.34 0 1 

Indicator variable for location (1 = threequarter point inside curve (PC+3/4L), 0 = 

otherwise) 

23450 0.13 0.34 0 1 

Indicator variable for location (1 = point of 

tangency (PT), 0 = otherwise) 23450 0.13 0.34 0 1 

Indicator variable for curve direction to the left 

(1=left, 0=right) 23450 0.33 0.47 0 1 

28 
Table 7. Descriptive statistics for dataset of five critical horizontal curve locations. 

Variable Obs. Mean Std. 

Dev. Min. Max. 

Speed (mph) 15449 29.98 7.73 7.98 63.82 

Lateral position (ft) 15449 1.32 2.19 -22.92 20.48 

Inverse of horizontal curve radius (ft) 15449 0.0043 0.0029 0.0009 0.0119 

Vertical grade (%) 15449 -2.12 7.37 -15.94 12.43 

Indicator variable for flashing rate and 

sequence (1 = low rate toward the driver, 

0=otherwise) 

15449 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = low rate with flashing in 

unison, 0=otherwise) 

15449 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = low rate away from the 

driver, 0=otherwise) 

15449 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = high rate toward the driver, 

0=otherwise) 

15449 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = high rate with flashing in 

unison, 0=otherwise) 

15449 0.11 0.31 0 1 

Indicator variable for flashing rate and 

sequence (1 = high rate away from the 

driver, 0=otherwise) 

15449 0.11 0.32 0 1 

29 
Indicator variable for control scenario (1 = 

control, 0=otherwise) 15449 0.34 0.47 0 1 

Indicator variable for location (1 = point of 

curvature (PC), 0 = otherwise) 15449 0.2 0.4 0 1 

Indicator variable for location (1= middle 

point of curve (PC+1/2L), = 0 = otherwise) 15449 0.2 0.4 0 1 

Indicator variable for location (1 = quarter 

point inside curve (PC+1/4L) , 0 = 

otherwise) 

15449 0.2 0.4 0 1 

Indicator variable for location (1 = threequarter point inside curve (PC+3/4L), 0 = 

otherwise) 

15449 0.2 0.4 0 1 

Indicator variable for location (1 = point of 

tangency (PT), 0 = otherwise) 15449 0.2 0.4 0 1 

Indicator variable for curve direction to the 

left (1=left, 0=right) 15449 0.35 0.48 0 1 

30 
Table 8. Descriptive statistics for lane departure dataset. 

Variable Obs. Mean Std. 

Dev. Min. Max. 

Speed at upstream 200 ft (mph) 3087 35.02 9.04 8.46 63.79 

Speed at upstream 150 ft (mph) 3087 34.81 8.74 9.77 63.88 

Speed at upstream 100 ft (mph) 3087 34.31 8.34 10.63 63.93 

Speed at upstream 50 ft (mph) 3087 33.38 7.97 11.59 63.93 

Speed at PC (mph) 3087 32.01 7.82 8.85 63.82 

Speed at PC+1/4L (mph) 3087 30.61 7.83 8.34 59.81 

Speed at PC+1/2L (mph) 3087 29.41 7.82 10.08 56.21 

Speed at PC+3/4L (mph) 3087 28.79 7.47 9.06 55.35 

Speed at PT (mph) 3087 29.11 7.20 7.98 55.10 

Inverse of horizontal curve radius (ft) 3087 0.0043 0.0029 0.0009 0.0119 

31 
Average vertical grade (%) 3087 -2.12 6.84 -13.26 9.94 

Lane departure (1=yes, 0= otherwise) 3087 0.58 0.49 0.00 1.00 

Lateral Position at upstream 200 ft (ft) 3087 1.27 2.43 -14.59 8.90 

Lateral Position at upstream 150 ft (ft) 3087 1.09 2.33 -13.21 10.04 

Lateral Position at upstream 100 ft (ft) 3087 0.92 2.13 -18.95 14.59 

Lateral Position at upstream 50 ft (ft) 3087 0.92 1.88 -21.41 19.37 

Lateral Position at PC (ft) 3087 1.21 1.61 -9.53 12.09 

Lateral Position at PC+1/4L (ft) 3087 1.25 1.88 -8.26 9.54 

Lateral Position at PC+1/2L (ft) 3087 1.52 2.42 -13.11 9.21 

Lateral Position at PC+3/4L (ft) 3087 1.16 2.38 -18.75 14.92 

Lateral Position at PT (ft) 3087 1.49 2.45 -22.92 20.48 

32 
Indicator variable for curve direction to the left (1=left, 

0=right) 3087 0.35 0.48 0.00 1.00 

Indicator variable for flashing rate and sequence (1 = low 

rate toward the driver, 0=otherwise) 3087 0.11 0.31 0.00 1.00 

Indicator variable for flashing rate and sequence (1 = low 

rate with flashing in unison, 0=otherwise) 3087 0.11 0.31 0.00 1.00 

Indicator variable for flashing rate and sequence (1 = low 

rate away from the driver, 0=otherwise) 3087 0.11 0.31 0.00 1.00 

Indicator variable for flashing rate and sequence (1 = 

high rate toward the driver, 0=otherwise) 3087 0.11 0.31 0.00 1.00 

Indicator variable for flashing rate and sequence (1 = 

high rate with flashing in unison, 0=otherwise) 3087 0.11 0.31 0.00 1.00 

Indicator variable for flashing rate and sequence (1 = 

high rate away from the driver, 0=otherwise) 3087 0.11 0.32 0.00 1.00 

Indicator variable for control scenario (1 = control, 

0=otherwise) 3087 0.34 0.47 0.00 1.00 

ANALYSIS RESULTS 

Verification of Speed and Lateral Position Performance in Simulator 

An effort to verify the speed and lateral position behavior of drivers in the simulator environment 

created for this study was conducted using previous empirical research that studied vehicle speed 

and lateral position “in the field.” First, an ordinary least squares, linear regression model was 

estimated with the recorded simulator speed for this study at the curve midpoint as a dependent 

variable. The inverse of horizontal curve radius and vertical grade were defined as the right-hand 

side (RHS) independent variables. The resulting model is shown in figure 7. 

33 
ܸ ൌ െ1655.48 െ 0.344393 ൈ ܩ ൅ 35.81 ܴ

Figure 7. Equation. Expected mean speed at horizontal curve midpoint. 

Where: 

V = expected mean speed at horizontal curve midpoint (mph) 

R = radius of horizontal curve (ft) 

G = vertical grade (%) 

The expected speed and the variability around the regression line were then used to estimate an 

85th-percentile speed in the driving simulator at the horizontal curve midpoints. 

Next, several existing speed models for rural, two-lane horizontal curves were identified. Table 9 

shows various 85th-percentile speed prediction models identified in the rural, two-lane highway 

literature. For the first three models in table 9, estimation used data from roads with significantly 

higher posted speed limits than the section of PA Route 851 used for this study. However, 

developed a speed model applicable to roads with lower posted speed limits (30 mph-40 mph). 

(Banihashemi et al. 2011) The posted speed limit is 40-45 mph on the test segments of PA Route 851. 

A plot of these various speed prediction models for rural, two-lane highways is shown in figure 8. 

Overall, results show very similar speed behavior between drivers in the simulator study 

conducted as part of this research effort and drivers observed in the field as part of these previous 

efforts. Findings are very comparable to those from Banihashemi et al., the most important 

comparison given Banihashemi et al.’s focus on roadways with posted speed limits similar to PA 

Route 851.(Banihashemi et al. 2011) It is important to note here that drivers in this study were not asked 

to “obey” the posted speed limit. The nature of this particular road (e.g., alignment, cross section, 

roadside) led to what some might consider the “slower” operating speeds in figure 6, around or 

below the posted speed limit. In other words, the posted speed limits on the simulated PA Route 

851 appeared to be very credible given the alignment and cross section of the road. 

Table 9. Published 85th-percentile speed models. 

Authors Models 

Taragin (1954) V85=(88.87-2554.76/R)*0.621 

Glennon et al. (1985) V85=(103.96-4524.94/R)*0.621 

Krammes (1995) V85=(102.44-2471.81/R+0.012Lc-0.10Δ)*0.621 

Banihashemi et al.(2011) V85=44.25-1462/R 

Note: Lc represents length of horizontal curve, Δ represents defection angle, R represents horizontal curve radius. 

34 
60 

50 ➔ 

:2 40 ➔ 

a. 

E 

-0 

QI 

QI 

a. 

Vl 30 

20 

10 

0 

0 

I 

85th Percentile Speed vs Radius: Model Comparison 

---

200 400 

-----

600 

Radius{ft) 

------------

- Taragin 

--Glennon et al. 

Krammes 

- - - Simulator LRM with 85th speed 

--Banihashemi et al. 

800 1000 1200 

Figure 8. Graph. Modeled 85th- percentile speed versus radius of horizontal curve. 

To verify the lateral vehicle position measurements in the simulator, a comparison was made 

between the simulator data and one previous field study. Table 10 shows the mean distance 

between the roadway centerline and the center of the simulator vehicle under different flashing 

treatments and the control condition in this study. The lateral vehicle positions were closer to the 

middle of the travel lane for each treatment when compared to the control condition, but in all 

cases were more than 1 ft to the right of the center of the lane. 

Table 11 presents the descriptive statistics for the lateral vehicle position, again measured as the 

vehicle centroid position in relation to the centerline, from a published field study on centerline 

rumble strips.(Porter et al. 2004) The field study data also showed vehicles positioned to the right of the 

center of the lane, but not as far right as in the driving simulator. Variability in lateral vehicle 

placement was also smaller in the field than in the simulator. Observed differences may be due to 

the simulator’s “open cockpit,” with only one seat (the driver seat). Still, the numbers are within 

35 
reason and indicate that lateral position behavior was generally replicated in the simulator 

environment. 

In summary, the results of the validation effort increased the research team’s confidence in their 

ability to capture actual speed and lateral vehicle position behavior in the driving simulator. 

Table 10. Distance between the center of the simulator vehicle and the middle of the lane. 

Treatment n 

Lane Width 

(ft) Std. Mean (ft) Lower Bound* 

Upper 

Bound* 

Low toward 1688 10.42 2.23 6.51 6.40 6.61 

Low 

simultaneously 1705 10.41 2.21 6.49 6.39 6.60 

Low away 1710 10.44 2.25 6.45 6.35 6.56 

High toward 1711 10.42 2.40 6.51 6.40 6.62 

High 

simultaneously 1715 10.41 2.23 6.44 6.34 6.55 

High away 1735 10.44 2.17 6.49 6.39 6.59 

Control 5185 10.43 2.05 6.64 6.59 6.70 

Note: * denotes the 95-percent confidence interval. 

36 
Table 11. Distance between vehicle centroid and centerline for CRS sites (from Porter et al., 

2004). 

Category Designation Period 

Lane 

Width 

(ft) 

n 

Mean 

(ft) Std. Lower Bound 

Upper 

Bound 

1 Treatment Before 12 107 6.16 1.71 5.84 6.48 

1 Treatment After 12 171 6.62 1.31 6.42 6.82 

2 Comparison Before 12 157 6.19 1.29 5.99 6.39 

2 Comparison After 12 190 6.22 1.35 6.03 6.41 

3 Treatment Before 11 280 6.02 1.2 5.88 6.16 

3 Treatment After 11 278 6.27 1.17 6.13 6.41 

4 Comparison Before 12 199 6.1 1.35 5.91 6.29 

4 Comparison After 12 294 6.18 1.34 6.03 6.33 

Note: * denotes the 95-percent confidence interval. 

Aggregate Analysis of Speed and Lateral Position 

The data described in previous sections of this report were analyzed with a focus on lateral 

vehicle positions and speeds. Occurrences of lane departures are analyzed at a more disaggregate 

level (e.g., probability of a vehicle lane departure) in the next section. As already noted, there 

were three curve sections identified in the PA 851 scenario with six flashing device treatments 

and the existing TCDs (with no flashing devices) control group (for additional details, see 

Appendix B). 

In general, the aggregate analyses do not appear to show any statistically significant differences 

in mean speed across the various flashing treatments. The mean speeds are generally lowest for 

the control condition. With regards to lateral vehicle position, there were no consistent and 

statistically significant differences in lateral position across all treatment conditions and the 

control. 

Disaggregate Analysis of Lane Departure Probability 

This section describes an analysis of lane departures under the various treatments and the control 

condition. Binary logit models were estimated using the occurrence (or not) of one or more lane 

departures as a driver traverses a horizontal curve as the dependent variable and roadway 

characteristics (including curve treatments) as independent variables. In other words, an 

observation was considered one pass of a driver through a horizontal curve from the PC to the 

37 
PT. Lane departures were defined as any part of the simulator vehicle leaving the appropriate 

travel lane either to the driver’s left or right. Descriptive statistics for the lane departure database 

were provided in table 8. 

In the binary logit model, a set of linear functions (Sin), shown in general form in figure 9, is 

used to define how lane departure outcome i (i.e., one or more lane departures or no lane 

departures) for observation n (i.e., one pass of a driver through a horizontal curve from the PC to 

the PT) is determined. 

Sin = Xinβi + εin

 Figure 9. Equation. Linear function of binary logit model. 

Where: 

Xin = a row of observed characteristics (e.g., driver, vehicle, roadway, environment) associated 

with observation n that have an impact on lane departure outcome i; 

βi = a vector of parameters to be estimated that quantify how the characteristics in Xin impact lane 

departure outcome i; and 

εin = a disturbance term that accounts for unobserved and unknown characteristics of observation 

n that impact lane departure outcome i. 

There are as many such linear functions as there are possible lane departure outcomes. In this 

case, there are two possible outcomes: (1) one or more lane departures occur as a driver traverses 

a horizontal curve, or (2) no lane departures occur as a driver traverses a horizontal curve. With 

the disturbance terms of the two “Sin functions” for these outcomes identically and independently 

distributed as extreme value, the binary logit model results, shown in figure 10. 

ܧܺܲሺܺ݅݊ߚ ሻ ܲ݊ ሺ݅ሻ ൌ ݅

ሻ݅ߚܺ݅݊ሺܺܲܧ ൅ 1

Figure 10. Equation. Binary logit model for the probability of lane departures. 

Model outcome i in this analysis represented the outcome of one or more lane departures 

occurring; therefore, Pn(i) represents the probability of a lane departure as a subject traverses a 

horizontal curve for observation n. 

The estimation results for the binary logit model of lane departures are shown in table 12. For 

ease of interpretation, the odds ratios (as opposed to the actual parameter estimate) are reported. 

An odds ratio of more than one in this table indicates that the variable is positively associated 

with the possibility of a lane departure (i.e., an increase in that variable’s value increases the 

probability of one or more lane departures); an odds ratio less than one indicates the variable is 

negatively associated with the possibility of a lane departure (i.e., an increase in that variable’s 

value reduces the probability of one or more lane departures). 

38 
The odds ratio for the inverse of horizontal curve radius indicates smaller radii will increase the 

probability of one or more lane departures, as expected. Vertical grade indicates that more 

positive and steeper vertical grades will also increase the probability of one or more lane 

departures. However, the indicator variables for the six treatments indicated that none of the six 

applied treatments showed statistically significant impacts on lane departure probabilities at the 

95 or 90 percent confidence level compared to the control condition. However, the treatment of 

low flashing rate away from the driver is associated with a lower probability of one or more lane 

departures and the odds ratio is statistically significant at slightly more than the 85 percent 

confidence level. This is particularly interesting because findings to be discussed in the next 

section also show the “low-away” treatment resulting in a horizontal curve speed nearly 4 mph 

less than the control condition for drivers that approach a horizontal curve at 50 mph or more. 

Table 12. Binary logit model of lane departure occurrence. 

Variable Odds Ratio Standard Error p-value 

Inverse of horizontal curve radius (ft) 1.60E+59 2.59E+60 <0.001 

Average Vertical Grade (%) 1.014635 0.006607 0.026 

Indicator variable for curve direction to the left 

(1=left, 0=right) 0.450576 0.036931 <0.001 

Indicator variable for flashing rate and sequence 

(1 = low rate toward the driver, 0=otherwise) 0.984032 0.129083 0.902 

Indicator variable for flashing rate and sequence 

(1 = low rate with flashing in unison, 

0=otherwise) 

1.048085 0.137075 0.720 

Indicator variable for flashing rate and sequence 

(1 = low rate away from the driver, 0=otherwise) 0.817384 0.106161 0.121 

Indicator variable for flashing rate and sequence 

(1 = high rate toward the driver, 0=otherwise) 1.09018 0.143449 0.512 

Indicator variable for flashing rate and sequence 

(1 = high rate with flashing in unison, 

0=otherwise) 

1.085951 0.142125 0.529 

Indicator variable for flashing rate and sequence 

(1 = high rate away from the driver, 0=otherwise) 1.111789 0.145497 0.418 

Constant 1.059988 0.100821 0.540 

Note: Number of obs. = 3087; Log_likelihood = -1993.7254. 

39 
݅ߝ ൅ ܺ݅ߚ ൌ ܸ݅

Regression Analysis to Detect Possible Speed-Activation Thresholds 

While not explicitly included in the experimental design, the richness of the data collected 

allowed the research team to indirectly explore possible speed-activation thresholds through the 

data analysis activities. The results of this exploration are reported in this section. The end goal 

of the speed-activation threshold analysis was insights and recommendations that inform the 

follow-on field study (see Chapter 4 of this report), which will further advance the exploration of 

these threshold levels in an outdoor setting. 

Ordinary least squares regression was used to model the potential linear-in-parameters 

relationship between the six different treatments, the control condition, and mean horizontal 

curve speed, while also controlling for the effect of other driver and roadway characteristics. The 

basic structure of the OLS regression model is shown in the equation in figure 11. 

Figure 11. Equation. Regression equation of horizontal curve speed. 

Where: 

ܸ௜ = mean horizontal curve speed (mean of instantaneous speed at PC, PC+1/4L, PC+1/2L, 

PC+3/4L, and PT);

ߚ =	regression coefficients to be estimated; 

ߝ = ௜disturbance terms for each observation; and 

ܺ௜ = explanatory variables that are associated with Vi, including the curve treatments. 

Model estimation was repeated with five different datasets: 

1. All data (which reinforced findings already reported of no statistically significant 

differences between treatments and controls for speed except for, in some cases, a lower 

expected speed for the control condition. 

2. Data for observations of mean horizontal curve speed only for cases where the curve 

approach speed (measured at 200 ft upstream from the PC) was 40 mph or more. 

3. Data for observations of mean horizontal curve speed only for cases where the curve 

approach speed (measured at 200 ft upstream from the PC) was 45 mph or more. 

4. Data for observations of mean horizontal curve speed only for cases where the curve 

approach speed (measured at 200 ft upstream from the PC) was 50 mph or more.; 

5. Data for observations of mean horizontal curve speed only for cases where the curve 

approach speed (measured at 200 ft upstream from the PC) was 55 mph or more. 

Model estimation results are reported in table 13. As already noted, estimation results using all 

data generally reinforced findings already reported of no statistically significant differences 

between treatments and controls for speed except for, in some cases, a lower expected speed for 

the control condition. Estimation results using data only for cases where the curve approach 

speed was either 40 mph or more, or 45 mph or more, are generally consistent with the “all data” 

40 
model. Some interesting results are uncovered in the estimation results using data only for cases 

where the curve approach speed was 50 mph or more. These results show four different flashing 

treatments associated with lower speeds than the control condition: low toward, low 

simultaneous, low away, and high simultaneous by 0.89, 1.0, 3.8, and 2.4 mph, respectively. The 

findings for low-away and high-simultaneous are statistically significant at the 95 percent 

confidence level. A curve approach speed of 50 mph or more represents an operating speed that 

is at least 5 to 10 mph above the posted speed of PA Route 851, depending on the location. This 

indicates that while the flashing treatments were not associated with any change in speed across 

all drivers, they may be effective at reducing speeds for drivers approaching a curve at a “high” 

operating speed (identified for this scenario as 50 mph or more, at least 5 to 10 mph above the 

posted speed). The low-away treatment was associated with the lowest mean curve speed of all 

treatment and control conditions (and from the previous section, with a reduction in lane 

departure probability compared to all other treatments and the control condition). Estimation 

results using only observations where the curve approach speed (measured at 200 ft upstream 

from the PC) was 55 mph or more are reported in the shaded columns, but sample size issues 

make it difficult to have any confidence in the estimates. 

In summary, the findings presented in this section are promising in terms of the flashing 

treatments resulting in speed reductions for drivers approaching horizontal curves at “excessive 

speeds.” Excessive speeds were identified for this stretch of PA Route 851 as 50 mph or more. 

The low-away and high-simultaneous treatments had the greatest and most statistically 

significant effects on this faster group of drivers. It is likely that the threshold speed over which 

the flashing treatments show this type of speed reduction effect will vary from location to 

location and will be a function of the road design characteristics. 

41 
Table 13. Comparison of mean speed models for various approach speeds. 

All Mean 

Speed 

Mean Speed 

>40mph 

Mean Speed 

>45mph 

Mean Sp

>50 mp

Variables Coef. P>z Coef. P>z Coef. P>z Coef. 

