Safety Evaluation of Signalized Restricted

Crossing U-Turn Intersections

PUBLICATION NO. FHWA-HRT-17-082 DECEMBER 2017

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

The research documented in this report was conducted as part of the Federal Highway 

Administration’s (FHWA’s) Evaluation of Low-Cost Safety Improvements Pooled Fund Study 

(ELCSI-PFS). FHWA established this PFS in 2005 to conduct research on the effectiveness of 

the safety improvements identified by the National Cooperative Highway Research Program 

Report 500 Guides as part of the implementation of the American Association of State Highway 

and Transportation Officials (AASHTO) Strategic Highway Safety Plan. The ELCSI-PFS studies 

provide a crash modification factor and benefit– cost (B/C) economic analysis for each of the 

targeted safety strategies identified as priorities by the pooled fund member States.

A restricted crossing U-turn (RCUT) intersection is defined as a three- or four-approach 

intersection where minor street left-turn and through movements (if any) are rerouted to one-way

downstream U-turn crossovers. This study collected and analyzed crash data before and after 

conversion of 11 intersections from conventional to RCUT design. The intersections were in 

suburban areas on four- or six-lane arterials. Study results show a signalized RCUT to be 

effective to reduce total crashes, and reduce injury crashes since they generally reduce the more 

severe angle and turning crashes. The study estimated B/C ratio for installing an RCUT shows 

that this strategy, when considering safety and operations, is cost beneficial. This report is 

intended for safety engineers, highway designers, planners, and practitioners at State and local 

agencies involved with AASHTO Strategic Highway Safety Plan implementation.

Jonathan Porter, Ph.D.

Acting Director, Office of Safety

Research and Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-17-082

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Safety Evaluation of Signalized Restricted Crossing U-Turn 

Intersections

5. Report Date

December 2017

6. Performing Organization Code

7. Author(s)

Joseph E. Hummer, Ph.D., P.E., and Sathish Rao

8. Performing Organization Report No.

VHB

8300 Boone Blvd., Ste. 700

Vienna, VA 22182-2626

Wayne State University

Department of Civil and 

Environmental Engineering

Detroit, MI 48202

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-13-D-00001

12. Sponsoring Agency Name and Address

Federal Highway Administration

Office of Safety Research and Development

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period 

Covered

Safety Evaluation

14. Sponsoring Agency Code:

FHWA

15. Supplementary Notes

The Federal Highway Administration (FHWA) Office of Safety Research and Development managed this study

under the Development of Crash Modification Factors (DCMF) program. The FHWA Office of Safety Research 

and Development Program and Task Manager was Roya Amjadi (HRDS-20).

16. Abstract

This study evaluated restricted crossing U-turn (RCUT) intersection and was conducted by the DCMF program 

for the Evaluation of Low-Cost Safety Improvements Pooled Fund Study. RCUT is defined as a three-approach or

four-approach intersection where minor street left-turn and through movements (if any) are rerouted to one-way 

downstream U-turn crossovers. RCUTs are also known as superstreets, J-turns, reduced conflict intersections, and 

synchronized streets. Previous research has shown that unsignalized RCUTs are generally safer than conventional 

options. However, there are no known studies specific to the safety of signalized RCUTs. The objective of this 

effort was to collect and analyze crash data to develop a crash modification factor (CMF) for signalized RCUTs. 

This study collected and analyzed crash data before and after conversion of 11 intersections from conventional to 

RCUT design. The intersections were in suburban areas on four- or six-lane arterials. For most individual sites 

and groups of sites examined, odds ratio tests showed that there were high-quality comparison sites available, and 

regression to the mean was not an issue. The project team recommends a CMF of 0.85 for overall crashes and 

0.78 for injury crashes for the conversion of a conventional intersection to an RCUT intersection.

Based on those CMFs, the project team produced an estimated benefit-to-cost ratio of 3.6 to 1.0 when considering 

safety and operations or 2.6 to 1.0 considering safety only. 

17. Key Words

RCUT, restricted crossing U-turn, superstreet, J-turn, 

synchronized street, signalized, safety, CMF

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

70

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
ii

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 

in2square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m

2

yd2 square yard 0.836 square meters m

2

ac acres 0.405 hectares ha

mi2square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m

3 

yd3 cubic yards 0.765 cubic meters m

3 

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") 

TEMPERATURE (exact degrees) 

o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2poundforce 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 

mm2square millimeters 0.0016 square inches in2 

m

2

square meters 10.764 square feet ft2 

m

2

square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m

3 cubic meters 35.314 cubic feet ft3 

m

3 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 o

F 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2candela/m

2

0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

TABLE OF CONTENTS

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

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

BACKGROUND ON STUDY...................................................................................................3

BACKGROUND ON STRATEGY ..........................................................................................3

CHAPTER 2. LITERATURE REVIEW.....................................................................................9

CHAPTER 3. METHODOLOGY..............................................................................................11

OVERVIEW OF STUDY DESIGNS .....................................................................................11

BEFORE–AFTER STUDY METHODS ...............................................................................11

Naive Analysis...................................................................................................................11

Adjustment for Traffic Volume.......................................................................................11

Comparison Group ...........................................................................................................12

Empirical Bayesian ...........................................................................................................14

CHAPTER 4. SELECTION OF BEST COMPARISON SITES.............................................15

SUPPLEMENTAL ANALYSIS .............................................................................................16

CHAPTER 5. RESULTS.............................................................................................................17

SITES........................................................................................................................................17

Adjustment for Traffic Volume.......................................................................................23

Comparison Groups .........................................................................................................27

Injury Crashes...................................................................................................................34

Spatial Patterns.................................................................................................................41

Other Variables.................................................................................................................41

CHAPTER 6. ECONOMIC ANALYSIS...................................................................................43

CHAPTER 7. CONCLUSIONS..................................................................................................47

APPENDIX. ANALYSIS OF OTHER CRASH VARIABLES ...............................................49

ACKNOWLEDGMENTS ...........................................................................................................61

REFERENCES.............................................................................................................................63
iv

LIST OF FIGURES

Figure 1. Illustration. Schematic of signalized RCUT.....................................................................4

Figure 2. Illustration. Conventional intersection conflict points (four-approach)...........................5

Figure 3. Illustration. RCUT conflict points (four-approach)..........................................................5

Figure 4. Chart. Hypothetical time series plot of treatment and comparison group......................13

Figure 5. Equation. Comparison group method.............................................................................13

Figure 6. Equation. Computing the variance of the sequence of sample odds ratios....................13

Figure 7. Equation. CMF estimate.................................................................................................14

Figure 8. Equation. Variance estimate...........................................................................................14

Figure 9. Graph. Crash frequency in the before period for the Ohio-Symmes treatment site

and its recommended comparison sites................................................................................33

Figure 10. Graph. Crash frequency in the before period for all treatment sites and the 

recommended set of comparison sites..................................................................................33
v

LIST OF TABLES

Table 1. Conflict points at conventional intersections and RCUTs.................................................4

Table 2. Results of major safety studies of unsignalized RCUTs....................................................6

Table 3. RCUT treatment sites.......................................................................................................17

Table 4. Important design features of sites after RCUT installation .............................................18

Table 5. Reported crashes by year for the RCUT treatment sites..................................................22

Table 6. Crash frequency at RCUT treatment sites in the year before construction

compared to previous years..................................................................................................23

Table 7. Traffic volume data for each site (in thousands of vehicles per day)..............................24

Table 8. Results from traffic volume analysis of each site and groups of sites.............................26

Table 9. Potential comparison sites ...............................................................................................28

Table 10. Comparison site test results ...........................................................................................31

Table 11. Results from comparison site analysis of each site and groups of sites ........................35

Table 12. Results from traffic volume analysis of each site and groups of sites for injury

crashes..................................................................................................................................36

Table 13. Comparison site test results for injury crashes ..............................................................37

Table 14. Results from comparison site analysis of sites and groups of sites for 

injurycrashes.........................................................................................................................40

Table 15. Estimated construction cost by site................................................................................43

Table 16. Sensitivity of B/C ratios.................................................................................................45

Table 17. Other crash variables at Alabama-Plum site..................................................................49

Table 18. Other crash variables at Alabama-Retail site.................................................................50

Table 19. Other crash variables at North Carolina site..................................................................51

Table 20. Other crash variables at Ohio-Symmes site...................................................................52

Table 21. Other crash variables at Ohio-Tylersville site ...............................................................53

Table 22. Other crash variables at Ohio-Hamilton-Mason site .....................................................54

Table 23. Other crash variables at Texas-Evans site .....................................................................55

Table 24. Other crash variables at Texas-Stone Oak site ..............................................................56

Table 25. Other crash variables at Texas-New Guibeau site .........................................................57

Table 26. Other crash variables at Texas-Shaenfield site ..............................................................58

Table 27. Other crash variables at Texas-71 site ...........................................................................59
vi

LIST OF ABBREVIATIONS

B/C benefit–cost

CMF crash modification factor

CRF crash reduction factor

DCMF Development of Crash Modification Factors (program)

EB empirical Bayesian

FHWA Federal Highway Administration

RCUT restricted crossing U-turn
1

EXECUTIVE SUMMARY

A restricted crossing U-turn (RCUT) intersection is defined as a three-approach or four-approach 

intersection where minor street left-turn and through movements (if any) are rerouted to one-way 

downstream U-turn crossovers. RCUTs are also known as superstreets, J-turns, reduced conflict 

intersections, and synchronized streets. Ten States have installed at least 50 RCUTs since the late 

1980s. At least five States have installed signalized RCUTs—those at which the major street 

crossover(s) and U-turn crossover(s) are under the control of traffic signals. Studies have shown 

RCUTs to have advantages over traditional intersections in terms of travel time and delay, signal 

progression, pedestrian crossing, and transit service. 

While there are theoretical reasons that support the relative safety benefits of RCUTs as 

compared to conventional intersections, it is also possible that certain RCUT elements could 

diminish or negate these benefits. For example, signalized RCUTs involve a greater number of 

signals at the U-turn crossover(s) and require that some users travel longer overall distances. 

There is no known completed research on the safety of signalized RCUTs. 

The objective of this evaluation was to develop a crash modification factor (CMF) for the 

replacement of a traditional signalized intersection with a signalized RCUT. The project team 

also intended for the evaluation to conduct a qualitative analysis of crash data at signalized 

RCUTs to provide information to designers on expected crash patterns and trends. Finally, the 

research team developed a benefit–cost (B/C) ratio. 

The project team selected the before–after analysis with comparison sites methodology for this 

evaluation. The method accounts for simultaneous event biases, which the project team thought 

to be the most threatening potential bias to the evaluation. Simultaneous event biases that could 

have been important include the recession of the late 2000s and development in the area of the 

study sites. An empirical Bayesian methodology was unnecessary in this case because regression 

to the mean was not a serious threat to the validity of the analysis. The method relies on highquality comparison sites, and the quality of the comparison sites is testable. The project team 

collected data from 11 treatment sites in 4 States: Alabama, North Carolina, Ohio, and Texas. 

The treatment sites were all in suburban areas, on four-lane or six-lane divided arterials, and 

characterized by high-speed traffic and minimal crossing pedestrians. The project team analyzed 

four potential comparison sites per treatment site. In addition to the before–after analyses of all 

reported crashes, the project team conducted a before–after analysis of fatal and injury crashes. 

The research resulted in estimated CMFs of 0.85 for overall crashes and 0.78 for injury crashes.

These CMFs are not statistically significant at the 95- or 90-percent confidence level. The injury 

CMF is significantly different from 1.0 at the 68-percent confidence interval. This is the first 

effort to develop a CMF for this strategy. Future evaluations can expand the sample size and use 

more robust methods that may result in CMFs with higher confidence. The CMFs suggest that 

signalized RCUTs will generally produce a crash reduction. 

Economic analysis resulted in an estimated total annualized RCUT cost of $369,000. The 

estimated costs included costs for construction and signal operation and maintenance. The 

estimated benefits included savings of travel time and crashes. The 11 RCUTs were estimated to 
2

save 103 hours of motorist time per workday, which equates to $388,000 per year. The safety 

benefit was a savings of 3.0 property-damage-only crashes per year and 2.3 injury and fatal 

crashes per year, which results in an annual monetary savings of $948,000. Thus, the B/C ratio 

was 2.6 to 1.0 considering the safety benefits only and 3.6 to 1.0 considering the combined safety 

and operational benefits. 
3

CHAPTER 1. INTRODUCTION

BACKGROUND ON STUDY

The Federal Highway Administration (FHWA) established the Development of Crash 

Modification Factors (DCMF) program in 2012 to address highway safety research needs for 

evaluating new and innovative safety strategies (improvements) by developing reliable 

quantitative estimates of their effectiveness in reducing crashes. The goal of the DCMF program 

is to save lives by identifying new safety strategies that effectively reduce crashes and promote 

those strategies for nationwide implementation by providing measures of their safety 

effectiveness and benefit–cost (B/C) ratios through research. State transportation departments 

and other transportation agencies need to have objective measures for safety effectiveness and 

B/C ratios before investing in new strategies for statewide safety improvements. Forty State 

transportation departments provide technical feedback on safety improvements to the DCMF 

program and implement new safety improvements to facilitate evaluations. These States are 

members of the Evaluation of Low-Cost Safety Improvements Pooled Fund Study, which 

functions under the DCMF program.

