Safety Effectiveness of

Intersection Left- and Right-Turn

Lanes

July 2002
Technical Report Documentation Page

1. Report No.

FHW A-RD-02-089

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

4. Title and Subtitle

Safety Effectiveness of Intersection Left- and Right-Turn Lanes

5. Report Date

July 2002

8. Performing Organization Report No.

104584.1.006 7. Author(s)

D.W . Harw ood, K.M . Ba uer, I.B. Potts, D .J. Torbic, K.R. Richa rd, 

E.R. Kohlman R abbani, E. Hauer, and L. Elefteriadou

9. Performing Organization Name and Address

M idw est Research Institute

425 Volke r Bo ulevard

Kansas City, MO 64110-2299

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH 61-96-C-00055

12. Sponsoring Agency Name and Address

Federal Highway Administration

Office of Safety Research and Development

6300 G eorgetown Pike

McLean, Virginia 22101-2296

13. Type of Report and Period Covered

Final Report

October 1996-July 2002

14. Sponsoring Agency Code

15. Supplementary Notes

CO TR: Michael S. Griffith, HRDS-02

16. Abstract

This report presents the results of research that performed a well-designed before-after evaluation of the safety effects

of providing left- and right-turn lanes for at-grade intersections. Geometric design, traffic control, traffic volume, and

traffic accident data were gathered for a total of 280 improved intersections, as well as 300 similar intersections that

were not improved during the study period. The types of improvement projects evaluated included installation of added

left-turn lanes, added right-turn lanes, and extension of the length of existing left- or right-turn lanes. An observational

before-after evaluation of these projects was performed using several alternative evaluation approaches. The three

contrasting approaches to before-after evaluation used were the yoked comparison or matched-pair approach, the

comparison group approach, and the Empirical Bayes approach. The research not only evaluated the safety

effectiveness of left- and right-turn lane improvements, but also compared the performance of these three alternative

approaches in making such evaluations. The research developed quantitative safety effectiveness measures for

installation design improvements involving added left-turn lanes and added right-turn lanes. The research concluded

that the Empirical Bayes method provided the most accurate and reliable results. Further use of this method is

recommended.

17. Key Words

Intersection safety Before-after evaluation

Left-turn lanes Empirical Bayes

Right-turn lanes Comparison group

Safety effectiveness

18. Distribution Statement

No restrictions. This document is available to the public

through the National Technical Information Service,

Springfield, Virginia 22161

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages 22. Price

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AREA AREA 

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fl oz fluid ounces 29.57 milliliters mL mL milliliters 0.034 fluid ounces fl oz gal gallons 3.785 liters L L liters 0.264 gallons gal ft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.71 cubic feet ft3 yd3 cubic yards 0.765 cubic meters m3 m3 cubic meters 1.307 cubic yards yd3 NOTE: volumes greater than 1000 shall be shown in m3

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TEMPERATURE (exact degrees) TEMPERATURE (exact degrees) 

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fc foot-candles 10.76 lux lx lx lux 0.0929 foot-candles fc fl foot-Lamberts 3.426 candela/m2 cd/m2 cd/m2 candela/m2 0.2919 foot-Lamberts fl 

FORCE and PRESSURE or STRESS FORCE and PRESSURE or STRESS 

lb pounds 4.45 Newtons N N Newtons 0.225 pounds lb lb/in2 (psi) pounds per square inch 6.89 kiloPascals kPa kPa kiloPascals 0.145 pounds per square inch lb/in2 (psi) k/in2 (ksi) kips per square inch 6.89 megaPascals MPa MPa megaPascals 0.145 kips per square inch k/in2 (ksi) 

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lb/ft3 (pcf) pounds per cubic foot 16.02 kilograms per cubic meter kg/m3 kg/m3 pounds per cubic foot 0.062 kilograms per cubic meter lb/ft3 (pcf) *SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. (Revised September 1993) 

ii 
iii

Table of Contents

List of Figures ........................................................... v

List of Tables ........................................................... vii

1. INTRODUCTION ...................................................... 1

2. LITERATURE REVIEW ON SAFETY EFFECTS OF INTERSECTION DESIGN

ELEMENTS ......................................................... 3

Overview ............................................................ 3

Intersection Geometric Design Features .................................... 5

Traffic Control and Operational Features .................................. 16

Traffic Characteristics ................................................. 20

Summary ........................................................... 20

3. SELECTION OF EVALUATION SITES .................................. 23

Evaluation Priorities for Intersection Improvement Types ..................... 23

Identification of Candidate Intersections .................................. 24

Selection of Improved or Treatment Sites ................................. 25

Selection of Comparison Sites .......................................... 31

Number and Characteristics of Matching Improved and Comparison Sites ........ 34

Selection of Reference Sites ............................................ 34

4. DATA COLLECTION ................................................. 41

Geometric Design and Traffic Control Data ................................ 41

Traffic Volume Data .................................................. 43

Traffic Accident Data ................................................. 46

5. EVALUATION PLAN ................................................. 55

Target Accident Types and Locations .................................... 55

Accident Severity Levels .............................................. 63

Evaluation Approaches ................................................ 64

Before-After Evaluation with Yoked Comparisons .......................... 69

Before-After Evaluation with a Comparison Group .......................... 78

Before-After Evaluation with the Empirical Bayes Approach .................. 90

Execution of the Evaluation Plan ........................................ 97

6. EVALUATION RESULTS ............................................ 101

Evaluation Results for Specific Safety Measures ........................... 101

Evaluation Results for Specific Project Types ............................. 112

Comparison of Alternative Evaluation Approaches ......................... 128

Projects Involving Added Left-Turn Lanes ............................... 131

Projects Involving Added Right-Turn Lanes .............................. 135

Projects Involving Added Left- and Right-Turn Lanes ...................... 137
iv

Projects Involving Extension of the Length of Existing Turn Lanes ............ 139

Supplementary Analysis Results ........................................ 139

Recommended Accident Modification Factors ............................ 140

Economic Evaluations ............................................... 142

7. CONCLUSIONS AND RECOMMENDATIONS ........................... 153

Conclusions ........................................................ 153

Recommendations ................................................... 154

8. REFERENCES ...................................................... 157

Appendices

Appendix A—Summary of Intersection Safety Studies

Appendix B—Negative Binomial Regression Models

Appendix C—Detailed Evaluation Results

Appendix D—Field Data Collection for Geometric Design and Traffic Control Features
v

List of Figures

Figure 1. Target Area for Evaluation of an Intersection with a Right-Turn Lane 

Added on One Approach ....................................... 57

Figure 2. Target Area for Evaluation of an Intersection with Right-Turn Lanes 

Added on Two Approaches ..................................... 58

Figure 3. Target Area for Evaluation of an Intersection with a Left-Turn Lane 

Added on One Approach ....................................... 60

Figure 4. Plot of Observed vs. Expected Accident Frequencies ................. 73

Figure 5. Typical Regression Relationship for Predicting Intersection

Accident Frequency as a Function of Entering Traffic Volume .......... 81

Figure 6. Comparison of Accident Experience for Treatment and Comparison 

Groups for Rural Unsignalized Intersections at Which Left-Turn Lanes 

Were Added ................................................. 87

Figure 7. Use of Regression Relationship in the EB Approach .................. 93
vi
vii

List of Tables

Table 1. Intersection Features Addressed in the Literature Review ............... 4

Table 2. Accident Modification Factors for Installation of Left-Turn Lanes on the

Major-Road Approaches to Intersection on Two-Lane Rural Highways .... 7

Table 3. Summary of Research Results Concerning the Safety Effectiveness of

Installing Left-Turn Lanes ....................................... 8

Table 4. Accident Modification Factors for Installation of Right-Turn Lanes on the

Major-Road Approaches to Intersection on Two-Lane Rural Highways .... 9

Table 5. Summary of Research Results Concerning the Safety Effectiveness of

Installing Right-Turn Lanes ..................................... 10

Table 6. Number of Improved Intersection Sites by Area Type and State ......... 28

Table 7. Number of Improved Sites at Rural Intersections .................... 29

Table 8. Number of Improved Sites at Urban Intersections .................... 30

Table 9. Number of Improved Intersections and Number of Turn-Lanes Added or

Extended in Intersection Improvement Projects ...................... 32

Table 10. Year Completed for Projects at Improved Intersections ............... 33

Table 11. Number of Matched Pairs of Improved and Comparison Sites by Area 

Type and State ............................................... 35

Table 12. Number of Matched Pairs of Improved and Comparison Sites at Rural

Intersections ................................................. 36

Table 13. Number of Matched Pairs of Improved and Comparison Sites at Urban

Intersections ................................................. 37

Table 14. Number of Improved Intersections and Number of Turn-Lanes Added in

Projects at Improved Sites with Matched Comparison Sites in Candidate

Intersection Improvement Projects ................................ 38

Table 15. Completion Date for Intersection Improvement Projects With Matched

Comparison Sites ............................................. 39

Table 16. Number of Comparison and Reference Sites by Area Type, Traffic 

Control Type, and State ........................................ 40

Table 17. ADT Volumes for All Improved and Comparison/Reference Sites ....... 45

Table 18. ADT Volumes for Matched Improved and Comparison Sites ........... 47

Table 19. Summary of Accident Database .................................. 48

Table 20. Safety Performance of Matched Improved and Comparison Sites at Rural

Intersections ................................................. 50

Table 21. Safety Performance of Matched Improved and Comparison Sites at Urban

Intersections ................................................. 52

Table 22. Accident Data Layout for a Before-After Evaluation with Yoked

Comparisons ................................................. 71

Table 23. Calculations of P

2

 Treatment, P

2

 Homogeneity, and P

2

 Total for 

Before-After Evaluation with Yoked Comparisons ................... 75

Table 24. Accident Data Layout for Before-After Evaluation with 

Comparison Group ............................................ 82

Table 25. Observed and Expected Accident Counts .......................... 82
viii

Table 26. Comparison of Accident Frequency Time Series for Treatment 

and Control Groups in the Time Period Before Improvement of the 

Treatment Sites ............................................... 88

Table 27. Accident Data Layout for Before-After Evaluation

with the Empirical Bayes Approach ............................... 91

Table 28. Evaluation Results for Total Intersection Accidents at Four-Leg 

Intersections ................................................ 102

Table 29. Evaluation Results for Fatal and Injury Intersection Accidents at 

Four-Leg Intersections ........................................ 103

Table 30. Evaluation Results for Project-Related Intersection Accidents at 

Four-Leg Intersections ........................................ 104

Table 31. Evaluation Results for Project-Related Fatal and Injury Accidents at 

Four-Leg Intersections ........................................ 105

Table 32. Evaluation Results for Total Accidents on Individual Intersection 

Approaches at Four-Leg Intersections ............................ 106

Table 33. Evaluation Results for Fatal and Injury Accidents on Individual

Intersection Approaches at Four-Leg Intersections .................. 107

Table 34. Evaluation Results for Project-Related Accidents on Individual

Intersection Approaches at Four-Leg Intersections .................. 108

Table 35. Evaluation Results for Total Intersection Accidents at 

Three-Leg Intersections ....................................... 109

Table 36. Evaluation Results for Fatal and Injury Intersection Accidents at 

Three-Leg Intersections ....................................... 110

Table 37. Evaluation Results for Project-Related Intersection Accidents at 

Three-Leg Intersections ....................................... 111

Table 38. Evaluation Results for Total Accidents on Individual Intersection 

Approaches at Three-Leg Intersections ........................... 113

Table 39. Evaluation Results for Fatal and Injury Accidents on Individual

Intersection Approaches at Three-Leg Intersections ................. 114

Table 40. Evaluation Results for Project-Related Accidents on Individual

Intersection Approaches at Three-Leg Intersections ................. 115

Table 41. Evaluation Results for Projects Involving Added Left-Turn Lanes 

at Four-Leg Intersections ...................................... 116

Table 42. Evaluation Results for Projects Involving Added Right-Turn Lanes at 

Four-Leg Intersections ........................................ 118

Table 43. Evaluation Results for Projects Involving Added Left- and 

Right-Turn Lanes at Four-Leg Intersections ....................... 120

Table 44. Evaluation Results for Projects Involving Extension of the Length 

of Existing Turn Lanes at Four-Leg Intersections ................... 122

Table 45. Evaluation Results for Projects Involving Added Left-Turn Lanes at 

Three-Leg Intersections ....................................... 124

Table 46. Evaluation Results for Projects Involving Added Right-Turn Lanes 

at Three-Leg Intersections ..................................... 126

Table 47. Comparison of Evaluation Approaches ........................... 130
ix

Table 48. Final Evaluation Results Involving Added Left-Turn Lanes for 

Four-Leg Intersections ........................................ 132

Table 49. Final Evaluation Results Involving Added Left-Turn Lanes for 

Three-Leg Intersections ....................................... 133

Table 50. Final Evaluation Results for Projects Involving Added 

Right-Turn Lanes for Four-Leg Intersections ....................... 136

Table 51. Final Evaluation Results for Projects Involving Added 

Right-Turn Lanes for Three-Leg Intersections ...................... 138

Table 52. Final Evaluation Results for Projects Involving Added Left- and 

Right-Turn Lanes for Four-Leg Intersections ....................... 138

Table 53. Comparison of Safety Effectiveness of Added Left-Turn Lanes 

With Curbed and Painted Channelization ......................... 140

Table 54. Comparison of Safety Effectiveness of Added Left-Turn Lanes With

Protected and Protected/Permissive Signal Phasing ................. 140

Table 55. Recommended Accident Modification Factors for Installation of 

Left-Turn Lanes on the Major-Road Approaches to Rural Intersections . . 141

Table 56. Recommended Accident Modification Factors for Installation of 

Left-Turn Lanes on the Major-Road Approaches to 

Urban Intersections ........................................... 141

Table 57. Recommended Accident Modification Factors for Installation of 

Right-Turn Lanes on the Major-Road Approaches to Rural and Urban

Intersections ................................................ 142

Table 58. Economic Evaluation for Rural Three-Leg Unsignalized Intersections 

with Minor-Road ADT Equal to 10 Percent of Major-Road ADT ...... 144

Table 59. Economic Evaluation for Rural Three-Leg Unsignalized Intersections 

with Minor-Road ADT Equal to 50 Percent of Major-Road ADT ...... 145

Table 60. Economic Evaluation for Rural Four-Leg Unsignalized Intersections 

With Minor-Road ADT Equal to 10 Percent of Major-Road ADT ...... 146

Table 61. Economic Evaluation for Rural Four-Leg Unsignalized Intersections 

With Minor-Road ADT Equal to 50 Percent of Major-Road ADT ...... 147

Table 62. Economic Evaluation for Urban Four-Leg Unsignalized Intersections 

With Minor-Road ADT Equal to 10 Percent of Major-Road ADT ...... 148

Table 63. Economic Evaluation for Urban Four-Leg Unsignalized Intersections 

With Minor-Road ADT Equal to 50 Percent of Major-Road ADT ...... 149

Table 64. Economic Evaluation for Urban Four-Leg Signalized Intersections 

With Minor-Road ADT Equal to 25 Percent of Major-Road ADT ...... 150

Table 65. Economic Evaluation for Urban Four-Leg Signalized Intersections 

With Minor-Road ADT Equal to 50 Percent of Major-Road ADT ...... 151
1

1. INTRODUCTION

This report presents the results of research on the safety effectiveness of geometric

design improvements for at-grade intersections. The objective of the research was to

perform a well-designed before-after evaluation of selected types of intersection design

improvements. The research was performed as part of a pooled-fund study; a portion of

the funding for the research was contributed by highway agencies in the District of

Columbia and the states of Iowa, Illinois, Louisiana, Minnesota, Montana, Nebraska, New

Jersey, North Carolina, Oregon, and Virginia.

Representatives of the participating highway agencies met on three occasions during

the study to assist in guiding the research. In particular, the types of intersection

improvements to be evaluated in this study were selected in consultation with the

participating state highway agencies. Based on a review of safety literature concerning a

broad range of intersection design improvements, presented in section 2 of this report, it

was decided that the before-after evaluation should focus on intersection design

improvements involving left- and right-turn lanes.

Geometric design, traffic control, traffic volume, and traffic accident data were

gathered for a total of 280 improved sites under the jurisdiction of the participating states,

as well as 300 similar intersections that were not improved during the study period. The

types of improvement projects evaluated included installation of added left-turn lanes,

installation of added right-turn lanes, installation of added left- and right-turn lanes as part

of the same project, and extension of the length of existing left- or right-turn lanes. An

observational before-after evaluation of these projects was performed using evaluation

approaches recommended in a recent report by Griffin and Flowers(1) and a recent book by

Hauer.(2) Three contrasting approaches to before-after evaluation were used—the yoked

comparison or matched-pair approach, the comparison group approach, and the Empirical

Bayes approach. The research not only evaluated the safety effectiveness of left- and rightturn lane improvements, but also compared the performance of these three alternative

approaches in making such evaluations.

This report presents the research methodology and evaluation approaches used to

evaluate left- and right-turn lane projects and presents the results of the evaluations. 

Section 2 of the report presents a review of the safety literature concerning intersection

design improvements; the results of this review were considered in the decision to focus

the study of left- and right-turn lane improvements. Section 3 describes the selection of the

evaluation sites. The collection of data concerning those evaluation sites is described in

section 4. Section 5 presents the evaluation plan for the study, including the three specific

evaluation approaches that were used, while section 6 presents and interprets the evaluation

results. The conclusions and recommendations of the study are presented in section 7.

Appendix A summarizes the results of safety studies concerning intersection 

improvements published in the literature. Appendix B presents the results of negative

binomial regression modeling on relationships between intersection accident frequency and
2

traffic volumes performed for use with the comparison group and Empirical Bayes

approaches. Appendix C presents the detailed results of all before-after evaluations

performed in the research; this appendix includes all evaluation results that are discussed in

Section 6 of the report, as well as other evaluations whose results were not statistically

significant and were, therefore, not used. Finally, appendix D presents the definitions for

geometric design and traffic control data items that were collected in the field concerning

the study intersections.
3

2. LITERATURE REVIEW ON SAFETY EFFECTS OF

INTERSECTION DESIGN ELEMENTS

This section of the report presents the results of the literature review that was

conducted as part of the research. This literature review covered all aspects of intersection

safety. Based on this review, a decision was reached to focus the research on the safety

effectiveness of left- and right-turn lanes at intersections. Therefore, these issues are

addressed in greater detail than most other issues.

Overview

The scope of the literature review includes studies related to the safety effects of a

wide variety of geometric design, traffic, and control elements of at-grade intersections. 

Although the research presented in this report focuses on the safety effectiveness of

intersection left- and right-turn lanes, the initial scope of the research was not limited to

this topic and could potentially have included the safety evaluation of any type of 

intersection design improvement. Therefore, this literature review is organized to

emphasize studies related to the safety effectiveness of turn lanes, but it also includes a

review of all geometric, traffic, and control elements that affect the safety of at-grade

intersections.

The review identifies studies that address general intersection geometric design,

traffic, and control issues with emphasis on studies that provide a quantitative estimate of

the factor of interest. Some studies find a factor to be related to safety, but do not quantify

the effect of that factor. With minor exceptions, the review does not address studies that

investigated a factor but did not find it to be important or statistically significant. In such

cases, it would be difficult without more detailed review of the study to judge whether the

lack of an observed effect resulted from the true lack of a relationship of that factor to

safety or from a limited sample size or poor study design. The review considers both

studies that directly evaluated relationships between the factors of interest and safety and

studies that summarized and synthesized past research. The latter were included to take

advantage of the judgements made by previous reviewers.

Table 1 presents a list of the intersection features that are discussed in this review. 

The table is organized into three categories: intersection geometric design features, traffic

control and operational features, and traffic characteristics. The specific topics that are

most directly related to the safety effectiveness of turn lanes are listed first under each

category.

The remainder of this section of the report presents the findings of the literature review

of the specific topics identified in table 1. The findings are also presented in an extensive

summary table in appendix A.
4

Table 1. Intersection Features Addressed in the Literature Review.

Intersection geometric design features Traffic control and operational features Traffic characteristics

Left-turn lanes

S offset left-turn lanes

Right-turn lanes

Channelization

S island design

Number of intersection legs (e.g., 3, 4, 5)

Intersection type (e.g., cross, T, Y, offset)

Roundabouts

Angle of intersection (e.g., skew)

Curb return radius

Sight distance

S intersection sight distance

S stopping sight distance

S sight distance to traffic control device

Approach width

Number of approach lanes

Median width and type

Vertical alignment on approaches

Horizontal alignment on approaches

Type of traffic control:

S uncontrolled

S YIELD-controlled

S STOP-controlled

S signal-controlled

S roundabouts

Turn prohibitions

Presence and type of crosswalks

Posted speed limit on approaches

Advance warning signs

Lighting

Average daily traffic (ADT)

S total entering ADT (all approaches)

S entering ADTs for major and minor

 approaches

Turning movements

Peak hour approach volumes

Vehicle mix/percent trucks

Distribution of total entering volume by hour

of day

Distribution of approach volume by hour of

day

Average approach speed

Volum e of bicycle traffic

Volum e of pedestrian traffic
5

Intersection Geometric Design Features

Left-Turn Lanes

Installation of left-turn lanes has been the focus of many research studies. Various

safety-related impacts have been documented depending upon the type of intersection

(signalized, unsignalized, four-leg, etc.) where the left-turn treatment was implemented, as

well as the different types and/or severity of accidents. Parker(3) determined that the

addition of left-turn lanes at rural intersections along two-lane highways can reduce the

potential for passing-related accidents. On urban four-lane roadways, McCoy and

Malone(4) found that installation of left-turn lanes reduced rear-end, sideswipe, and left-turn

accidents. Foody and Richardson(5) found that accident rates decreased by 38 percent with

the addition of a left-turn lane at signalized intersections and by 76 percent at unsignalized

intersections. Gluck et al.(6) reported accident rate reductions ranging from 18 to 77 percent

due to the installation of left-turn lanes, based on the review of work by the New Jersey

Department of Transportation,(7,8) Griewe,(9) Agent,(10) Ben-Yakov and Craus,(11) Craus and

Mahalel,

(12) Tamburri and Hammer,(13) and Wilson et al.(14)

When implemented with additional safety measures, left-turn lanes have been very

effective in increasing safety. Haler reported that left-turn channelization reduced

accidents to varying degrees depending upon the intersection configuration.(15) Based on a

synthesis of work by McFarlane,(16) Haler reported that the provision of left-turn lanes at

unsignalized intersections, when combined, with installation of curbs or raised medians,

reduced accidents by 70, 65, and 60 percent in urban, suburban, and rural areas,

respectively. When the channelization was painted rather than raised, accidents decreased

only by 15, 30, and 50 percent in urban, suburban, and rural areas, respectively. At

signalized intersections, installation of left-turn channelization accompanied by a left-turn

signal phase reduced accidents by 36 percent; however, without the left-turn phase,

accidents decreased only by 15 percent.(16) At unsignalized intersections, findings of a

California study indicate greater reductions in accidents with the use of a left-turn lane in a

raised median than with painted left-turn lanes.(17) Similarly, Lacy(18) found that a left-turn

lane, when coupled with several other safety improvements, reduced accident frequency by

35 percent and accident severity by 80 percent. Dale(19) found that installation of a traffic

signal and left-turn channelization at intersections along rural two-lane highways reduced

the total number of accidents by 20 percent, while the installation of a traffic signal without

any channelization reduced the total number of accidents by only 6 percent.

Not all studies, however, have shown that left-turn lanes reduce accidents. Bauer and

Harwood(20) found that left-turn lanes were associated with higher frequencies of both total

multiple-vehicle accidents and fatal and injury multiple-vehicle accidents. However, this

result was not advanced by the authors as a basis for policy because the directions of

specific effects in predictive models often represent the surrogate effects of other variables,

rather than the true effect of the variable of interest. At unsignalized intersections, McCoy

and Malone determined there was a significant increase in right-angle accidents.(4)
6

However, at unsignalized intersections on rural two-lane highways, McCoy et al.(21) found

no significant difference in rear-end and left-turn accident rates between intersections with

and without left-turn lanes. Poch and Mannering(22) also found some situations in which

accidents of specific types increased with installation of left-turn lanes.

Several predictive models and accident modification factors have been developed that

indicate left-turn lanes have a positive effect on safety. Maze et al.(23) developed a model

that predicted a reduction in left-turn accident rate of 6 percent due to the installation of a

left-turn lane with permitted signal phasing and a reduction of approximately 35 percent

from installation of a left-turn lane with protected/permitted signal phasing. Vogt(24)

developed a model for a four-leg rural intersection of a four-lane major road with

STOP-controlled two-lane minor roads which yielded an accident reduction factor for total

accidents of 38 percent due to the installation of a left-turn lane along the major road.

In another study, Harwood et al.(25) developed algorithms to predict the expected safety

performance of rural two-lane highways. The prediction algorithms combined elements of

historical accident data, predictions from statistical models, results of before-after studies,

and expert judgements made by experienced engineers. As part of the research, an expert

panel of safety researchers developed accident modification factors (AMFs) for specific

geometric design and traffic control features. AMFs are used in the accident prediction

algorithms to represent the effects of safety of the respective features. The base value of

each AMF is 1.0. Any feature associated with a higher accident experience than the base

condition has an AMF with a value greater than 1.0, and any feature associated with lower

accident experience than the base condition has an AMF with a value less than 1.0.

In developing AMFs for the installation of left-turn lanes on the major-road

approaches to intersections on two-lane rural highways, the expert panel conducted an

extensive review of past research on the safety effectiveness of left-turn lanes, including

most of the studies discussed above. The panel was charged with defining the safety

effectiveness of intersection left-turn lanes based on the best study of this issue or based on

results from a combination of studies. The panel concluded that there have been no

well-designed before-after evaluations of intersection left-turn lanes and no single study

that was considered more reliable than others. Therefore, the panel combined results from

several studies and developed AMFs for left-turn lanes, which are presented in Table 2. 

The AMFs represent a judgement by the panel. The panel estimated that installation of a

left-turn lane along one major approach reduces intersection-related accidents by 18 to

24 percent, depending upon the type of traffic control and the number of legs, and

installation of left-turn lanes along both major approaches to a four-leg intersection reduces

intersection-related accidents by 33 to 42 percent, depending upon the type of traffic

control. These results are presented in table 2 in the form of AMFs, as defined above.

No research was found that quantifies the safety effectiveness of extending the length

of existing left-turn lanes to eliminate traffic overflows into through travel lanes and to

allow a greater proportion of vehicle deceleration to occur in the turn lane rather than in the

through travel lanes.
7

Table 2. Accident Modification Factors for Installation of Left-Turn Lanes on the 

 Major-Road Approaches to Intersection on Two-Lane Rural Highways(25).

Intersection type

Intersection

traffic control

Number of major-road approaches on which

left-turn lanes are installed

One approach Both approaches

Three-leg intersection STOP signa

Traffic signal

0.78

0.85

—

—

Four-leg intersection STOP signa

Traffic signal

0.76

0.82

0.58

0.67

a

 STOP signs on minor-road approach(es)

An emerging issue in the design of left-turn channelization is the restriction in sight

distance that opposing left-turn vehicles cause one another. As an indication of this safety

problem, David and Norman(26) determined that for average daily traffic (ADT) volumes

between 10,000 and 20,000 veh/day, four-leg intersections with opposing left-turn lanes

had more accidents than those without. A potentially effective countermeasure for safety

problems where opposing left-turn lanes are present is to eliminate the sight restrictions by

offsetting the left-turn lanes. Harwood et al.(27) reviewed the safety performance of a

limited set of tapered and parallel offset left-turn lanes and found no safety problems. Both

McCoy et al.(28) and Joshua and Saka(29) developed procedures to compute the amount of

offset required for clear sight lines. However, no evaluations of the accident reduction

effectiveness of offset left-turn lanes have been found.

Table 3 summarizes the results of those studies that provided quantitative estimates of

the effectiveness of installing left-turn lanes at intersections.

Right-Turn Lanes

Compared to left-turn lanes, very few studies have been conducted on the safety

effectiveness of right-turn lanes. Bauer and Harwood(20) indicate that right-turn

channelization resulted in a decrease in both total multiple-vehicle accidents and fatal and

injury multiple-vehicle accidents. However, Vogt and Bared(30) modeled accidents for

three-leg unsignalized intersections along rural two-lane highways, and based upon the

prediction model, the presence of a right-turn lane increases intersection-related accidents

by 27 percent.

The expert panel discussed above also developed estimates of the safety effectiveness

of right-turn lanes; these AMFs are presented in table 4.(25) In their review of information,

the expert panel did not find any well-designed before-after studies on the accident

reduction effectiveness of right-turn lanes. Based on a review of the available studies, the

expert panel estimated the presence of a right-turn lane along one approach to a rural

STOP-controlled intersection reduces intersection-related accidents by 5 percent, and the 
8

Table 3. Summary of Research Results Concerning the Safety Effectiveness of Installing

Left-Turn Lanes.

Source

Reported LTL effectiveness

(percent change in accident

frequency)

Conditions/comments

Total

intersection

accidents

Left-turn

accidents

Harwood et al. [2000]

(25) -18 to -24 — two-lane highway; LTL on one majorroad approach

-32 to !42 — two-lane highway; LTLs on two

major-road approaches

Vogt [1999](24) -38 — LTL at four-leg rural intersection with

four-lane major road and two-lane

minor road

Maze et al [1994](23) -6 — signalized intersection; LTL with

permitted phasing

-35 — signalized intersection; LTL with

protected/permitted phasing

New Jersey Department

of Transportation

[1993](8)

-35 to -51 — LTL installation on Route 130 in 

New Jersey

Griewe [1986](9) -58 -62 eight LTLs added by restriping

Agent [1983](10) -77 — unsignalized intersection

-54 — signalized intersection

Ben Yahov and Craus

[1980](11)/Craus and

Mahalel [1980](12)

-38 — LTL installation

McFarlane [1979](16) -70 — LTL with curbed median; urban

-65 — LTL with curbed median; suburban

-60 — LTL with curbed median; rural

-15 — LTL with painted median; urban

-30 — LTL with painted median; suburban

-50 — LTL with painted median; rural

-36 — signalized intersection with LTL and

exclusive phase

-15 — signalized intersection with LTL but

no exclusive phase
Table 3. Summary of Research Results Concerning the Safety Effectiveness of Installing

Left-Turn Lanes (Continued).

Source

Reported LTL effectiveness

(percent change in accident

frequency)

Conditions/comments

Total

intersection

accidents

Left-turn

accidents

9

Foody and Richardson

[1973](5)

-38 — signalized intersections

-76 — unsignalized intersections

Dale [1973](19) -20 — two-lane highway intersection;

installation of signal with LTL

Lacy [1972](18) -35 — installation of LTL with other

improvements

Tamburri and Hamm er

[1968](13)/ Wilson et al

[1967](14)

-18 — unsignalized intersection

Table 4. Accident Modification Factors for Installation of Right-Turn Lanes on the 

Major-Road Approaches to Intersection on Two-Lane Rural Highways.(25)

Intersection

traffic control

Number of major-road approaches on which right-turn lanes are installed

One approach Both approaches

STOP signa0.95 0.90

Traffic signal 0.975 0.95

a

 STOP signs on minor-road approach(es)

presence of a right-turn lane along both major approaches reduces intersection-related

accidents by 10 percent. Similarly for rural signalized intersections, the expert panel

estimated a reduction of 2.5 percent in total intersection-related accidents due to the

presence of a right-turn lane along one major-road approach and 5 percent for right-turn

lanes along both major-road approaches.

No research was found that quantifies the safety effectiveness of extending the length

of existing right-turn lanes to eliminate traffic overflows into through travel lanes.

Table 5 summarizes the results of available studies on the safety effectiveness of

right-turn lanes.
10

Table 5. Summary of Research Results Concerning the Safety Effectiveness of Installing

Right-Turn Lanes.

Source

Reported LTL effectiveness

(percent change in accident

frequency)

Conditions/comments

Total

intersection

accidents

Right-turn

accidents

Harwood et al. [2000](25) -5 — two-lane highway; RTL on one major

road approach to an unsignalized

intersection

-10 — two-lane highway; RTLs on two

major-road approaches to an

unsignalized intersection

-2.5 — two-lane highway; RTL on one m ajorroad approach to an unsignalized

intersection

-5 — two-lane highway; RTLs on two

major-road approaches to signalized

intersection

Vogt and Bared

[1998](30)

27 — based on multivariate modeling with

Minnesota data

Channelization

Four functional objectives form the basis for channelization design concepts:(31,32)

• Limiting the points of conflict.

• Limiting the complexity of the conflict area.

• Limiting the conflict frequency.

• Limiting the conflict severity.

A variety of measures such as designation and arrangement of traffic lanes, traffic islands,

median dividers, and various signs, signals, and markings may be used for channelization

purposes. Studies on channelization by David and Norman,(26) Exnicios,(33) and Rowan and

Williams(34) in general indicate that channelization improves safety. Exnicios(33) found

reductions in accidents as high as 100 percent over a 26-month period. Haler(15) also

reported that channelization can reduce accidents.

Channelizing islands are defined areas between traffic lanes that control vehicle

movements and serve as refuge points for pedestrians.(35) Islands also provide suitable

locations to place traffic control devices. Islands vary in both size and shape, as well as the

type of surfacing material used.
11

Washington et al.(36) found that intersection approaches with raised medians had

accident rates 40 percent lower than intersection approaches with flush medians. 

Forrestel(37) found that installation of a raised median island reduced the pedestrian accident

rate by 11.5 percent. In another study, Templer(32) found that a raised median reduced the

number of conflicts between pedestrians and vehicles, but the difference was not

statistically significant.

Number of Intersection Legs

There is broad agreement in the literature that four-leg intersections experience more

accidents than comparable three-leg intersections. This finding is logical because four-leg

intersections have more conflict points than three-leg intersections and, therefore, present

more opportunities for accidents to occur. Four studies have quantified this effect.

Bauer and Harwood(20) found that both rural and urban STOP-controlled intersections

with four legs experienced approximately twice as many accidents as three-leg

intersections. Specifically, rural four-leg STOP-controlled intersections experienced an

average of 1.1 accidents per year, while three-leg intersections experienced 0.6 accidents

per year. Urban four-leg STOP-controlled intersections experienced 2.2 accidents per year,

while three-leg intersections experienced 1.3 accidents per year.

Predictive models developed by Harwood et al.(27) showed that typical divided highway

intersections with four legs had about twice as many accidents as three-leg intersections for

narrow medians and more than five times as many accidents as three-leg intersections for

wide medians.

Hanna et al.(38) found that, in rural areas, four-leg intersections experience

approximately 69 percent more accidents than T intersections. T intersections are three-leg

intersections at which the legs meet at a right angle, while Y intersections are three-leg

intersections where one or more of the legs are skewed. David and Norman(26) found that

for STOP-controlled intersections in urban areas with total entering traffic volumes under

20,000 veh/day, the accident frequencies for three- and four-leg intersections were very

similar; however, for intersections with total entering volumes over 20,000 veh/day, fourleg intersections experienced twice as many accidents as three-leg intersections.

Intersection Type

The review of intersection type focused on the differences between conventional and

offset four-leg intersections and between T and Y three-leg intersections. Lau and

May(39,40) found these differences to be statistically significant in modeling of injury

accidents at both signalized and unsignalized intersections, but their classification and

regression tree (CART) analysis results are difficult to interpret as a specific effect of these
12

factors. Lau and May also modeled fatal and property-damage-only (PDO) accidents but,

for the sake of simplicity, the discussions in this paper focus on the findings of injury

accident modeling that are typical of the others.

Hanna et al.(38) found that, for three-leg intersections, Y intersections have accident

rates approximately 50 percent higher than T intersections; for four-leg intersections, offset

intersections had accident rates that were approximately 43 percent of the accident rate of

conventional four-leg intersections. The effect observed by Hanna et al. is interesting. The

operating experience of some highway agencies indicates that offset intersections can

create operational and safety problems as through vehicles on the crossroad must turn onto

and off of the major road rather than making a simple crossing maneuver. A number of

projects have been constructed to realign the legs of offset intersections to convert them to

conventional four-leg intersections. However, the results of Hanna et al. suggest the

opposite—that offset intersections operate more safely than conventional four-leg

intersections. This finding may indicate that, where there is little through traffic on the

crossroad, two T intersections operate more safely than one conventional four-leg

intersection.

Roundabouts

Roundabouts are a unique topic. They can be considered both an intersection

geometric design feature and a form of intersection traffic control. Because roundabouts

are classified as a form of intersection traffic control in Roundabouts: An Informational

Guide, the safety effectiveness of roundabouts is discussed in the section on type of traffic

control.

(41)

Angle of Intersection

The angle between the legs of an intersection, particularly whether the legs intersect at

a right or an oblique angle, has long been considered to affect the safety performance of the

intersection. McCoy et al.(42) found that accidents at rural two-way STOP-controlled

intersections increase with increasing skew angle; this result applies to both three-leg and

four-leg intersections. In addition, the previously discussed difference in safety

performance between three-leg T and Y intersections found by Hanna et al.(38) represents an

effect of the angle of the intersection.

Harwood et al.(25) incorporated AMFs for intersection skew angle when they developed

algorithms to predict the expected safety performance of rural two-lane highways. The

AMFs for intersection skew angle were derived from statistical modeling and apply to total

intersection-related accidents. Thus, the AMFs were formulated from data and do not

represent judgements by the expert panel on the accident reduction effectiveness of this

design feature. For a three-leg STOP-controlled intersection, the AMF was calculated as:
13

(1)

For a four-leg STOP-controlled intersection, the AMF was calculated as:

(2)

where:

SKEW = intersection skew angle (degrees), expressed as the absolute value of

the difference between 90 degrees and the actual intersection angle.

Curb Return Radius

The curb return radius of an intersection controls the turning speed for vehicles

making right turns. In addition, larger curb return radii make it possible for intersections to

better accommodate right turns by large trucks. Haler(15) cited curb return radius as an

important factor in safe intersection operations, but apparently no specific evaluations of

the effect of curb return radius on safety have been conducted.

Sight Distance

Sight distance is the distance ahead or along an intersecting roadway that a driver can

see from any location on the roadway system. Provision of adequate sight distance is

fundamental to the design of roadways and intersections for safe operations. Three types of

sight distance are particularly critical to the safe operation of at-grade intersections:

intersection sight distance, stopping sight distance, and sight distance to traffic control

devices.

Three studies have addressed the safety effects of intersection sight distance. David

and Norman(26) found that within specific ADT levels the reduction in accident experience

from a sight distance improvement was, in most cases, highest for intersection approaches

whose initial sight distance was lowest. Hanna et al.(38) found that intersections with

“poor” sight distance had an observed accident rate of 1.33 accidents per million entering

vehicles, while intersections as a whole had an accident rate of 1.13 accidents per million

entering vehicles. Mitchell(43) found that total intersection accidents were reduced by

67 percent when intersection sight obstructions were removed. Unfortunately, none of

these studies were specific concerning the magnitude of the sight distance improvements

made.
14

Fambro et al.(44) found that accident rates were high for intersections located on crest

vertical curves with limited sight distance. The results of another recent study by Fambro

et al.(45) are consistent with that finding.

No evaluations were found of the safety effects of limited sight distance to traffic

control devices, such as STOP signs and signals.

The expert panel of safety researchers discussed earlier reviewed several sources of

information to evaluate the effects of intersection sight distance on intersection-related

accidents. The panel did not find any single evaluation to be the most credible. Therefore,

the AMFs established by the panel represent the panel’s best judgment on the safety effects

of intersection sight distance. The AMFs are as follows for intersection sight distance at

intersections with STOP control on the minor leg(s):

• 1.05 if sight distance is limited in one quadrant of the intersection.

• 1.10 if sight distance is limited in two quadrants of the intersection.

• 1.15 if sight distance is limited in three quadrants of the intersection.

• 1.20 if sight distance is limited in four quadrants of the intersection.

In applying these AMFs, sight distance in a quadrant of an intersection is considered

limited if the available sight distance is less than the sight distance specified by AASHTO

policy for a design speed of 20 km/h (12 mph) less than the major road-design speed and

the sight distance restrictions are due to roadway alignment and/or terrain.

Approach Width

The width of an intersection approach includes the combined widths of the approach

lanes and, in some cases, the width of the shoulder, as well. Studies by Bauer and

Harwood,(20) Neuman,(31) and Lacy(18) found that increasing the approach width to an

intersection reduces the accident rate along the approach. Bauer and Harwood(20) found

that as lane width decreases on an intersection approach, accidents tended to increase. 

Similarly, Neuman(31) indicated that accidents may be reduced by widening the shoulder at

intersections on narrow two-lane roadways. Widening of the shoulders may reduce

accidents by providing space for collision-avoidance maneuvers and by providing better

sight lines if sight distance is limited on the approach. Lacy(18) also found that widening the

approaches, combined with other safety improvements, decreased accident frequency by

35 percent and accident severity by 80 percent. By contrast, David and Norman(26) did not

find any evidence that incremental changes in lane or shoulder width near intersections

affects accident rates.
15

Number of Approach Lanes

The number of lanes on an intersection approach is determined primarily by traffic

demand and the desired level of service. Intuitively, one might assume that the number of

accidents is proportional to the number of lanes (i.e., as the number of lanes increases so

would the total number of accidents, since the potential number of conflicts would appear

to increase). However, Bauer and Harwood(20) found that for unsignalized intersections in

both rural and urban areas, the number of accidents tended to be higher on facilities with

one approach lane and accidents tended to be lower at intersections with two or more

approach lanes. The opposite appears to be the case for urban, four-leg, signalized

intersections. David and Norman(26) also indicated that accident frequencies can be reduced

for intersections with total entering volumes under 10,000 veh/day by adding through

lanes. It should be noted that with a demand-related design parameter such as number of

lanes, it is difficult to assess directly whether any observed safety effects are due to the

number of lanes or to the traffic volume on the approach.

Median Type and Width

The width of a divided highway median influences the safety performance of

intersections on that highway. Harwood et al.(27) found that accident frequencies at rural

four-leg signalized intersections decrease as median width increases. In contrast, at both

signalized and unsignalized intersections in urban and suburban areas, accident frequencies

were found to increase with increasing median width. Similar results for rural divided

highway intersections were found in an earlier Ohio study by Priest.(46) An Indiana study

by Van Maren found no statistically significant relationship between median width and

accident rates at divided highway intersections.(47)

Vertical Alignment

Crest and sag vertical curves are used to provide a smooth transition between roadway

segments with different grades. From a safety standpoint, it is undesirable to locate

intersections on steep grades or on crest vertical curves with limited sight distance. Steep

upgrade approaches to intersections cause difficulty because vehicles accelerate more

slowly, resulting in increased time during which the vehicle is exposed in the conflict area

of the intersection. Steep downgrade approaches to intersections result in longer stopping

distances, which may cause potential problems, as well. Surprisingly, however,

Hanna et al.(38) found the accident rates for intersections with grades steeper than

five percent to be lower than the average accident rate for all intersections. The average

accident rates were 0.97 and 1.13 accidents per million entering vehicles for intersections

with steep grades and for all intersections, respectively.
16

As discussed above, vertical curves cause potential problems at intersections where

sight distance is limited. In particular, Fambro et al.(44) concluded that accident rates were

high at intersections on crest vertical curves where sight distance was limited.(27)

Horizontal Alignment

From a safety standpoint, it is desirable for the alignment of intersecting roadways to

be straight as practical. Horizontal curves on the approaches to intersections make it

difficult for a driver to discern the proper path of travel and also affect a driver’s visual

perspective, since the driver’s focus is directed tangentially to the travel path.(48)

Horizontal curves also add complexity to the driving environment. Past research has

shown that the distance from a horizontal curve to the nearest intersection is related to

safety.(49) However, no studies were found which indicate that any specific threshold value

for degree of curvature adversely affects safety on intersection approaches.

Traffic Control and Operational Features

Type of Traffic Control

A variety of different traffic control types are used for at-grade intersections including

no control, YIELD-control, STOP-control, signal control, and roundabouts.

Poch and Mannering(22) indicated that intersections with no control on any of the

approaches experience fewer total and angle accidents than intersections with other types

of traffic control. However, this effect could have been observed solely because

uncontrolled intersections typically have lower traffic volumes than other intersection

types.

Hauer,(15) in a synthesis of past research, noted that conversion from no control to

YIELD control reduced accidents by 44 to 52 percent in one study and by 23 to 63 percent

in another.

Hall et al.(50) found that accidents can be reduced by 20 to 60 percent by proper use of

YIELD signs. However, little additional benefit was found if the YIELD signs were

replaced by STOP signs. Agent and Deen(51) found that at YIELD-controlled intersections,

over half of the accidents were rear-end collisions, while angle collisions made up over half

of the accidents at STOP-controlled intersections. Hanna et al.(38) found that accident rates

at STOP-controlled intersections were lower than those at intersections having higher

traffic flow.
17

No safety evaluations were found in the literature for intersections where flashing

beacons were used in conjunction with STOP signs at either two-way or all-way

STOP-controlled intersections.

Research by Hanna et al.(38) indicates that signalization of intersections that are

currently unsignalized typically results in a slight increase in accident rate, a substantial

increase in rear-end collisions, and a comparable decrease in angle collisions. Poch and

Mannering(22) found that total and angle accidents for signal-controlled intersections were

lower than for other traffic control types.

Maze et al.(23) developed predictive models which indicate that a protected left-turn

signal phase without a left-turn lane has a positive effect on safety. A numerical example

developed by the authors indicates an anticipated reduction in left-turn accidents of

50 percent from installation of a left-turn signal phase. David and Norman(26) found that in

urban areas, multiphase traffic signals appear to have lower percentages of fatal and injury

accidents than two-phase signals. King and Goldblatt(52) found that signalization leads to a

reduction in angle collisions and an increase in rear-end collisions; their results also

indicate that signalized intersections have higher accident rates, although this is often offset

by reduced accident severity.

U.S. experience with roundabouts is rather limited, but interest has increased recently,

partially due to the operational and safety benefits being reported in documents such as

Roundabouts: An Informational Guide.(41) The Informational Guide indicates roundabouts

may improve the safety of intersections by eliminating or altering conflict types, by

reducing speed differentials at intersections, and by decreasing overall speeds into and

through intersections. The Informational Guide summarizes the overall safety performance

of roundabouts in various countries, including the U.S. After converting intersections with

conventional traffic control to roundabouts, a reduction in accidents is reported of about

37 percent for all accidents and 51 percent for injury accidents. These values are consistent

with experiences in the U.S. and internationally. Persaud et al.(53) found similar results after

performing a before-after accident analysis following the conversion of twenty-three

intersections from STOP-control and signal-control to roundabouts. Persaud et al. reported

a 40 percent reduction in total accidents, an 80 percent reduction for all injury accidents,

and about a 90 percent reduction of fatal and incapacitating injury accidents.

Turn Prohibitions

Research by Lau and May(39,40) found that left-turn prohibitions were a significant

factor in predicting injury accidents at both signalized and unsignalized intersections. 

However, the results of this CART analysis are difficult to interpret in order to obtain an

explicit estimate of this effect.
18

Presence and Types of Crosswalk

The purpose of marked crosswalks is to guide pedestrians across a busy roadway, as

well as to increase drivers’ awareness of pedestrians. Some intersections provide

designated crosswalks for pedestrians, while others do not. Research results provide

conflicting conclusions as to whether the provision of marked crosswalks actually improves

safety for pedestrians. Several studies have concluded that marked crosswalks decrease

accident rates, in some cases by as much as 50 percent.(15,54) On the other hand, perhaps the

best-known study on crosswalks, conducted by Herms(55) in 1970, concluded that

approximately twice as many pedestrian accidents occurred in marked crosswalks as in

unmarked crosswalks. Another study found that pedestrian accidents increased by

86 percent after crosswalks were marked.(15) As Herms pointed out, the increase in

accident rates resulting from marked crosswalks may “not be due to the crosswalk being

marked as much as it is a reflection on the pedestrian’s attitude and behavior when using

the marked crosswalk.” Other factors which may affect the safety of marked crosswalks

include visibility, intersection type, and signal timing.

Although crosswalks typically affect pedestrian safety, it is also important to note that

vehicular accident rates may also be affected. Hauer(15) noted that rear-end collisions

increase after crosswalks are marked. Thus, the need for crosswalks should be examined

from the standpoint of both pedestrian safety and vehicular safety.

Posted Speed Limit

It is rational to assume that the likelihood and severity of accidents on an intersection

approach increases as the posted speed limit on the approach increases. Higher posted

speed limits are generally associated with higher approach speeds, which require longer

distances to bring an approaching vehicle to a complete stop. Therefore, drivers must react

more quickly to potential conflicts encountered at the intersections. However, no studies

were found that quantify the extent to which accidents increase or decrease with changes in

posted speed limits or operating speeds on intersection approaches.

Advance Warning Signs

Advance warning signs are intended to increase a driver’s awareness of upcoming

traffic situations. Studies of specific types of advance warning signs provide varied results. 

Gattis and Iqbal(56) found that most drivers do not abide by the “Do Not Block Intersection”

sign. Washington(36) found that accident rates increased for approaches to skewed

intersections where advance warning signs were provided. Pant and Huang(57) found the

“Prepare To Stop When Flashing” sign raised conflict rates by 15 percent on curved

approaches but had no influence on conflict rates on tangent approaches. Pant and Huang

also noted that the flashing symbol “Signal Ahead” sign had no impact on traffic conflict

rates.
19

Research has also shown positive effects for certain supplements to advance warning

signs. Washington(36) found that advance warning signs with flashers (AWFs) can reduce

approach accident rates at high-speed signalized intersections by as much as 50 percent. 

He also concluded that right-angle accidents were reduced when route markers and/or

advance warning signs were present. Klugman(58) found that total accident rates decreased

from 1.22 to 1.09 accidents per million entering vehicles at AWF-equipped intersections;

right-angle and rear-end accident rates also decreased from 0.68 to 0.63 accidents per

million entering vehicles. In other related work, Styles

(59) concluded that the “Red Signal

Ahead” warning sign reduced right-angle accident rates by 42 percent on intersection

approaches with crest vertical curves and reduced the total accident rate on intersection

approaches with horizontal curves and tangent alignments by 14 and 41 percent,

respectively. In a separate study, Styles(60) found that flashing red strobe lights are also

effective in reducing right-angle accidents.

It is important to note that many of the studies related to advance warning signs stress

the importance of factors such as approach alignment, type of sign, and type of accident as

influencing the accident reduction effectiveness for such devices.

Lighting

Intersection lighting is potentially effective as a countermeasure to reduce nighttime

accidents. Bauer and Harwood(20) found that rural, four-leg, STOP-controlled intersections

that were lighted would be expected to experience 21 percent fewer fatal and injury

accidents than unlighted intersections. However, for other intersection types, no similar

effect was observed and, in some cases, an opposite effect that may represent a surrogate

effect of some other variable was observed. It is important to note that this study evaluated

total accidents (daytime plus nighttime), rather than nighttime accidents alone.

Box(61) found that improved lighting reduced the proportion of pedestrian/bicycle,

fixed-object, sideswipe, and other accidents that occurred at night on a 4.5-km (2.8-mi)

section of a suburban arterial in Illinois. Only nighttime head-on accidents increased as a

proportion of total (daytime plus nighttime) accidents.

An extensive study in Los Angeles found no statistically significant reduction in

nighttime accidents due to lighting improvements at intersections. Statistically significant

reductions in nighttime accidents were found for a few intersections.