Inverse of horizontal curve radius (ft) -1152 0.000 -696 0.000 -640 0.000 -813 0

Average Vertical Grade (%) -0.218 0.000 0.041 0.131 0.068 0.145 -0.046 0

Speed at PC-50(ft) 0.613 0.000 0.861 0.000 0.991 0.000 0.963 0

Indicator variable for flashing rate and sequence (1=low rate toward the driver, 0=otherwise) 0.170 0.468 0.597 0.149 0.913 0.154 -0.893 0Indicator variable for flashing rate and sequence (1=low rate with flashing in unison, 0=otherwise) 0.068 0.773 0.319 0.431 0.104 0.866 -1.033 0Indicator variable for flashing rate and sequence (1=low rate away from the driver, 0=otherwise) -0.004 0.985 -0.273 0.513 -0.339 0.617 -3.841 0

Indicator variable for flashing rate and sequence (1=high rate toward the driver, 0=otherwise) 0.181 0.440 0.328 0.419 1.059 0.091 0.588 0Indicator variable for flashing rate and sequence (1=high rate with flashing in unison, 0=otherwise) -0.064 0.785 -0.365 0.387 -0.096 0.880 -2.404 0

Indicator variable for flashing rate and sequence (1=high rate away from the driver, 0=otherwise) 0.519 0.025 0.515 0.217 1.073 0.089 0.478 0

Constant 13.938 0.000 1.736 0.086 -4.965 0.007 -2.787 0

Number of observation 3087 944 424 140 

42 
SUMMARY OF SIMULATOR STUDY 

The objective of this simulator study was to explore optimum flash rates, sequencing patterns, 

and thresholds for speed-activated TCDs on horizontal curves along rural, two-lane roads to 

inform follow-on field testing of these devices. A driving simulator located in the University of 

Utah’s Utah Traffic Lab was used to conduct the simulator study and a PA Route 851 scenario, 

used for some previous FHWA research efforts, was ultimately used for the simulator 

experiments. Sixty-eight subjects (35 male, 33 female) between the ages of 18 and 25 

participated in this research; data from 63 subjects were useable for analysis. A 3 (flash pattern) 

x 2 (flashing rate) experimental design was used with a hanging control group. The three flashing 

patterns that were explored were flashing in sequence toward the driver, flashing in sequence 

away from the driver, and flashing in unison. The two flashing rates that were used were slow 

flashing, defined as one flash per second, and fast flashing, defined as five flashes per second. 

Overall, six distinct simulator scenarios were created, each with three distinct flashing treatments. 

A seventh scenario was created where each of the curves had the actual PA Route 851 TCDs, but 

without flashing. The precise scenarios driven by each subject were assigned in a 

counterbalanced order so that all treatment-curve combinations were driven an equal number of 

times over the course of the study and to minimize the effect of ordering/learning. 

All scenarios represented nighttime, clear-weather conditions on the six-mile stretch of PA Route 

851. An effort to verify the speed and lateral position behavior of drivers in the simulator 

environment created for this study was conducted using previous empirical research that studied 

vehicle speed and lateral position “in the field.” The results of the validation effort increased the 

research team’s confidence in their ability to capture actual speed and lateral vehicle position 

behavior in the driving simulator. 

Data analysis considered various measures of speed, lateral position, and lane departures. An 

aggregate analysis of all data points showed no statistically significant differences in mean speed 

across the various flashing treatments. Generally speaking, mean speeds were lowest for the 

control condition and this difference was statistically significant at several locations. Similarly, 

there were no consistent and statistically significant differences in lateral position across all 

treatment conditions and the control. 

None of the six applied treatments showed statistically significant impacts on lane departure 

probabilities at the 95th- or 90th-percentile confidence level compared to the control condition. 

However, the “low flashing rate away from the driver” treatment was associated with a lower 

probability of one or more lane departures and the odds ratio was statistically significant at 

slightly more than the 85th-percentile confidence level. 

The research team also indirectly explored possible speed-activation thresholds through the data 

analysis activities. Results showed that while the flashing treatments were not associated with 

any change in speed across all drivers, they may be effective at reducing speeds for drivers 

approaching a curve at a “high” operating speed (identified for this experiment as 50 mph or 

more, at least 5 to 10 mph above the posted speed). For these higher approach speeds, four 

43 
different flashing treatments were associated with lower speeds than the control condition: low 

toward, low simultaneous, low away, and high simultaneous by 0.89, 1.0, 3.8, and 2.4 mph, 

respectively. The low-away treatment was associated with the lowest mean curve speed of all 

treatment and control conditions, followed by the high-simultaneous flashing treatment. Sample 

size limitations did not allow the research team to determine if this trend continued for approach 

speeds of 55 mph or more. 

Limiting participant ages to fall between 18 and 25 years old is one limitation of the study. This 

simulator study was part of a larger, multifaceted study on roadway departure crashes. There 

were some practical limitations on this study’s size and cost. This particular trade-off associated 

with age ranges was made to give the research team more efficient access to potential subjects, 

and fewer possible cases of simulator sickness. Older drivers are likely to exhibit different visual 

scanning patterns and have different nighttime driving performance capabilities and behaviors. It 

is possible that results could be different for older drivers. Future research should therefore 

expand subject age ranges to increase generalizability of findings. 

As already noted, findings of this speed threshold analysis were promising in terms of the 

flashing treatments resulting in speed reductions for drivers approaching horizontal curves at 

“excessive speeds.” The low-away and high-simultaneous treatments had the greatest and most 

statistically significant effects on this faster group of drivers. It is likely that the threshold speed 

over which the flashing treatments show this type of speed reduction effect will vary from 

location to location and will be a function of the road design characteristics. Additional work on 

how to determine this speed threshold from site-to-site is needed. 

The following recommendations are based on the overall findings of this simulator study and are 

offered for consideration as this study moves forward to the field evaluation: 

 Continue to explore “low-away” and “high-simultaneous” flashing treatments, as both 

resulted in the greatest and most statistically significant speed reduction effects on drivers 

approaching horizontal curves at higher speeds; “low-away” also resulted in the lowest 

probability of one or more lane departures along the horizontal curves; 

 Expand the driver age ranges beyond 18-25 years old to include older drivers; and 

 Establish a procedure for determining a “threshold speed” over which the flashing 

treatments will be effective that can be applied across a range of roadway designs; using 

an empirical approach, the simulator study showed this threshold speed to be 50 mph for 

the study section of PA Route 851. 

44 
CHAPTER 4. OUTDOOR FIELD STUDY EVALUATION 

OBJECTIVES 

The objective of this study was to perform additional field studies of the SDCWS at some of the 

same sites included in the Smadi et al. study to validate the findings of the indoor simulator 

experiment described in Chapter 3 of this report.(Smadi et al. 2015) For this evaluation, vehicle 

operating speeds were collected at several locations approaching and within a horizontal curve to 

determine the optimal SDCWS flash rates, speed activation, and flashing sequence settings. The 

settings that were tested included the following: 

 Baseline: the chevrons are not flashing, so that a baseline condition can be established 

for a static array of chevrons. This is equivalent to the “before” condition used in the 

Smadi et al. study.(Smadi et al. 2015) 

 Condition #1: speed-activation threshold is 5 mph above the advisory speed for the 

horizontal curve; flash sequence is simultaneous; flash rate is three flashes per second. 

 Condition #2: speed activation threshold is 10 mph above the advisory speed for the 

horizontal curve: flash sequence is simultaneous; flash rate is three flashes per second. 

 Condition #3: speed-activation threshold is 5 mph above the advisory speed for the 

horizontal curve; flash sequence is away from the driver; flash rate is one flash per 

second. 

 Condition #4: speed activation threshold is 10 mph above the advisory speed for the 

horizontal curve: flash sequence is away from the driver; flash rate is one flash per 

second. 

STUDY SITES 

The study sites for the present study are located in Wisconsin. Characteristics of the sites are 

shown in table 14. It should be noted that a driveway was present near the midpoint of the 

horizontal curve at the Wisconsin Route 213 site, so the chevron array began immediately after 

the driveway and did not span the entire length of the curve. 

45 
Table 14. Characteristics of Wisconsin study sites. 

Route Direction 

AADT 

(veh/day) 

Lane 

Width 

(ft) 

Shoulder 

Width 

(ft) 

Posted 

Speed 

Limit 

(mph) 

Curve 

Advisory 

Speed Limit 

(mph) 

Curve 

Radius* (ft) 

Curve 

Length* (ft) 

N

20 WB 3,583 12 2 55 30 259.23 473.05 

67 SB 3,494 12 3 55 25 249.86 531.80 

213 SB 2,369 12 3 55 50 464.16 511.56 

* = Curve radius, curve length and chevron sign spacing were estimated from aerial imagery. 

46 
~800 feet I 

1~<:-----------~) ~ 

Travel 

Direction • • 

Midpoint of Horizontal Curve 

• Hi-star Sensor 

The existing SDCWS at the study sites is equipped with a radar device that can detect vehicles 

approximately 800 ft in advance of the horizontal curve, and is set to activate only when it 

detects approaching vehicles exceeding a certain speed threshold. A wireless communication 

system maintains synchronization among the chevron signs within the system. 

SPEED LIMIT COMPLIANCE AND OPERATING SPEED FIELD ASSESSMENT 

The research team collected observational speed data at the three locations identified in table 14. 

A sample of speeds were collected at each site under the five conditions noted in the “Objectives” 

section above, beginning with the baseline condition. For each, operating speeds were measured 

at a point upstream (and beyond the detection range of the SDCWS) to act as a control point, 

which allowed the research team to confirm that speeds were not changing at the data collection 

locations due to ancillary factors in the area. The control point also provided an indication of the 

voluntary speed limit compliance rate by vehicles on the roadways prior to reaching the SDCWS 

curve locations. A sample data collection configuration is shown in figure 12. 

Figure 12. Graphic. Data collection layout. 

Figure 12 shows a data collection point approximately 800 ft prior to the beginning of a 

horizontal curve, where the SDCWS first detects vehicles. Some field experimentation prior to 

beginning the data collection effort was undertaken to confirm this location at each study site. 

The upstream sensor was moved to a location just beyond the limit of the detection range prior to 

commencing field data collection, and was placed as close as possible to the previous field data 

collection location used by Smadi et al.(Smadi et al. 2015) Another data collection location was 

positioned at the beginning of the horizontal curve. The mid-point of the horizontal curve was 

the final data collection location at each Wisconsin site. 

47 
The results of the field study are presented in the “Analysis Results” section that follows. The 

data collection protocols, sample size requirements, and data analysis methods are described in 

detail in Appendix C. The field evaluation methods and data compiled during the field study are 

also detailed in Appendix C. 

Variables 

The primary data in the present study were the free-flow operating speeds of vehicles at all study 

locations. A sample of observed operating speeds was used to create speed distributions that 

show the range of driver behavior at each study site. These distributions were used to calculate 

measures of performance, such as the mean operating speed, standard deviation (or variance) of 

speed, specific speed percentiles (e.g., 85th-percentile operating speed), proportion of vehicles 

traveling within the pace (defined as the 10 mph range that has the highest proportion of 

observed speeds), and the proportion of vehicles exceeding the horizontal curve advisory speed 

(a measure of speed limit non-compliance). These measures of performance served as the 

dependent variables in the operating speed assessment. Several independent variables were 

considered in the SDCWS evaluation, including the following: 

 Time of day (night vs. day). 

 Flashing condition (baseline and conditions #1 through #4). 

 Data collection location (~800 ft before the curve, PC location, and midcurve location). 

ANALYSIS RESULTS 

The analyses from this part of the study are presented in detail in Appendix D of the report. The 

analyses are organized into four sections. First, the difference in the mean and 85th-percentile 

operating speeds at each data collection location for each condition is presented. This analysis 

includes tabular and graphical summaries for the entire data collection period at each site, and is 

then disaggregated by daytime and nighttime speeds. The second section presents the difference 

in mean and 85th-percentile speeds between adjacent data collection locations at each site. These 

data are presented for each data collection period, and then disaggregated by daytime and 

nighttime periods. The third section presents information about the proportion of vehicles 

exceeding the posted speed limit as well as the horizontal curve advisory speed. The final part of 

this section includes all of the statistical tests that were completed to compare the various speed 

metrics across the test conditions. 

Speed Difference Results 

The purpose of this analysis was to compare the mean and 85th-percentile operating speeds 

between each data collection condition at each data collection location. Appendix D presents 

detailed results in three subsections. The first considers all of the data collection time periods 

combined (daytime and nighttime). The second subsection considers only the daytime data 

collection period, while the final subsection considers only the nighttime data collection period. 

48 
Summary of Speed Difference Results 

When considering the collective results of the speed difference analysis, there appears to be a 

more significant operating speed benefit for the sequential flashing signs during nighttime 

conditions than during the daytime conditions. This was expected because the signs may be one 

of only a few cues that drivers received at night, while other cues likely exist during the day, 

when traversing a horizontal curve on rural two-lane highways. During the nighttime conditions, 

condition #1 (simultaneous flashing pattern at a rate of 3Hz, with a speed activation threshold 5 

mph above the advisory speed) produced the lowest mean and 85th-percentile operating speeds 

relative to the baseline and other active, sequential flashing patterns. For condition #1, the mean 

operating speeds at the midpoint of the curve were 0.0 to 3.3 mph lower during the active 

condition relative to the baseline (inactive) condition, while the 85th-percentile operating speeds 

were 0 to 4 mph lower than the baseline condition, at the three study sites. At the beginning of 

the curve, the speed reduction effect of condition #1 was more significant. Mean speeds were 1.1 

to 3.3 mph lower than the baseline condition, while the 85th-percentile operating speeds were 2.5 

to 4.0 mph lower than the baseline condition. Condition #2 (same as condition #1 with speed 

activation threshold that is 10 mph above the curve advisory speed) produced operating speeds at 

night that were also generally lower than the baseline condition. Conditions #3 and #4 (flashing 

pattern away from the driver) did not have a consistent, significant effect on vehicle operating 

speeds at night in the present study. 

Operating Speed Differences between Data Collection Locations 

The analysis considered the mean and 85th-percentile operating speed differences between each 

data collection location for all five conditions (baseline plus conditions #1 through #4). For each 

condition, the data were aggregated for the entire data collection period (daytime and nighttime 

combined) and then disaggregated into daytime and nighttime periods only. The speed 

differences were calculated between the following data collection locations: 

 Upstream (800 ft before the curve) minus the point of curvature. 

 Upstream minus the midpoint of the horizontal curve. 

 PC minus the midpoint of the horizontal curve. 

In this analysis, all of the speed differences have a positive value, indicating that the speed at the 

upstream location is greater than the operating speed at a downstream location. A summary of 

the data used for this analysis is shown in table 15 through table 23. In the present study, an 

active flashing condition for the sequential chevron array was considered effective if the 

difference between the upstream and midpoint of horizontal curve locations exceeded the speed 

difference at these same points during the baseline (inactive flashing) condition. 

49 
Table 15. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 213 upstream-PC. 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 5.39 4.60 6.35 4.00 3.00 5.00 

Condition #1 5.71 4.53 6.56 5.00 4.00 7.00 

Condition #2 5.74 5.39 6.07 6.00 6.00 5.00 

Condition #3 4.89 4.69 5.16 4.00 4.00 5.00 

Condition #4 5.70 5.25 6.25 4.00 3.00 5.00 

Table 16. Difference in mean and 85th- percentile operating speeds between data collection - 

WI 213 upstream - midcurve. 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 10.33 9.38 11.50 8.00 7.00 10.00 

Condition #1 10.38 9.03 11.37 9.00 8.00 9.00 

Condition #2 9.19 8.05 10.29 7.00 7.00 7.00 

Condition #3 7.98 6.81 9.51 5.00 5.00 7.00 

Condition #4 8.88 7.87 10.09 6.00 4.00 8.00 

50 
Table 17. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 213 PC-midcurve. 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 4.94 4.77 5.15 4.00 4.00 5.00 

Condition #1 4.67 4.50 4.80 4.00 4.00 2.00 

Condition #2 3.45 2.66 4.22 1.00 1.00 2.00 

Condition #3 3.09 2.12 4.35 1.00 1.00 2.00 

Condition #4 3.17 2.62 3.84 2.00 1.00 3.00 

Table 18. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 20 upstream-PC. 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 11.44 11.31 11.69 12.00 11.00 13.00 

Condition #1 12.80 11.90 14.10 14.00 13.00 16.00 

Condition #2 11.69 11.34 12.25 12.00 11.00 12.00 

Condition #3 12.92 12.87 12.99 13.00 12.00 14.00 

Condition #4 13.63 13.98 13.26 13.00 13.00 13.00 

51 
Table 19. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 20 upstream-midcurve. 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 18.06 17.87 18.41 18.00 17.00 18.00 

Condition #1 17.85 17.34 18.60 19.00 18.00 20.00 

Condition #2 16.87 17.10 16.49 18.00 17.00 16.00 

Condition #3 18.58 18.69 18.43 19.00 19.00 19.00 

Condition #4 18.38 19.33 17.35 18.00 20.00 17.00 

Table 20. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 20 PC-midcurve. 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 6.62 6.57 6.72 6.00 6.00 5.00 

Condition #1 5.06 5.44 4.50 5.00 5.00 4.00 

Condition #2 5.18 5.75 4.24 6.00 6.00 4.00 

Condition #3 5.66 5.83 5.44 6.00 7.00 5.00 

Condition #4 4.75 5.35 4.10 5.00 7.00 4.00 

52 
Table 21. Difference in mean and 85th- percentile operating speeds between data collection 

locations – WI 67 upstream-PC. 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 6.31 5.65 6.74 6.00 5.00 7.00 

Condition #1 7.25 6.15 9.20 8.50 7.00 11.00 

Condition #2 7.51 6.45 8.64 9.00 7.00 9.00 

Condition #3 6.58 6.21 7.24 7.00 8.00 7.00 

Condition #4 6.12 6.21 7.24 7.00 8.00 7.00 

Table 22. Difference in mean and 85th- percentile operating speeds between data collection 

locations - WI 67 (upstream-midcurve). 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 10.54 10.19 10.77 11.00 11.00 11.00 

Condition #1 10.73 9.48 12.94 12.50 10.00 15.00 

Condition #2 11.51 10.40 12.72 13.00 12.00 12.00 

Condition #3 10.88 10.87 10.90 12.00 12.00 11.00 

Condition #4 10.74 10.82 10.63 12.00 11.00 12.00 

53 
Table 23. Difference in mean and 85th- percentile operating speeds between data collection 

locations - WI 67 (PC-midcurve). 

Condition 

Difference in Mean Speed Difference in 85th Speed 

All-day Daytime Nighttime All-day Daytime Nighttime 

Baseline 4.23 4.55 4.03 5.00 6.00 4.00 

Condition #1 3.48 3.33 3.74 4.00 3.00 4.00 

Condition #2 4.01 3.95 4.08 4.00 5.00 3.00 

Condition #3 4.29 4.66 3.66 5.00 4.00 4.00 

Condition #4 4.62 4.95 4.15 5.00 4.00 5.00 

When considering the difference between the upstream and midpoint curve locations, the 

baseline condition produced the highest mean operating speed difference at the Wisconsin Route 

213 site for the daytime period and the nighttime period. Condition #1 produced the highest 

mean speed difference for the combined daytime and nighttime conditions at the Wisconsin 

Route 213 site. Condition #1 produced the highest 85th-percentile speed difference for the 

daytime only period, as well as the combined daytime and nighttime periods. The baseline 

condition produced the highest 85th-percentile operating speed difference between the upstream 

location and midpoint of the horizontal curve during the nighttime only condition. There is little 

difference in the speed difference metrics between the baseline and condition #1 at the 

Wisconsin Route 213 site. 

At the Wisconsin Route 20 site, conditions #1, #3, and #4 all produced mean or 85th-percentile 

operating speed differences that exceeded the speed difference during the baseline condition, 

depending on the data collection period (daytime only, nighttime only, or daytime and nighttime 

combined). During the nighttime condition only, condition #1 produced the largest speed 

reduction between the upstream location and the midpoint of the horizontal curve. 

At the Wisconsin Route 67 site, conditions #1, #3, and #4 all produced mean or 85th-percentile 

operating speed differences that exceeded the speed difference during the baseline condition, 

depending on the data collection period (daytime only, nighttime only, or daytime and nighttime 

combined). During the nighttime condition only, condition #1 produced the largest speed 

reduction between the upstream location and the midpoint of the horizontal curve. 

Figure 13 through figure 18 show the speed difference data graphically for the sites on 

Wisconsin routes 213, 20, and 67, respectively. In the top panel of each figure, the mean speed 

difference is shown, while the bottom panel in each figure shows the 85th-percentile speed 

difference. 

This analysis generally supports the previous analysis, which indicates that the simultaneous 

flashing pattern, which is activated when vehicle operating speeds exceed the advisory curve 

54 
■ ■ ■ 

■ ■ ■ 

warning speed by 5 mph or more, produces the greatest speed difference between the upstream 

and midpoint curve locations at night. 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Upstream ‐ PC Upstream ‐Middle Curve PC ‐ Middle Curve

Difference in Mean Speed (mph)

WI 213

All‐day Daytime Nighttime

Figure 13. Graph. Mean operating speed difference at the WI 213 site. 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Upstream ‐ PC Upstream ‐Middle Curve PC ‐ Middle Curve

Difference in 85th Speed (mph)

WI 213

All‐day Daytime Nighttime

Figure 14. Graph. 85th-percentile operating speed difference at the WI 213 site. 

