Safety Evaluation of Turning

Movement Restrictions at

Stop-Controlled Intersections

PUBLICATION NO. FHWA-HRT-17-064 MARCH 2018

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) 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.(1) The ELCSI-PFS 

studies provide a crash modification factor and benefit–cost economic analysis for each of the 

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

This study evaluated the safety effects of converting full movement, stop-controlled intersections 

to right-in-right-out (RIRO) operation with physical barriers. The results indicated reductions for 

all crash types (i.e., total, all intersection-related and fatal and injury intersection-related) for 

stop-controlled intersections with RIRO compared to full movement intersections. While the 

economic analysis suggests the strategy can be cost-effective in reducing crashes at stopcontrolled intersections, potential costs and benefits need to be analyzed on a case-by-case basis.

This document is intended for safety engineers, highway designers, planners, and practitioners at 

State and local agencies involved with AASHTO Strategic Highway Safety Plan implementation.

Monique R. Evans, P.E., CPM

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 DOCUMENTATION PAGE

1. Report No.

FHWA-HRT 17-064

2. Government

Accession No.

3. Recipient's Catalog No.

4. Title and Subtitle

Safety Evaluation of Turning Movement Restrictions at Stop-Controlled

Intersections

5. Report Date

March 2018

6. Performing Organization Code

7. Author(s)

Thanh Le, Frank Gross, Tim Harmon, and Kimberly Eccles

8. Performing Organization Report

No.

9. Performing Organization Name and Address

VHB

8300 Boone Blvd., Ste. 700

Vienna, VA 22182-2626

10. Work Unit No.

11. Contract or Grant No.

DTFH61-13-D-00001

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period

Final Report; June 2015–September

2017

14. Sponsoring Agency Code

HRDS-20

15. Supplementary Notes

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

under the Development of Crash Modification Factors program. The FHWA Office of Safety Research and

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

16. Abstract

The Development of Crash Modification Factors program conducted a safety evaluation of turning movement

restrictions at stop-controlled intersections for the Evaluation of Low Cost Safety Improvements Pooled Fund

Study. This study evaluated the safety effects of converting full movement, stop-controlled intersections to rightin-right-out (RIRO) operation using physical barriers, as measured by the change in crash frequency. The project

team obtained geometric, traffic, and crash data for urban, three-legged, full movement and RIRO stop-controlled

intersections, as well as the downstream four-legged, stop-controlled or signalized intersection with full movement

in California. The team used a cross-sectional analysis to estimate the effects of turning movement restrictions

while controlling for other differences between sites with RIRO and full movement. Aggregate results indicated

reductions for all crash types analyzed (i.e., total, all intersection-related, and fatal and injury intersection-related)

for stop-controlled intersections with RIRO compared to full movement. Reductions were statistically significant at

the 95-percent confidence level for all crash types. Crash modification factors for total, all intersection-related, and

fatal and injury intersection-related crashes were 0.55, 0.32, and 0.20, respectively. Based on the disaggregate

results, RIRO intersections do not appear to have differing effects for different levels of traffic, design speed, or

number of lanes. Potential for crash migration in determining net benefits needs to be considered. Results indicated

potential increases at downstream intersections, but many increases are not statistically significant even at the 90-

percent confidence level. While the economic analysis suggests the strategy can be cost-effective in reducing

crashes at a hypothetical stop-controlled intersection, potential costs and benefits with site-specific values need to

be analyzed on a case-by-case basis.

17. Key Words

Turning movement restrictions, low-cost, safety

improvements, safety evaluations

18. Distribution Statement

No restrictions. This document is available to the

public through the National Techincal 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:

56

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized
ii
iii

TABLE OF CONTENTS

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

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

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

BACKGROUND ON STUDY..................................................................................................4

LITERATURE REVIEW.........................................................................................................4

CHAPTER 2. OBJECTIVE ..........................................................................................................7

CHAPTER 3. STUDY DESIGN ...................................................................................................9

ACCOUNTING FOR POTENTIAL ISSUES AND SOURCES OF BIAS..........................9

General Issues...........................................................................................................................9

Issues Specific to Cross-Sectional Models.............................................................................10

SAMPLE SIZE CONSIDERATIONS...................................................................................12

PROPENSITY SCORE MATCHING ..................................................................................13

CHAPTER 4. METHODOLOGY ..............................................................................................15

CHAPTER 5. DATA COLLECTION........................................................................................17

DATA SUMMARY .................................................................................................................20

CHAPTER 6. DEVELOPMENT OF CRASH PREDICTION MODELS..............................23

CRASH PREDICTION MODELS........................................................................................23

Total Crashes..........................................................................................................................23

Intersection-Related Crashes..................................................................................................24

Fatal and Injury, Intersection-Related Crashes ......................................................................24

Total Crashes at Downstream Intersections...........................................................................26

Total Intersection-Related Crashes at Downstream Intersections..........................................26

Fatal and Injury Intersection-Related Crashes at Downstream Intersections.........................27

CHAPTER 7. CROSS-SECTIONAL EVALUATION RESULTS..........................................29

AGGREGATE ANALYSIS....................................................................................................29

DISAGGREGATE ANALYSIS.............................................................................................33

CHAPTER 8. ECONOMIC ANALYSIS ...................................................................................35

EXAMPLE OF SINGLE INTERSECTION CONVERSION ............................................36

EXAMPLE OF MULTIPLE INTERSECTION CONVERSION ......................................36

SUMMARY .............................................................................................................................38

CHAPTER 9. SUMMARY AND CONCLUSIONS..................................................................39

APPENDIX. EXAMPLE INTERSECTIONS ...........................................................................41

ACKNOWLEDGEMENTS.........................................................................................................45

REFERENCES.............................................................................................................................47
iv

LIST OF FIGURES

Figure 1. Photo. RIRO stop-control intersection..............................................................................4

Figure 2. Chart. Relationship between total access points per mile and crash rate .........................5

Figure 3. Screenshot. Example intersection selection from Google® EarthTM..............................18

Figure 4. Screenshot. HSIS intersection inventory table ...............................................................19

Figure 5. Equation. Model for total crashes...................................................................................23

Figure 6. Equation. Model for intersection-related crashes, all severities.....................................24

Figure 7. Equation. Model for fatal and injury, intersection-related crashes.................................24

Figure 8. Equation. Model for total crashes at downstream intersections .....................................26

Figure 9. Equation. Model for total intersection-related crashes at downstream intersections .....27

Figure 10. Equation. Model for fatal and injury, intersection-related crashes at downstream 

intersections..........................................................................................................................27

Figure 11. Equation. Formula for estimating CMF from regression model ..................................29

Figure 12. Equation. Formula for estimating standard error of CMF from regression model.......30

Figure 13. Graphic. Illustration of upstream and downstream full movement intersections 

compared to upstream RIRO and downstream full movement intersections.......................31

Figure 14. Equation. Formula for estimating CMF from regression model with interaction 

term.......................................................................................................................................32

Figure 15. Equation. Formula for estimating standard error of CMF from regression model 

with interaction term.............................................................................................................32

Figure 16. Graphic. Illustration of corridor with multiple intersections converted from full 

movement to RIRO...............................................................................................................37

Figure 17. Photo. Example three-legged, stop-controlled, full movement intersection.................41

Figure 18. Photo. Example urban, three-legged, stop-controlled RIRO intersection ....................42

Figure 19. Photo. Example four-legged, signalized intersection downstream from RIRO 

parent intersection.................................................................................................................43
v

LIST OF TABLES

Table 1. Crashes per site-year from data collection sites...............................................................12

Table 2. Crashes per site-year from downstream intersections......................................................13

Table 3. Data summary for urban, three-legged, stop-controlled intersections with RIRO 

operation ...............................................................................................................................20

Table 4. Data summary for urban, three-legged, stop-controlled intersections with full 

movement .............................................................................................................................21

Table 5. Data summary for downstream intersections of locations with RIRO operation ............21

Table 6. Data summary for downstream intersections of locations with full movement...............21

Table 7. Model parameters for total crashes ..................................................................................24

Table 8. Model parameters for total intersection-related crashes ..................................................24

Table 9. Model parameters for fatal and injury, intersection-related crashes................................25

Table 10. Model parameters for total crashes at downstream intersections...................................26

Table 11. Model parameters for total intersection-related crashes at downstream 

intersections..........................................................................................................................27

Table 12. Model parameters for fatal and injury intersection-related crashes at downstream 

intersections..........................................................................................................................28

Table 13. Results for urban, three-legged, stop-controlled intersections with RIRO 

compared to full movement..................................................................................................29

Table 14. Results for urban signalized intersections downstream from stop-controlled 

intersections with RIRO compared to full movement ..........................................................33

Table 15. Results for urban stop-controlled intersections downstream from stop-controlled 

intersections with RIRO compared to full movement ..........................................................33

Table 16. Applying CMFs to a corridor scenario...........................................................................38

Table 17. Recommended CMFs for urban, three-legged, stop-controlled intersections with

RIRO compared to full movement .......................................................................................39

Table 18. Recommended CMFs for urban signalized intersections downstream from stopcontrolled intersections with RIRO compared to full movement.........................................39

Table 19. Recommended CMFs for urban stop-controlled intersections downstream from 

stop-controlled intersections with RIRO compared to full movement.................................40
vi

LIST OF ABBREVIATIONS

AADT annual average daily traffic

B/C benefit–cost

CMF crash modification factor

DCMF Development of Crash Modification Factors (program)

FHWA Federal Highway Administration 

FID feature identifier, feature ID

GIS Geographic Information System

HSIS Highway Safety Information System

KML Keyhole Markup Language

MVMT million vehicle miles traveled

NCHRP National Cooperative Highway Research Program

RIRO right-in-right-out

USDOT United States Department of Transportation
1

EXECUTIVE SUMMARY

The Federal Highway Administration 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 DCMF program’s ultimate goals are to save lives by 

identifying new safety strategies that effectively reduce crashes and promote those strategies for 

nationwide implementation by providing safety effectiveness measures and benefit–cost (B/C) 

ratios through research. State and local transportation agencies need objective measures for safety 

effectiveness and B/C ratios before investing in broad applications of safety improvement 

strategies. Forty State transportation departments provide technical feedback on safety 

improvements to the DCMF program and implement 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.