(62)
20

Traffic Characteristics

Average Daily Traffic (ADT) Volume

Many studies have found approach traffic volumes to have a strong relationship to

intersection accidents. A number of studies have used the total entering ADT as an

exposure measure in determining intersection accident rates. Bauer and Harwood(20) found

better results in accident prediction modeling when the major-road and crossroad ADTs

were treated as separate independent variables than when they were combined as a product

or a sum. Lau and May(39,40) represented the relative traffic volumes on the intersecting

roadways by the ratio of the crossroad volume to the total entering ADT, expressed as a

percentage.

Turning Movements

Hauer et al.(63) developed relationships between accident frequency for specific

accident types (e.g., left-turn accidents) and the turning movement volumes most

specifically related to that accident type.

Other Traffic Characteristics

No studies were found relating the following traffic flow measures to accidents:

• Peak hour approach volumes.

• Vehicle mix/percent trucks.

• Distribution of total entering volume by hour of the day.

• Distribution of approach volume by hour of the day.

• Average approach speed.

• Volume of bicycle traffic.

• Volume of pedestrian traffic.

Summary

The scope of this literature review covers the safety effectiveness of general

intersection geometric design features, traffic control elements, and traffic characteristics,

focusing on studies that provide a quantitative estimate of the factor of interest. Based

upon the review, it is evident that many design features have the capability to improve the

safety of an at-grade intersection. It is also evident by the quantity of the studies related to

left- and right-turn lanes that there is considerable interest in quantifying their safety

effectiveness. This interest has been stimulated by the number of highway agencies that
21

have installed turn lanes and by the results of previous studies that give a strong indication

that installation of left- or right-turn lanes improves the safety of at-grade intersections. 

Based on these considerations, representatives of the state highway agencies in this

pooled-fund study decided to focus this research on the evaluation of the safety

effectiveness of intersection left- and right-turn lanes.
22
23

3. SELECTION OF EVALUATION SITES

This section of the report describes the process of selecting evaluation sites for the

evaluation of left- and right-turn lane projects and summarizes the characteristics of the

sites that were used. The first portion of this section documents the types of projects that

were evaluated. The next subsection describes the overall process of identifying candidate

intersections, including three types of sites: improved or treatment sites, comparison sites,

and reference sites. These three types of sites are defined later in this section. The

identification and screening of each site type is described and the number and

characteristics of sites of each type are summarized.

Evaluation Priorities for Intersection Improvement Types

Based on the results of the literature review and, most especially, on the assessments

of the participating state highway agencies, a decision was reached to focus the safety

evaluation on projects involving intersection left- and right-turn lanes. In particular, a

decision was made to focus the evaluation on the following four project types for which it

appeared that sufficient improved sites for an evaluation were likely to be available:

• Installation of a left-turn lane on one or more major-road approaches to an

existing intersection where no turn lane was present.

• Installation of a right-turn lane or a right-turn channelizing island on one or more

major-road approaches to an existing intersection where no turn lane was present. 

In some cases, there may have been an existing right-turn channelizing island

prior to the project.

• Installation of both left- and right-turn lanes on one or more major-road

approaches to an intersection.

• Projects that involved extending the length of an existing left- or right-turn lane,

without adding a new turn lane.

The following types of improvement projects were not evaluated:

• Projects in which no left- or right-turn lanes were installed. In particular,

candidate projects that involved signal modifications only, such as the addition of

exclusive turn phases, were not considered.

• Projects in which through lanes were added on the major road. In particular,

corridor improvement projects in which the through roadway was widened along

an entire corridor, but turn lanes also were added at selected intersections, were

not considered. However, a few projects that installed a two-way left-turn lane
24

(TWLTL) along the major road, thus providing conventional left-turn lanes at one

or more intersections, were retained.

• Projects in which existing through lanes were converted to left- or right-turn

lanes.

• Projects in which minor-road approaches were realigned to convert two nearby

three-leg intersections into a single four-leg intersection.

Identification of Candidate Intersections

Candidate intersections were identified and reviewed as potential evaluation sites in

cooperation with the participating state highway agencies. Three types of sites were

considered:

• Improved or treatment sites, which were intersections at which one of the project

types described above was implemented.

• Comparison sites, which were intersections similar in geometric design, traffic

control, and traffic volume characteristics to the improved sites, but were not

improved. The objective was to identify matched pairs of improved and

comparison sites with similar characteristics.

• Reference sites, which were sites that were not improved, but also were not

matched to any particular improved site.

The general characteristics that all study sites were expected to meet were as follows:

• Only three- and four-leg intersections were considered. Multileg intersections

were excluded because they typically incorporate unique features that are not

representative of most intersections.

• Only intersections with two-way STOP control and signal control were

considered. A few intersections with other types of control (e.g., four-way STOP

control) were suggested by the participating states, but these other types were not

present in sufficient numbers for evaluation.

• Only intersections between public roads were considered. Intersections at which

the minor-road leg was a driveway to a shopping center or school were eliminated

because it was considered unlikely that reliable traffic volume data for the minor

leg by hour of the day exist and those data that do exist are likely to be atypical. 

However, some three-leg intersections between public roads at which the fourth

leg of the intersection is a residential or commercial driveway were retained.
25

The selection process and characteristics for each type of intersection are discussed below

in more detail.

Selection of Improved or Treatment Sites

Improved or treatment sites are sites at which an improvement project was constructed

at an intersection. The project types considered were adding left-turn lanes, adding rightturn lanes, adding both left- and right-turn lanes, or extending an existing left- or right-turn

lane. This following discussion addresses the identification and screening of candidate

projects and summarizes the number and characteristics of the selected sites.

Identification and Screening of Candidate Projects

Candidate projects were identified with the assistance of eight of the participating state

highway agencies. The participating states were: Illinois, Iowa, Louisiana, Minnesota,

Nebraska, North Carolina, Oregon, and Virginia. Each state identified candidate

intersection improvement projects that were constructed from 1994 to 1997. A few

projects that were constructed in earlier years (1989-93) or in a later year (1998) were also

identified.

The participating states initially suggested nearly 800 candidate intersection

improvement projects. These projects were subjected to a screening review that involved

reviewing construction plans and project memoranda, reviewing photologs, and, in some

cases, visiting the site in the field. From these 800 candidates, a total of 280 improved

intersections were selected that met all of the criteria for the study. The reasons for

eliminating sites were as follows:

• The site was located at a multileg intersection; only three- and four-leg

intersections were included in the study.

• The site had a traffic control other than two-way STOP or signal control.

• The site was not located at a public road intersection. Intersections at which the

minor-road leg was a driveway to a shopping center or a school were excluded. 

Every selected study site had at least one minor-road leg that was a public road.

• Left- or right-turn lanes were added only on minor-road approach(es) to two-way

STOP-controlled intersections. Where no traffic signals were present, only

projects involving major-road turn lanes were considered. However, at signalized

intersections, projects involving added turn lanes or any approach were

considered.
26

• The planned project was never constructed.

• The added turn lane served a new intersection, not an intersection that existed

before the improvement.

• Multiple improvements were made as part of the same project, such that the safety

effects of the turn-lane improvement would be confounded with the effects of

other improvement types. As much as possible, “clean” projects in which a single

type of improvement was made were sought; complex projects involving multiple

improvements at the same intersection were not considered. The one exception to

this criterion was that existing unsignalized intersections where traffic signals

were added at the same time the turn lanes were built were retained in the study. 

There were a substantial number of projects of this type for evaluation and it is

certainly of potential interest to highway agencies, although it is unlikely that the

safety effects of signalizing the intersection and adding turn lanes can be

separated.

• Additional improvements were made within two years before or two years after

the turn-lane improvement, such that any evaluation of the turn-lane improvement

would be confounded by the other improvements.

• The intersection had unusual features that could have confounded the evaluation

of the turn-lane improvement. For example, one intersection at which a left-turn

lane was added was eliminated from consideration because there was a railroad

crossing running diagonally across the intersection.

• Data needed for before-after evaluation of the project were not available. 

Geometric design and traffic control data were gathered by the research team and,

therefore, were potentially available for any intersection of interest. However,

traffic accident and traffic volume data were obtained through the assistance of

the participating highway agencies. The data needs of the study for traffic

accident and traffic volume data are described in Section 4 of this report. If traffic

accident or traffic volume data were not available for a particular site, that site

was eliminated from consideration.

These screening criteria were applied during the site-selection phase of the study, which

reduced the number of candidate sites from approximately 800 to 388. An additional 108

improved sites were eliminated during the data collection process because unexpected

features of the intersection or the project were discovered or because needed data were

unavailable.

Number and Characteristics of Improved Sites

As stated above, a total of 280 improved or treatment sites were available for

evaluation. The distribution of these sites by state, area type (rural/urban), project type,

and project year are described below.
27

Table 6 presents the distribution of improved sites by state and area type. The tables

shows that there are nearly equal numbers of projects at rural and urban sites. 

Approximately 45 percent of the improved sites (126 out of 280) are located in Illinois; the

study was very fortunate that the Illinois Department of Transportation had conducted a

substantial number of improvement projects that were suitable for the evaluation. No other

state contributed more than 12.5 percent of the sites. An evaluation of sites at which leftor right-turn lanes were added found no major differences in site characteristics between

the intersections of specific types in Illinois and those of the same type in other states. 

Thus, it does not appear to be a source of bias that 45 percent of the improved sites were

located in a single state.

Sites were classified as rural or urban based on posted and operating speed, character

of adjacent development, and location with respect to population centers, with speed being

the single most important factor. Sites with posted speed limits and operating speeds of

88 km/h (55 mi/h) or more were generally classified as rural unless there was good reason

based on development or location to do otherwise. Sites with posted speed limits and

operating speeds less than 88 km/h (55 mi/h) were generally classified as urban unless

there was good reason based on development or location to do otherwise. The urban

classification included sites in both urban and suburban areas.

Table 7 presents the distribution of the improved sites at rural intersections by traffic

control type and project type. The table shows that, in rural areas, the project types with

the largest sample sizes are:

• Added left-turn lanes at existing unsignalized intersections (61 projects).

• Added right-turn lanes at existing unsignalized intersections (41 projects).

• Added left- and right-turn lanes at existing unsignalized intersections

(27 projects).

Table 8 presents comparable data for improved sites at urban intersections. The table

shows that, in urban areas, the project types with the largest sample sizes are:

• Added left-turn lanes at existing unsignalized intersections (20 projects).

• Added left-turn lanes at existing signalized intersections (43 projects).

• Added right-turn lanes at existing signalized intersections (21 projects).

• Added left- and right-turn lanes at existing unsignalized intersections

(12 projects).

• Added left-turn lanes at newly signalized intersections (32 projects).

The other project types in Tables 7 and 8, not listed above, may not be present in sufficient

numbers to permit a reliable evaluation.
2

8

Table 6. Number of Improved Intersection Sites by Area Type and State.

Number of improved sites by area type

State Rural Urban Total

Iowa (IA) 15 17 32

Illinois (IL) 61 65 126

Louisiana (LA) 0 12 12

Minnesota (MN) 1 10 11

North Carolina (NC) 18 5 23

Nebraska (NE) 4 9 13

Oregon (OR) 21 14 35

Virginia (VA) 23 5 28

Total 143 137 280
2

9

Table 7. Number of Improved Sites at Rural Intersections.

Intersection

traffic control Project type

Number of improved sites by state

IA IL LA MN NC NE OR VA Total

Existing unsignalized

intersections

Added LTLs 0 21 0 1 14 4 14 7 61

Added RTLs 14 18 0 0 0 0 5 4 41

Added both LTLs and RTLs 1 21 0 0 1 0 2 2 27

Extended LTLs 0 0 0 0 0 0 0 2 2

Extended both LTLs and

RTLs

0 0 0 0 0 0 0 0 0

Existing signalized

intersections

Added LTLs 0 0 0 0 0 0 0 0 0

Added RTLs 0 0 0 0 0 0 0 0 0

Added both LTLs and RTLs 0 0 0 0 0 0 0 0 0

Extended LTLs 0 0 0 0 0 0 0 7 7

Extended both LTLs and

RTLs

0 0 0 0 0 0 0 1 1

Newly signalized

intersections

Added LTLs 0 0 0 0 0 2 0 0 2

Added RTLs 0 0 0 0 0 1 0 0 1

Added both LTLs and RTLs 0 1 0 0 0 0 0 0 1

Total 15 61 0 1 15 7 21 23 143

LTL = Left-turn lane

RTL = Right-turn lane
3

0

Table 8. Number of Improved Sites at Urban Intersections.

Intersection

traffic control Project type

Number of improved sites by state

IA IL LA MN NC NE OR VA Total

Existing unsignalized

intersections

Added LTLs 2 6 1 2 0 5 4 0 20

Added RTLs 1 3 0 0 0 0 0 0 4

Added both LTLs and RTLs 0 1 0 0 0 0 0 0 1

Extended LTLs 0 0 0 0 0 0 0 4 4

Extended both LTLs and 

RTLs

0 0 0 0 0 0 0 0 0

Existing signalized

intersections

Added LTLs 9 17 5 3 2 3 4 0 43

Added RTLs 1 17 2 0 0 0 0 1 21

Added both LTLs and RTLs 3 7 1 1 0 0 0 0 12

Extended LTLs 0 0 0 0 0 0 0 0 0

Extended both LTLs and 

RTLs

0 0 0 0 0 0 0 0 0

Newly signalized

intersections

Added LTLs 1 14 3 4 3 1 6 0 32

Added RTLs 0 0 0 0 0 0 0 0 0

Added both LTLs and RTLs 0 0 0 0 0 0 0 0 0

Total 17 65 12 10 5 9 14 5 137

LTL = Left-turn lane

RTL = Right-turn lane
31

Table 9 presents not only the number of intersections by area type, traffic control type,

and project type, but also the number of added or extended left- and right-turn lanes. The

added or extended turn lanes include only major-road turn lanes at unsignalized

intersections, but may include both major- and minor-road turn lanes at signalized

intersections. The table shows that the 280 improved sites include 411 added or extended

left-turn lanes and 185 added or extended right-turn lanes.

Table 10 presents the distribution of improved sites by the year in which the project

was constructed. The table shows that 268 of the 280 projects (94 percent) were

constructed during the years from 1994 to 1997, inclusive. The earliest project was

constructed in 1989 and the latest project was constructed in 1998. Virtually all of the

projects were simple enough that their construction was begun and completed during a

single calendar year.

Selection of Comparison Sites

Evaluation of the safety effectiveness of the projects implemented at the improved

sites requires a method for estimating the changes in safety that would have occurred at the

improved sites had the improvements not been made. This is normally accomplished with

data from sites that are not improved during the study period. 

Later sections of this report present three alternative evaluation approaches that were

used during the project. One of the alternative approaches considered relies on one-to-one

matching between improved and similar unimproved sites, while two others rely on

predictive models developed from groups of unimproved sites. The sites selected as

similar to the improved sites through a one-to-one matching process are referred to in this

report as comparison sites. The identification and selection of these comparison sites and

the number and characteristics of such sites are described below. This is followed by a

description of other unimproved sites that were included in the development of predictive

models, but were not matched to any particular improved site; such sites are referred to in

this report as reference sites, and their selection and characteristics are discussed later in

this section of the report.

Identification and Screening of Candidate Comparison Sites

Candidate comparison sites were identified by the research team, with assistance from

the participating highway agencies. Screening of candidate comparison sites was

conducted both from office and photolog data and in field visits. The criteria for a

comparison site to match a particular treatment site were as follows:

• Located in the same state as the improved site.
3

2

Table 9. Number of Improved Intersections and Number of Turn-Lanes Added or Extended in 

Intersection Improvement Projects.

Intersection

traffic

control

Project

type

Number of intersections and added or extended turn lanes

Rural intersections Urban intersections Combined

No. of

intersections

No. of added

or extended No. of

intersections

No. of added

or extended No. of

intersections

No. of added or

extended

LTLs RTLs LTLs RTLs LTLs RTLs

Existing

unsignalized

intersections

Added LTLs 61 81 – 20 30 – 81 111 –

Added RTLs 41 – 57 4 – 6 45 – 63

Added both LTLs

and RTLs

27 45 40 1 2 2 28 47 42

Extended LTLs 7 7 – 4 6 – 11 13 –

Extended both

LTLs and RTLs

1 2 2 0 0 0 1 2 2

Existing

signalized

intersections

Added LTLs 0 0 – 43 128 – 43 128 –

Added RTLs 0 – 0 21 – 46 21 – 46

Added both LTLs

and RTLs

0 0 0 12 42 29 12 42 29

Extended LTLs 2 2 – 0 0 – 2 2 –

Extended both

LTLs and RTLs

0 0 0 0 0 0 0 0 0

Newly

signalized

intersections

Added LTLs 2 4 – 32 60 – 34 64 –

Added RTLs 1 – 2 0 – 0 1 – 2

Added both LTLs

and RTLs

1 2 1 0 0 0 1 2 1

Total 143 143 102 137 268 83 280 411 185

LTL = Left-turn lane

RTL = Right-turn lane
33

Table 10. Year Completed for Projects at 

Improved Intersections.

Year completed No. of projects Percent of projects

1989

1991

1992

1993

1994

1995

1996

1997

1998

1

2

7

5

71

82

55

55

 2

280

0.4

0.7

2.5

1.8

25.4

29.3

19.6

19.6

0.7

• Located geographically as close as possible to the improved site. Whenever

possible, a matched comparison site was chosen on the same highway or in the

same general area as the improved site; however, where a close geographical

location was not possible, a similar intersection in a different part of the state was

selected.

• Same number of intersection legs as the improved site (i.e., both were either

three-leg or four-leg intersections).

• Same traffic control as the improved site (i.e., both were either two-way

STOP-controlled or signal-controlled intersections).

• Similar geometrics to the improved site (e.g., if the improved site was a skewed

intersection or was located on a multilane highway, then a skewed intersection or

an intersection on a multilane highway was selected as the comparison site, if

possible).

• Similar ADT to the improved site; however, ADT matching was approximate

because some comparison sites were selected before the minor-road ADT was

known.

• No major geometric or traffic control changes during the study period (generally

1988 to 1999).

It was the original intention that the geometrics of the matched comparison site should

resemble the geometrics of the improved site in the period before the improvement was

made; in other words, the comparison sites would be intersections without major-road turn

lanes. This criterion proved impractical because, especially for urban signalized

intersections, candidate comparison sites with no turn lanes were very hard to find. 

Therefore, a decision was made that the geometrics of the matched comparison site should
34

resemble the geometrics of the improved site in either its condition before improvement or

after improvement, with matching to the condition before improvement being preferred. In

all cases, the matched comparison site must have undergone no major geometric or traffic

control improvement during the study period. In other words, if the matched comparison

site had major-road left-turn lanes, it must have had those lanes in place during the periods

both before and after the project at the improved site.

For improved sites at which both signalization and turn lanes were installed, the

matched comparison site was a similar unsignalized intersection that remained

unsignalized throughout the study period.

Number and Characteristics of Matching Improved and

Comparison Sites

Matched comparison sites were identified for 260 of the 280 improved sites

(93 percent). The other 20 sites were sufficiently unique that a satisfactory matching

comparison site could not be found.

The characteristics of the 260 pairs of matching improved and comparison sites are

summarized in tables 11 through 15, which are analogous to tables 5 through 10 presented

above for the improved sites. Tables 11 through 15 represent the characteristics of the

260 improved sites (a subset of the 280 improved sites presented earlier). Section 4 of this

report presents further data on the characteristics of the matched improved and comparison

sites.

Selection of Reference Sites

As described earlier, a portion of the improved sites’ evaluation uses predictive models

developed with data from unimproved sites. An advantage of this approach is that one-onone matching of improved and unimproved sites is not required. In fact, the larger the data

set of unimproved sites on which predictive models are based the better, so it is desirable to

have data for more unimproved sites than improved sites.

To increase the sample of unimproved sites, additional reference sites were selected. 

The selection of the additional reference sites and the number and characteristics of the

combined data set of comparison and reference sites is described below.
3

5

Table 11. Number of Matched Pairs of Improved and Comparison Sites by Area Type and State.

Number of matched sites by area type

State Rural Urban Total

Iowa (IA) 15 17 32

Illinois (IL) 59 54 113

Louisiana (LA) 0 11 11

Minnesota (MN) 1 10 11

North Carolina (NC) 18 5 23

Nebraska (NE) 4 8 12

Oregon (OR) 17 14 31

Virginia (VA) 22 5 27

Total 136 124 260
3

6

Table 12. Number of Matched Pairs of Improved and Comparison Sites at Rural Intersections.

Intersection

traffic control Project type

Number of matched pairs of sites by state

IA IL LA MN NC NE OR VA Total

Existing unsignalized

intersections

Added LTLs 0 20 0 1 14 4 11 7 57

Added RTLs 14 17 0 0 0 0 4 4 39

Added both LTLs and RTLs 1 21 0 0 1 0 2 2 27

Extended LTLs 0 0 0 0 0 0 0 2 2

Extended both LTLs and

RTLs

0 0 0 0 0 0 0 0 0

Existing signalized

intersections

Added LTLs 0 0 0 0 0 0 0 0 0

Added RTLs 0 0 0 0 0 0 0 0 0

Added both LTLs and RTLs 0 0 0 0 0 0 0 0 0

Extended LTLs 0 0 0 0 0 0 0 6 6

Extended both LTLs and

RTLs

0 0 0 0 0 0 0 1 1

Newly signalized

intersections

Added LTLs 0 0 0 0 2 0 0 0 2

Added RTLs 0 0 0 0 1 0 0 0 1

Added both LTLs and RTLs 0 1 0 0 0 0 0 0 1

Total 15 59 0 1 18 4 17 22 136

LTL = Left-turn lane

RTL = Right-turn lane
3

7

Table 13. Number of Matched Pairs of Improved and Comparison Sites at Urban Intersections.

Intersection

traffic control Project type

Number of matched pairs of sites by state

IA IL LA MN NC NE OR VA Total

Existing unsignalized

intersections

Added LTLs 2 4 1 2 0 5 4 0 18

Added RTLs 1 0 0 0 0 0 0 0 1

Added both LTLs and RTLs 0 1 0 0 0 0 0 0 1

Extended LTLs 0 0 0 0 0 0 0 0 0

Extended both LTLs and 

RTLs

0 0 0 0 0 0 0 0 0

Existing signalized

intersections

Added LTLs 9 16 4 3 2 2 4 0 40

Added RTLs 1 15 2 0 0 0 0 1 19

Added both LTLs and RTLs 3 6 1 1 0 0 0 0 11

Extended LTLs 0 0 0 0 0 0 0 4 4

Extended both LTLs and 

RTLs

0 0 0 0 0 0 0 0 0

Newly signalized

intersections

Added LTLs 1 12 3 4 3 1 6 0 30

Added RTLs 0 0 0 0 0 0 0 0 0

Added both LTLs and RTLs 0 0 0 0 0 0 0 0 0

Total 17 54 11 10 5 8 14 5 124

LTL = Left-turn lane

RTL = Right-turn lane
3

8

Table 14. Number of Improved Intersections and Number of Turn-Lanes Added in Projects at Improved 

 Sites with Matched Comparison Sites in Candidate Intersection Improvement Projects.

Intersection

traffic

control

Project

type

Number of intersections and added or extended turn lanes

Rural intersections Urban intersections Combined

No. of

intersections

No. of added

or extended No. of

intersections

No. of added

or extended No. of

intersections

No. of added or

extended

LTLs RTLs LTLs RTLs LTLs RTLs

Existing

unsignalized

intersections

Added LTLs 57 75 – 18 26 – 75 101 –

Added RTLs 39 – 54 1 – 1 40 – 55

Added both LTLs and RTLs 27 45 40 1 2 2 28 47 42

Extended LTLs 6 6 – 4 6 – 10 12 –

Extended both LTLs and RTLs 1 2 2 0 0 0 1 2 2

Existing

signalized

intersections

Added LTLs 0 0 – 40 120 – 40 120 –

Added RTLs 0 – 0 19 – 41 19 – 41

Added both LTLs and RTLs 0 0 0 11 38 28 11 38 28

Extended LTLs 2 2 – 0 0 – 2 2 –

Extended both LTLs and RTLs 0 0 0 0 0 0 0 0 0

Newly

signalized

intersections

Added LTLs 2 4 – 30 56 – 32 60 –

Added RTLs 1 – 2 0 – 0 1 – 2

Added both LTLs and RTLs 1 2 1 0 0 0 1 2 1

Total 136 136 99 124 248 72 260 384 171

LTL = Left-turn lane

RTL = Right-turn lane
39

Table 15. Completion Date for Intersection Improvement Projects 

 With Matched Comparison Sites.

Year completed No. of projects Percent of projects

1989

1991

1992

1993

1994

1995

1996

1997

1998

1

1

7

4

67

73

54

51

 2

260

0.4

0.4 

2.7

1.5

25.8

28.1

20.8

19.6

0.8

Identification and Screening of Candidate Reference Sites

Reference sites were intersections similar to the sites that were improved, but not

matched to any particular improved site. Reference sites were of the same area types and

traffic control types as the improved sites, but must have been free of unusual features and

undergone no major geometric or traffic control improvements during the study period.

The research team identified candidate reference sites with assistance from

participating State highway agencies. Many of the reference sites were candidate

comparison sites that did not match any particular improved site. The candidate reference

sites were screened both from office and photolog data and in field visits. Reference sites

were retained only if traffic volume and traffic accident data for the site were available. 

Number and Characteristics of Comparison and Reference Sites

A total of 40 additional reference sites were selected and included in the data

collection effort described in Section 4 of this report. Thus, there were a combined total of

300 unimproved sites available for use in comparison groups and for development of

predictive models, 260 matched comparison sites and 40 additional reference sites. 

Table 16 presents the distribution of the 300 comparison and reference sites by area

type, traffic control type, and state. The comparison and reference sites include only 6 rural

signalized intersections, but at least 50 rural unsignalized, urban unsignalized, and urban

signalized intersections. Approximately 53 percent of the comparison and reference sites

are in rural areas, and 47 percent in urban areas. Approximately 69 percent of the

comparison and reference sites are at unsignalized intersections, and 31 percent are at

signalized intersections. Approximately 33 percent of the comparison and reference sites

are at three-leg intersections, and 67 percent are at four-leg intersections.

Section 4 of this report presents data on the traffic volumes and accident experience at

the comparison and reference sites.
4

0

Table 16. Number of Comparison and Reference Sites by Area Type, Traffic Control Type, and State.

Area type

Traffic control

type

Number of

intersection legs

State

IA IL LA MN NC NE OR VA Total

Rural Unsignalized 3 2 32 0 0 12 0 12 14 72

4 15 40 0 1 6 8 7 5 82

Total 17 72 0 1 18 8 19 19 154

Rural Signalized 3 0 0 0 0 0 0 0 1 1

4 0 0 0 0 0 0 0 5 5

Total 0 0 0 0 0 0 0 6 6

Urban Unsignalized 3 1 9 2 0 0 0 6 0 8

4 3 12 2 6 3 5 5 0 36

Total 4 21 4 6 3 5 11 0 54

Urban Signalized 3 1 4 1 0 0 0 0 1 7

4 12 42 6 4 2 5 4 4 79

Total 13 46 7 4 2 5 4 5 86

Both Both 3 4 45 3 0 12 0 18 16 98

4 30 94 8 11 11 18 16 14 202

Total 34 139 11 11 23 18 34 30 300

41

4. DATA COLLECTION

This section of the report documents the data collection performed for the intersection

sites selected for the safety evaluation of left- and right-turn lanes. The types of data

collection addressed includes geometric design and traffic control data, traffic volume data,

and traffic accident data. Each type of data is addressed below.

Geometric Design and Traffic Control Data

Data were collected on the geometric design and traffic control features of each

improved, comparison, and reference site. Nearly all of the study sites were visited in the

field by a research team member to obtain geometric design and traffic control data. In

addition, geometric design and traffic control data were obtained from the following

sources, whenever available:

• Construction or as-built plans.

• Intersection drawings or sketches.

• Project reports.

• Highway agency project memoranda.

These sources were also useful in documenting what specific geometric changes were

made as part of a project.

Field Visits

The field visits provided a key opportunity to observe the characteristics of each site

and record data of interest. Some intersections were visited twice, once during the

selection of improved sites and once during the data collection activities. Time spent in the

field in each state was also used to identify or review candidate comparison and reference

sites.

The field activities involved visits to both highway agency offices and field sites, and

had multiple purposes including:

• Reviewing each intersection in the field.

• Taking photographs and/or making a videotape of site conditions for later

reference during the evaluation.

• Obtaining documentation of the geometrics and traffic control of each intersection

both before and after the project.
42

• Obtaining documentation on the reasons why the project was implemented.

• Obtaining documentation on the starting and completion dates for each project.

• Interviewing the engineers most familiar with development of each project and its

operational and safety effects.

The vast majority of field visits to improved sites were made after completion of project

construction. This provided an opportunity to verify in the field that the project had, in

fact, been constructed and that its geometrics in the period after construction matched the

data provided in the office. The geometrics before construction were often evident in the

field, due to differences in pavement surfaces, but were also documented from office

records. Relying on both office and field data, a record was made of the geometric design

and traffic control changes made as part of the improvement project (see appendix D).

Geometric Design and Traffic Control Variables

Geometric design and traffic control data were collected for each study intersection. 

For each individual intersection approach, the geometric design and traffic control

variables obtained were:

• Number of through lanes.

• Number of left-turn lanes.

• Number of right-turn lanes.

• Type of left-turn channelization.

• Type of right-turn channelization.

• Horizontal alignment.

• Approach grades.

• Presence of crest/sag vertical curves.

• Total through lane width.

• Right shoulder type.

• Right shoulder width.

• Total left-turn lane width.

• Total left-turn lane length.

• Total right-turn lane width.

• Total right-turn lane length.

• Presence of median (divided/undivided).

• Median width.

• Median type.

• One-way vs. two-way operation.

• Left-turn prohibition.

• Number of driveways within 76 m (250 ft).

• Type of driveways.

• Curb parking within 76 m (250 ft).
43

• Type of traffic control.

• Type of left-turn phasing (if signalized).

• Presence of pedestrian signals (if signalized).

• Presence of advance warning signs.

• Posted speed limit.

For the intersection as a whole, variables obtained were:

• Number of intersection legs.

• Angle of intersection.

• Area type (rural/urban).

• Character of development.

• Lighting.

• Level of pedestrian activity.

The set of geometric design and traffic control variables obtained was purposely broader

than needed for the planned analyses so that issues beyond those planned could be

addressed, as needed. It was never envisioned that all of these variables could, or should,

be related to traffic accidents, but they were obtained to assure that the documentation of

each study intersection was very complete. Appendix D provides definitions of the

measurement methods and codes used for each of these geometric design and traffic

control variables.

Traffic Volume Data

Traffic volume data were obtained for each study intersection. The desirable traffic

volume data set for any study intersection included:

C Major- and minor-road ADTs for each year of the study period.

C Intersection turning movement counts for morning and evening peak periods.

It was found, as a practical matter, that the participating states nearly always had ADT data

on file for the major-road in the vicinity of each intersection. Minor-road ADT data were

often, but not always, available for the improved sites; ADT data were likely to be

available for the improved sites because there had often been a traffic count made at the

intersection as part of the design of the project. Minor-road ADT data for comparison and

reference sites were available for virtually every intersection of potential interest in some

states and only for a very limited number of intersections in other states.

Intersection turning movement counts were of direct interest to the study. In

evaluating the safety effectiveness of intersection left- and right-turn lanes, it would be

valuable to know the volume of vehicles turning left or right and using the turn lanes of

interest. However, turning movement volumes were not available for most of the
44

intersections. In particular, turning movement volumes were only available for less than

10 percent of the improved sites, and an even smaller percentage of the comparison and

reference sites. Therefore, as a practical matter, it was not feasible to use intersection

turning volumes in the safety evaluation because the sample size for any given type of

project would have been substantially reduced.

It was decided that, for a intersection to be used in the evaluation, ADT data should be

available for both the major- and minor-road legs of the intersection for at least one year

during the study period. If this minimal traffic volume data set was not available, any

improved, comparison, or reference site was dropped from the study. For most

intersections, major-road ADT data for several years and minor-road ADT data for at least

one year were available. These ADT data came from many sources in the participating

highway agencies, including state ADT maps and logbooks, county and city ADT maps,

traffic volume data bases and manual files, and, in some cases, traffic counts made

specifically for this evaluation. However, as stated above, no intersection was used unless

major- and minor-road ADT data were available for at least one year.

Some of the analyses performed required separate estimates of intersection ADTs for

each year of the study period. These estimates for each individual year were obtained by

interpolation and extrapolation from the ADT data obtained from the participating states. 

All extrapolations were checked very carefully to assure that the rates of ADT growth or

decline were reasonable for the site conditions and consistent with ADT growth or decline

patterns at nearby sites. Where ADT data were available for only one year, extrapolations

to earlier and later years were made using the following data sources for guidance:

• Minor-road ADTs at a given intersection were extrapolated, where possible, using

the growth or decline rate for the major road at the same intersection.

• Major- and minor-road ADTs at one intersection were extrapolated based on ADT

growth or decline patterns for a nearby intersection, such as the matched

comparison site, or a nearby set of intersections.

• Major- and minor-road ADTs at one intersection were extrapolated based on ADT

growth or decline rates from a nearby continuous count station, if available.

Table 17 presents the distribution of ADTs for the improved and comparison/reference

sites, including mean, minimum, and maximum values of the ADT for the year 1999, and

annualized percentage growth rates in ADT over the period from 1988 to 1999, for

major-road ADT, minor-road ADT, and total ADT entering the intersection. Where the

ADTs at an intersection differ between the two major-road approaches or the two

minor-road approaches, Table 17 is based on the larger of the two major- or minor-road

ADT values; for this reason, the mean total entering ADT is not necessarily equal to the

sum of the mean major- and minor-road ADTs. Table 18 presents comparable data for the

matched improved and comparison sites. The 260 matched improved sites shown in

Table 18 are a subset of the 280 total improved sites shown in table 17.
4

5

Table 17. ADT Volumes for All Improved and Comparison/Reference Sites.

Area

type

Traffic control

type Site type

Number

of 

intersections

Ma jor-ro ad AD T (v eh/day) Min or-ro ad AD T (v eh/day) To tal en tering A DT (veh /day)

Mean

1999

Min imum

1999

Ma ximum

1999

Gro wth

rate

1988-

1999

Mean

1999

Min imum

1999

Ma ximum

1999

Gro wth

rate

1988-

1999

Mean

1999

Min imum

1999

Ma ximum

1999

Gro wth

rate

1988-

1999

Rural Unsignalized Improved 131 9,100 1,600 32,400 2.6 1,400 50 11,800 2.4 9,700 2,000 32,000 2.6

Comparison/

Reference

154 8,100 1,100 26,800 2.5 900 25 6,400 2.4 8,500 1,100 26,700 2.5

Rural Signalized Improved 8 15,100 10,700 20,000 1.8 5,600 2,500 8,400 2.5 17,800 14,800 22,900 2.3

Co m pariso n/ 

Reference

6 20,300 14,500 26,000 3.1 5,900 1,300 11,400 3.7 22,600 19,000 31,700 2.8

Rural Newly signalized Improved 4 11,900 4,200 17,700 3.2 5,200 9,200 6,400 3.3 16,400 11,400 21,900 3.1

Comparison/

Reference

— — — — — — — — — — — — —

Urban Unsignalized Improved 25 14,500 1,520 40,600 2.1 1,800 200 8,0001 2.2 15,500 1,800 41,200 2.1

Comparison/

Reference

54 14,400 2,000 25,600 2.2 2,400 80 6,300 2.2 15,500 2,60 26,500 2.2

Urban Signalized Improved 80 21,300 7,200 55,100 1.1 7,900 550 26,000 1.8 26,800 7,500 61,000 1.2

Comparison/

Reference

86 21,500 5,800 55,100 2.0 7,400 100 25,700 1.9 26,600 6,800 62,300 1.9

Urban Newly signalized Improved 32 16,700 4,600 40,300 2.2 4,300 100 13,700 2.2 19,600 5,400 43,800 2.2

Comparison/

Reference

— — — — — — — — — — — — —
46

Traffic Accident Data

Traffic accident data for the study intersections were obtained from the computerized

accidents records of the participating state highway agencies. In some cases, the

computerized data were supplemented with collision diagrams prepared by manual or

computer means.

Accident data were obtained for all study intersections in each state for a period of 9 to

13 years. Table 19 shows the specific time periods for which data were available in each

state. In most states, the study period began with the calendar year 1988. However,

because of limitations on data availability and changes in data formats, data for Minnesota

and Virginia were obtained for a period beginning in 1990 and data for North Carolina for

a period beginning in 1991. The final year of the study period was 1999 for all states

except one; in Oregon, the study was extended to include data for the year 2000 because

both accident and ADT data for that period were available.

Data were requested from each state for all accidents during the study period that

occurred on any intersection leg within 300 meters (1,000 feet) of each intersection. The

300-meter (1,000-foot) distance was not selected because accidents that far from the

intersection are necessarily related to the intersection, but simply to assure that all accidents

of potential interest were available and that no request for supplementary data would need

to be available. 

After evaluation of the available data, a criterion for identifying intersection-related

accidents of interest to the evaluation was established. Intersection-related accidents were

selected from the available data including accidents assigned mileposts within 75 meters

(250 feet) of the study intersection, and had were designated by the investigating officer or

accident data coder that they were related to the operation of the intersection. Where

closely spaced intersections were present, the 75-meter (250 foot) boundary was decreased

to a point half the distance to the adjacent intersection. Accidents indicated as being

non-intersection-related or driveway-related were excluded from the evaluation. Table 19

includes data only for accidents that meet this definition of being related to the intersection.

The one exception to this procedure described above was in accident data from

Illinois. Illinois codes all intersection-related accidents to the milepost of the intersection. 

Therefore, the milepost cannot be used to distinguish the distance of a collision from the

intersection in question. In Illinois data, all accidents assigned to the intersection milepost

are presumed to be related to the operation of the intersection and were included in the

analyses. 
4

7

Table 18. ADT Volumes for Matched Improved and Comparison Sites.

Area

type

Traffic control

type Site type

Number

of 

intersections

Ma jor-ro ad AD T (v eh/day) Min or-ro ad AD T (v eh/day) To tal en tering A DT (veh /day)

Mean

1999

Min imum

1999

Ma ximum

1999

Gro wth

rate

1988-

1999

Mean

1999

Min imum

1999

Ma ximum

1999

Gro wth

rate

1988-

1999

Mean

1999

Min imum

1999

Ma ximum

1999

Gro wth

rate

1988-

1999

Rural Unsignalized Matched

Improved

125 9,100 1,600 32,400 2.6 1,300 50 6,800 2.3 9,700 2,000 32,000 2.5

Matched

Comparison

125 7,700 1,100 26,800 2.6 900 25 6,400 2.3 8,100 1,100 26,300 2.6

Rural Signalized Matched

Improved

7 15,000 10,700 20,000 1.7 5,600 2,500 8,400 2.4 17,700 14,800 22,900 2.3

Marked

Comparison

7 20,300 14,500 26,000 3.1 5,900 1,300 11,400 3.7 22,600 19,000 31,700 2.8

Rural Newly signalized Matched

Improved

4 11,900 9,200 17,700 3.2 5,200 4,200 6,400 3.3 16,400 11,400 21,900 3.2

Matched

Comparison

4 10,800 7,500 18,000 3.0 1,900 700 2,900 3.1 12,600 8,000 19,600 3.2

Urban Unsignalized Matched

Improved

20 14,900 1,600 40,600 2.4 1,900 200 8,000 2.3 15,900 1,900 41,200 2.4

Matched

Comparison

20 13,900 2,000 25,600 2.3 1,400 100 4,400 2.0 14,500 2,600 26,500 2.3

Urban Signalized Matched

Improved

74 21,100 7,200 55,100 1.1 7,800 550 26,000 1.8 26,600 7,500 61,100 1.2

Matched

Comparison

74 21,300 5,800 55,100 1.9 7,200 100 25,700 2.0 26,100 6,800 62,300 1.9

Urban Newly signalized Matched

Improved

30 15,800 4,600 38,200 2.5 4,200 100 13,700 2.3 18,600 5,400 40,300 2.5

Matched

Comparison

30 14,200 4,000 36,500 2.1 2,800 80 10,200 2.3 15,760 4,800 38,000 2.1
4

8

Table 19. Summary of Accident Database.

State

Accident data period Number

of 

intersections

Total

 No. of

accidents

Accidents by severity level

Percentage of accidents by

severity level

First year Last year

Total No.

of years Fatal Injury PDO Fatal Injury PDO

Iowa (IA) 1988 1999 12 66 3,611 15 1,522 2,074 0.4 42.1 57.5

Illinois (IL) 1988 1999 12 265 12,875 57 4,537 8,281 0.4 35.5 64.1

Louisiana (LA) 1988 1998 11 23 2,668 8 994 1,666 0.3 37.3 62.4

Minnesota

(MN)

1990 1999 10 22 890 5 334 551 0.6 37.5 61.9

North Carolina

(NC)

1991 1999 9 46 1,055 5 508 542 0.5 48.2 51.3

Nebraska (NE) 1988 1999 12 31 1,681 6 726 949 0.4 43.2 56.4

Oregon (OR) 1988 2000 13 69 1,860 14 946 900 0.8 50.9 48.3

Virginia (VA) 1990 1999 10 58 1,416 13 636 767 0.9 44.9 54.2

Total 580 26,056 123 10,203 15,730 0.5 39.2 60.3
49

In some states, the accident location milepost or reference point assigned to an

intersection may change from year to year. These changes were accounted for so that a

consistent set of accident data from year to year were extracted from the available accident

data.

The accident data elements obtained from each state varied; in most cases, the accident

data provided by the state included more accident descriptors than were needed for the

study. Both accident-level and vehicle-level accident descriptors were obtained. The

variables that were actually used in preliminary investigations and in the safety evaluation

itself were:

• Date of accident (month/day/year).

• Accident location (typically by county, route, and milepost or reference point).

• Accident severity (fatal/injury/property damage only).

• Number of vehicles involved.

• Accident type/manner of collision.

• Direction of travel of involved vehicles.

• Actual or intended movement of involved vehicles (through/left turn/right turn/U

turn).

• Relationship to intersection (at intersection/not at intersection but intersection

related/not intersection related).

• Vehicle and party types involved (passenger car/truck/bus/pedestrian/bicycle).

The dates for which accident data were obtained are shown in Table 19. The table

shows that the periods for which accident data were available varied among the states. In

each state, the accident data period extends back to 1988, whenever possible; where a later

date is shown for the beginning of the accident data period, data before that date were

unavailable. Study periods before and after improvement of each treated site were

determined based on criteria described in Section 5 of this report.

Table 19 documents the magnitude of the available accident data base. The table

shows that there were a total of 26,056 intersection-related accidents during the study

period for all 580 intersections combined. Approximately 49 percent of the accidents

occurred in Illinois which, as documented above, included approximately 45 percent of the

study intersections. The table also shows the distribution of accident severity, by state and

overall; accidents involving fatalities ranged from 0.3 to 0.9 percent of all accidents, and

accidents involving non-fatal injuries ranged from 35.5 to 50.9 percent of all accidents.

Table 20 compares the total intersection accident experience, exposure, and accident

rate per million entering vehicles, for periods before and after the improvement projects,

for the 260 matched improved and comparison sites at rural intersections. Table 21

presents comparable data for urban intersections.
5

0

Table 20. Safety Performance of Matched Improved and Comparison Sites at Rural Intersections.

Area type

Traffic control

type Project type Site type

Before period After period

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/

MEV)

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/

MEV)

Rural Unsignalized Added LTLs Improved 648 10,000 1288.4 0.50 271 11,400 963.1 0.28

Comparison 321 7,300 915.6 0.35 346 8,600 730.3 0.47

Rural Unsignalized Added RTLs Improved 240 4,700 445.2 0.54 142 4,750 335.1 0.42

Comparison 178 5,800 440.2 0.40 108 5,900 354.2 0.30

Rural Unsignalized Added both

LTLs and

RTLs

Improved 234 8,500 568.3 0.41 150 10,400 427.3 0.35

Comparison 152 7,200 478.2 0.32 94 8,400 344.1 0.27

Rural Unsignalized Extended

LTLs

Improved 28 12,600 64.3 0.44 14 14,200 20.7 0.68

Comparison 6 9,400 48.1 0.13 1 11,900 17.4 0.06

Rural Unsignalized Extended

both LTLs

and RTLs

Improved — — — — — — — —

Comparison — — — — — — — —

Rural Signalized Added LTLs Improved — — — — — — — —

Comparison — — — — — — — —

Rural Signalized Added RTLs Improved — — — — — — — —

Comparison — — — — — — — —

Rural Signalized Added both

LTLs and

RTLs

Improved — — — — — — — —

Comparison — — — — — — — —

Rural Signalized Extended

LTLs

Improved 89 14,600 213.2 0.42 28 17,500 88.2 0.32

Comparison 139 18,700 267.7 0.52 69 21,700 114.5 0.60

Rural Signalized Extended

both LTLs

and RTLs

Improved 31 13,800 35.2 0.88 8 17,000 12.4 0.65

Comparison 20 17,200 44.1 0.45 9 20,700 15.1 0.60

MEV = million entering vehicles
51

Table 20. Safety Performance of Matched Improved and Comparison Sites at Rural Intersections (Continued).

Area type

Traffic control

type Project type Site type

Before period After period

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/

MEV)

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/

MEV)

Rural Newly

signalized

Added LTLs Improved 40 15,800 39.6 1.01 62 18,400 61.2 1.01

Comparison 7 13,000 32.0 0.22 31 15,000 50.4 0.62

Rural Newly

signalized

Added RTLs Improved 27 12,600 18.4 1.47 44 14,900 21.7 2.03

Comparison 11 9,100 13.3 0.83 20 10,800 15.7 1.27

Rural Newly

signalized

Added both

LTLs and

RTLs

Improved 23 7,500 22.1 1.04 6 10,700 11.7 0.51

Comparison 4 5,100 14.8 0.27 1 7,500 8.2 0.12

MEV = million entering vehicles
5

2

•

Table 21. Safety Performance of Matched Improved and Comparison Sites at Urban Intersections.

Area type

Traffic control

type Project type Site type

Before period After period

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/ MEV)

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/

MEV)

Urban Unsignalized Added LTLs Improved 352 13,500 595.5 0.59 152 16,100 451.5 0.34

Comparison 216 12,700 567.0 0.38 221 14,900 411.5 0.54

Urban Unsignalized Added RTLs Improved 3 1,700 5.4 0.56 0 2,000 1.5 0.00

Comparison 8 2,400 7.9 1.03 1 2,700 2.0 0.50

Urban Unsignalized Added both

LTLs and RTLs

Improved 12 18,400 46.9 0.26 4 18,900 27.6 0.14

Comparison 17 24,500 62.6 0.27 6 26,500 38.6 0.16

Urban Unsignalized Extended LTLs Improved — — — — — — — —

Comparison — — — — — — — —

Urban Unsignalized Extended both

LTLs and RTLs

Improved — — — — — — — —

Comparison — — — — — — — —

Urban Signalized Added LTLs Improved 2,707 23,700 2,306.7 1.17 1,108 24,800 1,467.0 0.76

Comparison 2,246 20,500 1,958.1 1.15 1,404 22,900 1,371.9 1.02

Urban Signalized Added RTLs Improved 1,551 24,400 1,298.7 1.19 666 26,400 568.3 1.17

Comparison 1,502 24,600 1,315.4 1.14 538 27,900 585.8 0.92

Urban Signalized Added both

LTLs and RTLs

Improved 867 23,700 716.8 1.21 320 28,800 380.7 0.84

Comparison 796 21,800 662.5 1.20 314 26,700 348.4 0.90

Urban Signalized Extended LTLs Improved 162 32,800 277.7 0.58 133 35,000 159.8 0.83

Comparison 141 30,600 265.3 0.53 111 36,300 161.7 0.69

Urban Signalized Extended both

LTLs and RTLs

Improved — — — — — — — —

Comparison — — — — — — — —

MEV = million entering vehicles
5

3

Table 21. Safety Performance of Matched Improved and Comparison Sites at Urban Intersections (Continued).

Area type

Traffic control

type Project type Site type

Before period After period

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/

MEV)

No. of

accidents

Average

ADT

(veh/day)

Exposure

(MEV)

Accident

rate

(acc/

MEV)

Urban Newly signalized Added LTLs Improved 1,008 15,100 1,155.0 0.87 416 18,100 735.3 0.57

Comparison 564 13,500 1,029.7 0.55 354 15,500 632.5 0.56

Urban Newly signalized Added RTLs Improved — — — — — — — —

Comparison — — — — — — — —

Urban Newly signalized Added both

LTLs and

RTLs

Improved — — — — — — — —

Comparison — — — — — — — —

MEV = million entering vehicles
54
55

5. EVALUATION PLAN

This section of the report presents the evaluation plan for determining the safety

effectiveness of intersection improvement projects involving left- and right-turn lanes. 

The discussion includes the target accident types and locations for the improvement

projects to be evaluated and the accident severity levels considered. An overview and

comparison of three alternative statistical approaches to before-after evaluation are

presented together with a detailed discussion of each of those three approaches. All three

alternative statistical approaches were used in the evaluation and the results obtained from

each approach are presented in section 6.

Target Accident Types and Locations

As part of the evaluation, data were obtained for each accident that occurred at or near

each study intersection in specified time periods both before and after the projects were 

evaluated. A decision was made concerning which accidents were the “target accidents” to

which the evaluation should be applied. Target accidents included all accidents that

occurred at or near each intersection during the study period. In addition, it was also

considered desirable to limit the evaluation to those accidents of collision types or collision

locations that were likely to be affected by the improvements being evaluated. If the

accident data for both the before and after periods included accidents of types that could

not conceivably be affected by the improvement, then this “noise” would have introduced

unnecessary variability into the accident counts that may have prevented the researchers’

ability to observe the effect of the improvement. 

Thus, while the effect of the improvement projects on total intersection accidents

should be considered, it also is desirable to consider specific subsets of total intersection

accidents as the target accidents for the evaluation. On the other hand, the effects of some

improvement types may be so pervasive that nearly every intersection accident may be

affected. Clearly, the appropriate target accidents depend on the nature of the

improvement being evaluated.

Section 3 of this report highlights four key types of intersection improvement projects

to be considered in the before-after evaluation:

• Addition of left-turn lanes.

• Addition of right-turn lanes.

• Addition of both left- and right-turn lanes.

• Extension of the length of existing left- and right-turn lanes.

These project types vary in the portions of the intersection and the accident types they

potentially affect. The effects of right-turn lanes appear to be the simplest and, therefore,

are addressed first. Projects involving left-turn lanes, both left- and right-turn lanes, and

extended turn lanes will then be addressed. 
56

Projects Involving Addition of Right-Turn Lanes

The safety effects of adding a right-turn lane on an intersection approach are expected

to be limited to only certain types of intersection accidents. For example, it might be

supposed that installation of a right-turn lane on a particular approach would affect

primarily the following accident types:

• Rear-end collisions between vehicles on the treated approach.

• Sideswipe, same-direction collisions between vehicles on the treated approach.

• Angle or sideswipe collisions between a right-turning vehicle and a vehicle within

the intersection or on the departing roadway of the intersecting street.

However, because other collision types could potentially be affected by installation of a

right-turn lane, a particular portion of the intersection can be designated as the “target

area” rather than designating particular accident types as target accidents. Figure 1

illustrates the portion of the intersection area that would be expected to be affected by

installation of a right-turn lane on a major-road approach. Only accidents occurring within

the target area would be expected to be affected by the addition of the right-turn lane.

Since only a limited portion of the intersection area is potentially affected by

installation of a right-turn lane, it may also be possible to use the accidents that occur in

the portions of the intersection outside the target area as a comparison group in the

analysis. This idea is explored further below.