The restricted crossing U-turn (RCUT) intersection strategy was selected for evaluation as part 

of this effort. 

BACKGROUND ON STRATEGY

An RCUT is defined as a three-approach or four-approach intersection where minor street leftturn and through movements (if any) are rerouted to one-way downstream U-turn crossovers. 

RCUTs are also known as superstreets, J-turns, reduced conflict intersections, and synchronized 

streets. Figure 1 shows a schematic of a signalized RCUT with four approaches. Ten States have 

collectively installed at least 50 RCUTs since the late 1980s.

(1) At least five States have installed 

signalized RCUTs where the major street crossover(s) and U-turn crossover(s) are controlled by 

traffic signals. Studies have shown RCUTs to have advantages over traditional intersections in 

terms of travel time and delay, signal progression, pedestrian crossing, and transit service.

(1)
4

Source: FHWA.

Figure 1. Illustration. Schematic of signalized RCUT.

(1)

There are several theoretical reasons to believe that RCUTs have the potential to reduce crash 

frequency and severity compared to conventional intersections. First and foremost, RCUTs 

reduce the number of conflict points, as shown in table 1 (and illustrated in figure 2 and figure 

3). The number of conflict points at an intersection is commonly thought to be related to the 

number of crashes, and the RCUT design reduces the number of decision points for drivers. This 

is also true for pedestrian movements. At signalized four-approach RCUTs, pedestrians should 

experience conflicts when crossing a major street only if a driver violates a red signal or if the

pedestrian jaywalks. In addition, the types of vehicle-to-vehicle conflicts change at an RCUT

compared to conventional intersections. This includes fewer right-angle conflicts, which 

generally result in more severe crashes. There is also more distance between conflict points at an 

RCUT compared to conventional intersections, which provides drivers more space and time to 

perceive and react to potential safety issues. 

Table 1. Conflict points at conventional intersections and RCUTs.

Number of 

Approaches Type of Conflict

Conventional 

Intersection Conflict 

Points

RCUT Conflict 

Points

3 Vehicle-to-vehicle 9 7

3 Vehicle-to-pedestrian 12 5

4 Vehicle-to-vehicle 32 14

4 Vehicle-to-pedestrian 24 8
5

Source: FHWA.

Figure 2. Illustration. Conventional intersection conflict points (four-approach). 

Source: FHWA.

Figure 3. Illustration. RCUT conflict points (four-approach).

Conflict Points

Crossing

Merging

Diverging
6

There are several unique elements of signalized RCUTs that are expected to improve intersection 

safety.(1) For example, the signal progression possible along an RCUT corridor can help manage 

speed and decrease stops, which may produce safety benefits. RCUT signals may function using 

as few as two phases, simplifying related decisionmaking and reducing opportunities for signal 

violations and traps. Additionally, most RCUTs feature medians to divide directions of traffic 

along the major road, potentially resulting in fewer opposite direction head-on and drivewayrelated crashes sometimes associated with these types of corridors.

Conversely, the following RCUT elements could negate or outweigh the expected safety benefits 

as compared to a traditional intersection:

(1)

• Left-turn and through vehicles from the minor street travel longer overall distances at an 

RCUT—to the downstream U-turn crossover and back—which could lead to an increase 

in the number of crashes related to distance traveled (e.g., animal crashes). U-turn 

crossovers at signalized RCUTs are typically 500 to 800 ft from the main intersection, so 

total added distances traveled are not insignificant. 

• The movements from the minor street—where a right-turn/U-turn combination replaces a 

direct left-turn left or straight through—may be counterintuitive to unfamiliar drivers and 

may lead to maneuvers that could result in crashes. 

• Signalized RCUTs with two or more lanes in the crossovers or from minor streets involve 

side-by-side turns, which could lead to sideswipe crashes. 

• RCUTs have more signals (two at a three-legged intersection and four at a four-legged 

intersection) than a conventional intersection with one signal, which may contribute to

rear-end crashes. 

Three major studies with relatively large crash samples and accepted analysis methods have been 

published in recent years analyzing the installation of unsignalized RCUTs at sites that 

previously were conventional unsignalized intersections.

(1) The percentage decreases in crashes,

shown in table 2, were statistically different from 0 at the 95-percent level or greater. These 

studies suggest that unsignalized RCUTs can be effective safety countermeasures and offer 

promise that signalized RCUTs may also be effective safety countermeasures.

Table 2. Results of major safety studies of unsignalized RCUTs.

(1)

Parameter North Carolina Maryland Missouri

Number of RCUT sites 13 9 5

Type of traffic control Stop Merge Stop

Decrease in total crashes 27% 44% 35%

Decrease in injury crashes 51% 42% 54%

More recently, RCUTs were featured in the FHWA Every Day Counts 2 program, as well as in 

projects to develop informational videos, capacity models for the Highway Capacity Manual,

and informational guides for planners and designers.(2) Some States have also sponsored 
7

meaningful research on RCUT operations, and, as previously discussed, thoroughly researched 

the safety of unsignalized RCUTs. Although research and experience support the implementation 

of signalized RCUTs to improve traffic operations, research was needed on the safety 

effectiveness.

The objective of this evaluation was to develop a crash modification factor (CMF) for the 

replacement of a traditional signalized intersection with a signalized RCUT. The evaluation was 

also intended to conduct a qualitative analysis of crash data at signalized RCUTs to provide 

information to designers on expected crash patterns and trends. The evaluation considered 28 

signalized RCUT sites across 5 States to understand its safety effectiveness. Finally, the 

evaluation provided a range of B/C ratios for signalized RCUT installation. Achieving these 

objectives should help transportation agencies select appropriate locations to install signalized 

RCUTs to improve intersection safety and operations.

9

CHAPTER 2. LITERATURE REVIEW

Kramer first developed RCUT intersections in the mid-1980s.(1) A similar concept, called a Jturn intersection, was developed independently in Maryland in the late 1980s. Since the 

development of the concept, there has been a steady stream of published literature on the design. 

Recently, FHWA commissioned an exhaustive review of this literature for its Restricted 

Crossing U-Turn Intersection Informational Guide.

(1) The Guide summarized the literature in 

nine chapters, including the following:

• Multimodal considerations.

• Safety.

• Operational characteristics.

• Operational analysis.

• Geometric design.

• Traffic control devices.

• Construction and maintenance.

The Guide posits that, while there is still much unknown about RCUTs, significant knowledge 

has been gained in the past 25 years that highway agencies can use to design and operate 

RCUTs.

The Guide found no previous papers making substantial contributions on the safety of signalized 

RCUTs. However, and as previously noted, the Guide did review three papers that made 

substantial contributions to the knowledgebase on the topic of the safety of unsignalized RCUT 

intersections.(1) The first of these three major studies was completed in North Carolina in 2010.(1)

The authors examined 13 rural sites where a two-lane minor road met a four-lane divided major 

road. Before the conversion to an RCUT, the intersection used two-way stop control. STOP signs 

controlled the RCUT crossovers. The authors used comparison groups to account for other 

changes from the before to the after periods and used an empirical Bayesian (EB) method to 

account for potential regression to the mean. Total crashes decreased between 27 and 74 percent,

depending on the analysis method employed. The number of fatal and injury, angle, and left-turn 

crashes decreased substantially with conversion to the RCUT design, while the number of 

sideswipe, rear-end, and other crashes decreased slightly or increased.

The second major study to examine the safety of unsignalized RCUTs was completed in 

Maryland in 2012.(3) The nine sites were like those in the North Carolina study, except the U-turn 

crossovers and minor street right turns had merges, while the left-turn crossovers had YIELD 

signs. The authors reported results from several types of analyses, but they considered an EB

analysis with a calibrated safety performance function that produced a 44-percent decrease in 

total crashes from the before to the after period to be the most reliable.

Edara et al. produced the third major study to examine the safety of unsignalized RCUT 

intersections.(4) The five rural sites had STOP signs on the minor street approaches and 

crossovers before and after conversion to the RCUT design. The EB analysis procedure 

accounted for regression-to-the-mean bias using the crash prediction model from the Highway 

Safety Manual. The results from the Missouri study showed a 35-percent reduction in total 
10

crashes and a 54-percent reduction in injury crashes. Similar to the research conducted in North 

Carolina, researchers in Missouri observed a large reduction in angle crashes with RCUT 

installation.
11

CHAPTER 3. METHODOLOGY

OVERVIEW OF STUDY DESIGNS

Before–after and cross-sectional regression analyses are the two most common methodologies 

for developing CMFs for installed countermeasures using crash data. The preferred before–after 

method relies on recent countermeasure installations. This method uses the safety performance at 

a treatment site before countermeasure installation to predict what would have happened at the 

site without the countermeasure. Hauer’s Observational Before–After Studies in Road Safety on 

before–after evaluations is still the primary guidance for those types of studies.(5) A crosssectional analysis develops an equation to predict the number of crashes at highway locations, 

where the equation contains a term for the countermeasure of interest. Before–after studies have 

an advantage in that using the before-period data to predict what would have happened reduces 

much of the site-to-site variation that must be accounted for with cross-sectional regression 

analysis. Thus, before–after studies can provide powerful results with relatively low sample sizes

when data are available from the specific timeframe and certain biases are mitigated.

The project team selected a before–after analysis for this evaluation due to the availability of

RCUT treatment sites. 

BEFORE–AFTER STUDY METHODS

Hauer describes four primary before–after analysis methods, each accounting for a different type 

of bias.

(5) The following provides an overview of the four methods.

Naive Analysis

Naive analyses only adjust for a difference in the number of years of crash data from the before 

period to the after period.(5) Naive analyses are not trustworthy when more serious biases may be 

present. Therefore, the naive analysis results are not presented.

Adjustment for Traffic Volume

Adjustments for changes in traffic volume are a standard feature of many crash studies, as traffic 

volume is highly related to crash frequency. The method is described in Chapter 8 of Hauer.(5)

Adjusting for traffic volume accounts for some threats to study validity; however, this method 

does not account for other events that would cause a bias in study results. Therefore, the project 

team relied more heavily on the results from other methods in drawing conclusions.

Many CMF studies do not report the results from an analysis adjusting for traffic volume. 

However, it is an important step when using comparison groups to account for simultaneous 

event bias (detailed in the following section) does not include an adjustment for changing traffic 

volumes. Hauer does not provide a method for estimating the CMF and the standard deviation 

(SD) of the CMF while adjusting for both comparison groups and changing traffic volume.(5) For 

the purpose of this study, the project team analyzed the adjustment for traffic flow and the 

adjustment for comparison groups separately.
12

As detailed in the following chapters, traffic volumes in the dataset were fairly constant and 

experienced small changes across years at most sites over the study period. This may be due to 

stable volumes at the sites or a result of traffic volume estimates based on interpolations from 

relatively rare field counts. Due to the small changes in traffic volume from year to year and a 

lack of calibrated safety performance functions for signalized intersections in each of the four

studied States, the project team assumed a proportional relationship between crashes and volume 

for this evaluation. Additionally, the traffic volume adjustment method should properly consider

a coefficient of variation in the traffic volume data, which is a measure of how much traffic 

volumes at the site vary from day to day. Coefficients of variation were not available for the 

treatment sites in this evaluation, so the project team used a middle range value of 0.1 for the

calculations.(5)

Comparison Group

One of the most important potential biases to a before–after crash study are events that occur 

during the study periods at the treatment sites that change the predicted number of crashes

regardless of the treatment. Common simultaneous event biases include significant weather 

events like hurricanes or ice storms and changes in vehicle and crash reporting characteristics. 

To account for simultaneous event biases, the project team used comparison sites as described by 

Hauer.

(5) The team identified four potential comparison sites for each treatment site. Potential 

comparison sites were large surface street intersections (to ensure adequate sample sizes) near 

the treatment site (to ensure that the same events occurred at both places). Aerial photographs 

were reviewed to ensure the sites did not undergo any discernible treatment during the study 

period.