55 
11111111111111 111111 111 Ill 111 

■ ■ ■ 

1111 111111111 . Ill Ill 111 11, II, 

■ ■ ■ 

0.00

5.00

10.00

15.00

20.00

25.00

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Upstream ‐ PC Upstream ‐Middle Curve PC ‐ Middle Curve

Difference in Mean Speed (mph)

WI 20

All‐day Daytime Nighttime

Figure 15. Graph. Mean operating speed difference at the WI 20 site. 

0.00

5.00

10.00

15.00

20.00

25.00

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Upstream ‐ PC Upstream ‐Middle Curve PC ‐ Middle Curve

Difference in 85th Speed (mph)

WI 20

All‐day Daytime Nighttime

Figure 16. Graph. 85th-percentile operating speed difference at the WI 20 site. 

56 
111111 111111111 I Ill HI 111111111 

• • • 

111 11 111 111111 .111111 111 Ill Ill-

• • • 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Upstream ‐ PC Upstream ‐Middle Curve PC ‐ Middle Curve

Difference in Mean Speed (mph)

WI 67

All‐day Daytime Nighttime

Figure 17. Graph. Mean operating speed difference at the WI 67 site. 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Upstream ‐ PC Upstream ‐Middle Curve PC ‐ Middle Curve

Difference in 85th Speed (mph)

WI 67

All‐day Daytime Nighttime

Figure 18. Graph. 85th-percentile operating speed difference at the WI 67 site. 

Proportion of Vehicles Exceeding the Posted Speed Limit 

This analysis considered the proportion of vehicles exceeding the advisory speed limit by 5 and 

10 mph, respectively. Operating speed data were collected at three locations at each Wisconsin 

study site: 800 ft before the beginning of the horizontal curve, the point of curvature, and the 

midpoint of the horizontal curve. A lower proportion of vehicles exceeding the posted speed 

limit indicated a desirable outcome for the chevron condition being evaluated. The data 

57 
presented in table 24 through table 26 are for a full data collection period (daytime and nighttime 

combined), daytime only, and nighttime only, respectively. 

Table 24. Proportion of vehicles exceeding the advisory speed (daytime and nighttime 

combined – WI 213). 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 76.25% 43.44% 24.06% 40.31% 16.56% 3.75% 

Condition #1 75.92% 36.73% 20.82% 43.67% 12.24% 2.04% 

Condition #2 77.16% 38.58% 29.44% 40.61% 11.17% 6.60% 

Condition #3 78.64% 43.73% 34.58% 40.68% 18.31% 10.85% 

Condition #4 84.13% 39.05% 30.16% 40.63% 15.24% 9.84% 

Table 25. Proportion of vehicles exceeding the advisory speed (daytime and nighttime 

combined – WI 20). 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 98.73% 89.81% 54.14% 98.73% 60.19% 18.15% 

Condition #1 96.94% 82.99% 52.04% 94.56% 45.24% 12.24% 

Condition #2 99.46% 81.23% 49.06% 97.59% 43.43% 14.75% 

Condition #3 100.00% 85.76% 54.85% 98.79% 55.45% 17.88% 

Condition #4 98.83% 85.38% 57.31% 98.54% 50.29% 21.05% 

58 
Table 26. Proportion of vehicles exceeding the advisory speed (daytime and nighttime 

combined – WI 67). 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 97.98% 100.00% 99.60% 95.97% 96.37% 85.48% 

Condition #1 98.42% 99.47% 96.58% 95.26% 93.16% 81.58% 

Condition #2 98.63% 99.32% 96.58% 95.67% 93.39% 79.04% 

Condition #3 99.16% 99.72% 96.91% 98.03% 96.35% 87.36% 

Condition #4 97.65% 99.36% 97.65% 96.79% 96.37% 84.40% 

In table 27, the lowest proportion of vehicles exceeding the curve advisory speed by 5 mph 

resulted during condition #1 at the midpoint of the horizontal curve for Wisconsin Route 213. 

Condition #2 produced the lowest proportion of vehicles exceeding the curve advisory speed at 

Wisconsin routes 20 and 67. When considering the proportion of vehicles exceeding the advisory 

speed plus 10 mph, condition #1 produced the lowest proportion at Wisconsin routes 213 and 20. 

The lowest proportion of vehicles exceeding the curve advisory speed by 10 mph was found 

during condition #2 at the midpoint horizontal curve location along the Wisconsin Route 67 

study site. These findings were similar at the point of curve data collection location, where 

conditions #1 and #2 both produced the smallest proportions among the five conditions evaluated. 

Table 27 through table 29 shows the proportion of vehicles exceeding the advisory curve speed 

during the daytime only. At the midpoint of the horizontal curve, the lowest proportion of 

vehicles exceeding the posted speed limit occurred in condition #1 at all three Wisconsin sites. 

The only exception was for Wisconsin Route 67, when considering the proportion of vehicles 

exceeding the advisory speed plus 10 mph – in this case, Condition #2 resulted in the lowest 

proportion of vehicles exceeding the curve advisory speed. Condition #1 and #2 produced the 

lowest proportion of vehicles exceeding the curve advisory speed at the point of curvature. 

59 
Table 27. Proportions of vehicles exceeding the curve advisory speed during the daytime 

only – WI 213. 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 80.11% 49.43% 29.55% 39.20% 17.05% 3.98% 

Condition #1 82.52% 51.46% 23.30% 43.69% 15.53% 2.91% 

Condition #2 80.41% 40.21% 34.02% 43.30% 14.43% 9.28% 

Condition #3 76.65% 41.32% 37.72% 41.92% 16.17% 11.38% 

Condition #4 84.30% 40.70% 34.88% 42.44% 16.28% 12.21% 

Table 28. Proportions of vehicles exceeding the curve advisory speed during the daytime 

only – WI 20. 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 99.03% 89.37% 51.69% 99.03% 59.42% 16.91% 

Condition #1 95.40% 79.89% 44.83% 91.95% 42.53% 10.34% 

Condition #2 99.57% 83.55% 46.75% 97.40% 44.16% 10.39% 

Condition #3 100.00% 83.60% 48.15% 98.94% 50.26% 12.70% 

Condition #4 99.44% 80.90% 47.75% 98.88% 44.94% 14.61% 

60 
Table 29. Proportions of vehicles exceeding the curve advisory speed during the daytime 

only – WI 67. 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 97.98% 100.00% 98.99% 95.96% 94.95% 84.85% 

Condition #1 97.94% 99.59% 96.30% 95.47% 94.24% 84.77% 

Condition #2 98.25% 99.12% 96.93% 94.74% 95.61% 79.82% 

Condition #3 99.12% 100.00% 97.79% 97.35% 96.46% 87.61% 

Condition #4 97.81% 98.91% 96.35% 97.08% 95.62% 82.85% 

Table 30 through table 32 shows the proportion of vehicles exceeding the advisory curve speed 

during the nighttime only. At the midpoint of the horizontal curve, the lowest proportion of 

vehicles exceeding the curve advisory speed by 10 mph occurred for condition #1 at all three 

Wisconsin sites. When using the advisory speed plus 5 mph criterion, the lowest proportion of 

vehicles exceeding this speed at the midpoint of the curve occurred during the baseline at 

Wisconsin Route 213, during condition #2 for Wisconsin Route 20, and during condition #3 for 

Wisconsin Route 67. When considering the point of curvature location, the lowest proportion of 

vehicles exceeding the advisory speeds by 5 and 10 mph were during conditions #1 and #2. 

Table 30. Proportion of vehicles exceeding the curve advisory speed during the nighttime 

only - WI 213. 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 71.53% 36.11% 17.36% 41.67% 15.97% 3.47% 

Condition #1 71.13% 26.06% 19.01% 43.66% 9.86% 1.41% 

Condition #2 74.00% 37.00% 25.00% 38.00% 8.00% 4.00% 

Condition #3 81.25% 46.88% 30.47% 39.06% 21.09% 10.16% 

Condition #4 83.92% 37.06% 24.48% 38.46% 13.99% 6.99% 

61 
Table 31. Proportion of vehicles exceeding the curve advisory speed during the nighttime 

only - WI 20. 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 98.13% 90.65% 58.88% 98.13% 61.68% 20.56% 

Condition #1 99.17% 87.50% 62.50% 98.33% 49.17% 15.00% 

Condition #2 99.30% 77.46% 52.82% 97.89% 42.25% 21.83% 

Condition #3 100.00% 88.65% 63.83% 98.58% 62.41% 24.82% 

Condition #4 98.17% 90.24% 67.68% 98.17% 56.10% 28.05% 

Table 32. Proportion of vehicles exceeding the curve advisory speed during the nighttime 

only - WI 67. 

Period 

Percent of vehicles exceeding advisory 

speed by 5 mph 

Percent of vehicles exceeding advisory 

speed by 10 mph 

800 ft prior PC Midcurve 800 ft prior PC Midcurve 

Baseline 97.99% 100.00% 100.00% 95.97% 97.32% 85.91% 

Condition #1 99.27% 99.27% 97.08% 94.89% 91.24% 75.91% 

Condition #2 99.05% 99.53% 96.21% 96.68% 91.00% 78.20% 

Condition #3 99.23% 99.23% 95.38% 99.23% 96.15% 86.92% 

Condition #4 97.42% 100.00% 99.48% 96.39% 97.42% 86.60% 

Figure 19 through figure 27 show the analysis results graphically. In figure 19 through figure 21, 

the proportion of vehicles exceeding the curve advisory speed is shown for all three data 

collection sites when aggregating the daytime and nighttime data. Figure 22 through figure 24 

includes data for the daytime period only, while figure 25 through figure 27 includes data for the 

nighttime period only. In each figure, all three data collection sites are shown separately. The 

proportion of vehicles exceeding the advisory speed by 5, 10, 15, and 20 mph are shown in each 

figure. As expected, the proportion of vehicles exceeding the advisory speeds generally declines 

from the location 800 ft prior to the horizontal curve to the midpoint of the horizontal curve. 

62 
■ ■ ■ 

■ ■ ■ 

I I, ~ 11 11 I. I. I. I. 11 I_ •- I_ 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Percent of vehicles

WI 213

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 19. Graph. Proportion of vehicles exceeding the advisory speed (daytime and 

nighttime combined) – WI 213. 

0%

20%

40%

60%

80%

100%

120%

Percent of vehicles

WI 20

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 20. Graph. Proportion of vehicles exceeding the advisory speed (daytime and 

nighttime combined) – WI 20. 

63 
■ ■ ■ 

111111~111,~~~~~llll 

■ ■ ■ 

111 ,~ lh 111 111 1.. I.. I.. 1.. L. ·-I. I. L_ •-- - - • • -

0%

20%

40%

60%

80%

100%

120%

Percent of vehicles

WI 67

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 21. Graph. Proportion of vehicles exceeding the advisory speed (daytime and 

nighttime combined) – WI 67. 

0%

20%

40%

60%

80%

100%

Percent of vehicles

WI 213

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 22. Graph. Proportion of vehicles exceeding the advisory speed during the daytime 

– WI 213. 

64 
■ ■ ■ 

11 111 11 ~ 11 1.11. I. I. 1. I._ I._ I._ I •. I •. I. I. I_ I. L 

■ ■ ■ 

I 1111 1~ ~~~,~llll~ 

0%

20%

40%

60%

80%

100%

120%

Percent of vehicles

WI 20

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 23. Graph. Proportion of vehicles exceeding the advisory speed during the daytime 

– WI 20. 

0%

20%

40%

60%

80%

100%

120%

Percent of vehicles

WI 67

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 24. Graph. Proportion of vehicles exceeding the advisory speed during the daytime 

– WI 67. 

65 
■ ■ ■ 

l1. l11 111 11 h I,. I._ I.. I,. I •• I._ •- I. ••- •-- I - - ■ 

■ ■ ■ 

0%

20%

40%

60%

80%

100%

Percent of vehicles

WI 213

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 25. Graph. Proportion of vehicles exceeding the advisory speed during the nighttime 

– WI 213. 

0%

20%

40%

60%

80%

100%

120%

Percent of vehicles

WI 20

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 26. Graph. Proportion of vehicles exceeding the advisory speed during the nighttime 

– WI 20. 

66 
■ ■ ■ 

0%

20%

40%

60%

80%

100%

120%

Percent of vehicles

WI 67

800 ft prior PC Middle curve

Exceeding 5 mph Exceeding 10 mph Exceeding 15 mph Exceeding 20 mph 

Figure 27. Graph. Proportion of vehicles exceeding the advisory speed during the nighttime 

– WI 67. 

Summary of Proportion of Vehicles Exceeding Advisory Speed Analysis 

This analysis considered the proportion of vehicles exceeding the advisory speed by increments 

of 5 mph. In general, conditions #1 and #2 (simultaneous flashing pattern, with a flashing rate of 

3 Hz, and speed activation thresholds 5 and 10 mph above the advisory speed) produced the 

greatest level of speed compliance among the five conditions tested. A large portion of the 

observed operating speeds exceeding the curve advisory warning speed by 5 mph at all three 

study sites; however, the proportion of vehicles exceeding the curve advisory speed by 15 or 

more mph was very low at all three study sites. 

Statistical Tests 

Several statistical tests were performed to compare the active flashing sequential conditions 

(conditions #1 through #4) to the baseline condition. These included the following: 

 Comparison of mean speeds 

 Comparison of speed variance 

 Test of proportions for vehicles exceeding the horizontal curve advisory speed 

All of these tests were performed for the daytime and nighttime data collection periods combined, 

the daytime period only, and the nighttime period only. The results of the statistical tests are 

presented in appendix E. 

Comparison of Speed Measures 

The results of the present study were compared to the results from a previous study of sequential 

flashing chevron signs.(Smadi et al. 2015) Only the three Wisconsin study sites were compared. All of 

the data were aggregated into combined daytime and nighttime operating speeds. The three data 

collection locations were located at the same points in both studies. The flashing pattern 

67 
evaluated in the previous study was set to flash away from the driver at a rate of one flash per 

second (1 Hz). The speed activation threshold in the previous study was set equal to the curve 

speed advisory. In the present study, four conditions were evaluated. The results of the speed 

comparisons are shown in table 33 through table 38. The baseline and before periods are the 

same – an array of static chevrons were present at the study site without any active flashing 

pattern. The four conditions tested in the present study are based on operating speed data 

collected during a period approximately 24 hours after the flashing settings were programmed. 

As such, the most meaningful comparison to the previous study is for the period 1, 12, 18, and 24 

months after the setting was programmed. 

When comparing the baseline and before conditions in the following tables, the mean speed at 

the location 800 ft before the beginning of the horizontal curve was identical (59.4 mph) at 

Wisconsin Route 213. The baseline and before period mean speeds differed by approximately 

2.3 mph at the Wisconsin Route 20 site (53.6 mph in the present study versus 55.9 mph in the 

previous study). Finally, the baseline and before period mean speeds differed by approximately 

0.5 mph at the Wisconsin Route 67 site (51.6 mph in the present study versus 51.1 mph in the 

previous study). This suggests that operating speeds approaching the horizontal curve at each site 

were generally consistent during two different time periods, so drivers were generally selecting a 

speed that was similar when the static signs were present without an active flashing pattern. 

When comparing the baseline mean speeds at the PC and midpoint of the horizontal curve from 

the present study, to the mean speeds at these same locations 24 months after the flashing pattern 

was established in the previous study, there are similarities. At Wisconsin Route 213, the mean 

speed at the PC in the baseline condition of the present study was 54.0 mph. In the previous 

study, the mean speed at the PC 24 months after the flashing pattern was set was 54.2 mph. At 

the Wisconsin Route 20 and 67 sites, the baseline mean speeds at the PC and midpoint curve 

locations are similar to the mean speeds 24 months after the flashing pattern was set in the 

previous study. This suggests that there is a long-term effect of the flashing pattern from the 

previous study. 

The final set of comparisons are for each condition tested in the present study to the period 1, 12, 

18, and 24 months after the flashing pattern was set in the previous study. The mean and 85thpercentile operating speeds for conditions #1 and #2 in the present study were generally about 

one to two mph lower than the mean and 85th-percentile operating speeds from the previous 

study at all three study sites. The mean and 85th-percentile operating speeds for conditions #3 

and #4 were similar to the mean and 85th-percentile operating speeds from the previous study 

when compared to the speeds 1 month, 12 months, 18 months, and 24 months after the flashing 

pattern was established in the previous study. In the present study, however, the speed variance 

was generally higher for conditions #1 and #2 when compared to the previous study. This 

suggests that the simultaneous flashing pattern from the present study (conditions #1 and #2) 

may produce lower mean and 85th-percentile operating speeds when compared to the sequential 

flashing pattern used in the present study; however, the speed variability associated with the 

simultaneous flashing pattern may be associated with an increase in the speed variance. 

68 
Table 33. Operating speed for WI 213 – this study. 

Period 

(Obs) 

Location Mean SD 85th speed 

Baseline 

(N=320) 

800 ft prior 59.41 6.17 65 

PC 54.02 6.98 61 

Midcurve 49.08 8.82 57 

Condition #1 

(N=245) 

800 ft prior 59.18 5.59 65 

PC 53.47 6.24 60 

Midcurve 48.8 7.32 56 

Condition #2 

(N=197) 

800 ft prior 59.44 5.68 65 

PC 53.7 6.28 59 

Midcurve 50.25 8.15 58 

Condition #3 

(N=295) 

800 ft prior 59.56 5.82 65 

PC 54.66 6.39 61 

Midcurve 51.58 8.22 60 

Condition #4 

(N=315) 

800 ft prior 59.86 4.92 65 

PC 54.15 5.91 61 

Midcurve 50.98 8.3 59 

69 
Table 34. Operating speed for WI 213 – Smadi et al., 2015. 

Period 

(Obs) 

Location Mean SD 85th speed 

Before* 

(N=773) 

800 ft prior 59.4 -- --

PC 56.2 5.9 62 

Midcurve 55.5 7.1 62 

After 1 Month* 

(N=785) 

800 ft prior 60.1 -- --

PC 55 5.8 61 

Midcurve 54.3 7.1 61 

After 12 Months 

(N=775) 

800 ft prior 60.9 -- --

PC 56.2 5.6 62 

Midcurve 56 6.7 62 

After 18 Months 

(N=694) 

800 ft prior 60.7 -- --

PC 54.9 5.5 60 

Midcurve 55.9 6.5 62 

After 24 Months 

(N=1,473) 

800 ft prior 59.6 -- --

PC 54.2 5.5 60 

Midcurve 54.6 6.6 61 

Note: -- no data available, * denotes the 95-percent confidence interval. 

70 
Table 35. Operating speed for WI 20 – this study. 

Period 

(Obs) 

Location Mean SD 85th speed 

Baseline 

(N=314) 

800 ft prior 53.57 5.39 59 

PC 42.13 5.48 47 

Midcurve 35.52 5.66 41 

Condition #1 

(N=294) 

800 ft prior 52.87 6.8 59 

PC 40.07 5.41 45 

Midcurve 35.01 5.46 40 

Condition #2 

(N=373) 

800 ft prior 52 5.62 58 

PC 40.31 5.67 46 

Midcurve 35.13 5.4 40 

Condition #3 

(N=330) 

800 ft prior 54.53 5.59 60 

PC 41.61 6.02 47 

Midcurve 35.95 5.5 41 

Condition #4 

(N=342) 

800 ft prior 54.76 5.73 60 

PC 41.13 5.8 47 

Midcurve 36.38 5.84 42 

71 
Table 36. Operating speed for WI 20 – Smadi et al., 2015. 

Period 

(Obs) 

Location Mean SD 85th speed 

Before 

(N=743) 

800 ft prior 55.9 -- --

PC 43.3 5.3 49 

Midcurve 38.4 4.4 43 

After 1 Month 

(N=740) 

800 ft prior 56.7 -- --

PC 41 4.7 46 

Midcurve 36.5 3.9 40 

After 12 Months 

(N=775) 

800 ft prior 56.4 -- --

PC 40.9 5.1 46 

Midcurve 37.5 4.6 42 

After 18 Months 

(N=688) 

800 ft prior 56.5 -- --

PC 42.1 5 47 

Midcurve 37.2 4.3 42 

After 24 Months 

(N=1,470) 

800 ft prior 55.3 -- --

PC 41 5 46 

Midcurve 36.8 4.1 41 

Note: * denotes the 95-percent confidence interval; -- no data available. 

72 
Table 37. Operating speed for WI 67 – this study. 

Period 

(Obs) 

Location Mean SD 85th speed 

Baseline 

(N=248) 

800 ft prior 51.63 6.59 57 

PC 45.32 5.57 51 

Midcurve 41.08 4.98 46 

Condition #1 

(N=380) 

800 ft prior 50.23 6.98 56.5 

PC 42.98 5.01 48 

Midcurve 39.5 4.9 44 

Condition #2 

(N=439) 

800 ft prior 50.62 6.52 57 

PC 43.11 5.01 48 

Midcurve 39.1 4.69 44 

Condition #3 

(N=356) 

800 ft prior 51.35 6.4 57 

PC 44.77 4.93 50 

Midcurve 40.47 4.75 45 

Condition #4 

(N=468) 

800 ft prior 51.01 6.73 57 

PC 44.89 5.27 50 

Midcurve 40.27 5.07 45 

73 
Table 38. Operating speed for WI 67 – Smadi et al., 2014. 

Period 

(Obs) 

Location Mean SD 85th speed 

Before 

(N=905) 

800 ft prior 51.1 -- --

PC 47.4 5.3 53 

Midcurve 40.7 4.7 45 

After 1 Month 

(N=1,008) 

800 ft prior 50.2 -- --

PC 45.4 5.7 51 

Midcurve 38.3 4.7 43 

After 12 Months 

(N=912) 

800 ft prior 51.1 -- --

PC 46 5.4 51 

Midcurve 39.7 4.7 45 

After 18 Months 

(N=910) 

800 ft prior 52 -- --

PC 47 5.4 53 

Midcurve 40.1 4.9 45 

After 24 Months 

(N=1,848) 

800 ft prior 51.7 -- --

PC 46.1 5.3 51 

Midcurve 40.3 4.6 45 

Note: * denotes the 95-percent confidence interval; -- no data available. 