Figure 2 illustrates an intersection at which right-turn lanes have been added on both

major-road approaches. Here there are separate target areas for the two improvements that

touch but do not overlap. Thus, it appears to be possible to evaluate each added right-turn

lane separately, and accidents outside the target areas (i.e., on the minor-road approaches

and on the departing roadways) could still be considered as part of a comparison group,

unaffected by the improvement.

A review of the descriptors of individual accidents available in data from the

participating states established that there are no data to explicitly identify accidents within

the target areas shown in figures 1 and 2 (essentially accidents that occur on a particular

intersection approach). It is possible, however, to identify:

• Accidents that involved vehicles that passed through the approach in question

(i.e., vehicles with a particular initial direction of travel). This is very close to the

target area definitions shown in figures 1 and 2, but could include collisions

involving vehicles on the approach of interest after they have left the shaded areas

shown in the figures.
57

Figure 1. Target Area for Evaluation of an Intersection with a Right-Turn Lane Added on 

 One Approach.

Minor Road
58

Minor Road

Figure 2. Target Area for Evaluation of an Intersection with Right-Turn lanes Added on 

 Two Approaches.
59

• Accidents that involved vehicles that passed through the approach in question and

were making or intending to make a particular movement (straight ahead, right

turn, or left turn). Of these, collisions involving vehicles making the right turn of

interest are obviously most relevant to evaluation of a particular right-turn lane.

The preceding discussion leads to definitions of three accident categories that can be used

to evaluate added right-turn lanes. These are:

• Total intersection accidents: all accidents that occur or are related to the

intersection being evaluated.

• Intersection approach accidents: all accidents involving one or more vehicles

that were on or had passed through the approach(es) on which the right-turn

lane(s) being evaluated were installed.

• Project-related accidents: all accidents involving one or more vehicles that had

made, were making, or intended to make the specific right-turn maneuver(s) for

which the right-turn lane(s) being evaluated were installed.

Projects Involving Addition of Left-Turn Lanes

The safety effects of adding a left-turn lane on an intersection approach are also

limited to only a portion of the intersection area, but that portion is larger for an added leftturn lane than for an added right-turn lane. Figure 3 illustrates the target area affected by

addition of a left-turn lane on one intersection approach. An added left-turn lane affects a

primary target area, where collisions may occur between a vehicle turning left and

same-direction or opposing-direction vehicles. In addition, collisions could also occur in

secondary target areas on the other approaches. Furthermore, at a signalized intersection,

accidents on all of the approaches may be indirectly affected if installation of a left-turn

lane results in changes in the signal phasing or timing.

Figure 3 illustrates that the primary and secondary target areas for installation of a

left-turn lane can include nearly the entire intersection. If left-turn lanes were installed on

opposing approaches at the same intersection, the coverage of the intersection area would

be even greater. 

As in the evaluation of right-turn lanes, the available accident data could not be used

to identify explicitly whether any particular collision occurred within the primary or

secondary target areas shown in Figure 3. However, the following accident categories can

be evaluated:
60

Figure 3. Target Area for Evaluation of an Intersection with a Left-Turn Lane Added on 

 One Approach.

Minor Road
61

• Total intersection accidents: all accidents that occur at or are related to the

intersection being evaluated.

• Approach-related accidents: all accidents involving one or more vehicles that

were on or had passed through the approach(es) on which the left-turn lane(s)

being evaluated were installed.

• Project-related accidents: all accidents involving one or more vehicle that had

made, were making, or intended to make the specific left-turn maneuver(s) for

which the left-turn lane(s) being evaluated were installed.

Projects Involving Addition of Both Left- and Right-Turn Lanes

Where both left- and right-turn lanes are added at a single intersection approach, the

area of the intersection affected by the project is the combined extent of the shaded areas

shown in figures 1 and 3. For such projects, total intersection accidents and intersection

approach accidents can be defined as they are for the left- and right-turn lane project types

discussed above. However, if both a left- and right-turn lane are installed on the same

intersection approach, the definition of project-related accidents must combine the separate

definitions for left- and right-turn lane projects given above.

Projects Involving Extension of the Length of Existing Turn Lanes

The target area for projects in which the length of an existing turn lane is extended

could potentially be smaller than the target areas for right- and left-turn lanes shown in

Figures 1 and 3, respectively. Accidents susceptible to correction by extending a turn lane

should probably include only those accidents that occur in the through travel lane in the

area upstream of the location where the turn lane begins in the condition before

improvement. However, there is no way to identify such accidents explicitly in the

available accident data. Therefore, projects involving extension of the length of existing

left-turn, right-turn, or left- and right-turn lanes have been evaluated with the same target

accident types defined above for addition of those specific types of turn lanes.

Accident Classification by Approach and Movement

The intersection improvement projects being evaluated, by definition, would be

expected to directly affect some parts of the intersection and not others. For example,

installation of a left-turn lane on one intersection approach would certainly directly affect

accidents involving vehicles using that approach, but only indirectly would affect

intersection accidents that did not involve that approach. Therefore, researchers developed

a method for classifying intersection accidents by the approach and actual or intended

turning movement.
62

Each accident-involved vehicle was classified by its initial direction of travel and

intended movement (i.e., by the same data that are normally used to construct a collision

diagram). The categories used were as follows:

• 11 - major-road approach 1 - through movement.

• 12 - major-road approach 1 - right-turn movement.

• 13 - major-road approach 1 - left-turn movement.

• 21 - major-road approach 2 - through movement.

• 22 - major-road approach 2 - right-turn movement.

• 23 - major-road approach 2 - left-turn movement.

• 31- minor-road approach 1 - through movement.

• 32 - minor-road approach 1 - right-turn movement.

• 33 - minor-road approach 1 - left-turn movement.

• 41 - minor-road approach 2 - through movement.

• 42 - minor-road approach 2 - right-turn movement.

• 43 - minor-road approach 2 - left-turn movement.

Thus, at four-leg intersections, there are 12 approach/movement combinations. Major-road

approach 1 is the northbound or eastbound approach, while major-road approach 2 is the

southbound or westbound approach. Similarly, minor-road approach 1 is the northbound or

eastbound approach, while minor-road approach 2 is the southbound or westbound

approach. At three-leg intersections, 6 of the 12 approach/movement combinations do not

exist. In the case of three-leg intersections, the one minor-road approach is designated as

minor-road approach 1; approach/movement combinations 31, 41, 42, 43, and either 12

and 23 or 13 and 22 do not exist at a three-leg intersection.

Each two-vehicle accident was classified by the appropriate pair of

approach/movement codes for the involved vehicle. For example, a collision between a

northbound major-road vehicle turning left and a southbound through vehicle was

classified as a 13/21 collision. Accidents involving only a single vehicle were classified

by the approach/movement code for that vehicle. Accidents involving more than two

vehicles were classified by the approach/movement codes of the first two involved

vehicles (whichever vehicles were designated as Vehicle 1 and Vehicle 2 in the accident

record).

Accidents were classified by involved approaches using the approach/movement

classes shown above. For example, accidents involving the northbound major-road

approach to a particular intersection were identified as all accidents for which the

approach/movement code for one of the involved vehicles was 11, 12, or 13. Multiplevehicle accidents involving vehicles from different approaches were counted as approachrelated for each of those approaches.

The same concept was used to identify accidents related to particular improvement

projects. For example, if the improvement project at a particular intersection involved

installing left-turn lanes on both the northbound and southbound major-road approaches,
63

then all accidents for which the approach/movement code of one of the involved vehicles

was either 13 or 23 would be considered to be project-related accidents. For sites at which

left-turn lanes were added, approximately 11 percent of total intersection accidents before

project construction were of accident types related to the project. The comparable

percentages of project-related accidents before project construction were 18 percent for

installation of right-turn lanes, 28 percent for installation of both left- and right-turn lanes,

and 7 percent for extension of the length of existing turn lanes.

This accident classification system was applied to both improved and comparison sites

so that when approach-related or project-related accidents were evaluated for an improved

site, accidents with comparable approach/movement combinations could be selected for

the comparison site.

Accident Severity Levels

Traffic accidents are generally classified into three severity levels—fatal, injury, and

property-damage-only—based on the severity of the most serious injury suffered by any

party to the accident. Injury accidents are often subdivided further based on the severity of

the injury. Property-damage-only accidents are often subdivided further by the amount of

property damage sustained in the accident or by whether one or more vehicles are towed

from the accident scene.

Accident severity levels are an important consideration in planning a safety

evaluation, because the completeness of accident reporting varies by severity level. All

accidents involving a fatality or a personal injury are required to be reported to police

authorities. Fatal accidents are nearly always reported to police authorities and, therefore,

become part of accident databases. Injury accidents are less completely reported. 

Estimates of the reporting of accidents involving personal injuries vary from 50 to 90

percent and are undoubtedly dependent on the injury severity. The threshold amount at

which property-damage accidents are required to be reported to police authorities varies

from jurisdiction to jurisdiction. Even those property-damage-only accidents that meet the

reporting threshold, and are thus required to be reported, are, in fact, reported in less than

50 percent of cases. 

Reporting is presumed to be higher for the most severe property-damage-only

accidents in which at least one vehicle is towed from the scene; police authorities are

usually aware of cases when a tow truck is summoned. However, not all jurisdictions

identify in their accident records whether accident-involved vehicles have been towed

from the scene. Unfortunately, most of the eight states that contributed data to this study

did not have a code in their data indicating that one or more involved vehicles were towed

from the scene. Therefore, data on property-damage-only accidents involving a towaway

were not used as a separately category in the study because they could not be consistently

identified.
64

Because of the concerns about incomplete accident reporting, some evaluations have

focused exclusively on fatal and injury accidents, excluding property-damage-only

accidents because of their low reporting percentage. However, some intersection design

improvements are implemented to mitigate patterns of minor accidents that often involve

property-damage only. Thus, the evaluation did not focus exclusively on fatal and injury

accidents.

The accident severity levels considered in the study were:

• All severity levels combined (fatal, injury, and property-damage-only accidents).

• Fatal and injury accidents only (all property-damage-only accidents excluded).

Evaluation Approaches

This section of the report discusses and compares three alternative approaches to

before-after evaluation that were utilized in the research. These are:

• Before-after evaluation with yoked comparisons.

• Before-after evaluation with a comparison group.

• Before-after evaluation with the Empirical Bayes approach.

An overview of each approach is presented below, followed by a discussion of other

analysis considerations. A more detailed discussion of the three evaluation approaches is

then presented. This later discussion includes a conceptual overview, the statistical

analysis approach, and the strengths and weaknesses of the approaches.

The three alternative evaluation approaches combine evaluation concepts

recommended in two sources. These are: a recent FHWA report by Griffin and Flowers

entitled A Discussion of Six Procedures for Evaluating Highway Safety Projects;

(1) and the

recently published book by Hauer, entitled Observational Before-After Studies in Road

Safety.

(2) These sources share some of the same concepts but use different terminology and

notation. To make an appropriate comparison of these concepts, we have introduced a

common terminology and notation drawing liberally (but not exclusively) on the

terminology and notation used by Hauer.(2)

Before-After Evaluation with Yoked Comparisons

The first of the three analysis approaches is the before-after evaluation with yoked

comparisons, or the YC approach. This is a traditional approach to the evaluation of traffic

accident countermeasures and involves one-to-one matching between intersections that

have been improved by the addition of left- or right-turn lanes and similar intersections

that have not been improved. The purpose of the matched or yoked comparison sites is to

account for the effects of time trends. This approach has been recommended by Griffin
65

and Flowers.(1) The one-to-one matching of treatment and comparison intersections

requires selection of intersections that are similar in key characteristics such as area type

(rural/urban), traffic control (signalized/unsignalized), number of legs, and traffic volume. 

In the YC approach, it is assumed that the change in accidents from before to after the

improvement at each treatment site, had the site been left unimproved, would have been in

the same proportion as at the matching comparison site. Under this assumption, the

accident frequency at each treatment site in the before period would be multiplied by the

ratio of “after-to-before” accidents at the comparison site to predict what would have been

the expected number of accidents in the after period at the treated site without the

improvement.

The specific YC approach that has been employed is one of the designs recommended

by Griffin and Flowers.(1) The YC approach is described in more detail later in this section

of the report. 

Before-After Evaluation with a Comparison Group

The second of the three evaluation approaches that have been utilized in the research

is before-after evaluation with a comparison group, which will be termed the CG approach.

 This is a variation of the YC approach to the evaluation of traffic accident

countermeasures and is intended to estimate the safety effectiveness of an improvement, or

combination of improvements, while controlling for time-trend effects. This is achieved

by careful selection of a suitable comparison group of intersections to match the improved

intersections, so that the above-mentioned effects will be manifested equally in the

treatment and the comparison groups. The before-after approach with comparison groups

differs from the stronger before-after approach with randomized control groups in that the

choice of whether or not to improve an intersection was already made by the participating

highway agency prior to the study and is therefore not within the control of the research

team. In the CG approach, it is assumed that the change in accidents from before to after

at the improved intersections, had they been left unimproved, would have been in the same

proportion as in the comparison group. Under this assumption, the “before” accident

frequency would be multiplied by the ratio of the after-to-before accidents in the

comparison group to predict what would have been the expected number of accidents in

the “after” period without the improvement. Similar procedures can be used to adjust for

differences in traffic volumes (e.g., exposure) at the improved intersections between the

before and after periods.

The proper choice of comparison intersections with similar characteristics to those of

the improved intersections is, therefore, very important to a valid before-after evaluation. 

Intersections in the treatment (improved) and comparison groups are matched on their

geometric features, traffic control features, and traffic volumes, but not necessarily on their

accident experience. Before-period accident frequencies for the treatment and comparison

groups do not necessarily need to be similar since the assumption is made in this design
66

that if the improvements were not undertaken, then the change in accidents from before to

after conditions for the improved sites would be similar to that for the comparison sites.

The specific CG approach used in the study is a variation of that recommended by

Hauer.(2) This approach incorporates a multivariate formula to adjust for the differences in

traffic volume between the before and after periods and between treatment and comparison

sites. While Hauer develops the CG approach as far as possible within its conceptual

limitations, it should be noted that Hauer considers the Empirical Bayes approach,

discussed below, to be superior to the CG approach. The CG approach is discussed in

more detail later in this section of the report. 

Before-After Evaluation with the Empirical Bayes Approach

An alternative analysis approach used in the study is the Empirical Bayes (EB)

method. The distinctive features of the EB method are threefold. First, since there is

potential for selection bias in the choice of improvement sites, the EB method attempts to

account for that bias, which neither the YC nor the CG approach can. Second, the EB

method attempts to account explicitly for changes from “before” to “after” in causal

factors such as traffic volume. This is particularly important for intersections, since the

expected number of accidents at an intersection is a nonlinear combination of the various

conflicting flows, and it is often inappropriate to use a simple accident rate to account for

the influence of changes in traffic volume.(2,20) Third, in the CG approach, it is common to

use only two to three years of “before” accident data for fear that older accident counts are

no longer relevant; the EB method can correctly exploit the information in older accident

counts, which is particularly important for intersection types that experience only a limited

number of accidents per year.

The EB method requires richer data and more effort in analysis. What is referred to

above as a comparison group is referred to in the EB method as a sample from a reference

population or reference group. For the reference group, data are required not only about

accidents, but also about traffic flow (and perhaps other variables). Using these, one then

estimates suitable multivariate models linking accident frequency and causal variables. 

The result of this modeling then accounts for both selection bias and changes in causal

variables. The EB approach has recently been implemented in an evaluation of the safety

effects of resurfacing projects by Hauer, Terry, and Griffith(64) and has been used by

Persaud et al.(53) to evaluate the safety effects of converting conventional intersections to

roundabouts.

The specific EB method used in this study is based on the recently published book by

Hauer, entitled Observational Before-After Studies in Road Safety.

(2) This method is

described in more detail later in this section of the report.
67

Choice Between Alternative Analysis Approaches

The three alternative analysis approaches described above have the same goal but use

different methods. In each case, there is a comparison or reference group to provide a

means for estimating the accident experience that would have been observed in the after

period at the treated sites if no treatment had been made. The YC approach does this by

assuming that accidents at each treated site would change between the before and after

periods as the accidents did at a similar comparison site. The CG approach replaces the one

matched comparison site with a group of similar sites. The EB approach relies on a

regression relationship from a group of similar sites (called a reference group rather than a

comparison group) to estimate the accident experience in the after period at a treated site if

no treatment had been made. 

Each of the three approaches described above are valid alternative approaches to the

before-after evaluation of intersection design improvements. The EB approach appears to

have advantages over the YC and CG methods in its ability to address the effect of

regression to the mean, but the EB approach also requires more complete data and greater

analysis effort. The CG approach is generally considered to be preferable to the YC

approach, because the CG approach relies on multiple sites in a comparison group, while

the YC approach relies on a single comparison site. This research has used all three

approaches rather than making an a priori choice between them. An assessment of the

relative performance of the three approaches is presented later in the report. 

Dependent Variables

The dependent variable in any statistical analysis is the variable whose value is to be

determined or predicted. For all analyses in this study, the single most important dependent

variable to assess safety effectiveness as a result of an improvement is the accident

frequency at the selected intersections. Yearly accident frequencies, with a minimum of

three years (and preferably five years) of “before” data and as much “after” data as possible

have been obtained and analyzed. The option of analyzing yearly accident rates (number of

accidents per million entering vehicles) is less desirable because accident rates presume a

linear relationship between accidents and traffic volume, while most previous studies have

shown such relationships to be nonlinear.

Data on all accidents occurring at the intersections during study periods before and

after construction of each improvement project have been obtained from the participating

states. Accident severity levels that have been used as the dependent variable are:

• Total accidents (fatal, injury, and property-damage-only accidents).

• Fatal and injury accidents (excluding property-damage-only accidents).
68

The eight specific safety measures considered in the evaluation are:

• Total intersection accidents.

• Fatal and injury intersection accidents.

• Project-related intersection accidents.

• Project-related fatal and injury intersection accidents.

• Total accidents for individual intersection approaches.

• Fatal and injury accidents for individual intersection approaches.

• Project-related accidents for individual intersection approaches.

• Project-related fatal and injury accidents for individual intersection approaches.

Independent Variables

The independent variables in an accident study are those variables whose effects on

accidents are to be determined or controlled in the analysis. The primary independent

variable is the implementation of the improvement project whose effectiveness is to be

determined. Independent variables have been used in several ways in the study:

• To adjust for changes from the before to the after period (e.g., in traffic volume).

• To match an appropriate yoked comparison site to a treatment site in the YC

approach (e.g., area type, traffic control, number of legs, and traffic volume).

• To estimate multivariate models from a reference group to adjust for traffic

volumes in the CG approach and to determine expected values of accidents in the

EB approach.

• To examine how safety may depend on the characteristics of a site (e.g.,

intersection geometrics or traffic control).

The primary independent variables included in the study are those geometric, traffic control,

and traffic volume variables obtained from the participating state highway agencies and in

field visits to the study sites.

Before and After Study Periods

Accident data for each site were obtained from the participating state highway agencies

for specific time periods. These periods are presented in table 19. Thirteen years of

accident data were available for one State, 12 years were available for three States, 11 years

were available for one State, 10 years were available for two States, and 9 years were

available for one State. As shown in tables 10 and 15, the projects evaluated were

constructed on various dates during the period for which these accident data were obtained.
69

The study periods before and after improvement of each site were selected as follows:

• The before study period extended from the beginning of the first year for which

accident data for the site were available to the end of the last calendar year before

construction of the project.

• The after study period extended from the beginning of the year after the project

was completed to the end of the last year for which accident data for the site were

available.

Both the before and after study periods were composed of complete calendar years. Partial

years were not used because they are subject to seasonal effects which could bias the

evaluation.

The entire calendar year during which the improvement project was constructed was

omitted from the evaluation. This approach avoided the use of partial years of accident

data. In addition, because projects in many parts of the country are completed during the

summer construction season, exclusion of the entire construction year provides a buffer

period of several months between the end of construction and the beginning of the “after”

study period. This buffer period provides an opportunity for drivers to become familiar

with the improved intersection before the assessment of the project’s effectiveness begins.

For the improved or treatment sites as a whole, the “before” study periods ranged from

1 to 10 years in duration, with a mean duration of 6.7 years. The “after” study periods also

ranged from 1 to 10 years in duration, with a mean duration of 3.9 years.

Before-After Evaluation with Yoked Comparisons

The first of the three alternative evaluation approaches that will be presented is the

before-after evaluation with yoked comparisons, referred to as Design 4 by Griffin and

Flowers.(1) Of the six evaluation designs presented by Griffin and Flowers, this is the most

appropriate for evaluation of intersection design improvements. 

Conceptual Overview

The before-after evaluation with yoked comparisons involves a one-to-one matching

between treatment and comparison sites. Thus, for each improved or treatment site, a

comparison site with similar features was identified. Each selected comparison site was 

similar to the corresponding treatment site with respect to area type (rural/urban),

intersection type (three-leg or four-leg), traffic control (signalized/two-way STOP),

geometric design, and traffic volumes. The matched treated and comparison sites were

always located in the same state and, usually (but not necessarily) in the same geographic

region of the state. The comparison site had to have undergone no geometric design or
70

traffic control improvements (beyond routine maintenance) during the periods for which

data were available before and after improvements of the corresponding treatment site. 

Thus, for any project type of interest, there were n pairs of treatment and comparison sites

for consideration in the evaluation. The term “yoked comparison” used by Griffin and

Flowers refers to the one-to-one matching between the treatment and comparison sites.(1) 

Accident data were obtained for periods as long in duration as possible before and after

the improvement at each treated site and for the same time periods at the matched

comparison site. Griffin and Flowers assume that the durations of the before and after

periods are identical at any given treated site, although they may vary from one treated site

to another. Despite this assumption, there is no particular reason that the duration of the

before and after periods need to be identical, because the adjustment to account for the

difference between, for example, a three-year “before” period and a two-year “after” period

is obvious.

The key assumption in the YC approach is that the change in accidents between the

before and after periods at any comparison sites is representative of the change in accidents

that would have occurred at the corresponding treatment site had the improvement at that

site not been made. Thus, it is postulated that, if the implementation of the improvement

project at any treatment site was beneficial to safety, it resulted in the number of crashes at

the treatment site falling more rapidly, or rising less rapidly, than accidents at the

comparison site. 

The YC approach, as formulated by Griffin and Flowers,(1) does not include a

mechanism to account for changes in traffic volume between the before and after periods

at the treatment and control sites. Since traffic volume for the before and after periods are 

available for each site, we have modified the YC approach to adjust for the effects of

traffic volume, assuming that those effects are linear (i.e., proportional to the changes in

volume). 

Table 22 illustrates the accident data that was gathered to employ the before-after

design with yoked comparisons for any given type of project. In the table, the values of K,

L, M, and N are counts of the number of accidents observed during periods before and

after the treatments. The values of B, 2, and E are statistics derived from these data. The

analyses employed to derive these measures are described below. 
71

 Table 22. Accident Data Layout for a Before-After Evaluation 

 with Yoked Comparisons.

Treatment sites Comparison sites Expected number

of accidents on

treatment site

during after

period in the

absence of

treatment

Observed accident

reduction effectiveness

Site

number State

Number of

accidents

during

before

period

Number of

accidents

during

after 

period

Number of

accidents

during

before

period

Number of

accidents

during

after 

period

Odds

ratio

Percentage

reduction

1 1K1 L1 M1 N1 B1 21 E1

2 1K2 L2 M2 N2 B2 22 E2

3 1K3 L3 M3 N3 B3 23 E3

4 2K4 L4 M4 N4 B4 24 E4

! 2 ! ! ! ! ! !!

i 3Ki Li Mi Ni Bi 2i Ei

! 10 ! ! ! ! ! !!

n 10 Kn Ln Mn Nn Bn 2n En

Statistical Analysis

For any pair of treatment and comparison sites (designated by subscript i), the

expected number of accidents at the treated site in the “after” period, had no improvement

been made (Bi

Ê

), is best estimated as:

(3) πi i

i

i

 K 

N

M = 



 





The best estimate of the expected number of accidents after the treatment (8i

Ê

) is the

observed accident frequency. In other words:

(4) 

λi i = L

The expected number of accidents without the treatment, Bi, is then compared to the

observed number of accidents, 8i

Ê

 or Li, to assess the accident reduction effectiveness of

the project at that site. The accident reduction effectiveness of the project can then be

assessed as the ratio of what the accident experience was with the treatment to what it

would have been without the treatment:
72

(5)   θ λπ π /  /  i i i ii = = L

or, equivalently:

(6)   θ λπ/  i i

i i

i i

L M

K N = =

When 2i

Ê

 < 1, the accident frequency has decreased, and the treatment appears to be

effective; when 2i

Ê

 > 1, accident frequency has increased, and the treatment appears to be 

harmful to safety. The treatment effectiveness can also be expressed as the percentage

change in the expected accident frequency, E, estimated as 100 (2 - 1). A negative value

of E represents a reduction in accident frequency. If the before and after periods differ in

duration, or if traffic volumes have changed between the before and after periods, the

proportional changes need to be incorporated in Equations (3) through (5). (More

sophisticated methods of accounting for the traffic volume effects will be discussed in

conjunction with the CG and EB approaches.)

The first step in the analysis is simply to plot the pairs of observed and expected

accident frequencies for the “after” period (known as Li

 and (i

Ê

, respectively), as illustrated

in Figure 4. If all of the data points were to fall on the diagonal line in the figure, then one

would conclude that the treatment had no effect. Points that fall below the diagonal line

suggest that the treatment was beneficial, while points that fall above the diagonal line

suggest that the treatment was harmful.

Equations (3) through (6) address the estimated treatment effectiveness at a single site. 

An overall estimate of the treatment effectiveness can be derived from the effectiveness

estimates for the individual sites using a weighted average. The weight, wi, for each site

represents the reciprocal of the squared standard error of the log odds ratio, Ri, generated

from the data for that site, or:

R (7)

L M

K N i

i i

i i

= i





 

 ln ln  = 

θ

The squared standard error for Ri is calculated as:

R (8) KLMN i se

ii i i

( )

2 11 1 1

= ++ +
73

Figure 4. Plot of Observed vs. Expected Accident Frequencies.

from which the weight, wi

, is simply calculated as:

w R (9) i i se =1 2 / ( )

A weighted average (mean) log odds ratio across all n pairs of sites can be determined

as:

R (10)

w R

w mean

i i

i

= Σ

Σ

By exponentiating Equation (10), an overall average (mean) odds ratio, or project

effectiveness, can be obtained for the n sites as:

θ (11) mean

R

e mean =

Thus, the overall mean percentage accident reduction effectiveness of a treatment can

be estimated as:
74

E (12) mean mean = 100 1 ( ) θ −

The next step in the analysis is to assess whether the estimated effectiveness, 2mean, is

statistically significantly different from one, or whether the mean percentage accident

reduction effectiveness is statistically significantly different from zero. Since Rmean is

asymptotically normally distributed, a z-test is used to test for significance, as follows. 

The standard error of Rmean is computed as:

R w (13) mean se i ( ) =1/ Σ

A standard normal z-score can then be obtained as:

zR R (14) = mean mean se / ( )

If z falls within the interval from -1.96 to +1.96, there is no apparent treatment effect

at the 95 percent confidence level. If z falls outside the interval from -1.96 to +1.96, there

is a statistically significant treatment effect (beneficial if z is negative, harmful if z is

positive).

The approach described above is also used to estimate a 95 percent confidence interval

for the estimated treatment effect, 2mean. First, the upper and lower 95 percent confidence

limits around Rmean would be estimated as:

R RR (15) mean upper mean mean se ( ) () = +196 .

R RR (16) mean lower mean mean se ( ) () = −196 .

Next, the upper and lower 95 percent confidence limits for the treatment effect

expressed as a weighted average log odds ratio would be determined by exponentiating

Equations (15) and (16) as:

θ (17) mean upper

L

e mean upper

( )

( ) =

θ (18) mean lower

L

e mean lower

( )

( ) =
75

Finally, substituting 2mean (upper) and 2mean (lower) for 2mean in Equation (12) provides a

95-percent confidence interval for the estimated treatment effect expressed as a percentage

accident reduction:

E [ ] (19) mean upper mean upper () () = − 100 1 θ

E [ ] (20) mean lower mean lower () () = − 100 1 θ

Thus, it can be said that the estimated percentage effect of the treatment is, on the

average, Emean, with 95-percent confidence that it is in the range from Emean (lower) to Emean

(upper). For example, it might be concluded that the estimated effectiveness of a particular

intersection improvement project type in reducing accidents is 25 percent, on the average,

with 95-percent confidence that it is in the range from 9 percent to 38 percent. 

Using Equations (11), (12), and (13), the standard error of Emean is computed as:

E w mean se mean i (21) ( ) =100 θ / ∑

To complete the analysis and estimation of the mean measure of effectiveness, the

homogeneity of the individual estimated treatment effects, Ri

, is tested. The plot of the

pairs of observed versus expected accidents in Figure 4 provides a view of the scatter of

the data around the diagonal. A chi-square (P2) analysis that partitions the total chi-square

value into a chi-square for treatment and a chi-square for homogeneity will be performed

to determine whether the scatter of the data points about the overall estimate of treatment

effectiveness is within expectations. The calculations for the P2 analysis are shown in

Table 23.

Table 23. Calculations of P2

 Treatment, P2 Homogeneity, and P2 Total

 for Before-After Evaluation with Yoked Comparisons.

Source Chi-square (P2

)

Degrees of 

freedom (df) Probability

Treatment Rmean

2

 (Ewi)1 pT

Homogeneity Ewi (Ri-Rmean)

2 n–1 pH

Total Ewi (Ri)

2 n —

The probability, or significance, levels pT and pH, associated with the treatment and

homogeneity effects, respectively, provide a measure of statistical significance of these two

sources of variability in the data. pT provides the same test for significance as did

Equation (13). If pH is less than 0.05 (i.e., a significance level of 5 percent), then Griffin
76

and Flowers(1) recommend a conclusion that the data are not homogeneous across pairs of

sites and that some other factors besides treatment are affecting the analysis. In such a

case, Griffin and Flowers maintain that the data should not be combined into a single

measure of effectiveness of the treatment. By contrast, Hauer(2) maintains that variations in

treatment effectiveness between sites are to be expected and that otherwise valid

effectiveness measures should not necessarily be excluded on the basis of lack of

homogeneity. In this research, the test for homogeneity recommended by Griffin and

Flowers was performed and the results were documented in appendix C of this report, but

no effectiveness measures were excluded due to lack of homogeneity.

The statistical analysis described above has been programmed in the commercially

available SAS software package and performed for a variety of intersection and treatment

types as described in section 6 of this report. 

Adjustment for Traffic Volume and Study Period Duration

The equations presented above are applicable if both the treatment and comparison

sites have the same traffic volumes, both before and after the project, and if the duration of

the before and after periods for both the treated and comparison sites are equal. In most

cases, these assumptions are not appropriate. Typically, the traffic volumes of the treated

and matched comparison sites differ and, for both sites, the traffic volumes typically

change from the before to the after period; traffic volume growth over time is most

common, but in some cases traffic volumes may actually decline from before to after the

improvement project. Furthermore, it is not uncommon for the durations of the before and

after periods to differ.

An adjustment factor for traffic volume in the yoked comparison analysis can be

computed as:

(22) ( )

( ) Adj

ADT ADT

ADT ADT

AT BT

AC BC

1 = /

/

where: Adj1 = Traffic volume adjustment factor.

ADTBT = Traffic volume (veh/day) for treated site in the before period.

ADTAT = Traffic volume (veh/day) for treated site in the after period.

ADTBC = Traffic volume (veh/day) for comparison site in the before period.

ADTAC = Traffic volume (veh/day) for comparison site in the after period.

An adjustment factor for duration of study periods in the yoked comparison analysis

can be computed as:

(23) ( )

( ) Adj

YEARS YEARS

YEARS YEARS

AT BT

AC BC

2 = /

/
77

where: Adj2 = Duration adjustment factor.

YEARSBT = Duration of before period for treated site (years).

YEARSAT = Duration of after period for treated site (years).

YEARSBC = Duration of before period for comparison site (years).

YEARSAC = Duration of after period for comparison site (years).

Normally, YEARSBT and YEARSBC are equal, as are YEARSAT and YEARSAC, so that

ADj2 is usually equal to 1.0.

To apply these adjustments in the YC analysis, Equation (6) must be recast as:

(24) 

θi

i i

i i

L M

K N Adj Adj = 1 2

The remainder of the analysis proceeds as described above.

Strengths and Weaknesses of This Evaluation Approach

The strength of the YC approach is its simplicity and its conceptual appeal to

engineers. Since there is one and only one matching comparison site for each treatment

site, care can be taken in assuring that the treatment and comparison sites are similar in a

number of engineering factors such as traffic volume, geometric design, and traffic control. 

Also, the data needs for this approach are readily apparent and known in advance.

The YC approach has three major weaknesses, however. First, by relying on only one

comparison site for each treated site, the YC approach relies on very limited data in

estimating the values of Bi, the accident experience that would have been observed at

particular treatment sites if no treatment had been made. If the treatment and comparison

sites are well matched, the values of Mi and Ni will be similar in magnitude to Ki and Li

(i.e., often quite small) and will be highly variable. The YC approach does not utilize data

from other similar treatment sites to increase the magnitudes of Mi

 and Ni and, therefore,

the reliability of the estimates that can be made with them. Thus, the YC approach is likely

to produce accident reduction effectiveness estimates with relatively wide confidence

limits.

Second, the YC approach cannot deal with the well-known phenomenon of 

“regression to the mean.” If the treated sites have been selected for improvement because

of high short-term accident experience in the before period, then simple probability theory

suggests that accident experience is likely to be lower in the after period even if no

improvement is made. Thus, the effect of the treatment is likely to be partially confounded

with the expected decrease in accident experience from regression to the mean. Regression

to the mean can only be accounted for with knowledge of the “normal” or expected value

of before-period accident experience at the treated sites and the YC approach cannot supply

such information.
78

Third, the yoked comparison approach has difficulty dealing with accident frequencies

with values equal to zero. Specifically, if either Ki or Ni is zero in Equation (6) or

Equation (24), then the effectiveness of the treatment is undefined. This problem is usually

resolved by substituting 0.5 for zero as the value of Ki

 or Ni in these equations, but the

existence of this problem represents a conceptual weakness of the YC approach.

Despite these weaknesses, the YC approach has been used in this research because one

objective of the study is to assess the performance of alternative analysis approaches. In

particular, the YC approach was useful because the matched comparison sites required by

the YC approach also served as the foundation for a comparison group for the CG approach

and a reference group for the EB approach. 

Before-After Evaluation with a Comparison Group

The second of the three alternative evaluation approaches used in the study is beforeafter evaluation with a comparison group. This approach has been formulated based on

recommendations by Hauer,(2) with variations to handle the adjustments for traffic volume,

study period duration, and state-to-state differences inherent in this study. The CG

approach overcomes one weakness of the YC approach—the limitation to a single

comparison site for each treatment site. For each treatment site, the CG approach replaces

the yoked or matched comparison site with a group of comparison sites so that the accident

experience of the entire comparison group is used in estimating what the accident

experience of each treatment site would have been had the improvement not been made. It

should be noted that, although Hauer has developed the CG approach as far as its

limitations permit, he considers the EB approach, presented below, to be conceptually

superior to the CG approach.

Conceptual Overview

In the CG approach, the idea of one-to-one matching of the treatment and comparison

sites is discarded and the available comparison sites, taken as a whole, are considered as a

comparison group. Indeed, the comparison group should preferably include more sites

than the treatment group. The purpose of the comparison group is still to estimate the

change in accidents that would have occurred at the treated sites if the treatment had not

been made. 

The CG approach has been implemented as follows. For any particular project type,

such as those discussed in section 3 of this report, a certain number of intersections at

which improvements of that particular type have been made were available for evaluation. 

These intersections will be referred to as the treatment group. Researchers identified a

second group of intersections at which no geometric design or traffic control changes

(other than routine maintenance) were made during the time periods for which data are

available for the treated intersections. This second group of intersections constitutes the
79

comparison group. Accident and traffic volume data were generally available for the same

time periods for both the comparison group and the treatment group.

The comparison sites would normally be similar to the treatment group in geometric

design and traffic control features, although Hauer does not consider close physical

similarity between the treatment and comparison sites to be critical. Instead, Hauer

maintains that close agreement between the treatment and comparison groups in the

monthly or yearly time series of accident frequencies during the period before

improvement of the treated sites is more important.(2) In other words, Hauer considers that

it is not vital that the comparison sites look like the treatment sites, but it is vital that the

comparison sites have accident histories similar to the accident histories for treatment sites

for the period before improvement of those sites. Such similarity in accident experience

during the period before improvement increases confidence (but cannot assure) that the

comparison group will behave as the treatment group would have behaved had the

improvement not been made. Hauer suggests a statistical technique to assess analytically

the appropriateness of any particular comparison group for the corresponding treatment

group.(2) Hauer’s approach of matching on the basis of safety rather than physical

characteristics was not fully implemented in this study, because researchers had the

matched comparison sites that were used in the yoked comparison analysis available for

use as a comparison group. The matching comparison sites had been selected to be

physically similar to the individual treatment sites, so the matched comparison sites, as a

group, were physically similar to the treatment sites as a group. The treatment and

comparison sites were also similar in traffic volume levels. Additional reference sites with

similar physical characteristics and traffic volumes were added to increase the size of the

comparison group.

The key features that distinguish the CG approach from the YC approach are as

follows:

• The estimate of the odds ratio, 2i

, for each treated site are based on a comparison

group rather than a single yoked comparison site. Even where the same

comparison group is used for all intersections of a specific project type, the

comparison group data vary because the dates of the before and after periods for

specific treated sites vary. Section 9.5 of Hauer’s book provides an appropriate

procedure for analyzing such data.(2)
80

• Before-to-after changes in traffic volume at the treated sites are accounted for

explicitly using safety performance functions (i.e., multivariate regression

relationships) like those used in Chapter 8 of Hauer’s book. Figure 5 illustrates

the use of a regression relationship between accident frequency and traffic volume

as a safety performance function to adjust for a change in traffic volumes between

the before and after periods. The adjustment factor for the effect on accidents of a

change in traffic volume is rtf, defined as shown in the figure.

• The YC approach incorporates a test for homogeneity of the treatment effects, as

illustrated in table 23. If the chi-square value for homogeneity is too large, Griffin

recommends that the data from different sites not be combined into a single

accident reduction factor.(1) As noted in the preceding discussion of the YC

approach, Hauer assumes that it is natural for the effect of the same treatment to

vary from site to site.(2) Therefore, in the CG approach, an average effect is

determined and its precision is assessed by examining its site-to-site variance.

The CG approach leads to a very similar formulation of the data for the evaluation to

that used in the YC approach. Table 24 is analogous to table 22 and differs only in that the

columns formerly headed “Comparison sites” are now headed “Comparison group.” In

each row of table 24, Mi

 and Ni are based on an entire comparison group and not just on

one matching site. However, the values of Mi and Ni in table 24 may vary even among the

sites that use the same comparison group if the time periods on which Ki

 and Li are based

vary. Furthermore, the data for the individual comparison sites that are combined to

determine Mi

 and Ni must first be adjusted for differences in traffic volume between the

treatment and comparison sites. An adjustment for state-to-state differences in accident

frequency is also needed where a specific comparison state is located in a different state

than the treatment site.
81

Figure 5. Typical Regression Relationship for Predicting Intersection

 Accident Frequency as a Function of Entering Traffic Volume.
82

Table 24. Accident Data Layout for Before-After Evaluation

 with Comparison Group.

Treatment sites Comparison group Expected number

of accidents on

treatment site

during after

period in the

absence of

treatment

Observed accident

reduction effectiveness

Site

number State

Number of

accidents

during

before

period

Number of

accidents

during

after 

period

Number of

accidents

during

before

period

Number of

accidents

during

after 

period

Odds

ratio

Percentage

reduction

1 1K1 L1 M1 N1 B1 21 E1

2 1K2 L2 M2 N2 B2 22 E2

3 1K3 L3 M3 N3 B3 23 E3

4 2K4 L4 M4 N4 B4 24 E4

! 2 ! ! ! ! ! !!

i 3Ki Li Mi Ni Bi 2i Ei

! 10 !! ! ! ! !!

n 10 Kn Ln Mn Nn Bn 2n En

The statistical analysis methodology used to determine the values of B, 2, and E in the

CG approach is explained below. 

Statistical Analysis

The statistical analysis methodology for the CG approach must be explained in terms

of both the observed accident counts and their expected values. As shown in Table 25, the

observed accident counts that correspond to the row and column headings shown in the

table will be referred to as K, L, M, and N, and their expected values, which are unknown,

will be referred to by the Greek letters 6, 8, :, and <.

Table 25. Observed and Expected Accident Counts.

Treatment group Comparison group

Before K, 6 M, :

After L, 8 N, <
83

The comparison ratio, rC, for the comparison group is defined as the ratio of the

expected accident count during the after period to the expected accident count in the before

period. In other words:

r (25) C =ν μ/

The CG approach assumes that the expected number of accidents for the treatment

group in the after period, had no treatment been implemented, can be predicted as:

π κ = r (26) C

Implicit in Equation (25) is the assumption that the corresponding ratio for the

treatment group, had no treatment been implemented: 

r (27) T = π κ/

is equal to rC. In other words:

r r or equivalently r r (28) T C CT = = , ,/ 1

The ratio rC/rT is also known as the odds ratio, Ti.

The customary effectiveness of a treatment at a given site is the same as that derived

for the YC approach in Equations (3) through (6). However, for the CG approach, the

subscript i in these equations represents the appropriate data for a particular treatment site. 

Each treatment site has a corresponding comparison group, and the individual sites in that

comparison group will be represented here by the subscript j.

The CG analysis proceeds in a manner similar to the YC analysis except that for any

given treated site, Mi

 and Ni in Equation (6) must be determined as the sum of the adjusted

accident frequencies for the individual comparison sites within the comparison group. 

Adjustments to accident frequencies for the individual comparison sites are needed when

(1) the treatment and comparison sites have different traffic volume levels, (2) the study

periods for the treatment and comparison sites have different durations, or (3) the treatment

and comparison sites are located in different states, and the safety performance of the

particular intersection type in question differs between those states.

Because of the need to make these adjustments, the computation of the comparison

group analysis is more complex than suggested by Equations (6) through (11). 

Specifically, a set of adjustments were first made to the data for the comparison group sites

that correspond to each treatment site. Then, the comparison group sites corresponding to

each treatment site were combined to determine pooled accident frequencies for the

comparison group as a whole. Finally, an adjustment was made to the combined treatment

and comparison site data in determining the treatment site odds ratio.
84

In the comparison group analysis, the traffic volume adjustment was made using a

regression relationship between accident frequency and traffic volume, rather than

assuming that the adjustment should be proportional to traffic volumes as shown in

Equation (22). These regression relationships were developed with negative binomial

regression and involved separate coefficients for major- and minor-road traffic volumes, as

described in the next section. These regression relationships also involved a coefficient to

represent the differences in accident frequency between states.

For each individual comparison site, its accident frequency was adjusted to be

comparable to its equivalent value under the same conditions (traffic volume, duration of

study period, and state) as the treatment site. The adjustment factor for the comparison site

in the before period is:

(29) ( )

( ) Adj f MajADT MinADT STATE YEARS

f MajADT MinADT STATE YEARS B

BT BT T BT

BC BC C BC

= , ,

, ,

where: AdjB = Adjustment factor for comparison site accident frequency in the

before period.

f(x,y,z) = Predicted accident frequency as a function of major-road traffic

volume (x), minor-road traffic volume (y), and state (z) from a

negative binomial regression relationship (see discussion later

in this section).

MajADTBT = Major-road traffic volume (veh/day) in the before period at the

treatment site.

MajADTBC = Major-road traffic volume (veh/day) in the before period at the

comparison site.

MinADTBT = Minor-road traffic volume (veh/day) in the before period at the

treatment site.

MinADTBC = Minor-road traffic volume (veh/day) in the before period at the

comparison site.

YEARSBT = Duration of before period for treatment site (years).

YEARSBC = Duration of before period for comparison site (years).

Similarly, the adjustment factor for the comparison site in the after period is:

(30) ( )

( ) Adj f MajADT MinADT STATE YEARS

f MajADT MinADT STATE YEARS A

AT AT T AT

AC AC C AC

= , ,

, ,

where: AdjA = Adjustment factor for comparison site accident frequency in the

after period.

MajADTAT = Major-road traffic volume (veh/day) in the after period at the

treatment site.
85

MajADTAC = Major-road traffic volume (veh/day) in the after period at the

comparison site.

MinADTAT = Minor-road traffic volume (veh/day) in the after period at the

treatment site.

MinADTAC = Minor-road traffic volume (veh/day) in the after period at the

comparison site.

YEARSAT = Duration of after period for treatment site (years).

YEARSAC = Duration of after period for comparison site (years).

With these adjustment factors, the adjusted accident frequencies for an individual

comparison site can be determined as:

MADJ M Adj (31) j jB =

and

NADJ N Adj (32) j jA =

Then, the values of the adjusted before period accident frequencies, MADJj, are

summed over all of the comparison sites corresponding to a specific treatment site, i, to

calculate the total adjusted before-period comparison group accident frequency, Mi. 

Similarly, the values of the adjusted after-period accident frequencies, NADJj, are summed

over all comparison sites to calculate the total adjusted after-period comparison group

accident frequency, Ni.

In combining the treatment site and comparison group accident frequencies, a final

adjustment must be made to the after-period accident frequency for the treatment site. 

This adjustment also uses the negative binomial regression relationships. The adjustment

is determined as:

(33) ( )

( ) Adj f MajADT MinADT STATE

f MajADT MinADT STATE

AT AT T

BT BT T

3 = , ,

, ,

where: Adj3 = Adjustment factor applied to after-period accident frequency

[analogous to Adj1 in Equation (22)].

This adjustment is applied by modifying the value of the observed after-period

accident frequency for the treatment site, Li

N

, as follows:

L L Adj (34) i i = '/ 3

The odds ratio is then determined as in Equation (6), and the analysis proceeds as in

the YC approach.
86

Appropriateness of Comparison Groups

Hauer (2) states explicitly that the foundation of the CG method rests on the assumption

(or the hope) that Equation (28) is correct. While Hauer discusses the importance of

agreement in key safety measures between the treatment and comparison groups, no

specific statistical methodology for assessing the level of agreement between the treatment

and comparison groups is presented. Therefore, such a methodology has been developed

for use in the current study. 

Confirming the degree of agreement between the group of treatment or improved sites

and the comparison group is an important aspect in the before-after analysis. The

comparison sites were selected because they were similar in physical characteristics and

traffic volumes to the treatment sites, but there was no a priori assurance that the treatment

and comparison sites were similar in safety performance in the time period before

improvement of the treatment sites. A statistical approach to providing this assurance was

developed and implemented using groups of treatment and matched comparison sites over

the entire period before improvement of the treatment sites. For each combination of area

type, traffic control, and project type, a time series of total intersection accidents at the

treatment sites was compared to the time series of accidents at the matching comparison

sites. An example pair of treatment and comparison time series is illustrated in Figure 6. 

Each of the two time series shown in the figure represents the series of average accident

frequencies per site per year over the entire before period. Each time series is based on the

same number of intersections, and the number of intersections included decreases from

year to year as more and more treatment sites reach the year during which they were

improved. 

The evaluation approaches used in this report do not require that the treatment and

comparison time series shown in figure 6 coincide. However, if the treatment and

comparison groups are well matched, the average annual accident frequency for the

comparison group should rise when the average for the treatment group rises, and fall

when the average for the treatment groups falls. A perfect match in accident trends

between the treatment and comparison groups would exhibit a pair of two jagged but

parallel lines in plots like figure 6.
87

Figure 6. Comparison of Accident Experience for Treatment and Comparison Groups for 

 Rural Unsignalized Intersections at Which Left-Turn Lanes Were Added.

The objective of the statistical assessment of the degree of agreement between 

treatment and control groups is to test whether the corresponding time series, like those

shown in the figure, are parallel. A basic two-way analysis of repeated measures approach

with interaction was used to test whether the two time series (treatment and comparison) of

yearly accidents deviate significantly from parallelism. The two main factors used in the

analysis of variance are the type of site (i.e., treatment vs. comparison) and the year (i.e.,

the sequence of calendar years as shown in the figure). The various sites were nested

within their respective type of site. The repeated measures nature of the design refers to

the yearly measurements (observed accident counts) made on the same sites. In addition, a

first-order autoregressive covariance structure was assumed for the accidents to reflect the

property of correlations being larger for nearby times than for far-apart times. In this

approach, the evaluation of the interaction between the two factors, type of site and year,

provides a test for parallelism. PROC MIXED of SAS was used to perform the analyses

of variance. The 10-percent significance level was used to assess the results of these

analyses of variance.

This approach was implemented for selected treatment and comparison groups for two

safety performance measures—total intersection accidents and fatal and injury intersection

accidents. The results of 20 analyses are shown in table 26. This table presents results for

all treatment and comparison groups that included more than 20 site-years of data. For

each safety performance measure, site type, and improvement project type, the table shows

the number of site-years in the individual treatment or comparison groups and an

indication of whether the two time series can be considered to be parallel at the 90 percent
88

confidence level. For the cases shown in the table, there were no significant effects for

lack of parallelism. Based on these results, it was concluded that the groups of treatment

and comparison sites are comparable in their safety performance.

 Table 26. Comparison of Accident Frequency Time Series for Treatment and Control 

 Groups in the Time Period Before Improvement of the Treatment Sites.

Area 

type

Traffic control

 type Project type

Number of

improved or

treated siteyears in before

period

Test for lack of

parallelism:

significant at

10% level?

Total Intersection Accidents

Rural Signalized Extended LTLs 40 No

Rural Unsignalized Added LTLs 340 No

Rural Unsignalized Added both LTLs and 184 No

Rural Unsignalized Added RTLs 266 No

Urban Newly Signalized Added LTLs 203 No

Urban Signalized Added LTLs 267 No

Urban Signalized Added both LTLs and 84 No

Urban Signalized Added RTLs 141 No

Urban Signalized Extended LTLs 23 No

Urban Unsignalized Added LTLs 123 No

Fatal and Injury Intersection Accidents

Rural Signalized Extended LTLs 40 No

Rural Unsignalized Added LTLs 340 No

Rural Unsignalized Added both LTLs and 184 No

Rural Unsignalized Added RTLs 266 No

Urban Newly Signalized Added LTLs 203 No

Urban Signalized Added LTLs 267 No

Urban Signalized Added both LTLs and 84 No

Urban Signalized Added RTLs 141 No

Urban Signalized Extended LTLs 23 No

Urban Unsignalized Added LTLs 123 No
89

Negative Binomial Regression Relationships for Traffic Volume and State

Adjustments

The adjustments for traffic volume and state effects presented above (AdjA, AdjB, and

Adj3) are based on negative binomial regression relationships for predicting accident

frequency as a function of major-road traffic volume, minor-road traffic volume, and state. 

Because the relationship between accident frequency and traffic volume is generally

nonlinear (as illustrated in figure 5), the regression relationship can provide a more

accurate adjustment for the effect of traffic volume than the proportional adjustment used

in the YC approach [see Equation (22)]. State-to-state differences were included in the

negative binomial regression models where they were found to be statistically significant.

Regression relationships were developed for complected as many combinations of the

following intersection characteristics as possible using the comparison and reference site

data:

• Area type (urban/rural).

• Type of traffic control (signalized/unsignalized).

• Number of intersection legs (three or four).