Hauer recommends testing potential comparison sites prior to an analysis.(5) The test consists of 

calculations of odds ratios of the changes in crash frequency at the treatment sites and potential 

comparison sites from one year to the next before the countermeasure was installed. If this 

calculation is performed over a series of years before the treatment was installed, a look at the 

mean of the odds ratios and the SD of the mean will be revealing. The mean should be close to 

1.0—and the SD close to 0—for successful comparison sites that are tracking closely to the 

treatment sites in the years before countermeasure installation. This concept is illustrated in

figure 4. 
13

Source: FHWA.

Figure 4. Chart. Hypothetical time series plot of treatment and comparison group.(6)

Figure 5 illustrates how the CMF is estimated using the comparison group method.

(5)

Figure 5. Equation. Comparison group method. 

Where:

= predicted crashes in the treatment group in the after period had the treatment not been 

implemented.

M = crashes in the comparison group before the implementation of the treatment.

N = crashes in the comparison group after the implementation of the treatment.

K = crashes in the treatment group before the implementation of the treatment. 

Figure 6 illustrates the next step in the process. 

Figure 6. Equation. Computing the variance of the sequence of sample odds ratios.

Where is variance of the sequence of sample odds ratios that are calculated based on 

the time series of crash counts from the treatment and comparison sites. 

π = K × 

N

M

 

π

Var π = π

2 

1

K

+

1

M

+

1

N

+ Var(ω) 

Var ω 
14

As mentioned by Hauer, for an ideal comparison group, the mean of the odds ratios is very close 

to 1, and is very close to 0.

(5) Figure 7 and figure 8 show how to estimate the CMF and 

variance.

Figure 7. Equation. CMF estimate.

Figure 8. Equation. Variance estimate.

Where:

= crashes in the treatment group after the implementation of the treatment.

Var ( ) = assuming reasonably that the crash frequency follows a Poisson distribution.

The project team used a before–after analysis with comparison sites for this evaluation. The 

method accounts for simultaneous event biases, which the project team thought to be the most 

threatening potential bias to the evaluation. The method relies on high-quality comparison sites, 

but, as mentioned, the quality of the comparison sites is testable and is provided later in this 

report.

Empirical Bayesian

Before–after studies most commonly use the EB method to account for regression-to-the-mean

bias. Regression to the mean is the tendency of an abnormally high or low value recorded in one 

time period to return to a value much closer to the long-run average in the next time period. This

is important in most safety studies because transportation agencies typically install 

countermeasures at high-crash locations and must account for regression to the mean in the after 

period. Fortunately, regression to the mean was not an important threat to the validity of this 

evaluation because the treatment sites were not chosen on the basis of any type of hazardous site 

identification process. Instead, the agencies selected the sites for RCUT installation primarily to 

relieve congestion. Therefore, the project team did not employ the popular EB method described 

in Chapter 11 of Hauer.

(5) With complex intersections spread over four States, an EB analysis 

would have been very complicated for this evaluation. The comparison site method proposed 

herein efficiently accounted for the most important potential study bias—simultaneous events—

using comparison sites that passed a quality test. Chapter 4 describes this comparison site 

method in detail.

Var ω 

CMF =

λ

π

 1 +

Var(π)

π

2

 

Var CMF =

CMF

2 

Var(λ)

λ

2 +

Var(π)

π

2

 

 1 +

Var(π)

π

2

 

2

CMF =

λ

π

 1 +

Var(π)

π

2

 

Var CMF =

CMF

2 

Var(λ)

λ

2 +

Var(π)

π

2

 

 1 +

Var(π)

π

2

 

2

λ

λ λ
15

CHAPTER 4. SELECTION OF BEST COMPARISON SITES

The quality of the result from the comparison group method relies on the selection of highquality comparison sites. The project team collected data from four States: Alabama, North 

Carolina, Ohio, and Texas. To ensure high quality, the project team undertook the following 

steps during comparison site selection:

1. Identified large intersections near the treatment sites using aerial photographs to serve as 

possible comparison sites. They identified four potential comparison sites for each 

RCUT. The project team examined the available time series of aerial photos to ensure 

there were no major changes to the roadways or surroundings near the potential 

comparison sites during the study periods.

2. Contacted the respective State agencies to confirm the comparison sites were appropriate 

in terms of the trends in traffic volumes and the surrounding environment.

3. Obtained crash data for the comparison sites and the RCUTs from the State agencies.

4. Employed the odds ratio test (previously discussed) to determine the appropriate set of 

comparison sites for each treatment site. The project team tested each potential 

comparison site separately and all combinations of comparison sites.

5. In cases where odds ratio tests showed that all four potential comparison sites—and all 

combinations of those four—were unfit for use, computed odds ratios for the comparison 

sites associated with other treatment sites in the same metropolitan area. 

After identifying the best comparison sites and computing CMFs for each individual treatment 

site, the project team analyzed groups of sites. Analyzing groups of sites boosted sample sizes 

and generally decreased the SDs of the estimates of the CMFs. The project team analyzed the 

following:

• Groups of treatment sites from the same State.

• Groups of treatment sites with the longest available before periods (Alabama, North 

Carolina, and Ohio).

• All sites together.

In all group analyses, the project team used all years of available data at each site. This meant

that the group results are somewhat biased in favor of the sites with more data.

A difficulty in analyzing groups of sites was determining the best set of comparison sites. The 

project team again used odds ratio tests to identify the best set of comparison sites, starting its 

search with the best comparison sites identified during the individual site analyses.

A group analysis built the sample size and averaged the different sites to overcome the physical 

or data shortcomings present at each site. The group analyses resulted in the overall signalized 
16

RCUT CMF that is considered a predictor of future general signalized RCUT safety 

performance.

SUPPLEMENTAL ANALYSIS

In addition to the before–after analyses of all reported crashes previously discussed, the project 

team conducted a before–after analysis of fatal and injury crashes. The sample of fatal and injury 

crashes was much smaller than the sample of all crashes, which further emphasized the 

importance of the group analyses.

The project team analyzed other key crash variables at the individual site and group levels, 

including day/night and crash type, to determine any changes between the before and after 

periods. The variable analyses help signalized RCUT designers identify any issues with early 

installations.

After the project team estimated the CMF for signalized RCUT installation, they developed a 

B/C ratio for the improvement. Participating States provided information on the cost of the 

RCUT installation at each study site. The project team based the benefit on the estimated crash 

reduction (if any) and on an estimate of the travel time savings experienced by motorists using 

the RCUT. They derived the B/C ratio employing similar methods used in CMF research and 

development. 
17

CHAPTER 5. RESULTS

SITES

The project team identified 28 signalized RCUTs from 5 States (Alabama, Michigan, North 

Carolina, Ohio, and Texas). After some investigation, they eliminated two sites in Michigan 

because the RCUTs were installed in the mid-1990s, and crash data were no longer available 

from the before period. The project team obtained aerial photos of the remaining 26 sites to 

review for substantial changes in the roadways or surrounding land uses between the before 

period and after period. Such changes would likely confound the RCUT installation and make 

determination of the effects of the RCUT installation impossible. On this basis, the project team 

eliminated 15 sites. The most common confounding change resulting in site elimination was that 

the RCUT was installed at the same time the intersection traffic control changed from 

unsignalized to signalized. Table 3 identifies the final sites selected for evaluation, which 

included 11 sites in 4 States where signalized RCUTs were installed during years lending 

themselves to evaluation, and the RCUT installation was the only substantial change at the 

location.

Table 3. RCUT treatment sites.

State County Intersection

Alabama Houston US-231 NW of Dothan at Plum Rd.

Alabama Houston US-231 NW of Dothan at Retail Dr.

North Carolina New Hanover US-421 south of Piner Rd., Wilmington

Ohio Butler OH-4 in Hamilton at Symmes Rd.

Ohio Butler OH-4 in Hamilton at Tylersville Rd.

Ohio Butler OH-4 in Hamilton at Hamilton-Mason Rd.

Texas Bexar US-281 north of San Antonio at Evans Rd.

Texas Bexar US-281 north of San Antonio at Stone Oak Pkwy./TPC

Pkwy.

Texas Bexar Loop-1604 west of San Antonio at New Guibeau

Texas Bexar Loop-1604 west of San Antonio at Shaenfield

Texas Travis TX-71 in Del Valle east of Austin at FM-973/Fallwell Ln.

Table 4 shows the major geometric characteristics of the treatment sites after RCUT 

construction. Two numbers in a cell means that two approaches to the intersection had those two 

different values for the parameter. The treatment sites have much in common besides having 

signalized RCUTs. They are all in suburban areas, on four-lane or six-lane divided arterials, and 

characterized by high-speed traffic and minimal crossing pedestrians; as a result, the CMFs

emerging from this effort will apply to this type of site. Most of the signalized RCUT

installations of which the project team is aware that are planned for the next few years share 

these characteristics.
18

Table 4. Important design features of sites after RCUT installation.

Parameter 1 2 3 4 5 6 7 8 9 10 11

Intersection angle 

(degrees)

90 70 

80

70 

80

80 60 80 90 90 70 90 70 

80

Main street speed limit 

(mph)

45 40 45 50 50 55 60 60 60 60 60 

65

Minor street speed limit 

(mph)

30 20 25 40 40 40 40 40 35 40 35

55

Main street number of 

through lanes each 

direction

3 3 2 3 3 2 2

3

2 2 

3

3 3

Minor street number of 

lanes entering 

intersection each

direction

2 1

2

2 3 2 

3

2

3

3 3 2 3 1

2

North or west left-turn 

crossover number of 

lanes

2 2 1 1 1 1 2 2 2 N/A 1

South or east left-turn 

crossover number of 

lanes

2 1 1 1 1 1 2 2 N/A 2 1

North or west U-turn 

crossover number of 

lanes

1 1 1 2 2 1 2 2 2 N/A 2

South or east U-turn 

crossover number of 

lanes

1 1 1 2 1 2 2 2 N/A 2 2

Distance center of 

intersection to north or 

west U-turn crossover

(ft)

870 930 890 850 950 950 1,080 1,100 1,560 N/A 1,720

18
19

Parameter 1 2 3 4 5 6 7 8 9 10 11

Distance center of 

intersection to south or 

east U-turn crossover (ft)

750 620 1,290 830 1,000 1,060 1,080 1,310 N/A 1,340 1,180

Major street channelized 

right turns

No No No Yes Yes Yes Yes Yes Yes Yes No

Minor street channelized 

right turns

No No No No No No Yes Yes No Yes No

Crosswalks at main 

intersection

Yes Yes No No No No Yes Yes No Yes Yes

Major street median 

width (ft)

40 40 26 46 34 46 52 52 73 71 58

Minor street median 

width (ft)

14 12 12 12 12 12 21 2–42 15 4 4

Other interesting design 

features

None North 

U-turn 

at 

signalized 

intersection

Sharp 

curve 

on 

west 

leg

None None None None None 3-

legged

3-

legged

Signalized 

leftturn 

crossover 

west of 

main 

intersection

N/A = not applicable; 1 = Alabama-Plum; 2 = Alabama-Retail; 3 = North Carolina; 4 = Ohio-Symmes; 5 = Ohio-Tylersville; 6 = Ohio-Hamilton-Mason; 

7 = Texas-Evans; 8 = Texas-Stone Oak; 9 = Texas-New Guibeau; 10 = Texas-Shaenfield; 11 = Texas-71.

19
20

After identifying treatment sites and potential comparison sites, the project team contacted 

officials in each of the four States for the necessary data. State officials provided information on 

the RCUTs’ construction dates and crash data for the most recent 5 full years before the start of 

construction, through the construction year(s), up to the most recent available data. The project 

team decided 5 years of before data was desirable to boost the sample size and to provide the 

longest possible test of the quality of the comparison sites. The project team also reviewed the 

available aerial photos for each treatment site to ensure that no substantive changes had occurred 

during the 5-year before period.

States also provided data on all reported crashes that occurred within 1,500 ft of the main 

intersection along the major street and within 500 ft along the minor street for treatment sites. 

These distances ensured that all crashes related to the RCUT—especially those at the U-turn 

crossovers—were included in the dataset, while midblock crashes were excluded. The project 

team adjusted the data collection boundary in several instances due to geometric design 

differences. Two sites in Texas had longer distances to crossovers, so the boundary was 

extended. Two Alabama sites—US-231 northwest of Dothan at Plum Road and Retail Drive—

had other intersections 1,200 ft and 1,000 ft from the main intersections. Thus, the team set the 

data collection boundary at 1,000 ft at Plum Road and 800 ft at Retail Drive.

Additionally, States provided traffic volume data at and around each treatment site and 

information on changes to the intersection geometry accompanying the RCUT construction. The 

States also provided the following crash reporting thresholds:

• Alabama: By law, there is no crash reporting threshold.

• North Carolina: $1,000 total crash damage or a reportable injury.

• Ohio: Fatality, injury, or $1,000 or more in property damage.

