Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-23-049 APRIL 2023 

Safety Comparisons Between 

Interchange Types
FOREWORD

The Highway Safety Manual has made predictive safety analysis feasible for many basic 

roadway elements, including freeway and ramp segments and ramp terminals.(3) This capability 

raises the expectation for predicting the safety performance of more complex roadway facilities, 

such as interchanges. Although interchanges can be decomposed into basic road elements, the 

safety performance of interchanges cannot be derived by simply adding predictions from 

individual components.

The Federal Highway Administration (FHWA) supported a project to explore planning-level 

analysis of interchange configurations during alternatives analysis or Interchange Access 

Requests (IARs). FHWA sought to identify the most commonly considered configurations in 

IARs and develop a predictive model and implementation tool. The purpose of this predictive 

model and associated implementation tool is to use an approach with more robust considerations 

than a single crash modification factor and provide reliable predictions using information 

commonly known during interchange project planning. The model and implementation tool can 

be used to evaluate the predicted crash frequency and severity for interchange configurations 

under consideration using basic inputs for the entire interchange area.

This report documents the methods and processes used to identify applicable service interchange 

configurations for inclusion in the predictive method and provides details on data collection 

procedures and methods used for developing crash frequency and severity implementation 

models. The results of this research can be used to support planning-level analysis of various 

interchange configurations under consideration early in the project development process. 

Brian, Cronin, P.E. 

Director, Office of Safety and Operations

Research and Development

Notice

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

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

the use of the information contained in this document.

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

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

objective of the document. 

Quality Assurance Statement

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

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

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

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

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-23-049

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

4. Title and Subtitle

Safety Comparisons Between Interchange Types

5. Report Date

April 2023 

6. Performing Organization Code:

7. Author(s)

Scott Himes (ORCID: 0000-0003-2932-7691), Vikash Gayah

(ORCID: 0000-0002-0648-3360), Jeff Gooch (ORCID: 0000-

0003-1227-0967), Ginny O’Connor (ORCID: 0000-0002-

4668-6832), and Thanh Le (ORCID: 0000-0001-7558-8978) 

8. Performing Organization Report No.

9. Performing Organization Name and Address

VHB

940 Main Campus Dr. 

Suite 500

Raleigh, NC 27606 

10. Work Unit No.

11. Contract or Grant No.

DTFH6116D00040

12. Sponsoring Agency Name and Address

Office of Safety and Operations Research and Development

Federal Highway Administration 

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Report; September 2020–September 2022

14. Sponsoring Agency Code

HRSO-10

15. Supplementary Notes

The contract manager for this report was Wei Zhang, (HRSO-10; ORCID: 0000-0002-5175-7527). The technical 

advisory committee included Mark Doctor, Jeff Shaw, Tim Taylor, and Dave Petrucci.

16. Abstract

Documentation of potential safety performance impacts is required for Interchange Justification Reports (IJRs) to 

validate the need for new or modified interchanges.(2) Typically, IJRs are written early in the project planning and 

design process, with details generally consistent with conceptual design. As such, the design details required for 

using the Highway Safety Manual (HSM) Part C predictive models may not be known, only the general form of the 

interchanges.(1) Without these details, using the HSM models to develop an accurate prediction of crash frequency 

and severity of the design is difficult. In addition, aggregating site-by-site predictions may not fully capture the 

safety performance impacts when considering the project location as a whole. The Federal Highway 

Administration sought to develop planning-level models and tools to predict crash frequency and severity for an 

existing or proposed interchange to further explore and address an IJR application. This predictive model estimates 

crash frequency and severity for service interchange configurations comprising more than 75 percent of those 

typically considered in IJRs. The safety performance function includes freeway and crossroad annual average daily 

traffic (AADT) per lane, ramp AADT, and interchange configuration. The base estimate is adjusted for freeway 

lanes, crossroad lanes, area type, interchange skew, a nearby adjacent interchange gore, managed lanes, the 

number of crossroad left-turn lanes at terminals, and variation in ramp volumes.

17. Key Words

Interchange, configuration, safety, crash prediction 

model

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

https://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

103

22. Price

N/A

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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
iii

TABLE OF CONTENTS

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

Background ............................................................................................................................... 1

Objective ................................................................................................................................... 1

Purpose and Structure of the Guide ....................................................................................... 2

CHAPTER 2. IDENTIFY INTERCHANGE TYPES FOR INCLUSION IN THIS STUDY . 3

Objective ................................................................................................................................... 3

Survey Response ....................................................................................................................... 3

Discussion .................................................................................................................................. 7

CHAPTER 3. METHODOLOGY FOR PREDICTING SAFETY PERFORMANCE OF 

INTERCHANGES ....................................................................................................................... 11

Introduction ............................................................................................................................ 11

Interchange Influence Area ................................................................................................... 11

Interchange Area .................................................................................................................... 12

Crash Frequency Analysis Methodology ............................................................................. 16

Crash Severity Analysis Methodology .................................................................................. 19

Crash Prediction Goodness-of-Fit Measures ....................................................................... 19

CHAPTER 4. DATA COLLECTION ........................................................................................ 21

Introduction ............................................................................................................................ 21

Interchange Configurations Studied .................................................................................... 21

Crossroad Ramp Terminal Types and Traffic Control ...................................................... 28

Freeway Ramp Terminal Type ............................................................................................. 33

Freeway Ramp Type .............................................................................................................. 33

Data Collection Prioritization ............................................................................................... 34

Data Collection Process ......................................................................................................... 35

Identify Interchange Locations and Type ........................................................................... 36

LRS Download ................................................................................................................... 37

Collect and Integrate Roadway Inventory Data ................................................................. 39

Roadway Inventory Validation and Supplemental Data Collection .................................. 40

Crash Data Download ........................................................................................................ 44

Crash Data Integration ....................................................................................................... 45

Database Summary ................................................................................................................ 46

CHAPTER 5. PREDICTIVE MODEL DEVELOPMENT ...................................................... 51

Introduction ............................................................................................................................ 51

Modeling Approach................................................................................................................ 51

Statistical Analysis Methods.................................................................................................. 52

Fatal and Injury Crash Frequency Prediction Model ........................................................ 52

Model Development ........................................................................................................... 53

Model Estimation ............................................................................................................... 55

Model Evaluation ............................................................................................................... 56

Estimated AFs .................................................................................................................... 57

Sensitivity Analysis ............................................................................................................ 59

PDO Crash Frequency Prediction Model ............................................................................ 62
iv

Model Development ........................................................................................................... 62

Model Estimation ............................................................................................................... 62

Model Evaluation ............................................................................................................... 63

Estimated AFs .................................................................................................................... 64

Sensitivity Analysis ............................................................................................................ 66

CHAPTER 6. SDF DEVELOPMENT ....................................................................................... 69

Background ............................................................................................................................. 69

Database Summary ................................................................................................................ 69

Geometric and Operational Characteristics ........................................................................ 69

Crash Characteristics .......................................................................................................... 71

SDF Model Development................................................................................................... 72

CHAPTER 7. CONCLUSIONS AND RECOMMENDATIONS ............................................ 79

Conclusions ............................................................................................................................. 79