• Number of lanes on major road (two-lane/multilane).

A variety of dependent variables (accident frequency measures) were used in modeling,

including:

• Total intersection accidents.

• Total fatal and injury intersection accidents.

• Total project-related intersection accidents.

• Total fatal and injury project-related intersection accidents.

• Total accidents for individual intersection approaches.

• Total fatal and injury accidents for individual intersection accidents.

• Total project-related accidents for individual intersection approaches.

• Total fatal and injury project-related accidents for individual intersection

approaches.

These relationships were developed with negative binomial regression because

(1) negative binomial regression is well suited to deal with accident frequencies which are

frequently zero or very low numbers and (2) negative binomial regression provides an

overdispersion parameter that makes it useful in the EB analysis as well as the CG

analysis.

The model development process and the specific models developed are presented in

Appendix B of this report.
90

Strengths and Weaknesses of This Evaluation Approach

A strength of the CG approach, in contrast to the YC approach, is that it relies on a

group of similar sites, rather than a single site, to determine the values of Mi and Ni. The

increased size of the accident sample in the comparison group should decrease the variance

of the accident data and, thus, shrink the confidence limits for the accident reduction

effectiveness.

On the other hand, some unwanted variability in accidents may be introduced by the

inevitable diversity of the sites that make up the comparison group. Even if the comparison group as a whole resembles the treatment group as a whole, some of the comparison

sites are bound to be quite different in physical characteristics (and in accident experience)

from any given treatment site. 

Like the YC approach, the CG approach cannot determine the treatment effectiveness

when Ki

 or Ni is equal to zero. The same approximation used in the YC approach (setting

zero values equal to 0.5) is used in the CG approach.

An important weakness of the CG approach is that, like the YC approach, it cannot

address the bias created by regression to the mean. This weakness will be addressed by the

EB approach.

Before-After Evaluation with the Empirical Bayes Approach

The third of the three alternative evaluation approaches that was used in the research

is before-after evaluation with the Empirical Bayes (EB) approach. This approach was

formulated by Hauer(2) and is the only known approach to before-after evaluation that

directly addresses regression to the mean.

Conceptual Overview

In the EB approach, the comparison group discussed in the CG approach is replaced

with a reference group that is used to model the relationship between accident frequency

and fundamental intersection descriptors such as traffic volume. These models are then

used together with the observed accident counts at the treated sites in the before period to

estimate the number of accidents that would have occurred at the treated sites in the after

period if no improvement had been made.

To accomplish this, the reference group should consist of intersections that are similar

to the treated intersections before they were treated (i.e., intersections with left- and rightturn lanes), or intersections whose safety performance is similar to that of such

intersections. In this research, the reference groups for the EB approach were drawn from
91

the same sites used as the comparison group for the CG approach, including both matched

comparison sites from the YC approach and additional reference sites.

The regression relationships used in the EB approach were the same negative binomial

regression relationships used for the CG approach and discussed earlier in this section. A

more detailed discussion of the development of these regression relationships is presented

in Appendix B of this report. Separate regression relationships were developed for specific

combinations of the following intersection characteristics:

• Area type (urban/rural).

• Traffic control type (signalized/unsignalized).

• Number of intersection legs (three or four).

• Number of lanes on major road (two-lane/multilane).

The EB approach leads to another variation of the data layout for the evaluation. 

Table 27 is analogous to Tables 22 and 24 but is adapted to fit the EB approach. A key

difference of Table 27 from Tables 22 and 24 is that the accident experience of the

reference group does not appear explicitly. Instead, the reference group is used to develop

regression models that are used in estimating the values of B shown in the table. Once the

values of B have been determined, the computation of 2 and E is much as presented

previously. 

Table 27. Accident Data Layout for Before-After Evaluation

 with the Empirical Bayes Approach.

Treatment sites

Observed accident

reduction effectiveness

Site

number State

Number of

accidents

during before

period

Expected

number of

accidents

during after

period in the

absence of

treatment

Observed

number of

accidents

during after

period

Odds

ratio

Percentage

reduction

1 1K1 B1 L1 21 E1

2 1K2 B2 L2 22 E2

3 1K3 B3 L3 23 E3

4 2K4 B4 L4 24 E4

! 2 ! ! ! !!

i 3Ki Bi Li 2i Ei

! 10 ! ! ! !!

n 10 Kn Bn Ln 2n En
92

The statistical analysis methodology for the EB approach is explained below.

Statistical Analysis

The statistical analysis methodology for the EB approach revolves around the use of

the reference group to develop regression relationships between accident experience and

key site characteristics such as traffic volumes. Figure 5, which is presented in the

discussion of the CG approach of this report, shows a typical regression relationship of this

type. The abscissa in figure 5 is a measure of traffic volume such as the total volume per

day entering the intersection. Analogous relationships have been developed using both the

major- and minor-road average daily traffic volumes (ADTs) as predictor variables; such

relationships have been used in the evaluation, but cannot be illustrated in a twodimensional graph. 

A key change in the EB approach from the YC approach and the CG approach is in

the treatment of the observed accident count in the period before improvement of the

treated sites. In both the YC and CG approaches, this observed value is used as the best

available estimate of safety at the treated site during the before period. The EB approach

recognizes that the expected value of the accident count for a site, as indicated by a

regression relationship such as that shown in figure 5, has value as well and may, in some

cases, be a much more important piece of information than the observed accident count. In

other words, the observed accident count in the before period is simply one observation of

a random process and better evaluations will result if, in addition to knowing the observed

accident count, the process itself is understood.

To illustrate this process, consider the example in figure 7, which utilizes the same

regression relationship shown in figure 5. Figure 7 shows the observed accident count for

an intersection in the before period as a point above the regression line for the

corresponding traffic volume. This indicates that the observed accident count is higher

than expected. Such higher-than-expected accident counts are subject to regression to the

mean (as are lower-than-expected accident counts). The best estimate of accident

experience at this site for the before period, given both the observed accident frequency

and that from the regression relationship, is the value corresponding to the x between the

observed point and the expected value.
93

Figure 7. Use of Regression Relationship in the EB Approach.
94

Hauer provides an analytical procedure to estimate the position of the x in figure 7. 

This analytical procedure involves, in essence, a weighted average of the observed and

expected values. This analytical procedure is fundamental to the EB approach. Hauer

refers to the observed accident count as K and the expected value of the accident count as

E(6). E(6) is an expected value that can be estimated from a regression relationship like

that shown in figure 7. As noted above, the regression relationships actually used in the

EB analysis are the same negative binomial regressions that were described earlier for use

in the CG analysis. These negative binomial regressions predict accidents for a one-year

period, so the equation for E(6) also incorporates the duration of the before period:

E f MajADT MinADT STATE YEARS (35) BT BT T BT () ( , , ) κ =

The x in figure 7, denoted as E{6|K}, is estimated using a weight factor, ", as follows:

EK E K { } { } (36) i i κ ακ α = +− i ( ) 1

The subscript i in Equation (34) indicates that the values apply to a specific treatment

site. Hauer demonstrates that to estimate E{6*K}i

 with maximum precision, " must have

the value indicated below:

(37) { }

{ }

α

κ

κ

=

+

1

1

VAR

E

i

i

The value of E{6}i in Equation (35) can be obtained from the regression relationship

shown in figure 7. The value of VAR{6}i

 can be obtained from analysis of the residuals

from that regression relationship.

It can also be shown that " can be estimated from the overdispersion parameter of the

negative binomial regression relationship and the expected before period accident

frequency for the treatment site:

α (38)

κ = +

1

1 d E i ( )

where: d = overdispersion parameter of the appropriate negative binomial regression

relationship

In applying Equation (36), the value of E(6) for a single year was used so that " would

not depend on the duration of the before period. The value of E{6*K}i

 is determined from

Equation (36) using the weight, ", determined from Equation (38).
95

The variance of E{6*K}i is then determined as:

Var E K E K { } { } (39) i iκ ακ = − ( ) 1

As in the YC and CG approaches [see Equation (4)], the best estimate of the expected

accident frequency after the treatment, 8i

Ê

, is the observed accident frequency after the

treatment, Li.

Adjustment factors are now needed to account for differences in traffic volume and

duration between the before and after study periods. The traffic volume adjustment, rTF,

uses the appropriate negative binomial regression relationship:

(40) ( )

( ) r

f MajADT MinADT STATE

f MajADT MinADT STATE TF

AT AT T

BT BT T

= , ,

, ,

The adjustment factor for the duration of the study period is:

r (41)

YEARS

YEARS d

AT

BT

=

The next step is to estimate Bi, the expected value of the accident count that would

have occurred during the after period if the improvement had not been made. This value is

estimated as:

π κ  { } (42) i i = E K rr d tf

where rd is the ratio of the durations of the before and after periods and rtf is the ratio of the

expected accident counts for the traffic volume levels (or traffic flow rates) at the

intersection during the before and after periods, as illustrated in f7igure 5. (NOTE: rtf is

not equal to the ratio of the before and after traffic volumes unless the regression

relationship being used is linear).

The customary estimate of the effectiveness of the treatment is ^2i, which can be

determined as:

(43)   θ λπ/  i ii =

The overall effectiveness of a group of treatments at similar sites can be determined by

summing and then combining values of 8i

Ê

 and ^Bi. The overall treatment effectiveness is

equal to:
96

(44)  





 θ

λ

π

λ

π = = ΣΣ

i

i

However, the use of ^2 in this form is not recommended because, even if ^8 and ^B are

unbiased estimators of 8 and B, the ratio ^

8/

^

B is a biased estimator of ^^

2. Although this bias

is often small, Hauer(2) recommends that removing it is a worthwhile precaution. An

approximately unbiased estimator for ^

2 is given by:

[ { } ] (45)  *  θ θ ππ = + / 1  /  2 VAR

Investigation during the current research confirmed that the difference between ^2 and ^

2* is very small, usually affecting only the third or fourth significant digit of the treatment

effectiveness. Nevertheless, because of the potential bias in ^

2, the value of ^2* was used as

the treatment effectiveness estimate in this research.

The variance of ^

8 can be determined as:

VAR{ } i (46)   λ λ = Σ

The variance of ^B can be determined as:

VAR VAR E K r r { } { } (47) i d tf π κ  = Σ 2 2

Finally, the variance of ^2* can be estimated as:

VAR VAR VAR VAR { } [ ] { } { } [ { } ] (48)  *  (  θ θ λλ ππ ππ ≅ ++ / )(  / /  / 22 2 2 21

As indicated in Equation (12), the effectiveness of the treatment can be expressed as a

percentage accident reduction in the form:

E = − 100 1 ( (49) θ * )

Typically, Hauer(2) does not estimate a 95 percent confidence interval for 2 or E. 

Rather, Hauer reports the percentage accident reduction effectiveness, E, and its standard

deviation, also expressed as a percentage. Hauer also suggests that the estimated effect, E,

should not be relied upon unless its estimated standard deviation is two to three times

smaller than E. In this research, only results where the standard deviation of E was less

than half of E were used. This is nearly equivalent to the Z value of 1.96 used in the YC

and CG approaches.
97

Hauer has also formulated a more sophisticated EB approach in which the prediction

of accident frequencies from the appropriate negative binomial regression relationship to

determine the value of E{6}i

 is done on a year-by-year basis, rather than for the before

period as a whole. This more sophisticated EB approach was applied to 32 EB analyses,

which constitute all of the EB analyses presented in Section 6 of this report that had

sample sizes of 20 improved intersections or more. The differences in effectiveness

measures between the EB approach described above and Hauer’s more sophisticated

approach ranged as high as 6 percent, but was typically less than 2 percent. The mean of

the differences in effectiveness measures between the two approaches was 1.3 percent. 

While Hauer’s more sophisiticated approach is desirable on theoretical grounds, and can

be applied when year-by-year traffic volume data for the before period are available, the

actual change in the results obtained in this study was minimal.

Strengths and Weaknesses of This Evaluation Approach

The major strength of the EB approach is that, among the evaluation approaches

presented here, only the EB approach can address the potential bias created by regression

to the mean. Neither the YC approach nor the CG approach can do this. In his recent

book, Hauer presents a strong theoretical case for the advantages of the EB approach.(2)

Another strength of the EB approach is that, since regression modeling makes very

efficient use of data, reference groups needed for the EB approach should be smaller than

the comparison groups that would be required for the CG approach. This should enable

percentage accident reduction for specific projects types to be assessed within the desired

precision level in cases where this was not possible with the CG approach.

The EB approach eliminates the difficulty with zero values of accident frequency that

is inherent in both the YC and CG approaches. In the EB approach, a value of Ki

 equal to

zero can be treated naturally without arbitrarily substituting a value of 0.5. The weighting

provided by Equation (36) will result in a non-zero value of E{6*K}i

, because E{6}i will

be greater than zero even when Ki is not.

Execution of the Evaluation Plan

The evaluation plan is presented in this section of the report. The YC, CG, and EB

evaluation approaches were applied to the database discussed in sections 3 and 4 of this

report.

Yoked Comparison Evaluations

A total of 214 YC evaluations were performed for specific combinations of:

• Eight safety measures.
98

– total intersection accidents

– fatal and injury intersection accidents

– project-related intersection accidents

– project-related fatal and injury intersection accidents

– total accidents for individual intersection approaches

– fatal and injury accidents for individual intersection approaches

– project-related accidents for individual intersection approaches

– project-related fatal and injury accidents for individual intersection approaches

• Two area types.

– rural

– urban

• Two intersection types.

– three-leg intersections

– four-leg intersections

• Three traffic control types.

– unsignalized (two-way stop-control)

– signalized

– newly signalized (i.e., signalized in conjunction with left-turn installation)

• Five project types.

– added left-turn lanes

– added right-turn lanes

– added left- and right-turn lanes

– extension of the length of existing left-turn lanes

– extension of the length of existing left- and right-turn lanes

The YC approach using one-to-one matching of treatment and comparison sites was

executed as described above. The sample sizes for the 214 evaluations performed ranged

from 1 to 35 intersections and from 1 to 116 intersection approaches. Statistically

significant effectiveness measures were found for 37 of the 214 evaluations performed. 

Obviously, the evaluations with larger sample sizes are more likely to provide statistically

significant results.

The results of the YC evaluations are presented in tables C-1 through C-10 in

appendix C of this report and are discussed in section 6. The tables in Appendix C show

the number of sites included in each evaluation; these are, in some cases, smaller than the

total number of sites for that intersection and project type shown in section 4 of this report

because of outliers that were omitted from the analysis. Outliers consisted of anomalous

sites at which substantial unexplained increases in accident frequency occurred. For

example, one site experienced 1 accident in 4 years before the turn lane project and 53

accidents in 6 years after the project. Such large increases in accident frequency suggest

problems in accident reporting rather than actual project effects. In all analyses performed

in this study, sites were excluded as outliers if the apparent treatment effectiveness was

greater than equal to a 100 percent increase in accident frequency (i.e., if the odds ratio, ^

2i,
99

was greater than or equal to 2.0). Sites with less than 5 accidents in the before period were

not excluded as outliers, even if ^2i was greater than or equal to 2.0, because such variations

in accident frequency over time are not unusual when the accident frequencies are very

small.

Comparison Group Evaluations

A total of 150 CG evaluations were performed for specific combinations of:

• Six safety measures.

• Two area types.

• Two intersection types.

• Three traffic control types.

• Five project types.

The CG analysis omitted two safety measures that were considered in the YC analysis. 

These were:

• Project-related fatal and injury intersection accidents.

• Project-related fatal and injury accidents for individual approaches.

The CG analysis used the negative binomial regression relationships shown in Appendix B

to adjust accident frequencies for differences in traffic volume between the before and

after study periods and between the treatment and comparison sites. These negative

binomial regression relationships also accounted for state-to-state differences in accident

frequency in cases where treatment and comparison sites were located in different states. 

The state-to-state differences in accident frequencies in this multistate database were, in

some cases, substantial (see appendix B). In evaluations for which no satisfactory

regression models were available, proportional traffic volume adjustments based on

Equation (22) were made.

The CG approach, using a comparison group to replace the single comparison site

used in the YC analysis, was executed as described above. The sample sizes for the 150

evaluations ranged from 1 to 39 for intersections and from 1 to 155 for intersection

approaches. Statistically significant effectiveness measures were found for 47 of the 150

evaluations performed. The resulting CG evaluations are presented in tables C-11 through

C-16 in appendix C of this report and are discussed in Section 6.

Empirical Bayes Evaluations

A total of 108 EB evaluations were performed for specific combinations of:

• Six safety measures.

• Two area types.
100

• Two intersection types.

• Three traffic control types.

• Five project types.

The same six safety measures are used as in the CG evaluation, because negative binomial

regression relationships, needed for the CG and EB evaluations, could not be developed

for the safety measures based on project-related fatal and injury accidents. Several other

cases also had to be omitted from the EB analysis because of unsatisfactory models.

The EB approach, using the negative binomial regression relationship in place of the

comparison sites used in the YC and CG approaches, was executed as described above. 

The sample sizes for the 108 evaluations ranged from 1 to 39 for intersections and from

2 to 148 for intersection approaches. Statistically significant effectiveness measures were

found for 48 of the 108 evaluations performed. The results of the EB evaluations are

presented in table C-17 through C-22 in appendix C of this report and are discussed in

section 6.

101

6. EVALUATION RESULTS

This section of the report presents and interprets the results of the evaluations

conducted using the YC, CG, and EB approaches. Detailed results of all evaluations

conducted as part of the study are presented in appendix C. This section focuses on those

evaluation results that were found to be statistically significant. All tests of statistical

significance in this report were performed at the 5 percent significance level (95 percent

confidence level) unless otherwise stated.

The evaluation results are tabulated in several different ways in this section. First,

results tables for each dependent variable and target area are presented. Then, the same

results are tabulated and reviewed by project type.

Evaluation Results for Specific Safety Measures

Tables 28 through 40 present the evaluation results for specific safety measures. The

results for four-leg intersections are presented first. There are more statistically significant

analysis results for four-leg intersections than for three-leg intersections because there were

more treated sites and more accidents per site for four-leg intersections. The tables and the

safety measures presented for four-leg intersections include:

• Table 28—total intersection accidents.

• Table 29—fatal and injury intersection accidents.

• Table 30—project-related intersection accidents.

• Table 31—project-related fatal and injury intersection accidents.

• Table 32—total accidents for individual intersection approaches.

• Table 33—fatal and injury accidents for individual intersection approaches.

• Table 34—project-related accidents for individual intersection approaches.

Each table shows all treatment effectiveness measures that were obtained for the YC, CG,

and EB approaches. Only those results that were statistically significant are included. 

There is no table for project-related fatal and injury accidents on individual intersection

approaches because none of the evaluation results for that safety measure were statistically

significant for the YC approach and, because of low accident frequencies, appropriate

regression models to conduct the CG and EB approaches could not be developed.

The tables and the safety measures presented for three-leg intersections include:

• Table 35—total intersection accidents.

• Table 36—fatal and injury intersection accidents.

• Table 37—project-related intersection accidents.
10

2

Table 28. Evaluation Results for Total Intersection Accidents at Four-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change

in accident

frequency

No. of

improved

sites

Percent change

in accident

frequency

No. of 

improved

sites

Percent change

in accident

frequency

for 

entire 

project

for one

turn

lane

for

entire

project

for one

turn

lane

for

 entire

 project

for one

turn

lane

Rural Newly Signalized Added LTLs 2 !70.3 !35.2

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 21 !58.8 !32.1 25 !60.6 !33.7 25 !49.6 !27.6

Rural Unsignalized Added LTLs and RTLs 15 !25.2 !12.6

Rural Unsignalized Added RTLs 29 !35.1 !22.6 28 !22.0 !14.0

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs 24 !43.8 !22.4 28 !46.4 !24.1 25 !20.0 !10.4

Urban Signalized Added LTLs 33 !42.0 !13.0 37 !18.3 !5.8 39 !29.5 !9.5

Urban Signalized Added LTLs and RTLs 9 !21.3 !5.5 10 !26.6 !6.8 10 !27.8 !7.1

Urban Signalized Added RTLs 18 !9.0 !4.1

Urban Signalized Extended LTLs 3 42.3 25.4 3 49.5 29.7

Urban Unsignalized Added LTLs 8 !70.5 !35.2 9 !53.4 !26.7

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs 3 !67.1 !40.3

Note: Only statistically significant evaluation results are shown.
10

3

Table 29. Evaluation Results for Fatal and Injury Intersection Accidents at Four-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change

in accident

frequency

No. of

improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change

in accident

frequency

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

 lane

for

entire

project

for one

turn

lane

Rural Newly Signalized Added LTLs 2 !82.6 !41.3 2 !57.5 !28.7

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 22 !70.4 !39.7 25 !73.9 !41.0 24 !63.4 !35.4

Rural Unsignalized Added LTLs and RTLs 15 !44.7 !22.3

Rural Unsignalized Added RTLs 29 !37.2 !23.4

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs 23 !42.7 !21.8 28 !48.7 !25.2 14 !54.2 !28.1

Urban Signalized Added LTLs 35 !39.5 !12.4 39 !18.0 !5.8 39 !28.4 !9.2

Urban Signalized Added LTLs and RTLs 10 !45.9 !11.8 10 !45.2 !11.6

Urban Signalized Added RTLs 17 !20.6 !9.2

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs 8 !79.5 !39.7 9 !58.8 !29.4

Note: Only statistically significant evaluation results are shown.
10

4

Table 30. Evaluation Results for Project-Related Intersection Accidents at Four-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change

in accident

frequency

No. of 

improved

 sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 23 !66.2 !37.2

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs 35 !39.1 !12.6

Urban Signalized Added LTLs and RTLs 7 !59.8 !15.5 9 !40.2 !10.3

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs 8 !79.0 !39.5 9 !60.4 !30.2 7 !51.2 !25.6

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
10

5

Table 31. Evaluation Results for Project-Related Fatal and Injury Accidents at Four-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUPa EMPIRICAL BAYESa

No. of

 improved

 sites

Percent change in

accident

frequency

No. of

 improved

sites

Percent change in

accident

frequency

No. of 

improved

 sites

Percent change in

accident

frequency

for

 entire

 project

for one

turn

lane

for

entire

 project

for one

turn

lane

for

 entire

 project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs

Urban Signalized Added LTLs and RTLs

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs 8 !81.5 !40.8

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.

a Because of small accident frequencies, regression models could not be developed for this safety measure. Therefore, the CG and EB

evaluations could not be performed.
10

6

Table 32. Evaluation Results for Total Accidents on Individual Intersection Approaches at Four-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change in

accident

frequency

No. of

improved

sites

Percent change in

accident

frequency

No. of 

improved

sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

 project

for one

turn

lane

for

entire

project

for one

turn

lane

Rural Newly Signalized Added LTLs 4 !67.5 !67.5 4 !44.1 !44.1

Rural Newly Signalized Added LTLs and RTLs 2 !68.8 !34.4 2 !61.4 !30.7

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 40 !47.3 !47.3 50 !61.0 !61.0 50 !54.6 !54.6

Rural Unsignalized Added LTLs and RTLs 30 !27.9 !14.0

Rural Unsignalized Added RTLs 58 !31.6 !31.6 57 !26.7 !26.7

Rural Unsignalized Extended LTLs 4 !43.0 !43.0

Urban Newly Signalized Added LTLs 47 !55.0 !55.0 56 !45.7 !45.7 49 !28.0 !28.0

Urban Signalized Added LTLs 106 !42.0 !42.0 147 !28.0 !28.0 148 !34.2 !34.2

Urban Signalized Added LTLs and RTLs 32 !30.9 !15.4 39 !34.5 !17.2 38 !32.5 !16.2

Urban Signalized Added RTLs 28 !25.7 !25.7 67 !17.6 !17.6

Urban Signalized Extended LTLs 12 45.3 45.3 11 57.8 57.8

Urban Unsignalized Added LTLs 16 !69.4 !69.4 18 !54.4 !54.4 17 !20.1 !20.1

Urban Unsignalized Added LTLs and RTLs 2 !66.3 !33.1

Urban Unsignalized Added RTLs 6 !75.8 !75.8

Note: Only statistically significant evaluation results are shown.
10

7

Table 33. Evaluation Results for Fatal and Injury Accidents on Individual Intersection Approaches at 

 Four-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

 sites

Percent change in

accident

frequency

No. of

improved

sites

Percent change in

accident

frequency

No. of

improved

sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

for 

entire 

project

for one

turn

lane

Rural Newly Signalized Added LTLs 4 !76.4 !76.4 4 !42.1 !42.1

Rural Newly Signalized Added LTLs and RTLs 2 !55.4 !27.7

Rural Newly Signalized Added RTLs 2 !65.6 !65.6

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 41 !55.0 !55.0 50 !70.8 !70.8 49 !61.0 !61.0

Rural Unsignalized Added LTLs and RTLs 49 !48.1 !24.1

Rural Unsignalized Added RTLs 58 !37.0 !37.0 55 !24.3 !24.3

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs 49 !58.1 !58.1 55 !46.9 !46.9 48 !43.2 !43.2

Urban Signalized Added LTLs 114 !40.0 !40.0 154 !22.6 !22.6 122 !35.3 !35.3

Urban Signalized Added LTLs and RTLs 34 !35.4 !17.2 39 !49.7 !24.9 35 !53.4 !26.7

Urban Signalized Added RTLs 64 !22.2 !22.2

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs 16 !77.8 !77.8 18 !55.4 !55.4

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
10

8

 Table 34. Evaluation Results for Project-Related Accidents on Individual Intersection Approaches at 

 Four-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change in

accident

frequency

No. of

 improved

sites

Percent change in

accident

frequency

No. of 

improved

sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

 project

for one

turn

lane

for

 entire

 project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs 115 !33.9 !33.9 127 !40.4 !40.4

Urban Signalized Added LTLs and RTLs 32 !59.7 !29.8 38 !39.1 !19.6 34 !49.5 !24.8

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs 16 !78.1 !78.1 18 !60.5 !60.5 14 !50.5 !50.5

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
10

9

Table 35. Evaluation Results for Total Intersection Accidents at Three-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change

in accident

frequency

No. of

improved

sites

Percent change

in accident

frequency

No. of 

improved

sites

Percent change

in accident

frequency

for 

entire 

project

for one

turn

lane

for

entire

project

for one

turn

lane

for

 entire

 project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 31 !63.7 !63.7 35 !53.5 !53.5 36 !43.7 !43.7

Rural Unsignalized Added LTLs and RTLs 12 !29.4 !23.5

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs

Urban Signalized Added LTLs and RTLs

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs 10 !35.0 !35.0 8 !33.2 !33.2

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
110

Table 36. Evaluation Results for Fatal and Injury Intersection Accidents at Three-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change

in accident

frequency

No. of

improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 34 !58.6 !58.6 35 !54.8 !54.8

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs

Urban Signalized Added LTLs and RTLs

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs

Note: Only statistically significant evaluation results are shown.
111

Table 37. Evaluation Results for Project-Related Intersection Accidents at Three-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change

in accident

frequency

No. of 

improved

 sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 35 !62.3 !62.3

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs

Urban Signalized Added LTLs and RTLs

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
112

• Table 38—total accidents for individual intersection approaches.

• Table 39—fatal and injury accidents for individual intersection accidents.

• Table 40—project-related accidents for individual intersection approaches.

These tables are comparable to those presented above for four-leg intersections. There is

no table for project-related fatal and injury accidents at three-leg intersections because no

evaluation results for that accident type were statistically significant.

Evaluation Results for Specific Project Types

The evaluation results for specific project types are presented in tables 41 through 46. 

Specifically, the evaluation results by project type for four-leg intersections are presented

in the following tables:

• Table 41—projects involving added left-turn lanes.

• Table 42—projects involving added right-turn lanes.

• Table 43—projects involving added left- and right-turn lanes.

• Table 44—projects involving extension of the length of existing turn lanes.

The evaluation results by project type for three-leg intersections are presented in:

• Table 45—projects involving added left-turn lanes.

• Table 46—projects involving added right-turn lanes.

There were no statistically significant evaluation results for projects involving addition of

both left- and right-turn lanes or extension of the length of existing turn lanes at three-leg

intersections, so no tables of evaluation results are presented for these project types.

The results in tables 41 through 46 are drawn from, and are identical to, the results in

tables 28 through 40. However, results for the safety measures involving project-related

fatal and injury accidents are omitted because only the YC approach could be evaluated for

those cases and, even for the YC approach, very few statistically significant results were

obtained due to low accident frequencies analyzed.

The next section addresses the choice among the alternative analysis methods

presented in these tables. Then, the results for the specific project types can be interpreted.
113

Table 38. Evaluation Results for Total Accidents on Individual Intersection Approaches at Three-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change in

accident

frequency

No. of

improved

sites

Percent change in

accident

frequency

No. of 

improved

sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

 project

for one

turn

lane

for

entire

project

for one

turn

lane

Rural Newly Signalized Added LTLs 4 !67.5 !67.5

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 34 !47.5 !47.5 70 !51.9 !51.9 62 !45.2 !45.2

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs 9 !49.3 !49.3

Urban Signalized Added LTLs and RTLs

Urban Signalized Added RTLs 3 !44.5 !44.5

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs 10 !55.4 !55.4 20 !54.4 !54.4 16 !32.3 !32.3

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
114

 Table 39. Evaluation Results for Fatal and Injury Accidents on Individual Intersection Approaches at 

 Three-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

 sites

Percent change in

accident

frequency

No. of

improved

sites

Percent change in

accident

frequency

No. of

improved

sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

project

for one

turn

lane

for 

entire 

project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 70 !43.6 !43.6

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs 9 !47.6 !47.6

Urban Signalized Added LTLs and RTLs

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
115

Table 40. Evaluation Results for Project-Related Accidents on Individual Intersection Approaches at 

 Three-Leg Intersections.

Area

type

Traffic control

type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of

improved

sites

Percent change in

accident

frequency

No. of

 improved

sites

Percent change in

accident

frequency

No. of 

improved

sites

Percent change in

accident

frequency

for

entire

project

for one

turn

lane

for

entire

 project

for one

turn

lane

for

 entire

 project

for one

turn

lane

Rural Newly Signalized Added LTLs

Rural Newly Signalized Added LTLs and RTLs

Rural Newly Signalized Added RTLs

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and RTLs

Rural Unsignalized Added LTLs 70 !64.3 !64.3

Rural Unsignalized Added LTLs and RTLs

Rural Unsignalized Added RTLs

Rural Unsignalized Extended LTLs

Urban Newly Signalized Added LTLs

Urban Signalized Added LTLs

Urban Signalized Added LTLs and RTLs

Urban Signalized Added RTLs

Urban Signalized Extended LTLs

Urban Unsignalized Added LTLs

Urban Unsignalized Added LTLs and RTLs

Urban Unsignalized Added RTLs

Note: Only statistically significant evaluation results are shown.
116

Table 41. Evaluation Results for Projects Involving Added Left-Turn Lanes at Four-Leg Intersections.

Area

 type

Traffic control 

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved 

sites

Percent change in

accident frequency

No. of

 improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

 project

for one

 turn 

lane

for

 entire

 project

for one

turn

lane

TOTAL INTERSECTION ACCIDENTS

Rural Newly Signalized 2 !70.3 !35.2

Rural Unsignalized 21 !58.8 !32.1 25 !60.6 !33.7 25 !49.6 !27.6

Urban Newly Signalized 24 !43.8 !22.4 28 !46.4 !24.1

Urban Signalized 33 !42.0 !13.0 37 !18.3 !5.8 39 !29.5 !9.5

Urban Unsignalized 8 !70.5 !35.2 9 !53.4 !26.7

FATAL AND INJURY INTERSECTION ACCIDENTS

Rural Newly Signalized 2 !82.6 !41.3 2 !57.5 !28.7

Rural Unsignalized 22 !70.4 !39.7 25 !73.9 !41.0 24 !63.4 !35.4

Urban Newly Signalized 23 !42.7 !21.8 28 !48.7 !25.2 14 !54.2 !28.1

Urban Signalized 35 !39.5 !12.4 39 !18.0 !5.8 39 !28.4 !9.2

Urban Unsignalized 8 !79.5 !39.7 9 !58.8 !29.4

PROJECT-RELATED INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 23 !66.2 !37.2

Urban Newly Signalized

Urban Signalized 35 !39.1 !12.6

Urban Unsignalized 8 !79.0 !39.5 9 !60.4 !30.2 7 !51.2 !25.6

TOTAL ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized 4 !67.5 !67.5 4 !44.1 !44.1

Rural Unsignalized 40 !47.3 !47.3 50 !61.0 !61.0 50 !54.6 !54.6

Urban Newly Signalized 47 !55.0 !55.0 56 !45.7 !45.7 49 !28.0 !28.0

Urban Signalized 106 !42.0 !42.0 147 !28.0 !28.0 148 !34.2 !34.2

Urban Unsignalized 16 !69.4 !69.4 18 !54.4 !54.4 17 !20.1 !20.1

Note: Only statistically significant evaluation results are shown.
117

Table 41. Evaluation Results for Projects Involving Added Left-Turn Lanes at Four-Leg Intersections (Continued).

Area

 type

Traffic control 

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved 

sites

Percent change in

accident frequency

No. of

 improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

 project

for one

 turn 

lane

for

 entire

 project

for one

turn

lane

FATAL AND INJURY ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized 4 !76.4 !76.4 4 !42.1 !42.1

Rural Unsignalized 41 !55.0 !55.0 50 !70.8 !70.8 49 !61.0 !61.0

Urban Newly Signalized 49 !58.1 !58.1 55 !46.9 !46.9 48 !43.2 !43.2

Urban Signalized 114 !40.0 !40.0 154 !22.6 !22.6 122 !35.3 !35.3

Urban Unsignalized 16 !77.8 !77.8 18 !55.4 !55.4

PROJECT-RELATED ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized

Urban Newly Signalized

Urban Signalized 115 !33.9 !33.9 127 !40.4 !40.4

Urban Unsignalized 16 !78.1 !78.1 18 !60.5 !60.5 14 !50.5 !50.5

Note: Only statistically significant evaluation results are shown.
.

.

.

118

Table 42. Evaluation Results for Projects Involving Added Right-Turn Lanes at Four-Leg Intersections

Area type

Traffic control

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved sites

Percent change in

accident frequency

No. of improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one 

turn

lane

for

entire

project

for one

turn

lane

TOTAL INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 29 !35.1 !22.6 28 !22.0 !14.0

Urban Signalized 18 !9.0 !4.1

Urban Unsignalized 3 !67.1 !40.3

FATAL AND INJURY INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 29 !37.2 !23.4

Urban Signalized 17 !20.6 !9.2

Urban Unsignalized

PROJECT-RELATED INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized

Urban Signalized

Urban Unsignalized

TOTAL ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized 58 !31.6 !31.6 57 !26.7 !26.7

Urban Signalized 28 !25.7 !25.7 67 !17.6 !17.6

Urban Unsignalized

Note: Only statistically significant evaluation results are shown.
119

Table 42. Evaluation Results for Projects Involving Added Right-Turn Lanes at Four-Leg Intersections (Continued).

Area type

Traffic control

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved sites

Percent change in

accident frequency

No. of improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one 

turn

lane

for

entire

project

for one

turn

lane

FATAL AND INJURY ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized 2 !65.6 !65.6

Rural Unsignalized 58 !37.0 !37.0 55 !24.3 !24.3

Urban Signalized 64 !22.2 !22.2

Urban Unsignalized

PROJECT-RELATED ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized

Urban Signalized

Urban Unsignalized

Note: Only statistically significant evaluation results are shown.
12

0

Table 43. Evaluation Results for Projects Involving Added Left- and Right-Turn Lanes at Four-Leg Intersections.

Area type

Traffic control 

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved sites

Percent change in

accident frequency

No. of improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one

 turn 

lane

for

entire

project

for one

turn

lane

TOTAL INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 15 !25.2 !12.6

Urban Signalized 9 !21.3 !5.5 10 !26.6 !6.8 10 !27.8 !7.1

Urban Unsignalized

FATAL AND INJURY INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 15 !44.7 !22.3

Urban Signalized 10 !45.9 !11.8 10 !45.2 !11.6

Urban Unsignalized

PROJECT-RELATED INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized

Urban Signalized 7 !59.8 !15.5 9 !40.2 !10.3

Urban Unsignalized

TOTAL ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized 2 !68.8 !34.4 2 !61.4 !30.7

Rural Unsignalized 30 !27.9 !14.0

Urban Signalized 32 !30.9 !15.4 39 !34.5 !17.2 38 !32.5 !16.2

Urban Unsignalized 2 !66.3 !33.1

Note: Only statistically significant evaluation results are shown.
121

Table 43. Evaluation Results for Projects Involving Added Left- and Right-Turn Lanes 

 at Four-Leg Intersections (Continued).

Area type

Traffic control 

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved sites

Percent change in

accident frequency

No. of improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one

 turn 

lane

for

entire

project

for one

turn

lane

FATAL AND INJURY ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized 2 !55.4 !27.7

Rural Unsignalized 49 !48.1 !24.1

Urban Signalized 34 !35.4 !17.2 39 !49.7 !24.9 35 !53.4 !26.7

Urban Unsignalized

PROJECT-RELATED ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized

Urban Signalized 32 !59.7 !29.8 38 !39.1 !19.6 34 !49.5 !24.8

Urban Unsignalized

Note: Only statistically significant evaluation results are shown.
12

2

Table 44. Evaluation Results for Projects Involving Extension of the Length of Existing Turn Lanes 

at Four-Leg Intersections

Area

type

Traffic

control type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No.

 of 

improved

 sites

Percent change in

accident

frequency No.

 of

 improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one 

turn

lane

for

entire

project

for one

turn

lane

TOTAL INTERSECTION ACCIDENTS

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and

RTLS

Rural Unsignalized Extended LTLs

Urban Signalized Extended LTLs 3 42.3 25.4 3 49.5 29.7

FATAL AND INJURY INTERSECTION ACCIDENTS

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and

RTLs

Rural Unsignalized Extended LTLs

Urban Signalized Extended LTLs

PROJECT-RELATED INTERSECTION ACCIDENTS

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and

RTLs

Rural Unsignalized Extended LTLs

Urban Signalized Extended LTLs

Note: Only statistically significant evaluation results are shown.
12

3

Table 44. Evaluation Results for Projects Involving Extension of the Length of Existing 

 Turn Lanes at Four-Leg Intersections (Continued).

Area

type

Traffic

control type Project type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No.

 of 

improved

 sites

Percent change in

accident

frequency No.

 of

 improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one 

turn

lane

for

entire

project

for one

turn

lane

TOTAL ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and

RTLs

Rural Unsignalized Extended LTLs 4 !43.0 !43.0

Urban Signalized Extended LTLs

FATAL AND INJURY ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and

RTLs

Rural Unsignalized Extended LTLs

Urban Signalized Extended LTLs

PROJECT-RELATED ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Signalized Extended LTLs

Rural Signalized Extended LTLs and

RTLs

Rural Unsignalized Extended LTLs

Urban Signalized Extended LTLs

Note: Only statistically significant evaluation results are shown.
12

4

Table 45. Evaluation Results for Projects Involving Added Left-Turn Lanes at Three-Leg Intersections

Area

 type

Traffic control 

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved 

sites

Percent change in

accident frequency

No. of

 improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

 project

for one 

turn

lane

for

 entire

 project

for one

turn

lane

TOTAL INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 31 !63.7 !63.7 35 !53.5 !53.5 36 !43.7 !43.7

Urban Newly Signalized

Urban Signalized

Urban Unsignalized 10 !35.0 !35.0 8 !33.2 !33.2

FATAL AND INJURY INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 34 !58.6 !58.6 35 !54.8 !54.8

Urban Newly Signalized

Urban Signalized

Urban Unsignalized

PROJECT-RELATED INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized 35 !62.3 !62.3

Urban Newly Signalized

Urban Signalized

Urban Unsignalized

Note: Only statistically significant evaluation results are shown.
12

5

Table 45. Evaluation Results for Projects Involving Added Left-Turn Lanes at Three-Leg Intersections (Continued).

Area

 type

Traffic control 

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved 

sites

Percent change in

accident frequency

No. of

 improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

 project

for one 

turn

lane

for

 entire

 project

for one

turn

lane

TOTAL ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized 4 !67.5 !67.5

Rural Unsignalized 34 !47.5 !47.5 70 !51.9 !51.9 62 !45.2 !45.2

Urban Newly Signalized

Urban Signalized 9 !49.3 !49.3

Urban Unsignalized 10 !55.4 !55.4 20 !54.4 !54.4 16 !32.3 !32.3

FATAL AND INJURY ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized 70 !43.6 !43.6

Urban Newly Signalized

Urban Signalized 9 !47.6 !47.6

Urban Unsignalized

PROJECT-RELATED ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized 70 !64.3 !64.3

Urban Newly Signalized

Urban Signalized

Urban Unsignalized

Note: Only statistically significant evaluation results are shown.
,

.

12

6

Table 46. Evaluation Results for Projects Involving Added Right-Turn Lanes at Three-Leg Intersections

Area type

Traffic control

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved sites

Percent change in

accident frequency

No. of improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one 

turn

lane

for

entire

project

for one

turn

lane

TOTAL INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized

Urban Signalized

Urban Unsignalized

FATAL AND INJURY INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized

Urban Signalized

Urban Unsignalized

PROJECT-RELATED INTERSECTION ACCIDENTS

Rural Newly Signalized

Rural Unsignalized

Urban Signalized

Urban Unsignalized

TOTAL ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized

Urban Signalized 3 !44.5 !44.5

Urban Unsignalized

Note: Only statistically significant evaluation results are shown.
12

7

Table 46. Evaluation Results for Projects Involving Added Right-Turn Lanes at Three-Leg Intersections (Continued).

Area type

Traffic control

type

YOKED COMPARISON COMPARISON GROUP EMPIRICAL BAYES

No. of 

improved sites

Percent change in

accident frequency

No. of improved

sites

Percent change in

accident frequency

No. of

improved

sites

Percent change in

accident

frequency

for 

entire

project

for one

turn

lane

for

entire

project

for one 

turn

lane

for

entire

project

for one

turn

lane

FATAL AND INJURY ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized

Urban Signalized

Urban Unsignalized

PROJECT-RELATED ACCIDENTS FOR INDIVIDUAL INTERSECTION APPROACHES

Rural Newly Signalized

Rural Unsignalized

Urban Signalized

Urban Unsignalized

Note: Only statistically significant evaluation results are shown.
128

Comparison of Alternative Evaluation Approaches

The tables presented above include results from the YC, CG, and EB approaches. For

example, table 41 presents the results of 30 before-after evaluations for projects involving

added left-turn lanes at four-leg intersections. Of these 30 evaluations, there are:

• Fourteen evaluations for which all three evaluation approaches provided

statistically significant results.

• Four evaluations for which only the YC and CG approaches provided statistically

significant results.

• One evaluation for which only the YC and EB approaches provided statistically

significant results.

• One evaluation for which only the CG and EB approaches provided statistically

significant results.

• Two evaluations for which only the YC approach provided statistically significant

results.

• One evaluation for which only the EB approach provided statistically significant

results.

• Five evaluations for which none of the approaches provided statistically

significant results.

In any evaluation for which more than one approach provides statistically significant

results, a key issue is to determine which results should be used.

The discussion of the three evaluation methods in section 5 of this report makes clear

that, on conceptual and theoretical grounds, the EB approach appears to be the most

desirable of the three approaches. The primary reason for this is that, among the three

approaches, only EB can account for regression to the mean. When comparing the CG and

YC methods, the CG method is most desirable on conceptual and theoretical grounds

because it uses a group of comparison sites, rather than a single site, to determine what

would have happened at the treatment site had the improvement not been made. The use of

multiple comparison sites should reduce the variance of the treatment effect and provide

more accurate results. Thus, we began the study with the idea that the three evaluation

approaches, in descending order of appropriateness, were EB, CG, and YC.

The results in tables 41 through 46 have been reviewed for confirmation of our initial

expectations concerning the suitability of the evaluation approaches. Table 46 presents a

summary of the frequency with which various types of results were obtained.
129

Table 47 is interpreted as follows. First, the table shows that, for the 110 analyses

performed, there were 46 statistically significant results for the EB approach, 45 for the

CG approach, and 34 for the YC approach. While not definitive, this result is consistent

with the theoretical expectation that the EB and CG approaches are preferable to the YC

approach.

Second, for 32 cases where statistically significant results were obtained with the EB

approach and at least one of the other approaches, the project effectiveness determined

with the EB approach was lower than with the YC and CG in 18 cases and was higher in

only 6 cases. The generally lower project effectiveness estimates obtained with the EB

approach are consistent with the approach being less affected by regression to the mean

than the YC and CG approaches.

Both of these observations from table 47 appear to confirm that the EB approach is the

most suitable approach, followed by the CG approach, and then the YC approach. These

findings support the use of the EB results in favor of the CG and YC results whenever the

EB results are statistically significant. When the EB results are not statistically significant,

the choice of which results to report is complex. One could:

• Use the EB results, even though the results are not statistically significant.

• Use the statistically significant CG or YC results, even though the results may be

subject to regression to the mean.

• Report inconclusive results because no completely satisfactory result was

obtained.

In the cases where the EB result was not statistically significant but the YC or CG

result was statistically significant, we reviewed both the nonsignificant EB result and the

significant YC or CG result. On engineering grounds, we generally found the significant

YC or CG results to be more credible than the nonsignificant EB results. For example, at

four-leg urban unsignalized intersections where left-turn lanes were added, the CG analysis

shows a statistically significant decrease of 27 percent in total accidents, while the EB

analysis shows a statistically non-significant decrease in total accidents of 0.1 percent. 

Both results are based on a limited sample of nine improved sites. The EB result suggests

that installing left-turn lanes at urban unsignalized intersections has no safety benefit. By

contrast, the 27 percent effectiveness estimate from the CG analysis for added left-turn

lanes at four-leg urban unsignalized intersections is very consistent with the EB

effectiveness estimate of 28 percent for four-leg rural intersections. We are not prepared

to believe that this project type reduces total intersection accidents by 28 percent at rural

unsignalized intersections, but has no effect at urban unsignalized intersections. However,

it is also evident that the analysis results for this case are based on a very limited sample

size and that a further evaluation with a larger sample of improved sites would be

desirable.
13

0

Table 47. Comparison of Evaluation Approaches.

Project type

Total number of

evaluations

performeda

Number of evaluations

with statistically

significant results

Number of

evaluations with

statistically

significant results

for EB approach

and at least one

other approach

Relative magnitude of EB effectiveness

estimates

EB below

YC and CG

EB between

YC and CG

EB above

YC CG EB YC and CG

Added LTLs 40 28 27 24 21 13 6 2

Added RTLs 30 1 5 10 3 3 0 0

Added LTLs and RTLs 26 5 11 9 6 2 2 2

Extended LTLs and RTLs 14 0 2 3 2 0 0 2

110 34 45 46 32 18 8 6

a

 based on these evaluations included in Tables 40 through 45
131

For the reasons presented above, tables of final evaluation results have been prepared

by applying the following rules to the results in tables 41 through 46:

• Use the effectiveness measure determined from the EB approach, if it is

statistically significant.

• If the effectiveness measure determined from the EB approach is not statistically

significant, but the effectiveness measure from the CG approach is statistically

significant, use the CG result.

• If the effectiveness measures from both the EB and CG approaches are not

statistically significant, but the effectiveness measure from the YC approach is

statistically significant, use the YC result.

Projects Involving Added Left-Turn Lanes

Table 48 presents final evaluation results for projects involving added left-turn lanes at

four-leg intersections. These results were derived from the results presented in table 41

using the guidelines for choice of evaluation approach presented above. All of the results

in Table 48 are presented as percentage changes in accident frequency for installing one

turn lane. Table 49 presents comparable effectiveness estimates for projects involving

added left-turn lanes at three-leg intersections.

Each entry in the tables is presented in the format:

Percentage change ± standard error of percentage change

The percentage change is normally a negative value that represents the mean reduction

in accident frequency that is expected to result from a specific type of improvement at a

specific type of intersection. The standard error is a measure of the precision of the mean

percentage change in accident frequency. The smaller the standard error, the smaller the

magnitude of site-to-site and year-to-year variations in results would be expected. The

standard error does not directly provide a confidence interval for the mean percentage

change. In fact, as shown in the tables in appendix C of this report, the actual confidence

intervals for the mean percentage change are asymmetrical (i.e., the width of the

confidence interval below the mean is not the same as that above the mean). Thus, the

interval containing one standard error on either side of the mean does not necessarily

represent any particular proportion of the variation in the mean. Nevertheless, the standard

error shown in tables 48 and 49 is useful as a measure of the relative precision of each

result.
13

2

Table 48. Final Evaluation Results Involving Added Left-Turn Lanes for Four-Leg Intersections.

Percent change in accident frequency for adding one turn lane ± standard error

Total intersection accidents Intersection approach accidents

All accidentsa

Fatal and injury

accidents

Project-related

accidentsa All accidentsa

Fatal and injury

accidents

Project-related

accidentsa

RURAL INTERSECTIONS

Unsignalized !28 ± 2.6 !35 ± 3.0 !37 ± 7.4 !55 ± 2.4 !61 ± 3.2 G

Newly Signalizedb

!35 ± 7.6 !29 ± 6.3 G !44 ± 7.3 !42 ± 7.6 G

URBAN INTERSECTIONS

Unsignalizedb!27 ± 3.0 !29 ± 4.0 !25 ± 7.2 !20 ± 4.4 !55 ± 4.8 !51 ± 7.3

Signalized !10 ± 0.8 !9 ± 1.3 !13 ± 3.2 !34 ± 0.8 !35 ± 1.3 !40 ± 1.8

Newly Signalizedb!24 ± 2.8 !28 ± 5.0 G !28 ± 2.9 !43 ± 4.0 G

Note: Results for unsignalized intersections apply only to left-turn lanes on major-road approaches.

a

 includes accidents of all severity levels

b

 based on a limited number of sites
13

3

Table 49. Final Evaluation Results Involving Added Left-Turn Lanes for Three-Leg Intersections.

Percent change in accident frequency for adding one turn lane ± standard error

Total intersection accidents Intersection approach accidents

All accidentsa

Fatal and injury

accidents

Project-related

accidentsa All accidentsa

Fatal and injury

accidents

Project-related

accidentsa

RURAL INTERSECTIONS

Unsignalized !44 ± 5.5 !55 ± 8.3 !62 ± 14.5 !45 ± 6.5 !44 ± 10.9 !64 ± 10.5

Newly Signalizedb

G G G !68 ± 9.3 G G

URBAN INTERSECTIONS

Unsignalizedb!33 ± 12.1 G G !32 ± 13.1 G G

SignalizedbG G G !49 ± 13.9 !48 ± 23.4 G

Newly SignalizedbG G G G G G

Note: Results for unsignalized intersections apply only to left-turn lanes on major-road approaches.

a

 includes accidents of all severity levels

b

 based on a limited number of sites
134

For rural unsignalized intersections with two-way stop control, installation of a majorroad left-turn lane was found to reduce total accidents at four-leg intersections by

28 percent. The corresponding reduction in fatal and injury intersection accidents was

slightly larger, at 35 percent. In general, the effectiveness estimate for installing a left-turn

lane was higher for the approach on which the turn lane was installed than for the

intersection as a whole. Accident frequency was reduced by 55 percent for total accidents

and by 61 percent for fatal and injury accidents on the specific intersection approach where

the turn lane was installed.

For newly signalized four-leg intersections, the effectiveness of adding a left-turn lane

appears to be slightly larger than at unsignalized intersections for total intersection

accidents and slightly smaller than unsignalized intersections for individual intersection

approaches.