Speed Differential Comparisons between Study Periods 

The differences in mean and 85th-percentile operating speeds were compared from the present 

and previous studies. For this comparison, the differences in operating speeds between the 

flashing pattern and the baseline (or before period from the previous study) were computed. A 

negative value indicates that the operating speeds were lower for the flashing pattern relative to 

the baseline (or before period) condition. Like the previous comparisons, the present study 

results were compared to the results obtained 1, 12, 18, and 24 months after the flashing pattern 

was set in the previous study. The results of the comparison are shown in table 39 through table 

41. 

74 
Table 39. Comparison of speed differentials between present and previous study – WI 2

Location 

This Study Smadi et al., 201

Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

After 1 

month 

After 12 

months 

Aft

mo

Change in Mean Speed 800 ft prior -0.23 0.03 0.15 0.45 0.7 1.5 

Change in Mean Speed PC -0.55 -0.32 0.64 0.13 -1.2 0 -Change in Mean Speed Midcurve -0.28 1.17 2.5 1.9 -1.2 0.5 

Change in 85th Speed 800 ft prior 0 0 0 0.00 -- -- 

Change in 85th Speed PC -1 -2 0 0 -1 0 

Change in 85th Speed Midcurve -1 1 3 2 -1 0 

Note: -- no data available; * negative value is the speed reduction between the flashing condition and the baseline (or before perio

75 
Table 40. Comparison of speed differentials between present and previous study – WI 2

Location 

This Study Smadi et al., 201

Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

After 1 

month 

After 12 

months 

Afte

mon

Change in Mean Speed 800 ft prior -0.71 -1.58 0.96 1.19 0.8 0.5 0

Change in Mean Speed PC -2.06 -1.83 -0.52 -1 -2.3 -2.4 -1

Change in Mean Speed Midcurve -0.5 -0.38 0.44 0.87 -1.9 -0.9 -1

Change in 85th Speed 800 ft prior 0 -1 1 1 -- -- -

Change in 85th Speed PC -2 -1 0 0 -3 -3 -Change in 85th Speed Midcurve -1 -1 0 1 -3 -1 -

Note: -- no data available; * negative value is the speed reduction between the flashing condition and the baseline (or before peri

76 
Table 41. Comparison of speed differentials between present and previous study – WI 6

Location 

This Study Smadi et al., 2015 

Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

After 1 

month 

After 12 

months 

After 

month

Change in Mean Speed 800 ft prior -1.4 -1.01 -0.27 -0.61 -0.9 0 0.9 

Change in Mean Speed PC -2.34 -2.21 -0.55 -0.43 -2 -1.4 -0.4 

Change in Mean Speed Midcurve -1.58 -1.98 -0.61 -0.81 -2.4 -1 -0.6 

Change in 85th Speed 800 ft prior -0.5 0 0 0 -- -- -- 

Change in 85th Speed PC -3 -3 -1 -1 -2 -2 0 

Change in 85th Speed Midcurve -2 -2 -1 -1 -2 0 0 

Note: -- no data available; * negative value is the speed reduction between the flashing condition and the baseline (or before pe

77 
Based on the data shown in table 39 through table 41, the operating speed data collected 1 month 

after establishing the flashing pattern in the previous study shows the most significant reduction 

in mean and 85th-percentile operating speeds relative to an inactive flashing pattern. The flashing 

pattern used in the previous study was away from the driver, with a flashing rate of 1 Hz. The 

speed activation threshold was set equal to the curve advisory speed. Conditions #1 and #2 in the 

present study also produced a negative speed differential, indicating that both patterns produced 

operating speeds lower than the baseline (or inactive flashing) condition. However, the 

magnitude of these speed reductions was not as large in most cases to the flashing pattern used in 

the previous study. 

Operating Speed Differences along Horizontal Curve Comparisons 

The mean and 85th-percentile operating speeds were compared between data collection locations 

along the horizontal curve, and compared between the present and previous studies. The 

operating speeds for this comparison all produced a positive value, because the operating speed 

differences were computed for an upstream location relative to a downstream location. For 

example, the difference in operating speeds between the upstream locations (800 ft prior to the 

horizontal curve) were higher than the operating speeds at the midpoint of the horizontal curve. 

Larger positive values in the table 42 through table 44 indicate a more desirable outcome. 

Like the previous comparisons, the present study data were compared to the data 1, 12, 18, and 

24 months after the flashing was set in the previous study. When comparing each flashing 

condition in the present study to the baseline, condition #1 generally produced a larger speed 

reduction between data collection locations along the horizontal curve than other conditions. For 

example, consider Wisconsin Route 213 operating speed differences between the upstream and 

midpoint curve locations. During the baseline condition (no active flashing pattern), the mean 

and 85th-percentile speed differences were 10.33 and 8.00 mph, respectively. Condition #1 

produced mean and 85th-percentile operating speed differences of 10.38 and 9.00 mph, 

respectively, which is 0.05 and 1.00 mph greater than the baseline. From the previous study, this 

same site produced mean and 85th-percentile operating speed differences of 3.90 and 9.00 mph, 

respectively, for the before condition. One, 12,18, and 24 months after the flashing pattern was 

set in the previous study, the mean and 85th-percentile operating speed differences were 5.80 and 

11.00 mph, respectively, which is 1.90 and 2.00 mph larger than the before period. In general, 

the flashing pattern in the previous study produced larger mean and 85th-percentile operating 

speed differences along the horizontal curve than any of the flashing conditions tested in the 

present study. 

78 
Table 42. Comparison of speed differentials across data collection locations between present and previou

Location 

This Study Smadi et al., 201

Baseline Condition #1 Condition #2 Condition #3 Condition #4 Before After 1 month After 12 months 

Upstream-PC Mean 5.39 5.71 5.74 4.89 5.70 3.20 5.10 4.80 

Upstream-PC 85th 4.00 5.00 6.00 4.00 4.00 8.00 10.00 10.00 

UpstreamMidcurve Mean 

10.33 10.38 9.19 7.98 8.88 3.90 5.80 5.00 

UpstreamMidcurve 85th 8.00 9.00 7.00 5.00 6.00 9.00 11.00 10.00 

PC-Midcurve Mean 4.94 4.67 3.45 3.09 3.17 0.60 0.70 0.20 

PC-Midcurve 85th 4.00 4.00 1.00 1.00 2.00 2.00 2.00 1.00 

79 
Table 43. Comparison of speed differentials across data collection locations between present and previous 

Location 

This Study Smadi et al., 20

Baseline Condition #1 Condition #2 Condition #3 Condition #4 Before After 1 month After 12 months 

Upstream-PC Mean 11.44 12.80 11.69 12.92 13.63 12.60 15.60 15.50 

Upstream-PC 85th 12.00 14.00 12.00 13.00 13.00 18.00 20.00 20.00 

UpstreamMidcurve Mean 18.06 17.85 16.87 18.58 18.38 17.60 20.20 18.90 

UpstreamMidcurve 85th 18.00 19.00 18.00 19.00 18.00 23.00 25.00 24.00 

PC-Midcurve Mean 6.62 5.06 5.18 5.66 4.75 4.90 4.60 3.40 

PC-Midcurve 85th 6.00 5.00 6.00 6.00 5.00 8.00 8.00 6.00 

80 
Table 44. Comparison of speed differentials across data collection locations between present and previo

Location 

This Study Smadi et al., 20

Baseline Condition #1 Condition #2 Condition #3 Condition #4 Before After 1 month After 12 months 

Upstream-PC Mean 6.31 7.25 7.51 6.58 6.12 3.70 4.80 5.10 Upstream-PC 85th 6.00 8.50 9.00 7.00 7.00 11.00 13.00 12.00 

UpstreamMidcurve Mean 10.54 10.73 11.51 10.88 10.74 10.40 11.90 11.40 UpstreamMidcurve 85th 11.00 12.50 13.00 12.00 12.00 18.00 21.00 19.00 PC-Midcurve Mean 4.23 3.48 4.01 4.29 4.62 6.60 7.00 6.20 PC-Midcurve 85th 5.00 4.00 4.00 5.00 5.00 9.00 10.00 9.00 

81 
Comparison of Proportion of Vehicles Exceeding the Advisory Curve Speed 

The proportion of vehicles exceeding the horizontal curve advisory speed was computed and 

compared between the present and previous studies. A higher proportion indicates that more 

vehicles are exceeding the advisory speed warning, so a lower proportion is a desirable outcome. 

The results of the comparisons are shown in table 45 through table 56. Table 45 through table 50 

show the comparison for instances when the vehicle operating speeds exceeded the curve 

advisory speed by 5 and 10 mph. Table 51 through table 56 shows the comparisons when the 

operating speeds exceed the curve advisory speed by 15 and 20 mph. 

The proportions are presented for only the PC and midpoint of the horizontal curves. The 

baseline condition in the present study shows that fewer vehicles exceeded the curve advisory 

speed plus 5 mph relative to the before period in the previous study. This may be the result of a 

“carryover” effect from the flashing pattern used in the previous study being deactivated for the 

present study baseline condition. In the present study, Conditions #1 and #2 (simultaneous 

flashing at a rate of 3 Hz) produce the lowest proportion of vehicles exceeding the advisory 

speed by 5 and 10 mph relative to the other conditions tested in the present study. When 

compared to the previous study during the period 1, 12, 18, and 24 months after the flashing 

pattern was established, the proportion of vehicles exceeding the curve advisory speed by 5 and 

10 mph was lower for Conditions #1 and #2 relative to the previous study. 

Table 51 through table 56 show the proportion of vehicles exceeding the curve advisory speed by 

15 and 20 mph. Again, only data collected at the PC and midpoint of the horizontal curve 

locations are shown. In the present study, conditions #1 and #2 (simultaneous flashing at a rate of 

3 Hz) produce the lowest proportion of vehicles exceeding the advisory speed by 15 and 20 mph 

relative to the other conditions tested in the present study. When compared to the previous study 

during the period 1, 12, 18, and 24 months after the flashing pattern was established, the 

proportion of vehicles exceeding the curve advisory speed by 15 and 20 mph was similar for 

conditions #1 and #2 relative to the previous study. This suggests that the previous sequential 

flashing pattern (flashing away from the driver at 1 Hz) and simultaneous flashing patterns 

(flashing at a rate of 3 Hz) were equally effective in limiting the proportion of drivers exceeding 

the advisory curve speeds by 15 and 20 mph. 

Table 45. Comparison of proportion of vehicles exceeding curve advisory speed by 5 

and 10 mph at WI 213 – this study.. 

Period 

Percent of Vehicles Exceeding 

AS by 5 mph 

Percent of Vehicles 

Exceeding AS by 10 mph 

PC MC PC MC 

Baseline 43.44% 24.06% 16.56% 3.75% 

Condition #1 36.73% 20.82% 12.24% 2.04% 

Condition #2 38.58% 29.44% 11.17% 6.60% 

Condition #3 43.73% 34.58% 18.31% 10.85% 

Condition #4 39.05% 30.16% 15.24% 9.84% 

82 
Table 46. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 213 – Smadi et al., 2015. 

Period 

Percent of Vehicles Exceeding 

AS by 5 mph 

Percent of Vehicles 

Exceeding AS by 10 mph 

PC MC PC MC 

Before 67.00% 66.00% 31.00% 29.00% 

1 Month 59.00% 57.00% 23.00% 23.00% 

12 Months 66.00% 67.00% 30.00% 31.00% 

18 Months 56.00% 66.00% 19.00% 29.00% 

24 Months 51.00% 57.00% 17.00% 23.00% 

Table 47. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 20 – this study. 

Period 

Percent of Vehicles Exceeding 

AS by 5 mph 

Percent of Vehicles 

Exceeding AS by 10 mph 

PC MC PC MC 

Baseline 89.81% 54.14% 60.19% 18.15% 

Condition #1 82.99% 52.04% 45.24% 12.24% 

Condition #2 81.23% 49.06% 43.43% 14.75% 

Condition #3 85.76% 54.85% 55.45% 17.88% 

Condition #4 85.38% 57.31% 50.29% 21.05% 

Table 48. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 20 – Smadi et al., 2015. 

Period 

Percent of Vehicles Exceeding 

AS by 5 mph 

Percent of Vehicles 

Exceeding AS by 10 mph 

PC MC PC MC 

Before 95.00% 83.00% 77.00% 38.00% 

1 Month 92.00% 69.00% 63.00% 20.00% 

12 Months 91.00% 75.00% 64.00% 33.00% 

18 Months 93.00% 75.00% 72.00% 30.00% 

24 Months 91.00% 72.00% 64.00% 25.00% 

Table 49. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 67 – this study. 

Period 

Percent of Vehicles Exceeding 

AS by 5 mph 

Percent of Vehicles 

Exceeding AS by 10 mph 

PC MC PC MC 

Baseline 100.00% 99.60% 96.37% 85.48% 

Condition #1 99.47% 96.58% 93.16% 81.58% 

Condition #2 99.32% 96.58% 93.39% 79.04% 

Condition #3 99.72% 96.91% 96.35% 87.36% 

Condition #4 99.36% 97.65% 96.37% 84.40% 

83 
Table 50. Comparison of proportion of vehicles exceeding curve advisory speed by 5 and 10 

mph at WI 67 – Smadi et al., 2015. 

Period 

Percent of Vehicles Exceeding 

AS by 5 mph 

Percent of Vehicles 

Exceeding AS by 10 mph 

PC MC PC MC 

Before 99.00% 99.00% 99.00% 92.00% 

1 Month 99.00% 97.00% 97.00% 80.00% 

12 Months 99.00% 98.00% 97.00% 88.00% 

18 Months 99.00% 99.00% 99.00% 88.00% 

24 Months 99.00% 99.00% 98.00% 90.00% 

Table 51. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 

20 mph at WI 213 – this study. 

Period 

Percent of Vehicles Exceeding 

AS by 15 mph 

Percent of Vehicles 

Exceeding AS by 20 mph 

PC MC PC MC 

Baseline 3.44% 0.63% 0.63% 0.31% 

Condition #1 2.86% 0.41% 0.82% 0.00% 

Condition #2 2.54% 0.00% 0.00% 0.00% 

Condition #3 5.08% 1.02% 1.02% 0.00% 

Condition #4 3.17% 2.22% 0.63% 0.32% 

Table 52. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 

20 mph at WI 213 – Smadi et al., 2015. 

Period 

Percent of Vehicles Exceeding 

AS by 15 mph 

Percent of Vehicles 

Exceeding AS by 20 mph 

PC MC PC MC 

Before 4.00% 3.00% 1.00% 0.00% 

1 Month 3.00% 2.00% 0.00% 0.00% 

12 Months 4.00% 5.00% 0.00% 0.00% 

18 Months 2.00% 4.00% 1.00% 1.00% 

24 Months 1.00% 2.00% 0.00% 0.00% 

Table 53. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 

20 mph at WI 20 – this study. 

Period 

Percent of Vehicles Exceeding 

AS by 15 mph 

Percent of Vehicles 

Exceeding AS by 20 mph 

PC MC PC MC 

Baseline 27.39% 2.55% 6.69% 0.96% 

Condition #1 12.24% 1.36% 4.08% 0.34% 

Condition #2 16.09% 1.34% 3.75% 0.00% 

Condition #3 22.42% 4.24% 8.79% 0.61% 

Condition #4 19.59% 5.26% 5.85% 1.46% 

84 
Table 54. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 

20 mph at WI 20 – Smadi et al., 2015. 

Period 

Percent of Vehicles Exceeding 

AS by 15 mph 

Percent of Vehicles 

Exceeding AS by 20 mph 

PC MC PC MC 

Before 39.00% 9.00% 13.00% 1.00% 

1 Month 22.00% 2.00% 4.00% 0.00% 

12 Months 23.00% 6.00% 4.00% 1.00% 

18 Months 32.00% 3.00% 6.00% 0.00% 

24 Months 25.00% 3.00% 4.00% 0.00% 

Table 55. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 

20 mph at WI 67 – this study. 

Period 

Percent of Vehicles Exceeding 

AS by 15 mph 

Percent of Vehicles 

Exceeding AS by 20 mph 

PC MC PC MC 

Baseline 81.45% 56.85% 48.79% 16.53% 

Condition #1 69.74% 43.68% 28.16% 9.47% 

Condition #2 69.70% 35.76% 29.84% 8.66% 

Condition #3 82.30% 47.75% 42.98% 12.92% 

Condition #4 79.70% 48.50% 45.30% 14.96% 

Table 56. Comparison of proportion of vehicles exceeding curve advisory speed by 15 and 

20 mph at WI 67 – Smadi et al., 2015. 

Period 

Percent of Vehicles Exceeding 

AS by 15 mph 

Percent of Vehicles 

Exceeding AS by 20 mph 

PC MC PC MC 

Before 92.00% 61.00% 74.00% 19.00% 

1 Month 84.00% 40.00% 56.00% 9.00% 

12 Months 89.00% 52.00% 63.00% 16.00% 

18 Months 92.00% 55.00% 67.00% 18.00% 

24 Months 89.00% 58.00% 63.00% 17.00% 

85 
SUMMARY OF FIELD STUDY 

This study collected data at three two-lane rural highway sites with sequential flashing chevron 

signs. Five data collection periods were included in the evaluation: baseline (inactive flashing 

chevron) and four different flashing patterns. The mean and 85th-percentile operating speeds, 

speed deviation, and proportion of vehicles exceeding the advisory curve speed were also 

computed and compared. Among the conditions tested in the present study, the simultaneous 

flashing pattern, set to active 5 mph above the curve advisory speed at a rate of 3 Hz, produced 

the most desirable outcome based on the speed metrics considered in the present study. When 

compared to the previous study, several operating speed comparisons indicate that a flashing 

pattern away from the driver, with a flashing rate of 1 Hz set to active when operating speeds 

exceed the curve advisory speed, is the optimal among all conditions tested in the present and 

previous studies based on data collected in Wisconsin. 

86 

CHAPTER 5. DISCUSSION AND RECOMMENDATIONS 

In the Federal Highway Administration’s Manual on Uniform Traffic Control Devices 

(MUTCD), the Chevron Alignment sign (W1-8) is used as a horizontal alignment warning sign 

to provide additional emphasis and guidance to motorists when there is a change in the 

horizontal alignment of a roadway.(FHWA, 2009) The placement of the chevron alignment sign is 

based on the difference between the posted speed limit and the curve advisory speed. According 

to MUTCD (Table 2C-5), when this difference is 5 mph, the use of the chevron alignment sign is 

optional.(2009) When this difference is 10 mph, the chevron alignment sign is recommended. In 

locations where the difference in the posted speed limit and curve advisory speed exceeds 15 

mph, chevron alignment signs are required. 

According to the MUTCD, chevron alignment signs shall be installed on the outside of a turn or 

curve, in line with and at approximately a right angle to approaching traffic. The signs shall be 

installed at a minimum height of 4 ft, measured vertically from the bottom of the sign to the 

elevation of the near edge of the traveled way. The typical spacing of the chevron alignment 

signs on horizontal curves is shown in table 57. 

Table 57. Typical spacing of chevron alignment signs on horizontal curves 

Advisory Speed Curve Radius Sign Spacing 

15 mph or less Less than 200 ft 40 ft 

20 to 30 mph 200 to 400 ft 80 ft 

35 to 45 mph 401 to 700 ft 120 ft 

50 to 60 mph 701 to 1,250 ft 160 ft 

More than 60 mph More than 1,250 ft 200 ft 

Regarding the three study sites, the differences between posted speed limit and curve advisory 

speed at WI 20 and WI 67 sites were more than 25 mph, so the chevron alignment signs are 

required. For the WI 213 site, the use of chevron alignment signs is optional based on the 

MUTCD guidance. With respect to the spacing of signs, the WI 20 and WI 67 sites have curve 

advisory speeds of 30 mph and 25 mph, respectively, so the spacing between each chevron 

alignment sign should be 80 ft (measured from PC). The curve advisory speed at the WI 213 site 

was 50 mph, so the spacing should be 160 ft. The chevron spacing at the WI 20 and WI 213 sites 

was 50 ft; which is closer than the MUTCD recommended spacing. The chevron sign spacing at 

the WI 67 site was 100 ft, which is slightly longer than recommended in the MUTCD. 

Nevertheless, all of the sequential flashing chevron arrays were effective in reducing vehicle 

operating speeds and the proportion of vehicles exceeding the speed limit when the array was 

operational. 

88 
Based on the indoor simulator study, a flashing sequence away from the driver at a low flash rate 

(1 Hz), or a simultaneous flashing pattern with a high flash rate (e.g., 3 Hz), offered two 

conditions that produced the greatest speed reduction effects on drivers approaching horizontal 

curves on two-lane rural highways. The flashing sequence away from the driver also produced 

the lowest probability of a roadway departure event. These two conditions were then evaluated in 

the field at three locations in Wisconsin. In addition to the flashing pattern and flash rate, the 

speed-activation threshold was also varied. 