Recommendations for Future Efforts .................................................................................. 80

APPENDIX A: SURVEY PROVIDED TO DIVISIONS.......................................................... 83

APPENDIX B: ADDITIONAL SURVEY COMMENTS ......................................................... 89

ACKNOWLEDGMENTS ........................................................................................................... 91

REFERENCES ............................................................................................................................. 93
v 

LIST OF FIGURES

Figure 1. Graphic. Interchange configurations considered in the survey. ........................................ 5

Figure 2. Graphic. Agencies installing roundabouts at ramp terminals. .......................................... 7

Figure 3. Graphic. Area of influence for proposed interchange analysis in Florida.(5) .................. 12

Figure 4. Graphic. Study interchange area definition.(8) ................................................................ 14

Figure 5. Graphic. Example crossroad interchange area for an SPDI.(9) ....................................... 15

Figure 6. Photo. Example crossroad interchange area.(10) .............................................................. 15

Figure 7. Equation. HSM CPM. ..................................................................................................... 16

Figure 8. Equation. Interchange crash frequency CPM. ................................................................ 17

Figure 9. Graph. Example CURE plot.(13) ...................................................................................... 20

Figure 10. Photo. Aerial image of a conventional diamond interchange.(10) ................................. 23

Figure 11. Photo. Aerial image of a CD interchange.(10)................................................................ 23

Figure 12. Photo. Aerial image of a TDI.(10) .................................................................................. 24

Figure 13. Photo. Aerial image of a DDI.(10) .................................................................................. 24

Figure 14. Photo. Aerial image of an SPDI.(10) .............................................................................. 25

Figure 15. Photo. Aerial image of a parclo A2 interchange.(10) ..................................................... 25

Figure 16. Photo. Aerial image of a parclo A4 interchange.(10) ..................................................... 26

Figure 17. Photo. Aerial image of a parclo B2 interchange.(10) ..................................................... 26

Figure 18. Photo. Aerial image of a parclo B4 interchange.(10) ..................................................... 27

Figure 19. Photo. Aerial image of a parclo AB2 interchange.(10)................................................... 27

Figure 20. Photo. Aerial image of a parclo AB4 interchange.(10)................................................... 28

Figure 21. Graphic. D4 ramp terminals.(1) ..................................................................................... 30

Figure 22. Graphic. A4 ramp terminals.(1) ..................................................................................... 30

Figure 23. Graphic. B4 ramp terminals.(1)...................................................................................... 30

Figure 24. Graphic. A2 ramp terminals.(1) ..................................................................................... 31

Figure 25. Graphic. B2 ramp terminals.(1) ...................................................................................... 31

Figure 26. Graphic. TDI.(9) ............................................................................................................. 31

Figure 27. Graphic. SPDI.(9) ........................................................................................................... 32

Figure 28. Graphic. DDI ramp terminal. ........................................................................................ 32

Figure 29. Graphic. Roundabout ramp terminal. ........................................................................... 33

Figure 30. Photo. Aerial view of an interchange containing diamond ramps, loop ramps, 

and direct connection ramps.(10)............................................................................................ 34

Figure 31. Photo. Example interchange area in GIS.(15) ................................................................. 39

Figure 32. Graphic. Sample component breakdown of a parclo AB2 interchange.(10) .................. 41

Figure 33. Photo. Interchange with crash points.(15) ...................................................................... 46

Figure 34. Photo. Interchange showing crashes on the inventory side of the roadway.(15) ............ 46

Figure 35. Equation. Structure of prediction models. .................................................................... 52

Figure 36. Equation. Generalized KABC CPM. ............................................................................ 53

Figure 37. Equation. KABC SPF. .................................................................................................. 53

Figure 38. Equation. Freeway AF for five and six lanes................................................................ 53

Figure 39. Equation. Freeway AF for more than six lanes. ........................................................... 53

Figure 40. Equation. Crossroad AF for more than four lanes. ....................................................... 54

Figure 41. Equation. Area type AF. ............................................................................................... 54

Figure 42. Equation. Interchange skew AF. ................................................................................... 54

Figure 43. Equation. Nearby gore AF. ........................................................................................... 54
vi 

Figure 44. Equation. Managed-lane AF. ........................................................................................ 54

Figure 45. Equation. Crossroad left-turn lanes AF. ....................................................................... 54

Figure 46. Equation. Ramp volume COV AF. ............................................................................... 54

Figure 47. Graph. CURE plot for KABC crashes. ......................................................................... 57

Figure 48. Equation. Freeway with five and six lanes KABC AF value. ...................................... 57

Figure 49. Equation. Freeway with more than six lanes KABC AF value. ................................... 57

Figure 50. Equation. Crossroad through lanes KABC AF value. .................................................. 58

Figure 51. Equation. Area type KABC AF value. ......................................................................... 58

Figure 52. Equation. Nearby gore KABC AF value. ..................................................................... 58

Figure 53. Equation. Managed lane KABC AF value. ................................................................... 58

Figure 54. Equation. Interchange skew angle KABC AF value. ................................................... 59

Figure 55. Equation. Crossroad number of left-turn lanes KABC AF value. ................................ 59

Figure 56. Equation. Ramp AADT COV KABC AF value. .......................................................... 59

Figure 57. Graph. Predicted KABC crash frequency for scenario 1. ............................................. 60

Figure 58. Graph. Predicted KABC crash frequency for scenario 2. ............................................. 61

Figure 59. Graph. Predicted KABC crash frequency for scenario 3. ............................................. 61

Figure 60. Graphic. CURE plot for PDO crashes. ......................................................................... 64

Figure 61. Equation. Freeway with five and six lanes PDO AF value. ......................................... 64

Figure 62. Equation. Freeway with more than six lanes PDO AF value. ...................................... 64

Figure 63. Equation. Crossroad through lanes PDO AF value. ..................................................... 65

Figure 64. Equation. Area type PDO AF value. ............................................................................. 65

Figure 65. Equation. Nearby adjacent interchange PDO AF value. .............................................. 65

Figure 66. Equation. Managed lane PDO AF value. ...................................................................... 65

Figure 67. Equation. Interchange skew angle PDO AF value. ...................................................... 66

Figure 68. Equation. Crossroad number of left-turn lanes PDO AF value. ................................... 66

Figure 69. Equation. Ramp AADT COV PDO AF value. ............................................................. 66

Figure 70. Graph. Predicted PDO crash frequency for scenario 1. ................................................ 67

Figure 71. Graph. Predicted PDO crash frequency for scenario 2. ................................................ 67

Figure 72. Graph. Predicted PDO crash frequency for scenario 3. ................................................ 68

Figure 73. Equation. Probability of outcome m. ............................................................................ 72

Figure 74. Equation. Probability of KA severity outcome. ............................................................ 73

Figure 75. Equation. Probability of B severity outcome. ............................................................... 73

Figure 76. Equation. Probability of C severity outcome. ............................................................... 73

Figure 77. Equation. Deterministic component of outcome m. ..................................................... 73

Figure 78. Equation. Projected probability of severity outcome m. ............................................... 75

Figure 79. Equation. Projected deterministic component of KA crash severity. ........................... 75