Table 48 shows that the effectiveness of adding a major-road left-turn lane at an

unsignalized intersection in an urban area is about the same as at a rural unsignalized

intersection, although the urban result is based on a limited sample size. The effectiveness

of adding a left-turn lane at an urban signalized intersection is a 10 percent reduction in

total intersection accidents, which is substantially smaller than for urban unsignalized

intersections.

The effectiveness measures for total intersection accidents in Table 48 address

installation of a turn lane on a single major-road approach. If turn lanes are installed on

both major-road approaches, the effectiveness measure for total intersection accidents

would be expected to increase as follows:

(50)

where: E2 = accident reduction effectiveness for adding turn lanes on two major-road

approaches to an intersection

E1 = accident reduction effectiveness for adding a turn lane on one major-road

approach to an intersection

Equation (50) indicates that the second turn lane is effective in reducing only those

intersection accidents not reduced by the first turn lane. Thus, the value of E2 is always

less than twice the value of E1

. Equation (50) is applicable only to the effectiveness

measure for total intersection accidents, not those for accidents on individual intersection

approaches.

For three-leg intersections, table 49 shows that total intersection accidents decreased

by 44 percent with the addition of a major-road left-turn lane at rural unsignalized

intersections and by 33 percent at urban unsignalized intersections.
135

The effectiveness of left-turn lanes in reducing accidents was generally higher for

individual intersection approaches than for the intersection as a whole and generally higher

for project-related accidents than for all accidents.

The results shown in table 48 are reasonably consistent with previous evaluations of

left-turn lane installation. Table 3 shows a broad range of effectiveness measures for

left-turn lane projects at unsignalized intersections—a reduction in total intersection

accidents from 18 to 76 percent. Most of these projects were constructed at rural

unsignalized intersections. The comparable result from table 48 is an accident reduction of

28 percent. While 28 percent is in the range from 18 to 76 percent reported in the

literature, almost any credible evaluation result would also be in this range.

A more relevant comparison can be made with the results of the expert panel review of

previous studies reported by Harwood et al.(25) This expert panel, in reviewing the

literature, made estimates of the effectiveness of installing left turns at rural two-lane

highway intersections. For four-leg unsignalized intersections, the expert panel estimated

an effectiveness of 24 percent for major-road left-turn lane installation, while this study

estimated 28 percent; thus, the results of the current study are quite comparable to previous

studies. For three-leg unsignalized intersections, the expert panel estimated effectiveness

of 22 percent for major-road left-turn lane installation, while the current study estimated

44 percent; thus, for three-leg unsignalized intersections this study estimates substantially

more effectiveness than previous studies. It should be kept in mind that none of those

previous studies used the formal evaluation approaches that have been used in this study.

Table 3 shows a range of effectiveness measures for installation of left-turn lanes at

signalized intersections, most of them in urban and suburban areas, ranging from 6 to

70 percent. The effectiveness measure for urban signalized intersections found in this

study is an accident reduction of 10 percent, which falls in the lower end of this range. For

rural signalized intersections, the expert panel estimated the effectiveness of installing a

left-turn lane as 18 percent.(25) No comparable effectiveness estimate was developed in this

study, but the effectiveness of left-turn installation at urban signalized intersections was

estimated as 10 percent.

Many of the results in the current study show lower effectiveness estimates for

improvements at urban intersections than for comparable improvements at rural

intersections.

Projects Involving Added Right-Turn Lanes

Table 50 presents the final evaluation results for projects involving added right-turn

lanes at four-leg intersections. In general, the accident reduction effectiveness of installing

right-turn lanes for total intersection accidents or total approach accidents is substantially

smaller than for installing left-turn lanes. This is to be expected because right-turn 
13

6

Table 50. Final Evaluation Results for Projects Involving Added Right-Turn Lanes for Four-Leg Intersections.

Percent change in accident frequency for adding one turn lane ± standard error

Total intersection accidents Intersection approach accidents

All

accidentsa

Fatal and

injury accidents

Project-related

accidentsa

All

accidentsa

Fatal and

injury accidents

Project-related

accidentsa

RURAL INTERSECTIONS

Unsignalized !14 ± 5.2 !23 ± 6.6 G !27 ± 5.3 !24 ± 7.9 G

Newly SignalizedbG G G G !66 ± 7.6 G

URBAN INTERSECTIONS

Unsignalizedb !40 ± 10.1 G G G G G

Signalized !4 ± 2.0 !9 ± 3.0 G !18 ± 2.0 !22 ± 3.1 G

Note: Results for unsignalized intersections apply only to right-turn lanes on major-road approaches.

a

 includes accidents of all severity levels

b

 based on a limited number of sites
137

collisions are typically less frequent than left-turn collisions. For the most part, statistically

significant effectiveness measures for installation of right-turn lanes were obtained only for

unsignalized intersections in rural areas and signalized intersections in urban areas.

At rural unsignalized four-leg intersections, right-turn lane projects reduced total

intersection accidents by 14 percent and intersection approach accidents by 27 percent. 

The effectiveness measure of 14 percent for total intersection accidents is higher than the

comparable value of 5 percent estimated by the expert panel convened by Harwood et al.(25)

At urban signalized intersections, right-turn lane projects reduced total intersection

accidents by 4 percent and total intersection-approach accidents by 18 percent.

Where right-turn lanes are installed on two major-road approaches to an intersection,

the combined effectiveness measure for both turn lanes should be determined using

Equation (50).

Table 51 presents the final evaluation results for projects involving added right-turn

lanes at three-leg intersections. Only limited results were obtained for this type of project.

Projects Involving Added Left- and Right-Turn Lanes

Table 52 presents final evaluation results for projects involving the addition of both 

left- and right-turn lanes at four-leg intersections. These projects combine installation of

both left- and right-turn lanes on a single approach. For this reason, one would expect the

results in table 52 to be between the results in tables 48 and 50; this appears to be the case

for total intersection accidents at urban signalized intersections, but not at rural

unsignalized intersections. The total effect of installing both left- and right-turn lanes on

two major-road approaches can be obtained with Equation (50). There were no statistically

significant results for projects involving the addition of both left- and right-turn lanes at

three-leg intersections.

There is no obvious method to separate the effects of left- and right-turn lanes in

table 52. A preferable method of determining the effects of adding both left- and right-turn

lanes is to combine the relevant effectiveness measures from table 48 or 49 with those from

table 50 or 51. For example, it can be shown from tables 48 and 50 and Equation (50) that

at an urban four-leg signalized intersection, the addition of two major-road left-turn lanes

would be expected to reduce total intersection accidents by 19 percent. The addition of

two major-road right-turn lanes would be expected to reduce total intersection accidents by

8 percent. The combined effectiveness would be computed as 1- (1 - 0.19) (1 - 0.08) =

0.25, or a 25 percent reduction in total intersection accidents.
13

8

Table 51. Final Evaluation Results for Projects Involving Added Right-Turn Lanes for Three-Leg Intersections.

Percent change in accident frequency for adding one turn lane ± standard error

Total intersection accidents Intersection approach accidents

All

accidentsa

Fatal and

injury accidents

Project-related

accidentsa

All

accidentsa

Fatal and

injury accidents

Project-related

accidentsa

RURAL INTERSECTIONS

Unsignalized G G !46 ± 38.6 G G G

Newly Signalized G G G G G

URBAN INTERSECTIONS

Unsignalized G G G G G G

SignalizedbG G G !45 ± 10.4 G G

Note: Results for unsignalized intersections apply only to right-turn lanes on major-road approaches.

a

 includes accidents of all severity levels

b

 based on a limited number of sites

Table 52. Final Evaluation Results for Projects Involving Added Left- and Right-Turn Lanes for Four-Leg Intersections.

Percent change in accident frequency for adding one turn lane

Total intersection accidents Intersection approach accidents

All

accidentsa

Fatal and

injury accidents

Project-related

accidentsa

All

accidentsa

Fatal and

injury accidents

Project-related

accidentsa

RURAL INTERSECTIONS

Unsignalized !13 ± 5.3 !22 ± 6.1 G !14 ± 5.7 G G

Newly SignalizedbG G G !31 ± 8.2 !28 ± 11.6 G

URBAN INTERSECTIONS

Unsignalizedb

G G G !33 ± 9.9 G G

Signalized !7 ± 1.2 !12 ± 1.7 !10 ± 2.2 !16 ± 1.1 !27 ± 1.5 !25 ± 1.8

Note: Results for unsignalized intersections apply only to turn lanes on major-road approaches. 

a

 includes accidents of all severity levels

b

 based on a limited number of sites
139

Projects Involving Extension of the Length of Existing Turn

Lanes

No separate table of final evaluation results is presented for projects involving the

extension of the length of existing turn lanes. The available results, which are quite sparse,

are presented in table 44.

Table 44 shows that for three projects in which the existing left-turn lanes at urban

four-leg signalized intersections were extended, total intersection accidents increased by

approximately 30 percent. This increase may result from substantial growth in left-turn

volumes at these signalized intersections that was not accounted for in the evaluation. A

weakness of this evaluation is that, while growth in the total major- and minor-road

average daily traffic volumes was accounted for, no data on the growth in left-turn volumes

are available. The decision by the highway agency to extend the length of the left-turn lane

suggests that left-turn volumes were growing, but it is not known whether they were

growing faster than the major-road volume as a whole.

At rural unsignalized four-leg intersections, for four intersection approaches where

existing major-road left-turn lanes were extended in length, the effect of the projects was to

reduce total accident frequency on the intersection approach by 43 percent. None of the

other safety measures for these projects had statistically significant changes.

No statistically significant analysis results were found for the extension of the length

of existing turn lanes at three-leg intersections.

Because the analyses of the extended-turn-lane projects are based on small sample

sizes, no overall conclusions have been drawn from the evaluation of these projects.

Supplementary Analysis Results

Two supplementary analyses were conducted to evaluate intersection design and traffic

control features that, because of sample size considerations, could be evaluated for some,

but not all, intersection types. These supplementary analyses addressed the relative safety

effectiveness of curbed vs. painted channelization for left-turn lanes and of protected vs.

protected/permissive left-turn signal phasing. These analyses were conducted using the EB

approach for three intersection types with sufficient data to make these comparisons.

Table 53 shows that at rural unsignalized intersections there appears to be a definite

indication that left-turn lanes with curbed channelization are more effective than left-turn

lanes with painted channelization. This appears to be particularly the case for rural fourleg unsignalized intersections in which channelized left-turn lanes reduced accidents by

57 percent while painted left-turn channelization reduced accidents by only 23 percent. By

contrast, there appears to be no difference between the safety effectiveness of curbed and
140

painted left-turn channelization for urban four-leg signalized intersections. However, the

sample sizes for these comparisons are too small for the results to be definitive.

Table 53. Comparison of Safety Effectiveness of Added Left-Turn Lanes With Curbed and 

 Painted Channelization.

Area

 type

Type of traffic

control

No. of

 legs

Number of improved sites

Percentage reduction in

accidents from left-turn lane

installation

All LTL

 types

Curbed

LTLs

Painted

LTLs

All LTL

 types

Curbed

 LTLs

Painted

 LTLs

Rural Unsignalized 3 36 5 31 !44 !49 !43

Rural Unsignalized 4 24 6 18 !28 !57 !23

Urban Signalized 4 38 8 30 !10 !10 !9

Table 54 shows a similar evaluation for protected and protected/permissive signal

phasing at urban four-leg signalized intersections. The results suggest that there is

essentially no effect of the type of signal phasing on the safety effectiveness of left-turn

lanes. However, as in the previous analysis, there are too few data to obtain definitive

results. Signalized intersections with no separate left-turn phasing were not included in the

evaluation because data were available for only two sites without left-turn phasing.

Table 54. Comparison of Safety Effectiveness of Added Left-Turn Lanes With Protected 

 and Protected/Permissive Signal Phasing.

Area

 type

Type

of

 traffic

control

No. of

 legs

Number of improved sites

Percentage reduction in

accidents from left-turn lane

installation

Combined Protected

Protected/

permissive Combined Protected

Protected/

permissive

Urban Signalized 4 36 5 31 !10 !10 !9

Recommended Accident Modification Factors

AMFs have been developed based on the results presented above for potential use to

replace the AMFs for rural intersections presented in tables 2 and 4. In addition, AMFs for

turn-lane installation at urban intersections have also been devleoped.

AMFs for installation of left-turn lanes at rural intersections are presented in table 55. 

The AMFs for STOP-controlled intersections are based on the results of the current study. 

The AMFs for signalized intersections are those developed by the expert panel convened

by Harwood et al.,(25) since no results for rural signalized intersections were obtained in the

current study.
141

 Table 55. Recommended Accident Modification Factors for Installation of Left-Turn 

 Lanes on the Major-Road Approaches to Rural Intersections.

Intersection type

Intersection

traffic control

Number of major-road approaches on which leftturn lanes are installed

One approach Both approaches

Three-leg intersection STOP signa

Traffic signal

0.56b

0.85c

—

—

Four-leg intersection STOP signa

Traffic signal

0.72d

0.82c

0.52d

0.67c

a

 STOP signs on minor-road approach(es)

b

 based on results in Table 49

c

 based on results in Reference 25

d

 based on results in Table 48

AMFs for installation of left-turn lanes at urban intersections are presented in

Table 56. All of the AMFs in the table are based on the results of the current study, except

for the AMF for three-leg signalized intersections. Since no results for three-leg signalized

intersections in rural areas were obtained in the current study, the AMF of 0.93 in the table

was derived by using the same proportional difference between the AMFs for three- and

four-leg signalized intersections shown in table 55 (i.e., 0.90 x 0.85/0.83 = 0.93).

 Table 56. Recommended Accident Modification Factors for Installation of Left-Turn 

 Lanes on the Major-Road Approaches to Urban Intersections.

Intersection type

Intersection

traffic control

Number of major-road approaches on which leftturn lanes are installed

One approach Both approaches

Three-leg intersection STOP signa

Traffic signal

0.67b

0.93c

—

—

Four-leg intersection STOP signa

Traffic signal

0.73d

0.90d

0.53d

0.81d

a

 STOP signs on minor-road approach(es)

b

 based on Table 49

c

 estimated from Table 48 and Reference 25

d

 based on Table 48

Table 57 presents AMFs for installation of right-turn lanes based on the results of the

current study. These AMFs, based on results obtained in the current study for rural

unsignalized intersections and urban signalized intersections, should be applied to all rural

and urban intersections, because no better estimates are available.
142

 Table 57. Recommended Accident Modification Factors for Installation of Right-Turn 

 Lanes on the Major-Road Approaches to Rural and Urban Intersections.

Intersection

traffic control

Number of major-road approaches on which right-turn lanes are installed

One approach Both approaches

STOP signa0.86b0.74b

Traffic signal 0.96c

0.92c

 

a

 STOP signs on minor-road approach(es)

 

b

 based on rural unsignalized intersection results in Table 50

 

c

 based on urban signalized intersection results in Table 50

It is recommended that the AMFs presented in tables 55 through 57 be used for safety

prediction in the FHWA Interactive Highway Safety Design Model (IHSDM) and in other

ongoing initiatives such as the FHWA Comprehensive Highway Safety Improvement

Model (CHSIM).

Economic Evaluation

Tables 58 through 65 present the results of an economic evaluation of the installation

of left-turn lanes at intersections of various types. The primary measure of the cost

effectiveness of improvement projects shown in the tables is the benefit-cost ratio (B/C),

which is determined as the present value of future accident costs reduced, divided by the

estimated cost of constructing the left-turn lanes. When the benefit-cost ratio is greater

than 1.0, this indicates that the anticipated benefit of adding a left-turn lane will exceed its

cost.

Each table presents an economic analysis for adding left-turn lanes at specific

intersection types under specific traffic volume assumptions. The intersection types

considered are:

• Rural three-leg unsignalized intersections.

• Rural four-leg unsignalized intersections.

• Urban four-leg unsignalized intersections.

• Urban four-leg signalized intersections.

The traffic volume assumptions are:

• Major-road ADT from 1,000 to 10,000 veh/day for unsignalized intersections.

• Major-road ADT from 10,000 to 40,000 veh/day for signalized intersections.

• Minor-road ADT equal to either 10 or 50 percent of major-road ADT for

unsignalized intersections.
143

• Minor-road ADT equal to either 25 or 50 percent of major-road ADT for

signalized intersections.

For each intersection type and traffic volume level, the expected number of accidents per

year was estimated from the negative binomial regression models for total intersection

accidents presented in appendix B. The AMFs for left-turn installation are those presented

in tables 55 and 56. The number of accidents reduced per year by left-turn installation was

derived by combining the expected number of accidents per year and the AMF.

The costs of accidents reduced were derived from FHWA estimates for 1994, updated

to 2002 using the GDP implicit price deflator. These values are:

• Fatal and injury accidents—$103,000.

• Property-damage-only accidents—$2,300.

The present value of accident costs reduced was derived with the uniform series present

worth factor based on the assumptions of a project service life of 30 years and a minimum

attractive rate of return (MARR) of 4 percent.

The average cost of installing a single left-turn lane is $85,000 based on estimates

from four of the states that participated in this study.

Tables 58 and 59 present the economic evaluation results for installing a single majorroad left-turn lane at a rural three-leg unsignalized intersection. These results indicate that

left-turn lane installation would become cost effective for a major-road ADT of 4,000

veh/day with 10 percent of the major-road volume on the minor road and at 2,000 veh/day

with 50 percent of the major-road volume on the minor road.

Tables 60 and 61 present comparable data for rural four-leg unsignalized intersections. 

Left-turn lane installation would become cost-effective for a major-road ADT of

3,000 veh/day with 10 percent of the major-road volume on the minor road. With a

minor-road volume equal to 50 percent of the major-road volume, left-turn lane installation

would be cost effective at all of the major-road volume levels considered.

Tables 62 and 63 present comparable data for urban four-leg unsignalized

intersections. Left-turn lane installation would become cost-effective for a major-road

ADT of 2,000 veh/day with both 10 and 50 percent of the major-road volume on the minor

road.

Tables 64 and 65 present comparable data for urban four-leg signalized intersections. 

Left-turn lane installation was found to be cost-effective for all combinations of major- and

minor-road ADTs considered.
14

4

 Table 58. Economic Evaluation for Rural Three-Leg Unsignalized Intersections with Minor-Road ADT Equal to 10 Percent of 

 Major-Road ADT.

ADT

Expected 

number of 

accidents

per year AMF

No. of 

accidents

reduced

 per year

No. of

LTLs 

installed

Cost per

 turn

lane

 installed

($)

Total

cost ($)

Accident costs ($)

Percent

fatal

and injury

accidents

Service life

(years) MARR

Percent

value

of 

accident

costs

Major Minor reduced ($) B/C

Fatal & 

Injury PDO

1,000 100 0.03 0.56 0.01 1 85,000 85,000 103,000 2,300 46.0 30 4.0 10,246 0.1

2,000 200 0.09 0.56 0.04 1 85,000 85,000 103,000 2,300 46.0 30 4.0 31,780 0.4

3,000 300 0.17 0.56 0.07 1 85,000 85,000 103,000 2,300 46.0 30 4.0 61,618 0.7

4,000 400 0.27 0.56 0.12 1 85,000 85,000 103,000 2,300 46.0 30 4.0 98,567 1.2

5,000 500 0.38 0.56 0.17 1 85,000 85,000 103,000 2,300 46.0 30 4.0 141,901 1.7

6,000 600 0.52 0.56 0.23 1 85,000 85,000 103,000 2,300 46.0 30 4.0 191,113 2.2

7,000 700 0.66 0.56 0.29 1 85,000 85,000 103,000 2,300 46.0 30 4.0 245,818 2.9

8,000 800 0.83 0.56 0.36 1 85,000 85,000 103,000 2,300 46.0 30 4.0 305,713 3.6

9,000 900 1.00 0.56 0.44 1 85,000 85,000 103,000 2,300 46.0 30 4.0 370,550 4.4

10,000 1,000 1.19 0.56 0.52 1 85,000 85,000 103,000 2,300 46.0 30 4.0 440,117 5.2

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
14

5

 Table 59. Economic Evaluation for Rural Three-Leg Unsignalized Intersections with Minor-Road ADT Equal to 50 Percent of 

 Major-Road ADT.

ADT Expected

number of

accidents

per year AMF

No. of

accidents

reduced

per year

No. of

LTLs

installed

Cost per 

turn

lane

installed

($)

Total 

cost ($)

Accident Costs ($) Percent

fatal 

and injury

accidents

Service life

(years) MARR

Percent

value

 of

 accident

costs

Major Minor reduced ($) B/C

Fatal &

 injury PDO

1,000 500 0.08 0.56 0.03 1 85,000 85,000 103,000 2,300 46.0 30 4.0 28,380 0.3

2,000 1,000 0.24 0.56 0.10 1 85,000 85,000 103,000 2,300 46.0 30 4.0 88,023 1.0

3,000 1,500 0.46 0.56 0.20 1 85,000 85,000 103,000 2,300 46.0 30 4.0 170,669 2.0

4,000 2,000 0.74 0.56 0.32 1 85,000 85,000 103,000 2,300 46.0 30 4.0 273,011 3.2

5,000 2,500 1.06 0.56 0.47 1 85,000 85,000 103,000 2,300 46.0 30 4.0 393,038 4.6

6,000 3,000 1.43 0.56 0.63 1 85,000 85,000 103,000 2,300 46.0 30 4.0 529,343 6.2

7,000 3,500 1.84 0.56 0.81 1 85,000 85,000 103,000 2,300 46.0 30 4.0 680,866 8.0

8,000 4,000 2.29 0.56 1.01 1 85,000 85,000 103,000 2,300 46.0 30 4.0 846,764 10.0

9,000 4,500 2.77 0.56 1.22 1 85,000 85,000 103,000 2,300 46.0 30 4.0 1,026,347 12.1

10,000 5,000 3.30 0.56 1.45 1 85,000 85,000 103,000 2,300 46.0 30 4.0 1,219,035 14.3

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
14

6

 Table 60. Economic Evaluation for Rural Four-Leg Unsignalized Intersections With Minor-Road ADT Equal to 10 Percent of 

 Major-Road ADT.

ADT

Expected

number of

accidents

per year AMF

No. of

accidents

reduced

per year

No. of

LTLs

installed

Cost per

turn

lane

installed

($)

Total

cost ($)

Accident costs ($)

Percent

fatal 

and injury

accidents

Service life

(years) MARR

Percent

value

 of

accident

costs

reduced

Major Minor ($) B/C

Fatal &

injury PDO

1,000 100 0.12 0.52 0.06 2 85,000 170,000 103,000 2,300 46.3 30 4.0 47,994 0.3

2,000 200 0.26 0.52 0.13 2 85,000 170,000 103,000 2,300 46.3 30 4.0 106,802 0.6

3,000 300 0.42 0.52 0.20 2 85,000 170,000 103,000 2,300 46.3 30 4.0 170,525 1.0

4,000 400 0.59 0.52 0.28 2 85,000 170,000 103,000 2,300 46.3 30 4.0 237,666 1.4

5,000 500 0.76 0.52 0.36 2 85,000 170,000 103,000 2,300 46.3 30 4.0 307,469 1.8

6,000 600 0.93 0.52 0.45 2 85,000 170,000 103,000 2,300 46.3 30 4.0 379,469 2.2

7,000 700 1.12 0.52 0.54 2 85,000 170,000 103,000 2,300 46.3 30 4.0 453,350 2.7

8,000 800 1.30 0.52 0.63 2 85,000 170,000 103,000 2,300 46.3 30 4.0 528,879 3.1

9,000 900 1.49 0.52 0.72 2 85,000 170,000 103,000 2,300 46.3 30 4.0 605,879 3.6

10,000 1,000 1.68 0.52 0.81 2 85,000 170,000 103,000 2,300 46.3 30 4.0 684,211 4.0

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
14

7

 Table 61. Economic Evaluation for Rural Four-Leg Unsignalized Intersections With Minor-Road ADT Equal to 50 Percent 

 of Major-Road ADT

ADT

Expected

number of

accidents

per year AMF

No. of 

accidents

reduced

per year

No. of

LTLs

installed

Cost per

turn

lane

installed

($)

Total

cost ($)

Accident costs ($)

Percent

fatal 

and injury

accidents

Service life

(years) MARR

Percent

value

 of

 accident

costs

reduced

Major Minor ($) B/C

Fatal &

injury PDO

1,000 500 0.47 0.52 0.22 2 85,000 170,000 103,000 2,300 46.3 30 4.0 190,331 1.1

2,000 1,000 1.04 0.52 0.50 2 85,000 170,000 103,000 2,300 46.3 30 4.0 423,543 2.5

3,000 1,500 1.67 0.52 0.80 2 85,000 170,000 103,000 2,300 46.3 30 4.0 676,249 4.0

4,000 2,000 2.32 0.52 1.11 2 85,000 170,000 103,000 2,300 46.3 30 4.0 942,510 5.5

5,000 2,500 3.00 0.52 1.44 2 85,000 170,000 103,000 2,300 46.3 30 4.0 1,219,326 7.2

6,000 3,000 3.71 0.52 1.78 2 85,000 170,000 103,000 2,300 46.3 30 4.0 1,504,857 8.9

7,000 3,500 4.43 0.52 2.13 2 85,000 170,000 103,000 2,300 46.3 30 4.0 1,797,843 10.6

8,000 4,000 5.16 0.52 2.48 2 85,000 170,000 103,000 2,300 46.3 30 4.0 2,097,367 12.3

9,000 4,500 5.92 0.52 2.84 2 85,000 170,000 103,000 2,300 46.3 30 4.0 2,402,727 14.1

10,000 5,000 6.68 0.52 3.21 2 85,000 170,000 103,000 2,300 46.3 30 4.0 2,713,367 16.0

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
14

8

 Table 62. Economic Evaluation for Urban Four-Leg Unsignalized Intersections With Minor-Road ADT Equal to 10 Percent of 

 Major-Road ADT.

ADT Expected

 number of 

accidents

per year AMF

No. of

accidents

reduced

per year

No. of

LTLs 

installed

Cost per

 turn

lane

 installed

($)

Total 

cost ($)

Accident costs ($) Percent

 fatal

and injury

accidents

Service life

(years) MARR

Percent

value

 of 

accident

costs

Major Minor reduced ($) B/C

Fatal &

injury PDO

1,000 100 0.27 0.53 0.13 2 85,000 170,000 103,000 2,300 42.4 30 4.0 98,923 0.6

2,000 200 0.48 0.53 0.23 2 85,000 170,000 103,000 2,300 42.4 30 4.0 176,464 1.0

3,000 300 0.68 0.53 0.32 2 85,000 170,000 103,000 2,300 42.4 30 4.0 247,567 1.5

4,000 400 0.86 0.53 0.40 2 85,000 170,000 103,000 2,300 42.4 30 4.0 314,786 1.9

5,000 500 1.04 0.53 0.49 2 85,000 170,000 103,000 2,300 42.4 30 4.0 379,259 2.2

6,000 600 1.21 0.53 0.57 2 85,000 170,000 103,000 2,300 42.4 30 4.0 441,624 2.6

7,000 700 1.37 0.53 0.65 2 85,000 170,000 103,000 2,300 42.4 30 4.0 502,288 3.0

8,000 800 1.54 0.53 0.72 2 85,000 170,000 103,000 2,300 42.4 30 4.0 561,534 3.3

9,000 900 1.69 0.53 0.80 2 85,000 170,000 103,000 2,300 42.4 30 4.0 619,567 3.6

10,000 1,000 1.85 0.53 0.87 2 85,000 170,000 103,000 2,300 42.4 30 4.0 676,544 4.0

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
14

9

 Table 63. Economic Evaluation for Urban Four-Leg Unsignalized Intersections With Minor-Road ADT Equal to 50 Percent of 

 Major-Road ADT.

ADT Expected

 number of 

accidents

per year AMF

No. of 

accidents

reduced

per year

No. of

LTLs 

installed

Cost per

turn

lane

installed

($)

Total

cost ($)

Accident costs ($) Percent

fatal

and injury

accidents

Service life

(years) MARR

Percent

value

 of 

accident

costs

Major Minor reduced ($) B/C

Fatal & 

injury PDO

1,000 500 0.37 0.53 0.17 2 85,000 170,000 103,000 2,300 42.4 30 4.0 135,175 0.8

2,000 1,000 0.66 0.53 0.31 2 85,000 170,000 103,000 2,300 42.4 30 4.0 241,133 1.4

3,000 1,500 0.93 0.53 0.43 2 85,000 170,000 103,000 2,300 42.4 30 4.0 338,293 2.0

4,000 2,000 1.18 0.53 0.55 2 85,000 170,000 103,000 2,300 42.4 30 4.0 430,146 2.5

5,000 2,500 1.42 0.53 0.67 2 85,000 170,000 103,000 2,300 42.4 30 4.0 518,246 3.0

6,000 3,000 1.65 0.53 0.78 2 85,000 170,000 103,000 2,300 42.4 30 4.0 603,466 3.5

7,000 3,500 1.88 0.53 0.88 2 85,000 170,000 103,000 2,300 42.4 30 4.0 686,362 4.0

8,000 4,000 2.10 0.53 0.99 2 85,000 170,000 103,000 2,300 42.4 30 4.0 767,320 4.5

9,000 4,500 2.32 0.53 1.09 2 85,000 170,000 103,000 2,300 42.4 30 4.0 846,620 5.0

10,000 5,000 2.53 0.53 1.19 2 85,000 170,000 103,000 2,300 42.4 30 4.0 924,477 5.4

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
15

0

 Table 64. Economic Evaluation for Urban Four-Leg Signalized Intersections With Minor-Road ADT Equal to 

 25 Percent of Major-Road ADT.

ADT Expected 

number of

accidents

per year AMF

No. of

 accidents

reduced

 per year

No. of 

LTLs 

installed

Cost per

 turn

lane

 installed

($)

Total 

cost ($)

Accident costs ($) Percent

fatal

and injury

accidents

Service life

(years) MARR

Percent

value

 of

 accident

costs

Major Minor reduced ($) B/C

Fatal &

injury PDO

10,000 2,500 1.80 0.81 0.34 2 85,000 170,000 103,000 2,300 39.5 30 4.0 248,458 1.5

11,000 2,750 2.10 0.81 0.40 2 85,000 170,000 103,000 2,300 39.5 30 4.0 290,660 1.7

12,000 3,000 2.43 0.81 0.46 2 85,000 170,000 103,000 2,300 39.5 30 4.0 335,417 2.0

13,000 3,250 2.77 0.81 0.53 2 85,000 170,000 103,000 2,300 39.5 30 4.0 382,651 2.3

14,000 3,500 3.13 0.81 0.59 2 85,000 170,000 103,000 2,300 39.5 30 4.0 432,294 2.5

15,000 3,750 3.50 0.81 0.67 2 85,000 170,000 103,000 2,300 39.5 30 4.0 484,282 2.8

16,000 4,000 3.90 0.81 0.74 2 85,000 170,000 103,000 2,300 39.5 30 4.0 538,560 3.2

17,000 4,250 4.30 0.81 0.82 2 85,000 170,000 103,000 2,300 39.5 30 4.0 595,075 3.5

18,000 4,500 4.73 0.81 0.90 2 85,000 170,000 103,000 2,300 39.5 30 4.0 653,779 3.8

19,000 4,750 5.17 0.81 0.98 2 85,000 170,000 103,000 2,300 39.5 30 4.0 714,629 4.2

20,000 5,000 5.62 0.81 1.07 2 85,000 170,000 103,000 2,300 39.5 30 4.0 777,585 4.6

22,000 5,500 6.58 0.81 1.25 2 85,000 170,000 103,000 2,300 39.5 30 4.0 909,662 5.4

24,000 6,000 7.59 0.81 1.44 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,049,736 6.2

26,000 6,500 8.66 0.81 1.65 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,197,564 7.0

28,000 7,000 9.79 0.81 1.86 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,352,928 8.0

30,000 7,500 10.96 0.81 2.08 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,515,633 8.9

32,000 8,000 12.19 0.81 2.32 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,685,502 9.9

34,000 8,500 13.47 0.81 2.56 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,862,373 11.0

36,000 9,000 14.80 0.81 2.81 2 85,000 170,000 103,000 2,300 39.5 30 4.0 2,046,098 12.0

38,000 9,500 16.18 0.81 3.07 2 85,000 170,000 103,000 2,300 39.5 30 4.0 2,236,538 13.2

40,000 10,000 17.60 0.81 3.34 2 85,000 170,000 103,000 2,300 39.5 30 4.0 2,433,566 14.3

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
151

 Table 65. Economic Evaluation for Urban Four-Leg Signalized Intersections With Minor-Road ADT Equal to 

 50 Percent of Major-Road ADT.

ADT Expected

number of

accidents

per year AMF

No. of

accidents

reduced

 per year

No. of

LTLs

installed

Cost per

 turn

lane

 installed

($)

Total 

cost ($)

Accident costs ($) Percent

fatal

and injury

accidents

Service life

(years) MARR 

Percent

value

 of 

accident

costs 

Major Minor reduced ($) B/C

Fatal &

injury PDO

10,000 5,000 3.15 0.81 0.60 2 85,000 170,000 103,000 2,300 39.5 30 4.0 435,901 2.6

11,000 5,500 3.69 0.81 0.70 2 85,000 170,000 103,000 2,300 39.5 30 4.0 509,941 3.0

12,000 6,000 4.26 0.81 0.81 2 85,000 170,000 103,000 2,300 39.5 30 4.0 588,464 3.5

13,000 6,500 4.86 0.81 0.92 2 85,000 170,000 103,000 2,300 39.5 30 4.0 671,334 3.9

14,000 7,000 5.49 0.81 1.04 2 85,000 170,000 103,000 2,300 39.5 30 4.0 758,428 4.5

15,000 7,500 6.15 0.81 1.17 2 85,000 170,000 103,000 2,300 39.5 30 4.0 849,638 5.0

16,000 8,000 6.83 0.81 1.30 2 85,000 170,000 103,000 2,300 39.5 30 4.0 944,864 5.6

17,000 8,500 7.55 0.81 1.43 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,044,015 6.1

18,000 9,000 8.30 0.81 1.58 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,147,008 6.7

19,000 9,500 9.07 0.81 1.72 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,253,765 7.4

20,000 10,000 9.87 0.81 1.87 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,364,216 8.0

22,000 11,000 11.54 0.81 2.19 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,595,936 9.4

24,000 12,000 13.32 0.81 2.53 2 85,000 170,000 103,000 2,300 39.5 30 4.0 1,841,686 10.8

26,000 13,000 15.20 0.81 2.89 2 85,000 170,000 103,000 2,300 39.5 30 4.0 2,101,038 12.4

28,000 14,000 17.17 0.81 3.26 2 85,000 170,000 103,000 2,300 39.5 30 4.0 2,373,613 14.0

30,000 15,000 19.23 0.81 3.65 2 85,000 170,000 103,000 2,300 39.5 30 4.0 2,659,068 15.6

32,000 16,000 21.39 0.81 4.06 2 85,000 170,000 103,000 2,300 39.5 30 4.0 2,957,091 17.4

34,000 17,000 23.64 0.81 4.49 2 85,000 170,000 103,000 2,300 39.5 30 4.0 3,267,398 19.2

36,000 18,000 25.97 0.81 4.93 2 85,000 170,000 103,000 2,300 39.5 30 4.0 3,589,729 21.1

38,000 19,000 28.38 0.81 5.39 2 85,000 170,000 103,000 2,300 39.5 30 4.0 3,923,843 23.1

40,000 20,000 30.88 0.81 5.87 2 85,000 170,000 103,000 2,300 39.5 30 4.0 J4,269,515 25.1

MARR = minimum attractive rate of return

B/C = benefit-cost ratio
152
153

7. CONCLUSIONS AND RECOMMENDATIONS

This section presents the conclusions and recommendations of the research on the

effectiveness of left- and right-turn lane improvements for at-grade intersections.

Conclusions

The conclusions of the study are as follows:

1. Added left-turn lanes are effective in improving safety at signalized and unsignalized

intersections in both rural and urban areas. Installation of a single left-turn lane on a

major-road approach would be expected to reduce total intersection accidents at rural

unsignalized intersections by 28 percent for four-leg intersections and by 44 percent

for three-leg intersections. At urban unsignalized intersections, installation of a leftturn lane on one approach would be expected to reduce accidents by 27 percent for

four-leg intersections and by 33 percent for three-leg intersections. At four-leg urban

signalized intersections, installation of a left-turn lane on one approach would be

expected to reduce accidents by 10 percent. Installation of left-turn lanes on both

major-road approaches to a four-leg intersection would be expected to increase, but

not quite double, the resulting effectiveness measures for total intersection accidents;

the increased effectiveness measure for adding left-turn lanes on both major-road

approaches can be determined using Equation (50). The complete set of effectiveness

measures for left-turn lane installation is presented in Tables 48 and 49.

2. Added right-turn lanes are effective in improving safety at signalized and unsignalized

intersections in both rural and urban areas. Installation of a single right-turn lane on a

major-road approach would be expected to reduce total intersection accidents at rural

unsignalized intersections by 14 percent and accidents at urban signalized intersections

by 4 percent. Right-turn lane installation reduced accidents on individual approaches

to four-leg intersections by 27 percent at rural unsignalized intersections and by

18 percent at urban signalized intersections. Only limited results were found for rightturn lane installation at three-leg intersections. Installation of right-turn lanes on both

major-road approaches to a four-leg intersections would be expected to increase, but

not quite double, the resulting effectiveness measures for total intersection accidents;

the increased effectiveness measure for adding right-turn lanes on both major-road

approaches can be determined using Equation (50). The complete set of effectiveness

measures for right-turn lane installation is presented in tables 50 and 51.

3. For both left- and right-turn lane improvements, the results obtained from this study

are within the range of all previous studies reported in the literature, but are slightly

higher than the best estimates from previous studies recently made by an expert panel.
154

4. Evaluation results for adding both left- and right-turn lanes at the same intersection are

presented in table 52.

5. A small sample of projects involving extension of the length of existing turn lanes at

rural unsignalized and urban signalized intersections was evaluated. However, no

reliable effectiveness measures could be developed from this small sample.

6. In general, turn-lane improvements at rural intersections resulted in larger percentage

reductions in accident frequency than comparable improvements at urban

intersections.

7. In the various evaluations performed, the effectiveness of turn-lane improvements in

reducing fatal and injury accidents was greater than for total accidents in some cases,

and less than for total accidents in others. Overall, there is no indication that any type

of turn-lane improvement is either more or less effective for different accident severity

levels.

8. Tables 48 through 52 include estimates of the standard error of the mean improvement

effectiveness. The standard error is a measure of the precision of the mean

improvement effectiveness (i.e., smaller standard errors represent more precise

estimates). The most precise effectiveness estimates were generally obtained for the

project and intersection types with the largest sample sizes, particularly added left-turn

lanes at rural four-leg unsignalized intersections and at urban four-leg signalized

intersections.

9. The results of economic analyses for addition of left-turn lanes at typical rural and

urban intersections, as a function of traffic volume, are presented in tables 58 through

65. These economic analyses are based on the effectiveness estimates derived in this

study and illustrate the traffic volume levels at which installation of left-turn lanes

becomes cost effective.

10. The EB approach to observational before-after evaluations of safety improvements

appears to perform effectively. Comparisons of the EB approach to the YC and CG

approaches found that the EB approach was more likely to provide statistically

significant effectiveness measures. Furthermore, the effectiveness measures obtained

from the EB approach were generally smaller than those from the other approaches;

this may have resulted from reduced effect of the regression-to-the-mean phenomenon;

compensation for regression to the mean is highly desirable in providing accurate

evaluation results.

Recommendations

The recommendations of the study are as follows:
155

1. The effectiveness measures for left-turn improvements in tables 48 and 49 and for

right-turn improvements in tables 50 and 51 should be considered by highway

agencies in evaluating potential improvements at intersections.

2. FHWA should consider incorporating these results in the AMFs used for safety

prediction in the Interactive Highway Safety Design Model (IHSDM) and in other

ongoing initiatives, such as the Comprehensive Highway Safety Improvement Model

(CHSIM). Tables 55 through 57 present revised AMFs for use in these models.

3. The EB approach should be considered the most desirable approach for observational

before-after evaluation of safety improvements. The EB approach is the only

evaluation approach with the potential to compensate for regression to the mean. 

Where the EB approach cannot be applied, the CG and YC approaches should be

considered as preferable to evaluation designs without comparison sites. The CG

approach should generally be considered as preferable to the YC approach, because it

incorporates a comparison group consisting of multiple sites. However, both the CG

and YC approaches are likely to provide overly optimistic evaluation results.
156
157

8. REFERENCES

1. Griffin, L. I., and R. J. Flowers. “A Discussion of Six Procedures for Evaluating

Highway Safety Projects,” Federal Highway Administration, December 1997.

2. Hauer, E. Observational Before-After Studies in Road Safety, Pergamon/Elsevier

Science, Inc., Tarrytown, New York, 1997.

3. Parker, M. R., et al. Geometric Treatments for Reducing Passing Accidents at Rural

Intersections on Two-Lane Rural Highways: Vol. I—Final Report, Report No.

FHWA/RD-83/074, Federal Highway Administration, 1983.

4. McCoy, P. T., and M. S. Malone. “Safety Effects of Left-Turn Lanes on Urban FourLane Roadways,” Transportation Research Record 1239, Transportation Research

Board, 1989.

5. Foody, T. J., and W. C. Richardson. “Evaluation of Left Turn Lanes as a Traffic

Control Device,” Ohio Department of Transportation, 1973.

6. Gluck, J., H.S. Levinson, and V. Stover. Impacts of Access Management Techniques,

NCHRP Report 420, Transportation Research Board, 1999.

7. New Jersey Department of Transportation. 1992.

8. New Jersey Department of Transportation. Route 130 Accident Study, 1993.

9. Greiwe, R.R. “Intersection Management Techniques for the Left-Turning Vehicle,”

ITE Journal, June 1986.

10. Agent, K.R. “Warrants for Left-Turn Lanes,” Transportation Quarterly, Vol. 37,

No. 1, Eno Foundation for Transportation, Inc., 1983.

11. Ben-Yakov, Y., and J. Craus. “Warrants for Left-Turn Lanes on Two-Lane Roads,”

Publication No 80-28, Transportation Research Institute, Technion-Israel Institute of

Technology, 1980.

12. Craus, J., and D. Mahalel. “Analysis of Operations and Safety Characteristics of LeftTurn Lanes,” ITE Journal, July 1986.

13. Tamburri, T.N. and C.G. Hammer. Evaluation of Minor Improvements: (Part 5) LeftTurn Channelization, State of California, Division of Highways, Traffic Department,

1968.
158

14. Wilson, J.C. et al. “Simple Types of Intersection Improvements,” Improved Street

Utilization Through Traffic Engineering Special Report 93, Highway Research Board,

1967.

15. Hauer, E. “The Safety of Older Persons at Intersections,” Transportation in an Aging

Society: Improving Mobility and Safety for Older Persons, Volume 2, Special Report

218, Transportation Research Board, 1988, pp. 194-252.

16. McFarland, W. F., et al. Assessment of Techniques for Cost-Effectiveness of Highway

Accident Countermeasures, Report No. FHWA-RD-79-53, Federal Highway

Administration, 1979.

17. California Department of Public Works. Evaluation of Minor Improvements: Flashing

Beacons, Safety Lighting, Left-Turn Channelization, Traffic Department, 1967.

18. Lacy, J. D. “Traffic Operations Program to Increase Capacity and Safety,” Traffic

Quarterly, Vol. 26, No. 3, July 1972, pp. 327-340.

19. Dale, C.W. Cost-Effectiveness of Safety Improvement Projects, Federal Highway 

Administration, Washington, DC, May 1973.

20. Bauer, K. M., and D. W. Harwood. Statistical Models of At-Grade Intersection

Accidents, Report No. FHWA-RD-96-125, Federal Highway Administration, 1996.

21. McCoy, P. T., W. J. Hoppe, and D. V. Dvorak. “Benefit-Cost Evaluation of Left-Turn

Lanes on Uncontrolled Approaches of Rural Intersections,” Transportation Research

Record 1026, Transportation Research Board, 1985. 

22. Poch, M., and F. L. Mannering. “Negative Binomial Analysis of Intersection Accident

Frequencies,” presented at the 75th annual meeting of the Transportation Research

Board, January 1996.

23. Maze, T. H., J. L. Henderson, and S. Sankar. Impacts on Safety of Left-Turn

Treatment at High-Speed Signalized Intersections, Iowa Highway Research Board

Project HR-347, Iowa State University, January 1994.

24. Vogt, A. Crash Models for Rural Intersections: Four-Lane by Two-Lane StopControlled and Two-Lane by Two-Lane Signalized, Report No. FHWA-RD-99-128,

Federal Highway Administration, October 1999.

25. Harwood, D.W., F.M. Council, E. Hauer, W.E. Hughes, and A. Vogt. Prediction of

the Expected Safety Performance of Rural Two-Lane Highways, Report No. FHWARD-99-207, Federal Highway Administration, December 2000.
159

26. David, N. A., and J. R. Norman. Motor Vehicle Accidents in Relation to Geometric

and Traffic Features of Highway Intersections: Vol II - Research Report, Report No.

FHWA-RD-76-129, Federal Highway Administration, 1976.

27. Harwood, D. W., M. T. Pietrucha, M. D. Wooldridge, R. E. Brydia, and K. Fitzpatrick. 

Median Intersection Design, NCHRP Report 375, Transportation Research Board,

1995.

28. McCoy, P. T., U. R. Navarro, and W. E. Witt. “Guidelines for Offsetting Opposing

Left-Turn Lanes on Four-Lane Divided Roadways,” Transportation Research Record

1356, Transportation Research Board, 1992.

29. Joshua, S. C., and A. A. Saka. “Mitigation of Sight Distance Problem for Unprotected

Left-Turning Traffic at Intersections,” Transportation Research Record 1356,

Transportation Research Board, 1992.

30. Vogt, A., and J. Bared. “Accident Models for Two-Lane Rural Segments and

Intersections,” Transportation Research Record 1635, Transportation Research Board,

1998.

31. Neuman, T. R. Intersection Channelization Design Guide, NCHRP Report 279,

Transportation Research Board, November 1985.

32. Templer, J. A. Provisions for Elderly and Handicapped Pedestrians, Volume 3: The

Development and Evaluation of Countermeasures, Report No. FHWA/RD-79/3,

Federal Highway Administration, 1980.

33. Exnicios, H. F. “Accident Reduction Through Channelization of Complex

Intersections,” Improved Street Utilization Through Traffic Engineering, Special

Report 93, Highway Research Board, 1967, pp. 160-165.

34. Rowan, N. J., and T. G. Williams. Channelization, Report 19-4 (Final), Texas

Transportation Institute, March 1966.

35. American Association of State Highway and Transportation Officials. A Policy on

Geometric Design of Highways and Streets, Washington, DC, 2001.

36. Washington, S. P. et al. Evaluation of High-Speed Isolated Intersections in

California, Report No. FHWA/CA/TO/91-2, Federal Highway Administration, June

1991.

37. Forrestel, R. R. Design and Safety of Pedestrian Facilities, prepared by: ITE Technical

Council Committee 5A-5, December 1994.
160

38. Hanna, J. T., T. E. Flynn, and L. T. Webb. “Characteristics of Intersection Accidents

in Rural Municipalities,” Transportation Research Record 601, Transportation

Research Board, 1976.

39. Lau, M. Y., and A. D. May. Accident Prediction Model Development: Signalized

Intersections, Research Report No. UCB-ITS-RR-88-7, Institute of Transportation

Studies, University of California-Berkeley, December 1988.

40. Lau, M. Y., and A. D. May. Accident Prediction Model Development: Unsignalized

Intersections, Research Report No. UCB-ITS-RR-89-12, Institute of Transportation

Studies, University of California-Berkeley, May 1989.

41. Robinson, B.W., L. Rodegerdts, W. Scarborough, W. Kittelson, R. Troutbeck, W.

Brilon, L. Bondzio, K. Courage, M. Kyte, J. Mason, A. Flannery, E. Myers, J. Bunker,

and G. Jacquemart. An Informational Guide, Report No. FHWA-RD-00-67, Federal

Highway Administration, June 2000.

42. McCoy, P. T., E. J. Tripi, and J. A. Bonneson. Guidelines for Realignment of Skewed

Intersections, Nebraska Department of Roads Research Project Number RES1 (0099)

P471, 1994.

43. Mitchell, R. “Identifying and Improving Highway Accident Locations,” Public

Works, December 1972.

44. Fambro, D. B. Geometric Design Consideration for Rural Roads, Report 1125-1F,

Texas Transportation Institute, 1989.

45. Fambro, D. B., K. Fitzpatrick, and R. Koppa. Determination of Stopping Sight

Distances, Draft Final Report of NCHRP Project 3-42, Texas Transportation Institute,

1996.

46. Priest, R. V. “Statistical Relationships Between Traffic Volume, Median Width, and

Accident Frequency on Divided Highway Grade Intersections,” Highway Research

News, No. 13, Highway Research Board, June 1964.

47. Van Maren, P. A. Correlation of Design and Control Characteristics with Accidents

at Rural Multi-Lane Highway Intersections, Purdue University and Indiana State

Highway Commission, July 1980.

48. Transportation Research Board. Designing Safer Roads: Practices for Resurfacing,

Restoration, and Rehabilitation, Special Report 214, 1987.

49. Kuciemba, S. R., and J. A. Cirillo. “Safety Effectiveness of Highway Design

Features,” Volume V: Intersections, Report No. FHWA/RD-91/048, Federal Highway

Administration, 1992.
161

50. Hall, D. L., K. C. Sinha, and H. Michael. “Comprehensive Evaluation of

Nonsignalized Control at Low Volume Intersections,” Transportation Research

Record 681, Transportation Research Board, 1978.

51. Agent, K. R., and R. C. Deen. “Relationship Between Roadway Geometrics and

Accidents,” Transportation Research Record 541, Transportation Research Board,

1975.

52. King, G. F., and R. B. Goldblatt. “Relationship of Accident Patterns to Type of

Intersection Control,” Transportation Research Record 540, Transportation Research

Board, 1975.

53. Persaud, B.N., R.A. Retting, P.E. Garder, and D. Lord. “Observational Before-After

Study of the Safety Effect of U.S. Roundabout Conversions Using the Empirical Bayes

Method,” presented at the 80th Annual Meeting of the Transportation Research Board,

January 2001, Preprint CD-ROM, ID# 01-0562.

54. Smith, S. A., and R. Knoblauch. “Guidelines for the Installation of Crosswalk

Markings,” Transportation Research Record 1141, Transportation Research Board,

1987.

55. Herms, B. F. Pedestrian Crosswalk Study: Accidents in Painted and Unpainted

Crosswalks, City of San Diego, California, 1970.

56. Gattis, J. L., and Z. Iqbal. “Effectiveness of Do Not Block Intersection Signs,”

Transportation Research Record 1456, Transportation Research Board, 1994.

57. Pant, P. D., and X. H. Huang. “Active Advance Warning Signs at High-Speed

Signalized Intersections: Results of a Study in Ohio,” Transportation Research Record

1368, Transportation Research Board, 1992.

58. Klugman, A. et al. Use and Operation of Advance Warning Flashers at Signalized

Intersections, Report No. MN/RC-93/01, Minnesota Department of Transportation.

November 1992.

59. Styles, W. J. Evaluation of the Flashing RED SIGNAL AHEAD Sign, Report No.

FHWA/MD-83/06, Maryland State Highway Administration, June 1982.

60. Styles, W. J. Evaluation of Flashing Red Strobe Signal, Report No. FHWA/MD83/07, Maryland State Highway Administration, June 1982.