The present field study found that, among the conditions tested, the simultaneous flashing pattern, 

set to active 5 mph above the curve advisory speed at a rate of 3 Hz, produced the most desirable 

outcome based on the speed metrics considered in the present study. When compared to the 

previous study by Smadi et al., several operating speed comparisons indicate that a flashing 

pattern away from the driver, with a flashing rate of 1 Hz set to active when operating speeds 

exceed the curve advisory speed, is the optimal among all conditions tested in the present and 

previous studies based on data collected in Wisconsin.(Smadi et al. 2015) 

89 
APPENDIX A 

INTAKE QUESTIONNAIRE 

1. Date of birth (mm/dd/yy) ____/____/___¬_ 

2. Gender Male Female 

3. Do you have normal or corrected-to-normal vision? 

Yes No 

4. Are you color blind? * If you don't know, please tell us. 

Yes No 

5. Are you a native or equally fluent speaker of English? 

Yes No 

6. Have you had your normal amount of caffeine today? 

Yes No 

7. Did you get a normal amount of sleep last night? 

Yes No 

If no, please specify the number of hours of sleep you got: ___________ 

8. Do you have a valid driver's license? 

90 
Yes No 

9. Have you ever participated in a study in which you drove or observed someone drive in 

an instrumented vehicle? 

Yes No 

91 
APPENDIX B 

As noted in Chapter 3, there were three curve sections identified in the PA 851 scenario with six 

flashing device treatments and the existing TCDs (with no flashing devices) control group. These 

are described in greater detail below. 

Figure 28 through figure 30 show the mean speed at each curve location (i.e., PC, PC+1/4L, 

PC+1/2L, PC+3/4L, PT, PC-50ft, PC-100ft, PC-150ft, and PC-200ft) for each treatment and the 

control. The error bars represent “plus or minus” two standard errors of the mean. If the 

counterbalanced experimental design was successful, a direct comparison of these estimates 

should provide an indication of whether or not the treatments were associated with speed and 

lateral position at the various points along and upstream of the horizontal curve. Generally 

speaking, the mean speed is decreasing in all scenarios (treatment scenarios or control) as the 

drivers approach the curve. The lowest mean speeds are observed inside of the curves at 

locations PC+1/2L or PC+3/4L, at which point drivers begin to accelerate as they exit the curve. 

This replicates speed behavior that has been observed in the field on other research efforts. 

There do not appear to be any statistically significant differences in mean speed across the 

various flashing treatments. Generally speaking, mean speeds are lowest for the control condition 

and this difference is statistically significant at various locations, primarily in curve section 3. 

While this may at first seem surprising, other previous and ongoing research has indicated a 

possible increase in speeds as nighttime curve delineation improves. 

Figure 31 through figure 33 show the mean lateral position at each curve location (i.e., PC, 

PC+1/4L, PC+1/2L, PC+3/4L, PT, PC-50ft, PC-100ft, PC-150ft, and PC-200ft) for each 

treatment and the control. The error bars represent “plus or minus” two standard errors of the 

mean. Generally speaking, there are no consistent and statistically significant differences in 

lateral position across all treatment conditions and the control. 

At this point, the research team also executed a number of other analyses to quantify the 

association between speed, lateral position, and the treatment/control conditions, including 

ordinary least squares (OLS) regression, fixed-effects models (representing a within-subjects 

estimator), and random-effects models (representing a weighted average of within and between 

subjects estimators). All model estimation results reinforced the patterns shown in figure 28 

through figure 33: no statistically significant differences between treatments and controls for 

speed and lateral position were noted except for, in some cases, a lower expected speed for the 

control condition. 

92 
 

Figure 28. Graph. Mean speed ± two standard errors of mean in curve section 1. 

93 
 

       

 

 

  

  

 

 

 

 

 

 

 

 

   

 

  

 

  

 

  

 

 

 

  

 

 

Figure 29. Graph. Mean speed ± two standard errors of mean in curve section 2. 

94 
 

 

 

  

 

 

 

 

 

 

 

 

 

       

 

 

 

 

 

   

 

  

 

  

 

  

 

 

   

 

Figure 30. Graph. Mean speed ± two standard errors of mean in curve section 3. 

95 
 

Figure 31. Graph. Mean lateral placement ± two standard errors of mean in curve sectio

96 
 

     

 

  

  

 

 

 

 

 

 

 

  

   

 

 

   

 

  

 

  

 

  

 

 

   

 

 

Figure 32. Graph. Mean lateral placement ± two standard errors of mean in curve sectio

97 
 

Figure 33. Graph. Mean lateral placement ± two standard errors of mean in curve sectio

98 
APPENDIX C 

Data Collection Protocols 

The research team collected vehicle operating speed data at three point locations shown in figure 

12 and at the three sites shown in table 14. At each location, operating speeds were measured in 

the direction in which the SDCWS were visible to the driver. The research team collected data 

during five periods at each site (baseline and conditions #1 through #4) in November 2016. 

Data were collected on weekdays to eliminate any irregularities in traffic flow resulting from 

recreational traffic or special events. The weather was clear during all data collection periods. At 

each site, the data collection session included both daytime and nighttime conditions. The data 

collection period lasted approximately 16 hours (from 3 p.m. to 5 a.m.), which was long enough 

to capture the most free-flow vehicles traveling at all sites and satisfy the sample size 

requirements after the data were screened (see “Sample Size Determination” section below). The 

speed data were collected using Nu-Metrics Hi-Star on-pavement sensors. These sensors 

measure 6.5 x 5.5 x 0.63 inches, and are preferred because they are less conspicuous than other 

data collection equipment.(Poe et al., 1996) All Fthree of the Wisconsin study sites had an asphalt 

pavement surface. A 22-caliber nail gun was used to fasten the sensors to the pavement surface, 

and a black, rubber mat was used to cover the sensor, in order to further conceal it and protect it 

from traffic. 

Each sensor was installed in the center of the travel lane. The intent of the data collection effort 

was to collect free-flow vehicle speeds, which represent driver speeds that are uninfluenced by 

leading or following vehicles. In this case, operating speeds are a function of the roadway 

features, traffic control devices, and other site-specific features. The definition of a free-flow 

vehicle is provided below. 

After each data collection period, the sensors were removed and the data were downloaded to a 

laptop computer. Data were then transferred into Microsoft Excel to develop the analysis data 

files. 

Sample Size Determination 

There are two sample size considerations in collecting an adequate number of vehicle operating 

speeds in field evaluations. The first corresponds to the number of observations needed during 

any specific data collection period (i.e., baseline condition and conditions #1 through #4) to 

minimize sampling error. A second considers the differences in measures of effectiveness 

(MOEs) between data collection periods to detect a statistically significant difference in the 

MOE. The following discussion provides a brief overview of the methods that were used to 

compute minimum sample size requirements in the present study. 

99 
 

 

When the mean speed is the variable of interest, the minimum sample size needed for any data 

collection period is based the equation shown in figure 34.(Institute of Transportation Engineers 2010) 

Figure 34. Equation. Minimum number of measured free-flow operating speeds. 

Where: 

ܰ = minimum number of measured free-flow operating speeds 

ܵ = estimated sample standard deviation (mph) 

ܭ = constant corresponding to the desired confidence level 

ܧ = permitted error in the average operating speed estimate (mph) 

To obtain a range of possible sample sizes, multiple values for the confidence level K were input 

into the equation. The values correspond to confidence levels of 90, 95, and 99 percent. The 

permitted error in the average speed estimate, E, was input as the most conservative value of ±1 

percent. The estimate of sample standard deviation, S, is a function of traffic area and highway 

type. A standard deviation of 5.3 mph was representative of a rural, two-lane highway.(Institute of 

Transportation Engineers 2010) The resulting sample size estimates, based on the varying input parameters, 

are shown in table 58. 

Table 58. Values for speed sample size determination. 

ࡺ ࡱ ࡷ ࡿ

1.64 (90%) ±1 76 

5.3 1.96 (95%) ±1 108 

2.58 (99%) ±1 187 

For comparative research studies, such as a before-after observational evaluation, the expected 

difference in means (or proportions) between two different data collection periods can be used to 

estimate sample sizes. An equation used to estimate the sample size required in an observational 

before-after study is shown figure 35:(Snedecor and Cochran 1989) 



Figure 35. Equation. Speed sample in comparison groups. 

100 
Where: 

ܰ = sum of the sample in both comparison groups (before-after); 

ݏ = standard deviation of the comparison groups (assumed equal) (mph); 

ݖ௜௥௖௧௜௖௔௟ = level of statistical significance assumed from the standard normal distribution 

ݖ௢௣௪௘௥ = desired statistical power from the standard normal distribution 

ܦ =	minimum expected difference between the sample means (mph). 

In considering the speed example from above, table 59 shows different sample size estimates, 

assuming that the standard deviation in speeds is 5.3 mph, for varying levels of critical and 

power statistics, and for different minimum expected differences in sample means. 

It is common in traffic engineering field studies to use the 95th-percentile confidence level to 

estimate sample size requirements; therefore, based on the data shown in table 58, a minimum 

sample of 108 free-flow operating speed during each data collection period was considered 

desirable. From the literature review, it is common that the presence of enforcement reduces 

mean operating speeds by 5.9 km/h (3.67 mph).(Hauer et al. 1981) Based on the 95th-percentile critical 

values in table 59, the level of speed reduction corresponds to a minimum sample size of 73 freeflow vehicle speeds. For the present study, the research team used the more conservative sample 

size (i.e., the largest), thus a sample of at least 100 free-flow vehicle operating speeds was 

desirable for each data collection period (day and night), at each study location. 

Table 59. Sample size estimates for comparative research studies. 

Difference in 

Means (mph) 

90% Level of Statistical Significance 

(zcritical = 1.645) 

95% Level of Statistical Significance 

(zcritical = 1.96) 

90% Desired 

Statistical Power 

(zpower = 1.282) 

95% Desired 

Statistical Power 

(zpower = 1.645) 

90% Desired 

Statistical Power 

(zpower = 1.282) 

95% Desired 

Statistical Power 

(zpower = 1.645) 

1.0 500 822 709 1169 

2.0 125 206 177 292 

3.0 56 91 79 130 

4.0 31 51 44 73 

5.0 20 33 28 47 

101 
(X i -X~) l = -,::::::;:,=:::::;,=-

s i s; -Tnl n~ 

Data Reduction and Analysis 

Prior to data analysis, the raw data from the study sites were screened to separate larger vehicles 

from passenger vehicles, and to identify vehicles whose operations may have been affected by 

the presence of other vehicles (i.e., vehicles that are not free-flow). Single-unit trucks were 

included in the data set, but larger vehicles (e.g., buses with more than two axles, combination 

trucks, and vehicles with trailers) were not, because the sample size requirement for these vehicle 

types could not be met during a 24-hour data collection session. Vehicles were also excluded if 

they were closely following another vehicle through the study site. Based on a previous study, 

vehicles with a time-headway less than 4 sec were considered to not be traveling in free-flow and 

were eliminated from consideration in the evaluation.(Mahoney et al. 2003) Free-flow vehicles were 

identified using the time-stamp included in the on-pavement sensor output. Because the data 

were time-stamped, they could be “tracked” through each study site so that individual driver 

speed choice could be evaluated during each data collection period. 

After data screening, mean and 85th-percentile operating speeds, as well as the standard deviation 

of speed, were calculated at each sensor location for all sites and all data collection periods. The 

percentage of vehicles exceeding the posted speed limit was also determined, as was the pace 

and percentage of vehicles in the pace. 

An initial comparison between corresponding speed parameters (mean speed, 85th-percentile 

speed, standard deviation of speed, percentage of vehicles exceeding the posted speed limit) at 

each site for each data collection period was made by calculating the numerical differences in 

these speed parameters. The control location that was located prior to the sequential flashing 

chevron array was used to confirm that the speed metrics were consistent from one data 

collection period to the next. 

The t-statistic for independent samples was applied to determine if the differences in mean speed 

were statistically significant at all data collection locations and among all time periods. 

Statistically significant changes in mean operating speeds indicated that the observed speeds 

were different in the two time periods being compared. The t-statistic is commonly used to test 

the hypothesis of differences in population parameters.(Washington et al. 2003) In this study the null 

hypothesis for testing the differences in two mean speed measures was the samples were equal. 

The alternative hypothesis was that the difference in the two observed speed samples was less 

than zero (i.e., the speed in the first sample was less than the speed from the second sample). 

The t-statistic for two independent samples is computed as show in figure 36. 

Figure 36. Equation. Formula of t-statistics. 

102 
0 oR 

p = _:: X 100 

n 

Where: 

X1, X2= mean speed for the first and second periods; 

S1, s2 = standard deviation of speed for the first and second periods; 

n1, n2 = sample size in first and second periods. 

In figure 36, conditions 1 and 2 represent the two data collection periods being compared. For 

example, the baseline condition is period 1 and condition #1 is period 2, or the baseline condition 

is period 1 and condition #2 is period 2. Each SDCWS setting was compared to the baseline. The 

optimal outcome was the SDCWS condition that produced the lowest mean operating speed 

relative to the baseline condition. 

The point speed measurements provide an indication of how vehicle operating speeds differ at 

each data collection location and during each time period. Because the data collection effort 

produced time stamps, it was possible to “track” each vehicle through a data collection site. In 

this case, the difference in operating speeds between two data collection points (e.g., speed 800 ft 

before the curve and the midpoint of the horizontal curve, or the difference in operating speeds 

between the PC and midpoint locations of a horizontal curve) was also computed. The t-test 

described above was used to compare the mean vehicle operating speeds between two point 

locations to assess how individual drivers modify their operating speeds when the SDCWS are in 

operation. 

In addition to the t-test, the percentage of vehicles exceeding the posted or advisory speed limit 

at the data collection locations was calculated and compared between data collection periods. 

The percentage of speeding vehicles, PS, was computed as show in figure 37. 

Figure 37. Equation. Percentage of vehicles exceeding posted or advisory speed limit. 

Where: 

x = number of vehicles exceeding the posted or advisory speed limit; and 

n = the total number of vehicles in the sample. 

By comparing the number of vehicles exceeding the posted or advisory speed limit between two 

data collection periods, it can be determined if the SDCWS was associated with a reduction in 

the proportion of posted or advisory speed limit violations. The percent reduction of speeding 

vehicles, %RS, between two periods, 1 and 2, at the SDCWS locations was computed as shown in 

figure 38. 

Figure 38. Equation. Percentage reduction of speeding vehicles between two periods. 

103 
Where: 

PS1 = the proportion of vehicles speeding during the first data collection period; and 

PS2 = the proportion of vehicles speeding during the second data collection period. 

In order to determine if the proportion of vehicles exceeding the posted or advisory speed limit at 

the SDCWS locations changes between any of the data collection periods, a Z-test for 

independent samples was computed. The null hypothesis for this test was that the two 

proportions were equal, while the alternative hypothesis was that the two samples differed. The 

Z-statistic used to determine the statistical difference between the two proportions was computed 

as show in figure 39. 

Figure 39. Equation. Formula of Z-statistic. 

Where PS1 and PS2 are the sample proportions from the equation in figure 39, n1 and n2 are 

sample sizes for the corresponding proportions being considered, and P is the combined 

proportion in both samples, computed as show in figure 40. 

Figure 40. Equation. Combined proportion of speeding vehicles in two samples. 

The final speed performance metric considered in the present study was speed variance. A twosided F-test was used to compare the variances of vehicle operating speeds during any two data 

collection periods. The F-test is computed as show in figure 41. 

Figure 41. Equation. Formula of F-statistic. 

Values of speed variance considered in this study are those related to the time of day (day or 

night) and data collection time period (baseline and conditions #1 through #4). 

Summary 

The operating speed field evaluation of the SDCWS considered three locations in Wisconsin. 

These three locations had differing site characteristics, including the number of chevrons in the 

SDCWS array, horizontal curve radii, and posted (and advisory) speed limits. The data collection 

protocol considered three data collection points at each study site. These included operating 

speed measurement locations approaching and within the limits of a horizontal curve. The 

flashing rates, sequences, and speed-activation thresholds are consistent with those found to 

104 
produce the lowest curve operating speeds in the indoor driving simulator study described in 

Chapter 3 of this report. The measures and statistical comparisons considered in the present 

study are shown in table 60. 

Table 60. Summary of data collection periods, measures of effectiveness, and statistical 

tests. 

Statistical Measure Speed Variable Comparisons Statistical Test(s) 

Mean Speed (mph) 

Point Speeds at all Four 

Locations 

Speed Differentials 

between Approach and 

Midcurve Locations* 

Baseline → Condition #1 

Baseline → Condition #2 

Baseline → Condition #3 

Baseline → Condition #4 

t-test 

85th-percentile Speed 

(mph) 

Point Speeds at all Four 

Locations 

Speed Differentials 

between Approach and 

Midcurve Locations 

Baseline → Condition #1 

Baseline → Condition #2 

Baseline → Condition #3 

Baseline → Condition #4 

Descriptive 

Statistics Only 

Standard Deviation of 

Speed (mph) or Speed 

Variance (mph2

) 

Point Speeds at all Four 

Locations 

Speed Differentials 

between Approach and 

Midcurve Locations 

Baseline → Condition #1 

Baseline → Condition #2 

Baseline → Condition #3 

Baseline → Condition #4 

F-test 

Percent Exceeding 

Posted or Advisory 

Speed Limit (%) 

Point Speeds at all Four 

Locations 

Baseline → Condition #1 

Baseline → Condition #2 

Baseline → Condition #3 

Baseline → Condition #4 

z-test 

Pace (mph) and Percent 

Vehicles in Pace 

Point Speeds at all Four 

Locations 

Baseline → Condition #1 

Baseline → Condition #2 

Baseline → Condition #3 

Baseline → Condition #4 

Descriptive 

Statistics Only 

*The speed differentials were determined using “tracked” vehicles and computed as follows: (1) difference in individual driving 

speeds between the sensor located 300 ft prior to the beginning of the curve and the mid-point of the curve, and (2) difference in 

individual driving speeds between the sensor located at the beginning of the curve and the midpoint of the curve. 

105 
DESCRIPTIVE STATISTICS 

The field data collection effort took place over a period of two weeks in November 2016. Both 

daytime and nighttime speeds were collected at all three study sites. The first condition observed 

was the baseline, in which the flashing sequential chevron signs were inactive, so they appeared 

much like the W1-8 horizontal alignment warning sign found in the Manual on Uniform Traffic 

Control Devices.(FHWA 2009) After collecting speed data in the baseline condition, the following 

four conditions were programmed for a period of nearly 24 hours each to permit a full period of 

daytime and nighttime data collection: 

 Condition #1: Speed-activation threshold is 5 mph above the advisory speed; flash 

sequence is simultaneous; flash rate is three flashes per second (3 Hz) 

 Condition #2: Speed-activation threshold is 10 mph above the advisory speed; flash 

sequence is simultaneous; flash rate is three flashes per second 

 Condition #3: Speed activation threshold is 5 mph above the advisory speed; flash 

sequence is away from the driver; flash rate is one flash (1 Hz) per second 

 Condition #4: Speed activation threshold is 10 mph above the advisory speed; flash 

sequence is away from the driver; flash rate is one flash per second 

Table 61 through table 63 shows the descriptive statistics for all of the speed data collected at 

each study site, for all five data collection conditions. The number of free-flow vehicle operating 

speeds collected at each site, the mean speed, standard deviation of speed, 85th-percentile 

operating speed, percentage of vehicles exceeding the posted and advisory speed limits, the 10 

mph pace, and the percentage of vehicles in the pace are also included in table 61 through table 

69. Table 64 through table 66 show the same descriptive statistics for the daytime and table 67 

through table 69 show the same descriptive statistics for nighttime periods. 

Because the operating speed data were collected on approach tangents and within the limits of a 

horizontal curve, it was expected that the mean and 85th-percentile operating speeds would be 

highest at the measurement location approximately 800 ft prior to the beginning of the curve. It 

was also expected that the mean and 85th-percentile operating speeds would be lowest at the 

midpoint of the horizontal curve. The descriptive statistics in table 61 through table 69 confirm 

that vehicles were decreasing their operating speed from the approach tangent through the 

midpoint of the horizontal curve. It was also anticipated that the standard deviation of speed 

would increase as vehicles move from the approach tangent through the horizontal curve. The 

data summarized in table 61 through table 69 are consistent with this expectation. Because the 

horizontal curves all have chevron signs, it was expected that the percentage of vehicles 

exceeding the posted speed limit would be lower at the midpoint of the horizontal curve than on 

the approach tangent or at the beginning of the curve due to the restrictive curve feature. Finally, 

the operating speeds during the daytime generally appear to be higher than the nighttime 

operating speeds. 