• Texas: Fatality, injury, or $1,000 or more in property damage to any one person’s 

property.

The thresholds did not change in any State during the study period. It is important to note the 

difference between Alabama’s threshold and the other three States’ threshold. 

In the course of contacting officials in the 4 States for crash and other data for the 11 RCUT 

treatment sites and associated potential comparison sites, the project team also asked the officials 

why the RCUTs were installed. This information was important when selecting the analysis 

methodology. If treatments were installed as the result of a high-crash site identification process, 

regression to the mean is likely to result in a natural decrease in the number of crashes in the 

after period; methods to adjust for this threat should be used. On the other hand, if the treatments 

were installed for reasons other than to mitigate safety concerns, regression to the mean is not a 

potential bias to the study. Regression to the mean may occur from the before period to the after

period, but it is just as likely to result in an increase as a decrease in the number of crashes. In 

this case, methods to adjust for regression to the mean are not needed and may be harmful if they 

are not executed appropriately. The responding officials in all four participating States indicated 

that all RCUTs were chosen for operational reasons, including to accommodate future growth in 
21

traffic, not due to a high number of crashes. All officials indicated safety was considered and that 

RCUTs were expected to perform well on the safety dimension but was not a case of selecting

the design as a safety treatment for an identified high-crash site.

Table 5 summarizes the crash data provided for the treatment sites. There were some deviations 

from the desired 5 years of before-period data at some sites due to data availability, most 

prominently at the Texas sites where no crash data were available before 2009. Also, beforeperiod data from 2003 to 2006 were collected at the Hamilton-Mason Road site in Ohio because 

crash data from 2007 to 2009 had reporting inconsistencies. The project team collected the oldest 

5 years of after-period data available for the North Carolina site. 

One way to confirm whether regression to the mean is a serious threat to the validity of an 

analysis is to look for a significant drop in crashes on average in the year before treatment 

compared to previous years. The decision to install the countermeasure is likely made without 

benefit of the crash data from that last year before construction, so if regression to the mean were

to manifest in the time series, it would likely do so starting in that year. The project team was 

able to examine this trait at 8 of the 11 treatment sites, since data for Ohio-Hamilton-Mason were 

not available in the year before construction, and only 1 year of before-period data was available

at the Texas-Evans and Texas-Stone Oak sites. Table 6 shows a summary of the year before

construction data and reveals that only two of the eight sites—Alabama-Retail and TexasShaenfield—had a significant drop in crashes during the year before construction. At the other 

six sites, the number of crashes either matched the average from the previous years or rose. It 

appears that regression to the mean was indeed not a serious threat to the validity of this analysis.
22

Table 5. Reported crashes by year for the RCUT treatment sites.

Site 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

AL-Plum -- -- 38 31 19 45 35 * * * 14 26 --

AL-Retail -- -- 13 12 10 6 3 * * * 0 3 --

NC 44 32 46 38 40 * 36 25 36 26 36 -- --

OH-Symmes -- -- -- 24 24 19 24 22 * * 27 43 --

OH-Tylersville -- -- -- 22 24 16 16 26 * * 16 8 --

OH-Hamilton-Mason -- 16 12 19 33 -- -- -- * * 2 7 --

TX-Evans -- -- -- -- -- -- -- 103 * 97 93 135 --

TX-Stone Oak -- -- -- -- -- -- -- 42 * 40 44 85 --

TX-New Guibeau -- -- -- -- -- -- -- 52 51 * 32 30 --

TX-Shaenfield -- -- -- -- -- -- -- 50 31 * 36 46 --

TX-71 -- -- -- -- -- -- -- 41 40 30 45 * 16

AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

*Construction years.

--No data. 

22
23

Table 6. Crash frequency at RCUT treatment sites in the year before construction 

compared to previous years.

Site

Average Number of Crashes per 

Year in Years Before the Last 

Year Prior to Construction

Number of Crashes in the Last 

Year Prior to Construction

AL-Plum 33 35

AL-Retail 10 3

NC 40 40

OH-Symmes 23 22

OH-Tylersville 20 26

TX-New Guibeau 52 51

TX-Shaenfield 50 31

TX-71 37 45

AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

Adjustment for Traffic Volume

This section reports the results from the analysis that adjusted for an estimated change in traffic 

volume between the before and after periods. Traffic volume may change due to development in 

the area, economic changes that affect travel, or changes in overall levels of motorization. Except 

for the Texas-71 site, the project team obtained traffic volume data from at least one official 

count or estimate by the responsible agency for at least one station near each site in the before

period and the after period. If the responsible agency provided a count or estimate at more than 

one station and/or year in a before or after period at a particular site, the project team averaged 

all available counts. Table 7 summarizes the traffic volume data. The data for the Texas-Evans 

and Texas-Stone Oak sites are from counts on the major and minor streets, while count data for 

the other sites are from the major street only.
24

Table 7. Traffic volume data for each site (in thousands of vehicles per day).

Site 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

AL-Plum 26 26 27 27 25 25 25* 26* 25* 27 27

AL-Retail 36 36 36 36 36 35 36* 37* 36* 39 37

NC 35 -- 37 -- 38* -- 36 -- 38 -- --

OH-Symmes 20 -- -- -- 19 -- -- * * 25 --

OH-Tylersville 20 -- -- -- 19 -- -- * * 17 --

OH-Hamilton-Mason 12 -- -- -- 12 -- -- * 16* 17 --

TX-Evans -- -- 93 102 112 102 104 133* 110 110 110

TX-Stone Oak -- -- 84 80 78 80 80 80* 89 89 89

TX-New Guibeau -- -- 61 71 69 71 71 65 80* 83 83

TX-Shaenfield -- -- 61 66 69 67 66 50 70* 79 79

TX-71 10 9 9 10 11 -- 10 10 10 10 12*

AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

*Construction years.

--No data. 

24
25

Table 8 shows the results from the analysis of all crashes after accounting for changes in traffic 

volumes from the before period to the after period. The safety effect is represented by a CMF. 

The crash reduction factor (CRF) is calculated as 1 minus the CMF value, converted from a 

proportion to a percentage. As discussed in the methodology section, a CMF equal to 1.0 implies 

that the treatment is not expected to change the number of crashes, while a CMF below 1 implies 

that the treatment is expected to reduce crashes. There was no available traffic volume estimate 

for the Texas-71 site; therefore, the project team could not compute CMF with this method. 

Table 8 also shows the estimated SD of each CMF, which can be used to produce an 

approximate confidence interval for the CMF and determine whether it is significantly different 

from 1.0 at a particular significance level. If a CMF is 1 SD from 1.0, that corresponds to a 68-

percent confidence level that the actual value is different from 1.0. If a CMF is 1.96 SDs from 

1.0, that corresponds to a 95-percent confidence level. Among other things, the SD of a CMF is a 

useful indicator of the adequacy of the sample size, with small SDs usually associated with large 

samples of crashes.

As shown in table 8, eight sites had a CMF less than 1.0, and two sites had CMF values greater 

than 1.0. The North Carolina site, both Alabama sites grouped together, and all three Ohio sites 

grouped together had CMF values well under 1.0, while the Texas sites as a group had a CMF 

close to 1.0. All 10 sites grouped together had a CMF of more than 1.0, due to the influence of 

the large sample size from the Texas-Stone Oak site sample size. SDs were reasonable, ranging 

from 0.09 to 0.25.

The results in table 8 are based on the sums of the traffic volumes from the individual sites. This 

gives rise to the possibility that the results were skewed by higher volume sites. To account for 

this possibility, the project team reanalyzed the last five rows in table 8 using volume indices 

rather than totals (i.e., normalizing the volume at each site in the before period to a value of 1.0). 

The reanalysis provided only minor changes in the CMFs and the SDs of the CMFs, suggesting 

that summing the volumes was an acceptable technique for this evaluation.
26

Table 8. Results from traffic volume analysis of each site and groups of sites.

Site

BeforePeriod 

Crashes

BeforePeriod 

Traffic 

Volume 

(Vehicle/Day)

AfterPeriod 

Crashes

After-Period 

Traffic 

Volume 

(Vehicle/Day)

Percent 

Change in 

Traffic 

Volume CMF SD of CMF

AL-Plum 168 26,000 40 27,000 3 0.56 0.12

AL-Retail 44 36,000 3 38,000 6 0.15 0.09

NC 200 36,000 159 37,000 3 0.76 0.13

OH-Symmes 113 20,000 70 25,000 25 1.20 0.24

OH-Tylersville 104 20,000 24 17,000 −13 0.64 0.17

OH-Hamilton-Mason 80 12,000 9 17,000 48 0.15 0.05

TX-Evans 103 104,000 325 110,000 6 0.97 0.17

TX-Stone Oak 42 80,000 169 89,000 11 1.16 0.25

TX-New Guibeau 103 68,000 62 83,000 22 0.48 0.10

TX-Shaenfield 81 58,000 82 79,000 36 0.72 0.15

All AL 212 62,000 43 65,000 5 0.47 0.10

All OH 297 50,000 103 59,000 19 0.67 0.12

All TX except TX-71 329 310,000 638 361,000 16 0.98 0.15

AL, NC, and OH 709 148,000 305 161,000 9 0.75 0.12

All except TX-71 1,038 458,000 943 522,000 14 1.09 0.16

AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

26
27

Comparison Groups

The primary analysis method in this evaluation used comparison sites to adjust for the possibility 

of changes during the study period, such as development patterns, significant weather events, 

driver behavior, vehicle fleets, and crash reporting tendencies. As discussed in the methodology 

section, the key to a successful comparison site analysis is to find comparison sites that match 

the crash patterns of the treatment sites year by year in the before period. It is believed that the 

comparison sites change year by year in the after period, reflecting what would have happened at 

the treatment sites if the treatment had not been installed.

The project team selected comparison sites using the following criteria:

• Geographically located in proximity to the treatment site to be affected by the same 

trends.

• Geographically located far enough from the treatment site so as not to be affected by the 

treatment itself.

• Suitable crash sample.

• No obvious changes (e.g., large development or construction projects) between the before 

and after periods that would affect the crash pattern at a comparison site.

• Sites similar to the treatment sites in terms of design, such as large signalized 

intersections.

The project team narrowed the list of potential comparison sites using the time series of aerial 

photos available via the Internet. This review revealed construction or development at many of 

the potential comparison sites during the time periods of interest, thus disqualifying those sites 

from further analysis. The project team consulted with local or State transportation officials 

responsible for the remaining potential sites to gather additional input on high-quality sites.

Table 9 provides a list of potential comparison sites that met the criteria to warrant the collection 

of crash data. The before and after periods of crash data collected at all comparison sites were

equal to the periods for the corresponding treatment site. There were four potential comparison

sites for each treatment site.
28

Table 9. Potential comparison sites.

RCUT 

Treatment Site

Potential 

Comparison 

Site 1

Potential 

Comparison 

Site 2

Potential 

Comparison 

Site 3

Potential 

Comparison 

Site 4

US-231 NW of 

Dothan at Plum 

Rd.

US-231 NW of 

Dothan at 

Redmond Rd.

US-231 NW of 

Dothan at John 

D. Odom Rd.

US-231 NW of 

Dothan at 

University Dr.

US-231 NW of 

Dothan at 

County Road 59 

and Midtown 

Ave.

US-231 NW of 

Dothan at Retail 

Dr.

US-231 SE of 

Dothan at Ross 

Clark Cir.

US-231 NW of 

Dothan at 

Murphy Mill Rd.

US-231 NW of 

Dothan at Napier 

Field Rd.

US-231 NW of 

Dothan at North 

Cherokee Ave.

US-421 south of 

Piner Rd., 

Wilmington

US-421 at 

Sanders Rd., 

Wilmington

US-421 at Silva 

Terra Dr., 

Wilmington

US-421 at 

Myrtle Grove 

Rd., Wilmington

US-421 at Sea 

Breeze, 

Wilmington

OH-4 at 

Symmes Rd., 

Hamilton

OH-4 Dixie 

Hwy. and Ross 

Rd. in Fairfield

OH-4 Dixie 

Hwy. and

Seward Rd., 

Fairfield

OH-4 Port 

Union Rd., 

Hamilton

South Gilmore 

Rd. and Mack 

Rd., Fairfield

OH-4 at 

Tylersville Rd., 

Hamilton

OH-747 

Princeton 

Glendale Rd. 

and Tylersville 

Rd., West 

Chester Twp.

South Ross Rd. 

and Mack Rd., 

Fairfield

OH-747 

Princeton 

Glendale Rd. 

and Union 

Centre Blvd., 

West Chester 

Twp.

OH-747 

Princeton 

Glendale Rd. 

and Port Union 

Rialto, West 

Chester Twp.