This study investigated the safety effects of converting full movement, stop-controlled 

intersections to right-in-right-out (RIRO) intersections using physical barriers. The intent of this

strategy was to reduce the frequency and severity of crashes by eliminating left turns into and out 

of the target intersections—the highest severity conflicts at an intersection. Studies have explored 

various access management techniques and the installation of median barriers at the corridor 

level. However, no quantitative information is available to quantify the safety effects of 

restricting left turns at specific intersections and the effects of shifting traffic to downstream 

intersections.

The project team obtained geometric, traffic, and crash data for intersections along urban, fourand six-lane corridors in California. These intersections included three-legged, stop-controlled

intersections with full movement (no physical turning restrictions) and RIRO operations (curbed 

or other positive median barrier) as well as the immediate downstream four-legged, stopcontrolled or signalized intersections with full movement. The project team conducted a crosssectional analysis to estimate the effects of turning movement restrictions while controlling for 

other factors. To select full movement intersections with similar characteristics to the RIRO

intersections, the project team used propensity score matching. Analyses focused on safety effects 

at target stop-controlled intersections (i.e., those with turning movement restrictions) and 

downstream intersections. The analysis controlled for changes in safety due to differences in 

traffic volume and differences between intersections with and without turning restrictions.

The aggregate results indicated reductions for all crash types analyzed (i.e., total, all intersectionrelated, and fatal and injury intersection-related) for urban, three-legged, stop-controlled 

intersections with RIRO compared to full movement. The reductions were statistically significant 

at the 95-percent confidence level for all crash types. The crash modification factors (CMFs) for

total, all intersection-related, and fatal and injury intersection-related crashes, were 0.547, 0.324, 

and 0.199, respectively. Potential for crash migration in determining net benefits needs to be 

considered. Results indicated potential increases at the downstream intersection for most of the

crash types analyzed. While many of the increases at downstream intersections were not 

statistically significant, even at the 90-percent confidence level, a rigorous analysis would include 

these potential increases in estimating the net benefit conservatively.
2

The disaggregate analysis sought to identify those conditions under which the strategy was most 

effective by including interaction terms for variables of interest, including number of lanes on the 

mainline, traffic volumes on the major and minor road, and design speed. Results indicated that 

interaction terms were not statistically significant at even an 80-percent confidence level for any 

the variables of interest. This was consistent for all crash types. 

Assuming a hypothetical scenario with a single stop-controlled intersection and a downstream 

signalized intersection, a 20-year service life, conservative costs, and benefits for fatal and injury

crashes, the B/C ratio is 9.6 to 1. With the U.S. Department of Transportation recommended 

sensitivity analysis, these values could range from 5.4 to 1 up to 13.5 to 1. While these results 

suggest the strategy can be cost-effective in reducing crashes at a hypothetical stop-controlled 

intersection, there is a need to analyze potential costs and benefits on a case-by-case basis with 

site-specific values.
3

CHAPTER 1. INTRODUCTION

BACKGROUND ON STRATEGY

Improving access management near unsignalized intersections and reducing the frequency and 

severity of intersection conflicts are two objectives to improve unsignalized intersection safety in 

the National Cooperative Highway Research Program (NCHRP) Report 500 Guide, Volume 5: A 

Guide for Addressing Unsignalized Intersection Collisions.

(1) Restricting or eliminating turning 

maneuvers is a key element in related strategies.

Turning movement restrictions are access management strategies used to improve the safety of

stop-controlled intersections and driveways. Restricted and prohibited turn movements reduce the 

number of turning conflict points at intersections, which is generally known to reduce crash 

risk.

(2) Transportation agencies commonly use signs, pavement markings, or geometrics to 

prohibit turning movements. In almost all cases, one or more left-turn movements are prohibited,

and right-turning vehicles are allowed to operate as normal. Left-turn movements cross a 

conflicting direction of traffic and present risk for crashes. Right turns at most stop-controlled 

intersections are essentially merging movements and do not present the same level of safety risks 

as left turns. 

Turning operations at most stop-controlled intersections can be categorized into one of the three 

following groups:

• Full movement.

• Left turn from mainline only.

• Right-in-right-out (RIRO).

Full movement implies no turning restrictions; most stop-controlled intersections operate with 

full movement. Left turn from mainline only prohibits left turns out of the minor road, such as a 

restricted crossing U-turn intersection. This study did not evaluate the left turn from mainline 

only strategy due to sample size limitations. RIRO eliminates left turns into and out of the minor 

road. A positive or curbed median barrier on the mainline is a common strategy for creating a

RIRO at minor road stop-controlled intersections. The median physically blocks left turns into 

and out of the intersecting street. Figure 1 presents a photograph of a stop-controlled intersection 

with turning movements restricted to RIRO. Refer to the appendix for examples of intersections 

similar to the ones observed in this study.

As with all access management techniques, agencies must strike a balance between the safety and 

operational efficiency of intersections and maintaining access to properties along and adjacent to 

the roadway. While restricting turns is expected to provide a safety improvement in most cases, 

limited information is available about the quantitative safety effects of these practices and the 

effects on downstream intersections. This study addressed the need for research into the safety 

effects of turning movement restrictions at stop-controlled intersections.
4

© VHB.

Figure 1. Photo. RIRO stop-control intersection.

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 DCMF program’s goals are

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

those strategies for nationwide implementation by providing measures of their safety 

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

agencies need objective measures for safety effectiveness and B/C ratios before investing in 

broad strategic applications of 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.

LITERATURE REVIEW

The literature review focused on the safety effects of converting full movement to RIRO

operations at intersections, which are most commonly implemented with a raised median 

preventing all left turns. Most or all evaluations to date have examined corridor and segment 

impacts of installing raised medians rather than the effects of turning restrictions at intersections 
5

and downstream intersections. The following is a summary of salient research related to specific 

strategies.

Research by Schultz, Braley, and Boschert in Correlating Access Management to Crash Rate, 

Severity, and Collision Type indicated that the presence of a raised median corresponded to a 

reduction of 1.23 crashes per million vehicle miles traveled (MVMT).

(3)

In addition, raised 

medians were negatively correlated with right-angle collisions.

Research performed by Gluck, Levinson, and Stover for NCHRP Report 420 also investigated the 

relationship between median type and crash rates.

(4) Figure 2 illustrates the relationship between 

crash rate and roadway cross-section.

Adapted from Gluck, J., Levinson, H.S., and Stover, V. (1999). NCHRP Report 420: Impacts of Access Management 

Techniques, figure 24, p. 57.

Figure 2. Chart. Relationship between total access points per mile and crash rate.

(4)

NCHRP Report 395 compared outcomes from crash prediction models developed by various 

researchers.(5) A composite finding suggested, in general, a raised median was safer than 

undivided roadways, especially on roads with above 20,000 vehicles per day.

Eisele and Frawley in Estimating the Safety and Operational Impact of Raised Medians and 

Driveway Density: Experiences from Texas and Oklahoma Case Studies investigated the 

relationship between access density and crash rate for raised median and nonraised median 

corridors separately.(6) Both relationships showed positive correlation with crashes, but the trend 

line was slightly steeper for nonraised than for raised median corridors. The researchers 

concluded that the reduced slope of the regression line for raised median corridors demonstrated
6

that relatively lower crash rates in corridors with raised medians were due to reduced conflict 

points.

Hallmark et al. indicated in the Toolbox to Assess Tradeoffs between Safety, Operations, and Air 

Quality for Intersection and Access Management Strategies: Final Report that a FHWA

evaluation involving data from seven States suggested raised medians reduced crashes at least 40

percent in urban settings.

(7)
7

CHAPTER 2. OBJECTIVE

The project team for this evaluation examined safety impacts of physically restricting turning 

movements to RIRO from full movement at stop-controlled intersections in California. The 

objective was to estimate the safety effectiveness of this strategy as measured by crash frequency. 

Target crash types included the following:

• Total: all crashes within 100 ft of intersection (all types and severities combined).

• Intersection-related: all crashes within 100 ft of intersection defined as “intersectionrelated” by the reporting officer (all types and severities combined).

• Fatal and injury: all injury crashes within 100 ft of intersection defined as “intersectionrelated” by the reporting officer (fatal injury, incapacitating injury, non-incapacitating

injury, and possible injury).

• Multivehicle: all multiple-vehicle crashes within 100 ft of intersection defined as 

“intersection-related” by the reporting officer (all types and severities combined). Because 

all intersection-related crashes within 100 ft of the intersections included multiple 

vehicles, the project team dropped this category from the remainder of the analysis, as it 

was redundant.

A further objective was to address the following ways in which effects may vary:

• By lane configuration of intersection (i.e., four mainline lanes and two cross-street lanes 

versus six mainline lanes and two cross-street lanes).

• By level of traffic volume.

• By design speed on the major route.

• By type of traffic control at downstream intersections (i.e., signalized or minor road stopcontrol).

• By the presence of turn lanes at downstream intersections.

Evaluation of overall effectiveness included consideration of installation costs and crash savings 

in terms of the B/C ratio. 

Meeting these objectives placed some special requirements on the data collection and analysis 

tasks, including the following needs:

• Select a sufficiently large sample size of sites with and without physical turning 

restrictions to detect, with statistical significance, what may be small differences in safety 

for some crash types after controlling for differences due to factors unrelated to turning 

restrictions.
8

• Identify appropriate reference sites without turning restrictions.