106 
Table 61. Descriptive statistics for all operating speeds – WI 213. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline (N=320) 

800 ft prior 59.41 6.17 65 76.25% 93.75% 54 - 6

PC 54.02 6.98 61 43.44% 72.81% 51 - 6

Midcurve 49.08 8.82 57 24.06% 49.69% 47 - 5

Condition #1 (N=245) 

800 ft prior 59.18 5.59 65 75.92% 93.88% 55 - 6

PC 53.47 6.24 60 36.73% 68.57% 48 - 5

Midcurve 48.80 7.32 56 20.82% 42.86% 46 - 5

Condition #2 (N=197) 

800 ft prior 59.44 5.68 65 77.16% 96.45% 54 - 6

PC 53.70 6.28 59 38.58% 73.10% 49 - 5

Midcurve 50.25 8.15 58 29.44% 55.33% 49 - 5

Condition #3 (N=295) 

800 ft prior 59.56 5.82 65 78.64% 95.93% 55 - 6

PC 54.66 6.39 61 43.73% 72.20% 50 - 6

Midcurve 51.58 8.22 60 34.58% 62.71% 50 - 6

Condition #4 (N=315) 

800 ft prior 59.86 4.92 65 84.13% 97.46% 55 - 6

PC 54.15 5.91 61 39.05% 75.56% 48 - 5

Midcurve 50.98 8.3 59 30.16% 57.78% 49 - 5

107 
Table 62. Descriptive statistics for all operating speeds – WI 20. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=320) 

800 ft prior 53.57 5.39 59 36.94% 99.68% 50 - 6

PC 42.13 5.48 47 1.91% 98.41% 37 - 4

Midcurve 35.52 5.66 41 0.00% 80.57% 32 - 4

Condition #1 

(N=245) 

800 ft prior 52.87 6.8 59 36.39% 99.66% 50 - 6

PC 40.07 5.41 45 0.68% 95.92% 35 - 4

Midcurve 35.01 5.46 40 0.34% 80.27% 29 - 3

Condition #2 

(N=197) 

800 ft prior 52.00 5.62 58 26.27% 100.00% 47 - 5

PC 40.31 5.67 46 1.34% 97.59% 35 - 4

Midcurve 35.13 5.4 40 0.00% 82.57% 31 - 4

Condition #3 

(N=295) 

800 ft prior 54.53 5.59 60 44.24% 100.00% 50 - 6

PC 41.61 6.02 47 2.12% 97.27% 35 - 4

Midcurve 35.95 5.5 41 0.00% 86.06% 31 - 4

Condition #4 

(N=315) 

800 ft prior 54.76 5.73 60 47.66% 99.71% 50 - 6

PC 41.13 5.8 47 1.75% 97.66% 36 - 4

Midcurve 36.38 5.84 42 0.29% 85.67% 31 - 4

108 
Table 63. Descriptive statistics for all operating speeds – WI 67. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=248) 

800 ft prior 51.63 6.59 57 26.61% 100.00% 48 - 5

PC 45.32 5.57 51 2.82% 100.00% 40 - 5

Midcurve 41.08 4.98 46 0.00% 100.00% 35 - 4

Condition #1 

(N=380) 

800 ft prior 50.23 6.98 56.5 18.42% 99.74% 46 - 5

PC 42.98 5.01 48 0.79% 100.00% 37 - 4

Midcurve 39.50 4.9 44 0.53% 99.47% 35 - 4

Condition #2 

(N=439) 

800 ft prior 50.62 6.52 57 19.82% 100.00% 47 - 5

PC 43.11 5.01 48 0.91% 100.00% 38 - 4

Midcurve 39.10 4.69 44 0.23% 99.54% 34 - 5

Condition #3 

(N=356) 

800 ft prior 51.35 6.4 57 23.88% 100.00% 47 - 5

PC 44.77 4.93 50 1.40% 100.00% 40 - 5

Midcurve 40.47 4.75 45 0.28% 99.72% 36 - 4

Condition #4 

(N=468) 

800 ft prior 51.01 6.73 57 21.58% 100.00% 46 - 5

PC 44.89 5.27 50 2.14% 100.00% 40 - 5

Midcurve 40.27 5.07 45 0.21% 99.15% 35 - 4

109 
Table 64. Descriptive statistics for daytime operating speeds – WI 213. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=176) 

800 ft prior 59.20 5.26 64 80.11% 94.32% 53 - 6

PC 54.60 7.08 61 49.43% 77.27% 51 - 6

Midcurve 49.82 8.64 57 29.55% 51.70% 47 - 5

Condition #1 

(N=103) 

800 ft prior 59.74 4.68 65 82.52% 98.06% 55 - 6

PC 55.20 5.88 61 51.46% 80.58% 51 - 6

Midcurve 50.71 6.43 57 23.30% 55.34% 47 - 5

Condition #2 

(N=97) 

800 ft prior 60.11 5.36 66 80.41% 97.94% 54 - 6

PC 54.72 5.56 60 40.21% 79.38% 49 - 5

Midcurve 52.06 6.84 59 34.02% 60.82% 49 - 5

Condition #3 

(N=167) 

800 ft prior 59.32 6.05 65 76.65% 94.61% 55 - 6

PC 54.63 6.03 61 41.32% 73.65% 50 - 6

Midcurve 52.51 7.71 60 37.72% 70.66% 50 - 6

Condition #4 

(N=172) 

800 ft prior 59.92 4.73 64 84.30% 97.67% 55 - 6

PC 54.67 5.73 61 40.70% 81.40% 48 - 5

Midcurve 52.06 7.96 60 34.88% 64.53% 47 - 5

110 
Table 65. Descriptive statistics for daytime operating speeds – WI 20. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=207) 

800 ft prior 53.11 5.17 58 33.82% 99.52% 49 - 5

PC 41.80 5.38 47 1.45% 98.07% 37 - 4

Midcurve 35.24 5.52 41 0.00% 78.74% 31 - 4

Condition #1 

(N=174) 

800 ft prior 51.64 7 58 27.01% 99.43% 49 - 5

PC 39.75 5.5 45 0.00% 94.25% 35 - 4

Midcurve 34.30 5.59 40 0.83% 75.86% 30 - 4

Condition #2 

(N=231) 

800 ft prior 51.61 5.46 57 25.11% 100.00% 47 - 5

PC 40.27 5.51 46 0.87% 97.40% 35 - 4

Midcurve 34.52 5.54 40 0.00% 78.79% 30 - 4

Condition #3 

(N=189) 

800 ft prior 53.75 5.08 59 39.15% 100.00% 49 - 5

PC 40.88 5.55 47 1.59% 97.35% 34 - 4

Midcurve 35.06 5.46 40 0.00% 82.54% 31 - 4

Condition #4 

(N=99) 

800 ft prior 54.56 5.7 60 43.82% 100.00% 49 - 5

PC 40.58 6.12 47 2.81% 97.75% 34 - 4

Midcurve 35.24 5.66 40 0.00% 81.46% 30 - 4

111 
Table 66. Descriptive statistics for daytime operating speeds – WI 67. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=99) 

800 ft prior 50.80 6.28 56 21.21% 100.00% 48 - 5

PC 45.15 5.8 51 5.05% 100.00% 40 - 5

Midcurve 40.61 4.73 45 0.00% 100.00% 35 - 4

Condition #1 

(N=243) 

800 ft prior 49.59 6.83 55 14.81% 100.00% 44 - 5

PC 43.44 4.94 48 0.82% 100.00% 38 - 4

Midcurve 40.11 5.01 45 0.41% 99.18% 36 - 4

Condition #2 

(N=228) 

800 ft prior 49.48 6.47 55 14.04% 100.00% 44 - 5

PC 43.03 4.89 48 0.88% 100.00% 37 - 4

Midcurve 39.08 4.45 43 0.00% 99.56% 35 - 4

Condition #3 

(N=226) 

800 ft prior 51.21 6.47 57 22.12% 100.00% 47 - 5

PC 45.00 4.7 49 1.33% 100.00% 40 - 5

Midcurve 40.35 4.26 45 0.00% 100.00% 35 - 4

Condition #4 

(N=274) 

800 ft prior 51.06 6.76 57 22.26% 100.00% 46 - 5

PC 45.19 5.45 50 2.19% 100.00% 40 - 5

Midcurve 40.24 5.43 46 0.36% 98.54% 36 - 4

112 
Table 67. Descriptive statistics for nighttime operating speeds – WI 213. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=144) 

800 ft prior 59.67 7.14 66 71.53% 93.06% 54 - 6

PC 53.32 6.83 61 36.11% 67.36% 49 - 5

Midcurve 48.17 8.98 56 17.36% 47.22% 46 - 5

Condition #1 

(N=142) 

800 ft prior 58.77 6.15 65 71.13% 90.85% 55 - 6

PC 52.21 6.21 58 26.06% 59.86% 48 - 5

Midcurve 47.41 7.63 56 19.01% 33.80% 41- 5

Condition #2 

(N=100) 

800 ft prior 58.78 5.92 64 74.00% 95.00% 54 - 6

PC 52.71 6.79 59 37.00% 67.00% 50 - 6

Midcurve 48.49 8.94 57 25.00% 50.00% 49 - 5

Condition #3 

(N=128) 

800 ft prior 59.87 5.52 66 81.25% 97.66% 53 - 6

PC 54.71 6.86 61 46.88% 70.31% 51 - 6

Midcurve 50.36 8.72 59 30.47% 52.34% 50 - 6

Condition #4 

(N=143) 

800 ft prior 59.78 5.16 65 83.92% 97.20% 54 - 6

PC 53.52 6.07 60 37.06% 68.53% 48 - 5

Midcurve 49.69 8.55 57 24.48% 49.65% 47 - 5

113 
Table 68. Descriptive statistics for nighttime operating speeds – WI 20. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=107) 

800 ft prior 54.47 5.7 60 42.99% 100.00% 50 - 6

PC 42.78 5.63 47 2.80% 99.07% 37 - 4

Midcurve 36.06 5.92 42 0.00% 84.11% 32 - 4

Condition #1 

(N=120) 

800 ft prior 54.64 6.11 60.5 50.00% 100.00% 50 - 6

PC 40.54 5.27 44.5 1.67% 98.33% 36 - 4

Midcurve 36.04 5.12 40.5 0.00% 86.67% 32 - 4

Condition #2 

(N=142) 

800 ft prior 52.63 5.85 58 28.17% 100.00% 47 - 5

PC 40.37 5.95 46 2.11% 97.89% 34 - 4

Midcurve 36.13 5.04 42 0.00% 88.73% 32 - 4

Condition #3 

(N=141) 

800 ft prior 55.57 6.07 62 51.06% 100.00% 51 - 6

PC 42.59 6.49 48 2.84% 97.16% 38 - 4

Midcurve 37.15 5.35 43 0.00% 90.78% 33 - 4

Condition #4 

(N=164) 

800 ft prior 54.98 5.77 60 51.83% 99.39% 50 - 6

PC 41.73 5.39 47 0.61% 97.56% 36 - 4

Midcurve 37.63 5.8 43 0.61% 90.24% 33 - 4

114 
Table 69. Descriptive statistics for nighttime operating speeds – WI 67. 

Condition 

(Obs) 

Location Mean SD 85th speed 

% of vehicles 

exceeding 

PSL 

% of vehicles 

exceeding 

AS 

Pace 

Baseline 

(N=149) 

800 ft prior 52.17 6.76 58 30.20% 100.00% 48 - 5

PC 45.43 5.42 51 1.34% 100.00% 40 - 5

Midcurve 41.40 5.13 47 0.00% 100.00% 37 - 4

Condition #1 

(N=137) 

800 ft prior 51.36 7.13 58 24.82% 99.27% 47 - 5

PC 42.17 5.05 47 0.73% 100.00% 36 - 4

Midcurve 38.42 4.52 43 0.73% 100.00% 33 - 4

Condition #2 

(N=211) 

800 ft prior 51.84 6.37 57 26.07% 100.00% 47 - 5

PC 43.19 5.15 48 0.95% 100.00% 38 - 4

Midcurve 39.12 4.94 45 0.47% 99.53% 34 - 4

Condition #3 

(N=130) 

800 ft prior 51.60 6.28 57 26.92% 100.00% 46 - 5

PC 44.36 5.29 50 1.54% 100.00% 40 - 5

Midcurve 40.70 5.5 46 0.77% 99.23% 36 - 4

Condition #4 

(N=194) 

800 ft prior 50.94 6.7 57 20.62% 100.00% 45 - 5

PC 44.47 4.99 50 2.06% 100.00% 39 - 4

Midcurve 40.31 4.53 45 0.00% 100.00% 35 - 4

115 

APPENDIX D 

The analyses presented here are organized into four sections. First, the difference in the mean 

and 85th-percentile operating speeds at each data collection location for each condition is 

presented. This analysis includes tabular and graphical summaries for the entire data collection 

period at each site, and is then disaggregated by daytime and nighttime speeds. The second 

section presents the difference in mean and 85th-percentile speeds between adjacent data 

collection locations at each site. These data are presented for each data collection period, and 

then disaggregated by daytime and nighttime periods. The third section presents information 

about the proportion of vehicles exceeding the posted speed limit as well as the horizontal curve 

advisory speed. The final part of this section includes all of the statistical tests that were 

completed to compare the various speed metrics across the test conditions. 

Daytime and Nighttime Conditions Combined 

The data used in this analysis are summarized in table 70 through table 75 . These data include 

the entire data collection period (daytime and nighttime) at each study location. The difference in 

the operating speed during condition i (i = 1, 2, 3, or 4) was compared to the operating speed 

during the baseline condition using the equation in figure 42. 

Figure 42. Equation. Speed difference between data conditions and periods. 

Where: 

V = difference in mean or 85th-percentile operating speed (mph) 

Vi = mean or 85th-percentile operating speed during condition i, i = 1, 2, 3, 4 

VBaseline = mean or 85th-percentile operating speed during the baseline condition. 

In this analysis, a positive value for V indicates that an active condition for the sequential 

flashing chevrons was associated with higher operating speeds than the baseline condition. A 

negative value for V indicates that an active condition for the sequential flashing chevrons was 

associated with lower operating speeds than the baseline condition. The speed differences are 

shown in table 76 through table 78. At the location 800 ft before the beginning of the horizontal 

curve, it was anticipated that the mean and 85th-percentile operating speeds would be similar 

across all conditions. The data in table 76 through table 78 indicate that mean and 85th-percentile 

speeds varied by less than 1.6 mph between the baseline and all four conditions at a location 800 

ft before the beginning of the horizontal curve. Conditions #1 and #2 produced the lowest mean 

and 85th-percentile operating speed difference relative to the baseline condition at the point of 

curvature and the midpoint of the horizontal curve relative to the baseline condition when 

aggregating the operating speed data across both the daytime and nighttime data collection 

periods. 

117 
Because the purpose of the sequential flashing chevron signs is to warn drivers of a horizontal 

curve, the condition that produced the lowest operating speed among the sample of drivers was 

considered the most effective condition in the present study. Figure 43 through figure 45 show 

the mean and 85th-percentile operating speeds for each condition at all three data collection sites. 

Table 70. Mean speed data for all time periods – WI 213. 

Period 800 ft prior PC Midcurve 

Baseline 59.41 54.02 49.08 

Condition #1 59.18 53.47 48.80 

Condition #2 59.44 53.7 50.25 

Condition #3 59.56 54.66 51.58 

Condition #4 59.86 54.15 50.98 

Table 71. 85th-percentile operating speed data for all time periods – WI 213 

Period 800 ft prior PC Midcurve 

Before 65 61 57 

1 Month 65 60 56 

12 Months 65 59 58 

18 Months 65 61 60 

24 Months 65 61 59 

Table 72. Mean speed data speed for all time periods – WI 20. 

Period 800 ft prior PC Midcurve 

Baseline 53.57 42.13 35.52 

Condition #1 52.87 40.07 35.01 

Condition #2 52 40.31 35.13 

Condition #3 54.53 41.61 35.95 

Condition #4 54.76 41.13 36.38 

Table 73. 85th-percentile operating speed data for all time periods – WI 20. 

Period 800 ft prior PC Midcurve 

Before 59 47 41 

1 Month 59 45 40 

12 Months 58 46 40 

18 Months 60 47 41 

24 Months 60 47 42 

118 
Table 74. Mean speed data for all time periods – WI 67. 

Period 800 ft prior PC Midcurve 

Baseline 51.63 45.32 41.08 

Condition #1 50.23 42.98 39.5 

Condition #2 50.62 43.11 39.1 

Condition #3 51.35 44.77 40.47 

Condition #4 51.01 44.89 40.27 

Table 75. 85th-percentile operating speed data for all time periods – WI 67. 

Period 800 ft prior PC Midcurve 

Before 57 51 46 

1 Month 56.5 48 44 

12 Months 57 48 44 

18 Months 57 50 45 

24 Months 57 50 45 

At Wisconsin Route 213 (see figure 43), condition #1 produced the lowest mean and 85thpercentile operating speed at the midpoint of the horizontal curve. All other active flashing 

conditions produced operating speeds that exceeded the baseline condition (inactive flashing) 

when aggregating all of the data collection periods. The mean speed associated with condition #1 

at the midpoint of the horizontal curve is the only active flashing condition that produced 

operating speeds below the advance curve warning speed limit. At the point of curvature, 

conditions #1 and #2 produced operating speeds that were lower than the baseline condition 

when aggregating all of the data collection periods. 

At Wisconsin Route 20 (see figure 44), conditions #1 and #2 produced the lowest mean and 85thpercentile operating speeds at the midpoint of the horizontal curve. All other active flashing 

conditions produced operating speeds that exceeded the baseline condition (inactive flashing) 

when aggregating all of the data collection periods. All of the mean and 85th-percentile operating 

speeds at the midpoint of the curve were at least 5 mph higher than the curve advisory warning 

speed limit. At the point of curvature, conditions #1 and #2 produced operating speeds that were 

lower than the baseline condition when aggregating all of the data collection periods. 

119 
Table 76. Difference in mean and 85th-percentile operating speeds between all conditions 

for all time periods – WI 213. 

Performance Measure Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

Change in Mean Speed 800 ft prior -0.23 0.03 0.15 0.45 

Change in Mean Speed PC -0.55 -0.32 0.64 0.13 

Change in Mean Speed Midcurve -0.28 1.17 2.50 1.90 

Change in 85th Speed 800 ft prior 0.00 0.00 0.00 0.00 

Change in 85th Speed PC -1.00 -2.00 0.00 0.00 

Change in 85th Speed Midcurve -1.00 1.00 3.00 2.00 

Table 77. Difference in mean and 85th-percentile operating speeds between all conditions 

for all time periods – WI 20. 

Performance Measure Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

Change in Mean Speed 800 ft prior -0.71 -1.58 0.96 1.19 

Change in Mean Speed PC -2.06 -1.83 -0.52 -1.00 

Change in Mean Speed Midcurve -0.50 -0.38 0.44 0.87 

Change in 85th Speed 800 ft prior 0.00 -1.00 1.00 1.00 

Change in 85th Speed PC -2.00 -1.00 0.00 0.00 

Change in 85th Speed Midcurve -1.00 -1.00 0.00 1.00 

Table 78. Difference in mean and 85th-percentile operating speeds between all conditions 

for all time periods – WI 67. 

Performance Measure Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

Change in Mean Speed 800 ft prior -1.40 -1.01 -0.27 -0.61 

Change in Mean Speed PC -2.34 -2.21 -0.55 -0.43 

Change in Mean Speed Midcurve -1.58 -1.98 -0.61 -0.81 

Change in 85th Speed 800 ft prior -0.50 0.00 0.00 0.00 

Change in 85th Speed PC -3.00 -3.00 -1.00 -1.00 

Change in 85th Speed Midcurve -2.00 -2.00 -1.00 -1.00 

At Wisconsin Route 67 (see figure 45), conditions #1 and #2 produced the lowest mean and 85thpercentile operating speeds at the midpoint of the horizontal curve when aggregating all of the 

data collection periods. All of the mean and 85th-percentile operating speeds at the midpoint of 

the curve were at least 10 mph higher than the curve advisory warning speed limit. At the point 

of curvature, conditions #1 and #2 produced operating speeds that were lower than the baseline 

condition when aggregating all of the data collection periods. 

120 
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----

40

45

50

55

60

65

70

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1

Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 213

Mean Speed 85th Speed PSL Adv Speed

Figure 43. Graph. Mean and 85th-percentile operating speeds at WI 213 site for all time 

periods. 

25

30

35

40

45

50

55

60

65

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 20

Mean Speed 85th Speed PSL Adv Speed

121 
■ ■ ■ ■ ■ 

.. ------------------------- ■ ■ ■ ■ ~ ■ ------- ■ ■ 

...._____ • .--■ ■ ......... ■ ■ ■ 

.. ■ ■ ■ ■ 

----

Figure 44. Graph. Mean and 85th-percentile operating speeds at WI 20 site for all time 

periods. 

20

25

30

35

40

45

50

55

60

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 67

Mean Speed 85th Speed PSL Adv Speed

Figure 45. Graph. Mean and 85th-percentile operating speeds at WI 67 site for all time 

periods. 

Daytime Conditions Only 

The data used in this analysis are for the daytime period only, and are summarized in table 79 

through table 84. The difference in the operating speed during condition i (i = 1, 2, 3, or 4) was 

compared to the operating speed during the baseline condition using the equation shown in figure 

42. The mean and 85th-percentile operating speeds decreased from the approach tangent (800 ft 

prior to the beginning of the horizontal curve) to the midpoint of the horizontal curve at all sites 

during all data collection periods. 

The speed differences for the daytime operating speed data are shown in table 85 through table 

84. At the location 800 ft before the beginning of the horizontal curve, it was anticipated that the 

mean and 85th-percentile operating speeds would be similar across all conditions. The data 

indicate that mean and 85th-percentile speeds varied by not more than 2.0 mph between the 

baseline and all four conditions at this location. Conditions #1 and #2 generally produced the 

lowest mean and 85th-percentile operating speed difference relative to the baseline condition at 

the point of curvature and the midpoint of the horizontal curve relative to the baseline condition 

when aggregating the operating speed data across both the daytime and nighttime data collection 

periods. Figure 43 through figure 45 show the mean and 85th-percentile operating speeds for each 

condition at all three data collection sites. 