Figure 80. Equation. Projected deterministic component of B crash severity. .............................. 75

Figure 81. Equation. Constant value for outcome m. ..................................................................... 75

Figure 82. Equation. Freeway AADT severity AF. ....................................................................... 75

Figure 83. Equation. Crossroad AADT severity AF. ..................................................................... 75

Figure 84. Equation. Freeway posted speed limit severity AF. ..................................................... 75

Figure 85. Equation. Crossroad posted speed limit severity AF. ................................................... 75

Figure 86. Equation. Parclo type A or B severity AF. ................................................................... 75

Figure 87. Equation. Parclo type AB severity AF. ......................................................................... 76

Figure 88. Equation. SPDI, DDI, or TDI severity AF. .................................................................. 76

Figure 89. Equation. Roundabout diamond severity AF. ............................................................... 76

Figure 90. Equation. Nearby adjacent intersection severity AF. ................................................... 76
vii 

Figure 91. Equation. Nearby adjacent interchange gore severity AF. ........................................... 76

Figure 92. Equation. Pedestrian right-turn conflict severity AF. ................................................... 76

Figure 93. Equation. Number of freeway lanes severity AF. ......................................................... 76

Figure 94. Equation. Number of crossroad lanes severity AF. ...................................................... 76

Figure 95. Equation. Variance equation for the predictive model. ................................................ 80

Figure 96. Graphic. Interchange configurations considered in the survey. .................................... 85
viii

LIST OF TABLES

Table 1. Responses to questions 6 through 11. ................................................................................ 4

Table 2. Number of responses to frequency of consideration and selection of various 

interchange types. ................................................................................................................... 6

Table 3. Responses to whether agencies install roundabouts at terminals. ...................................... 6

Table 4. Estimated number of IJRs by interchange configuration per year. .................................... 8

Table 5. Interchange configuration ranking by score. ...................................................................... 9

Table 6. Descriptions of interchange configurations. .................................................................... 22

Table 7. Summary of crossroad ramp terminal configurations. ..................................................... 29

Table 8. Summary of number of responses by State for interchange data collection. ................... 35

Table 9. Interchanges that overlapped. ........................................................................................... 39

Table 10. Data elements for ramp segments. ................................................................................. 42

Table 11. Data elements for freeway and crossroad segments. ..................................................... 43

Table 12. Data elements for crossroad ramp terminals. ................................................................. 44

Table 13. Summary data for geometric, operational, and crash data. ............................................ 47

Table 14. Summary information for geometric, operational, and crash data. ................................ 48

Table 15. Summary information for categorical data. ................................................................... 49

Table 16. Final predictive model and parameters for KABC crash frequency. ............................. 56

Table 17. Final predictive model and parameters for PDO crash frequency. ................................ 63

Table 18. Summary of SDF dataset geometric and operational characteristics. ............................ 70

Table 19. Summary of SDF information for categorical data. ....................................................... 71

Table 20. Summary of crash severity data by State. ...................................................................... 72

Table 21. Summary of crash severity data by interchange configuration. ..................................... 72

Table 22. Summary of pooled data from SDF model. ................................................................... 78

Table 23. Annual IARs reviews by interchange configuration. ..................................................... 86

Table 24. Additional comments on IJR (IAR) practices. ............................................................... 89
ix

LIST OF ABBREVIATIONS

A incapacitating injury

AADT annual average daily traffic

AASHTO American Association of State Highway and Transportation Officials

AF adjustment factor

ANSI American National Standards Institute

B non-incapacitating injury

C possible injury

CD compressed diamond

CLF full cloverleaf

COV coefficient of variation

CPM crash prediction model 

CURE cumulative residual

DDI diverging diamond interchange 

DLT displaced left-turn interchange

DOT department of transportation

FHWA Federal Highway Administration

FI fatal injury

GIS geographic information system

GPS Global Positioning System 

HSIS Highway Safety Information System

HSM Highway Safety Manual

IAR Interchange Access Request

ID identity

IJR Interchange Justification Report

IOR Interchange Operations Report

K fatal

KA fatal and severe injury

KABC fatal and injury crash categories

KABCO injury classification scale

LRS linear referencing system 

Parclo partial cloverleaf

PDO property damage only 

PM parallel merge

SDF severity distribution function 

SPDI single-point diamond interchange

SPF safety performance function

SPUI single-point urban interchange

SR single roundabout interchange 

TDI tight diamond interchange 

VIEDA variable introduction exploratory data analysis

1 

CHAPTER 1. INTRODUCTION

BACKGROUND

In 2014, the American Association of State Highway and Transportation Officials (AASHTO) 

published a supplement to the Highway Safety Manual (HSM) containing crash prediction 

models (CPMs) for freeway segments, ramp segments, and ramp terminals.(1) To predict crashes 

for these facilities, these models require detailed sets of inputs, down to specific design 

characteristics for features such as lane width, shoulder width, and clear zone. One potential use 

of the HSM freeway and interchange models is to predict the potential safety performance effects 

of new interchanges or modifications to existing interchanges. 

Documentation of potential safety performance impacts is required for the justification and 

documentation necessary to substantiate any proposed changes in access to the Interstate 

System.(2)

 Typically, Interchange Justification Reports (IJRs) are written early in the project 

planning and design process, with details generally consistent with conceptual design. As such, 

the design details required for using the HSM CPMs may not be known; only the general form of 

the interchanges is known. Without these details, using the HSM models to develop an accurate 

prediction of crash frequency and severity for the design is difficult. In addition, aggregating 

component-by-component (e.g., individual ramp and freeway segment) predictions may not fully 

capture the safety performance impacts when considering the project location as a whole.

To further explore and address an IJR application, the Federal Highway Administration (FHWA) 

sought to develop planning-level models and tools to predict crash frequency and severity for an 

existing or proposed interchange. These planning-level models will allow analysts to compare the 

potential safety performance effects of freeway access and interchange design decisions at the 

planning level. Model inputs were limited to details that are generally known at the planning and 

conceptual design level and are expected to affect crash frequency and severity (e.g., traffic 

volume, number and forms of ramps, number and types of ramp terminals, land use, and mainline 

cross section). 

Since most IJRs focus on a few specific interchange types, the scope of the planning-level models

focuses on those that are most often considered. This research identified those interchange 

configurations that State agencies most commonly consider in IJRs (not just those that are 

selected), identified features related to safety performance that are typically assessed during the 

IJR review process, and developed a model for assessing those particular factors. The project 

team developed a spreadsheet implementation tool to support analysts using the methods 

described in this report. The spreadsheet tool directly implements the methods, geometric and 

operational characteristics, and parameters described in this report.

OBJECTIVE

The objective of the research study was to develop a planning-level safety assessment tool and 

interchange safety comparison process for FHWA and State department of transportation (DOT) 

use for IJR reviews. The safety assessment tool will allow agencies to quantify the safety 

performance of proposed designs against a base (or reference) condition.
2 

PURPOSE AND STRUCTURE OF THE GUIDE 

This report aims to document the process used to develop the planning-level predictive model, 

including factors considered and not included in the final application. The lessons learned from 

this project can inform planning-level predictive safety analysis and future improvements to the 

IJR process and predictive approach in the HSM.(3) This report is organized into the following

seven chapters:

1. Introduction: Provides an overview of this research project and the purpose of this report.

2. Identify Interchange Types for Inclusion in This Study: Discusses the survey and 

identifies the interchange types reviewed for IJRs and recommended for further analysis. 