• Properly account for changes in safety due to differences in traffic volume and other

differences between intersections with and without physical turning restrictions.
9

CHAPTER 3. STUDY DESIGN

While the current state of the art for developing high-quality crash modification factors (CMFs)

employs an empirical Bayes before–after study design, several factors can preclude its use. One 

factor is the availability of treatment information, including installation date and location for the 

treatment of interest. For strategies such as restricting turning movements, there is often 

insufficient information to determine the exact location and timing of the treatment. Using 

FHWA’s A Guide to Developing Quality CMFs, the project team determined that a rigorous 

cross-sectional study design would serve as a suitable alternative.(8) The following study design

considerations included steps to account for potential biases and sample size considerations in 

cross-sectional analysis.

ACCOUNTING FOR POTENTIAL ISSUES AND SOURCES OF BIAS

A cross-sectional study design is an observational study used to analyze a representative sample 

at a specific point in time. The safety effect is estimated by taking the ratio of the average crash 

frequency for two groups—one with and the other without the feature of interest. In this case, the 

feature of interest is RIRO operation. For this method to work, the two groups should be similar 

in all regards except for the feature of interest. In practice, this is difficult to accomplish, and 

multivariable regression models are typically used to estimate the feature of interest’s safety 

effects while controlling for characteristics that vary among sites.

Multivariable regression models use explanatory variables such as geometric and operational 

characteristics to predict a response variable, such as frequency of crashes. While these crosssectional models provide a means to estimate the safety effects of treatments, they are susceptible 

to biases that should be accounted for during sampling and modeling. The following issues and 

biases were identified from the Recommended Protocols for Developing CMFs and are 

potentially applicable to this study.

(9) General issues with safety evaluations are listed below, 

followed by a list of potential biases specific to cross-sectional studies. The project team made an 

effort to address all applicable biases.

General Issues

The following are descriptions of general issues:

• Measure of effectiveness: Direct measures of safety effectiveness, including crash 

frequency and severity, are preferred over surrogate measures. This study employed a 

crash-based analysis to evaluate the safety impacts of turning movement restrictions at 

stop-controlled intersections.

• Exposure: Neither crash frequency nor severity alone provides adequate information to 

determine the safety effectiveness of a particular design feature. Exposure is an important 

factor in assessing crash risks. This study uses traffic volumes on the major and minor 

roads (i.e., total entering volume) of each intersection as explanatory variables.
10

• Sample size: Because crashes are rare and random events, it is necessary to include a 

sufficient number of sites and/or years with enough crashes in the study sample to develop 

a valid relationship between the treatment and safety effect. The following section, 

Sample Size Considerations, presents a full discussion of sample size for this study.

• Site selection bias: In highway safety, sites are often selected for treatment based on need. 

In other words, sites with the highest crash frequency, severity, or potential for 

improvement are addressed first. When a countermeasure evaluation uses these types of 

sites exclusively, evaluation results are only applicable to sites with similar safety issues. 

The project team selected sites for this study based on the presence of the treatment and 

their geometric characteristics rather than crash experience; however, transportation 

agencies may implement the treatment based on crash experience. The potential for 

regression-to-the-mean was a non-issue in this study because the project team employed a 

cross-sectional analysis, which included data for only the period after treatment. To select 

suitable reference sites and to mitigate potential site selection bias, the project team used 

propensity score matching, discussed later in this chapter.

• Crash data quality: There is no national standard for crash data reporting. Although many 

States adopt some or all of the Model Minimum Uniform Crash Criteria data elements, 

crash data lack uniformity across jurisdictions, and most crash data are susceptible to 

quality and timeliness issues. It is necessary to account for these types of issues in the 

study design and analysis. For example, if the reporting threshold varies among States in 

the study and crashes from those States are aggregated in modeling, then the analysis

should account for the difference in thresholds. The data used in this research is from the 

Highway Safety Information System (HSIS) database, which ensures a higher level of 

quality control and documentation in each participating State than data obtained directly 

from State agencies.

Issues Specific to Cross-Sectional Models

The following is a list of issues specific to cross-sectional models:

• Control of confounding factors: Confounding factors are those that are significant 

predictors of the response variable, and associated with the treatment in question. Turning 

movement restrictions at intersections are often present at high traffic volumes, but they 

are not a consequence of high volumes. Since traffic volume is also a significant predictor 

of crashes, it is a potential confounding factor and is accounted for as a variable in the 

model. It is difficult to control for all potential confounding factors, but these factors were 

considered and addressed to the extent possible in the study design and evaluation. The 

project team used propensity score matching, discussed later in this chapter, to select 

suitable reference sites and to mitigate potential confounding effects.

• Omitted variable bias: It is difficult to account for the potential effects of omitted variable 

bias in an observational cross-sectional study such as this. This study addressed omitted 

variable bias to the extent possible by carefully considering the roadway and traffic 

characteristics that should be included in the models. With the rich data in HSIS, the 

project team tested a wide range of variables in the models and selected suitable variables 

for final modeling. There is potential for omitted variable bias due to factors such as 
11

weather, driver population, and vehicle fleet that are not directly included as variables in 

the models.

• Selection of appropriate functional form: The project team applied generalized linear 

modeling techniques to calibrate crash prediction models. They specified a log-linear 

relationship using a negative binomial error structure, following state of the art procedures 

in modeling crash data. The negative binomial error structure is recognized as more 

appropriate for crash counts than the normal distribution used in conventional regression 

modeling. The negative binomial error structure also has advantages over the Poisson 

distribution in that it allows for overdispersion that is often present in crash data.

• Correlation among independent variables: Correlation refers to the degree of association 

among variables. A high degree of correlation among the predictor variables makes it 

difficult to reliably estimate the effects of specific predictor variables. The project team

examined the correlation matrix to determine the extent of correlation among independent 

variables and used it to prioritize variables for inclusion.

• Over-fitting of prediction models: Over-fitting is related to the concept of diminishing 

returns. At some point, it is not worth adding independent variables to the model because 

they do not significantly improve the model fit. Over-fitting also increases the opportunity 

to introduce inter-correlation between independent variables. The project team considered 

combinations of predictor variables and employed relative goodness-of-fit measures to 

penalize models with greater estimated parameters.

• Low sample mean and sample size: This study dismissed low sample mean as a potential 

issue as many sites had experienced one or more crashes during the study period. The 

study addressed sample size through preliminary sample size estimates (see Sample Size 

Considerations section) and during early stages of the study and analysis.

• Temporal and spatial correlation: Temporal correlation may arise if multiple 

observations are used for the same site. In this study, three years of data were aggregated 

into a single observation at each site. As a result, this study dismissed temporal correlation 

as a potential issue. Spatial correlation is a potential issue. To help account for spatial 

correlation, the study selected sample corridors from various regions in California to 

achieve diversity of sites with respect to weather, topography, and driver population.

• Endogenous independent variables: Endogeneity occurs when one or more independent 

variable depend on the dependent variable. For example, transportation agencies may 

install left-turn lanes due to the frequency of left-turn crashes at an intersection, and thus 

their presence depends on crash frequency. The potential concern in an observational 

cross-sectional study is that treatments can be incorrectly associated with higher crashes 

when compared with sites where the treatments are absent and which may be prone to 

lower crash frequency. The project team used propensity score matching, discussed later 

in this chapter, to select suitable reference sites and to mitigate potential endogeneity 

issues.
12

SAMPLE SIZE CONSIDERATIONS

For crash-based studies, the total number of crashes is the primary measure of sample size rather 

than sites or years. However, including a sufficient number of sites and years in the study was 

necessary to attain an adequate sample of crashes. Further, selecting sites based on features of 

interest—not crash history—was important to minimize the potential for site selection bias and 

increase the applicability of the results.

The number of locations required for multivariable regression models depended on a number of 

factors, including the following:

• Average crash frequency.

• Number of variables desired in a model.

• Level of statistical significance desired in a model.

• Amount of variation in each variable of interest across sample sites.

Determination of whether or not a sample size is adequate can only be made once preliminary 

modeling is complete. If the variables of interest are not statistically significant, then more data 

are required to detect statistically significant differences, or it is necessary to accept a lower level 

of confidence. Estimation of the required sample size for cross-sectional studies is difficult and 

requires an iterative process, although, through experience and familiarity with specific 

databases, it is possible to develop an educated starting guess.

Table 1 presents average crashes per site-year for 138 target stop-control intersections by number 

of lanes and turning movement condition (RIRO or full movement). The 138 sites represented

161 total crashes per year and were reasonably representative of the range of site characteristics 

for urban, three-leg, minor road stop-controlled intersections.

Table 1. Crashes per site-year from data collection sites.

Turning Movement 

Condition

Four-Lane 

Major

Crashes/SiteYear (Sites)

Six-Lane 

Major

Crashes/SiteYear (Sites)

Combined

Crashes/SiteYear (Sites)

RIRO 0.64 (24) 1.02 (34) 0.86 (58)

Full movement 1.08 (57) 2.16 (23) 1.39 (80)

Combined 0.95 (81) 1.48 (57) 1.09 (138)

Table 2 presents average crashes per site-year for the 109 downstream intersections by number of 

lanes and turning movement condition at the respective upstream stop-controlled intersection. 

The 109 sites experienced 463 total crashes per year. While there was no formal stratification of 

the data by site characteristics during site selection, the project team included sites with a range of 

traffic volumes and other characteristics among sites to increase the practical applicability of the 

results. This sample data were likely sufficient to develop reliable cross-sectional models based 

on experience with similar evaluations.
13

Table 2. Crashes per site-year from downstream intersections.