OH-4 at 

Hamilton-Mason 

Rd., Hamilton

OH-747 

Princeton 

Glendale Rd. 

and HamiltonMason Rd., 

West Chester 

Twp.

OH-4 and

Hamilton 

Princeton Rd., 

Hamilton

OH-4 HamiltonMiddletown Rd. 

at Indian 

Meadows Dr., 

Hamilton

Princeton Rd. 

and Morris Rd., 

Hamilton

US-281 and 

Evans Rd. north 

of San Antonio

North of San 

Antonio at 

Brook Hollow 

Blvd. and

Hemier Rd.

North of San 

Antonio at 

Bitters Rd. and

Hemier Rd.

North of San 

Antonio at South 

Tower Dr. and

West Bitters Rd.

North of San 

Antonio at West 

Ave. and West 

Blanco Rd.

US-281 north of 

San Antonio at 

Stone Oak 

Pkwy./TPC 

Pkwy.

US-281 north of 

San Antonio at 

Bulverde Rd.

US-281 north of 

San Antonio at 

Overlook Pkwy.

North of San 

Antonio at TPC 

Pkwy. and

Bulverde Rd.

North of San 

Antonio at Stone 

Oak Pkwy. and

Canyon Golf Rd.
29

RCUT 

Treatment Site

Potential 

Comparison 

Site 1

Potential 

Comparison 

Site 2

Potential 

Comparison 

Site 3

Potential 

Comparison 

Site 4

Loop-1604 west 

of San Antonio 

at New Guibeau

TX-16 Bandera 

Rd. at Tezel Rd. 

and Prue Rd., 

San Antonio

NW of San 

Antonio at 

Scenic Loop Rd. 

and Bandera Rd.

NW of San 

Antonio at 

Leslie and

Bandera

NW of San 

Antonio at West 

Hausman Rd. 

and Babcock Rd.

Loop-1604 west 

of San Antonio 

at Shaenfield 

Rd.

TX-16 Bandera 

Rd. at Mainland 

Dr.

Loop-1604 west 

of San Antonio 

at Wiseman 

Blvd.

West of San 

Antonio at 

Alamo Pkwy. 

and Culebra Rd.

Loop-1604 west 

of San Antonio 

at Military Dr. 

West

TX-71, Del 

Valle, east of 

Austin at FM973/Fallwell Ln.

TX-973 at 

Webberville Rd., 

Austin

TX-973, Del 

Valle, east of 

Austin at Pearce 

Ln.

TX-973, Del 

Valle, east of 

Austin at 

Burleson Rd.

TX-973 at 

Clinger Rd., 

Austin

OH = Ohio; TX = Texas.

The project team employed the odds ratio test, as Hauer described, to determine the best 

comparison site(s) for each treatment site or group of treatment sites.(5) The odds ratio test 

measured the change in crash frequency at a comparison site from one year to the next relative to 

the change in crash frequency at the treatment site. The project team employed the odds ratio test 

for each available pair of years in the before period at each site and group of sites. Thus, the

project team calculated four odds ratios at most at each site, with 5 years of crash data at most in 

the before period. An odds ratio of 1.0 meant that the comparison and treatment datasets moved 

together in complete harmony. The project team examined the mean of the odds ratios 

calculated, as well as the SD of that mean, with desired values of 1.0 for the mean and 0 for the 

SD. For each individual treatment site, the project team conducted the odds ratio test on each of 

its potential comparison sites, on potential comparison sites identified for nearby treatment sites,

and on each combination of potential comparison sites. The project team used a combination of 

the best comparison sites found for each individual treatment site for each group of treatment 

sites.

In this dataset, the project team conducted tests of comparison sites for 9 of the 11 sites where at 

least 2 years of before-period data were available. They did not test Texas-Evans Road and

Texas-Stone Oak comparison sites because only 1 year of before data were available at those 

sites. Rather, the project team conducted an analysis using all four comparison sites. 

Table 10 displays the test results for the best comparison site or sites with each of the nine other

treatment sites. To illustrate the quality of the comparison sites, figure 9 and figure 10 show the 

correspondence between the numbers of crashes per year in the before period at treatment and 

chosen comparison sites for the Ohio-Symmes site and for the set of all treatment sites, 

respectively. Most of the nine treatment sites had good to excellent comparison site(s), with 

means of the odds ratios within 0.1 of 1.0, SDs of the means less than 0.4, and relatively high 

sample sizes. The sites with the strongest comparison sites included the following: 

• Alabama-Plum: Retail 2 and 4.

• Alabama-Retail: 1.
30

• North Carolina: 1 and 3.

• Ohio-Symmes: 1 and 3.

• Ohio-Tylersville: 1.

• Ohio-Hamilton-Mason: 2.

• Texas-New Guibeau: 1, 2, 3, and 4.

• Texas-Shaenfield: 1, 2, 3, and 4.

• Texas-71: 1, 3, and 4.

Note that, at the Alabama-Plum Road site, the best comparison site was from the Retail Drive set 

of potential sites. This is due to the proximity of those two treatment sites.
31

Table 10. Comparison site test results.

Site Parameter Year 1 Year 2 Year 3 Year 4 Year 5

Mean of 

Odds Ratio SD of Mean

AL-Plum Treat crashes 38 31 19 45 35 -- --

AL-Plum Comp. crashes 48 46 33 49 52 -- --

AL-Plum Odds ratio -- 1.12 1.09 0.60 1.30 1.03 0.30

AL-Retail Treat crashes 13 12 10 6 3 -- --

AL-Retail Comp. crashes 52 57 55 47 42 -- --

AL-Retail Odds ratio -- 1.08 1.04 1.20 1.32 1.16 0.13

NC Treat crashes 44 32 46 38 40 -- --

NC Comp. crashes 43 46 37 45 61 -- --

NC Odds ratio -- 1.40 0.54 1.40 1.23 1.14 0.41

OH-Symmes Treat crashes 24 24 19 24 22 -- --

OH-Symmes Comp. crashes 29 36 34 48 47 -- --

OH-Symmes Odds ratio -- 1.15 1.10 1.04 1.00 1.08 0.07

OH-Tylersville Treat crashes 22 24 16 16 26 -- --

OH-Tylersville Comp. crashes 31 27 18 23 30 -- --

OH-Tylersville Odds ratio -- 0.74 0.91 1.14 0.74 0.88 0.19

OH-Hamilton-Mason Treat crashes 16 12 19 33 -- -- --

OH-Hamilton-Mason Comp. crashes 17 15 21 20 -- -- --

OH-Hamilton-Mason Odds ratio -- 1.03 0.79 0.51 -- 0.78 0.26

TX-New Guibeau Treat crashes 52 51 -- -- -- -- --

TX-New Guibeau Comp. crashes 79 79 -- -- -- -- --

TX-New Guibeau Odds ratio -- 0.99 -- -- -- 0.99 --

TX-Shaenfield Treat crashes 81 82 -- -- -- -- --

TX-Shaenfield Comp. crashes 24 13 -- -- -- -- --

TX-Shaenfield Odds ratio -- 0.81 -- -- -- 0.81 --

TX-71 Treat crashes 41 40 30 45 -- -- --

TX-71 Comp. crashes 25 19 18 31 -- -- --

TX-71 Odds ratio -- 0.73 1.16 1.07 -- 0.99 0.23

All AL Treat crashes 51 43 29 51 38 -- --

All AL Comp. crashes 100 103 88 96 94 -- --

31
32

Site Parameter Year 1 Year 2 Year 3 Year 4 Year 5

Mean of 

Odds Ratio SD of Mean

All AL Odds ratio -- 1.18 1.21 0.60 1.27 1.07 0.31

All OH Treat crashes 62 60 54 73 48 -- --

All OH Comp. crashes 77 78 73 91 77 -- --

All OH Odds ratio -- 1.02 1.01 0.90 1.25 1.04 0.15

All TX Treat crashes 288 122 30 45 -- -- --

All TX Comp. crashes 250 111 18 31 -- -- --

All TX Odds ratio -- 1.04 0.63 1.06 -- 0.91 0.24

AL, NC, and OH Treat crashes 157 135 129 162 126 -- --

AL, NC, and OH Comp. crashes 220 227 198 232 232 -- --

AL, NC, and OH Odds ratio -- 1.19 0.90 0.92 1.27 1.07 0.19

All Treat crashes 445 257 159 207 126 -- --

All Comp. crashes 470 338 216 263 232 -- --

All Odds ratio -- 1.24 1.02 0.93 1.43 1.16 0.23

AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

--No data. 

Comp. crashes = comparison crashes.

32
33

Source: FHWA.

Figure 9. Graph. Crash frequency in the before period for the Ohio-Symmes treatment site 

and its recommended comparison sites.

Source: FHWA.

Figure 10. Graph. Crash frequency in the before period for all treatment sites and the 

recommended set of comparison sites.

The most important results from the evaluation—the results for all crashes using comparison

sites to adjust for potential simultaneous event biases—are shown in table 11. This includes a 

statistic called Var{w}, which is an intermediate step in the calculation of the CMF and the SD

of the CMF.

(5) Anyone attempting to replicate the calculations will need Var{w}. For cases in 

which the Hauer formula suggested a negative value for Var{w}, the project team used a value 
34

of 0.0055, which is an intermediate value in the range suggested by Hauer.(5) Table 11 shows that 

eight sites had CMF values less than 1.0, and three sites had CMF values greater than 1.0. For 

nine sites, the CMF estimate was more than 1 SD away from 1.0. The group results showed CMF 

values less than 1.0. The CMF for all sites was 0.85 with an SD of 0.16, so the CMF was less 

than 1 SD away from 1.

Injury Crashes

The results in table 11 are based on all reported crashes. This subsection provides the results of 

the analysis on fatal and injury crashes. There were not enough fatal crashes in either the before 

or after periods to analyze in any detail. In the before period, the following was the fatal crash 

history at the treatment sites:

• Ohio-Hamilton-Mason: 1. 

• Texas-Evans: 1.

• Texas-71: 3.

There were no fatal crashes at any treatment site after converting it to an RCUT. 

The sample of injury crashes was large enough to allow for the same analyses as for all crashes, 

namely, traffic volume adjustment and comparison site adjustment. 

Table 12 contains the results for the analysis of injury crashes adjusting for changes in traffic 

volume. The analysis again did not use data from the Texas-71 site because no after-period 

volume data were available. The results are very similar to the results of the traffic volume 

adjustment analysis on all crash data, with seven sites having a CMF less than 1.0 and two sites 

having a CMF greater than 1.0. The overall CMF from this analysis was 1.07 with an SD of 0.17.

Table 13 shows the results from odds ratio tests on the best comparison site or sites for each 

treatment site or group of treatment sites for injury crashes. The odds ratios and SDs were not as 

strong for injury crashes as all crashes because the sample sizes were smaller. Nonetheless, the 

odds ratios were mostly at 0.85 or above, and most SDs were below 0.3.
35

Table 11. Results from comparison site analysis of each site and groups of sites.

Site

BeforePeriod 

Treatment 

Crashes

BeforePeriod 

Comp. 

Crashes

AfterPeriod 

Treatment 

Crashes

AfterPeriod 

Comp. 

Crashes Var{w} CMF SD of CMF

AL-Plum 168 228 40 104 0.0055 0.51 0.11

AL-Retail 44 253 3 97 0.0055 0.17 0.10

NC 200 232 159 264 0.075 0.64 0.18

OH-Symmes 113 194 70 78 0.0055 1.49 0.31

OH-Tylersville 104 129 24 50 0.0055 0.57 0.16

OH-Hamilton-Mason 80 79 9 17 0.02 0.47 0.20

TX-Evans 103 69 325 175 0.0055 1.20 0.23

TX-Stone Oak 42 53 169 168 0.0055 1.20 0.28

TX-New Guibeau 103 158 62 185 0.0055 0.50 0.10

TX-Shaenfield 81 37 82 49 0.0055 0.72 0.19

TX-71 156 93 16 23 0.0055 0.39 0.13

All AL 212 481 43 201 0.029 0.44 0.11

All OH 297 396 103 199 0.0055 0.98 0.16

All TX 485 410 654 600 0.022 0.88 0.15

AL, NC, and OH 709 1,109 305 664 0.011 0.71 0.09

All 1,194 1,519 959 1,264 0.036 0.85 0.16

Comp. crashes = comparison crashes.; AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

35
36

Table 12. Results from traffic volume analysis of each site and groups of sites for injury crashes.