3. Methodology for Predicting Safety Performance of Interchanges: Details the methodology 

used for developing the predictive models, the final crash frequency and severity models, 

and comparative application of the planning-level model relative to design-level models 

from the HSM. 

4. Data Collection: Describes the data collection that supported the development of the 

safety prediction model and the methodology for predicting safety performance. 

5. Predictive Model Development: Describes the development of the crash frequency 

prediction model, including a description of the planning-level predictive method for 

interchanges. 

6. Severity Distribution Function (SDF) Development: Describes the development of SDFs, 

including a description of the planning-level factors associated with crash severity, given 

a crash has occurred.

7. Conclusions and Recommendations: Summarizes the report’s findings and identifies 

future research possibilities.
3 

CHAPTER 2. IDENTIFY INTERCHANGE TYPES FOR INCLUSION IN THIS STUDY

OBJECTIVE 

This chapter describes the survey the project team administered to FHWA division offices to 

identify which interchange configurations are most often considered for IJR reviews. The survey 

aimed to identify interchange configurations that comprise at least 75 percent of those considered 

for inclusion in the predictive methodology developed in this research. The survey also solicited

information on approximately how many interchanges of each configuration are constructed (and 

considered) annually to determine the potential availability of data for developing the predictive 

method. Further survey questions identified States for data collection and determined features of 

interest for safety prediction during the planning stage, both of which the project team refined in 

the data collection and analysis study design. Following is an overview of the survey responses 

and FHWA recommendations for interchange configurations for inclusion in the study.

Appendix A includes the survey instrument. 

SURVEY RESPONSE

The project team provided an electronic survey to all FHWA divisions based on the FHWAprovided list of those most likely to be involved with reviewing IJRs. In a dedicated followup 

effort, the project team obtained responses from FHWA divisions in 45 States; Puerto Rico; and 

Washington, DC.

Table 1 summarizes the results of survey questions 6 through 11. Responses to questions 1 

through 5 are not included in this summary, as they simply collected contact information from the 

survey responder. Each question received responses that added up to 47 jurisdictions, except in 

cases where not everyone responded. 
4 

Table 1. Responses to questions 6 through 11. 

Question Response Number of Responses*

6. How many IJRs does your State typically 

submit per year?

0–5 35

6–10 8 

11–15 2 

16–20 0 

More than 20 1 

7. Of those IJRs, how many involve system 

interchanges (interchanges providing access 

from one freeway to another)?

0–2 39

3–5 6 

6–8 1 

More than 8 1 

8. Approximately what percentage of service 

interchange IJRs (interchanges that provide 

access between a freeway and a nonfreeway 

road network) are for rural interchanges (as 

opposed to urban/suburban interchanges)?

0–25 29

26–50 11

51–75 2 

More than 75 5 

9. Approximately how many service 

interchange IJRs involve a new interchange?

0–5 46

6–10 1 

11–15 0 

More than 15 0 

10. Approximately how many service 

interchange IJRs involve reconfiguring an 

existing interchange?

0–5 40

6–10 6 

11–15 0 

More than 15 1 

11. Approximately how many service 

interchange IJRs per year involve minor 

changes (such as modifying ramp terminal 

access control, adding lanes to an existing 

ramp, or relocating a ramp terminal to a 

different roadway)?

0–5 40

6–10 3 

11–15 2 

16–20 1 

More than 20 0 

No response 1 

*Categories with the highest number of responses are italicized.

The focus of this project, and predictive modeling, was on service interchanges. The results 

indicate that most divisions receive fewer than five IJRs per year, with most of those focused on 

service interchange projects. However, some larger States receive a substantial number of IJRs 

per year, with one agency reporting more than 20 IJRs per year. 

Predictably, question 8 responses indicate that the majority of IJRs focus on urban applications, 

particularly when weighted by the number of IJRs received (i.e., divisions with a higher 

percentage of rural IJRs tend to receive fewer IJRs). Additionally, more rural States, such as 

Wyoming, had a higher percentage of rural IJRs. 

The majority of respondents indicated reviewing 0 to 5 new interchange IJRs per year in 

question 9 and provided similar responses for reconfiguration of existing interchanges in question 

10; however, 6 respondents indicated 6 to 10 interchange reconfigurations per year, and 

1 respondent indicated more than 15 per year. The respondents provided similar responses for 

question 11 for minor interchange modifications. 
5 

For question 12, the project team provided respondents with 15 potential interchange 

configurations and asked how often each configuration was considered by each agency annually. 

Figure 1 provides an overview of the interchange configurations included in the survey. 

Source: FHWA. 

Figure 1. Graphic. Interchange configurations considered in the survey. 

The questionnaire included the following response categories for how often an interchange 

configuration is considered by each agency: 

1. Uncommon or rarely considered. 

2. Sometimes considered but rarely selected.

3. Commonly considered and selected zero to two times per year.

4. Commonly considered and selected three to five times per year.

5. Commonly considered and selected six to eight times per year.

6. Commonly considered and selected more than eight times per year.

7. No response recorded. 

Table 2 provides the results for question 12, which asked respondents to estimate the frequency 

of consideration and selection of various interchange types. Note that the number in the response 

column in table 2 corresponds to the response category defined in the previous list. Responses 1 

and 2 indicate the interchange configuration may or may not be considered but is not 

implemented by the agency. Responses 3 through 6 indicate the interchange configuration is 

considered and implemented. Response 7 provides a space for divisions that did not provide a 

response for their particular agency. 
6 

Looking at the responses in table 2, full cloverleaf (CLF), displaced left turn (DLT), single 

roundabout (SR), ramp to frontage road, and braided ramp interchanges were not common 

configurations implemented by many agencies. A follow-on question (question 13) asked 

respondents to identify any potential inclusions that were missing from the list in table 2. 

Respondents indicated double roundabout, or dogbone, interchanges were missing. However, the 

project team considered roundabouts a traffic control approach for ramp terminals, and too few 

agencies are currently using double roundabout interchanges; therefore, they were not prioritized 

for inclusion as a separate interchange type. Additional responses focused on system 

interchanges, partial interchanges, and split interchanges. 

Table 2. Number of responses to frequency of consideration and selection of various 

interchange types.

Response CLF D CD TDI SPDI DDI A B AB DLT SR RF BR 

1 24 6 8 6 9 8 9 13 16 36 26 22 23

2 13 3 8 8 14 5 12 13 13 4 6 9 10

3 6 24 22 25 19 25 20 15 13 0 6 9 7 

4 0 9 1 1 0 4 0 0 0 0 1 0 0 

5 0 1 1 0 0 0 0 0 0 0 1 0 0 

6 0 0 0 0 0 0 0 0 0 0 0 0 0 

7 4 4 7 7 5 5 6 6 5 7 7 7 7 

*Categories with the highest number of responses are italicized. 