Turning Movement 

Condition at 

Upstream Intersection

Four-Lane 

Major

Crashes/SiteYear (Sites)

Six-Lane 

Major

Crashes/SiteYear (Sites)

Combined

Crashes/SiteYear (Sites)

RIRO 3.92 (21) 7.70 (27) 6.05 (48)

Full movement 2.17 (43) 4.39 (18) 2.83 (61)

Combined 2.74 (64) 6.38 (45) 4.24 (109)

PROPENSITY SCORE MATCHING

In experimental studies, a sample is selected from the reference population and treatment is 

applied randomly to one group while leaving another group untreated for control purposes. Using 

this approach, the treatment and control groups are similar, and the only difference is the presence 

of treatment. This helps to ensure the treatment effect does not include safety effects due to other 

differences between the two groups.

In observational studies, it is desirable to replicate the random assignment of treatment while 

accounting for the fact that sites are often selected for treatment based on safety and operational 

performance measures. Matching treatment and reference sites to have similar characteristics

reduces the potential for site selection bias and confounding factors. Selecting reference sites that 

are geometrically and operationally similar to treatment sites provides a more reliable comparison 

in cross-sectional studies, and propensity score matching is a rigorous approach to match 

treatment and reference sites.

This study employed propensity score matching to select reference sites that closely matched the 

treatment sites in terms of general site characteristics. In this study, physical turning movement 

restrictions at treatment sites related to substantial geometric and operational differences from 

nontreatment sites where all turning movements were allowed. Propensity score matching 

techniques helped account for site selection bias and other differences among sites with and 

without physical turning movement restrictions.

Propensity score matching is based on regression modeling. The project team developed a 

regression model to estimate scores (i.e., the probability of treatment or nontreatment) for all 

treatment and nontreatment sites based on site characteristics. The project team then used 

propensity scores to select reference sites most comparable with treatment sites for forming the

study sample. Detailed discussions of propensity score matching and its application in traffic 

safety research are available in papers by Rosenbaum and Rubin (1983) and Sasidharan and 

Donnell (2013).(10,11)

15

CHAPTER 4. METHODOLOGY

The evaluation used a cross-sectional study design. At its most basic level, the safety effect is 

estimated by taking the ratio of the average crash frequency for two groups, one with the 

treatment and the other without the treatment. The two groups of sites should be similar in all 

regards except for the presence of the treatment. This is difficult to accomplish in practice, and 

the project team used propensity score matching to match sites with and without treatment and 

used multivariable regression modeling to control for characteristics that varied among sites. 

The project team used multivariable regression to develop the statistical relationships between the 

dependent variables and a set of predictor variables. In this case, crash frequency was the 

dependent variable, and the team considered predictor variables, including treatment presence, 

traffic volume, and other roadway characteristics. The team estimated regression coefficients 

during the modeling process for each predictor variable. The coefficients represented expected 

change in crash frequency due to a unit change in the predictor variable with all else being equal.

The project team applied generalized linear modeling techniques to develop the crash prediction 

models and specified a log-linear relationship using a negative binomial error structure. The 

negative binomial error structure has advantages over the Poisson distribution in that it allows for 

overdispersion of the variance that is often present in crash data. One concern was the possibility 

of site-selection bias if agencies installed turning movement restrictions to address safety issues.

The project team used propensity score matching to address potential site selection bias.

After developing a propensity score-based matched dataset, the project team employed the

following protocol to develop the multivariable models:

• Step 1: Identify base models with traffic volume only. 

• Step 2: Explore other predictor variables. 

• Step 3: Select final model. 

The project team determined the appropriate form for the base models (Step 1) according to the 

procedure outlined in Hauer.(12) The team added predictor variables to the base models and 

assessed them one at a time to determine the appropriate functional form and value added. They 

used various functional forms to assess potential relationships between crash frequency and 

continuous variables and to determine whether variables were best represented as continuous or 

indicator variables. The team used the correlation matrix to consider correlations among predictor 

variables and prioritized correlated variables for inclusion in the final models. Once a variable 

was included in the model, the project team examined estimated parameters and associated 

standard errors to determine the following:

• Was the direction of effect (i.e., expected decrease or increase in crashes) in general 

agreement with expectations? General expectations were based on previous research.

• Did the magnitude of the effect seem reasonable?
16

• Were the parameters of the model estimated with statistical significance?

• Did the estimated dispersion parameter improve significantly?
17

CHAPTER 5. DATA COLLECTION 

The majority of data for this study was collected under a previous project funded by FHWA,

entitled Safety Evaluation of Access Management Policies and Techniques.

(13) The current study 

relied on Geographic Information System (GIS) files compiled under the previous effort to 

identify candidate intersections for this evaluation. The GIS files provided the location and type 

of turning restriction (i.e., full movement or RIRO) for intersections across California. 

The GIS files were enriched with additional data from the HSIS database. The HSIS roadway 

inventory provided number of lanes, lane width, shoulder width, design speed, average annual 

daily traffic (AADT), and other geometric characteristics on the mainline roads. The intersection 

inventory from HSIS supplied routes, county numbers, and mileposts on the mainline of all 

intersections. The HSIS inventory also provided AADT of the cross street. The project team

verified HSIS data using Google® EarthTM.

The project team used milepost, county, and route number to identify and link crashes from the 

HSIS crash data files to each intersection. The team included all crashes that occurred within a 

200-ft influence zone from the center of the intersection (i.e., 100 ft upstream and 100 ft 

downstream). The team used location type information to identify and separate “intersectionrelated” crashes (loc_typ = “I”) and number of vehicles involved with crash severity to develop 

multiple vehicle and fatal and injury data categories.

The data collection process followed these key steps:

• Step 1: Using the existing intersection GIS files, the attribute tables were exported 

from ArcGIS into text file format.(14) Latitude and longitude of all intersections were 

calculated using the “calculate geometry” tool in ArcGIS prior to exporting the 

attribute table. Data files were imported into MS Excel and separated by traffic control

types (i.e., stop control or signalized).

• Step 2: Exported data from the previous step were processed and encoded into 

Keyhole Markup Language (KML) for Google® EarthTM. The project team created 

separate KML files for stop control and signalized intersections.

• Step 3: KML files were imported into Google® EarthTM, and each route was reviewed 

to select appropriate intersections for this study. Candidate intersections were marked 

in HSIS intersection inventory files. Stop-controlled sites with full movement or 

RIRO turning movements were selected as candidates if they at least fit the following 

criteria:

o 200 ft from another stop-controlled intersection.

o 100 ft away from the limits of a turning lane for a signalized intersection.

o 350 ft from a signalized intersection with no turning lanes.

The team used the Ruler tool in Google® EarthTM for distance measurement.
18

The feature identifier (FID) number, a unique identifier from ArcGIS and shown in the Google®

EarthTM KML files, was populated in the HSIS intersection inventory for candidate sites as a 

reference key. The team used cross-street names to relate sites across the two datasets. Street 

names of the upstream and downstream intersections were available for additional verification. 

Cross-street name for each intersection in the HSIS data file was an essential piece of information 

for this process. Having cross-street name and verifying it in Google® EarthTM allowed the 

project team to link and merge information across data files from different sources. This was also 

why California was the only State examined in this study. The project team considered other 

States where GIS data were collected. However, either these States did not have an HSIS 

intersection file to supplement the data or the intersection files did not have data elements to link 

GIS and HSIS data (e.g., cross-street name).

Figure 3 and figure 4 illustrate the site selection process for an intersection on Route 82 in 

northern California. This example identifies a stop-controlled intersection with RIRO operation 

(FID = 8) in Google® EarthTM. This intersection is approximately 260 ft from the left-turn lanes 

at the nearby signalized intersection (FID = 9), and there are no other stop-controlled 

intersections within 200 ft of this intersection. It met the three criteria listed above, and is located 

in the HSIS intersection inventory in Figure 4. The cross street name, Rice Way, from Google®

EarthTM, is shown in Figure 3 and was located in the HSIS intersection inventory in Figure 4. The 

project team used the nearby intersection (Branham Lane) to confirm the location of interest. 

Last, the team entered the FID (FID = 8) in the HSIS intersection inventory file.

© 2015 Google® EarthTM.

Figure 3. Screenshot. Example intersection selection from Google® EarthTM.

(15)
19

Source: FHWA, data from HSIS.

Figure 4. Screenshot. HSIS intersection inventory table.

In addition to collecting data for these stop-controlled intersections, the project team collected

data from downstream intersections. The goal was to examine possible crash migration from an

RIRO location to the nearest location where vehicles can make U-turns. In all cases, the 

downstream intersection was the next immediate intersection following the parent RIRO or full 

movement intersection. The project team collected the following elements for the downstream 

turning locations:

• Type of traffic control (i.e., signalized or minor road stop control). For signalized 

intersections, it was unknown if a protected phase could facilitate a U-turn.

• Presence of a dedicated turn lane.

• Distance from stop-controlled intersection to downstream turn lane (if applicable).

• Distance from stop-controlled intersection to downstream turning location (where U-turn 

was permitted).

The project team collected data for 333 candidate stop-controlled intersections and 202 

downstream intersections. During preliminary data analysis, the team decided to retain locations 

with four or six lanes on the mainline and drop all other locations. There were too few RIRO sites

along two-lane corridors to draw meaningful conclusions (five intersections in this category). 

Further, based on literature review, prior studies indicated little if any benefit for implementing 

RIRO at intersections and driveways where left-turning traffic only crossed one lane. Locations 

with five or more than six lanes were also dropped because of small sample sizes. 