Site

BeforePeriod 

Crashes

Before-Period 

Traffic Volume 

(Vehicle/Day)

AfterPeriod 

Crashes

After-Period 

Traffic Volume 

(Vehicle/Day)

Percent 

Change in 

Traffic 

Volume CMF

SD of 

CMF

AL-Plum 25 26,000 6 27,000 3 0.55 0.25

AL-Retail 8 36,000 1 38,000 6 0.26 0.24

NC 91 36,000 33 37,000 3 0.34 0.08

OH-Symmes 48 20,000 31 25,000 25 1.24 0.32

OH-Tylersville 35 20,000 9 17,000 -13 0.69 0.26

OH-Hamilton-Mason 22 12,000 3 17,000 48 0.17 0.10

TX-Evans 29 104,000 116 110,000 6 1.20 0.28

TX-Stone Oak 17 80,000 61 89,000 11 1.00 0.28

TX-New Guibeau 40 68,000 17 83,000 22 0.33 0.10

TX-Shaenfield 27 58,000 27 79,000 36 0.70 0.20

All AL 33 62,000 7 65,000 5 0.48 0.20

All OH 105 50,000 43 59,000 19 0.78 0.18

All TX (except TX-71) 113 310,000 221 361,000 16 0.98 0.17

AL, NC, and OH 229 148,000 83 161,000 9 0.63 0.12

All (except TX-71) 342 458,000 304 522,000 14 1.07 0.17

AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

36
37

Table 13. Comparison site test results for injury crashes.

Site Parameter Year 1 Year 2 Year 3 Year 4 Year 5

Mean of 

Odds 

Ratio SD of Mean

AL-Plum Treat crashes 5 4 4 7 5 -- --

AL-Plum Comp. crashes 5 4 5 8 6 -- --

AL-Plum Odds ratio -- 0.69 0.83 0.68 0.79 0.75 0.08

AL-Retail Treat crashes 4 1 2 1 0 -- --

AL-Retail Comp. crashes 3 2 3 3 4 -- --

AL-Retail Odds ratio -- 1.14 0.38 0.86 1.18 0.89 0.37

NC Treat crashes 21 14 23 17 16 -- --

NC Comp. crashes 12 9 5 6 7 -- --

NC Odds ratio -- 0.97 0.29 1.29 1.01 0.89 0.42

OH-Symmes Treat crashes 9 12 7 11 9 -- --

OH-Symmes Comp. crashes 4 5 4 9 6 -- --

OH-Symmes Odds ratio -- 0.70 1.02 1.07 0.67 0.86 0.21

OH-Tylersville Treat crashes 6 10 9 4 6 -- --

OH-Tylersville Comp. crashes 24 16 11 12 20 -- --

OH-Tylersville Odds ratio -- 0.35 0.65 1.83 0.89 0.93 0.64

OH-Hamilton-Mason Treat crashes 4 3 5 10 -- -- --

OH-Hamilton-Mason Comp. crashes 11 11 20 16 -- -- --

OH-Hamilton-Mason Odds ratio -- 0.94 0.85 0.35 -- 0.71 0.32

TX-New Guibeau Treat crashes 19 21 -- -- -- -- --

TX-New Guibeau Comp. crashes 14 17 -- -- -- -- --

TX-New Guibeau Odds ratio -- 0.98 -- -- -- 0.98 --

TX-Shaenfield Treat crashes 19 8 -- -- -- -- --

TX-Shaenfield Comp. crashes 11 5 -- -- -- -- --

TX-Shaenfield Odds ratio -- 0.89 -- -- -- 0.89 --

TX-71 Treat crashes 21 17 10 23 -- -- --

TX-71 Comp. crashes 5 5 3 5 -- -- --

TX-71 Odds ratio -- 0.98 0.79 0.53 -- 0.76 0.23

All AL Treat crashes 9 5 6 8 5 -- --

37
38

Site Parameter Year 1 Year 2 Year 3 Year 4 Year 5

Mean of 

Odds 

Ratio SD of Mean

All AL Comp. crashes 5 4 5 8 6 -- --

All AL Odds ratio -- 1.03 0.74 0.91 0.91 0.89 0.12

All OH Treat crashes 19 25 21 25 15 -- --

All OH Comp. crashes 39 32 35 37 26 -- --

All OH Odds ratio -- 0.58 1.21 0.83 1.07 0.92 0.27

All TX Treat crashes 100 46 10 23 -- -- --

All TX Comp. crashes 146 27 3 5 -- -- --

All TX Odds ratio -- 0.39 0.45 0.53 -- 0.46 0.07

AL, NC, and OH Treat crashes 45 43 48 49 36 -- --

AL, NC, and OH Comp. crashes 56 45 45 51 39 -- --

AL, NC, and OH Odds ratio -- 0.81 0.86 1.06 0.99 0.93 0.12

All Treat crashes 149 90 60 73 36 -- --

All Comp. crashes 205 74 51 59 43 -- --

All Odds ratio -- 0.59 1.00 0.92 1.42 0.98 0.34

AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

--No data. 

Comp. crashes = comparison crashes.

38
39

The strongest comparison sites included the following:

• Alabama-Plum: Retail 3 and 4.

• Alabama-Retail: 3.

• North Carolina: 3 and 4.

• Ohio-Symmes: 3.

• Ohio-Tylersville: 1 and 3.

• Ohio-Hamilton-Mason: 1, 2, 3, and 4.

• Texas-New Guibeau: 2, 3, and 4.

• Texas-Shaenfield: New Guibeau 1 and 2.

• Texas-71: 3 and 4.

Note that, at the Alabama-Plum and Texas-Shaenfield sites, the best comparison sites were from 

the Retail Drive and New Guibeau sets of potential sites, respectively. This is due to the 

proximity of those treatment sites to each other.

Table 14 shows the results from the analysis of injury crashes using comparison sites. Note that 

some of the crash counts for groups of comparison sites do not match the sums of the component 

individual sites because comparison site data were not double-counted. The CMF values were 

much like those in the previous table of results, with eight sites having CMF values less than 1.0 

and three sites having CMF values greater than 1.0. The CMF for all sites, 0.78, was lower than 

that for all crashes. This result suggests that signalized RCUTs have a larger positive effect on 

injury crashes than on property-damage-only crashes. The CMF for injury crashes at all sites was 

greater than 1 SD from a value of 1.0.
40

Table 14. Results from comparison site analysis of sites and groups of sites for injury crashes.

Site

BeforePeriod 

Treatment 

Crashes

BeforePeriod 

Comp. 

Crashes

AfterPeriod 

Treatment 

Crashes

AfterPeriod 

Comp. 

Crashes Var{w} CMF SD of CMF

AL-Plum 25 28 6 13 0.0055 0.45 0.22

AL-Retail 8 15 1 6 0.0055 0.23 0.20

NC 91 39 33 32 0.0055 0.41 0.12

OH-Symmes 48 28 31 8 0.0055 1.90 0.75

OH-Tylersville 35 83 9 32 0.0055 0.62 0.25

OH-Hamilton-Mason 22 58 3 32 0.0055 0.22 0.13

TX-Evans 29 21 116 60 0.0055 1.27 0.39

TX-Stone Oak 17 20 61 56 0.0055 1.13 0.39

TX-New Guibeau 40 31 17 32 0.0055 0.38 0.13

TX-Shaenfield 27 16 27 28 0.0055 0.50 0.19

TX-71 71 18 11 7 0.0055 0.33 0.15

All AL 33 28 7 13 0.0055 0.41 0.19

All OH 105 169 43 72 0.0055 1.06 0.25

All TX 184 106 232 183 0.0055 0.88 0.15

AL, NC, and OH 229 236 83 117 0.0055 0.63 0.11

All 413 357 315 306 0.068 0.78 0.20

Comp. crashes = comparison crashes.; AL = Alabama; NC = North Carolina; OH = Ohio; TX = Texas.

40
41

Spatial Patterns

The crash data obtained for this evaluation specified location well enough to allow for a spatial 

patterns analysis. Notable clusters of crashes included the following:

• Alabama-Plum: Clusters of rear-end crashes southbound on the major street.

• North Carolina: Large clusters of rear-end crashes in both directions of the major street.

• Ohio-Symmes: A cluster of rear-end crashes northbound on the major street and a cluster 

of rear-end crashes eastbound on the minor street.

• Ohio-Tylersville: Smaller clusters of rear-end and sideswipe crashes in both directions on 

the major street.

• Texas-Evans: Large clusters of rear-end crashes in both directions of the major street, a 

cluster of sideswipe crashes northbound on the major street, and a cluster of crashes 

involving northbound left-turn vehicles.

• Texas-Stone Oak: Large clusters of rear-end crashes on the major street southbound.

• Texas-New Guibeau: Clusters of rear-end crashes northbound on the major street on the 

side with the stem of the T-intersection.

• Texas-Shaenfield: Clusters of rear-end crashes in both directions on the major street and

notable clusters of sideswipe and fixed object crashes southbound on the major street on 

the side with the stem of the T-intersection.

Thus, with a few exceptions, the crash data mostly showed clusters of rear-end crashes occurring 

at the RCUT sites. These patterns are somewhat different from those seen at typical conventional 

intersections, which would tend to feature more prominent clusters of turning and angle crashes.

Other Variables

The collected data allowed for examination of other variables besides severity and location. The 

appendix provides more information on the changes to crash type, light, weather, road condition, 

and occasionally other variables (when available) from the before period to the after period at 

each site. The Alabama-Retail, Ohio-Hamilton-Mason, and Texas-71 sites did not have enough 

crashes in the after period to detect any important changes. Despite the available data at the 

Texas-Stone Oak site, no significant changes in the variables occurred at this site. Several 

important changes occurred at the remaining sites between the before and after periods, including

the following:

• Alabama-Plum: The crash type changed drastically, from 68.5 percent right rear angle 

crashes in the before period to 52.5 percent rear-end center in the after period.

• Alabama-Plum: The percentage of daylight crashes decreased from 74.4 to 60.
42

• Alabama-Plum: The percentage of crashes in the rain decreased from 20.2 to 7.5.

• North Carolina: The percentage of left-turn, same roadway crashes decreased from 29 to 

10.7.

• Ohio-Symmes: The percentage of fixed object crashes increased from 0 to 10.

• Ohio-Symmes: The percentage of wet crashes increased from 32.7 to 45.7.

• Ohio-Tylersville: The percentage of angle crashes decreased from 15.4 to 4.2.

• Ohio-Tylersville: The percentage of wet crashes decreased from 26 to 12.5.

• Texas-Evans: The percentage of same direction, both going straight, rear-end crashes 

increased from 11.7 to 22.8.

• Texas-Evans: The percentage of dark, no light crashes decreased from 15.5 to 4.9.

• Texas-New Guibeau: The percentage of same direction, both going straight, sideswipe 

crashes increased from 2.9 to 16.1, while the percentage of same direction, one straight, 

one stopped crashes decreased from 57.3 to 38.7.

• Texas-Shaenfield: The percentage of same direction, both going straight, sideswipe 

crashes increased from 2.5 to 19.5, while the percentage of same direction, one straight, 

one stopped crashes decreased from 60.5 to 24.4.

• Texas-Shaenfield: The percentage of dark, lighted crashes increased from 13.6 to 29.3.

The most prominent changes with RCUT installation appear to be decreases in angle crashes and 

increases in sideswipe crashes.
43

CHAPTER 6. ECONOMIC ANALYSIS

This evaluation included an economic analysis on the installation of a signalized RCUT. The 

results of this analysis can help inform transportation agencies on the benefits and costs of this 

countermeasure.

To conduct the analysis, the project team employed annualized benefits and costs. Benefits 

included fewer crashes and shorter travel times. The costs included construction of the RCUT 

and maintenance of the extra traffic signals required by the RCUT. These benefits and costs 

should entail the majority of all quantifiable impacts of RCUT installation. The analysis 

essentially estimated the effects of signalized RCUT installation at a site that has characteristics 

that are average compared to the 11 test sites in this research.

The project team obtained construction cost data for 9 of the 11 test sites, as shown in table 15. 

Officials in Alabama emphasized that cost estimates were for RCUT-related items only, while 

officials in Ohio stated that cost estimates included items that were indirectly related to the 

RCUT. In that regard, underestimates from Alabama might balance overestimates from Ohio and 

still provide what looks to be a reasonable average. 

Table 15. Estimated construction cost by site.

Site

Estimated Construction 

Cost

AL-Plum $500,000

AL-Retail $500,000

OH-Symmes $6,500,000

OH-Tylersville $10,700,000

OH-Hamilton-Mason $4,700,000

TX-Evans $2,580,000

TX-Stone Oak $2,580,000

TX-New Guibeau $2,850,000

TX-Shaenfield $2,850,000

Average $3,750,000

AL = Alabama; OH = Ohio; TX = Texas.

The following assumptions were made during the analysis: 

• All benefits and costs were in 2014 dollars.

• Inflation was not relevant to bring the construction costs in table 15 to 2014 dollars or to 

annualize other benefits and costs into the future.

• RCUTs have a 20-year useful life, like other intersection improvements.