D = diamond; CD = compressed diamond; TDI = tight diamond interchange; SPDI = single-point diamond 

interchange; DDI = diverging diamond interchanges; A = partial cloverleaf (parclo) A2 or A4; B = parclo B2 or B4; 

AB = parclo AB2 or AB4; RF = ramp to frontage road; BR = braided ramp.

To identify potential States for data collection, the project team asked follow-on questions about 

which agencies consider and implement alternative interchanges, including single-point diamond 

interchanges (SPDIs), diverging diamond interchanges (DDI), DLT interchanges, and SR 

interchanges. Most responding divisions indicated their agencies commonly consider and install 

alternative interchanges, except most New England States, Maryland, Nebraska, New Jersey, and 

Wyoming. Division representatives also indicated Alaska and Hawaii do not consider and install 

alternative interchanges as of the time of this survey.

Table 3 further indicates the number of agencies commonly considering or installing roundabouts 

at ramp terminals. Figure 2 shows agencies that typically install roundabouts at terminals and 

those that commonly consider installing them. The responses indicate an even divide across the 

country for agencies installing roundabouts, only considering roundabouts, or not installing 

roundabouts at ramp terminals. 

Table 3. Responses to whether agencies install roundabouts at terminals.

Response Number of Responses*

Does not commonly consider roundabouts 16 

Commonly considers roundabouts but has not installed many 16 

Commonly installs roundabouts as ramp terminals at appropriate 

interchange configurations 15 

*Categories with the highest number of responses are italicized. 
7 

Source: FHWA. 

Figure 2. Graphic. Agencies installing roundabouts at ramp terminals.

DISCUSSION

Table 4 summarizes the approximate number of IJRs by interchange configuration per year. The 

table includes a column for agencies considering, but not installing, the configuration. For this 

scenario, the project team assigned 0.25 points per agency to provide weight for agencies 

considering but not installing. Each of the other columns is weighted to the average number of 

years for the category selected (e.g., each interchange in the zero to two per year category 

receives a weight of one and interchanges in the three to five per year category receive a score of 

four). The total score comes to 357 interchanges per year when including considered interchanges 

and 276 for constructed interchanges. Additional comments provided by State agencies regarding 

their respective IJRs are listed in appendix B.
8 

Table 4. Estimated number of IJRs by interchange configuration per year.

Interchange 

Configuration

Considered 

Only

0–2 

Per 

Year

3–5 

Per 

Year

6–8 

Per 

Year

9+ Per 

Year

Total

Score

Total Score 

Constructed

Only

Score weight 0.25 1 4 7 9 — —

CLF 37 6 0 0 0 15.25 6

Conventional 

diamond 9 24 9 1 0 69.25 67 

CD 16 22 1 1 0 37 33

TDI 14 25 1 0 0 32.5 29

SPDI 23 19 0 0 0 24.75 19

DDI 13 25 4 0 0 44.25 41

Parclo A 21 20 0 0 0 25.25 20

Parclo B 26 15 0 0 0 21.5 15

Parclo AB 29 13 0 0 0 20.25 13

DLT 40 0 0 0 0 10 0

SR 32 6 1 1 0 25 17

Ramps to 

frontage road 31 9 0 0 0 16.75 9 

Braided ramp 33 7 0 0 0 15.25 7

Total 324 191 16 3 0 357 276

—No data.

Table 5 ranks interchange configurations by individual score (based on installations per year). 

The table indicates the interchange configurations that are expected to comprise at least 

75 percent of IJRs. Conventional diamond interchanges received the highest score, and partial 

cloverleaf (parclo) AB interchanges received the lowest score for those in table 5. When 

excluding parclo AB interchanges, the list still includes 78 percent of those interchanges 

considered and 87 percent of interchanges constructed; however, the project team felt parclo AB 

interchanges are consistent with parclo A and B interchanges and would be worth including in 

the dataset moving forward. 
9 

Table 5. Interchange configuration ranking by score.

Interchange Configuration

Total 

Score

Constructed 

Score

Conventional diamond 69.25 67

DDI 44.25 41

CD 37 33

TDI 32.5 29

Parclo A 25.25 20

SR 25 17

SPDI 24.75 19

Parclo B 21.5 15

Parclo AB 20.25 13

Total 299.75 254

Percentage 84 92

11 

CHAPTER 3. METHODOLOGY FOR PREDICTING SAFETY PERFORMANCE OF 

INTERCHANGES 

INTRODUCTION

This chapter describes the framework for developing planning-level CPMs for the more common 

interchange configurations identified in the survey results described in chapter 2. First, this 

chapter provides an overview of the interchange influence and interchange areas used to define 

the analysis coverage and applicable area for crash prediction. Next, this chapter presents the 

approach to predicting crash frequency using the interchange as the base unit of analysis. Finally, 

this chapter discusses the approach to predicting crash severity using a fatal or injury crash as the 

base unit of analysis. 

INTERCHANGE INFLUENCE AREA

The general study area, or area of influence, necessary for conducting safety and operational 

analysis for interchange access improvements can vary substantially in size and scope. While 

access improvements should be considered on a case-by-case basis, the study area should 

generally include the components of the highway network adjacent to the interchange of interest 

that could impact, or be impacted by, the presence of the interchange(s) of interest. This highway 

network includes, at a minimum, the upstream and downstream interchanges and to the first 

adjacent signals on the crossroad.(2,4) The area of influence should be increased if safety and 

operational influences are likely to exist beyond those noted to capture the impacts to the broader 

system (e.g., impacts of traffic pattern changes on the crossroad network).(2) Figure 3 provides a 

sample influence area along a freeway mainline and crossroad for Florida DOT Interchange 

Access Requests (IARs).

(5)

 The study area, in this case, includes the upstream and downstream 

existing interchanges. 
12 

© 2020 Florida DOT.

AOI = area of influence.

Figure 3. Graphic. Area of influence for proposed interchange analysis in Florida.(5)

INTERCHANGE AREA

Due to the case-by-case nature of the influence area, the project team determined a planning-level 

predictive method based on the influence area was impractical. Therefore, the project team 

proposed the predictive method focus on the interchange area. 

Given the turbulence caused in the traffic stream, it is still difficult to isolate the effects of a 

single interchange by determining the interchange area when using a one-size-fits-all approach. 

Isolating the effects can be especially challenging when comparing interchange alternatives for 

one location—some interchange configurations are inherently longer than others, particularly 

with respect to the dimension of the interchange along the crossroad. A diamond or parclo

interchange with widely spaced ramp terminals spans a much longer length of the crossroad than 

a smaller interchange such as a tight diamond interchange (TDI) or an SPDI. As such, it was 

important to develop clear but flexible criteria to define the interchange area, adapting to the 

specific model application. 

Criteria for defining the interchange area are dependent on the location/geometry of the gores and 

speed change lanes (acceleration and deceleration lanes) on the mainline and the ramp terminal(s) 

on the crossroad. When applying the predictive models for safety comparisons, users should 

determine the maximum possible interchange area dimensions among the alternatives considered 

and apply these dimensions to all alternatives. For alternative interchanges with a smaller 
13 

footprint, users would apply the HSM predictive models to account for differences in interchange 

area dimensions among the alternatives. Using HSM predictive models is similar to evaluating 

safety for a curve that has been reconfigured to have a larger radius. The curve is lengthened to 

accommodate the larger radius. If the analyst does not account for the reduction in tangents on 

each side of the curve, then the new curve may have more predicted crashes than the old curve 

due to the increased overall length. For a fair comparison, analysts should account for the safety 

effect of reducing the tangents.