The final dataset included 138 stop-controlled intersections with a mix of RIRO and full 

movement operations. The downstream intersection dataset included 109 intersections with a mix 

of stop- and signal-control. The number of downstream intersections was smaller because some 

three-legged, stop-controlled intersections had the same downstream intersection. This happened

when two parent intersections (i.e., three-legged stop-controlled) were located on opposite sides 

and opposite approaches to a four-legged intersection. When two stop-controlled intersections 

shared a downstream intersection, the project team confirmed that the type of operation (i.e., 

RIRO or full movement) was the same for the two parent intersections.
20

DATA SUMMARY

The project team collected and aggregated three years of data for the analysis. Table 3 and table 4

present a data summary for 138 urban, three-legged, stop-controlled intersections included in the 

primary analysis. Table 3 presents a summary of 58 locations with RIRO operations using 

physical barriers, and table 4 presents a summary 80 locations with full movement. Table 5 and 

table 6 present data summaries for 109 intersections downstream from the primary study

intersections. Table 5 presents a summary of 48 intersections downstream from intersections with 

RIRO operations, and table 6 presents a summary of 61 intersections downstream from 

intersections with full movement. 

For continuous variables (e.g., mainline AADT), the tables present the number of sites as well as 

the mean, standard deviation, minimum, and maximum value for each variable. For indicator 

variables (e.g., mainline six-lane indicator), the tables present the number of sites as well as the 

mean, minimum, and maximum value for each variable. Indicator variables are either 0 or 1, 

indicating the absence or presence of the characteristic, respectively. The mean value of an 

indicator variable indicates the proportion of sites with the attribute present (indicator value of 1). 

For example, the six-lane indicator in table 3 had a mean value of 0.586. This implied that 58.6

percent of locations had six lanes on the mainline (indicator value = 1), and 41.4 percent of 

locations had four lanes (indicator value = 0). Similarly, the mean value of the signalized 

indicator in table 5 was 0.771, indicating that 77.1 percent of the sample was signalized 

intersections.

Table 3. Data summary for urban, three-legged, stop-controlled intersections with RIRO 

operation.

Variable Sites Mean Std. Dev. Min Max

Mainline AADT 58 38,724 11,997 13,433 75,000

Cross street AADT 58 519 510 51 2,600

Mainline six-lane indicator (1 if six lanes, 0 if 

four lanes) 58 0.586 — 0 1

50+ mph indicator (1 if 50+ mph, 0 otherwise) 58 0.224 — 0 1

Total crashes 58 2.586 2.555 0 9

Intersection-related crashes 58 0.638 0.968 0 4

Fatal and injury, intersection-related crashes 58 0.190 0.438 0 2

—Not applicable.
21

Table 4. Data summary for urban, three-legged, stop-controlled intersections with full 

movement.

Variable Sites Mean Std. Dev. Min Max

Mainline AADT 80 34,271 11,719 9,940 75,000

Cross street AADT 80 765 759 51 3,650

Mainline six-lane indicator (1 if six lanes, 0 if 

four lanes) 80 0.288 — 0 1

50+ mph indicator (1 if 50+ mph, 0 otherwise) 80 0.500 — 0 1

Total crashes 80 4.163 3.777 0 17

Intersection-related crashes 80 2.025 2.658 0 11

Fatal and injury, intersection-related crashes 80 1.125 1.618 0 8

—Not applicable.

Table 5. Data summary for downstream intersections of locations with RIRO operation.

Variable Sites Mean Std. Dev. Min Max

Mainline AADT 48 39,148 12,723 22,010 75,000

Cross street AADT 48 6,686 9,656 201 56,000

Signalized indicator (1 if signalized, 0 

otherwise)

48 0.771 — 0 1

Mainline six-lane indicator (1 if six lanes, 0 if 

four lanes) 48 0.563 — 0 1

Total crashes 48 18.146 24.163 0 107

Intersection-related crashes 48 6.563 9.079 0 50

Fatal and injury, intersection-related crashes 48 2.771 3.508 0 12

—Not applicable.

Table 6. Data summary for downstream intersections of locations with full movement.

Variable Sites Mean Std. Dev. Min Max

Mainline AADT 61 34,573 12,489 8,867 75,000

Cross street AADT 61 3,918 5,459 51 25,390

Signalized indicator (1 if signalized, 0 

otherwise)

61 0.557 — 0 1

Mainline six-lane indicator (1 if six lanes, 0 if 

four lanes) 61 0.295 — 0 1

Total crashes 61 8.475 7.997 0 36

Intersection-related crashes 61 3.213 3.843 0 21

Fatal and injury, intersection-related crashes 61 1.705 1.986 0 8

—Not applicable.

23

CHAPTER 6. DEVELOPMENT OF CRASH PREDICTION MODELS

This chapter presents crash prediction models. The project team used generalized linear modeling 

to estimate model coefficients assuming a negative binomial error distribution, consistent with the 

state of research in developing these models. In specifying a negative binomial error structure, the 

team estimated the dispersion parameter, k, iteratively from the model and the data. For a given 

dataset, smaller values of k indicate relatively better models.

The project team calibrated crash prediction models separately for urban, three-legged, stopcontrolled intersections and downstream four-legged, stop-controlled, and signalized

intersections. The following sections present the crash prediction models developed. Variable 

definitions included in the final crash prediction models are as follows:

• TOTAL = predicted number of total crashes (all types and severities).

• TOTAL_INT = predicted number of total intersection-related crashes.

• FI_INT = predicted number of intersection-related fatal and injury crashes.

• ML_AADT = AADT on the mainline (two-way, vehicles/day). (Traffic volume estimates 

were from HSIS, representing AADT for the roadway section as a whole.)

• XST_AADT = AADT on the cross street (two-way, vehicles/day). (Traffic volume 

estimates were from HSIS, representing AADT for the roadway section as a whole.)

• RIRO = indicator for RIRO operation (1 if RIRO, 0 otherwise).

• LANE6 = indicator for number of mainline lanes (1 if six, 0 if four).

• SPD50PLUS = indicator for design speed (1 if ≥50 mph on mainline, 0 otherwise).

CRASH PREDICTION MODELS

Total Crashes

Figure 5 presents the functional form of the crash prediction model, and table 7 presents the 

model parameters for total crashes.

Figure 5. Equation. Model for total crashes.

TOTAL = ML_AADT

β1 × XST_AADTβ2 × e

 β3×RIRO+β4×LANE6+β6 
24

Table 7. Model parameters for total crashes.

Variable Coefficient Estimated Value Standard Error

Mainline AADT β1 0.834 0.232

Cross street AADT β2 0.285 0.078

RIRO operation β3 –0.604 0.156

Mainline with six lanes β4 0.355 0.163

Intercept term β6 –9.220 2.437

Dispersion parameter (k) — 0.364 0.094

—Not applicable.

Intersection-Related Crashes

Figure 6 presents the functional form of the crash prediction model, and table 8 presents the 

model parameters for intersection-related crashes.

Figure 6. Equation. Model for intersection-related crashes, all severities.

Table 8. Model parameters for total intersection-related crashes.

Variable Coefficient Estimated Value Standard Error

Mainline AADT β1 0.850 0.329

Cross street AADT β2 0.493 0.113

RIRO operation β3 –1.127 0.252

Mainline with six lanes β4 0.458 0.231

50 mph or higher design speed β5 0.442 0.219

Intercept term β6 –11.751 3.436

Dispersion parameter (k) — 0.500 0.170

—Not applicable.

Fatal and Injury, Intersection-Related Crashes

Figure 7 presents the functional form of the crash prediction model, and table 9 presents the 

model parameters for fatal and injury, intersection-related crashes.

Figure 7. Equation. Model for fatal and injury, intersection-related crashes.

TOTAL_INT = ML_AADT

β1 × XST_AADTβ2 × e

 β3×RIRO+β4×LANE6+β5×SPD50PLUS+β6

 

FI_INT = ML_AADT

β1 × XST_AADTβ2 × e

 β3×RIRO+β5×SPD50PLUS+β6 
25

Table 9. Model parameters for fatal and injury, intersection-related crashes.

Variable Coefficient Estimated Value Standard Error

Mainline AADT β1 0.957 0.379

Cross street AADT β2 0.413 0.132

RIRO operation β3 –1.616 0.361

50 mph or higher design speed β5 0.539 0.267

Intercept term β6 –12.821 3.983

Dispersion parameter (k) — 0.407 0.220

—Not applicable.

The team also developed crash prediction models for the immediate U-turn location downstream 

from the intersection of interest. The team considered five additional predictor variables beyond 

those considered in developing crash prediction models for the upstream “parent” full movement 

and RIRO intersections. Specifically, the team considered an indicator for the operation at the 

parent intersection (full movement or RIRO), an indicator for the traffic control (stop-controlled 

or signalized), an interaction term between these two indicators, an indicator for the presence of 

turn lanes, and the distance to the parent intersection of interest. While several of these variables 

were not statistically significant at the 90-percent confidence level, the team included the terms in 

the final models for predictive purposes. Specifically, the inclusion of these variables can help 

users differentiate between scenarios. The model development process revealed that including 

these variables did not statistically affect the indicator for RIRO operations. As such, the 

inclusion of these variables does not impact the recommended CMFs. The remainder of this

section presents the models and associated model parameters. Definitions of variables included in 

the final crash prediction models are as follows:

• TOTALDS = predicted number of total crashes (all types and severities) at downstream 

intersections.

• TOTAL_INTDS = predicted number of total intersection-related crashes (coded as 

intersection-related) at downstream intersections.

• FI_INTDS = predicted number of intersection-related fatal and injury crashes (coded as 

intersection-related) at downstream intersections.

• MLAADTDS = AADT on the mainline at downstream intersections (two-way, 

vehicles/day).

• XSTAADTDS = AADT on the cross street at downstream intersections (two-way, 

vehicles/day).

• PRIRO = indicator for RIRO operation at upstream “parent” intersection of interest (1 if 

RIRO, 0 otherwise).

• SIGNAL = indicator for signal control at downstream intersection (1 if signalized 

downstream intersection, 0 otherwise).