(7)

• There is a 7-percent-per-year discount rate.

• On average, the RCUT major street carries 52,000 vehicles per day.
44

• On average, the RCUT minor street carries 10,000 vehicles per day.

• There is no traffic growth during the analysis period.

• Thirty percent of daily traffic occurs during the 4 peak hours of the day.

• The RCUT saves 20 seconds per vehicle during the 4 peak hours of the day and has no 

net operational effect at other times of the day, in line with previous estimates of the 

effects signalized RCUTs.

(1)

• Time savings are valued at $15 per hour, in line with typical economic analyses.

• There are 250 working days per year that experience time savings.

• Each extra traffic signal costs $5,000 per year to maintain and provide electricity.

(8)

• Without the RCUT, crashes would have occurred at the same annual frequency as in the 

before period at the 11 test sites during the whole analysis period.

• The property-damage-only CMF was 0.85, equivalent to the total CMF in table 11.

• The injury and fatal CMF was 0.78, per table 14.

• The property-damage-only crash cost in 2014 was $18,000.(9,10)

• The injury and fatal crash cost in 2014 was $384,000.

(9,10)

The analysis resulted in a $354,000 annualized construction cost. When added to the annual 

maintenance cost for three extra signals at $15,000, this yielded a total annualized RCUT cost of 

$369,000. For benefits, using several of the assumptions above, the project team estimated the 

operations to save 103 hours of motorist time per workday, which equates to $388,000 per year.

The safety benefit was a savings of 3.0 property-damage-only crashes per year and 2.3 injury and 

fatal crashes per year, which results in an annual monetary savings of $948,000. Thus, the B/C 

ratio was 2.6 to 1.0 considering the safety benefits only and 3.6 to 1.0 considering the safety and 

operational benefits. 

The U.S. Department of Transportation (USDOT) recommends testing the sensitivity of 

economic analysis results to differences in crash costs by examining the results with 0.57 and 

1.41 times the recommended crash costs.(10) Table 16 provides the results from that test. Even at 

the low levels of crash cost, considering safety only, installing a signalized RCUT at sites like 

those tested under the assumptions previously listed will be beneficial, and at high crash cost

levels, the effort is very beneficial.
45

Table 16. Sensitivity of B/C ratios.

Parameter

Recommended Crash 

Cost Lower Crash Cost Higher Crash Cost

2014 cost per 

property-damageonly crash

$18,000 $10,000 $25,000

2014 cost per injury 

crash

$384,000 $219,000 $541,000

B/C ratio including 

safety only

2.6 1.5 3.6

B/C ratio including 

safety and operations

3.6 2.5 4.7

47

CHAPTER 7. CONCLUSIONS

There are theoretical reasons to believe that signalized RCUT intersections would be safer than 

similar conventional signalized intersections, and previous research has shown that unsignalized 

RCUTs are generally safer than conventional unsignalized options. However, there has never 

been a study of the safety of signalized RCUT intersections. Therefore, the objective of this 

evaluation was to develop a CMF for signalized RCUT intersections and examine injury crashes, 

spatial patterns, and other crash variables.

This evaluation collected and analyzed crash data before and after conversion of 11 intersections 

from conventional to RCUT design. The intersections were in suburban areas along four-lane or 

six-lane arterials. Available data included more than 2,000 crash reports at the treatment sites 

over 65 years of intersection operation, including more than 700 injury crashes. Analyses 

adjusted for changes in traffic volumes. The project team also collected data at 44 potential 

comparison sites for use in adjusting for simultaneous event and maturation biases.

For most individual sites and groups of sites examined, odds ratio tests showed that there were 

high-quality comparison sites available, which enhanced the strength of the analyses. Therefore, 

this evaluation determined the following as the best general estimates of CMFs for conversion of 

a conventional intersection to an RCUT intersection:

• Overall crashes: CMF = 0.85 (CRF = 15 percent).

• Injury crashes: CMF = 0.78 (CRF = 22 percent).

The SDs of the CMFs were 0.16 and 0.20, respectively. This indicates that the CMF for overall 

crashes was not significantly different from a neutral value of 1.0 at a 68-percent confidence 

level but that the CMF for injury crashes was significantly different from a neutral value of 1.0 at 

a 68-percent confidence level. Regardless, the results support the assumption that a signalized 

RCUT will generally produce a crash reduction. Also, the fact that RCUTs likely save more 

injury crashes than overall crashes should not be surprising, since they generally reduce the more 

severe angle and turning crashes. 

The evaluation also produced an estimated B/C ratio for installing an RCUT at the set of test 

intersections of 3.6 to 1.0 when considering safety and operations or 2.6 to 1.0 considering safety 

only. When examining the sensitivity of this result to changes in crash costs, the B/C ratio 

always exceeded 1.0. Installing signalized RCUT intersections at locations similar to those 

studied should generally lead to positive results in terms of expected crash reductions.

At the individual site level of analysis, 8 of the 11 sites showed decreases in overall and injury 

crashes after RCUT installation. The three sites with increases (Ohio-Symmes, Texas-Evans, and 

Texas-Stone Oak) were the only treatment sites with three lanes on both minor street approaches. 

The only other treatment sites with three lanes on minor street approaches were TexasShaenfield, a T-intersection, and the other two Ohio sites with three lanes on one minor street 

approach and two lanes on the other minor street approach. Therefore, it is likely that signalized 

RCUTs may be relatively safer when the minor streets are narrower and/or carry lower traffic 

volumes.
48

There were clusters of rear-end crashes on the major streets of the RCUTs. An examination of 

crash types before and after RCUT installation showed that there was generally a conversion 

from angle crashes to sideswipe crashes. 

While this study provided an estimated CMF and B/C ratio for signalized RCUTs, there is still a 

need for future research to add more knowledge. The project team recommends a followup 

before–after study with more sites, in more States, over more years, since RCUT installation is 

accelerating, and such a larger study will be possible in a few years. A larger study may shed 

more light on the spatial crash patterns and other variables that this study could only touch upon, 

as well as the circumstances that best favor RCUT installation. A second promising area for 

future research would be a study with a much larger sample size of sites where a research team 

could assemble a model of the safety effects of some of the important geometric features of 

RCUTs. Finally, the project team recommends studies similar to this one for the installation of 

other types of alternative and conventional intersections. A CMF for signalized RCUTs is only 

helpful if designers can compare it to CMFs for other intersection forms such as median U-turn, 

quadrant roadway, and continuous flow intersections, among others.
49

APPENDIX. ANALYSIS OF OTHER CRASH VARIABLES

Table 17 through table 27 provide more information on the crash variables from the RCUT test 

sites. 

Table 17. Other crash variables at Alabama-Plum site.

Variable Level

BeforePeriod 

Number of 

Crashes

BeforePeriod 

Percent of 

Total 

Crashes

AfterPeriod 

Number of 

Crashes

AfterPeriod 

Percent of 

Total 

Crashes

Right front angle crash 9 5.4 3 7.5

Left front angle crash 1 0.6 1 2.5

Head-on center crash 9 5.4 5 12.5

Undercarriage crash 1 0.6 0 0.0

Broadside right crash 5 3.0 1 2.5

Right rear angle crash 115 68.5 1 2.5

Rear-end center crash 5 3.0 21 52.5

Left rear angle crash 1 0.6 0 0.0

Broadside left crash 1 0.6 0 0.0

No second vehicle crash 10 6.0 1 2.5

No applicable crash type 10 6.0 7 17.5

Missing crash type 1 0.6 0 0.0

Daylight 125 74.4 24 60.0

Dark 39 23.2 13 32.5

Dusk 4 2.4 2 5.0

Dawn 0 0.0 1 2.5

Clear weather 93 55.4 27 67.5

Cloudy weather 40 23.8 10 25.0

Foggy weather 1 0.6 0 0.0

Rainy weather 34 20.2 3 7.5

Dry road condition 118 70.2 33 82.5

Wet road condition 50 29.8 4 10.0

Unknown road condition 0 0.0 2 5.0

Water buildup on road 0 0.0 1 2.5
50

Table 18. Other crash variables at Alabama-Retail site.

Variable Level

BeforePeriod 

Number of 

Crashes

Before-Period 

Percent of 

Total Crashes

AfterPeriod 

Number of 

Crashes

After-Period 

Percent of 

Total 

Crashes

Right front angle crash 1 2.3 0 0.0

Left front angle crash 0 0.0 1 33.3

Head-on center crash 4 9.1 0 0.0

Undercarriage crash 2 4.6 0 0.0

Broadside right crash 0 0.0 0 0.0

Right rear angle crash 26 59.1 0 0.0

Rear-end center crash 1 2.3 2 66.7

Left rear angle crash 1 2.3 0 0.0

Broadside left crash 1 2.3 0 0.0

No second vehicle crash 0 0.0 0 0.0

No applicable crash type 6 13.6 0 0.0

Missing crash type 2 4.6 0 0.0

Daylight 34 77.3 2 66.7

Dusk 3 6.8 0 0.0

Dark, roadway lighted 1 2.3 0 0.0

Dark, roadway not lighted 6 13.6 1 33.3

Clear weather 27 74.4 2 66.7

Cloudy weather 7 2.4 0 0.0

Foggy weather 0 5.4 0 0.0

Rainy weather 10 17.9 1 33.3

Dry road condition 33 75.0 2 66.7

Wet road condition 11 25.0 1 33.3
51

Table 19. Other crash variables at North Carolina site.

Variable Level

BeforePeriod 

Number of 

Crashes

Before-Period 

Percent of 

Total Crashes

AfterPeriod 

Number of 

Crashes

After-Period 

Percent of 

Total 

Crashes

Angle crash 4 2.0 2 1.3

Animal crash 1 0.5 4 2.5

Backing-up crash 1 0.5 1 0.6

Fixed object crash 2 1.0 4 2.5

Left-turn, different 

roadways crash

4 2.0 5 3.1

Left-turn, same roadway

crash

58 29.0 17 10.7

Movable object crash 1 0.5 2 1.3

Parked crash 0 0.0 1 0.6

Other crash with vehicle 1 0.5 0 0.0

Pedalcyclist crash 1 0.5 0 0.0

Ran off road, right crash 1 0.5 0 0.0

Rear-end crash 102 51.0 82 51.6

Right-turn, different 

roadways crash

12 6.0 22 13.8

Right-turn, same roadway 

crash

1 0.5 2 1.3

Sideswipe, opposite

direction crash

1 0.5 1 0.6

Sideswipe, same direction 

crash

10 5.0 16 10.1

Daylight 139 69.5 112 70.4

Dawn 18 9.0 16 10.1

Dusk 43 21.5 31 19.5

Clear weather 194 97.0 156 98.1

Foggy weather 3 1.5 2 1.3

Rainy weather 3 1.5 1 0.6

Dry road condition 165 82.5 129 81.1

Wet road condition 34 17.0 30 18.9

Icy road condition 1 0.5 0 0.00
52

Table 20. Other crash variables at Ohio-Symmes site.

Variable Level

BeforePeriod 

Number of 

Crashes

Before-Period 

Percent of 

Total Crashes

After-Period 

Number of 

Crashes

After-Period 

Percent of 

Total Crashes

Angle crash 10 13.3 7 10.0

Backing-up crash 1 1.3 1 1.4

Fixed object crash 0 0.0 7 10.0

Head-on crash 1 0.9 1 1.4

Left-turn crash 19 16.8 6 8.6

Parked vehicle crash 1 0.9 0 0.0

Other noncrash 0 0.0 2 2.9

Pedestrian crash 0 0.0 1 1.4

Rear-end crash 75 66.4 41 58.6

Sideswipe, meeting crash 2 1.8 0 0.0

Sideswipe, passing crash 4 3.5 4 5.7

Dark, lighted 26 23.0 11 15.7

Dark, no light 7 6.2 2 2.9

Dawn 2 1.8 2 2.9

Daylight 75 66.4 50 71.4

Dusk 2 1.8 1 1.4

Light not stated 1 0.9 4 5.7

No adverse weather 

condition

87 77.0 51 72.9

Rain 26 23.0 15 21.4

Other weather 0 0.0 2 2.9

Snow 0 0.0 2 2.9

Dry road condition 74 65.5 36 51.4

Snow road condition 1 0.9 2 2.9

Wet road condition 37 32.7 32 45.7

Road condition not stated 1 0.9 0 0.0

Compact causal vehicle 18 15.9 8 11.4

Full-size causal vehicle 10 8.8 16 22.9

Mid-size causal vehicle 35 31.0 18 25.7

Other vehicle 25 22.1 18 25.7

Pickup causal vehicle 17 15.0 8 11.4

School bus causal vehicle 1 0.9 0 0.0

Straight truck causal 

vehicle

2 1.8 1 1.4

Subcompact causal vehicle 1 0.9 0 0.0

Tractor semitrailer causal 

vehicle

2 1.8 1 1.4

Vehicle not stated 2 1.8 0 0.0
53

Table 21. Other crash variables at Ohio-Tylersville site.