The project team considered the following two methods for evaluating the interchange area:

1. Use the farthest end of the taper on each side of the crossroad to define freeway influence. 

In cases where a lane add or drop is used (i.e., no taper exists), then a length of 1,500 ft 

from the farthest painted gore from the crossroad on each side would be used. For cases of 

closely spaced interchanges, this length would be reduced to the midpoint between the 

adjacent interchanges (measured from the nearest painted gores between adjacent 

interchanges). 

2. Apply the American National Standards Institute (ANSI) definition of an interchange 

area.(6)

 Under this definition, the interchange area extends 100 ft beyond the physical gore 

point furthest from the crossroad. It is also defined on the crossroad as extending 100 ft 

beyond the outermost curb return for each terminal. 

The difference is that method 1 provides an opportunity to consider the entire length of a speed 

change lane when a taper is present but may reduce the influence area when shorter speed change 

lanes are present—suggesting safety is only impacted upstream/downstream when longer speed 

change lanes are present. This assumption is not consistent with the findings of Bonneson et al., 

which suggested that lane changing impacts safety up to one-half mi upstream or downstream of 

a gore; but the effect is reduced as distance increases.(7)

The project team opted to use a modified form of the ANSI definition, which includes 1,500 ft 

upstream and downstream of the painted gore. This definition is consistent with the HSM 

approach for painted gores and is consistent with planning-level safety analysis, which 

commonly defines interchange influence as within 1,500 ft of the crossroad. This approach is 

advantageous for several reasons:

• The first approach is dependent on the location of tapers for interchange speed change 

lanes. At the planning stage, the rough location of gores may be known, but it is likely 

that acceleration and deceleration lanes are not yet defined, and assumptions will be 

needed on the location of the taper. Using a uniform 1,500-ft influence value eliminates 

the need to make assumptions regarding taper locations.

• The approach is standardized for all interchange configurations and for entrance and exit 

conditions (e.g., lane add/drop). The project team included data identifying the influence 

of adjacent facilities on safety performance, negating the need to address more formally

those in the definition of the interchange area.
14 

• The approach provides more uniformity across interchange configurations of different 

sizes and scopes as well as for older interchanges with more compressed features relative 

to newer interchanges in western States where space may be of less consideration.

Figure 4 provides a graphical representation of the interchange area for a diamond interchange. 

For cases where interchanges are closely spaced, the midpoint between the interchanges is used 

in lieu of 1,500 ft. 

Source: FHWA.

Figure 4. Graphic. Study interchange area definition.(8)

The crossroad interchange area is defined by characteristics on the outside legs of the crossroad 

ramp terminal(s). For a crossroad ramp terminal, the crossroad interchange area is defined as 

100 ft outside of the gore or curb return of the outermost ramp connection for each terminal. This 

definition is based on the description of an interchange crash from ANSI D16.1-2007.(6) Figure 5

shows how this measurement can be determined for an SPDI, while figure 6 provides an aerial 

markup of the identification on a compressed diamond (CD) interchange.(9) The total crossroad 

distance was measured and explored as a potential predictor variable in the CPM. Additionally, 

the project team captured the distance to adjacent features related to safety as outlined in the data 

collection section.
15 

© 2021 Cooperative Research Program (CRP). 

Figure 5. Graphic. Example crossroad interchange area for an SPDI.(9)

Original map: © 2021 Google® EarthTM. Modifications by FHWA to show the example ramps (see 

Acknowledgments section). 

Figure 6. Photo. Example crossroad interchange area.(10)
16 

CRASH FREQUENCY ANALYSIS METHODOLOGY

For this research, the project team developed a predictive method for planning-level safety 

prediction to compare interchange configuration safety performance. The methodology is 

consistent with the predictive methods included in the HSM for project-level analysis, 

particularly freeway segment, ramp, and ramp terminal project-level analysis.

Project-level safety performance functions (SPFs) are part of an overall CPM in Part C of the 

HSM.(3) A CPM estimates the predicted average crash frequency of a specific type of site (e.g., 

interchange) with specific geometric design elements and traffic control features. Each CPM in 

the HSM has the form shown in figure 7. 

Figure 7. Equation. HSM CPM.

Where:

Np = predicted average crash frequency, crashes/year. 

C = local calibration factor. 

NSPF = predicted crash frequency for a site with base conditions. 

AFi = adjustment factor (AF) for geometric design element or traffic control feature i (i=1 

to n). 

n = total number of AFs.

Each CPM includes an SPF, one or more AFs, and a local calibration factor. The SPF predicts the 

crash frequency NSPF for a site having characteristics that match a specified set of “base 

conditions.” These conditions describe the typical site’s design elements and control features 

(e.g., no left-turn lanes at terminals). The set of AFs is used to adjust NSPF such that the CPM can 

provide reliable estimates of the predicted crash frequency Np for sites that do not match all base 

conditions. In the case of planning-level models for interchange configuration, AFs focus more 

on the types of features present (e.g., numbers of lanes by type or traffic control at terminals) 

rather than specific geometric conditions (e.g., outside shoulder width), as they are generally not 

known at this stage. The calibration factor simply adjusts the base SPF estimate up or down to 

adjust predictions to match observed crash frequency for a local jurisdiction.

The project team considered developing separate SPFs using the following characteristics:

• Interchange configuration.

• Crash type—Multiple-vehicle versus single-vehicle crashes.

• Crash severity—Fatal and injury (KABC on the KABCO injury classification scale, 

where K represents fatal, A incapacitating injury, B non-incapacitating injury, and C 

possible injury crashes) versus property damage only (PDO) crashes (O on the KABCO 

scale). 

As described in chapter 4, sample sizes were not sufficient to estimate SPFs reliably for 

individual interchange configurations or by crash type. Therefore, the SPFs focus on KABC

crash frequency and PDO crash frequency separately.
17 

The predictive method for interchange areas provides crash frequency and severity by type for 

consistent application as necessary. The predictive models are consistent with the project-level 

predictive models in HSM Part C but function as planning-level inputs and analysis.(3) This 

aspect of the research project is important as the predictive models for interchanges will likely 

need to be combined with other HSM predictive models to provide evaluations for the entire 

influence area for IJRs. 

Figure 8 serves as the foundation for the interchange crash frequency CPM.

Figure 8. Equation. Interchange crash frequency CPM.

Where:

Ny,cs = predicted average crash frequency for crash severity (KABC or PDO) and for year y. 

y = time interval for reported crashes (i.e., evaluation period) per year.

L = segment length (freeway, crossroad, and ramp lengths are considered). 

b0 = regression coefficient for constant.

b1, b2, b3 = regression coefficients for freeway, crossroad, and ramp annual average daily 

traffic (AADT), respectively. 

AADTfr = freeway AADT.

AADTxs = crossroad AADT.