• RIROSIG = interaction term between PRIRO and SIGNAL (= PRIRO × SIGNAL).
26

• TRNLN = indicator for the presence of turn lane(s) at downstream intersection, located on 

the same approach as intersection of interest (1 if turn lane, 0 otherwise). 

• TRNDIST = distance from the stop-controlled intersection of interest to its downstream 

intersection (immediate U-turn location).

• LANE6 = indicator for number of lanes on the mainline (1 if six, 0 if four).

• SPD50PLUS = indicator for design speed (1 if ≥50 mph on mainline, 0 otherwise).

Total Crashes at Downstream Intersections

Figure 8 presents the functional form of the crash prediction model, and table 10 presents the 

model parameters for total crashes at the downstream intersections.

Figure 8. Equation. Model for total crashes at downstream intersections.

Table 10. Model parameters for total crashes at downstream intersections.

Variable Coefficient Estimated Value Standard Error

Mainline AADT β1 0.918 0.268

Cross street AADT β2 0.254 0.078

RIRO operation at upstream 

intersection β3 0.494 0.328

Signal control β4 0.742 0.271

Interaction between RIRO and Signal β5 –0.402 0.373

Presence of turning lane(s) β6 0.107 0.265

Distance to turning point β7 –3.68E–04 2.42E–04

Mainline with six lanes β8 0.284 0.185

50 mph or higher design speed β9 0.168 0.181

Intercept term β10 –10.057 2.712

Dispersion parameter (k) — 0.503 0.087

—Not applicable.

Total Intersection-Related Crashes at Downstream Intersections

Figure 9 presents the functional form of the crash prediction model, and table 11 presents the 

model parameters for total intersection-related crashes at the downstream intersections.

TOTALDS

= MLAADTDS

β1 × XSTAADTDSβ2

× e

 β3×PRIRO +β4×SIGNAL+β5×RIROSIG +β6×TRNLN+β7×TRNDIST+β8×LANE6+β9×SPD50PLUS+β10 
27

Figure 9. Equation. Model for total intersection-related crashes at downstream 

intersections.

Table 11. Model parameters for total intersection-related crashes at downstream 

intersections.

Variable Coefficient Estimated Value Standard Error

Mainline AADT β1 0.780 0.319

Cross street AADT β2 0.413 0.093

RIRO operation at upstream 

intersection β3 0.938 0.390

Signal control β4 0.471 0.333

Interaction between RIRO and Signal β5 –0.917 0.447

Presence of turning lanes β6 0.096 0.313

Distance to turning point β7 –3.62E–04 2.75E–04

Mainline with six lanes β8 0.141 0.212

50 mph or higher design speed β9 0.245 0.217

Intercept term β10 –10.627 3.254

Dispersion parameter (k) — 0.563 0.119

—Not applicable.

Fatal and Injury Intersection-Related Crashes at Downstream Intersections

Figure 10 presents the functional form of the crash prediction model, and table 12 presents the 

model parameters for fatal and injury intersection-related crashes at the downstream intersections.

Figure 10. Equation. Model for fatal and injury, intersection-related crashes at downstream 

intersections.

TOTAL_INTDS

= MLAADTDS

β1 × XSTAADTDSβ2

× e

 β3×PRIRO+β4×SIGNAL+β5×RIROSIG +β6×TRNLN+β7×TURNDIST +β8×LANE6+β9×SPD50PLUS+β10 

TOTALDS

= MLAADTDS

β1 × XSTAADTDSβ2

× e

 β3×PRIRO +β4×SIGNAL+β5×RIROSIG +β6×TRNLN+β7×TRNDIST+β8×LANE6 +β9×SPD50PLUS+β10 
28

Table 12. Model parameters for fatal and injury intersection-related crashes at downstream 

intersections.

Variable Coefficient Estimated Value Standard Error

Mainline AADT β1 0.770 0.362

Cross street AADT β2 0.383 0.101

RIRO operation at upstream 

intersection β3 0.446 0.451

Signal control β4 0.332 0.374

Interaction between RIRO and Signal β5 –0.509 0.513

Presence of turning lane(s) β6 0.104 0.352

Distance to turning point β7 –2.42E–04 2.96E–04

Mainline with six lanes β8 –0.138 0.231

50 mph or higher design speed β9 0.056 0.236

Intercept term β10 –10.645 3.707

Dispersion parameter (k) — 0.515 0.152

—Not applicable.
29

CHAPTER 7. CROSS-SECTIONAL EVALUATION RESULTS

AGGREGATE ANALYSIS

Table 13 presents estimated CMFs and related standard errors for each target crash type at the 

stop-controlled intersections with RIRO compared to intersections with full movement. As above, 

following are the target crash types:

• Total: all crashes within 100 ft of intersection.

• Intersection-related: all crashes within 100 ft of intersection defined as “intersectionrelated.”

• Fatal and injury: all injury crashes within 100 ft of intersection defined as “intersectionrelated.”

Table 13. Results for urban, three-legged, stop-controlled intersections with RIRO 

compared to full movement.

Variable Total

IntersectionRelatedFatal & 

Injury

Observed crashes per site-year with RIRO 0.86 0.21 0.06

Observed crashes per site-year with full movement 1.39 0.68 0.38

Estimate of CMF 0.55* 0.32* 0.20*

Standard error of CMF 0.09 0.08 0.07

*Statistically significant results at the 95-percent confidence level.

The project team estimated CMFs and standard errors based on the model coefficients in table 7

to table 9. Figure 11 presents the formula for estimating CMF from a regression model where 

was the estimated coefficient from the regression model for the variable of interest, X was the 

condition without treatment, and Y was the condition with treatment. In this study, the variable of 

interest was RIRO, which is a binary variable where 1 indicates “with condition” (i.e., RIRO),

and 0 indicates “without condition” (i.e., full movement). As an example, consider the model 

estimates from table 7, where the estimated coefficient for RIRO was –0.604. The total crash 

CMF for converting full movement to RIRO was exp (–0.604 * (1 – 0)) = 0.55.

Figure 11. Equation. Formula for estimating CMF from regression model.

Figure 12 presents the formula for estimating the standard error of the CMF from a regression 

model. Again, β was the estimated coefficient from the regression model for the variable of 

interest, X was the condition without treatment, and Y was the condition with treatment. (Note: s

was the standard error of the estimated coefficient from the regression model for the variable of 

interest.) Continuing with the previous example, consider the model estimates from table 7 where 

β

CMF = exp(β ∗ Y − X )
30

the estimated coefficient for RIRO was –0.604, and the standard error of the coefficient was 

0.156. The standard error of the total crash CMF for converting full movement to RIRO was 0.09. 

Figure 12. Equation. Formula for estimating standard error of CMF from regression 

model.

The aggregate results indicated reductions for all crash types analyzed (i.e., total, all intersectionrelated, and fatal and injury intersection-related) at the stop-controlled intersections with RIRO 

compared to intersections with full movement. The reductions were statistically significant at the 

95-percent confidence level for all crash types. The CMF for total, all intersection-related, and 

fatal and injury intersection-related crashes were 0.55, 0.32, and 0.20, respectively.

Crash migration is a potential issue related to the physical restriction of turning movements at a 

given access point. This occurs when crashes at a treated site are shifted to another site. While 

RIRO operations eliminate left turns at the subject location, U-turn movements and related 

crashes have potential to increase at the next intersections upstream and downstream that allow 

U-turns. As such, at a full movement signalized intersection within a corridor, there could be an 

increase in U-turn movements from both directions along the mainline if the stop-controlled 

intersections are converted to RIRO along the corridor. To account for this in this analysis, only 

U-turns at one intersection (the downstream intersection) were paired with the RIRO intersection 

because U-turn movements at the upstream intersection were paired with another RIRO 

intersection, and the effect was counted. This avoided double counting U-turns and 

overestimating the effect.

Figure 13 illustrates the relocation of direct left turns at a parent full movement, stop-controlled 

intersection to a downstream intersection when the parent intersection was converted to RIRO. 

To estimate the change in safety performance, the combined safety performance of the full 

movement stop-controlled intersection and downstream intersection need to be compared with the 

combined safety performance of the RIRO stop-controlled intersection and downstream 

intersection. Table 13 provides CMFs for estimating the change in safety from converting full 

movement to RIRO at the parent stop-controlled intersection. The remainder of this section 

provides the CMFs needed to estimate change in safety performance at the downstream 

intersection.

SE CMF =

exp β + s ∗ Y − X − exp β − s ∗ Y − X 

2
31

© VHB.

Figure 13. Graphic. Illustration of upstream and downstream full movement intersections 

compared to upstream RIRO and downstream full movement intersections.

The project team considered the potential change in crashes at downstream locations. 

Specifically, the project team identified the nearest downstream intersection where U-turning was 

permitted and compared crashes at parent RIRO stop-controlled intersections with parent full 

movement stop-controlled intersections. The downstream intersections comprised both signalized 

and stop-controlled intersections. For downstream signalized intersections, the signal phasing was 

unknown and may include permissive, permissive-protected, and protected left-turn phasing. In 

this analysis, the team used an interaction term between the RIRO indicator for the upstream 

intersection and the signal control indicator for the downstream intersection. This revealed 

differences in crash migration effects by traffic control type (i.e., signal vs. stop control) at the

downstream U-turn location. The project team computed CMFs from the estimated model 

coefficients in table 10 to table 12 using the equation in figure 14.
32

Figure 14. Equation. Formula for estimating CMF from regression model with interaction 

term.

Figure 15 presents the equation to compute the standard error of the CMF.

(16)

Figure 15. Equation. Formula for estimating standard error of CMF from regression model

with interaction term.

Where:

β3 = model coefficient for RIRO operation at the upstream intersection of interest (table 10–

table 12).

β5 = model coefficient for the interaction term between RIRO operation at the upstream 

intersection of interest and the signal control at the downstream intersection (table 10–

table 12).