122 
Table 79. Mean speed data for daytime conditions – WI 213. 

Period 800 ft prior PC Midcurve 

Baseline 59.2 54.6 49.82 

Condition #1 59.74 55.2 50.71 

Condition #2 60.11 54.72 52.06 

Condition #3 59.32 54.63 52.51 

Condition #4 59.92 54.67 52.06 

Table 80. 85th-percentile operating speed data for daytime conditions – WI 213 

Period 800 ft prior PC Midcurve 

Before 64 61 57 

1 Month 65 61 57 

12 Months 66 60 59 

18 Months 65 61 60 

24 Months 64 61 60 

Table 81. Mean speed data for daytime conditions – WI 20. 

Period 800 ft prior PC Midcurve 

Baseline 53.11 41.8 35.24 

Condition #1 51.64 39.75 34.3 

Condition #2 51.61 40.27 34.52 

Condition #3 53.75 40.88 35.06 

Condition #4 54.56 40.58 35.24 

Table 82. 85th-percentile operating speed data for daytime conditions – WI 20. 

Period 800 ft prior PC Midcurve 

Before 58 47 41 

1 Month 58 45 40 

12 Months 57 46 40 

18 Months 59 47 40 

24 Months 60 47 40 

Table 83. Mean speed data for daytime conditions – WI 67. 

Period 800 ft prior PC Midcurve 

Baseline 50.8 45.15 40.61 

Condition #1 49.59 43.44 40.11 

Condition #2 49.48 43.03 39.08 

Condition #3 51.21 45 40.35 

Condition #4 51.06 45.19 40.24 

123 
Table 84. 85th-percentile operating speed data for daytime conditions – WI 67. 

Period 800 ft prior PC Midcurve 

Before 56 51 45 

1 Month 55 48 45 

12 Months 55 48 43 

18 Months 57 49 45 

24 Months 57 50 46 

At Wisconsin Route 213 (see figure 46), the baseline and condition #1 produced the lowest mean 

and 85th-percentile operating speeds at the midpoint of the horizontal curve. The mean operating 

speeds during these two conditions were nearly equal to the curve advisory warning speed limit. 

All other active flashing conditions produced operating speeds that exceeded the baseline 

condition (inactive flashing) and condition #1 at the midpoint of the horizontal curve. The mean 

and 85th-percentile operating speeds at the point of curvature were similar across the baseline and 

all four active conditions for the sequential flashing chevrons. 

At Wisconsin Route 20 (see figure 47), condition #1 produced the lowest mean and 85thpercentile operating speeds at the midpoint of the horizontal curve and at the beginning of the 

curve. The mean operating speed for condition #1 was approximately 5 mph above the advance 

curve warning speed limit. At the beginning of the horizontal curve, the mean speed for 

condition #1 was approximately 10 mph above the advance curve warning speed limit. At this 

site, all of the active flashing conditions generally produced vehicle operating speeds lower than 

the baseline condition. 

Table 85. Difference in mean and 85th-percentile operating speeds between all conditions 

for daytime conditions – WI 213. 

Performance Measure Condition #1 Condition #2 Condition #3 Condition #4 

Change in Mean Speed 800 ft 

prior 

0.54 0.91 0.12 0.73 

Change in Mean Speed PC 0.61 0.13 0.03 0.08 

Change in Mean Speed 

Midcurve 0.88 2.24 2.69 2.23 

Change in 85th Speed 800 ft 

prior 

1.00 2.00 1.00 0.00 

Change in 85th Speed PC 0.00 -1.00 0.00 0.00 

Change in 85th Speed 

Midcurve 0.00 2.00 3.00 3.00 

124 
Table 86. Difference in mean and 85th-percentile operating speeds between all conditions 

for daytime conditions – WI 20. 

Performance Measure Condition #1 Condition #2 Condition #3 Condition #4 

Change in Mean Speed 800 ft 

prior 

-1.47 -1.50 0.64 1.45 

Change in Mean Speed PC -2.05 -1.53 -0.92 -1.22 

Change in Mean Speed 

Midcurve -0.93 -0.72 -0.18 0.00 

Change in 85th Speed 800 ft 

prior 

0.00 -1.00 1.00 2.00 

Change in 85th Speed PC -2.00 -1.00 0.00 0.00 

Change in 85th Speed 

Midcurve -1.00 -1.00 -1.00 -1.00 

Table 87. Difference in mean and 85th-percentile operating speeds between all conditions 

for daytime conditions – WI 67. 

Performance Measure Condition #1 Condition #2 Condition #3 Condition #4 

Change in Mean Speed 800 ft 

prior 

-1.21 -1.32 0.41 0.26 

Change in Mean Speed PC -1.71 -2.12 -0.15 0.04 

Change in Mean Speed 

Midcurve -0.50 -1.52 -0.26 -0.37 

Change in 85th Speed 800 ft 

prior 

-1.00 -1.00 1.00 1.00 

Change in 85th Speed PC -3.00 -3.00 -2.00 -1.00 

Change in 85th Speed 

Midcurve 0.00 -2.00 0.00 1.00 

At Wisconsin Route 67 (see figure 48), condition #2 produced the lowest mean and 85thpercentile operating speeds at the midpoint of the horizontal curve during the daytime period. 

However, the baseline and other active flashing conditions produced vehicle operating speeds 

that were nominally higher than condition #2. At the point of curvature, conditions #1 and #2 

produced operating speeds that were lower than the baseline condition and other active flashing 

conditions. 

125 
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......... 

• --. -. -. -~ 

■ 

■ 

■ 

■ 

■ 

■ ■-­

■--

■ 

■ 

■ 

■ 

■ 

■ 

■ 

■ 

40

45

50

55

60

65

70

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1

Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 213

Mean Speed 85th Speed PSL Adv Speed

Figure 46. Graph. Mean and 85th-percentile operating speeds at WI 213 site for daytime 

period. 

25

30

35

40

45

50

55

60

65

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 20

Mean Speed 85th Speed PSL Adv Speed

Figure 47. Graph. Mean and 85th-percentile operating speeds at WI 20 site for daytime 

period. 

126 
• 

■-

• 

• 

---■ • •----------------------

• • • "------1■---•■to----- ......... • -

• • • • • 

20

25

30

35

40

45

50

55

60

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1

Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 67

Mean Speed 85th Speed PSL Adv Speed

Figure 48. Graph. Mean and 85th-percentile operating speeds at WI 67 site for daytime 

period. 

Nighttime Conditions Only 

The data used in this analysis are for the nighttime period only, and are summarized in table 88 

through table 93. The difference in the operating speed during condition i (i = 1, 2, 3, or 4) was 

compared to the operating speed during the baseline condition using the equation shown in figure 

42. The mean and 85th-percentile operating speeds decreased from the approach tangent (800 ft 

prior to the beginning of the horizontal curve) to the midpoint of the horizontal curve at all sites 

during all data collection periods. 

The speed differences for the nighttime operating speed data are shown in table 88 through table 

93. At the location 800 ft before the beginning of the horizontal curve, it was anticipated that the 

mean and 85th-percentile operating speeds would be similar across all conditions. The data 

indicate that mean and 85th-percentile operating speeds varied by not more than 2.0 mph between 

the baseline and all four conditions at each location. Conditions #1 and #2 generally produced 

the lowest mean and 85th-percentile operating speed difference relative to the baseline condition 

at the point of curvature and the midpoint of the horizontal curve relative to the baseline 

condition during the nighttime data collection period. Figure 49 through figure 51 show the mean 

and 85th-percentile operating speeds for each condition at all three data collection sites. 

127 
Table 88. Mean operating speed data for nighttime conditions WI 213. 

Period 800 ft prior PC Midcurve 

Baseline 59.67 53.32 48.17 

Condition #1 58.77 52.21 47.41 

Condition #2 58.78 52.71 48.49 

Condition #3 59.87 54.71 50.36 

Condition #4 59.78 53.52 49.69 

Table 89. 85th - percentile operating speed data for nighttime conditions WI 213. 

Period 800 ft prior PC Midcurve 

Before 66 61 56 

1 Month 65 58 56 

12 Months 64 59 57 

18 Months 66 61 59 

24 Months 65 60 57 

Table 90. Mean operating speed data for nighttime conditions WI 20. 

Period 800 ft prior PC Midcurve 

Baseline 54.47 42.78 36.06 

Condition #1 54.64 40.54 36.04 

Condition #2 52.63 40.37 36.13 

Condition #3 55.57 42.59 37.15 

Condition #4 54.98 41.73 37.63 

Table 91. 85th - percentile operating speed data for nighttime conditions WI 20. 

Period 800 ft prior PC Midcurve 

Baseline 60 47 42 

Condition #1 60.5 44.5 40.5 

Condition #2 58 46 42 

Condition #3 62 48 43 

Condition #4 60 47 43 

Table 92. Mean operating speed data for nighttime conditions WI 67. 

Period 800 ft prior PC Midcurve 

Baseline 52.17 45.43 41.4 

Condition #1 51.36 42.17 38.42 

Condition #2 51.84 43.19 39.12 

Condition #3 51.6 44.36 40.7 

Condition #4 50.94 44.47 40.31 

128 
Table 93. 85th - percentile operating speed data for nighttime conditions WI 67. 

Period 800 ft prior PC Midcurve 

Baseline 58 51 47 

Condition #1 58 47 43 

Condition #2 57 48 45 

Condition #3 57 50 46 

Condition #4 57 50 45 

At Wisconsin Route 213 (see figure 49), the baseline and condition #1 produced the lowest mean 

and 85th-percentile operating speeds at the midpoint of the horizontal curve. The mean operating 

speeds during these two conditions were lower than the curve advisory warning speed limit. All 

other active flashing conditions produced operating speeds that exceeded the baseline condition 

(inactive flashing) and condition #1 at the midpoint of the horizontal curve. The mean and 85thpercentile operating speeds at the point of curvature were lowest for conditions #1 and #2. The 

mean speeds during these conditions were approximately 2 mph higher than the curve advisory 

speed limit. 

At Wisconsin Route 20 (see figure 50), conditions #1 and #2 produced the lowest mean and 85thpercentile operating speeds at the midpoint and the beginning of the horizontal curve during the 

nighttime condition. The mean operating speed for conditions #1 and #2 was approximately 5 

mph above the advance curve warning speed limit. At the beginning of the horizontal curve, the 

mean speed for conditions #1 and #2 was approximately 10 mph above the advance curve 

warning speed limit. 

Table 94. Difference in mean and 85th-percentile operating speeds between all conditions 

for nighttime conditions – WI 213. 

Performance Measure Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

Change in Mean Speed 800 ft prior -0.89 -0.89 0.20 0.11 

Change in Mean Speed PC -1.11 -0.61 1.39 0.21 

Change in Mean Speed Midcurve -0.76 0.32 2.19 1.52 

Change in 85th Speed 800 ft prior -1.00 -2.00 0.00 -1.00 

Change in 85th Speed PC -3.00 -2.00 0.00 -1.00 

Change in 85th Speed Midcurve 0.00 1.00 3.00 1.00 

129 
Table 95. Difference in mean and 85th-percentile operating speeds between all conditions 

for nighttime conditions – WI 20. 

Performance Measure Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

Change in Mean Speed 800 ft prior 0.17 -1.84 1.11 0.51 

Change in Mean Speed PC -2.23 -2.40 -0.19 -1.05 

Change in Mean Speed Midcurve -0.01 0.08 1.09 1.57 

Change in 85th Speed 800 ft prior 0.50 -2.00 2.00 0.00 

Change in 85th Speed PC -2.50 -1.00 1.00 0.00 

Change in 85th Speed Midcurve -1.50 0.00 1.00 1.00 

Table 96. Difference in mean and 85th-percentile operating speeds between all conditions 

for nighttime conditions – WI 67. 

Performance Measure Condition 

#1 

Condition 

#2 

Condition 

#3 

Condition 

#4 

Change in Mean Speed 800 ft prior -0.81 -0.34 -0.57 -1.23 

Change in Mean Speed PC -3.26 -2.24 -1.07 -0.96 

Change in Mean Speed Midcurve -2.98 -2.28 -0.70 -1.09 

Change in 85th Speed 800 ft prior 0.00 -1.00 -1.00 -1.00 

Change in 85th Speed PC -4.00 -3.00 -1.00 -1.00 

Change in 85th Speed Midcurve -4.00 -2.00 -1.00 -2.00 

At Wisconsin Route 67 (see figure 51), conditions #1 and #2 produced the lowest mean and 85thpercentile operating speeds at the midpoint and at the beginning of the horizontal curve during 

the nighttime period. All of the flashing patterns produced mean and 85th-percentile operating 

speeds lower than the baseline condition (inactive flashing patterns) at the beginning and 

midpoint locations along the horizontal curve. At the Wisconsin Route 67 site, the mean and 

85th-percentile operating speeds were more than 10 mph higher than the curve advisory speed 

limit. 

130 
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---- ----

... ~--

----

■......__ ~-- ... .. --­ -...-..;..,.,_-1.---....------.... ..... 

• 

.. 

• 

• 

• 

• 

• 

• 

.. 

40

45

50

55

60

65

70

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1

Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 213

Mean Speed 85th Speed PSL Adv Speed

Figure 49. Graph. Mean and 85th-percentile operating speeds at WI 213 site for nighttime 

period. 

25

30

35

40

45

50

55

60

65

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 20

Mean Speed 85th Speed PSL Adv Speed

Figure 50. Graph. Mean and 85th-percentile operating speeds at WI 20 site for nighttime 

period. 

131 
• • • • • 

.. • • • .. ~ 

'=-----■■--...,·----· '--.------r-- ~ 

• • 

20

25

30

35

40

45

50

55

60

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

Baseline

Condition #1Condition #2Condition #3Condition #4

800 ft prior PC Middle curve

Speed (mph)

WI 67

Mean Speed 85th Speed PSL Adv Speed

Figure 51. Graph. Mean and 85th-percentile operating speeds at WI 67 site for nighttime 

period. 

132 
APPENDIX E 

Statistical Tests 

Several statistical tests were performed as part of the field studies to compare the active flashing 

sequential conditions (conditions #1 through #4) to the baseline condition. These included the 

following: 

 Comparison of mean speeds 

 Comparison of speed variance 

 Test of proportions for vehicles exceeding the horizontal curve advisory speed 

All of these tests were performed for the daytime and nighttime data collection periods combined, 

the daytime period only, and the nighttime period only. The results of the statistical tests are 

shown below. 

Difference in Mean Speeds 

For this test, the mean speed at all three data collection locations (800 ft before the horizontal 

curve, beginning of the curve, and midpoint of the horizontal curve) were compared between 

each condition (baseline and conditions #1 through #4). The null hypothesis is the difference in 

mean speed between two conditions (e.g., condition #1 relative to baseline, condition #2 relative 

to baseline, etc.) is equal to zero. The probability of type I error was set at 0.05, so the null 

hypothesis is rejected if the p-value exceeds 0.05. It should be noted that a one-sided t-test was 

used in this case, so a statistically significant test indicates that the mean speed for the baseline 

condition differs from the mean speed in the flashing condition. A negative sign for the t-test 

indicates that the baseline condition has a higher mean speed than the flashing condition, which 

is a desirable result. Table 97 through note: * denotes the 95-percent confidence interval. 

table 105 shows the statistical tests for the daytime and nighttime mean speeds combined, the 

daytime mean speeds only, and the nighttime speeds only, respectively. 

Table 97. Statistical tests for mean speeds during daytime and nighttime periods combined 

- WI 213. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

tstatistic p-value tstatistic p-value tstatistic p-value tstatistic p-value 

800 ft prior -0.457 0.324 0.05 0.52 0.309 0.621 1.01 0.844 

PC -0.976 0.165 -0.528 0.299 1.187 0.882 0.254 0.6 

Midcurve -0.405 0.343 1.508 0.934 3.626 1 2.799 0.997 

Note: * denotes the 95-percent confidence interval. 

133 
Table 98. Statistical tests for mean speeds during daytime and nighttime periods combined 

- WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

tstatistic p-value tstatistic p-value tstatistic p-value tstatistic p-value 

800 ft prior -1.423 0.078 -3.73 <0.001* 2.211 0.986 2.734 0.997 

PC -4.666 <0.001* -4.268 <0.001* -1.148 0.125 -2.27 0.012* 

Midcurve -1.112 0.133 -0.909 0.182 0.99 0.839 1.927 0.973 

Note: * denotes the 95-percent confidence interval. 

Table 99. Statistical tests for mean speeds during daytime and nighttime periods combined 

- WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure tstatistic p-value tstatistic p-value tstatistic p-value tstatistic p-value 

800 ft prior -2.504 0.006* -1.942 0.026* -0.506 0.307 -1.167 0.122 

PC -5.469 <0.001* -5.331 <0.001* -1.277 0.101 -1.008 0.157 

Midcurve -3.937 <0.001* -5.209 <0.001* -1.523 0.064 -2.055 0.020* 

Note: * denotes the 95-percent confidence interval. 

As shown in table 97 through note: * denotes the 95-percent confidence interval. 

table 99, the baseline to condition #1 and the baseline to condition #2 comparisons are almost all 

negative, which confirms that conditions #1 and #2 are generally producing lower mean 

operating speeds than the baseline condition. The two-sample t-test is statistically significant at 

the point of curve along Wisconsin Route 20 for both flashing conditions relative to the baseline 

(inactive) condition. All of the condition #1 and #2 speeds are lower than the baseline condition 

at Wisconsin Route 67 when combining the daytime and nighttime mean speeds. Conditions #3 

and #4 produced few statistically different mean operating speeds relative to the baseline 

condition. 

The daytime mean speed comparisons, which are shown in table 100 through note: * denotes the 

95-percent confidence interval. 

table 102table 100 also show several statistically significant differences between the baseline 

condition and conditions #1 and #2 during the daytime data collection period. The statistically 

significant results were found at the PC locations of Wisconsin routes 20 and 67, suggesting that 

the flashing sequence is effective in reducing mean operating speeds at the beginning of the 

curve when compared to the baseline condition. Conditions #3 and #4 produced few statistically 

different mean operating speeds relative to the baseline condition. 

134 
The nighttime mean speed comparisons, which are shown in note: * denotes the 95-percent confidence 

interval. 

table 103 through note: * denotes the 95-percent confidence interval. 

table 105, also show several statistically significant differences between the baseline condition 

and conditions #1 and #2. The statistically significant results were found at the PC locations of 

Wisconsin routes 20 and 67, suggesting that the flashing sequence is effective in reducing mean 

operating speeds at the beginning of the curve when compared to the baseline condition. 

Statistically significant differences in the mean speed at the midpoint of the horizontal curve 

were found at Wisconsin site 67 when comparing conditions #1 and #2 to the baseline condition. 

This indicates that the sequential flashing pattern was effective in reducing mean operating 

speeds at the midpoint of the horizontal curve at one site during the nighttime conditions. 

Conditions #3 and #4 produced few statistically different mean operating speeds relative to the 

baseline condition. 

Table 100. Statistical tests for mean speeds during daytime period – WI 213. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure tstatistic p-value tstatistic p-value tstatistic p-value tstatistic p-value 

800 ft prior 0.859 0.805 1.366 0.913 0.204 0.581 1.352 0.911 

PC 0.735 0.769 0.150 0.560 0.045 0.518 0.113 0.545 

Midcurve 0.903 0.816 2.199 0.986 3.031 0.999 2.508 0.994 

Table 101. Statistical tests for mean speeds during daytime period – WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure tstatistic p-value tstatistic p-value tstatistic p-value tstatistic p-value 

800 ft prior -2.350 0.010* -2.946 0.002* 1.241 0.892 2.617 0.995 

PC -3.674 <0.001* -2.940 0.002* -1.671 0.048* -2.077 0.019* 

Midcurve -1.633 0.052 -1.364 0.087 -0.323 0.373 -0.001 0.500 

Note: * denotes the 95-percent confidence interval. 

Table 102. Statistical tests for mean speeds during daytime period – WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure tstatistic p-value tstatistic p-value tstatistic p-value tstatistic p-value 

800 ft prior -1.521 0.065 -1.704 0.045* 0.536 0.704 0.339 0.633 

PC -2.760 0.003* -3.402 <0.001* -0.241 0.405 0.064 0.526 

Midcurve -0.849 0.198 -2.788 0.003* -0.491 0.312 -0.593 0.277 

Note: * denotes the 95-percent confidence interval. 

135 
Table 103. Statistical tests for mean speeds during nighttime period – WI 213. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure tstatistic p-value tstatistic p-value t-statistic p-value tstatistic p-value 

800 ft prior -1.132 0.129 -1.022 0.154 0.257 0.601 0.149 0.559 

PC -1.436 0.076 -0.688 0.246 1.675 0.952 0.269 0.606 

Midcurve -0.769 0.221 0.277 0.609 2.038 0.979 1.467 0.928 

Table 104. Statistical tests for mean speeds during nighttime period – WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

tstatistic p-value tstatistic p-value t-statistic p-value tstatistic p-value 

800 ft prior 0.222 0.588 -2.484 0.007* 1.460 0.927 0.721 0.764 

PC -3.089 0.001* -3.229 0.001* -0.238 0.406 -1.541 0.062 

Midcurve -0.020 0.492 0.112 0.544 1.521 0.935 2.164 0.984 

Note: * denotes the 95-percent confidence interval. 