AADTr = ramp AADT (explored as total ramp AADT and AADT by ramp type). 

bi = regression coefficient for jurisdiction (i.e., State) i. 

Ii = indicator variable for State i (=1.0 if data corresponds to site i; otherwise 0).

For the full model, the project team inferred AFs from the multiple-variable regression model. 

The project team considered AFs for planning-level features (chapter 4), such as distance to 

adjacent interchange ramp gore, where the safety impact is consistent across interchange 

configurations. Additionally, the project team considered interactions to identify where and when 

a safety effect may differ from one interchange configuration to another. Chapter 4 provides 

specific interchange components considered and included in final crash frequency models.

The project team used a generalized database to estimate the planning-level CPMs. The new 

CPMs were coded as a multiple-variable regression model, and regression analysis was used to 

compute the regression coefficients. The AFs developed for the predictive method were inferred 

from the multiple-variable model such that they can be used together. Separate CPMs were 

estimated for fatal injury (FI) and PDO crash frequency.

For crash frequency modeling, count models are traditionally used to quantify the relationship 

between crash frequency and traffic volumes, design elements, and traffic control features. 

Negative binomial regression has commonly been applied to account for the overdispersion 

inherently found in crash data. The overdispersion parameter estimated from the modeling 

process is used in the development of the weight factor in the empirical Bayes analysis method.
18 

Recently, researchers have applied more sophisticated versions of count models to account for 

temporal and spatial correlations. Depending on the assumption of the correlation between 

unobserved effects and right-hand side variables, fixed- and random-effects models have been 

applied to account for temporal and spatial correlations. As models become more sophisticated, 

bias and inconsistency are generally lessened, improving the transferability of the model. 

However, more sophisticated models can be more time consuming to estimate, limiting the 

number of models that can be estimated during the modeling process. Additionally, more 

sophisticated models can prove to be more difficult to reach convergence with a larger number of 

predictor variables, limiting the number of geometric and operational features that can be 

included in the model specification.

The project team considered fixed-effects, random-effects, and mixed-effects models to account 

for unobserved correlations inherent in the data. The project team focused on negative binomial 

count models, as is the current state of the practice.

The project team used the variable introduction exploratory data analysis (VIEDA) approach, 

which differs from the typical approaches (forward and backward selection) used for regression 

modeling.(11) The intent of VIEDA is to answer the following two questions: 

1. Is the variable related to safety?

2. What is the proper functional form the variable should take in the CPM? 

The VIEDA approach is a two-step process. The first step is to develop a foundational model that 

includes key variables that define the facilities and exposure (e.g., AADTs, component lengths, 

interchange configuration). The foundational model is then used to predict crashes for each of the 

interchanges included in the model. In the second step, the observed crashes and fitted crashes are 

compared for each variable considered for inclusion in the model. If the ratio of observed crashes 

to fitted crashes exhibits a regular relationship with the variable considered for addition to the 

model, the variable is introduced, and the relationship exhibited is used to identify the proper 

functional form of the new variable. This methodology helps to identify the best form for 

continuous variables, as well as helps to identify potential dividing points for creating indicator 

variables. Once the variable is introduced to the model, the estimated parameters and associated 

standard errors are examined for the following:

• Is the direction of effect (i.e., expected decrease or increase in crashes) in accord with 

expectations? 

• Does the magnitude of effect seem reasonable?

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

• Does the estimated overdispersion parameter improve?

The project team asked these questions whenever a new variable was introduced to the model. In 

this manner, the project team gained an intimate understanding of the data and potential 

relationships. The process was iterative and allowed the team to look at potential holes and errors 

in the data as well as correlations impacting the development and refinement of the overall CPM. 
19 

CRASH SEVERITY ANALYSIS METHODOLOGY

While the CPM predicts crash frequency (by combined injury severity categories and crash 

types), the project team developed SDFs to predict the proportion of K, A, B, and C crash 

severity categories. The probability of each severity category is predicted as a function of traffic 

volume, geometry, and other interchange characteristics. The proportion is multiplied by the 

predicted crash frequency to obtain an estimate of the crash frequency for the corresponding 

severity category. 

This approach was shown by Avelar, Dixon, and Ashraf to be reliable for predicting crash 

frequency for individual severity levels, as opposed to developing crash severity-specific 

SPFs.(12)

 This approach is consistent with the project-level SPFs for freeways, ramps, and ramp 

terminals found in the HSM supplement.(1)

The project team used a multinomial logit model to estimate SDFs for the KABC CPM. The 

multinomial logit model allows for some variables to be constrained to have the same effect on 

each severity level while allowing other variables to have a differentiated effect among levels. 

For SDFs, the database was restructured such that the observed unit was the crash instead of the 

interchange. The FI crashes CPM can be combined with the SDF to estimate the number of 

crashes of different severity levels.

CRASH PREDICTION GOODNESS-OF-FIT MEASURES

The project team assessed the performance of the individual crash frequency models and 

compared alternative models to determine the most appropriate predictive models. The following 

are appropriate goodness-of-fit measures the project team used to assess the performance of new 

predictive models:

• Mean absolute deviation: Measures the average magnitude of variability of prediction. 

Smaller values are preferred to larger values in comparing two or more competing 

models. 

• Modified R2: Measures the amount of systematic variation explained by the CPM. Larger 

values indicate a better fit for the data in comparing two or more competing models. 

• Dispersion parameter: Measures the amount of variance in the CPM. All else being equal, 

a CPM with less dispersion is preferred (i.e., a smaller value is preferred to a model with 

more dispersion).

• Cumulative residual (CURE) plots: Figure 9 illustrates CUREs (observed minus predicted 

crashes) against a variable of interest sorted in ascending order (e.g., freeway traffic 

volume). CURE plots such as the one shown help to identify the following concerns: 

o Long trends: Trends in the CURE plot (increasing or decreasing) indicate regions of 

bias.

o Percent exceeding the confidence limits: CUREs outside the confidence limits 

indicate a poor fit over that range in the variable. CUREs frequently outside the 
20 

confidence limits indicate notable bias in the model. A reasonable upper threshold for 

the percent of the CURE plot exceeding the 95-percent limits is 5 percent.

o Vertical changes: Large vertical changes in the CURE plot are potential indicators of 

outliers in the dataset.

The project team used goodness-of-fit measures to evaluate alternative CPMs developed using 

the methods described. Additionally, the project team used CURE plots to assess residuals to 

determine the overall model fit to observed data. 

© 2021 Ohio DOT. 

Figure 9. Graph. Example CURE plot.(13)
21 

CHAPTER 4. DATA COLLECTION

INTRODUCTION

This chapter describes this project’s overall data collection process and the data integration effort 

to develop a combined project database for CPM estimation. An overview of the interchange 

configurations, crossroad ramp terminal configurations, ramp types, data elements collected, data

collection methods, and data quality measures that the project team employed are included. This 

chapter also details the data integration approaches for developing the final generalized database

and concludes with sample size details.

INTERCHANGE CONFIGURATIONS STUDIED

After surveying the FHWA division offices (results provided in chapter 2) and holding 

discussions with the project panel, the project team identified the following interchange 

configurations for inclusion in this study: 

• Conventional diamond. 

• CD. 