RIROSIG = interaction term (1 if RIRO upstream and signalized downstream intersection; 0 

otherwise).

VAR(β3) = variance of β3 (from variance-covariance matrix of estimated coefficients).

VAR(β5) = variance of β5 (from variance-covariance matrix of estimated coefficients).

COV(β3

,

β5) = covariance of β3 and β5 (from variance-covariance matrix of estimated 

coefficients).

Table 14 and table 15 present the estimated CMFs and related standard errors for each target 

crash type and traffic control type combination at the downstream intersections. The CMFs 

represent the change in crashes at the immediate downstream full movement intersection from 

RIRO locations compared to an immediate downstream full movement intersection from full 

movement locations. Analysts should apply the downstream CMF for each upstream intersection. 

In other words, the CMFs represent the safety effect of each intersection conversion from full 

movement to RIRO. For example, if there were three full movement intersections converted to 

RIRO immediately upstream of a signalized intersection and the CMF for total crashes at the 

downstream signal was 1.10 for a single conversion, then the combined CMF for total crashes at 

the downstream signalized intersection was 1.10 * 1.10 * 1.10 = 1.33.

Table 14 presents the results for downstream intersections with signal control. Reductions were 

not statistically significant even at the 90-percent confidence level; however, there was potential 

for increased total and intersection-related crashes at downstream signalized intersections. CMFs 

for total, all intersection-related, and fatal and injury intersection-related crashes were 1.10, 1.02, 

and 0.94, respectively. A rigorous analysis would include the potential increases, even though 

these were not statistically significant, in estimating the net benefit conservatively. 

Table 15 presents the results for downstream intersections with stop-control. Increases were 

statistically significant at the 90-percent confidence level for two of the three CMFs, one of 

which was also statistically significant at the 95-percent confidence level. All three CMFs 

indicated potential for increased total, intersection-related, and fatal and injury intersectionrelated crashes at downstream stop-controlled intersections. CMFs for total, all intersection-

CMF = e

 β3+β5×RIROSIG 

SE CMF = VAR β3

 + RIROSIG2 × VAR β5 + 2 × RIROSIG × COV β3

, β5

 
33

related, and fatal and injury intersection-related crashes were 1.64, 2.55, and 1.56, respectively. A

rigorous analysis would include the potential increases, even those that were not statistically 

significant, in estimating the net benefit conservatively.

Comparing CMFs, results indicated small potential changes for all crash types at downstream 

signalized intersections relative to downstream stop-controlled intersections. Further, CMFs for 

downstream signalized intersections were not statistically significant at the 90-percent confidence 

level, while two of the three CMFs for downstream stop-controlled intersections were statistically 

significant at the 90-percent confidence level.

Table 14. Results for urban signalized intersections downstream from stop-controlled

intersections with RIRO compared to full movement.

Variable Total

IntersectionRelatedFatal and

Injury

Estimate of CMF (parent RIRO = 1 and 

downstream SIGNAL = 1)

1.10 1.02 0.94

Standard error of CMF (parent RIRO = 1 and 

downstream SIGNAL = 1)

0.20 0.24 0.26

Table 15. Results for urban stop-controlled intersections downstream from stop-controlled 

intersections with RIRO compared to full movement.

Variable Total

IntersectionRelatedFatal and

Injury

Estimate of CMF (parent RIRO = 1 and 

downstream SIGNAL = 0)

1.64* 2.55** 1.56

Standard error of CMF (parent RIRO = 1 and 

downstream SIGNAL = 0)

0.33 0.39 0.45

*Statistically significant result at the 90-percent confidence level.

**Statistically significant result at the 95-percent confidence level.

DISAGGREGATE ANALYSIS

The disaggregate analysis sought to identify those conditions under which the strategy is most 

effective. The project team considered several variables in the disaggregate analysis, including 

major and minor road traffic volume, number of mainline lanes, and design speed. The 

multivariable regression models included interaction terms to investigate the potential differential 
34

effects of RIRO with respect to the interacted variable. For example, the interaction term for 

major road traffic volume and RIRO was the product of the two variables. A statistically 

significant interaction term indicated an apparent differential effect of RIRO across varied traffic 

volumes.

Results indicated that interaction terms were not statistically significant at even an 80-percent 

confidence level for any of these interactions between RIRO and major road traffic volume, 

minor road traffic volume, design speed, and number of lanes on the mainline. This was 

consistent for all crash types.

Based on the disaggregate results, it did not appear that RIRO operations have differing effects 

for different levels of traffic on both mainline and cross-street, design speed, or number of lanes

on the mainline.
35

CHAPTER 8. ECONOMIC ANALYSIS

The project team conducted an economic analysis of a hypothetical scenario to estimate the costeffectiveness of converting full movement, stop-sontrolled intersections to RIRO operations 

using physical barriers. The team estimated the treatment cost based on the construction and 

maintenance costs associated with physical barriers. The team determined net benefits by 

considering the change in fatal and injury crashes at the hypothetical stop-controlled intersection 

as well as the next downstream median opening where drivers may make a U-turn. While this 

economic analysis focused on potential safety benefits in relation to installation and maintenance 

costs, other factors to consider included impacts to traffic operations (e.g., travel time and delay) 

and economic impacts to adjacent businesses.

For this analysis, the project team used sites with physical median barriers to create RIRO 

operations. Other agencies might have used other means to implement RIRO operations, such as 

cable barrier, rigid barrier, or with signs only. For estimating treatment costs, assumptions 

included an average median width of 4 ft at an average cost of $6 per square foot.(17)(Note: the 

median may be 6 ft wide for a portion of the length between full movement intersections, with

narrower sections at the ends to facilitate turning lanes.) Given these assumptions, the 

implementation cost was approximately $24 per linear foot (or $126,720 per mile). For cost 

estimation purposes, the project team assumed a distance of 1,210 ft, which represented the 

average distance between the centers of signalized intersections evaluated in this study, minus

100 ft to account for the intersection area. Given these assumptions, the average cost per 

installation between signalized intersections was approximately $26,500. Analysts should adjust 

this cost accordingly, based on the scenario of interest and typical costs in their area as necessary.

The project team assumed the useful service life for safety benefits was 20 yr. Based on Michigan 

and Ohio data, the annual maintenance costs per lane-mile for area mowing, curb sweeping, and 

curb and gutter repairs were less than $60 annually. Given the relatively low cost for these 

services, the team assumed these costs were negligible compared to the installation costs over the 

service life.

(18)

FHWA Office of Safety Research and Development suggested using the Office of Management 

and Budget Circular A-4 as a resource for the real discount rate of seven percent to calculate the 

present value B/C of the treatment over the service life.

(19) With this information, the project team

computed the capital recovery factor as 10.59.

For the benefit calculations, the project team used the most recent FHWA mean comprehensive 

crash costs disaggregated by crash severity and location type as a base.

(20) FHWA developed 

these costs based on 2001 crash costs, and the unit cost (in 2001 U.S. dollars) for a fatal and 

injury crash was $158,177. At the time of analysis, this was updated to 2015 U.S. dollars by 

applying the ratio of the U.S. Department of Transportation (USDOT) 2015 value of a statistical 

life of $9.4 million to the 2001 value of $3.8 million.(20,21) Applying this ratio of 2.47 to the unit 

cost resulted in an aggregate 2015 unit cost of $391,280 for a fatal and injury crash. 

To estimate the safety benefits of implementing RIRO operations, the project team analyzed two 

hypothetical sections: one with a single stop-controlled intersection leading to a signalized 

intersection and another more complex corridor example with multiple intersections. The team 
36

calculated the net change in crashes by adding the expected change in crashes at RIRO 

intersections to the expected change in crashes at the downstream intersections. In some cases,

there was an expected increase in crashes at downstream intersections from conversion of 

upstream full movement stop-controlled intersections to RIRO intersections.

This section illustrates the proper application of applicable CMFs with hypothetical examples. 

Analysts should conduct a similar economic analysis with site-specific conditions and data to 

estimate the safety performance and B/C ratio for their specific scenario of interest.

EXAMPLE OF SINGLE INTERSECTION CONVERSION

The first example is a simple case of converting a single, full movement, three-legged, stopcontrolled intersection to RIRO where the downstream intersection is signalized. Figure 13

illustrates this scenario. For the purpose of this analysis, the three-legged, stop-controlled 

intersection had an average crash frequency of 3.0 fatal and injury crashes per year before 

treatment, and the downstream signalized intersection had an average crash frequency of 5.0 fatal 

and injury crashes per year before treatment.

From table 13, the CMF for converting full movement stop-controlled intersections to RIRO was 

0.20 for fatal and injury crashes. From table 14, the CMF for the effect at downstream signalized 

intersections was 0.94 for fatal and injury crashes, which was not statistically significant at 90-

percent confidence level. While the CMF for the downstream intersection is included here for 

completeness, a conservative analysis could ignore the effects at the downstream signal because 

the CMF was less than 1.0, indicating a reduction in fatal and injury crashes.

Applying these CMFs to the average annual crashes before treatment yielded a reduction of 2.4

fatal and injury crashes per year (i.e., 3.0 * (1 – 0.20)) at the parent stop-controlled intersection

and a reduction 0.3 crashes per year (i.e., 5.0 * (1 – 0.94)) at the downstream signalized 

intersection. The net crash reduction was approximately 2.7 fatal and injury crashes per year (i.e., 

2.4 + 0.3). A conservative estimate of the crash reduction is 2.4 fatal and injury crashes per year, 

ignoring the potential reduction in crashes at the downstream signalized intersection.

The project team calculated annual economic benefits by multiplying the conservative crash 

reduction per year by the average cost of a fatal and injury crash. The team calculated the B/C 

ratio of 9.6 to 1 as the ratio of the present value of benefits to the present value of all costs. 