Table 105. Statistical tests for mean speeds during nighttime period – WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

tstatistic p-value tstatistic p-value t-statistic p-value tstatistic p-value 

800 ft prior -0.986 0.163 -0.480 0.316 -0.732 0.233 -1.681 0.047* 

PC -5.254 <0.001* -3.969 <0.001* -1.660 0.049* -1.702 0.045* 

Midcurve -5.193 <0.001* -4.252 <0.001* -1.104 0.135 -2.083 0.019* 

Note: * denotes the 95-percent confidence interval. 

Speed Variance 

For the analysis, the speed variance in the baseline condition was compared to the speed variance 

for each flashing condition. The null hypothesis was that the two conditions produced an equal 

speed variance. The probability of type I error was set at 0.05. If the null hypothesis was rejected, 

the variable in the baseline condition was either smaller or larger than the flashing condition. An 

F-statistic exceeding 1.0 indicates that the speed variance in the baseline condition exceeded the 

speed variance in the flashing condition, a desirable result. The statistical tests were conducted 

for all speeds combined (daytime and nighttime), daytime speeds only, and nighttime speeds 

only. The results of the speed variance tests are shown in table 106 through table 114, 

respectively. 

136 
Table 106. Speed variance tests for daytime and nighttime periods combined - WI 213. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 0.820 0.103 0.846 0.198 0.891 0.313 0.636 <0.001* 

PC 0.798 0.063 0.809 0.103 0.838 0.123 0.716 0.003* 

Midcurve 0.690 0.002* 0.855 0.228 0.869 0.220 0.887 0.285 

Note: * denotes the 95-percent confidence interval. 

Table 107. Speed variance tests for daytime and nighttime periods combined - WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 1.594 <0.001* 1.091 0.426 1.077 0.507 1.132 0.265 

PC 0.976 0.834 1.072 0.525 1.207 0.093 1.120 0.306 

Midcurve 0.930 0.528 0.911 0.386 0.944 0.603 1.064 0.574 

Note: * denotes the 95-percent confidence interval. 

Table 108. Speed variance tests for daytime and nighttime periods combined - WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 1.121 0.329 0.979 0.843 0.942 0.602 1.042 0.721 

PC 0.809 0.064 0.810 0.058 0.783 0.036* 0.897 0.321 

Midcurve 0.970 0.785 0.888 0.286 0.909 0.412 1.039 0.744 

Note: * denotes the 95-percent confidence interval. 

Few speed variance tests were statistically significant for the daytime and nighttime periods 

combined, as shown in table 106. In only one case (800 ft before the horizontal curve on 

Wisconsin Route 20) was the variance lower for the flashing condition relative to the baseline 

condition. This suggests that the flashing sequential chevron signs have little effect on the speed 

variance when combining the daytime and nighttime operating speed data. 

When considering the daytime speed variance only, there are several statistically significant 

differences between the speed variance in the baseline condition and several flashing conditions, 

as shown in table 109 through note: * denotes the 95-percent confidence interval. 

137 
table 111. The location 800 ft before the horizontal curve on Wisconsin Route 20 was the only 

location where the flashing condition (condition #1) produced a speed variance lower than the 

baseline condition during the daytime period. 

Table 109. Speed variance tests for daytime period – WI 213. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 0.792 0.198 1.036 0.831 1.323 0.068 0.808 0.162 

PC 0.690 0.041* 0.617 0.010* 0.726 0.038* 0.656 0.006* 

Midcurve 0.554 0.001* 0.626 0.012* 0.797 0.141 0.849 0.283 

Note: * denotes the 95-percent confidence interval. 

Table 110. Speed variance tests for daytime period – WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 1.834 <0.001* 1.116 0.422 0.967 0.815 1.219 0.171 

PC 1.044 0.765 1.047 0.740 1.061 0.677 1.291 0.078 

Midcurve 1.025 0.861 1.006 0.968 0.978 0.875 1.052 0.722 

Note: * denotes the 95-percent confidence interval. 

Table 111. Speed variance tests for daytime period – WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 1.183 0.341 1.064 0.734 1.063 0.742 1.161 0.390 

PC 0.724 0.049* 0.709 0.039* 0.656 0.011* 0.884 0.438 

Midcurve 1.123 0.514 0.888 0.469 0.811 0.209 1.320 0.109 

Note: * denotes the 95-percent confidence interval. 

When considering the nighttime speed variance only, there were very statistically significant 

differences between the speed variance in the baseline condition and several flashing conditions, 

as shown in table 112 through table 114. In none of these cases did the flashing condition 

produce a speed variance lower than the baseline condition during the nighttime period. 

138 
Table 112. Speed variance tests for nighttime period – WI 213. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 0.742 0.077 0.688 0.048* 0.598 0.003* 0.523 <0.001* 

PC 0.827 0.258 0.989 0.963 1.009 0.957 0.792 0.165 

Midcurve 0.723 0.054 0.991 0.969 0.943 0.736 0.906 0.557 

Note: * denotes the 95-percent confidence interval. 

Table 113. Speed variance tests for nighttime period – WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 1.147 0.473 1.052 0.788 1.132 0.503 1.022 0.911 

PC 0.877 0.484 1.118 0.548 1.332 0.122 0.917 0.616 

Midcurve 0.748 0.124 0.726 0.075 0.816 0.261 0.959 0.802 

Table 114. Speed variance tests for nighttime period – WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

Fstatistic p-value Fstatistic p-value Fstatistic p-value Fstatistic p-value 

800 ft prior 1.113 0.522 0.887 0.424 0.865 0.398 0.982 0.900 

PC 0.867 0.397 0.902 0.492 0.953 0.781 0.847 0.277 

Midcurve 0.779 0.139 0.930 0.627 1.152 0.405 0.780 0.106 

Test of Proportions for Vehicles Exceeding Advisory Speed Warning 

This analysis considered the proportion of vehicles exceeding the advisory speed. A test of 

proportions was used to compare the baseline condition to one of the four flashing chevron 

conditions. The probability of type I error was set equal to 0.05. The null hypothesis was that the 

baseline and the flashing chevron conditions were equal. A one-sided test of proportions was 

used to compare the baseline to each flashing condition. When the proportion of vehicles 

139 
exceeding the advisory speed for the baseline condition was higher than the proportion of 

vehicles exceeding the advisory speed for one of the flashing conditions, the z-statistic was 

positive, which was an undesirable result. 

Results of the analysis are shown in table 115 through table 123. In table 115 through note: * 

denotes the 95-percent confidence interval. 

table 117, the speed data for the daytime and nighttime conditions were combined. Table 118 

and table 123 show the results of the analysis for the daytime and nighttime conditions, 

respectively. Few flashing conditions produced speed compliance relative to the baseline 

condition. This analysis indicates that the flashing sequential conditions evaluated in the present 

study did not produce improved speed compliance relative to the baseline condition. 

 Table 115. Proportion of vehicles exceeding advisory speed (daytime and nighttime 

combined) - WI 213. 

Performance 

Measure 

Baseline →

Condition #1 

Baseline →

Condition #2 

Baseline →

Condition #3 

Baseline →

Condition #4 

zstatistic p-value zstatistic p-value z-statistic p-value z-statistic p-value 

800 ft prior -0.064 0.525 -1.340 0.910 -1.216 0.888 -2.277 0.989 

PC 1.100 0.136 -0.072 0.529 0.169 0.433 -0.792 0.786 

Midcurve 1.613 0.053 -1.246 0.894 0.894 0.999 -2.044 0.980 

Table 116. Proportion of vehicles exceeding advisory speed (daytime and nighttime 

combined) - WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

zstatistic p-value zstatistic p-value zstatistic p-value zstatistic p-value 

800 ft prior 0.043 0.483 -1.093 0.863 -1.028 0.848 -0.070 0.528 

PC 1.862 0.031* 0.758 0.224 0.992 0.161 0.689 0.246 

Midcurve 0.093 0.463 -0.675 0.750 -1.871 0.969 -1.747 0.960 

Note: * denotes the 95-percent confidence interval. 

140 
Table 117. Proportion of vehicles exceeding advisory speed (daytime and nighttime 

combined) - WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

zstatistic p-value zstatistic p-value zstatistic p-value zstatistic p-value 

800 ft prior 0.804 0.211 0.000 0.500 0.000 0.500 0.001 0.500 

PC 0.001 0.500 0.001 0.500 0.001 0.500 0.001 0.500 

Midcurve 1.148 0.125 1.148 0.125 0.834 0.202 1.456 0.073 

Table 118. Proportion of vehicles exceeding advisory speed during daytime – WI 213. 

Performance 

Measure 

Baseline →

Condition #1 

Baseline →

Condition #2 

Baseline →

Condition #3 

Baseline →

Condition #4 

z-statistic pvalue z-statistic p-value z-statistic p-value zstatistic p-value 

800 ft prior -1.486 0.931 -1.397 0.919 -0.117 0.547 -1.590 0.944 

PC -0.649 0.742 -0.403 0.657 0.779 0.218 -0.951 0.829 

Midcurve -0.588 0.722 -1.450 0.926 -3.597 1.000 -2.425 0.992 

Table 119. Proportion of vehicles exceeding advisory speed during daytime – WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

zstatistic p-value zstatistic p-value zstatistic p-value zstatistic p-value 

800 ft prior 0.122 0.452 -1.054 0.854 -0.954 0.830 -0.926 0.823 

PC 1.974 0.024 0.469 0.320 0.480 0.316 0.220 0.413 

Midcurve 0.670 0.252 -0.013 0.505 -0.954 0.830 -0.665 0.747 

Table 120. Proportion of vehicles exceeding advisory speed during daytime – WI 67. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

zstatistic p-value zstatistic p-value zstatistic p-value zstatistic p-value 

800 ft prior 0.001 0.500 0.001 0.500 0.001 0.500 0.001 0.500 

PC 0.001 0.500 0.001 0.500 0.001 0.500 0.001 0.500 

Midcurve 0.904 0.183 0.661 0.254 0.001 0.500 1.209 0.113 

141 
Table 121. Proportion of vehicles exceeding advisory speed during nighttime – WI 213. 

Performance 

Measure 

Baseline →

Condition #1 

Baseline →

Condition #2 

Baseline →

Condition #3 

Baseline →

Condition #4 

z-statistic p-value z-statistic p-value z-statistic p-value z-statistic p-value 

800 ft prior 0.687 0.246 -0.621 0.733 -1.776 0.962 -1.628 0.948 

PC 1.318 0.094 0.059 0.477 -0.524 0.700 -0.212 0.584 

Midcurve 2.311 0.010* -0.427 0.665 -0.843 0.800 -0.412 0.660 

Note: * denotes the 95-percent confidence interval. 

Table 122. Proportion of vehicles exceeding advisory speed during nighttime – WI 20. 

Baseline → Baseline → Baseline → Baseline →

Performance Condition #1 Condition #2 Condition #3 Condition #4 

Measure 

zstatistic p-value zstatistic p-value zstatistic p-value zstatistic p-value 

800 ft prior 0.000 0.500 0.000 0.500 0.000 0.500 0.809 0.209 

PC 0.487 0.313 0.734 0.232 1.060 0.145 0.903 0.183 

Midcurve -0.546 0.708 -1.064 0.856 -1.595 0.945 -1.508 0.934 

Table 123. Proportion of vehicles exceeding advisory speed during nighttime – WI 67. 

Performance 

Measure 

Baseline →

Condition #1 

Baseline →

Condition #2 

Baseline →

Condition #3 

Baseline →

Condition #4 

zstatistic p-value z-statistic p-value zstatistic p-value z-statistic p-value 

800 ft prior 1.045 0.148 0.000 0.500 0.000 0.500 0.000 0.500 

PC 0.000 0.500 0.000 0.500 0.000 0.500 0.000 0.500 

Midcurve 0.000 0.500 0.838 0.201 1.073 0.142 0.000 0.500 

142 
ACKNOWLEDGMENTS 

The Federal Highway Administration would like to express appreciation to the professional 

industry practitioners and researchers for providing information for the field study conducted as 

part of this research effort. 

143 

REFERENCES 

3M. A Before and After Study of 3M Driver Feedback Signs. Traffic Safety Systems, 3M United 

Kingdom PLC, 2006. 

Albin, R., V. Brinkly, J. Cheung, F. Julian, C. Satterfield, W. Stein, E. T. Donnell, H. McGee, A. 

Holzem, M. Albee, J. Wood, and F. Hanscom. Low-Cost Treatments for Horizontal Curve Safety 

2016. Report No. FHWA-SA-15-084, Federal Highway Administration, Washington, DC, 

January 2016. 

Babaee, S., Y. Shen, E. Hermans, G. Wets, T. Brijs, and C. Ariën. Assessing individual driver's 

relative performance at curve: Applying data envelopment analysis on simulator data. Transport 

Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment, 2014, 

10 pp. 

Banihashemi, M., M. Dimaiuta, and H. Wang. Operating Speed Model for Low-Speed Rural 

Two-Lane Highways. Transportation Research Record: Journal of the Transportation Research 

Board, No. 2223, 2011, pp. 63-71. 

Bertini, R. L., C. M. Monsere, C. Nolan, P. Bosa, and T. A. El-Seoud. Field Evaluation of the 

Myrtle Creek Advanced Curve Warning System. Oregon Department of Transportation, Report 

No. FHWA-OR-RD-06-13, 2006. 

Carson, J. L., J. Tydlacka, L. S. Gray, and A.P. Voigt. NCHRP Synthesis 380: Applications of 

Illuminated, Active, In-Pavement Marker Systems. Transportation Research Board of the 

National Academies, Washington, DC, 2008. 

City of Bellevue Transportation Department. Stationary Radar Sign Program Report. Accessed 

at: http://jkheneghan.com/city/meetings/2009/Apr/04302009_radar_sign_report_BellevueWA.pdf on 

November 28, 2017. 

Cruzado, I., and E. T. Donnell. Evaluating Effectiveness of Dynamic Speed Display Signs in 

Transition Zones of Two-Lane, Rural Highways in Pennsylvania. Transportation Research 

Record: Journal of the Transportation Research Board, No. 2122, Transportation Research 

Board of the National Academies, 2009, pp. 1–8. 

Diercksen, L., C. M. Monsere, and R. L. Bertini. A Second Look at the Effectiveness of the 

Myrtle Creek Dynamic Advanced Curve Warning System. Proceedings from 15th World 

Congress on Intelligent Transport Systems and ITS America’s 2008 Annual Meeting, New York, 

NY, November 2008, 12 pp. 

Fontaine, M., P. Carlson, and H. Hawkins. Evaluation of traffic control devices for rural highspeed maintenance work zones: Second year activities and final recommendations. Texas 

Transportation Institute, No. FHWA/TX-01/1879-2, College Station, TX, 2000. 

145 
Glennon, J. C., T. R. Neuman, and J. E. Leisch. Safety and Operational Considerations for 

Design of Rural Highway Curves. Report No. FHWA-RD-86-035, Federal Highway 

Administration, Washington, DC, 1985. 

Hallmark S. L., N. Hawkins, and O. Smadi. Evaluation of Dynamic Speed Feedback Signs on 

Curves: A National Demonstration Project. Report No. FHWA-HRT-14-020, Federal Highway 

Administration, Washington, DC, 2015. 

Hauer, E., F. J. Ahlin, and J. S. Bowser. Speed Enforcement and Speed Choice. Accident 

Analysis & Prevention, Vol. 14, No. 4, 1981, pp. 267-278. 

Institute of Transportation Engineers. Manual of Transportation Engineering Studies. Prentice 

Hall, Englewood Cliffs, NJ, 2010, 2nd Edition. 

Knapp, K. K., and F. Robinson. The Vehicle Speed Impacts of a Dynamic Horizontal Curve 

Warning Sign on Low-Volume Local Roadways. Report No. CTS 12-12, 2012, Humphrey School 

of Public Affairs, University of Minnesota, 96 pp. 

Krammes, R., R. O. Brackett, M. Shafer, J. Ottesen, I. Anderson, K. Fink, K. Collins, O. 

Pendleton, and C. Messer. Horizontal Alignment Design Consistency for Rural Two-Lane 

Highways. Report No. FHWA-RD94-034., Federal Highway Administration, Washington, DC, 

1995. 

Federal Highway Administration. Manual on Uniform Traffic Control Devices (MUTCD). 

Washington, DC, 2009. 

Mahoney, K. M., R. J. Porter, E. T. Donnell, D. Lee, and M. T. Pietrucha. Evaluation of 

Centerline Rumble Strips on Lateral Vehicle Placement and Speed on Two-Lane Highways. 

Report No. FHWA-PA-2002-034-97-04, Pennsylvania Transportation Institute, Penn State 

University, University Park, PA, 2003. 

Mattox III, J., W. Sarasua, J. H. Ogle, R. T. Eckenrode, and A. Dunning. Development and 

Evaluation of Speed-Activated Sign to Reduce Speeds in Work Zones. Transportation Research 

Record: Journal of the Transportation Research Board, No. 2015, Transportation Research 

Board of the National Academies, Washington, DC, 2008, pp. 3–11. 

Molino, J. A., B. Katz, and M. B. Hermosillo. Simulator Evaluation of Low-Cost Safety 

Improvements on Rural Two-Lane Undivided Roads: Nighttime Delineation for Curves and 

Traffic Calming for Small Towns. Report No. FHWA-HRT-09-061. Federal Highway 

Administration, McLean, VA, 2010. 

Monsere, C., C. Nolan, R. Bertini, E. Anderson, and T. El-Seoud. Measuring the Impacts of 

Speed Reduction Technologies: Evaluation of Dynamic Advanced Curve Warning System. 

Transportation Research Record: Journal of the Transportation Research Board, No. 1918, 

Transportation Research Board of the National Academies, Washington, DC, 2005, pp. 98–107. 

146 
Montella, A., F. Galante, F. Mauriello, and L. Pariota. Effects of Traffic Control Devices on 

Rural Curve Driving Behavior. Transportation Research Record: Journal of the Transportation 

Research Board, No. 2492, Transportation Research Board of the National Academies, 

Washington, DC, 2015, pp. 10–22. 

National Highway Traffic Safety Administration. Traffic Safety Facts 2015. Report No. DOT HS 

812 384, US Department of Transportation, Washington, DC, 2015. 

Permanent International Association of Road Congresses. Road Safety Manual – 

Recommendations from the World Road Association. 2003. 

Poe, C. M., J. P. Tarris, and J. M. Mason. Operating Speed Approach to Geometric Design of 

Low-Speed Urban Streets. Report No. PTI 9606, Pennsylvania Transportation Institute, Penn 

State University, University Park, PA, 1996. 

Porter, R. J., E. T. Donnell, and K. M. Mahoney, Evaluation of effects of centerline rumble strips 

on lateral vehicle placement and speed. Transportation Research Record: Journal of the 

Transportation Research Board, No.1882, Transportation Research Board of the National 

Academies, Washington, DC, 2004, pp.10-16. 

Preston, H., and T. Schoenecker. Potential Safety Effects of Dynamic Signing at Rural 

Horizontal Curves. Report No. MN/RC-2000-14, University of Minnesota, 1999. 

Santiago-Chaparro, K. R., M. Chitturi, A. Bill, and D. Noyce. Spatial Effectiveness of Speed 

Feedback Signs. Transportation Research Record: Journal of the Transportation Research 

Board, No. 2281, Transportation Research Board of the National Academies, Washington, DC, 

2012, pp. 8-15. 

Smadi, O., N. Hawkins, S. Knickerbocker, S. Hallmark, and A. Pike. Evaluation of the 

Sequential Dynamic Curve Warning System: Final Report. Report No. FHWA-15-CAI-012-B. 

Federal Highway Administration, Washington, DC, November 2015. 

Snedecor, G. W., and W. G. Cochran. Statistical Methods, 8th Edition. Iowa State University 

Press, Ames, IA, 1989. 

Sun, C., P. Edara, Y. Hou, and A. Robertson. Safety Evaluation of Sequential Warning Lights in 

Tapers at Nighttime Work Zones. Transportation Research Record: Journal of the 

Transportation Research Board, No. 2272, Transportation Research Board of the National 

Academies, Washington, DC, 2012, pp. 1–8. 

Taragin, A., and J. E. Leisch. Driver Performance on Horizontal Curves. Proceedings of the 33rd 

Annual Meeting of the Highway Research Board, Washington, DC, 1954, pp. 446-466. 

Tribbett, L., J. Mounce, and P. McGowen. An Evaluation of Dynamic Curve Warning Systems in 

the Sacramento River Canyon. Western Transportation Institute, Montana State University, 

Bozeman, MT, April 2000. 

147 
Vest, A., S. Nikiforos, A. Clayton, and J. Pigman. Effect of Warning Devices on Curve 

Operating Speeds. Report No. No. KTC-05-20/SPR-259-03-1F, Kentucky Transportation Center, 

Lexington, KY, 2005, 44 pp. 

Winnett, M. A., and A. H. Wheeler. Vehicle-activated Signs: A Large-scale Evaluation. Report 

No. TRL548, Department of Transport, UK, 2002. 

148 
FHWA, Offce of Safety 

Joseph Cheung 

202-366-6994

joseph.cheung@dot.gov

FHWA-SA-18-075