61. Box, P.C. “Major Road Accident Reduction by Modern Illumination,” presented at

Visibility Symposium in Minneapolis, Minnesota, Transportation Research Board,

1988 (cited in Reference 71).
162

62. City of Los Angeles. “Intersection Accident Reduction Through Street Lighting,”

OTS Project 127803, Bureau of Street Lighting, 1980 (cited in Reference 71).

63. Hauer, E., J.C.N. Ng, and J. Lovell. “Estimation of Safety at Signalized

Intersections,” Transportation Research Record 1185, Transportation Research

Board, 1988.

64. Hauer, E., D. Terry, and M. S. Griffith. “The Effect of Resurfacing on Safety of

Two-Lane Roads in New York State,” presented at the 73rd annual meeting of the

Transportation Research Board, 1994.

65. Shaw, R.B., and H.L. Michael. “Evaluation of Delays and Accidents to Warrant

Construction of a Median Lane,” Highway Research Record 257, Highway Research

Board, 1968.

66. Jorgensen and Associates, Inc. Cost and Safety Effectiveness of Highway Design

Elements, NCHRP Report 197, Transportation Research Board, 1978.

67. Hagenaur, G. F., J. Upchurch, D. Warren, and M. J. Rosenbaum. “Intersections,”

Synthesis of Safety Research Related to Traffic Control and Roadway Elements,

Report No. FHWA-TS-82-232, Federal Highway Administration, 1982.

68. Knoblauch, R. L., H. N. Tobey, and E. M. Shunaman. “Pedestrian Characteristics and

Exposure Measures,” Transportation Research Record 959, Transportation Research

Board, 1984.

69. “What Not to Expect from Crosswalk Signals,” Civic Administration, Vol. 19, No. 5,

May 1967, cited in J. P. Braaksma, A Survey of Special Crosswalks in Canada, Report

No. TP981-CR7708, Transport Canada, Ottawa, September 1976.

70. Untermann, R. K. Accommodating the Pedestrian—Adapting Towns and

Neighborhoods for Walking and Bicycling, Van Nostrand Reinhold, New York, 1984.

71. Baerwald, J. R. (ed.). Traffic Engineering Handbook, Third Edition, Institute of

Traffic Engineers, Washington, DC, 1965.

72. Keck, M.E. The Relationship of Fixed and Vehicular Lighting to Accidents, Report

No. FHWA-SA-91-019, Federal Highway Administration, March 1991.

73. “A Comparison of Pedestrian Safety of Median Islands and Marked Crossings,”

Western Roads, Western Australia, August 1978 (cited in Reference 37).

74. McCullagh, P., and Nelder, J. A. Generalized Linear Models, Second Edition (1989),

New York: Chapman & Hall.
163

75. SAS Institute Inc. SAS/STAT®

 User’s Guide, Version 8, Cary, NC: SAS Institute Inc.,

1999, pp. 1363-1464.

76. Fridstrøm, L., et al. “Measuring the Contribution of Randomness, Exposure, Weather,

and Daylight to the Variation in Road Accident Counts,” Accident Analysis and

Prevention (1995), Vol. 27, pp. 1-20.

77. Miaou, S-P. Measuring the Goodness-of-Fit of Accident Prediction Models, Report

No. FHWA-RD-96-040, Federal Highway Administration, December 1996.
Appendix A

Summary of Intersection Safety Studies
A-1

APPENDIX A. SUMMARY OF INTERSECTION SAFETY 

 STUDIES.

Overview

The findings of the literature review are presented in this extensive summary table,

table A-1. The table presents the following information about each source identified in

the literature:

C General topic (i.e., geometric design or traffic control element).

C Author, publication year, and reference number.

C Summary of major findings.

C Study type (e.g., before/after, comparative, predictive model).

C Data used in any analyses that were conducted (including the number of sites, if

available).

C Type of sites.

The reference numbers in the table provide a link to the reference list presented at the end

of this report. The summary of major findings describes the nature of the relationship

between particular geometric design, traffic control, or traffic volume factors and safety

indicated by each study. Where the safety relationship in a particular reference can be

expressed as a simple percentage difference or algebraic difference, that quantitative

value is presented in the major findings column. However, where the findings are more

complex, such as the results of predictive modeling or classification and regression tree

(CART) analysis, they could not always be presented quantitatively in the table.
A-2

Table A-1. Summary of Intersection Safety Studies of Intersection Design and Traffic Control Features.

Design and control

elements Related literature Major findings Study type Data used Type of site

Intersection Geometric Design Features

Left-turn lanes Harwood et al. [2000]

(25) • Based upon the judgement of an

expert panel, installation of a left-turn

lane along one major approach

reduces intersection-related accidents

by 18 to 24%, depending upon the

type of traffic control and number of

legs at the intersection.

• Based upon the judgement of an

expert panel, installation of left-turn

lanes along both major approaches to

a four-leg intersection reduces

intersection-related accidents by 33 to

42 percent, depending upon the type

of traffic control.

Accident prediction

algorithm using

negative binomial

distribution and

accident modification

factors developed by

expert panel.

Prediction

algorithm

combines

elements of

historical

accident data,

predictions from

statistical

models, results

of before-after

studies, and

expert

judgments made

by experienced

engineers.

Rural intersections along

two-lane highways.

Vogt [1999](24) For a four-lane by two-lane STOPcontrolled rural intersection, the

predictive model indicates installation of

left-turn lanes along the major approach

reduced total accidents by 38 percent.

Accident prediction

model using negative

binomial distribution.

72 four-leg

intersections in

California and

Michigan.

Four-lane by two-lane

STOP-controlled rural

intersections.

Gluck et al. [1999](6) Installation of left-turn lanes reduced the

accident rates per million entering

vehicles at unsignalized intersections by

50 percent and at signalized

intersections by 18 percent. Combined

the presence of left-turn lanes reduced

the accident rate by 35 percent. 

Synthesis of previous

research conducted

by Tamburri and

Hammer(13) and

Wilson et al.(14)

53 intersections

in California.

Gluck et al. [1999](6) Intersections without turn lanes had an

accident rate of 1.65 accidents per

million entering vehicles, while

intersections with left-turn lanes had an

accident rate of 0.59.

Synthesis of previous

research conducted

by Shaw and

Michael.(65)

Eight intersections without

lanes; three with

left-turn lanes.

Gluck et al. [1999](6) Installation of left-turn lanes reduced the

accident rate per million entering

vehicles by 38 percent.

Synthesis of previous

research conducted

by Ben-Yakov and

Craus(11) and Craus

and Mahalel.(12)

25 intersections.
A-3

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Left-turn lanes (continued) Gluck et al. [1999](6) Installation of left-turn lanes reduced the

accident rates per million left-turning

vehicles at unsignalized intersections by

77 percent and at signalized

intersections by 54 percent.

Synthesis of previous

research conducted

by Agent.(10)

Gluck et al. [1999](6) Restriping the lane assignments to

provide left-turn lanes reduced the

number of accidents at eight intersection

locations. The left-turn lanes reduced

left-turn accidents by 62 percent and all

accidents by 58 percent.

Synthesis of previous

research conducted

by Greiwe.(9)

Eight intersections in

Indiana.

Gluck et al. [1999](6) 1.8-mile section of four-lane roadway

was converted to three-lane crosssection. Total number of accidents

before conversion was 109, and 67

accidents occurred during after period.

Synthesis of previous

research conducted

by New Jersey

Department of

Transportation.(7)

1.8 miles of

Route 47 in New

Jersey converted from fourlane road to

three-lane road.

Gluck et al. [1999](6) • Installation of left-turn lanes along

eight-mile southern section of Route

130 reduced the accident rate per

million entering vehicles by

35 percent.

• Installation of left-turn lanes along

28-mile northern section of Route 130

reduced the accident rate per million

entering vehicles by 51 percent.

Synthesis of previous

research conducted

by New Jersey

Department of

Transportation.(8)

Eight mile

southern section

of Route 130 in

New Jersey and

28-mile northern

section.

Bauer and Harwood

[1996](20)

Left-turn channelization resulted in an

increase in total multiple-vehicle

accidents and fatal injury accidents.

Statistical modeling

with negative

binomial regression.

14,432 rural

intersections in

California.

Rural and urban signalized

and unsignalized

intersections.

Poch and Mannering

[1995](22)

Total accident frequencies were found to

be higher on intersection approaches

with a shared through-left lane and two

or more total lanes than on approaches

with other conditions. Approaches with

a left-turn lane, a through lane, and a

shared through-right lane had more rearend accidents than those with other

conditions.

Accident prediction

model using negative

binomial distribution.

63 intersections

in Bellevue,

Washington.

Urban areas. A large

number of intersections

were in residential areas

which are characterized by

low traffic volume. All

intersections had some

sort of operational

improvement during 1988-

92.
A-4

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Left-turn lanes (continued) Maze et al. [1994](23) Predictive models indicate that a leftturn lane with permitted phasing at a

signalized intersection has a positive

effect on safety. A typical example

developed by the authors indicates an

anticipated reduction in left-turn accident

rate of approximately 5.5 percent from

installation of a left-turn lane with

permitted phasing.

Statistical modeling

based on multiple

regression.

63 signalized

intersections,

including 248

intersection

approaches. 

Five years of

accident data

were considered

for each

intersection.

At-grade signalized

intersections in Iowa.

Maze et al. [1994](23) Predictive models indicate that a leftturn lane with protected/permitted

phasing at a signalized intersection has

a positive effect on safety. A typical

example developed by the authors

indicates an anticipated reduction in leftturn accident rate of approximately

35 percent from installation of a left-turn

lane with protected/permitted phasing.

Statistical modeling

based on multiple

regression.

63 signalized

intersections,

including 248

intersection

approaches. 

Five years of

accident data

were considered

for each

intersection.

At-grade signalized

intersections in Iowa.

McCoy and Malone 

[1989](4)

On urban four-lane roadways, left-turn

lanes at signalized and unsignalized

intersections significantly reduced rearend, sideswipe, and left-turn accidents. 

At unsignalized intersections with leftturn lanes, there was also a significant

increase in right-angle accidents.

Comparative. 63 intersections

on urban, fourlane roadways.

Urban signalized and

unsignalized intersections

in Nebraska.

Lau and May [1988](40) Left-turn channelization on the

crossroad was found to be a significant

factor in predicting injury accidents at

unsignalized intersections.

CART analysis of

residuals from base

model.

17,000 unsignalized intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.
A-5

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Left-turn lanes (continued) Hauer [1988](15) • Provision of left-turn channelization at

unsignalized intersections reduced

accidents by 70 percent in urban

areas when combined with curbs or

raised bars. Likewise, accidents were

reduced by 65 and 60 percent,

respectively, in suburban and rural

areas.

• When channelization was painted at

unsignalized intersections, accidents

decreased by 15, 30, and 50 percent

in urban, suburban, and rural areas,

respectively.

Synthesis of previous

research conducted

by McFarland et al.

[1979].(16)

Not available. Not available.

Hauer [1988](15) At signalized intersections, left-turn

channelization with a left-turn phase

reduced accidents by 36 percent and

without the left-turn phase by

15 percent.

Synthesis of previous

research conducted

by McFarland et al.

[1979].(16)

Not available. Not available.

Hauer [1988](15) Adding left-turn lanes reduced accidents

by varying amounts depending on the

type of intersection, whether it was

signalized or unsignalized, and whether

the intersection was rural or urban.

Synthesis of previous

research conducted

by R. Jorgensen and

Associates, Inc.

[1978].(66)

Not available. Not available.

McCoy et al. [1985](21) At unsignalized intersections on rural

two-lane highways, there was no

significant difference in rear-end and

left-turn accident rates between

intersections with left-turn lanes and

those without left-turn lanes.

Comparative. Intersections on

rural two-lane

highways in

Nebraska.

Unsignalized intersections.

Parker et al. [1983](3) Passing-related accidents at rural

intersections along two-lane highways

do not represent a major safety problem,

but when a left-turn lane is provided at

new or reconstructed intersections,

potential for passing-related accidents is

greatly reduced.

Benefit/cost analysis

not reviewed directly. 

Overview based on

synthesis of previous

research by

Kuciemba and Cirillo

[1992].(49)

Not available. Not available.
A-6

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Left-turn lanes (continued) David and Norman

[1976](26)

Signalized intersections with opposing

left-turn lanes were found to have

significantly more accidents than

intersections without opposing left-turn

lanes. Provision of opposing left-turn

lanes at four-leg signalized intersections

was found to increase accident

frequencies by 2.4 to 6.1 accidents per

year.

Comparative. 22 four-leg

intersections

with opposing

left-turn lanes; it

is not clear how

many four-leg

intersections

without left-turn

lanes were

available.

Three years of

accident data

were obtained

for each

intersection.

Urban intersections in the

San Francisco Bay Area of

California.

Dale [1973](19) At intersections along rural two-lane

highways, installation of a traffic signal

and left-turn lane reduced the total

number of accidents by 19.7 percent,

while the installation of a traffic signal

without left-turn channelization reduced

the total number of accidents by

6 percent.

Foody and Richardson

[1973](5)

• For signalized intersections, the

accident rate was reduced 38 percent

with the addition of left-turn lanes.

• For unsignalized intersections, the

accident rate was reduced 76 percent

with the addition of left-turn lanes.

Comparative. Not available. Not available.

Lacy [1972](18) Several improvements to the

intersection, which included extending

and rearranging the channelization and

adding separate left-turn lanes, reduced

the accident frequency by 35 percent

and the accident severity by 80 percent. 

Additional improvements to the

intersection included widening the

approaches and modifying the traffic

signals.

Before/After. One urban

intersection in

Peoria, Illinois.

Urban intersection.

Caltrans [1967](17) Reduction in accident rates at

unsignalized intersections was much

higher with use of raised barrier left-turn

lanes than with painted left-turn lanes.

Before/After. 53 safety

improvement

projects in

California.

Urban and rural areas,

including signalized and

unsignalized intersections.
A-7

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Offset left-turn lanes Harwood et al. [1995]

(27) A field review of traffic operations and an

office review of three years of accident

data were conducted for two signalized

intersections with tapered offset left-turn

lanes and one signalized intersection

with parallel offset left-turn lanes. This

review found no operational or accident

problems at the intersections related to

the offset left-turn lanes. However, no

measures of effectiveness comparing

offset and conventional left-turn lanes

were developed.

Operational and

safety review of

intersection

performance.

Field

observation of

undesirable

driver behavior

and three years

of accident data

for three

intersections.

Signalized intersections on

divided highways with

tapered and parallel offset

left-turn lanes.

McCoy et al. [1992](28) Developed guidelines concerning the

amount of offset between opposing leftturn lanes to provide adequate sight

distance, but performed no accident

studies.

Engineering analysis. Typical

intersection

geometrics.

Intersections with

opposing left-turn vehicles.

Joshua and Saka

[1992](29)

Developed guidelines concerning the

amount of offset between opposing leftturn lanes to provide adequate sight

distance, but performed no accident

studies.

Engineering analysis. Typical

intersection

geometrics.

Intersections with

opposing left-turn vehicles.

Right-turn lanes Harwood et al. [2000]

(25) • Based on the judgement of an expert

panel, presence of a right-turn lane

along one major approach to a rural

STOP-controlled intersection reduces

intersection-related accidents by

5 percent.

• Based on the judgement of an expert

panel, presence of right-turn lanes

along both major approaches to a rural

STOP-controlled intersection reduces

intersection-related accidents by

10 percent.

Accident prediction

algorithm using

negative binomial

distribution and

accident modification

factors developed by

expert panel.

Prediction

algorithm

combines

elements of

historical

accident data,

predictions from

statistical

models, results

of before-after

studies, and

expert judgments made by

experienced

engineers.

Rural intersections along

two-lane highways.
A-8

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Right-turn lanes (continued) Harwood et al. [2000]

(25) • Based on the judgement of an expert

panel, presence of a right-turn lane

along one major approach to a rural

signalized intersection reduces

intersection-related accidents by

2.5 percent.

• Based on the judgment of an expert

panel, presence of right-turn lanes

along both major approaches to a rural

signalized intersection reduces

intersection-related accidents by

5 percent.

Accident prediction

algorithm using

negative binomial

distribution and

accident modification

factors developed by

expert panel.

Prediction

algorithm

combines

elements of

historical

accident data,

predictions from

statistical

models, results

of before-after

studies, and

expert judgments made by

experienced

engineers.

Rural intersections along

two-lane highways.

Vogt and Bared [1998](30) Presence of right-turn lanes at three-leg

rural unsignalized intersections

increases the total number of

intersection-related accidents by

27 percent.

Poisson and negative

binomial modeling.

389 rural threeleg intersections

in Minnesota.

Unsignalized intersections

on rural two-lane

highways.

Bauer and Harwood

[1996](20)

Right-turn channelization resulted in an

increase in total multiple-vehicle

accidents and fatal injury accidents.

Statistical modeling

with negative

binomial regression.

14,432 rural

intersections in

California.

Rural and urban signalized

and unsignalized

intersections.

Channelization Hauer [1988](15) Channelization was found to reduce

accidents by 32 percent and injury

accidents by 50 percent. The average

benefit-cost ratio of channelization was

2.3.

Synthesis of previous

research conducted

by Hagenaur et al.

[1982](67)

Not available. Not available.

David and Norman

[1976](26)

Raised pavement markings tended to

decrease accidents, especially at cross

intersections.

Comparative. 558 intersections with

4,372 accidents

in three years in

the San

Francisco Bay

Area of

California.

Urban areas. 82 percent of

intersections had some

form of delineation.
A-9

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Channelization (continued) Exnicios [1967](33) Several safety measures, including rechannelization, were implemented at

three intersections. The improvements

resulted in a 31 percent reduction in total

accidents (over two years), a 58 percent

reduction in total accidents (over one

year), and a 100 percent reduction in

total accidents (over 26 months) at the

respective intersections.

Before/After. Three

intersections.

The intersections were

located in a residential

suburb in Chicago, in

metropolitan New Orleans,

and in Shreveport,

Louisiana.

Rowan and Williams

[1966](34)

Accident rates, personal injuries, and

rear-end type accidents were reduced

due to the introduction of channelization.

Before/After. US Route 290 in

northwest

Houston.

Arterial, four-lane, at

signalized intersections.

Channelization

• Island design

Forrestel [1994](37) The pedestrian accident rate at an

unsignalized intersection on a four-lane

arterial was reduced by 11.5 percent

when raised median islands were

installed.

Synthesis of previous

research efforts. 

Related article

entitled “A

Comparison of the

Pedestrian Safety of

Median Islands and

Marked Crossings.” 

[1978](49)

One intersection

in Western

Australia.

Four-lane, unsignalized

intersection.

Washington et al. 

[1990](36)

Intersection approaches with raised

medians have a 40 percent lower

accident rate than those with flush

medians.

Comparative. 40 intersections

in California.

Not available.

Templer [1980](32) Raised medians reduced the number of

conflicts between pedestrians and

vehicles. However, the difference was

not statistically significant.

Before/After. Two intersections in

Clearwater,

Florida.

Signalized T-intersections,

located in business and

recreational areas.

Number of intersection legs

(e.g., three, four, five) 

Bauer and Harwood

[1996](20)

Rural four-leg STOP-controlled intersections have about twice as many

accidents as rural three-leg STOPcontrolled intersections (1.1 vs. 0.6

accidents per intersection per year). A

similar pattern was found for urban

STOP-controlled intersections (2.2 accidents per intersection per year for fourleg intersections vs. 1.3 for three-leg

intersections).

Comparative. 8,525 at-grade

intersections in

California.

Rural four-leg, rural threeleg, urban four-leg, and

urban three-leg

intersections, all with

STOP control.
A-10

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Number of intersection legs

(e.g., three, four, five)

(continued)

Harwood et al. [1995]

(27) Predictive relationships were developed

for number of multiple-vehicle

intersection accidents per year as a

function of major-road ADT, crossroad

ADT, major-road median width, majorroad lane and shoulder widths, majorroad design speed, presence of left-turn

lanes, and terrain. Results show that

typical divided highway intersections

with four legs have about twice as many

accidents as three-leg intersections for

narrow medians and more than five

times as many accidents as three-leg

intersections for wide medians.

Statistical modeling

with Poisson

regression.

1,200 intersections on

California state

highways.

Urban/suburban

unsignalized intersections

on divided highways in

California.

Hanna et al. [1976](38) In rural areas, four-leg intersections

have higher accident rates than T

intersections (69 percent increase).

Comparative. 232 intersections in rural

municipalities in

Virginia.

Includes both STOPcontrolled and signalized

intersections.

David and Norman

[1976](26)

In urban areas at STOP-controlled

intersections, accident frequencies were

very similar for four-leg intersections and

T/Y-type intersections with ADT under

20,000 veh/day. Once above

20,000 veh/day, the accidents doubled

for four-leg intersections.

Comparative. 558 intersections with

4,372 accidents

in three years in

the San

Francisco Bay

Area of

California.

Of 558 intersections, 269

were three-leg intersections, and 289 were

four-leg intersections.

Intersection type (e.g.,

cross, T, Y, offset)

Lau and May [1988](39) Intersection type was found to be a

significant factor in predicting injury

accidents at signalized intersections.

CART analysis of

residuals from base

model.

2,488 signalized

intersections. 

seven years of

injury accident

data for each

intersection.

Signalized intersections on

California state highways.

Lau and May [1988](40) Intersection type was found to be a

significant factor in predicting injury

accidents at unsignalized intersections.

CART analysis of

residuals from base

model.

17,000 unsignalized intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.
A-11

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Intersection type (e.g.,

cross, T, Y, offset)

(continued)

Hanna et al. [1976](38) For three-leg intersections, Y intersections were found to have accident

rates approximately 50 percent higher

than T intersections.

Comparative. 232 intersections in rural

municipalities in

Virginia.

Includes both STOPcontrolled and signalized

intersections.

Hanna et al. [1976](38) For four-leg intersections, offset

intersections had accident rates that

were approximately 43 percent of the

accident rate of conventional four-leg

intersections.

Comparative. 232 intersections in rural

municipalities in

Virginia.

Includes both STOPcontrolled and signalized

intersections.

Angle of intersection (e.g.,

skew)

Harwood et al. [2000]

(25) AMFs for intersection skew angle were

derived from statistical modeling and

apply to total intersection-related

accidents. For a three-leg STOPcontrolled intersection, the AMF was

calculated as:

AMF = exp (0.0040 SKEW)

For a four-leg STOP-controlled

intersection, the AMF was calculated as:

AMF = exp (0.0054 SKEW)

where:

SKEW = intersection skew angle

(degrees), expressed as the absolute

value of the difference between 90

degrees and the actual intersection

angle.

Accident prediction

algorithm using

negative binomial

distribution and

accident modification

factors developed by

expert panel.

Prediction

algorithm

combines

elements of

historical

accident data,

predictions from

statistical

models, results

of before-after

studies, and

expert judgments made by

experienced

engineers.

Rural intersections along

two-lane highways.

Bauer and Harwood

[1996](20)

Angle of intersection was found to have

a statistically significant relationship to

multiple-vehicle accident frequency at

urban, four-leg, signalized intersections,

but the direction of the effect was

opposite to that expected. Skewed

intersections were found to have

accident frequencies approximately

20 percent less than 90° intersections. 

This finding may represent a surrogate

effect of some uncontrolled variable.

Statistical modeling

with negative

binomial regression.

198 intersections on

California state

highways.

Urban four-leg signalized

intersections in California.
A-12

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Angle of intersection (e.g.,

skew) (continued)

McCoy et al. [1994](42) At two-way STOP-controlled

intersections on rural two-lane highways,

the number of accidents per year

increases with traffic volume and skew

angle. Thus, more accidents will occur

with higher volumes and/or greater skew

angles. Three-leg intersections have

fewer accidents than four-leg intersections with equivalent traffic

conditions and skew angles.

Comparative. 29 skewed and

39 nonskewed

rural intersections in

Nebraska.

Two-way STOP-controlled

intersections on rural twolane highways. Included

three-leg and four-leg

intersections. Volumes on

the major and minor

roadways ranged from 400

to 5,200 veh/day and from

150 to 1,500 veh/day,

respectively.

Hauer [1988](15) Stated as an important safety factor. No

safety studies conducted.

Roundabouts See section of this table

on type of traffic control.

Curb return radius Hauer [1988](15) Stated as an important safety factor. No

safety studies conducted.

Sight distance

• Intersection sight

distance (clear sight

triangles in intersection

quadrants)

Harwood et al. [2000]

(25) Based upon the judgement of an expert

panel, the AMFs are as follows for

intersection sight distance at

intersections with STOP control on the

minor leg(s):

• 1.05 if sight distance is limited in one

quadrant of the intersection.

• 1.10 if sight distance is limited in two

quadrants of the intersection.

• 1.15 if sight distance is limited in three

quadrants of the intersection.

• 1.20 if sight distance is limited in four

quadrants of the intersection.

Sight distance in a quadrant is

considered limited if the available sight

distance is less than the sight distance

specified by AASHTO policy for a design

speed of 20 km/h less than the major

road-design speed and the sight

distance restrictions are due to roadway

alignment and/or terrain.

Accident prediction

algorithm using

negative binomial

distribution and

accident modification

factors developed by

expert panel.

Prediction

algorithm

combines

elements of

historical

accident data,

predictions from

statistical

models, results

of before-after

studies, and

expert judgments made by

experienced

engineers.

Rural intersections along

two-lane highways.
A-13

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Sight distance

• Intersection sight

distance (clear sight

triangles in intersection

quadrants) (continued)

David and Norman

[1976](26)

Developed estimates of the reduction in

annual accident frequency from

improving sight distance as a function of

the initial sight distance (termed “sight

radius” in the study) from the minor-road

approach and total entering ADT. The

results indicated that, in most cases, the

worse the initial sight distance, the

greater the accident reduction obtained

from a sight distance improvement. 

Magnitudes of the sight distance

improvements were not specified.

Comparative. 558 intersections with

4,372 accidents

in three years in

the San

Francisco Bay

Area of

California.

Urban areas where foliage

and buildings obstructed

the view of intersections.

Hanna et al. [1976](38) In this study, the average accident rate

for all intersections was 1.13, while the

average accident rate for intersections

with “poor sight distance” is 1.33

accidents per million entering vehicles.

Comparative. Examined 41

intersections in

rural area of

Virginia with

total of 366

accidents.

Rural municipalities,

including both STOPcontrolled and signalized

intersections.

Mitchell [1972](43) • Total accidents at intersections

dropped 67 percent when intersection

sight obstructions were removed.

• The greatest percentage of reduction

in accidents was experienced at the

intersections where the sight distance

was improved.

Before/After. Five intersections in

Concord,

California.

Sight distance at five

intersections that had

been improved.

Sight distance

• Stopping sight distance 

Fambro et al. [1989](44) Accident rates were high for

intersections located on crest vertical

curves with limited sight distance. 

Similar results were obtained in NCHRP

Project 3-42 [1996].(28)

Comparative. Rural two-lane roadways.

Sight distance

• Sight distance to traffic

control device (e.g.,

STOP sign, signal)

None found
A-14

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Approach width Bauer and Harwood

[1996](20)

It was found that as lane width

decreases, the total number of multiplevehicle accidents and fatal injury

accidents increases.

Statistical modeling

with negative

binomial regression.

2,999 intersections in

California.

Urban four-leg signalized

and unsignalized

intersections.

David and Norman

[1976](26)

Higher accident occurrence for narrow

streets was not evident.

Comparative. 558 intersections with

4,372 accidents

in three years in

the San

Francisco Bay

Area of

California.

269 T-intersections, 289

four-leg intersections. 298

of the intersections were

STOP-controlled.

Lacy [1972](18) Several improvements to the

intersection, which included widening

the approaches, reduced the accident

frequency by 35 percent and the

accident severity by 80 percent. Other

improvements to the intersection

included: extending and rearranging the

channelization, adding separate left-turn

lanes, and modifying the traffic signals.

Before/After. One urban

intersection in

Peoria, Illinois.

Urban intersection.

Number of approach lanes Bauer and Harwood

[1996](20)

In the models for total multiple-vehicle

accidents and fatal injury accidents at

rural and urban unsignalized intersections, approaches with one lane were

associated with higher accident

frequencies, while approaches with two

or more lanes were associated with

lower total accident frequencies. The

opposite appears to be the case for

urban, four-leg, signalized intersections.

Statistical modeling

with negative

binomial regression.

2,262 rural

intersections in

California.

Rural four-leg STOPcontrolled intersections.

Lau and May [1988](39) The number of lanes on the major

roadway and the crossroad were found

to be a significant factor in predicting

injury accidents at signalized

intersections.

CART analysis of

residuals from base

model.

2,488 signalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Signalized intersections on

California state highways.
A-15

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Number of approach lanes

(continued)

Lau and May [1988](40) The number of lanes on the major

roadway was found to be a significant

factor in predicting injury accidents at

unsignalized intersections.

CART analysis of

residuals from base

model.

17,000

unsignalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.

David and Norman

[1976](26)

For roadways with ADT under

10,000 veh/day, accident frequencies

can be reduced by providing through

lanes.

Comparative. 558 intersections with

4,372 accidents

in three years in

the San

Francisco Bay

Area of

California.

Of 558 intersections,

71 percent had 2x2

through lanes, 26 percent

had 2x4, and 3 percent

had 4x4 on the crossroad

and major road,

respectively.

Median width and type Harwood et al. [1995]

(27) • At rural four-leg unsignalized

intersections, accident frequency

decreases as median width increases.

• At urban/suburban intersections

(unsignalized and signalized),

accident frequency increases with

increasing median width.

Comparative study

and statistical

modeling with

Poisson regression.

2,140 divided

highway

intersections in

urban and rural

areas of

California.

Intersections include rural

and urban/suburban

unsignalized intersections

(four-leg and three-leg), as

well as urban/suburban

four-leg signalized

intersections.

Van Maren [1977](47) Found no statistically significant

relationship between median width and

intersection accident rate.

Priest [1964](46) Except at very low volume levels,

intersection accident frequencies

decrease as the median width

increases. The difference in intersection

accident rate between medians less

than 20 ft wide and medians 20 to 30 ft

wide is greater than the difference in

intersection accident rate between

medians with widths of 20 to 39 ft and

those of 40 ft or more.

Statistical modeling

with regression

analysis.

316 intersections in Ohio. 

Three years of

accident data

were available

for each

intersection.

Intersections on divided

highways with partial or no

access control in Ohio.

Vertical alignment on

intersection approaches

Hanna et al. [1976](38) Rural intersections with steep grades

(greater than 5 percent) “generally

operate safely.” These intersections

had an accident rate of 0.97 accidents

per million entering vehicles, compared

to an overall accident rate of 1.13.

Comparative. 232 intersections in

Virginia.

Rural areas.
A-16

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Horizontal alignment on

intersection approaches

None found

Design speed Bauer and Harwood

[1996](20)

As design speed decreases, there is an

increase in total multiple-vehicle

accidents and fatal injury accidents.

Statistical modeling

with negative

binomial regression.

1,434 rural

intersections in

California.

Rural and urban

intersections.

Traffic Control and Operational Features

Type of traffic control

• Uncontrolled

• YIELD control

Poch and Mannering

[1995](22)

With no control on an intersection

approach, total and angle accidents

decrease.

Statistical modeling

with negative

binomial regression.

63 intersections

in the city of

Bellevue,

Washington.

Urban areas. A large

number of intersections

are in residential areas

which are characterized by

low traffic volume. All

intersections had some

sort of operational

improvement during 1988-

92.

Hauer [1988](15) • 44 percent and 52 percent fewer

accidents after conversion to YIELDcontrol.

• Another study gives accident

reduction of 23 and 63 percent after

conversion of uncontrolled

intersections to YIELD-control.

Synthesis of previous

studies.

Not available. Not available.

Lau and May [1988](39) Traffic control type was found to be a

significant factor in predicting injury

accidents at unsignalized intersections. 

However, the traffic control type variable

was formulated in such a way that it is

not easy to distinguish among the traffic

control types used in this table.

CART analysis of

residuals from base

model.

17,000

unsignalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.

Hall et al. [1978](50) Accidents can be reduced by 20 to

60 percent through proper use of YIELD

signs at low-volume intersections. Little

additional reduction is obtained if YIELD

signs are replaced by STOP signs. 

Not available. Not available. Not available.

Agent and Deen [1975](51) At YIELD signs, over half of the

accidents were rear-end collisions, while

angle collisions made up over half the

accidents at STOP signs.

Comparative. Data for

intersections in

Kentucky. 

Three years of

accident data

were available.

Intersections in rural

areas.
A-17

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Type of traffic control 

• STOP control

Hanna et al. [1976](38) Accident rates at STOP-controlled

intersections were lower at those

intersections having high traffic flow.

Comparative. 232 intersections in

Virginia.

Rural areas.

Type of traffic control 

• STOP control with

flashing beacons

None found

Type of traffic control 

• Signal control (phasing,

timing, and operation)

Poch and Mannering

[1995](22)

With signal control intersections, the

total and angle accidents decrease.

Statistical modeling

with negative

binomial distribution.

63 intersections

in city of

Bellevue,

Washington.

Urban areas. All

intersections had some

sort of operational

improvement during 1988-

92.

Maze et al. [1994](23) Predictive models indicate that a

protected left-turn signal phase without a

left-turn lane has a positive effect on

safety. A typical example developed by

the authors indicates an anticipated

reduction in left-turn accident rate of

approximately 50 percent from

installation of a protected left-turn signal

phase.

Statistical modeling

based on multiple

regression.

63 signalized

intersections,

including 248

intersection

approaches. 

Five years of

accident data

were considered

for each

intersection.

At-grade signalized

intersections in Iowa.

Lau and May [1988](39) A control-type variable based on signal

phasing and actuation was found to be a

significant factor in predicting injury

accidents at signalized intersections. 

However, the control type variable was

defined in such a way that explicit

effects of phasing and actuation cannot

be determined. 

CART analysis of

residuals from base

model.

17,000

unsignalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.

Hanna et al. [1976](38) Installation of traffic signal controls could

result in slight increase in accident rates,

significant increase in rear-end

accidents, and comparable decreases in

angle collisions. 

Comparative. 232 intersections in

Virginia.

Rural municipality area.
A-18

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Type of traffic control 

• Signal control (phasing,

timing, and operation)

(continued)

David and Norman

[1976](26)

In urban areas, multi-phase traffic

signals appear to have lower

percentages of fatal and injury accidents

than two-phase signals.

Comparative. 558 intersections with

4,372 accidents

in 3 years in the

San Francisco

Bay Area of

California.

Of 558 intersections, 269

were T intersections and

289 were four-leg

intersections. 298 of the

intersections were STOPcontrolled.

King and Goldblatt

[1975](52)

• Signalization leads to a reduction in

right-angle accidents and an increase

in rear-end accidents.

• Signalized intersections have higher

accident rates, but this is usually

offset by less severity per accident.

Comparative

analyses and review

of related research.

Used a large

nationwide

accident data

base.

Not available.

Type of traffic control 

• Roundabouts

Persaud et al. [2001](53) Converting intersections with

conventional traffic control (i.e.,STOP,

signal) to roundabouts reduces all

accidents by 40 percent, injury accidents

by 80 percent, and fatal and

incapacitating injury accidents by

90 percent.

Before/After. 23 intersections

located in 7

states.

Mix of urban, suburban,

and rural environments.

Robinson et al. [2000](41) Converting intersections with

conventional traffic control (i.e.,STOP,

signal) to roundabouts reduces all

accidents by 37 percent and injury

accidents by 51 percent.

Synthesis of research

conducted in the U.S.

and internationally.

Not available. Not available.

Turn prohibitions Lau and May [1988](39) Left-turn prohibitions were found to be a

significant factor in predicting injury

accidents at signalized intersections.

CART analysis of

residuals from base

model.

2,488 signalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Signalized intersections on

California state highways.

Lau and May [1988](40) Left-turn prohibitions were found to be a

significant factor in predicting injury

accidents at unsignalized intersections.

CART analysis of

residuals from base

model.

17,000

unsignalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.
A-19

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Presence and type of

crosswalks

Hauer [1988](15) Marked crosswalks had more exposure,

but fewer accidents, than unmarked

crosswalks.

Synthesis of previous

research conducted

by Knoblauch et al.

[1984](68)

Not available. Not available.

Hauer [1988](15) Pedestrian accidents increased

86 percent after crosswalks were

marked, and rear-end collisions

increased 32 percent.

Synthesis of previous

research entitled

“What Not To Expect

from Crosswalk

Signals.” [1976]

(69)

Not available. Not available.

Hauer [1988](15) Pedestrian accidents may be reduced by

approximately 50 percent by marking

crosswalks.

Synthesis of previous

research conducted

by Untermann.

[1984](70)

Not available. Not available.

Hauer [1988](15) Painted crosswalks reduced violation of

the pedestrian’s right of way.

Synthesis of previous

research cited in

Traffic Engineering

Handbook. [1965](71)

Not available. Not available.

Smith and Knoblauch

[1987](53)

Accident analyses suggest use of 

crosswalks at all signalized

intersections.

Comparative. Not available. Intersections with marked

and unmarked crosswalks.

Herms [1970](55) More pedestrian accidents occurred in

marked crosswalks than in unmarked

crosswalks by a ratio of about 6 to 1. A

crosswalk usage count showed the

crosswalk use ratio was approximately 3

to 1, marked vs. unmarked. In terms of

usage, approximately twice as many

pedestrian accidents occurred in marked

crosswalks as compared to unmarked

crosswalks.

Comparative. 400 unsignalized intersections in San

Diego,

California.

Each intersection had one

marked and one unmarked

crosswalk crossing the

major flow of traffic.

Posted speed limits on

approaches

None found.
A-2

0

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Advance warning signs Gattis and Iqbal [1994](56) The “Do Not Block the Intersection” sign

was found to be ineffective in preventing

drivers from blocking intersections.

Before/After. Four

intersections.

Two street intersections

and two commercial

driveway intersections.

Klugman et al. [1992](58) Accident rates for intersections equipped

with advance warning signs with flashers

(AWFs) decreased from 1.22 to 1.09

accidents per million entering vehicles

for all accident types and decreased

from 0.68 to 0.63 for right-angle and

rear-end accidents.

Comparative and

Before/After Study.

14 intersections. Signalized intersections.

Pant and Huang [1992](57) • On tangent approaches to intersections, the Prepare to Stop When

Flashing (PTSWF) sign showed no

significant increase or decrease in

conflict rate.

• On curved approaches to intersections, the PTSWF sign increased

the conflict rate by at least 15 percent.

• The Flashing Symbolic Signal Ahead

(FSSA) sign showed no effect on

vehicle conflict rates.

Before/After. Not available. High-speed signalized

intersections on rural or

suburban highways in

Ohio.

Washington et al.

[1991](36)

Implementation of AWFs can reduce

approach accident rates at high-speed

isolated signalized intersections by as

much as 50 percent.

Comparative. 40 signalized

intersections in

California.

High-speed isolated

signalized intersections.

Washington et al.

[1991](36)

• Right-angle accidents were

significantly reduced with the

presence of route markers and/or

advance warning signs.

• Accident rates increased on horizontal

approaches with a skew that

contained advance warning signs.

Synthesis of prior

research conducted 

by Van Maren et al.

[1980](47)

Not available. Not available.

Styles et al. [1982](59) Red Signal Ahead signs reduced:

• Right-angle accident rates by

42 percent on approaches with sharp

vertical crests. 

• Total accident rates on horizontal

curve approaches by 14 percent.

• Total accident rates on flat

approaches by 41 percent.

Before/After. 20 intersections. High-speed signalized

intersection approaches.
A-21

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Advance warning signs

(continued)

Styles et al. [1982](60) All intersections showed a reduction in

right-angle accidents with the

implementation of a flashing red strobe

light. One of the intersections showed

reductions in right-angle, rear-end, and

total accidents of 83 percent,

60 percent, and 61 percent, respectively.

Before/After. Four

intersections in

Maryland.

High-speed signalized

intersection approaches.

Lighting Bauer and Harwood

[1996](20)

At rural, four-leg, STOP-controlled

intersections, lighted intersections had

21 percent fewer total and injury

accidents than unlighted intersections. 

However, no similar effect was observed

for total intersection accidents, and an

effect in the opposite direction,

indicating that lighted intersections had

more accidents than unlighted

intersections, was observed for urban

four-leg STOP-controlled intersections. 

These results were based on accidents

for all times of day (daytime plus

nighttime).

Statistical modeling

with negative

binomial regression.

2,262 rural fourleg STOPcontrolled

intersections

and 1,551 urban

four-leg STOPcontrolled

intersections.

At-grade intersections on

California state highways.

Box [1989]

(61) Lighting improvements along a suburban

arterial street were found to reduce the

percentage of total intersection

accidents that occur at night as follows:

 Accident Percent reduction

• Pedestrian/bicycle 50 to 33

• Fixed object 57 to 25

• Sideswipe 32 to 11

• Other 33 to 25

Only nighttime head-on accidents

increased as a proportion of total headon accidents from 33 percent to

43 percent. There is no indication

whether any of the observed changes in

nighttime accident proportions are

statistically significant.

Before/after

comparison of the

proportion of

nighttime accidents.

Two years of

accident data

before and

Two years after

the

improvement.

One 2.8-mi suburban

arterial highway in

Naperville (suburban

Chicago, Illinois).
A-2

2

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Lighting (continued) City of Los Angeles

[1980](62)

Found no statistically significant

reduction in nighttime accidents due to

lighting improvements at intersections. 

Significant reductions in nighttime

accidents were found for a few

intersections. The assessment of this

study is based on review by Keck

[1990].(70)

Before/after

evaluation based on

regression analysis.

528 urban

intersections;

2 years of data

for nighttime

accidents and

persons injured

at the study

intersections

were used.

Urban intersections on city

streets in Los Angeles,

California.

Traffic Characteristics

Average daily traffic

• Total entering ADT, all

approaches

Many studies used total

entering ADT as traffic

exposure in their studies

(e.g., to calculate

intersection accident

rates).

Hauer [1988](15) Explicitly examined safety effects of

signalized intersections on traffic flow

impacts. He concluded that logically

sound models require that the accidents

be related to the traffic flows to which

the colliding vehicles belong and not the

sum of the entering volumes.

Comparative and

review of past

studies.

Sample data

from past

studies and

simulated data.

Mostly urban settings.

Lau and May [1988](39) The total entering ADT on all

approaches combined was found to

have a statistically significant

relationship to injury accidents at

signalized intersections.

Linear regression

model.

2,488 signalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Signalized intersections on

California state highways.

Lau and May [1988](40) The total entering ADT on all

approaches combined was found to

have a statistically significant

relationship to injury accidents at

unsignalized intersections.

Linear regression

model.

17,000

unsignalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.
A-2

3

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Average daily traffic

• Entering ADTs for major

and minor approaches

Bauer and Harwood

[1996](20)

Major-road and crossroad ADT variables

were present in all models as significant

predictors of accident frequency. The

relative effects for major-road ADT

ranged from 1.77 to 2.68, depending on

intersection settings (rural/urban, fourleg/three-leg, signalized/STOPcontrolled). The relative effects of minorroad ADT ranged from 1.24 to 1.80.

Statistical modeling

with negative

binomial regression.

14,432

intersections in

California.

Rural and urban, four-leg

and three-leg intersections, STOP-controlled

and signalized. Intersection major-road ADT

above 400 veh/day, minorroad ADT above 100

veh/day.

Lau and May [1988](39) Percentage of total entering traffic on

crossroad was found to be a significant

factor in predicting injury accidents at

signalized intersections.

CART analysis of

residuals from base

model.

2,400 signalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Signalized intersections on

California state highways.

Lau and May [1988](40) Percentage of total entering traffic on

crossroad was found to be a significant

factor in predicting injury accidents at

unsignalized intersections.

CART analysis of

residuals from base

model.

17,000

unsignalized

intersections. 

Seven years of

injury accident

data for each

intersection.

Unsignalized intersections

on California state

highways.

Turning movements Hauer et al. [1988](63) Developed relationships between

accident frequency for specific accident

types (e.g., left turn) and associated

turning volumes.

Predictive modeling

with negative

binomial regression.

Three years of

accident data for

145 intersections in

Toronto,

Ontario,

Canada; turning

movement data

by approach and

time of day.

Urban, four-leg, fixed-time,

signalized intersections

with two-way traffic on all

approaches and no turn

restrictions.

Peak hour approach

volumes

None found

Vehicle mix / percent trucks None found

Distribution of total entering

volume by hour of the day

None found
A-2

4

Table A-1 (Continued).

Design and control

elements Related literature Major findings Study type Data used Type of site

Distribution of approach

volume by hour of the day

None found

Average approach speed None found

Volume of bicycle traffic None found

Volume of pedestrian traffic None found
Appendix B

Negative Binomial Regression Models
B-1

APPENDIX B. NEGATIVE BINOMIAL REGRESSION MODELS

In each of the three approaches to before-after evaluation discussed in Section 5, an

adjustment for differences in traffic volumes was made. In the YC approach, a simple

proportional traffic volume adjustment was used. In the CG and EB approaches, an

adjustment based on a regression relationship between accident frequencies and traffic

volumes was used. This appendix discusses the development of these regression

relationships through negative binomial modeling of accident frequencies as a function of

traffic volumes and other variables. The application of these models has been illustrated in

Figures 5 and 6 in the main text of this report.

Statistical Approach

Accident counts at a given intersection are inherently discrete, positive numbers, and

often small, as in the case of fatal and injury accidents. Furthermore, the distribution of

accidents is often skewed in that most sites experience few accidents while a small number

of sites experience relatively many more accidents. The Poisson distribution is generally

thought of when dealing with rare discrete events such as accidents. The Poisson

distribution has only one parameter, namely its mean. The variance of a Poisson

distribution is, by definition, equal to its mean. This relationship between the mean and the

variance (dispersion) is often violated for accident counts due to inherent overdispersion in

the data (i.e., the variance of accident counts typically exceeds the mean). A flexible

distribution that can be used to effectively model overdispersed count data is the negative

binomial (NB) distribution. This distribution has two parameters, the mean and a

dispersion parameter. When the dispersion parameter nears zero, the NB distribution

approaches the Poisson distribution. 

The relationship between the expected number of accidents, Yi, occurring at

intersection i with a set of q intersection parameters, Xi1, Xi2, ..., Xiq, is

(B-1)

where $0, $1 ..., $q are the regression coefficients and with the assumption that the number

of accidents, Yi

, follows a negative binomial distribution with parameters " and d (with

0 # " # 1 and d $ 0). That is, the probability that an intersection defined by a known set of

predictor variables, Xi1, Xi2, ..., Xiq, experiences Yi

 = yi

 accidents can be expressed as:

(B-2)

where yi

! denotes the factorial of yi

.
B-2

The mean and variance of the negative binomial distribution of accident counts can

then be expressed in terms of the parameters " and d as follows:

(B-3)

(B-4)

The term :i can be referred to as the Poisson variance function and :i

2

/d as the extra

component arising from combining the Poisson distribution with a gamma distribution for

the mean to obtain the negative binomial distribution. The overdispersion parameter d is

not known a priori, but can be estimated so that the mean deviance becomes unity or the

Pearson chi-square statistic equals its expectation (i.e., equals its degrees of freedom).(74)

The model regression coefficients, $0

, $1

 ..., $q

, are estimated by the method of

maximum likelihood. The asymptotic normality of maximum likelihood estimates is used

to obtain tests of significance of the parameters and goodness of fit measures for the

models.

The parameters " and k of the negative binomial distribution can be indirectly

estimated using a generalized linear model to obtain the model regression coefficients $0

,

$1 ..., $q. The commercially available software SAS provides a procedure,

PROC GENMOD (a generalized linear model procedure), that can be used to estimate the

regression coefficients.(75)

To assess the goodness of fit of a model, a number of statistics are available:

Model Statistic Explanation

Deviance/(n - p) The deviance of the model containing all the parameters

(including the intercept) divided by its degrees of freedom,

n - p. This statistic (mean deviance) provides a test for

overdispersion and a measure of fit of the model. 

Asymptotically, this value tends toward 1.(74)

Pearson chi-square/(n - p) The Pearson chi-square statistic divided by its degrees of

freedom, n - p. This statistic provides another measure of

fit of the model.(74)

R

2 A goodness-of-fit parameter based on the ordinary

multiple correlation coefficient.

R

2

FT A goodness-of-fit parameter based on the Freeman-Tukey

variance stabilizing transformation of variables discussed

in Fridstrøm et al.(76)
B-3

R

2

k A goodness-of-fit parameter proposed by Miaou,(77) a

function of the overdispersion parameter of the regression

model and that of a means only model. [This measure has

not been estimated in this study, but is being considered

for inclusion in the final report.]

Selection of Independent and Dependent Variables in the

Regression Model

Using the reference group data, yearly accident counts were modeled as a function of

three independent or explanatory variables, as appropriate:

C Major-road traffic volume in vehicles per day.

C Minor-road traffic volume in vehicles per day.

C State.

Based on experience in previous intersection modeling by Bauer and Harwood (20) and

preliminary modeling in this study, the regression models included separate terms for

major- and minor-road traffic volumes rather than a combined term for the total traffic

volume entering the intersection. In all of the NB models developed in this study, the

natural logarithm of the major-road and minor-road traffic volumes was used. Thus, in the

NB model described in Equation (B-1), X1 and X2 generally represent log(MajADT) and

log(MinADT), respectively.

A state factor was included because the multistate database assembled for the study

exhibited large state-to-state variations which needed to be accounted for in the CG and EB

approaches to insure that these state effects were not mistaken for treatment effects. In

most cases, the negative binomial modeling was limited to intersections in the comparison

and reference groups that had no existing turn lanes. For modeling of urban signalized

intersections, a fourth independent variable, the number of existing left-turn lanes was

added because there were not enough of such intersections without turn lanes in the

comparison and reference groups for modeling.

In summary, the multiplicative model relating the expected accident counts and the

selected independent variables can be rewritten as:

(B-5)

where $3 and $4, the coefficients for the categorical variables—state and existing left-turn

lanes—vary with the levels of the variables.
B-4

As discussed in section 5, a number of dependent variables (safety measures) were

considered for modeling, including:

C Total intersection accidents.

C Fatal and injury intersection accidents.

C Project-related intersection accidents.

C Fatal and injury project-related intersection accidents.

C Total accidents for individual intersection approaches.

C Fatal and injury accidents for individual intersection accidents.

C Project-related accidents for individual intersection approaches.

C Fatal and injury project-related accidents for individual intersection approaches.

Selection of Intersection Types

Regression relationships were developed for as many combinations of the following

intersection characteristics as possible using the comparison and reference site data:

C Area type (urban/rural).

C Type of traffic control (signalized/unsignalized).

C Number of intersection legs (three or four).

C Number of lanes on major road (two-lane/multilane).

Negative Binomial Repression Results

The coefficients $0, $1, $2, $3, and $4 of the negative binomial regression in Equation

(B-5) and the dispersion parameter, k, were estimated by maximum likelihood using PROC

GENMOD of SAS. In all cases, a 10 percent significance level was chosen. Of the 300

available sites in the reference group, models were developed for a total of 252 sites,

grouped as follows: 

C Rural, unsignalized, three- and four-leg, two- and multilane sites (only sites

without existing left- or right-turn lanes were included)—N=120.

C Urban, signalized, three- and four-leg, two- and multilane sites (including sites

with up to four existing left-turn lanes; no consideration was given to the number

of existing right-turn lanes)—N=86.

C Urban, unsignalized, three- and four-leg, two- and multilane sites (only sites

without existing left- or right-turn lanes were included)—N=46.