USDOT recommended a sensitivity analysis be conducted assuming values of a statistical life of 

0.55 and 1.38 times the recommended 2015 value.

(21) These factors can be applied directly to the 

estimated B/C ratios to get a range of from 5.4 to 1 to 13.5 to 1. Results of this hypothetical 

example suggested the RIRO strategy, with conservative assumptions in cost, service life, and 

value of a statistical life, can be cost effective for reducing fatal and injury crashes at similar stopcontrolled intersections; however, there is a need to consider potential costs and benefits with 

site-specific values on a case-by-case basis.

EXAMPLE OF MULTIPLE INTERSECTION CONVERSION

The project team examined a more complex example with multiple conversions from full 

movement to RIRO along a corridor. Figure 16 illustrates this example, which proposed a section 

of a corridor between two four-legged intersections, one signalized and one stop-controlled. The 
37

side of the road leading to the four-legged signal has two three-legged, stop-controlled 

intersections. There is one three-legged, stop-controlled intersection on the other side of the road 

leading to the full movement, four-legged, stop-controlled intersection. The analyst would like to 

estimate change in safety performance by installing a median, effectively converting the threelegged stop-controlled intersections to RIRO. 

© VHB.

Figure 16. Graphic. Illustration of corridor with multiple intersections converted from full 

movement to RIRO.

From table 13, the CMF for converting full movement stop-controlled intersections to RIRO is 

0.55 for total crashes. From table 14, the CMF for the effect at downstream signalized 

intersections was 1.10 for total crashes. From table 15, the CMF for the effect at downstream 

stop-controlled intersections was 1.64 for total crashes. To estimate the safety effect at a 

downstream intersection, an analyst must apply the applicable CMF once for each upstream 

intersection. In this example, there are two three-legged, stop-controlled intersections upstream of 

the signalized intersection, and the CMF for the effect at the signalized intersection is 1.10. The 

CMF is applied once for each upstream intersection, resulting in a combined CMF of 1.21 (i.e., 

1.10 * 1.10). 

Table 16 shows the average total crash frequency per year prior to treatment, applicable CMFs, 

estimated crashes per year after treatment (i.e., CMF * crashes per year before), and estimated 

change in crashes per year (i.e., estimated crashes per year after treatment minus average crashes 

per year before treatment) at each intersection.
38

Table 16. Applying CMFs to a corridor scenario.

Intersection

Average 

Crashes 

per Year 

Before 

Treatment CMF

Estimated 

Crashes 

per Year 

After 

Treatment

Estimated 

Change in 

Crashes 

per Year

STOP 1—upstream from four-legged STOP 1.0 0.55 0.55 –0.45

STOP 2—upstream from four-legged signal 2.0 0.55 1.10 –0.90

STOP 3—upstream from four-legged signal 3.0 0.55 1.65 –1.35

Downstream four-legged STOP 4.0 1.64 6.56 +2.56

Downstream four-legged signal 8.0 1.21* 9.68 +1.68

Total 18.0 N/A 19.54 +1.54

*CMF multiplied twice (i.e., 1.10 * 1.10 = 1.21) to reflect two upstream intersections.

As shown Table 16, the net effect of installing a median to implement RIRO in this corridor 

section was an increase of 1.54 total crashes per year across all sites. This treatment is not 

justified due to the estimated increase in crashes, and therefore there is no need to conduct further 

analysis to estimate the B/C ratio.

SUMMARY

The hypothetical scenarios presented in this section illustrate the range of potential results from 

converting full movement, three-legged, stop-controlled intersections to RIRO. In some cases, it 

was cost-beneficial to convert full movement to RIRO intersections. In other cases, the increase 

in crashes at downstream locations outweighed the benefits at the parent intersection converted 

from full movement to RIRO. Analysts should conduct an economic analysis with site-specific 

conditions and data to estimate the safety performance and B/C ratio for the scenario of interest. 

In general, the B/C ratio for this treatment depends primarily on the magnitude of crashes at the 

parent intersection(s) and the downstream intersection(s). Specifically, conversion from full 

movement to RIRO will generally result in safety benefits when there is a demonstrated safety 

issue at the parent intersections and relatively low crash history at the downstream full movement 

intersections.
39

CHAPTER 9. SUMMARY AND CONCLUSIONS

The objective of this study was to undertake a rigorous cross-sectional evaluation of the safety 

effectiveness, as measured by crash frequency, of physical turning movement restrictions at 

urban, three-legged, stop-controlled intersections. The study compared RIRO to full movement 

access using data from California to examine the effects on total, intersection-related, and fatal 

and injury intersection-related crashes. Based on the aggregate results, table 17 presents 

recommended CMFs for various crash types for urban, three-legged, stop-controlled intersections 

with RIRO compared to full movement. Aggregate results indicated reductions for all crash types

analyzed, and all reductions were statistically significant at the 95-percent confidence level.

Table 17. Recommended CMFs for urban, three-legged, stop-controlled intersections with 

RIRO compared to full movement.

Variable Total All Intersection-Related

Fatal and

Injury

Estimate of CMF 0.55* 0.32* 0.20*

Standard error of CMF 0.09 0.08 0.07

*Statistically significant results at the 95-percent confidence.

While results indicated crash reductions at stop-controlled intersections with RIRO compared to 

full movement, there is a need to consider the potential for crash migration in determining the net 

benefits. Table 18 and table 19 present recommended CMFs for various crash types for signalized 

and stop-controlled intersections, respectively, downstream from urban, three-legged, stopcontrolled intersections with RIRO compared to full movement. CMFs in table 18 and table 19

apply to the immediate full movement downstream intersection, and analysts should apply the 

downstream CMF once for each upstream intersection converted from full movement to RIRO. 

For example, if there are three full movement intersections converted to RIRO immediately 

upstream of a signalized intersection, and the CMF for total crashes at the downstream signal is 

1.10 for a single conversion, then the combined CMF for total crashes at the downstream 

signalized intersection is 1.10 * 1.10 * 1.10 = 1.33.

Table 18. Recommended CMFs for urban signalized intersections downstream from stopcontrolled intersections with RIRO compared to full movement.

Variable Total

All IntersectionRelatedFatal and

Injury

Estimate of CMF (signalized) 1.10 1.02 0.94

Standard error of CMF (signalized) 0.20 0.24 0.26

Note: apply CMFs once for each upstream intersection converted from full movement to RIRO.
40

Table 19. Recommended CMFs for urban stop-controlled intersections downstream from 

stop-controlled intersections with RIRO compared to full movement.

Variable Total

IntersectionRelatedFatal and

Injury

Estimate of CMF (stop-controlled) 1.64* 2.55** 1.56

Standard error of CMF (stop-controlled) 0.33 0.39 0.45

Note: apply CMFs once for each upstream intersection converted from full movement to RIRO.

*Statistically significant result at 90-percent confidence level.

**Statistically significant result at 95-percent confidence level.

Differences in crash migration effects depend on the downstream traffic control type. CMFs for 

downstream locations with signal control were close to 1.0, and estimates were not statistically 

significant at 90-percent confidence level. As such, there was relatively low chance of increased 

crashes for downstream signalized intersections. Conversely, results indicated likely increases at 

downstream stop-control intersections for all crash types analyzed. Increases were statistically 

significant at 90- and 95-percent confidence levels for total and total intersection-related crashes, 

respectively. 

The disaggregate analysis sought to identify those conditions under which the strategy is most 

effective. Variables of interest included number of lanes on the mainline and cross street, traffic 

volumes, and design speed. For major road traffic volume, minor road traffic volume, and design 

speed, the disaggregate analysis indicated no statistically significant differences in effects for 

various levels of these variables.

The B/C ratio for converting a hypothetical stop-controlled intersection from full movement to 

RIRO, estimated with conservative cost and service life assumptions, and considering the change 

in fatal and injury crashes with the potential for crash migration at a downstream signalized

intersection, was 9.6 to 1. With the USDOT recommended sensitivity analysis, these values could 

range from 5.4 to 1 to 13.5 to 1. The economic analysis is based on a single hypothetical stopcontrolled intersection and a downstream signalized intersection. RIRO operation improved B/C 

when the target stop-controlled intersections had relatively high safety risk compared to 

downstream intersections, particularly downstream stop-controlled intersections. While these 

results suggest the strategy can be cost-effective in reducing crashes at stop-controlled 

intersections, there is a need to analyze potential costs and benefits on a case-by-case basis with 

site-specific values.

This study estimated the safety effectiveness of physical turning movement restrictions at urban, 

three-legged, stop-controlled intersections. The study compared RIRO to full movement access 

using data from California to examine the effects for total, intersection-related, and fatal and 

injury intersection-related crashes. Future research needs include the opportunity to evaluate 

similar data from other States, examine the safety effects related to other crash types such as 

pedestrian crashes, and expand the analysis to include other facility types such as two-lane roads.
41

APPENDIX. EXAMPLE INTERSECTIONS

This appendix presents images of three types of intersections considered in this study. Figure 17

displays an example of a three-legged, stop-controlled intersection with full turning movements 

(i.e., no restrictions).

© VHB.

Figure 17. Photo. Example three-legged, stop-controlled, full movement intersection.

Figure 18 displays an example of an urban, three-legged, stop-controlled intersection with RIRO 

operations.
42

© VHB.

Figure 18. Photo. Example urban, three-legged, stop-controlled RIRO intersection.

Figure 19 displays an example of an urban, four-legged, signalized intersection downstream from 

a RIRO parent intersection.
43

© VHB.

Figure 19. Photo. Example four-legged, signalized intersection downstream from RIRO 

parent intersection.

45

ACKNOWLEDGEMENTS

This report was prepared for the FHWA Office of Safety Research and Development under 

Contract DTFH61-13-D-00001. The FHWA Contracting Officer’s program and Task Manager

for this project was Roya Amjadi.

47

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