Variable Level

BeforePeriod

Number of 

Crashes

BeforePeriod

Percent of 

Total 

Crashes

AfterPeriod

Number of 

Crashes

After-Period

Percent of 

Total 

Crashes

Angle crash 16 15.4 1 4.2

Backing-up crash 3 2.9 0 0.0

Fixed object crash 5 4.8 1 4.2

Head-on crash 0 0.0 0 0.0

Left-turn crash 6 5.8 1 4.2

Other non-collision 1 1.0 0 0.0

Rear-end crash 66 63.5 14 58.3

Sideswipe, meeting crash 1 1.0 0 0.0

Overturning crash 1 1.0 0 0.0

Sideswipe, passing crash 5 4.8 7 29.2

Dark, lighted 19 18.3 4 16.7

Dark, no light 7 6.7 1 4.2

Dawn 5 4.8 1 4.2

Daylight 71 68.3 18 75.0

Dusk 2 1.9 0 0.0

No adverse weather condition 82 78.8 21 87.5

Rain 18 17.3 2 8.3

Other weather condition 0 0.0 1 4.2

Snow 3 2.9 0 0.0

Fog 1 1.0 0 0.0

Dry 75 72.1 21 87.5

Snow 1 1.0 0 0.0

Wet 27 26.0 3 12.5

Ice 1 1.0 0 0.0

Compact causal vehicle 14 13.5 3 12.5

Full-size causal vehicle 14 13.5 3 12.5

Mid-size causal vehicle 30 28.8 9 37.5

Motorcycle causal vehicle 1 1.0 0 0.0

Panel truck causal vehicle 5 4.8 0 0.0

Other causal vehicle 22 21.2 6 25.0

Pickup causal vehicle 13 12.5 3 12.5

School bus causal vehicle 1 1.0 0 0.0

Straight truck causal vehicle 1 1.0 0 0.0

Subcompact causal vehicle 1 1.0 0 0.0

Tractor semitrailer causal 

vehicle

1 1.0 0 0.0

Causal vehicle not stated 1 1.0 0 0.0
54

Table 22. Other crash variables at Ohio-Hamilton-Mason site.

Variable Level

BeforePeriod

Number of 

Crashes

BeforePeriod

Percent of 

Total 

Crashes

AfterPeriod

Number of 

Crashes

After-Period

Percent of 

Total 

Crashes

Angle crash 8 10.0 0 0.0

Backing-up crash 2 2.5 0 0.0

Fixed object crash 4 5.0 0 0.0

Left-turn crash 2 2.5 0 0.0

Other noncrash 0 0.0 1 11.1

Rear-end crash 61 76.3 7 77.8

Sideswipe, passing 3 3.8 1 11.1

Dark, lighted 6 7.5 0 0.0

Dark, no light 8 10.0 2 22.2

Dawn 3 3.8 1 11.1

Daylight 62 77.5 6 66.7

Dusk 1 1.3 0 0.0

No adverse weather 

condition

64 80.0 8 88.9

Rain 10 12.5 1 11.1

Snow 6 7.5 0 0.0

Dry road condition 60 75.0 7 77.8

Snow road condition 4 5.0 0 0.0

Wet road condition 15 18.8 2 22.2

Ice road condition 1 1.3 0 0.0

Compact causal vehicle 16 20.0 3 33.3

Full-size causal vehicle 6 7.5 0 0.0

Mid-size causal vehicle 23 28.8 3 33.3

Panel truck causal vehicle 2 2.5 0 0.0

Other causal vehicle 17 21.3 3 33.3

Pickup truck causal vehicle 13 16.3 0 0.0

Straight truck causal vehicle 1 1.3 0 0.0

Subcompact causal vehicle 2 2.5 0 0.0
55

Table 23. Other crash variables at Texas-Evans site.

Variable Level

BeforePeriod 

Number of 

Crashes

BeforePeriod 

Percent of 

Total 

Crashes

AfterPeriod 

Number of 

Crashes

AfterPeriod 

Percent of 

Total 

Crashes

Angle, both going straight

crash

25 24.3 64 19.7

Opposite direction, both going 

straight crash 

0 0.0 1 0.3

Od, one straight, one left-turn

crash

9 8.7 21 6.5

One mv other crash 1 1.0 0 0.0

One mv going straight crash 4 3.9 22 6.8

One mv turning left crash 1 1.0 3 0.9

One mv turning right crash 1 1.0 1 0.3

Sd, both going straight, rearend crash12 11.7 74 22.8

Sd, both going straight, 

sideswipe crash

9 8.7 22 6.8

Sd, both left-turn crash 0 0.0 3 0.9

Sd, both right-turn crash 2 1.9 7 2.2

Sd, one straight, one left-turn

crash

1 1.0 1 0.3

Sd, one straight, one right-turn

crash

1 1.0 1 0.3

Sd, one straight, one stopped

crash

37 35.9 105 32.3

Dark, lighted 20 19.4 70 21.5

Dark, no light 16 15.5 16 4.9

Dawn 0 0.0 3 0.9

Daylight 64 62.1 235 72.3

Dusk 0 0.0 1 0.3

Dark, unknown lighting 3 2.9 0 0.0

Clear/cloudy weather 91 88.3 297 91.4

No weather data 2 1.9 0 0.0

Rain 10 9.7 26 8.0

Fog 0 0.0 2 0.6

Dry road condition 83 80.6 291 89.5

Wet road condition 18 17.5 34 10.5

No road condition data 2 1.9 0 0.0

Od = opposite direction; mv = motor vehicle; Sd = same direction.
56

Table 24. Other crash variables at Texas-Stone Oak site.

Variable Level

Before-Period 

Number of 

Crashes

BeforePeriod 

Percent of 

Total 

Crashes

AfterPeriod 

Number of 

Crashes

After-Period 

Percent of 

Total 

Crashes

Angle, both going straight

crash

5 11.9 21 12.4

Od, both going straight crash 0 0.0 1 0.6

Od, one straight, one left-turn

crash

4 9.5 6 3.6

One mv other crash 1 2.4 0 0.0

One mv going straight crash 6 14.3 26 15.4

One mv turning left crash 0 0.0 1 0.6

One mv turning right crash 0 0.0 2 1.2

Sd, both going straight, rearend crash8 19.0 44 26.0

Sd, both going straight, 

sideswipe crash

5 11.9 12 7.1

Sd, one straight, one left-turn

crash

0 0.0 1 0.6

Sd, one straight, one rightturn crash0 0.0 1 0.6

Sd, one straight, one stopped

crash

13 31.0 54 32.0

Dark, lighted 11 26.2 46 27.2

Dark, no light 2 4.8 12 7.1

Dawn 1 2.4 4 2.4

Daylight 25 59.5 105 62.1

Dusk 0 0.0 2 1.2

Dark, unknown lighting 1 2.4 0 0.0

Unknown lighting 2 4.8 0 0.0

Clear and cloudy weather 37 88.1 116 68.6

Unknown weather 1 2.4 33 19.5

Rain 4 9.5 18 10.7

Fog 0 0.0 2 1.2

Dry road condition 33 78.6 142 84.0

Wet road condition 8 19.0 27 16.0

No road condition data 1 2.4 0 0.0

Od = opposite direction; mv = motor vehicle; Sd = same direction.
57

Table 25. Other crash variables at Texas-New Guibeau site.

Variable Level

BeforePeriod 

Number of 

Crashes

BeforePeriod 

Percent of 

Total 

Crashes

AfterPeriod 

Number of 

Crashes

AfterPeriod 

Percent of 

Total 

Crashes

Angle, both going straight crash 4 3.9 6 9.7

Od, both going straight crash 0 0.0 1 1.6

Od, one straight, one left-turn

crash

5 4.9 1 1.6

One mv going straight crash 7 6.8 5 8.1

One mv turning right crash 1 1.0 0 0.0

Sd, both going straight, rear-end

crash

22 21.4 14 22.6

Sd, both going straight, 

sideswipe crash

3 2.9 10 16.1

Sd, both left-turn crash 1 1.0 1 1.6

Sd, both right-turn crash 1 1.0 0 0.0

Sd, one straight, one stopped

crash

59 57.3 24 38.7

Dark, lighted 24 23.3 10 16.1

Dark, no light 6 5.8 5 8.1

Daylight 71 68.9 43 69.4

Dusk 0 0.0 1 1.6

Dark, unknown lighting 2 1.9 3 4.8

Clear and cloudy weather 90 87.4 56 90.3

Unknown weather 4 3.9 1 1.6

Rain 8 7.8 4 6.5

Fog 1 1.0 1 1.6

Dry road condition 84 81.6 54 87.1

Wet road condition 15 14.6 7 11.3

No road condition data 4 3.9 1 1.6

Od = opposite direction; mv = motor vehicle; Sd = same direction.
58

Table 26. Other crash variables at Texas-Shaenfield site.

Variable Level

BeforePeriod

Number of 

Crashes

BeforePeriod

Percent of 

Total 

Crashes

AfterPeriod

Number of 

Crashes

AfterPeriod

Percent of 

Total 

Crashes

Angle, both going straight crash 9 11.1 13 15.9

Od, one straight, one left-turn

crash

1 1.2 0 0.0

One mv going straight crash 6 7.4 7 8.5

One mv turning left crash 0 0.0 1 1.2

One mv turning right crash 0 0.0 1 1.2

Sd, both going straight, rear-end

crash

13 16.0 20 24.4

Sd, both going straight, 

sideswipe crash

2 2.5 16 19.5

Sd, both left-turn crash 0 0.0 2 2.4

Sd, one straight, one left-turn

crash

0 0.0 1 1.2

Sd, one right, one stopped crash 1 1.2 1 1.2

Sd, one straight, one stopped

crash

49 60.5 20 24.4

Dark, lighted 11 13.6 24 29.3

Dark, no light 7 8.6 3 3.7

Dawn 1 1.2 1 1.2

Daylight 59 72.8 53 64.6

Dark, unknown lighting 3 3.7 1 1.2

Clear and cloudy weather 71 87.6 76 92.7

Rain 9 11.1 5 6.1

Fog 0 0.0 1 1.2

No weather data 1 1.2 0 0.0

Dry road condition 70 86.4 72 87.8

Wet road condition 10 12.3 10 12.2

No road condition data 1 1.2 0 0.0

Od = opposite direction; mv = motor vehicle; Sd = same direction.
59

Table 27. Other crash variables at Texas-71 site.

Variable Level

Before-Period 

Number of 

Crashes

BeforePeriod 

Percent of 

Total 

Crashes

AfterPeriod 

Number of 

Crashes

AfterPeriod 

Percent of 

Total 

Crashes

Angle, both going straight

crash

45 28.8 2 12.5

Od, both going straight crash 1 0.6 0 0.0

Od, one straight, one left-turn

crash

14 9.0 0 0.0

One mv going straight crash 19 12.2 2 12.5

One mv turning left crash 4 2.6 0 0.0

One mv turning right crash 1 0.6 1 6.2

Sd, both going straight, rearend crash21 13.5 4 25.0

Sd, both going straight, 

sideswipe crash

8 5.1 3 18.8

Sd, one straight, one left-turn

crash

0 0.0 1 6.2

Sd, one straight, one rightturn crash2 1.3 0 0.0

Sd, one straight, one stopped

crash

41 26.3 3 18.8

Dark, lighted 22 14.1 4 25.0

Dark, no light 20 12.8 4 25.0

Dawn 4 2.6 0 0.0

Daylight 104 66.7 6 37.5

Dusk 3 1.9 0 0.0

Dark, unknown lighting 3 1.9 2 12.5

Clear and cloudy weather 140 89.7 11 68.8

Unknown weather 1 0.6 0 0.0

Rain 15 9.6 4 25.0

Fog 1 6.2

Dry road condition 136 87.2 11 68.8

Sand, mud, dirt road 

condition

1 0.6 0 0.0

Wet road condition 18 11.5 5 31.2

No road condition data 1 0.6 0 0.0

Od = opposite direction; mv = motor vehicle; Sd = same direction.

61

ACKNOWLEDGMENTS

This report was prepared for the Federal Highway Administration, Office of Safety Research and 

Development, under Contract DTFH61-13-D-00001. The FHWA Program and Task Manager for 

this project was Ms. Roya Amjadi.

The project team gratefully acknowledges the participation and assistance of the following 

organizations in this study: the Alabama Department of Transportation, the North Carolina 

Department of Transportation, the Ohio Department of Transportation, and the Texas 

Department of Transportation.

63

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HRDS-20/12-17(200)E