The eight types of safety measures discussed above were then considered as dependent

variables in the NB modeling, resulting in 96 models to be estimated (8 safety measures x

12 combinations of types of sites).
B-5

In each case, a variation of the model shown in Equation (B-5) was investigated,

including either all possible independent variables, or excluding selected ones, to assess

which model best fits the data. The following four models (Model Types 1 through 4)

were investigated:

C Model Type 1: Major-road traffic volume, minor-road traffic volume, and

state—all intersection types.

C Model Type 2: Major-road traffic volume and minor-road traffic volume—all

intersection types.

C Model Type 3: Major-road traffic volume, minor-road traffic volume, state, and

number of existing left-turn lanes—urban, signalized intersections only.

C Model Type 4: Major-road traffic volume, minor-road traffic volume, and number

of existing left-turn lanes—urban, signalized intersections only.

In summary, an attempt was made to estimate the regression coefficients and dispersion

parameter of a total of 256 models (4 variations on 96 models), and of these 256, select the

best model, if one was available, for each of the 96 cases.

An investigation of the number of accidents in each of the eight safety measure

categories found that the number of some types of accidents in a group of intersections

defined by area type, traffic control, number of lanes, and number of legs was too small

(less than 10 over the entire study period) to warrant modeling. This was true in the

following situations:

C Fatal and injury project-related intersection accidents at all types of sites (12

cases).

C Fatal and injury project-related accidents for individual intersection approaches at

all types of sites (12 cases).

C Project-related intersection accidents at rural, unsignalized, three-leg, two-and

multilane intersections (two cases).

C Project-related intersection accidents at urban, signalized, three-leg, multilane

intersections (one case).

C Project-related intersection accidents at urban, unsignalized, three-leg, two-and

multilane intersections (two cases).

C Project-related accidents for individual intersection approaches at rural,

unsignalized, three-leg, two-and multilane intersections (two cases).

C Project-related accidents for individual intersection approaches at urban,

signalized, three-leg, multilane intersections (one case).

C Project-related accidents for individual intersection approaches at urban,

signalized, three-leg, two-and multilane intersections (two cases).

Thus no modeling was attempted in any of these 34 cases. This left a total of 62 (96-34)

models to estimate.
B-6

The significance of the model as a whole and of the regression coefficients in

particular, the magnitude and signs of the measures of fit discussed above, whether the

maximum likelihood algorithm to estimate the regression coefficients converged, and

whether the coefficients made engineering sense, were all part of the decision process in

choosing a model in a particular case. Using these criteria, models for the final 64 cases

were selected. The models were rated as follows:

C A statistically significant model could be estimated, satisfying engineering criteria

such as the coefficients of the two traffic volumes were positive and 1 or below.

C The model developed had all the proper attributed, e.g., the coefficients of the two

traffic volumes were positive and 1 or below, but was not statistically significant. 

In that case, the model was considered to provide the best available estimate of

accident counts and was therefore selected. Generally, the two measures of model

fit, R2 and RFT

2

, are also low in these cases.

C No model could be estimated.

The negative binomial regression results are shown in tables B-1 through B-6 for six

of the eight types of safety measures. No tables are shown for fatal and injury

project-related accidents because there were no statistically significant models for these

safety measures. Each table includes the following statistics:

C Intersection type.

C Model type (Model Types 0 through 4, where Model Types 1 through 4 were

defined above and Model Type 0 denotes that no model was available through

regression analysis).

C The number of site-years or approach-years, depending on the type of safety

measure.

C The regression coefficients, $0

, $1

, $2

, representing the intercept and the exponents

of major-road and minor-road traffic volumes, respectively.

C The state coefficients, $3.

C The coefficients for the number of existing left-turn lanes, $4 (applicable for

Model Types 3 and 4 only).

C The negative binomial dispersion parameter (d).

C The two measures of model fit, R2 and RFT

2

.
B-7

Table B-1. Negative Binomial Regression Models for Total Intersection Accidents.

Area

type

Tra ffic

control

type

No. of

intersection

legs

No . of 

lanes

on

major

road

(twolane 

or

 m ultilane)

Model

type

No . 

of 

siteyears Intercept

Traffic volume

coe fficients Sta te coe fficients

Exis ting leftturn lane

 coefficient

 (0 lanes)

Dispersion

parameter (d) R

2% RFT

2

LogMajADT LogMinADT IA IL LA MN NC NE OR VA

R U 3 M 2,2 63 !6.523 0.078 0.864 0 0 0 0 0 0 0 0 0 0.079 18.26 12.74

R U 3 T 1,1 579 !12.153 1.000 0.633 !2.232 !1.145 0 0 !0.406 0 !1.242 0 0 0.506 32.32 28.37

R U 4 M 2,1 80 !12.493 0.797 0.868 0 0 0 0 0 0 0 0 0 0.197 57.55 50.32

R U 4 M 2,1 662 !8.136 0.298 0.856 0 0 0 0 0 0 0 0 0 0.354 34.53 32.78

U S 3 M 0,3 34

U S 3 T 0,3 47

U S 4 M 3,1 747 !6.749 0.692 0.178 0.921 0.772 1.552 0.905 !0.788 0.444 !0.098 0 !0.123 0.371 32.65 31.10

U S 4 T 1,1 177 !12.231 0.835 0.811 0 !1.030 !0.908 0 !1.718 0 0 0 0 0.220 35.27 37.42

U U 3 M 0,3 25

U U 3 T 1,1 195 !8.887 0.745 0.293 0.815 !0.029 1.385 0 0 0 0 0 0 0.460 11.11 30.87

U U 4 M 1,2 121 !1.426 0.061 0.184 1.434 0.438 0 0.066 0 0.609 0 0 0 0.184 43.92 36.04

U U 4 T 1,1 200 !7.740 0.641 0.194 1.108 1.481 0 1.269 0.587 0 0 0 0 0.408 32.22 22.90
B-8

Table B-2. Negative Binomial Regression Models for Fatal and Injury Intersection Accidents.

Area

type

Tra ffic

control

type

No. of

intersection

legs

No . of 

lanes

on

major

road

(twolane 

or

 m ultilane)

Model

type

No . 

of 

site-yea rs

Intercept

Traffic volume

coefficients Sta te coe fficients

Exis ting leftturn lane

coefficient

 (0 lanes)

Dispersion

parameter (d) R

2% RFT

2

LogMajADT LogMinADT IA IL LA MN NC NE OR VA

R U 3 M 0.3 63

R U 3 T 0,3 579

R U 4 M 2,1 80 !13.081 0.933 0.676 0 0 0 0 0 0 0 0 0 0.432 25.95 31.38

R U 4 T 2,1 662 !8.365 0.233 0.877 0 0 0 0 0 0 0 0 0 0.377 24.89 22.33

U S 3 M 0,3 34

U S 3 T 0,3 47

U S 4 M 3,1 747 !6.055 0.521 0.178 1.060 0.752 1.498 0.904 !0.299 0.414 !0.154 0 !0.219 0.295 27.48 24.39

U S 4 T 1,1 177 !8.899 0.533 0.633 0 !1.228 !0.835 0 !1.380 0 0 0 0 0.162 10.62 13.29

U U 3 M 0,3 25

U U 3 T 1,2 195 !8.073 0.715 0.051 1.087 0.013 1.426 0 0 0 0 0 0 0.060 32.49 33.34

U U 4 M 0,3 121

U U 4 T 1.1 200 !10.709 0.824 0.294 0.997 1.113 0 0.654 0.541 0 0 0 0 0.375 23.91 17.89
B-9

Table B-3. Negative Binomial Regression Models for Project-Related Intersection Accidents.

Area

type

Tra ffic

control

type

No. of

intersection

legs

No . of 

lanes

on

major

road

(twolane 

or

 m ultilane)

Model

type

No . 

of 

siteyears Intercept

Traffic volume

coe fficients Sta te coe fficients

Exis ting leftturn lane

coefficient

 (0 lanes)

Dispersion

parameter

(d) R

2% RFT

2

LogMajADT LogMinADT IA IL LA MN NC NE OR VA

R U 3 M 0,0 63

R U 3 T 0.0 579

R U 4 M 2,2 80 !10.732 0.652 0.539 0 0 0 0 0 0 0 0 0 2.010 3.68 3.88

R U 4 T 2,1 662 !11.201 0.648 0.462 0 0 0 0 0 0 0 0 0 0.263 3.07 1.66

U S 3 M 0,0 34

U S 3 T 0,3 47

U S 4 M 0,3 747

U S 4 T 0,3 177

U U 3 M 0,0 25

U U 3 T 0,0 195

U U 4 M 2,2 121 !11.185 0.736 0.434 0 0 0 0 0 0 0 0 0 1.280 5.80 3.63

U U 4 T 0,3 200
B-10

Table B-4. Negative Binomial Regression Models for Total Accidents on Individual Intersection Approaches.

Area

type

Tra ffic

control

type

No. of

intersection

legs

No . of 

lanes

on

major

road

(twolane 

or

 m ultilane)

Model

type

No . 

of 

approach

years Intercept

Traffic volume

coe fficients Sta te coe fficients

Exis ting leftturn lane

coefficient

(0 lanes)

Dispersion

parameter

(d) R

2% RFT

2

LogMajADT LogMinADT IA IL LA MN NC NE OR VA

R U 3 M 0,3 126

R U 3 T 1,1 1,158 !11.966 0.974 0.519 !23.164 !1.250 0 0 !1 0 !1.493 0 0 1.049 13.59 10.59

R U 4 M 2,1 160 !13.413 0.818 0.863 0 0 0 0 0 0 0 0 0 0.142 43.52 40.55

R U 4 T 2,1 1,324 !9.347 0.297 0.918 0 0 0 0 0 0 0 0 0 0.274 25.89 23.61

U S 3 M 4,2 102 !11.606 0.880 0.409 0 0 0 0 0 0 0 0 0.343 0.348 21.12 20.20

U S 3 T 2,1 141 !14.419 0.642 0.905 0 0 0 0 0 0 0 0 0 0.462 9.42 6.39

U S 4 M 1,1 2,976 !7.620 0.740 0.107 0.715 0.576 1.456 0.655 !0.706 0.105 !0.209 0 0 0.479 29.38 32.89

U S 4 T 2,1 708 !9.908 0.974 0.156 0 0 0 0 0 0 0 0 0 0.373 21.62 21.87

U U 3 M 0,3 50

U U 3 T 1,1 390 !8.638 0.722 0.137 0.930 0.221 1.400 0 0 0 0 0 0 0.647 12.76 16.00

U U 4 M 1,2 242 !5.515 0.421 0.124 1.909 0.655 0 0.433 0 0.948 0 0 0 0.222 31.90 28.03

U U 4 T 1,1 400 !7.885 0.589 0.187 1.099 1.357 0 1.048 0.382 0 0 0 0 0.509 18.76 13.19
B-11

Table B-5. Negative Binomial Regression Models for Total Fatal and Injury Accidents on Individual Intersection Approaches.

Area

type

Tra ffic

control

type

No. of

intersection

legs

No . of 

lanes

on

major

road

(twolane 

or

 m ultilane)

Model

type

No . 

of 

approach

years Intercept

Traffic volume

coe fficients Sta te coe fficients

Exis ting leftturn lane

coefficient

 (0 lanes)

Dispersion

parameter

(d) R

2% RFT

2

LogMajADT LogMinADT IA IL LA MN NC NE OR VA

R U 3 M 0,3 126

R U 3 T 0,3 1,158

R U 4 M 1,2 160 !9.994 0.695 0.348 0 !0.367 0 0 0 0.958 0 0 0 0.235 24.9 29.21

R U 4 T 2,1 1,324 !9.935 0.281 0.932 0 0 0 0 0 0 0 0 0 0.381 16.04 13.90

U S 3 M 4,2 102 !11.609 0.796 0.401 0 0 0 0 0 0 0 0 0 0.318 5.96 4.97

U S 3 T 2,2 141 !13.457 0.980 0.313 0 0 0 0 0 0 0 0 0 0.383 2.33 0.68

U S 4 M 1,1 2,976 !9.073 0.798 0.080 0.996 0.685 1.554 0.811 !0.092 0.329 !0.151 0 0 0.459 21.94 22.10

U S 4 T 0,3 708

U U 3 M 0,3 50

U U 3 T 0,3 390

U U 4 M 1,2 242 !2.985 0.074 0.141 1.842 !0.312 0 !0.507 0 0.874 0 0 0 0.215 29.24 26.58

U U 4 T 2,1 400 !11.081 0.826 0.373 0 0 0 0 0 0 0 0 0 0.538 11.49 8.25
B-12

Table B-6. Negative Binomial Regression Models for Project-Related Accidents on Individual Intersection Approaches.

Area

type

Tra ffic

control

type

No. of

intersection

legs

No . of 

lanes

on

major

road

(twolane 

or

 m ultilane)

Model

type

No . 

of 

approach

years Intercept

Traffic volume

coe fficients Sta te coe fficients

Exis ting leftturn lane

coefficient

 (0 lanes)

Dispersion

parameter

(d) R

2% RFT

2

LogMajADT LogMinADT IA IL LA MN NC NE OR VA

R U 3 M 0,0 126

R U 3 T 0,0 1,158

R U 4 M 2,2 160 !12.004 0.745 0.492 0 0 0 0 0 0 0 0 0 2.816 2.73 1.03

R U 4 T 2,1 1,324 !12.162 0.679 0.466 0 0 0 0 0 0 0 0 0 0.000 1.67 0.85

U S 3 M 0,0 102

U S 3 T 0,3 141

U S 4 M 1,1 2,976 !13.191 0.827 0.279 2.081 1.859 3.220 2.467 1.194 1.292 2.309 0 0 2.661 9.54 6.99

U S 4 T 0,3 708

U U 3 M 0,0 50

U U 3 T 0,0 390

U U 4 M 2,2 242 !11.106 0.659 0.434 0 0 0 0 0 0 0 0 0 1.429 3.21 1.45

U U 4 T 0,3 400
B-13

Overall Assessment of the Final Models

The combination of types of sites and safety measures required a total of 96 models to

be estimated. Of these 96 models, 26 models (27 percent) could be estimated with fully

satisfactory results and 13 models (14 percent) could be developed, but were not

statistically significant. These latter models were used, despite the lack of statistical

significance, because they represented the best available model. No models could be

estimated in 23 cases (24 percent). In 34 cases (35 percent), models could not be

developed because of sparse accident data over the entire study period. The R2

 and RFT

2

values range from 1.7 to 57.6 percent and from 0.9 to 50.3 percent, respectively, for the 26

statistically significant models. The R2 and RFT

2 values range from 2.33 to 43.9 percent and

from 0.68 to 36.0 percent, respectively, for the 13 models that were not statistically

significant but were still used.

The types of model used in adjusting accidents frequencies for traffic volumes, state

effect and, where applicable, the effect of existing left-turn lanes in the CG and EB

approaches can be summarized as follows:

C Type 1—17 models, including major-road traffic volume, minor-road traffic

volume, and state (18 percent).

C Type 2—18 models, including major-road traffic volume and minor-road traffic

volume (19 percent).

C Type 3—two models, including major-road traffic volume, minor-road traffic

volume, state, and number of existing left-turn lane (urban, signalized

intersections only) (2 percent).

C Type 4—two models, including major-road traffic volume, minor-road traffic

volume, and number of existing left-turn lane (urban, signalized intersections

only) (2 percent).

C Type 0—57 models; in these cases, no usable model was available (59 percent).

Models like those in tables B-1 through B-6 are intended for predicting annual

accident frequencies. Caution should be exercised in interpreting the individual

coefficients in the model as representing the effect on an individual factor on safety. 

However, it is interesting to note that the coefficient of EXLEFT for urban four-leg

signalized intersections on multilane highways shown in table B-1, when evaluated with

the average of the eight state effects shown in the table, represents an accident reduction

effectiveness of 12 percent for installation of a left-turn lane on one intersection approach. 

This is in good agreement with the 10 percent effectiveness for this project type determined

with the EB approach, as shown in table 47.
B-14

Use of the Negative Binomial Regression Models in the CG

and EB Evaluation Approaches

The overall adjustment procedures to account for traffic volumes changes in the CG

and EB evaluation approaches are discussed separately in section 5. To use any of the

regression equations shown in tables B-1 through B-6, proceed as follows: (a) select the

proper table (i.e., type of safety measure) and type of site within that table; (b) use the

coefficients shown for the intercept, major-road and minor-road traffic volumes; (c) select

the coefficient for the appropriate state, if state is included in the model; and (d) select the

coefficient for the zero existing left-turn lanes, if that parameter is included in the model

(four cases only—Model Types 3 and 4). In these four cases, the number of existing leftturn lanes was set equal to zero because the models were applied to sites with no existing

turn lanes. 

When no usable model was available, a simple proportional adjustment for traffic

volume was made in the CG approach. When no usable model was available, sites of that

type were not used in the EB approach.
Appendix C

Detailed Evaluation Results
C-1

APPENDIX C. DETAILED EVALUATION RESULTS

This appendix presents the detailed results of the evaluation of the effectiveness of

left- and right-turn lane improvement projects performed in this study. The results for the

yoked comparison (YC), comparison group (CG), and Empirical Bayes (EB) approaches

are presented separately. This appendix presents the results of all evaluations that were

performed. Only those results that were found to be statistically significant are presented

and interpreted in the main text of this report (see section 6).

Yoked Comparison Evaluations

The results of the YC evaluations performed in this study are presented in tables C-1

through C-10. These tables include the results for four specific dependent variables, two

different target areas, and two different types of analysis approaches.

The YC approach to before-after evaluation has been described conceptually in

section 5 of this report. The procedures presented in that section were used to obtain the

analysis results in tables C-1 through C-10. These analyses involved one-to-one

matching of treatment and similar unimproved comparison sites. Two analyses presented

below involved only treatment site data; in this approach, the project-related accidents for

the treatment site were evaluated using the non-project-related accidents for that same site

as the “comparison site” data. These were referred to as auto-matching analyses.

Description of Results Tables

Each of the tables of YC results presents the results of a set of similar YC evaluation

for a specific dependent variable, target area, and analysis type. Each row in a table

presents the results of the evaluation performed for a specific type of improvement

project. The following discussion guides the interpretation of individual columns in these

tables.

Area type. The first column for each row of the table identifies the area type for the

intersections evaluated. The codes used in this column are:

R = rural

U = urban

Traffic control: The second column identifies the type of traffic control at the

intersections evaluated. The codes used in this column are:

N = Newly signalized intersection

S = Signalized intersection

U = Unsignalized intersection
C-2

Table C-1. Yoked Comparison Evaluation Results for Total Intersection Accidents.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated

H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 !70.31 !35.15 7.60 !44.56 !9.52 4.63 !2.37 SIG 0.39 Yes

R N LR 4 1 8.58 4.29 65.60 !44.92 529.83 0.07 0.07 n/a n/a

R N R 4 1 !10.38 !5.19 20.07 !31.38 57.82 0.26 !0.24 n/a n/a

R S XL 3 2 !27.76 !27.76 54.20 !83.40 214.40 0.51 !0.43 NS 0.41 Yes

R S XL 4 3 !30.64 !30.64 27.62 !68.21 51.37 1.11 !0.92 NS 0.87 Yes

R S XLR 4 1 !44.32 !11.08 7.85 !20.39 17.07 1.41 !1.04 n/a n/a

R U L 3 31 !63.68 !63.68 7.21 !75.39 !46.41 8.83 !5.10 SIG 0.20 Yes

R U L 4 21 !58.08 !32.10 4.70 !39.70 !20.78 6.83 !4.28 SIG 0.04 No

R U LR 3 11 !56.64 !44.50 15.08 !64.27 2.57 2.95 !1.89 NS 0.76 Yes

R U LR 4 12 1.96 0.98 13.39 !19.53 35.31 0.07 0.07 NS 0.74 Yes

R U R 3 11 88.66 88.66 77.59 !15.75 322.45 1.14 1.54 NS 0.86 Yes

R U R 4 27 !23.91 !15.37 10.99 !32.80 11.69 1.40 !1.22 NS 0.86 Yes

R U XL 4 1 82.06 82.06 222.08 !83.33 1,888.76 0.37 0.49 n/a n/a

U N L 3 4 !30.29 !20.19 14.09 !41.01 17.52 1.43 !1.19 NS 0.02 No

U N L 4 24 !43.77 !22.35 3.18 !27.95 !15.39 7.03 !5.20 SIG 0.03 No

U S L 3 3 !32.62 !32.62 30.80 !72.49 65.08 1.06 !0.86 NS 0.18 Yes

U S L 4 33 !42.00 !12.95 1.16 !15.09 !10.53 11.17 !8.40 SIG 0.00 No

U S LR 4 9 !21.25 !5.46 2.20 !9.35 !0.65 2.48 !2.20 SIG 0.01 No

U S R 3 1 !36.52 !18.26 15.54 !37.84 32.85 1.18 !0.93 n/a n/a

U S R 4 14 !13.65 !6.82 4.23 !14.37 2.32 1.61 !1.50 NS 0.00 No

U S XL 3 1 !25.11 !25.11 43.24 !75.85 132.21 0.58 !0.50 n/a n/a

U S XL 4 2 14.95 9.97 15.24 !14.77 46.50 0.65 0.70 NS 0.80 Yes

U U L 3 10 !36.55 !36.55 17.52 !63.07 9.00 2.09 !1.65 NS 0.28 Yes

U U L 4 8 !70.46 !35.23 2.92 !39.97 !28.24 12.07 !6.17 SIG 0.63 Yes

U U LR 4 1 !0.85 !0.42 37.06 !38.55 164.58 0.01 !0.01 n/a n/a

U U R 4 1 22.76 22.76 228.29 !96.79 4,599.26 0.10 0.11 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-3
C-4

Table C-2. Yoked Comparison Evaluation Results for Fatal and Injury Intersection Accidents.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 !82.61 !41.31 6.58 !48.03 !11.64 6.27 !2.31 SIG 0.69 Yes

R N LR 4 1 !3.95 !1.97 61.88 !46.16 550.18 0.03 !0.03 n/a n/a

R N R 4 1 !2.86 !1.43 32.03 !36.67 126.92 0.04 !0.04 n/a n/a

R S XL 3 2 !35.44 !35.44 63.34 !90.56 341.70 0.56 !0.45 NS 0.72 Yes

R S XL 4 3 63.45 63.45 85.33 !41.25 354.74 0.74 0.94 NS 0.98 Yes

R S XLR 4 1 18.67 4.67 24.66 !19.18 126.30 0.19 0.21 n/a n/a

R U L 3 34 !58.55 !58.55 11.78 !76.26 !27.63 4.97 !3.10 SIG 0.78 Yes

R U L 4 22 !70.41 !39.72 4.85 !46.97 !26.90 8.18 !4.19 SIG 0.88 Yes

R U LR 3 11 !51.67 !40.60 22.18 !66.48 40.71 1.83 !1.25 NS 0.99 Yes

R U LR 4 14 !7.72 !3.86 16.19 !26.80 41.77 0.24 !0.23 NS 0.98 Yes

R U R 3 11 18.83 18.83 62.88 !57.88 235.23 0.30 0.33 NS 1.00 Yes

R U R 4 28 !22.28 !14.51 15.09 !36.91 25.69 0.96 !0.85 NS 0.94 Yes

R U XL 4 2 156.22 156.22 335.09 !80.26 3,225.51 0.47 0.72 NS 0.27 Yes

U N L 3 4 !10.57 !7.04 28.32 !43.17 84.60 0.25 !0.24 NS 0.11 Yes

U N L 4 23 !42.68 !21.81 5.40 !30.70 !9.05 4.04 !3.02 SIG 0.67 Yes

U S L 3 3 !41.97 !41.97 40.03 !84.99 124.28 1.05 !0.79 NS 0.25 Yes

U S L 4 35 !39.35 !12.41 2.04 !16.01 !7.96 6.08 !4.69 SIG 0.00 No

U S LR 4 8 !27.75 !7.16 3.75 !13.23 1.84 1.91 !1.62 NS 0.36 Yes

U S R 3 1 !74.25 !37.13 12.09 !47.96 31.17 3.07 !1.44 n/a n/a

U S R 4 12 !20.22 !9.71 7.22 !21.54 7.41 1.34 !1.20 NS 0.38 Yes

U S XL 3 1 !0.14 !0.14 99.86 !85.93 608.91 0.00 0.00 n/a n/a

U S XL 4 3 !7.71 !4.62 19.19 !31.92 49.21 0.24 !0.23 NS 0.14 Yes

U U L 3 10 !41.59 !41.59 24.13 !74.01 31.27 1.72 !1.30 NS 0.67 Yes

U U L 4 8 !79.48 !39.74 3.04 !44.26 !31.65 13.06 !5.34 SIG 0.93 Yes

U U LR 4 1 !73.75 !36.88 18.17 !49.13 147.92 2.03 !0.97 n/a n/a

U U R 4 1 84.14 84.14 379.61 !96.76 10,370.69 0.22 0.30 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-5

Table C-3. Yoked Comparison Evaluation Results for Project-Related Intersection Accidents.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 !33.84 !16.92 44.21 !47.59 404.14 0.38 !0.31 NS 1.00 Yes

R N LR 4 1 !47.97 !23.99 68.83 !49.85 4,598.55 0.35 !0.25 n/a n/a

R N R 4 1 !0.01 0.00 141.41 !49.80 12,730.59 0.00 0.00 n/a n/a

R S XL 3 2 !7.57 !7.57 184.85 !98.17 4,558.33 0.04 !0.04 NS 0.98 Yes

R S XL 4 4 23.06 23.06 119.45 !81.64 724.77 0.19 0.21 NS 0.99 Yes

R S XLR 4 1 !2.90 !0.73 47.01 !24.45 1,055.24 0.02 !0.02 n/a n/a

R U L 3 34 !1.04 !1.04 47.90 !61.68 155.56 0.02 !0.02 NS 1.00 Yes

R U L 4 23 !38.43 !21.56 16.71 !42.72 33.06 1.29 !1.00 NS 1.00 Yes

R U LR 3 12 !15.23 !12.18 53.44 !65.53 237.78 0.23 !0.21 NS 1.00 Yes

R U LR 4 15 68.38 34.19 44.25 !19.95 185.88 0.77 0.99 NS 0.95 Yes

R U R 3 11 4.90 4.90 89.46 !80.28 458.06 0.05 0.06 NS 1.00 Yes

R U R 4 28 !7.41 !4.83 28.83 !41.50 88.81 0.17 !0.16 NS 1.00 Yes

R U XL 4 2 50.26 50.26 242.29 !93.63 3,443.23 0.21 0.25 NS 0.57 Yes

U N L 3 4 !32.95 !21.97 59.60 !63.39 543.20 0.37 !0.30 NS 0.99 Yes

U N L 4 26 22.55 11.73 23.61 !21.17 79.74 0.50 0.55 NS 0.76 Yes

U S L 3 3 0.06 0.06 155.01 !95.20 1,984.29 0.00 0.00 NS 1.00 Yes

U S L 4 35 !39.09 !12.55 3.17 !17.87 !5.25 3.96 !3.06 SIG 0.53 Yes

U S LR 3 2 222.83 148.55 351.45 !57.90 5,216.77 0.42 0.72 NS 0.96 Yes

U S LR 4 7 !59.77 !15.50 2.24 !19.09 !10.02 6.90 !4.23 SIG 0.84 Yes

U S R 3 1 !46.90 !23.45 35.12 !48.01 304.93 0.67 !0.48 n/a n/a

U S R 4 13 9.10 5.38 19.84 !23.82 58.74 0.27 0.28 NS 0.00 No

U S XL 3 1 299.43 299.43 1,018.34 !97.30 59,004.76 0.29 0.54 n/a n/a

U S XL 4 2 5.15 3.44 118.54 !64.12 1861.12 0.03 0.03 NS 0.78 Yes

U U L 3 10 12.61 12.61 100.01 !80.25 542.02 0.13 0.13 NS 1.00 Yes

U U L 4 8 !78.97 !39.49 4.87 !45.76 !23.93 8.11 !3.37 SIG 0.57 Yes

U U LR 4 1 162.46 81.23 215.63 !44.76 3236.40 0.38 0.59 n/a n/a

U U R 4 1 !7.93 !7.93 260.41 !99.64 23,435.42 0.03 !0.03 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-6

Table C-4. Yoked Comparison Evaluation for Project-Related Fatal and Injury Intersection Accidents.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 !28.78 !14.39 57.63 !48.51 799.48 0.25 !0.21 NS 0.83 Yes

R N LR 4 1 !47.97 !23.99 68.83 !49.85 4,598.55 0.35 !0.25 n/a n/a

R N R 4 1 !0.01 0.00 141.41 !49.80 12,730.59 0.00 0.00 n/a n/a

R S XL 3 2 !7.57 !7.57 184.85 !98.17 4,558.33 0.04 !0.04 NS 0.98 Yes

R S XL 4 4 177.73 177.73 311.15 !69.10 2,396.19 0.57 0.91 NS 0.86 Yes

R S XLR 4 1 45.65 11.41 80.05 !24.51 2,682.84 0.14 0.17 n/a n/a

R U L 3 34 1.31 1.31 49.14 !60.85 162.14 0.03 0.03 NS 1.00 Yes

R U L 4 23 !36.33 !20.38 18.87 !43.42 44.50 1.08 !0.85 NS 1.00 Yes

R U LR 3 12 !17.33 !13.87 53.37 !66.40 241.59 0.26 !0.24 NS 1.00 Yes

R U LR 4 15 !4.91 !2.46 30.40 !36.42 116.47 0.08 !0.08 NS 1.00 Yes

R U R 3 11 4.90 4.90 89.46 !80.28 458.06 0.05 0.06 NS 1.00 Yes

R U R 4 28 !4.40 !2.87 31.87 !42.30 104.69 0.09 !0.09 NS 1.00 Yes

R U XL 4 2 143.69 143.69 451.22 !93.53 9,082.86 0.32 0.48 NS 0.73 Yes

U N L 3 4 !3.68 !2.46 90.81 !62.65 959.95 0.03 !0.03 NS 1.00 Yes

U N L 4 26 !8.78 !4.57 22.33 !33.14 67.33 0.20 !0.20 NS 1.00 Yes

U S L 3 3 1.06 1.06 165.02 !95.88 2,380.82 0.01 0.01 NS 1.00 Yes

U S L 4 35 !32.67 !10.49 6.31 !19.91 6.21 1.66 !1.35 NS 1.00 Yes

U S LR 3 2 !25.28 !16.86 96.24 !65.54 2,131.34 0.18 !0.15 NS 0.89 Yes

U S LR 4 8 !49.63 !12.81 4.96 !19.65 1.64 2.58 !1.80 NS 0.65 Yes

U S R 3 1 !73.45 !36.72 33.85 !49.91 1,914.45 1.09 !0.52 n/a n/a

U S R 4 14 40.32 20.16 30.02 !19.67 112.29 0.67 0.79 NS 0.59 Yes

U S XL 3 1 99.71 99.71 528.39 !98.88 35,587.40 0.19 0.26 n/a n/a

U S XL 4 3 34.97 20.98 109.53 !54.29 1,087.46 0.19 0.22 NS 0.79 Yes

U U L 3 10 3.97 3.97 92.99 !81.99 500.14 0.04 0.04 NS 1.00 Yes

U U L 4 8 !81.50 !40.75 5.62 !47.19 -19.58 7.25 !2.78 SIG 0.95 Yes

U U LR 4 1 !47.51 !23.75 69.44 !49.85 4,639.94 0.34 !0.24 n/a n/a

U U R 4 1 !7.93 !7.93 260.41 !99.64 23,435.42 0.03 !0.03 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-7

Table C-5. Yoked Comparison Evaluation Results for Total Accidents on Individual Intersection Approaches.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 4 !67.45 !67.45 9.33 !44.71 0.07 3.61 !1.96 SIG 0.65 Yes

R N LR 4 2 !11.77 !5.88 47.18 !44.58 308.79 0.12 !0.12 NS 0.78 Yes

R N R 4 2 !7.11 !7.11 23.17 !32.53 73.49 0.15 !0.15 NS 0.49 Yes

R S XL 3 2 61.29 61.29 189.96 !83.96 1,522.35 0.32 0.41 NS 0.62 Yes

R S XL 4 4 !50.86 !50.86 26.61 !83.00 42.03 1.91 !1.31 NS 0.69 Yes

R S XLR 4 2 !39.42 !19.71 7.49 !14.31 26.55 0.88 !0.68 NS 0.41 Yes

R U L 3 34 !47.46 !47.46 16.56 !71.67 !2.55 2.87 !2.04 SIG 0.61 Yes

R U L 4 40 !47.32 !47.32 6.35 !35.29 !9.58 3.97 !2.84 SIG 0.70 Yes

R U LR 3 23 !51.34 !25.67 18.04 !63.87 17.14 2.26 !1.54 NS 0.99 Yes

R U LR 4 27 5.26 2.63 14.21 !19.00 39.36 0.19 0.19 NS 0.99 Yes

R U R 3 11 79.25 79.25 107.95 !44.94 483.59 0.73 0.97 NS 1.00 Yes

R U R 4 43 !18.72 !18.72 11.48 !28.98 17.69 0.96 !0.86 NS 1.00 Yes

R U XL 4 1 0.00 0.00 193.65 !97.75 4,350.15 0.00 0.00 n/a n/a

U N L 3 6 !25.86 !25.86 19.80 !41.41 46.43 0.78 !0.67 NS 0.24 Yes

U N L 4 47 !55.50 !55.50 3.36 !34.08 !20.70 8.43 !5.47 SIG 0.32 Yes

U S L 3 3 70.74 70.74 121.99 !57.92 592.68 0.58 0.75 NS 0.98 Yes

U S L 4 106 !42.02 !42.02 1.19 !13.98 !9.32 9.97 !7.50 SIG 0.00 No

U S LR 3 3 !8.02 !4.01 31.23 !41.79 107.29 0.15 !0.15 NS 0.46 Yes

U S LR 4 32 !30.94 !15.47 1.56 !8.74 !2.55 3.84 !3.17 SIG 0.65 Yes

U S R 3 2 !62.59 !62.59 15.45 !46.30 44.46 2.02 !1.19 NS 0.04 No

U S R 4 28 !25.74 !25.74 3.86 !16.67 !1.36 2.59 !2.23 SIG 0.04 No

U S XL 3 1 33.33 33.33 238.82 !96.02 4,362.97 0.14 0.16 n/a n/a

U S XL 4 4 !4.97 !4.97 15.07 !25.62 36.40 0.19 !0.18 NS 0.08 Yes

U U L 3 10 !55.44 !55.44 17.53 !79.39 !3.65 3.16 !2.05 SIG 0.61 Yes

U U L 4 16 !69.37 !69.37 3.26 !39.91 !26.76 10.64 !5.56 SIG 0.59 Yes

U U LR 4 2 53.64 26.82 80.23 !40.08 545.03 0.33 0.41 NS 0.18 Yes

U U R 4 1 !69.49 !69.49 71.55 !99.69 2,924.77 0.97 !0.51 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-8

Table C-6. Yoked Comparison Evaluation Results for Fatal and Injury Accidents on Individual Intersection Approaches.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 4 !78.75 !78.75 8.67 !47.85 2.62 4.54 !1.90 NS 0.65 Yes

R N LR 4 2 !24.25 !12.12 44.62 !46.24 331.17 0.27 !0.24 NS 0.50 Yes

R N R 4 2 32.74 32.74 48.91 !34.35 231.36 0.33 0.38 NS 0.80 Yes

R S XL 3 2 64.93 64.93 203.08 !85.24 1,742.63 0.32 0.41 NS 0.86 Yes

R S XL 4 4 21.37 21.37 78.62 !65.90 332.02 0.27 0.30 NS 1.00 Yes

R S XLR 4 2 21.91 10.96 24.06 !14.67 190.29 0.15 0.17 NS 0.51 Yes

R U L 3 34 !40.80 !40.80 22.53 !71.92 24.82 1.81 !1.38 NS 0.99 Yes

R U L 4 41 !54.97 !54.97 6.89 !39.60 !11.12 4.25 !2.77 SIG 1.00 Yes

R U LR 3 24 !28.23 !14.11 28.77 !58.50 73.32 0.78 !0.66 NS 1.00 Yes

R U LR 4 29 !6.43 !3.22 15.69 !25.76 40.29 0.20 !0.20 NS 1.00 Yes

R U R 3 11 26.71 26.71 86.25 !66.62 381.08 0.31 0.35 NS 0.99 Yes

R U R 4 43 !10.90 !10.90 15.76 !29.77 35.65 0.41 !0.38 NS 1.00 Yes

R U XL 4 2 121.96 121.96 319.21 !86.75 3,619.01 0.38 0.55 NS 0.30 Yes

U N L 3 6 !38.23 !38.23 24.38 !49.79 74.56 0.94 !0.73 NS 0.80 Yes

U N L 4 49 !58.14 !58.14 4.99 !37.53 !17.26 5.94 !3.73 SIG 1.00 Yes

U S L 3 3 210.12 210.12 284.05 !48.49 1,767.16 0.74 1.24 NS 0.94 Yes

U S L 4 114 !39.99 !39.99 1.93 !14.58 !6.97 5.81 !4.46 SIG 0.81 Yes

U S LR 3 4 55.16 27.58 74.90 !41.65 360.97 0.49 0.61 NS 0.88 Yes

U S LR 4 34 !34.35 !17.17 2.62 !10.95 !0.39 2.56 !2.06 SIG 0.92 Yes

U S R 3 2 !86.69 !86.69 9.25 !49.56 51.49 4.69 !1.45 NS 0.78 Yes

U S R 4 30 !29.85 !29.85 6.36 !21.30 4.57 1.78 !1.48 NS 0.98 Yes

U S XL 3 1 !28.57 !28.57 148.85 !98.80 4,143.93 0.19 !0.16 n/a n/a

U S XL 4 4 !53.37 !53.37 14.86 !48.21 22.37 2.05 !1.37 NS 0.30 Yes

U U L 3 10 !52.96 !52.96 26.42 !84.35 41.44 2.00 !1.34 NS 0.84 Yes

U U L 4 16 !77.79 !77.79 3.70 !44.22 !28.66 10.51 !4.52 SIG 0.90 Yes

U U LR 4 2 !62.87 !31.43 29.45 !49.17 365.89 1.07 !0.62 NS 0.58 Yes

U U R 4 1 !38.98 !38.98 149.46 !99.50 7,321.45 0.26 !0.20 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-9

Table C-7. Yoked Comparison Evaluation Results for Project-Related Accidents on Individual Intersection Approaches.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 4 !29.35 !29.35 40.58 !46.28 285.61 0.36 !0.30 NS 0.85 Yes

R N LR 4 2 !28.14 !14.07 69.42 !49.19 1,535.50 0.20 !0.17 NS 0.87 Yes

R N R 4 2 0.01 0.01 100.01 !49.01 2,470.19 0.00 0.00 NS 1.00 Yes

R S XL 3 2 !12.07 !12.07 175.87 !98.26 4,331.89 0.07 !0.06 NS 0.96 Yes

R S XL 4 4 15.21 15.21 111.82 !82.81 672.11 0.14 0.15 NS 0.98 Yes

R S XLR 4 2 14.57 7.29 32.12 !15.96 499.57 0.08 0.08 NS 0.67 Yes

R U L 3 34 2.03 2.03 49.49 !60.57 164.00 0.04 0.04 NS 1.00 Yes

R U L 4 41 !25.66 !25.66 15.50 !34.87 32.03 0.88 !0.76 NS 1.00 Yes

R U LR 3 24 !10.95 !5.48 40.46 !56.60 136.84 0.22 !0.20 NS 1.00 Yes

R U LR 4 30 19.17 9.58 26.34 !24.94 91.70 0.36 0.40 NS 1.00 Yes

R U R 3 11 5.84 5.84 90.26 !80.11 463.06 0.06 0.07 NS 1.00 Yes

R U R 4 43 !9.68 !9.68 21.27 !34.60 57.55 0.27 !0.25 NS 1.00 Yes

R U XL 4 2 30.05 30.05 209.71 !94.48 2,966.70 0.14 0.16 NS 0.57 Yes

U N L 3 6 !21.71 !21.71 51.73 !54.57 346.63 0.25 !0.22 NS 1.00 Yes

U N L 4 50 !8.21 !8.21 15.32 !26.36 37.90 0.27 !0.26 NS 1.00 Yes

U S L 3 3 !0.44 !0.44 162.58 !95.94 2,344.12 0.00 !0.00 NS 0.99 Yes

U S L 4 115 !33.88 !33.88 2.87 !14.34 !2.90 3.30 !2.66 SIG 1.00 Yes

U S LR 3 5 95.11 47.55 140.81 !49.82 1,109.84 0.42 0.58 NS 0.99 Yes

U S LR 4 32 !59.67 !29.83 1.77 !14.68 !7.51 6.69 !4.10 SIG 0.79 Yes

U S R 3 2 !41.79 !41.79 47.53 !48.81 664.46 0.44 !0.33 NS 0.88 Yes

U S R 4 34 66.04 66.04 15.90 1.30 66.00 1.64 2.09 SIG 0.01 No

U S XL 3 1 0.00 0.00 282.84 !99.61 25,462.64 0.00 0.00 n/a n/a

U S XL 4 5 108.84 108.84 113.24 !38.42 730.24 0.53 0.75 NS 0.71 Yes

U U L 3 10 14.06 14.06 101.30 !79.99 550.30 0.14 0.15 NS 1.00 Yes

U U L 4 16 !78.11 !78.11 5.15 !45.65 !22.50 7.59 !3.23 SIG 0.97 Yes

U U LR 4 2 141.99 70.99 169.28 !42.21 1,828.13 0.42 0.63 NS 0.65 Yes

U U R 4 1 !38.98 !38.98 172.58 !99.76 15,497.21 0.23 !0.17 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-10

 Table C-8. Yoked Comparison Evaluation Results for Project-Related Fatal and Injury Accidents on Individual 

 Intersection Approaches.

Area

 type

Traffic

control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 4 !15.07 !15.07 52.96 !46.32 439.40 0.14 !0.13 NS 1.00 Yes

R N LR 4 2 !28.14 !14.07 69.42 !49.19 1,535.50 0.20 !0.17 NS 0.87 Yes

R N R 4 2 0.01 0.01 100.01 !49.01 2,470.19 0.00 0.00 NS 1.00 Yes

R S XL 3 2 !12.07 !12.07 175.87 !98.26 4,331.89 0.07 !0.06 NS 0.96 Yes

R S XL 4 4 163.58 163.58 295.30 !70.67 2,268.98 0.55 0.87 NS 0.86 Yes

R S XLR 4 2 29.41 14.70 37.44 !15.95 631.00 0.13 0.15 NS 0.90 Yes

R U L 3 34 2.03 2.03 49.49 !60.57 164.00 0.04 0.04 NS 1.00 Yes

R U L 4 41 !23.73 !23.73 16.79 !35.19 38.08 0.75 !0.66 NS 1.00 Yes

R U LR 3 24 !11.66 !5.83 40.56 !57.05 137.67 0.23 !0.22 NS 1.00 Yes

R U LR 4 30 !2.49 !1.25 23.43 !30.99 75.04 0.05 !0.05 NS 1.00 Yes

R U R 3 11 5.84 5.84 90.26 !80.11 463.06 0.06 0.07 NS 1.00 Yes

R U R 4 43 !5.70 !5.70 23.30 !34.49 67.48 0.14 !0.14 NS 1.00 Yes

R U XL 4 2 110.86 110.86 390.43 !94.40 7,845.84 0.28 0.40 NS 0.73 Yes

U N L 3 6 2.78 2.78 71.21 -53.59 532.89 0.02 0.02 NS 1.00 Yes

U N L 4 50 !16.09 !16.09 15.63 !30.09 36.55 0.53 !0.48 NS 1.00 Yes

U S L 3 3 !0.44 !0.44 162.58 !95.94 2,344.12 0.00 0.00 NS 0.99 Yes

U S L 4 116 !2.23 !2.23 5.62 !9.66 13.00 0.11 !0.11 NS 1.00 Yes

U S LR 3 5 !3.01 !1.51 75.60 !57.24 636.13 0.02 !0.02 NS 1.00 Yes

U S LR 4 33 !50.96 !25.48 3.37 !14.93 !0.69 3.01 !2.06 SIG 1.00 Yes

U S R 3 2 !10.24 !10.24 89.76 !49.11 2,211.92 0.06 !0.05 NS 0.97 Yes

U S R 4 35 62.30 62.30 21.67 !6.69 84.83 1.12 1.41 NS 0.96 Yes

U S XL 3 1 0.00 0.00 282.84 !99.61 25,462.64 0.00 0.00 n/a n/a

U S XL 4 5 29.49 29.49 80.25 !47.48 584.96 0.20 0.23 NS 0.99 Yes

U U L 3 10 5.30 5.30 94.18 !81.76 507.82 0.06 0.06 NS 1.00 Yes

U U L 4 16 !68.60 !68.60 8.60 !44.63 !4.08 3.99 !2.12 SIG 1.00 Yes

U U LR 4 2 !27.38 !13.69 70.16 !49.18 1,552.27 0.20 !0.17 NS 0.86 Yes

U U R 4 1 !38.98 !38.98 172.58 !99.76 15,497.21 0.23 !0.17 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-11

Table C-9. Yoked Comparison Evaluation for Project-Related Intersection Accidents—Auto Matched.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 109.08 54.54 104.11 !35.16 686.24 0.52 0.74 NS 0.99 Yes

R N LR 4 1 283.33 141.67 393.27 !46.56 10,643.71 0.36 0.65 n/a n/a

R N R 4 1 !38.64 !19.32 61.82 !49.41 1,542.16 0.31 !0.24 n/a n/a

R S XL 3 2 350.52 350.52 705.43 !79.07 9,595.30 0.50 0.96 NS 0.94 Yes

R S XL 4 4 7.61 7.61 82.50 !76.06 383.60 0.09 0.10 NS 0.91 Yes

R S XLR 4 1 !9.38 !2.34 36.95 !24.07 528.79 0.06 !0.06 n/a n/a

R U L 3 34 123.68 123.68 83.83 7.30 366.29 1.48 2.15 SIG 1.00 Yes

R U L 4 22 3.70 2.09 20.20 !26.68 58.70 0.10 0.11 NS 0.90 Yes

R U LR 3 12 120.86 96.69 118.15 !32.36 575.28 0.82 1.18 NS 0.98 Yes

R U LR 4 15 66.35 33.17 30.01 !9.00 118.72 1.11 1.41 NS 1.00 Yes

R U R 3 11 7.02 7.02 75.92 !73.36 329.86 0.09 0.10 NS 1.00 Yes

R U R 4 28 27.10 17.64 30.75 !25.16 106.30 0.57 0.65 NS 1.00 Yes

R U XL 4 2 89.07 89.07 190.91 !73.87 1,268.04 0.47 0.63 NS 0.22 Yes

U N L 3 4 33.86 22.57 89.80 !54.25 574.73 0.25 0.29 NS 0.81 Yes

U N L 4 24 92.22 48.12 23.77 10.85 107.42 2.02 2.76 SIG 0.41 Yes

U S L 3 3 72.16 72.16 220.51 !86.02 2,019.53 0.33 0.42 NS 0.64 Yes

U S L 4 37 !0.32 !0.10 3.88 !6.86 8.50 0.03 !0.03 NS 0.05 Yes

U S LR 3 2 21.81 14.54 107.34 !60.58 1,016.60 0.14 0.15 NS 0.64 Yes

U S LR 4 9 !44.51 !11.45 2.30 !15.31 !6.15 4.98 !3.66 SIG 0.28 Yes

U S R 3 1 237.50 118.75 181.91 !29.60 1,345.85 0.65 1.13 n/a n/a

U S R 4 12 2.36 1.23 8.42 !12.97 20.58 0.15 0.15 NS 0.02 No

U S XL 3 1 100.00 100.00 406.20 !96.27 10,611.75 0.25 0.34 n/a n/a

U S XL 4 3 !31.92 !19.15 21.37 !45.35 53.87 0.90 !0.73 NS 0.78 Yes

U U L 3 10 202.64 202.64 213.44 !24.04 1,105.75 0.95 1.57 NS 0.90 Yes

U U L 4 8 !38.17 !19.09 12.07 !35.62 16.45 1.58 !1.23 NS 0.56 Yes

U U LR 4 1 66.67 33.33 115.87 !44.54 1,221.71 0.29 0.37 n/a n/a

U U R 4 1 500.00 500.00 1,509.97 !95.68 83,140.41 0.33 0.71 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-12

 Table C-10. Yoked Comparison Evaluation Results for Project-Related Fatal and Injury Intersection Accidents—

 Auto Matched.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 326.58 163.29 262.40 !30.87 2,327.76 0.62 1.18 NS 0.39 Yes

R N LR 4 1 225.00 112.50 338.02 !47.24 9,532.79 0.33 0.57 n/a n/a

R N R 4 1 !11.76 !5.88 89.61 !49.18 2,313.44 0.07 !0.06 n/a n/a

R S XL 3 2 311.85 311.85 700.77 !85.33 11,464.18 0.45 0.83 NS 0.84 Yes

R S XL 4 4 42.50 42.50 132.34 !76.91 779.64 0.32 0.38 NS 0.94 Yes

R S XLR 4 1 !35.00 !8.75 27.08 !24.38 400.92 0.32 !0.26 n/a n/a

R U L 3 34 117.81 117.81 87.87 !1.22 380.28 1.34 1.93 NS 1.00 Yes

R U L 4 23 72.85 40.87 38.93 !11.95 156.90 1.05 1.36 NS 0.98 Yes

R U LR 3 12 86.17 68.94 103.92 !42.06 504.69 0.66 0.89 NS 1.00 Yes

R U LR 4 15 58.74 29.37 39.34 !19.96 159.68 0.75 0.93 NS 1.00 Yes

R U R 3 11 !0.17 !0.17 75.13 !77.17 336.41 0.00 0.00 NS 1.00 Yes

R U R 4 28 34.30 22.34 38.64 !28.33 142.81 0.58 0.67 NS 1.00 Yes

R U XL 4 2 137.27 137.27 332.77 !84.81 3,607.34 0.41 0.62 NS 0.89 Yes

U N L 3 4 !15.45 !10.30 62.03 !60.14 420.50 0.17 !0.15 NS 0.86 Yes

U N L 4 25 107.23 55.85 34.87 5.22 151.21 1.60 2.26 SIG 0.95 Yes

U S L 3 3 2.14 2.14 143.17 !93.45 1,493.64 0.01 0.02 NS 0.64 Yes

U S L 4 36 15.98 5.09 7.60 !7.17 23.44 0.67 0.72 NS 0.67 Yes

U S LR 3 2 19.14 12.76 106.15 !60.88 1,023.81 0.12 0.13 NS 0.55 Yes

U S LR 4 9 !44.71 !11.50 4.42 !17.98 0.42 2.60 !1.91 NS 0.40 Yes

U S R 3 1 450.00 225.00 589.23 !45.87 18,280.96 0.38 0.80 n/a n/a

U S R 4 14 8.34 4.17 12.30 !15.28 34.52 0.34 0.35 NS 0.81 Yes

U S XL 3 1 100.00 100.00 412.31 !96.48 11,272.58 0.24 0.34 n/a n/a

U S XL 4 3 60.80 36.48 77.90 !40.18 409.57 0.47 0.59 NS 0.45 Yes

U U L 3 10 149.46 149.46 185.68 !42.00 972.97 0.80 1.23 NS 0.99 Yes

U U L 4 8 !15.40 !7.70 21.44 !34.34 64.22 0.36 !0.33 NS 0.92 Yes

U U LR 4 1 300.00 150.00 458.26 !47.76 17,790.77 0.33 0.61 n/a n/a

U U R 4 1 100.00 100.00 529.15 !98.88 35,638.72 0.19 0.26 n/a n/a

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-13

All of the unsignalized intersections evaluated in the study had two-way stop control; i.e.,

there were stop signs on the minor-road approach(es) and no control on the major road

approaches. A newly signalized intersection is one that was unsignalized before the

improvement and signalized after the improvement; i.e., the intersection was signalized in

conjunction with the turn lane improvement.

Project type: The third column identifies the type of improvement project that was

evaluated. the codes used in this column are:

L = Added left-turn lane(s)

LR = Added left- and right-turn lanes

R = Added right-turn lane(s)

XL = Extended the length of existing left-turn lane(s)

XLR = Extended the length of existing left- and right-turn lanes

Number of intersection legs: The fourth column identifies the number of intersection

legs for the intersections evaluated. Only three- and four-leg intersections were considered

in the evaluation.

Number of improved sites: The fifth column presents the number of improved or

treatment sites included in the evaluation in question. In tables C-1 through C-4, C-9, and

C-10, this represents the number of intersections evaluated. In tables C-5 through C-8, this

represents the number of intersection approaches evaluated. The number of improved sites

includes only those that were actually evaluated. Some sites were excluded because they

were found to be outliers (see discussion in section 6 of this report). In the CG and EB

analyses, some sites were excluded because no satisfactory regression model was available. 

For any given type of intersection and project type, the total number of sites available

before such exclusions can be determined from tables in section 4 of this report.

Percent change in accident frequency: The sixth through tenth columns present the

mean percent change in accident frequency determined in the YC analysis, the mean

percent change in accident frequency per turn lane installed, the standard error, and upper

and lower confidence limits of the mean change in accident frequency per turn lane

installed. A negative value of percent change in accident frequency represents a reduction

in accidents, while a positive value represents an increase in accidents. The values in these

five columns correspond to Emean for projects as a whole and Emean, Emean(se), Emean(upper), and

Emean(lower) per turn lane added, as determined in Equations (12), (19), (20), and (21).

The value in the seventh column is the value in the sixth column divided by the mean

number of turn lanes added in the projects evaluated. For example, the analysis in

table C-1 addresses total intersection accidents. However, in some projects, turn lanes

were added on one approach; in some projects, turn lanes were added on two approaches;

and in some projects at signalized intersections, left-turn lanes were added on all four

approaches. The value in this seventh column of the table expresses the effectiveness of
C-14

the project on a per-added-lane basis to facilitate comparisons across project types. It

represents the best overall measure of treatment effectiveness for the projects evaluated.

In results tables for analyses conducted for individual intersection approaches, the

project effectiveness estimate, Emean, and the effectiveness estimate on a per-added-lane

basis are usually the same because, in most cases, only one turn lane was added to each

approach. However, for projects involving the addition of both left- and right-turn lanes,

the two effectiveness measures may differ.

The standard error and confidence limits presented in the eighth through tenth columns

of the table are measures of the precision of the mean accident frequency per turn lane

added presented in the seventh column. The confidence interval for the analysis results,

shown by the limits in the ninth and tenth columns of the tables, is not symmetrical above

and below the mean because, as shown in section 5 of this report, the evaluation was

performed on a logarithmic scale. The standard error shown in the eighth column of the

tables is the best single estimate of the precision of the estimated mean project

effectiveness per turn lane installed.

Treatment effect: The eleventh through thirteenth columns of the tables provide an

assessment of the statistical significance of the treatment effect determined above. The

ratio in the eleventh column is the mean percent change in accident frequency per turn lane

installed divided by its standard error. While this ratio was not used directly in the YC and

CG approaches, a similar ratio was used to determine statistical significance for the EB

approach.

The twelfth and thirteen columns present the calculated Z-score for the treatment

effect and the significance of that Z-score at the 5 percent significance level (i.e., the 95-

percent confidence level). The codes for significance of the results are:

SIG = Treatment effect is statistically significant

NS = Treatment effect is not statistically significant

n/a = Significance of treatment effect cannot be determined (typically

because only one site is available for evaluation)

Only those effectiveness measures found to be statistically significant should be relied

upon.

Test for homogeneity: The fourteenth and fifteenth columns present the test for

homogeneity discussed for the YC approach in section 5 of this report. The fourteenth 

column gives the value of PH determined as shown in table 23. The fifteenth column

shows whether the treatment group is homogenous at the 5 percent significance level (i.e,

95-percent confidence level). The homogeneity result was noted, but no results were

excluded based on this criterion.
C-15

Specific Evaluation Results

Table C-1 presents the results of the evaluation of treatment effectiveness for accidents

of all severity levels and for a target area including the entire intersection (i.e., including all

at-intersection and intersection-related accidents). Analyses of this type are referred to in

this report as addressing total intersection accidents. Table C-2 presents comparable data

to table C-1 for a dependent variable that includes only fatal and nonfatal injury accidents

(i.e., property-damage-only accidents were excluded). Analyses of this type are referred to

in this report as addressing fatal and injury intersection accidents.

Table C-3 is analogous to table C-1 in that it addresses all accident severity levels and

includes the entire intersection as the target area, but it includes only project-related

accidents. Project-related accidents have been defined in section 4 of this report as those

accidents that involve a turning maneuver that was related to the added or extended turn

lane(s) being evaluated. Analyses of this type are referred to in this report as addressing

total project-related intersection accidents. Table C-4 is similar to table C-3, but includes

only fatal and nonfatal injury accidents. Analyses of this type are referred to in this report

as addressing fatal and injury project-related intersection accidents.

Tables C-5 through C-8 are analogous to tables C-1 through C-4 except that they

present results for evaluations in which each observation represents a treated intersection

approach rather than a treated intersection as a whole. Only those approaches at the

treated intersections on which a turn lane was added or extended were included in the

analyses. The comparison site for each treated approach was comparable to the

unimproved comparison site matched to that particular treatment site. Table C-5 presents

the results for total accidents for individual intersection approaches. Table C-6 presents

the results for fatal and injury accidents for individual intersection approaches. The

results for project-related accidents for individual intersection approaches are presented

in table C-7 and the results for project-related fatal and injury accidents for individual

intersection approaches are presented in table C-8.

The results of the auto-matching approach referred to above are presented in table C9 and C-10.

Comparison Group Evaluations

The results of the CG evaluation performed in this study are presented in tables C-11

through C-16. These tables include results for three of the four specific dependent

variables that were included in the YC evaluation and for the same two different target

areas included in the YC evaluation. No CG analyses were performed for project-related

fatal and injury accidents because the available sample size of such accidents was too

small to develop satisfactory regression relationships for use in the traffic volume

adjustments.
C-16

Table C-11. Comparison Group Evaluation Results for Total Intersection Accidents.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 !12.66 !6.33 9.22 !21.13 16.07 0.69 !0.64 NS 0.55 Yes

R N LR 4 1 !64.48 !32.24 9.19 !43.56 !1.05 3.51 !2.00 n/a n/a

R N R 4 1 18.80 9.40 14.97 !13.76 47.35 0.63 0.68 n/a n/a

R U L 3 35 !53.53 !53.53 5.63 !63.34 !41.08 9.52 !6.33 SIG 0.35 Yes

R U L 4 25 !60.61 !33.67 2.54 !38.14 !28.07 13.23 !8.01 SIG 0.00 No

R U LR 3 12 !37.67 !30.13 12.57 !49.57 1.72 2.40 !1.88 NS 0.20 Yes

R U LR 4 15 !25.20 !12.60 5.28 !21.64 !0.68 2.39 !2.06 SIG 0.31 Yes

R U R 3 12 4.98 4.98 30.96 !41.11 87.14 0.16 0.16 NS 1.00 Yes

R U R 4 29 !35.07 !22.60 5.16 !31.58 !11.17 4.38 !3.51 SIG 0.86 Yes

R U XL 4 2 !20.58 !20.58 27.89 !60.09 58.07 0.74 !0.66 NS 0.19 Yes

U N L 3 4 29.35 19.57 13.88 !3.77 51.55 1.41 1.60 NS 0.01 No

U N L 4 28 !46.43 !24.08 2.01 !27.75 !19.85 11.98 !8.63 SIG 0.00 No

U S L 3 3 21.24 21.24 50.03 !46.00 172.22 0.42 0.47 NS 0.04 No

U S L 4 37 !18.28 !5.78 1.05 !7.76 !3.64 5.50 !4.96 SIG 0.00 No

U S LR 3 2 5.44 3.63 15.27 !20.75 40.93 0.24 0.24 NS 0.45 Yes

U S LR 4 10 !26.59 !6.82 1.30 !9.20 !4.08 5.24 !4.47 SIG 0.11 Yes

U S R 3 1 !26.37 !13.19 15.84 !34.16 35.54 0.83 !0.71 n/a n/a

U S R 4 17 !8.82 !4.05 2.46 !8.61 1.07 1.64 !1.57 NS 0.00 No

U S XL 3 1 84.98 84.98 82.12 !22.51 341.59 1.03 1.39 n/a n/a

U S XL 4 3 42.34 25.40 11.20 6.04 50.44 2.27 2.69 SIG 0.60 Yes

U U L 3 10 !34.95 !34.95 13.11 !56.18 !3.43 2.67 !2.13 SIG 0.12 Yes

U U L 4 9 !53.42 !26.71 2.95 !31.83 !20.15 9.06 !6.04 SIG 0.20 Yes

U U LR 4 1 !59.39 !29.69 12.06 !43.66 15.03 2.46 !1.52 n/a n/a

U U R 3 1 129.38 129.38 403.35 !92.69 7,100.71 0.32 0.47 n/a n/a

U U R 4 3 !35.85 !21.51 23.30 !48.25 66.11 0.92 !0.73 NS 0.46 Yes

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-17

Table C-12. Comparison Group Evaluation Results for Fatal and Injury Intersection Accidents.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 !57.47 !28.74 6.30 !38.10 !12.01 4.56 !2.89 SIG 0.48 Yes

R N LR 4 1 !68.83 !34.42 9.97 !45.55 4.61 3.45 !1.82 n/a n/a

R N R 4 1 !40.11 !20.05 10.97 !35.39 11.39 1.83 !1.40 n/a n/a

R U L 3 35 !54.80 !54.80 8.30 !68.46 !35.23 6.60 !4.33 SIG 0.81 Yes

R U L 4 25 !73.90 !41.06 2.40 !45.08 !35.49 17.07 !8.10 SIG 0.41 Yes

R U LR 3 12 !32.38 !25.90 17.21 !51.00 20.92 1.51 !1.23 NS 0.80 Yes

R U LR 4 15 !44.67 !22.34 6.07 !32.01 !7.46 3.68 !2.70 SIG 0.77 Yes

R U R 3 12 5.33 5.33 40.07 !50.03 122.02 0.13 0.14 NS 0.99 Yes

R U R 4 29 !37.21 !23.98 6.59 !35.05 !8.76 3.64 !2.86 SIG 0.97 Yes

R U XL 4 2 24.09 24.09 64.90 !55.49 245.89 0.37 0.41 NS 0.05 Yes

U N L 3 4 32.94 21.96 24.56 !15.18 85.91 0.89 1.03 NS 0.07 Yes

U N L 4 28 !48.65 !25.23 3.12 !30.69 !18.35 8.08 !5.69 SIG 0.00 No

U S L 3 3 2.67 2.67 71.14 !73.60 299.23 0.04 0.04 NS 0.31 Yes

U S L 4 39 !17.95 !5.79 1.73 !8.97 !2.17 3.35 !3.03 SIG 0.00 No

U S LR 3 2 !0.94 !0.62 21.64 !31.92 58.84 0.03 !0.03 NS 0.83 Yes

U S LR 4 10 !45.93 !11.78 1.80 !14.89 !7.76 6.54 !4.73 SIG 0.54 Yes

U S R 3 1 !61.42 !30.71 17.17 !46.63 60.41 1.79 !1.07 n/a n/a

U S R 4 17 !8.61 !3.85 3.89 !10.81 4.54 0.99 !0.95 NS 0.01 No

U S XL 3 1 80.37 80.37 120.37 !51.23 567.14 0.67 0.88 n/a n/a

U S XL 4 3 8.75 5.25 15.70 !19.29 44.56 0.33 0.35 NS 0.36 Yes

U U L 3 10 !31.93 !31.93 20.81 !62.62 23.95 1.53 !1.26 NS 0.65 Yes

U U L 4 9 !58.81 !29.40 4.04 !35.98 !19.74 7.28 !4.52 SIG 0.39 Yes

U U LR 4 1 !73.31 !36.66 15.56 !48.64 81.13 2.36 !1.13 n/a n/a

U U R 3 1 135.84 135.84 425.90 !93.15 8,024.99 0.32 0.48 n/a n/a

U U R 4 3 !1.49 !0.89 47.52 !47.77 225.77 0.02 !0.02 NS 0.82 Yes

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-18

Table C-13. Comparison Group Evaluation Results for Project-Related Intersection Accidents.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 2 119.39 59.70 108.53 !34.22 712.68 0.55 0.79 NS 0.95 Yes

R N LR 4 1 105.35 52.67 206.39 !48.00 5,228.66 0.26 0.36 n/a n/a

R N R 4 1 11.81 5.91 112.35 !48.91 2,821.20 0.05 0.06 n/a n/a

R U L 3 35 !62.33 !62.33 14.53 !82.32 !19.76 4.29 !2.53 SIG 1.00 Yes

R U L 4 25 !38.23 !21.24 9.33 !35.41 2.90 2.28 !1.77 NS 0.99 Yes

R U LR 3 12 !50.64 !40.51 27.28 !69.80 72.91 1.49 !1.02 NS 1.00 Yes

R U LR 4 14 6.66 3.33 20.51 !24.90 63.32 0.16 0.17 NS 0.94 Yes

R U R 3 12 !46.82 !46.82 38.63 !87.19 120.84 1.21 !0.87 NS 1.00 Yes

R U R 4 29 0.88 0.57 20.49 !29.39 56.15 0.03 0.03 NS 1.00 Yes

R U XL 4 2 !30.26 !30.26 66.53 !89.25 352.44 0.45 !0.38 NS 0.39 Yes

R N L 3 4 119.85 79.90 149.53 !46.82 1,015.85 0.53 0.77 NS 0.99 Yes

U N L 4 27 38.91 20.21 15.26 !4.27 57.26 1.32 1.55 NS 0.49 Yes

U S L 3 3 44.59 44.59 185.80 !88.35 1,694.62 0.24 0.29 NS 0.82 Yes

U S L 4 39 !7.22 !2.33 3.16 !7.92 4.56 0.74 !0.71 NS 0.20 Yes

U S LR 3 2 249.52 166.35 356.90 !55.09 4,623.02 0.47 0.82 NS 0.51 Yes

U S LR 4 9 !40.18 !10.33 2.19 !14.07 !5.39 4.73 !3.62 SIG 0.09 Yes

U S R 3 1 !8.81 !4.40 55.35 !45.78 442.35 0.08 !0.08 n/a n/a

U S R 4 17 5.88 2.78 8.38 !11.22 22.21 0.33 0.34 NS 0.00 No

U S XL 3 1 683.87 683.87 1,960.49 !94.17 105,378.46 0.35 0.82 n/a n/a

U S XL 4 3 25.14 15.08 38.48 !32.51 145.03 0.39 0.44 NS 0.88 Yes

U U L 3 10 59.28 59.28 113.90 !60.78 546.90 0.52 0.65 NS 1.00 Yes

U U L 4 9 !60.42 !30.21 7.58 !40.66 !8.07 3.99 !2.42 SIG 0.78 Yes

U U LR 4 1 !5.67 !2.83 61.43 !46.33 555.81 0.05 !0.04 n/a n/a

U U R 3 1 48.58 48.58 401.88 !99.26 29,705.68 0.12 0.15 n/a n/a

U U R 4 3 36.47 21.88 91.63 !50.87 674.17 0.24 0.28 NS 0.81 Yes

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-19

Table C-14. Comparison Group Evaluation Results for Total Accidents on Individual Intersection Approaches.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 4 !44.09 !44.09 7.26 !33.19 !3.51 3.04 !2.24 SIG 0.60 Yes

R N LR 4 2 !68.84 !34.42 8.75 !44.82 !3.15 3.93 !2.08 SIG 0.90 Yes

R N R 4 2 !29.49 !29.49 10.30 !30.11 12.49 1.43 !1.20 NS 0.17 Yes

R U L 3 70 !51.91 !51.91 7.31 !64.29 !35.23 7.10 !4.82 SIG 0.99 Yes

R U L 4 50 !61.03 !61.03 2.74 !38.66 !27.81 12.38 !7.45 SIG 0.17 Yes

R U LR 3 24 !29.08 !14.54 16.75 !48.20 21.21 1.39 !1.16 NS 0.64 Yes

R U LR 4 30 !27.90 !13.95 5.70 !23.55 !0.85 2.45 !2.07 SIG 0.99 Yes

R U R 3 24 5.74 5.74 37.73 !47.46 112.78 0.15 0.16 NS 1.00 Yes

R U R 4 58 !31.55 !31.55 5.53 !29.95 !8.04 3.67 !3.02 SIG 1.00 Yes

R U XL 4 4 6.54 6.54 42.74 !51.47 133.90 0.15 0.16 NS 0.65 Yes

U N L 3 8 30.27 30.27 17.86 !8.64 63.30 1.13 1.29 NS 0.22 Yes

U N L 4 56 !45.73 !45.73 2.35 !27.96 !18.71 10.09 !7.32 SIG 0.02 No

U S L 3 9 34.12 34.12 58.51 !42.97 215.40 0.58 0.67 NS 0.57 Yes

U S L 4 147 !27.96 !27.96 1.03 !10.89 !6.85 8.70 !7.35 SIG 0.00 No

U S LR 3 6 !10.13 !5.06 14.65 !29.57 30.09 0.46 !0.44 NS 0.62 Yes

U S LR 4 39 !34.45 !17.23 1.24 !11.10 !6.23 7.14 !5.74 SIG 0.90 Yes

U S R 3 3 !23.41 !23.41 19.93 !36.19 56.22 0.59 !0.51 NS 0.25 Yes

U S R 4 66 !6.98 !6.98 2.59 !8.09 2.10 1.27 !1.22 NS 0.01 No

U S XL 3 1 !57.24 !57.24 66.62 !97.98 806.21 0.86 !0.55 n/a n/a

U S XL 4 12 45.26 45.26 12.11 6.38 54.43 2.24 2.69 SIG 0.08 Yes

U U L 3 20 !49.26 !49.26 11.15 !67.02 !21.93 4.42 !3.09 SIG 0.83 Yes

U U L 4 18 !54.42 !54.42 3.11 !32.56 !20.21 8.74 !5.75 SIG 0.39 Yes

U U LR 4 2 !56.21 !28.11 15.45 !44.51 37.27 1.82 !1.17 NS 0.21 Yes

U U R 3 2 !2.67 !2.67 141.65 !94.39 1,587.02 0.02 !0.02 NS 1.00 Yes

U U R 4 6 !20.14 !20.14 27.03 !44.14 84.77 0.45 !0.40 NS 0.90 Yes

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-2

0

 Table C-15. Comparison Group Evaluation Results for Fatal and Injury Accidents on 

 Individual Intersection Approaches.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 4 !76.35 !76.35 4.15 !44.06 !26.46 9.19 !4.11 SIG 0.84 Yes

R N LR 4 2 !69.73 !34.86 9.97 !45.84 5.02 3.50 !1.81 NS 0.43 Yes

R N R 4 2 !65.60 !65.60 7.55 !42.73 !9.32 4.34 !2.43 SIG 0.09 Yes

R U L 3 70 !43.57 !43.57 10.90 !61.35 !17.61 4.00 !2.96 SIG 1.00 Yes

R U L 4 50 !70.83 !70.83 2.73 !43.91 !33.01 14.41 !7.31 SIG 0.99 Yes

R U LR 3 24 !15.48 !7.74 23.51 !45.79 53.66 0.53 !0.48 NS 0.91 Yes

R U LR 4 30 !35.96 !17.98 7.57 !29.86 0.90 2.37 !1.88 NS 0.99 Yes

R U R 3 24 !20.87 !20.87 30.78 !63.09 69.62 0.68 !0.60 NS 1.00 Yes

R U R 4 58 !36.95 !36.95 6.33 !34.50 !9.31 3.76 !2.96 SIG 1.00 Yes

R U XL 4 4 100.52 100.52 121.69 !38.96 558.79 0.83 1.15 NS 0.75 Yes

U N L 3 8 !11.37 !11.37 21.91 !38.10 55.55 0.35 !0.33 NS 0.50 Yes

U N L 4 55 !46.93 !46.93 3.67 !30.68 !16.12 6.63 !4.75 SIG 0.92 Yes

U S L 3 9 18.52 18.52 73.44 !64.81 299.22 0.25 0.27 NS 0.95 Yes

U S L 4 154 !22.58 !22.58 1.71 !10.45 !3.71 4.26 !3.74 SIG 0.94 Yes

U S LR 3 6 !5.03 !2.51 23.09 !35.68 62.72 0.15 !0.14 NS 0.94 Yes

U S LR 4 39 !49.70 !24.85 1.83 !15.88 !8.62 6.98 !4.85 SIG 0.97 Yes

U S R 3 3 !31.42 !31.42 30.06 !43.85 141.11 0.52 !0.43 NS 0.68 Yes

U S R 4 70 1.18 1.18 4.61 !7.68 10.52 0.12 0.12 NS 0.28 Yes

U S XL 3 2 5.75 5.75 124.45 !89.47 961.68 0.05 0.05 NS 0.99 Yes

U S XL 4 12 0.93 0.93 15.15 !22.91 38.89 0.04 0.04 NS 0.99 Yes

U U L 3 20 !37.99 !37.99 19.50 !66.53 14.86 1.95 !1.52 NS 0.96 Yes

U U L 4 18 !55.38 !55.38 4.78 !35.34 !16.06 5.80 !3.77 SIG 0.72 Yes

U U LR 4 2 !41.19 !20.60 31.78 !46.46 194.57 0.65 !0.49 NS 0.41 Yes

U U R 3 2 !19.16 !19.16 123.35 !95.94 1,508.78 0.16 !0.14 NS 1.00 Yes

U U R 4 6 46.78 46.78 60.66 !37.17 279.74 0.46 0.56 NS 0.99 Yes

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-21

 Table C-16. Comparison Group Evaluation Results for Project-Related Accidents on 

 Individual Intersection Approaches.

Area

 type

Traffic

 control

Project

 type

No. of

 legs

No. of

 improved

 sites

Percent change in accident frequency Treatment effect Test for homogeneity

Mean

Mean

 per added

 lane

Standard

 error

Lower 95%

 confidence

 limit

Upper 95%

 confidence

 limit Ratioa

Calculated

Z

Significant 

at 5%

 level?

Calculated H

 probability

Homogeneous 

 at 5% level?

R N L 4 4 6.61 6.61 45.97 !40.17 238.93 0.07 0.07 NS 0.96 Yes

R N LR 4 2 83.23 41.61 130.98 !44.44 1,459.75 0.32 0.42 NS 1.00 Yes

R N R 4 2 !3.71 !3.71 68.71 !47.06 739.57 0.03 !0.03 NS 1.00 Yes

R U L 3 70 !64.29 !64.29 10.51 !79.95 !36.41 6.11 !3.50 SIG 1.00 Yes

R U L 4 50 !23.25 !23.25 10.03 !28.67 12.07 1.29 !1.12 NS 1.00 Yes

R U LR 3 24 !50.45 !25.23 20.80 !65.83 30.86 1.94 !1.34 NS 1.00 Yes

R U LR 4 30 11.61 5.80 16.19 !18.40 48.55 0.36 0.38 NS 1.00 Yes

R U R 3 24 !47.37 !47.37 30.26 !82.94 62.40 1.57 !1.12 NS 1.00 Yes

R U R 4 58 20.39 20.39 19.36 !16.87 62.08 0.68 0.74 NS 1.00 Yes

R U XL 4 4 !25.90 !25.90 62.07 !85.65 282.72 0.42 !0.36 NS 0.84 Yes

U N L 3 8 84.44 84.44 94.58 !39.44 488.65 0.60 0.80 NS 1.00 Yes

U N L 4 56 44.56 44.56 14.47 !0.51 57.58 1.60 1.91 NS 1.00 Yes

U S L 3 9 !0.47 !0.47 80.28 !79.52 383.64 0.01 !0.01 NS 1.00 Yes

U S L 4 155 7.92 7.92 3.46 !3.60 10.04 0.74 0.77 NS 1.00 Yes

U S LR 3 6 132.10 66.05 147.45 !42.76 934.97 0.60 0.88 NS 0.99 Yes

U S LR 4 38 !39.11 !19.56 2.28 !13.92 !4.88 4.41 !3.41 SIG 0.99 Yes

U S R 3 3 !21.65 !21.65 44.34 !45.74 310.13 0.24 !0.22 NS 1.00 Yes

U S R 4 72 !73.15 73.15 12.06 14.32 62.26 2.89 3.76 SIG 0.44 Yes

U S XL 3 3 156.21 156.21 385.19 !86.55 4,778.84 0.41 0.63 NS 1.00 Yes

U S XL 4 12 36.34 36.34 38.97 !27.85 148.12 0.56 0.65 NS 0.99 Yes

U U L 3 20 54.97 54.97 82.88 !45.67 342.06 0.66 0.82 NS 1.00 Yes

U U L 4 18 !60.46 !60.46 6.92 !40.04 !10.76 4.37 !2.65 SIG 0.99 Yes

U U LR 4 2 31.64 15.82 72.94 !42.50 527.63 0.22 0.25 NS 0.77 Yes

U U R 3 2 46.13 46.13 335.89 !98.39 13,121.00 0.14 0.17 NS 1.00 Yes

U U R 4 6 91.07 91.07 98.90 !38.87 561.85 0.55 0.75 NS 1.00 Yes

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-22

The CG approach to before-after evaluation has been described conceptually in

section 5 of this report. The procedures presented in that section have been used to derive

the analysis results in tables C-11 through C-16. These analyses involved the matching of

individual treatment sites to an entire group of similar unimproved comparison sites.

Description of Results Tables

The results tables for the CG approach are identical in format to the tables for the YC

approach.

Specific Evaluation Results

The results of the CG evaluation are presented in the following tables:

C Table C-11—total intersection accidents.

C Table C-12—fatal and injury intersection accidents.

C Table C-13—project-related intersection accidents.

C Table C-14—total accidents for individual intersection approaches.

C Table C-15—fatal and injury accidents for individual intersection approaches.

C Table C-16—project-related accidents for individual intersection approaches.

Empirical Bayes Evaluations

The results of the EB evaluation are presented in tables C-17 through C-22. The tables

include results for the same three dependent variables and the same two target areas that

were included in the CG evaluation. No EB analyses were performed for project-related

fatal and injury accidents because the available sample size of such accidents was too small

to develop satisfactory regression relationships to represent expected accident frequencies

in the EB analysis.

The EB approach to before-after evaluation has been described conceptually in

section 5 of this report. The procedures presented in that section have been used to derive

the analysis results in tables C-17 through C-22. These analyses involved weighing of

observed and expected accident frequencies to obtain the best estimate of accident

frequency for the before study period, which is then compared to the observed accident

frequency for the after period.
C-2

3

Table C-17. Empirical Bayes Evaluation Results for Total Intersection Accidents.

Area

type

Traffic

control

Project

type

No. of 

legs

No. of

improved

sites

Percent change in accident frequency

Ratioa Mean Significant?

Mean per

added lane

Standard

error

R N L 4 2 6.59 3.30 9.79 0.3 NS

R N LR 4 1 !55.96 !27.98 9.56 2.9 n/a

R N R 4 1 46.76 23.38 16.22 1.4 n/a

R U L 3 36 !43.67 !43.67 5.47 8.0 SIG

R U L 4 25 !49.61 !27.56 2.63 10.5 SIG

R U LR 3 12 !29.42 !23.54 10.96 2.1 SIG

R U LR 4 15 2.06 1.03 5.35 0.2 NS

R U R 3 11 20.54 20.54 26.28 0.8 NS

R U R 4 28 !21.97 !13.98 5.17 2.7 SIG

R U XL 4 2 !22.00 !22.00 22.51 1.0 NS

U N L 3 3 46.57 27.94 13.78 2.0 SIG

U N L 4 25 !19.96 !10.40 2.77 3.8 SIG

U S L 4 39 !29.53 !9.52 0.83 11.4 SIG

U S LR 4 10 !27.80 !7.13 1.21 5.9 SIG

U S R 4 18 !9.01 !4.05 1.96 2.1 SIG

U S XL 4 3 49.45 29.67 10.62 2.8 SIG

U U L 3 8 !33.15 !33.15 12.11 2.7 SIG

U U L 4 9 !0.33 !0.17 4.97 0.0 NS

U U LR 4 1 !57.63 !28.82 11.01 2.6 n/a

U U R 3 1 7.05 7.05 111.08 0.1 n/a

U U R 4 3 !67.11 !40.26 10.08 4.0 SIG

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-2

4

Table C-18. Empirical Bayes Evaluation Results for Fatal and Injury Intersection Accidents.

Area

type

Traffic

control

Project

type

No. of 

legs

No. of

improved

sites

Percent change in accident frequency

Ratioa Mean Significant?

Mean per

added lane

Standard

error

R N L 4 2 !18.46 !9.23 10.11 0.9 NS

R N LR 4 1 !48.10 !24.05 13.71 1.8 n/a

R N R 4 1 4.07 2.03 16.28 0.1 n/a

R U L 4 24 !63.41 !35.39 3.01 11.8 SIG

R U LR 4 15 !22.50 !11.25 6.25 1.8 NS

R U R 4 28 !15.86 !10.33 7.93 1.3 NS

R U XL 4 2 1.19 1.19 39.29 0.0 NS

U N L 3 3 100.06 60.04 38.69 1.6 NS

U N L 4 14 !54.19 !28.10 4.95 5.7 SIG

U S L 4 39 !28.40 !9.15 1.31 7.0 SIG

U S LR 4 10 !45.23 !11.60 1.68 6.9 SIG

U S R 4 17 !20.55 !9.19 2.99 3.1 SIG

U S XL 4 3 31.88 19.13 15.83 1.2 NS

U U L 3 8 !23.54 !23.54 19.75 1.2 NS

U U L 4 2 !7.87 !3.93 33.49 0.1 NS

U U R 3 1 149.31 149.31 250.55 0.6 n/a

U U R 4 2 !53.41 !35.61 31.50 1.1 NS

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency per turn lane installed.
C-2

5

Table C-19. Empirical Bayes Evaluation Results for Project-Related Intersection Accidents.

Area

type

Traffic

control

Project

type

No. of 

legs

No. of

improved

sites

Percent change in accident frequency

Ratioa Mean Significant?

Mean per

added lane

Standard

error

R N L 4 2 51.84 25.92 40.88 0.6 NS

R N LR 4 1 !100.00 !50.00 n/a

R N R 4 1 !100.00 !50.00 n/a

R U L 4 23 !66.23 !37.16 7.42 5.0 SIG

R U LR 4 14 53.05 26.52 22.68 1.2 NS

R U R 4 29 33.90 21.85 26.57 0.8 NS

R U XL 4 2 62.88 62.88 108.20 0.6 NS

U N L 4 13 6.03 3.13 9.49 0.3 NS

U U L 4 7 !51.15 !25.58 7.24 3.5 SIG

U U LR 4 1 !45.44 !22.72 29.05 0.8 n/a

U U R 4 1 !100.00 !50.00 n/a

a

 Ratio of mean percent change in accident frequency to standard deviation of percent change in accident frequency per turn lane installed.
C-2

6

Table C-20. Empirical Bayes Analysis Results for Total Accidents on Individual Intersection Approaches.

Area

type

Traffic

control

Project

type

No. of 

legs

No. of

improved

sites

Percent change in accident frequency

Ratioa Mean Significant?

Mean per

added lane

Standard

error

R N L 4 4 !22.62 !22.62 7.26 1.6 NS

R N LR 4 2 !61.41 !30.71 8.18 3.8 SIG

R N R 4 2 6.00 6.00 11.64 0.3 NS

R U L 3 62 !45.20 !45.20 6.50 7.0 SIG

R U L 4 50 !54.63 !54.63 2.41 12.6 SIG

R U LR 3 16 !30.38 !15.19 17.12 1.8 NS

R U LR 4 30 !16.76 !8.38 4.39 1.9 NS

R U R 3 18 104.41 104.41 61.09 1.7 NS

R U R 4 57 !26.66 !26.66 5.26 3.3 SIG

R U XL 4 4 !43.02 !43.02 17.03 2.5 SIG

U N L 3 6 44.68 44.68 18.88 1.4 NS

U N L 4 49 !28.02 !28.02 2.92 5.0 SIG

U S L 3 9 !49.26 !49.26 13.87 3.6 SIG

U S L 4 148 !34.15 !34.15 0.79 13.9 SIG

U S LR 3 6 !19.32 !9.66 9.15 1.4 NS

U S LR 4 38 !32.49 !16.24 1.14 7.3 SIG

U S R 3 3 !44.48 !44.48 10.42 2.1 SIG

U S R 4 67 !17.62 !17.62 1.96 4.2 SIG

U S XL 3 2 7.17 7.17 40.41 0.2 NS

U S XL 4 11 57.80 57.80 11.72 3.0 SIG

U U L 3 16 !32.28 !32.28 13.14 2.5 SIG

U U L 4 17 !20.13 !20.13 4.40 2.3 SIG

U U LR 4 2 !66.27 !33.13 9.93 3.3 SIG

U U R 3 2 !100.00 !100.00 NS

U U R 4 6 !75.80 !75.80 8.48 5.4 SIG

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency

per turn lane installed.
C-2

7

Table C-21. Empirical Bayes Evaluation Results for Fatal and Injury Accidents on Individual

Intersection Approaches.

Area

type

Traffic

control

Project

type

No. of 

legs

No. of

improved

sites

Percent change in accident frequency

Ratioa Mean Significant?

Mean per

added lane

Standard

error

R N L 4 4 !42.11 !42.11 7.57 2.8 SIG

R N LR 4 2 !55.36 !27.68 11.57 2.4 SIG

R N R 4 2 !22.53 !22.53 12.25 0.9 NS

R U L 4 49 !60.99 !60.99 3.15 10.8 SIG

R U LR 4 30 !10.73 !5.37 7.33 0.7 NS

R U R 4 55 !24.28 !24.28 7.94 2.0 SIG

R U XL 4 4 80.35 80.35 70.18 1.1 NS

U N L 4 48 !43.18 !43.18 3.99 5.6 SIG

U S L 3 9 !47.59 !47.59 23.93 2.0 SIG

U S L 4 122 !35.32 !35.32 1.25 8.9 SIG

U S LR 3 5 !8.05 !4.03 19.49 0.3 NS

U S LR 4 35 !53.41 !26.70 1.51 9.1 SIG

U S R 3 3 !38.69 !38.69 22.06 0.9 NS

U S R 4 64 !22.20 !22.20 3.07 3.4 SIG

U S XL 3 2 !43.84 !43.84 56.78 0.8 NS

U S XL 4 11 31.47 31.47 14.96 1.2 NS

U U L 4 17 !5.21 !5.21 7.80 0.3 NS

U U LR 4 2 !42.22 !21.11 29.10 0.7 NS

U U R 4 6 !41.08 !41.08 25.25 1.0 NS

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency

per turn lane installed.
C-2

8

Table C-22. Empirical Bayes Analysis Results for Project-Related Accidents on Individual Intersection

Approaches.

Area

type

Traffic

control

Project

type

No. of 

legs

No. of

improved

sites

Percent change in accident frequency

Ratioa Mean Significant?

Mean per

added lane

Standard

error

R N L 4 4 29.19 29.19 32.30 0.5 NS

R N LR 4 2 !100.00 !50.00 NS

R N R 4 2 !100.00 !100.00 NS

R U L 4 50 !22.11 !22.11 11.06 1.1 NS

R U LR 4 30 97.51 48.76 24.04 2.0 SIG

R U R 4 58 34.23 34.23 26.33 0.8 NS

R U XL 4 4 50.96 50.96 97.67 0.5 NS

U N L 4 26 6.24 6.24 9.10 0.4 NS

U S L 4 127 !40.40 !40.40 1.82 6.9 SIG

U S LR 4 34 !49.50 !24.75 1.80 7.1 SIG

U S R 4 67 1.98 1.98 5.22 0.2 NS

U S XL 4 12 108.85 108.85 50.09 1.3 NS

U U L 4 14 !50.53 !50.53 7.24 3.5 SIG

U U LR 4 2 !42.14 !21.07 30.13 0.7 NS

U U R 4 2 !100.00 !100.00 NS

a

 Ratio of mean percent change in accident frequency to standard error of percent change in accident frequency

per turn lane installed.
C-29

Description of Results Tables

The first seven columns of the results tables for the EB analysis are identical to the first

five columns of the results tables for the YC and CG analysis. In particular, the sixth

column presents the mean treatment effectiveness, ^2* from Equation (44), expressed as a

percentage change in accident frequency. The seventh column presents the mean

percentage change in accident frequency per turn lane added, determined in the same

manner that it was for the YC and CG analyses.

The eighth column of the results tables for the EB analysis presents the standard error of

the mean treatment effectiveness per turn lane added. This standard error is the square root

of VAR{^2*} from Equation (47), expressed as a percentage change in accident frequency.

The ninth column is a ratio determined as the mean treatment effectiveness (in the

seventh column) divided by the standard error of treatment effectiveness (in the eighth

column).

The tenth column shows the significance of the mean treatment effectiveness. The

treatment effectiveness is considered to be significant if the ratio in the ninth column is

greater than or equal to 2.0. This significance criterion is not a formal test of statistical

significance at a specified confidence interval but, rather, is a criterion recommended by

Hauer(2) for judging the results of EB analyses. This criterion is, however, equivalent to the

statistical significance criteria used for the YC and CG approaches.

Specific Evaluation Results

The results of the EB evaluation are presented in the following tables:

C Table C-17—total intersection accidents.

C Table C-18—fatal and injury intersection accidents.

C Table C-19—project-related intersection accidents.

C Table C-20—total accidents for individual intersection approaches.

C Table C-21—fatal and injury accidents for individual intersection approaches.

C Table C-22—project-related accidents for individual intersection approaches.
Appendix D

Field Data Collection for

Geometric Design and

Traffic Control Features
D-1

APPENDIX D. FIELD DATA COLLECTION FOR GEOMETRIC

DESIGN AND TRAFFIC CONTROL FEATURES

This appendix documents the definitions and codes used to record geometric and

traffic control data during the field data collection. Data for each intersection visited in

the field were recorded on a standard form (figure D-1). Most data items were recorded

for each of the three or four approaches to the intersection. A few items (angle of

intersection, lighting, character of development, and level of pedestrian activity) apply to

the intersection as a whole, rather than to any specific approach. Location information

was also collected on the form.

More types of data were gathered in the field than were used in the final analyses. 

The intent was to collect as broad a set of geometric and traffic control data as feasible

during the initial field visits so that, if a question arose during the analysis about the

geometrics or traffic control of a specific intersection, data from the field would be

available to answer the question. This would eliminate the need to make additional field

visits during the latter stages of the project. Photographs were also taken in the field on

each intersection approach.

Some data items were gathered solely for site selection purposes. For example, data

were recorded on whether each intersection leg had one-way or two-way operation. In

fact, all legs of the study intersections had two-way operation. However, if one-way

operation had been present, this data item would have alerted the research team to that

fact. 

Each data item in figure D-1 is discussed below:

1. Number of through lanes:

Number of lanes used by through traffic on each approach. This included all lanes

used exclusively by through traffic and lanes shared by through traffic and right- or

left-turning traffic.

2. Number of left-turn lanes:

Number of lanes used exclusively by left-turning traffic. A shared lane used by both 

through traffic and left-turning traffic was counted as a through lane, not as a left-turn

lane.

3. Number of right-turn lanes:

Number of exclusive right-turn lanes on the approach. A shared lane used by both

through and right-turning traffic was counted as a through lane, not as a right-turn

lane. If there was a separate right-turn roadway created by a channelizing island, the

number of right-turn lanes was recorded as 1 or more, even if vehicles entered the

channelizing roadway from a lane shared with through traffic (i.e., even if there was

no exclusive right-turn lane upstream of the right-turn roadway created by the

channelizing island).
D-2

Figure D-1. Key Intersection Geometric and Traffic Control Variables.

 Site Number _____________________ County _______________ Data Collector Name _________________

 Intersection __________________________________ B______ A______ C ______ Date _______ Time______

Acceptable

Codes

Major Road

NB or EB

Crossroad

NB or EB

Major Road

SB or WB

Crossroad

SB or WB

Comments

(use back if needed)

Name of Street:

 1. Number of through lanes Numeric

 2. Number of left-turn lanes Numeric

 3. Number of right-turn lanes Numeric

 4. Type of left-turn treatment N,C,P

 5. Type of right-turn treatment N,I,L,R

 6. Horizontal alignment T,G,M,S

 7. Approach grades L,M,S / U,D

 8. Crest/sag vertical curves N,C,S

 9. Total through-lane width (ft) Numeric

 10. Right shoulder type P,G,T,C

 11. Right shoulder width (ft) Numeric

 12. Total LTL width (ft) Numeric

 13. Total LTL length (ft) Numeric

 14. Total RTL width (ft) Numeric

 15. Total RTL length (ft) Numeric

 16. Divided/undivided D,U

 17. Median width (ft) Numeric

 18. Median type N,R,D,F

 19. One-way/two-way 1 or 2

 20. Left-turn prohibition N,A,M,E,B

 21. Number of driveways within 250 ft Numeric

 22. Type of driveways N,C,I,R

 23. Curb parking within 250 ft N,P,A

 24. Traffic control N,ST,SG

 25. Left-turn phasing (arrows) N,A,B

 26. Pedestrian signals Y,N

 27. Painted crosswalk on approach Y,N

 28. Advance warning signs Y,F,N

 29. Posted speed limit (mph) Numeric

 30. Angle of intersection Numeric

 31. Lighting N,H,S,I/Y,N

 32. Character of development C,B,I,M,R,X

 33. Level of pedestrian activity L,M,H
D-3

4. Type of left-turn treatment:

N = No left-turn lanes.

C = Left-turn channelization defined by raised (curbed) or depressed median.

P = Painted left-turn channelization (no median or flush median).

NOTE: If number of left-turn lanes was zero, the type of left-turn treatment was N.

5. Type of right-turn treatment:

N = None.

I = Right-turn roadway created by a channelizing island without an exclusive

right-turn lane upstream of it (i.e., traffic entered the right-turn roadway from

a shared lane used by both through and right-turning traffic).

L = Right-turn roadway created by a channelizing island with an exclusive rightturn lane upstream of it (i.e., traffic entered the right-turn roadway from an

exclusive right-turn lane).

R = Conventional exclusive right-turn lane with no channelizing island.

6. Horizontal alignment (of approach):

T = Tangent.

G = Gentle curve (radius over 600 m or 2,000 ft).

M = Moderate curve (radius from 150 to 600 m or 500 to 2,000 ft).

S = Sharp curve (radius less than 600 m or 500 ft).

NOTE: The G, M, and S codes were used if the intersection was located on a

horizontal curve or if there was a horizontal curve on the approach within 250 feet of

the intersection. The curve radius was estimated visually in the three categories

shown.

7. Approach grades (within 75 m or 250 ft of the intersection):

L = Level (less than 2 percent grade).

M = Moderate grade (2 to 4 percent grade).

S = Steep grade (over 4 percent grade).

NOTE: The percent grade was estimated visually.

8. Crest/sag vertical curve (on approach):

N = None.

C = Crest vertical curve on approach.

S = Sag vertical curve on approach.

NOTE: Recorded presence of crest and sag vertical curves that extended through the

intersection or were within 75 m (250 ft) of the intersection.

9. Total through-lane width (ft):

Combined total width of all the through lanes, including both shared left-turn and

right-turn lanes. Widths of exclusive right- and left-turn lanes were not included in the
D-4

total through-lane width. The number of lanes whose widths were measured matched

the number of through lanes recorded.

NOTE: The through-lane width was measured at the stop line or crosswalk with a

measuring wheel. The total through-lane width was recorded such that the total

through-lane width divided by the number of through lanes equaled the average lane

width for the through lanes.

10. Right shoulder type:

P = Paved.

G = Gravel.

T = Turf.

C = Curb.

11. Right shoulder width (ft):

Measured from the outside edge of the through lane or right-turn lane to the outside

edge of the shoulder. This measurement was made with a measuring wheel.

12. Total LTL width (ft):

Combined total width of all exclusive left-turn lanes. The number of lanes whose

widths were measured matched the total number of exclusive left-turn lanes recorded.

NOTE: The total left-turn lane width was measured at the stop line or crosswalk. This

measurement was made with a measuring wheel. The total left-turn lane width was

recorded such that the total left-turn lane width divided the total number of exclusive

left-turn lanes equaled the average left-turn lane width.

13. Total LTL length (ft):

Total length of all exclusive left-turn lanes.

NOTE: The total left-turn lane length was measured from the stop line or crosswalk to

the upstream end of the left-turn lane(s). This measurement was made with a

measuring wheel. If the left-turn lane included a taper at its upstream end, the length

of the left-turn lane was measured to the last point at which the left-turn lane had its

full width.

14. Total RTL width (ft):

Combined total width of all right-turn lanes. The number of lanes whose widths were

measured matched the total number of right-turn lanes recorded.

NOTE: The total right-turn lane width was measured at the stop line or crosswalk. This

measurement was made with a measuring wheel. The total right-turn lane width was

recorded such that the total right-turn lane width divided by the total number of rightturn lanes equaled the average right-turn lane width.
D-5

15. Total RTL length (ft):

Total length of all right-turn lanes.

NOTE: The total right-turn lane length was measured from the stop line or crosswalk to

the upstream end of the right-turn lane(s). This measurement was made with a

measuring wheel. If the right-turn lane included a taper at its upstream end, the length

of the right-turn lane was measured to the last point at which the right-turn lane had its

full width.

16. Divided/undivided:

D = Divided (a raised or depressed median, or a flush median at least 1.2 m (4 ft)

in width, was present between the lanes in opposing direction of travel).

U = Undivided (no median present; a roadway with a flush median less than 1.2 m

(4 ft) in width.

17. Median width (ft):

Measured from inside edge of the through lane to inside edge of through lane in the

opposite direction of travel (i.e., left-turn lanes cut into the median were included in

the median width). This measurement was made with a measuring wheel. If the

approach was undivided, the median width as recorded as 0 m (0 ft).

18. Median type:

N = No median.

R = Raised median (curbed with turf or pavement in the median).

D = Depressed median (turf median with no curbs). This type of median typically

had a ditch or swale below roadway grade.

F = Flush median (paved median that was flush with the roadway grade).

19. One-way/two-way operation:

1 = One-way traffic operation on the intersection leg containing the approach.

2 = Two-way traffic operation on the intersection leg containing the approach.

20. Left-turn prohibition:

N = No left-turn prohibition on this approach.

A = Left turns prohibited from this approach at all times.

M = Left turns prohibited from this approach during the morning peak period only,

but not at other times.

E = Left turns prohibited from this approach during the evening peak period only,

but not at other times.

B = Left turns prohibited from this approach during both peak periods, but not at

other times.

21. Number of driveways within 75 m or 250 ft:

Total number of driveways within 75 m (250 ft) of the intersection on both sides of the

street on the intersection leg containing the approach in question.
D-6

22. Type of driveways:

N = No driveways (recorded as such if the number of driveways was equal to

zero).

C = One or more commercial driveways included in the driveway count for this

leg of the intersection.

I = One or more industrial/institutional driveways included in the driveway count

for this leg, but no commercial driveways.

R = One or more residential driveways included in the driveway count for this leg,

but no commercial or industrial/institutional driveways.

NOTE: This category was intended to establish a hierarchy in which the driveway type

for the most heavily used driveway(s) was recorded. Commercial driveways are

usually more heavily used throughout the day than industrial/institutional driveways,

which in turn are usually more heavily used than residential driveways. 

Industrial/institutional driveways include those that serve factories, non-retail

businesses, government buildings, hospitals, schools, churches, and apartment

complexes (with more than 10 apartments).

23. Curb parking within 75 m or 250 ft:

N = No curb parking on the right side of the intersection approach within 250 ft of

the intersection.

P = Parallel parking on the right side of the intersection approach within 250 ft of

the intersection.

A = Angle parking on right side of the intersection approach within 250 ft of the

intersection.

NOTE: Width of angle parking area was not included in width of through lanes.

24. Traffic control:

N = None.

ST = STOP controlled.

SG = Signalized.

25. Left-turn phasing (arrows):

N = No protected left-turn phase (i.e., there was no green arrow so all left turns

were made on the green ball).

A = Protected left-turn phase with left turns allowed only during the protected

phase (i.e., all left turns were made with a green arrow, while no left turns

were allowed on green ball).

B = Protected left-turn phase with left turns permitted both during the protected

phase and on the green ball (i.e., protected/permissive operation).

26. Pedestrian signals:

Y = Pedestrian signals (WALK/DON'T WALK) present for crossing the approach

in question.

N = No pedestrian signals for crossing the approach.
D-7

27. Painted crosswalk on approach:

Y = Painted or marked pedestrian crosswalk present on the approach in question.

N = No painted crosswalk on the approach in question.

28. Advance warning signs:

Y = Advance warning signs (e.g., SIGNAL AHEAD) present on the approach in

question.

F = Advance warning signs present AND the warning signs were accompanied by

flashing beacons.

N = No advance warning signs on the approach.

NOTE: If there was an advance warning sign with any legend other than SIGNAL

AHEAD (or the SIGNAL AHEAD symbol sign), the sign legend was noted as a

comment. Advisory speed limits are not typically used in conjunction with SIGNAL

AHEAD signs; however, if an advisory speed limit was used on the approach (except

for a temporary work zone speed limit), the magnitude of the advisory speed limit was

noted as a comment.

29. Posted speed limit (mph):

The posted regulatory speed limit (mph) on each approach.

NOTE: Regulatory speed limit signs are normally repeated at intervals to make sure

that drivers are aware of the speed limit. If there were no speed limit signs within the

immediate vicinity of the intersection, data collectors drove up to 1.6 km (1 mile)

upstream to check for speed limit signs that applied to the approach in question. If

there were no regulatory speed limits signs on the street, the following default speed

limits were used:

25 mph = Business or residential district on a non-state highway.

55 mph = State highways or outside of business and residential areas on non-State

highways.

30. Angle of intersection:

The angle between the intersecting approaches. The angle entered was the smallest

angle between the intersecting approaches (i.e., entered as 90 degrees or an acute angle

between 0 and 90 degrees).

NOTE: If the angle was other than 90 degrees, a sketch was made of the three or four

approaches to illustrate which approaches intersected at acute, right, and obtuse angles.

31. Lighting:

N = None.

H = High-mast lighting (not expected at conventional highway intersections; more

typical of freeway interchanges).
D-8

S = Street lighting (individual luminaires) continuously along one or both

intersecting streets.

I = Street lighting (individual luminaires) at the intersection, but not along the

intersecting streets.

NOTE: Ambient light sources other than street lighting present at the intersection were

noted by a supplementary code of Y (Yes) or N (No).

32. Character of development:

A = Agricultural area.

C = Central business district/downtown.

B = Outlying commercial business district.

I = Industrial district.

M = Mixed commercial and residential development.

R = Residential development.

X = Other (describe in comment).

33. Level of pedestrian activity:

L = Low (almost no pedestrian activity).

M = Medium (pedestrian activity with some frequency).

H = High (pedestrian activity with some frequency).