• TDI. 

• DDI. 

• SPDI. 

• Parclo type A2 and A4. 

• Parclo type B2 and B4. 

• Parclo type AB2 and AB4. 

The survey respondents also indicated that SR interchanges are commonly considered and 

installed as service interchanges; however, the project team uncovered that when questionnaire 

respondents mentioned SR interchanges, they were usually referring to diamond interchanges 

with roundabouts at the crossroad ramp terminals.

To simplify the questionnaire, the project team had not differentiated between types of parclo 

configurations when surveying the FHWA division offices. However, for the study design, data 

collection, and analysis, the project team focused on parclo configurations that are most 

commonly considered and installed (A2, A4, B2, B4, AB2, and AB4) and developed models with 

the intent of capturing any safety performance differences between these parclo types. Due to the 

sheer number of possible combinations, the project team did not include other parclo 

configurations in this research. 

Table 6 provides descriptions of the selected interchange configurations for inclusion in the 

predictive method. Additionally, figure 10 through figure 20 provide examples of those 

interchange configurations for reference. 
22 

Table 6. Descriptions of interchange configurations.

Interchange 

Configuration General Description

Example 

Image 

Conventional 

diamond 

A traditional diamond interchange includes relatively 

straight entrance and exit ramps leading from the mainline 

to the crossroad. Two ramp terminals are spaced at least 

800 ft apart.

Figure 10 

CD A CD interchange is a diamond with two ramp terminals 

spaced 400–800 ft apart.(7) Figure 11 

TDI

A TDI is a diamond interchange with signalized ramp 

terminals spaced 200–400 ft apart.(7,9) The signals on the 

two ramp terminals act in a coordinated effort, and turn 

lanes are created prior to the ramp terminal. No turn lanes 

are created between the ramp terminals.

Figure 12 

DDI

A DDI is a diamond interchange that includes traffic 

crossovers on the crossroad, so turning movements to and 

from the ramps do not conflict with through traffic.

Figure 13 

SPDI

This interchange is a variation of a diamond interchange 

in which all ramps meet at a single signalized intersection 

with the crossroad.(9) 

Figure 14 

Parclo A

In parclo A interchanges, the loop entrance ramp and 

diamond exit ramps are placed ahead of the crossroad. 

Parclo A4 also includes diamond entrance ramps beyond 

the crossroad. 

Figure 15 (A2) 

Figure 16 (A4)

Parclo B

In parclo B interchanges, the loop exit ramps and diamond 

exit ramps occur beyond the crossroad. Parclo B4 also 

includes diamond exit ramps ahead of the crossroad. 

Figure 17 (B2) 

Figure 18 (B4)

Parclo AB

A parclo AB interchange includes a combination of parclo 

A and parclo B features; typically, one direction includes 

the parclo A ramp orientations, and the other direction 

includes parclo B ramp orientations. 

Figure 19

(AB2)

Figure 20

(AB4)
23 

© 2021 Google® EarthTM. 

Figure 10. Photo. Aerial image of a conventional diamond interchange.(10)

© 2021 Google® EarthTM. 

Figure 11. Photo. Aerial image of a CD interchange.(10)
24 

© 2021 Google® EarthTM. 

Figure 12. Photo. Aerial image of a TDI.(10)

© 2021 Google® EarthTM. 

Figure 13. Photo. Aerial image of a DDI.(10)
25 

© 2021 Google® EarthTM. 

Figure 14. Photo. Aerial image of an SPDI.(10)

© 2021 Google® EarthTM. 

Figure 15. Photo. Aerial image of a parclo A2 interchange.(10)
26 

© 2021 Google® EarthTM. 

Figure 16. Photo. Aerial image of a parclo A4 interchange.(10)

© 2021 Google® EarthTM. 

Figure 17. Photo. Aerial image of a parclo B2 interchange.(10)
27 

© 2021 Google® EarthTM. 

Figure 18. Photo. Aerial image of a parclo B4 interchange.(10)

© 2021 Google® EarthTM. 

Figure 19. Photo. Aerial image of a parclo AB2 interchange.(10)
28 

© 2021 Google® EarthTM.

Figure 20. Photo. Aerial image of a parclo AB4 interchange.(10)

CROSSROAD RAMP TERMINAL TYPES AND TRAFFIC CONTROL

While some crossroad ramp terminals are consistently used for certain interchange 

configurations, there can be some variance, such as a traditional versus roundabout configuration 

at a diamond interchange crossroad ramp terminal. Table 7 describes the crossroad ramp terminal 

configurations and related interchange configurations included in this study. Sketches in figure 

21 through figure 29 provide examples of these crossroad ramp terminal configurations.

The project team also identified the traffic control for the ramp. The following forms of traffic 

control are valid for crossroad ramp terminals in this project:

• Signal control. 

• Minor road stop control. 

• All-way stop control.

• Roundabout control.

• Yield control. 

• No control. 

Note that right-turn movements may operate under different traffic control than the main 

terminal.
29 

Table 7. Summary of crossroad ramp terminal configurations.

Ramp 

Terminal 

Configuration Description

Potential 

Configurations 

Example 

Image 

D4

The intersection of a crossroad with an 

entrance ramp on one side of the road and an 

exit ramp on the other. Typical for diamond 

interchanges. 

Diamond, CD Figure 21 

A4

The intersection of a crossroad with a 

diamond exit ramp and loop entrance ramp 

on one side and a diamond entrance ramp on 

the other. Typical for a parclo A4 

interchange. 

Parclo Figure 22 

B4

The intersection of a crossroad with a 

diamond entrance ramp and loop exit ramp 

on one side and a diamond exit ramp on the 

other. Typical for a parclo B4 interchange. 

Parclo Figure 23 

A2

The intersection of a loop entrance ramp and 

an exit diamond ramp on the same side of the 

crossroad.

Parclo Figure 24 

B2

The intersection of a loop exit ramp and a 

diamond entrance ramp on the same side of 

the crossroad. 

Parclo Figure 25 

TDI

Two closely spaced signalized ramp 

terminals that function in a coordinated 

manner. 

TDI Figure 26 

SPDI A single signalized ramp terminal with the 

crossroad and all ramps. SPDI Figure 27 

DDI

A diamond interchange ramp terminal in 

which crossroad traffic temporarily flows in 

the opposite direction after crossing over at a 

signal. 

DDI Figure 28 

Roundabout 

A roundabout ramp terminal in which the 

circulating traffic has the right-of-way and 

traffic entering the roundabout enters under 

yield control.

Diamond, CD, 

parclo Figure 29 
30 

© 2014 AASHTO.

Figure 21. Graphic. D4 ramp terminals.(1)

© 2014 AASHTO.

Figure 22. Graphic. A4 ramp terminals.(1)

© 2014 AASHTO.

Figure 23. Graphic. B4 ramp terminals.(1)
31 

© 2014 AASHTO. 

Figure 24. Graphic. A2 ramp terminals.(1)

© 2014 AASHTO.

Figure 25. Graphic. B2 ramp terminals.(1)

© 2021 CRP.

Figure 26. Graphic. TDI.

(9)
32 

© 2021 CRP.

Figure 27. Graphic. SPDI.(9)

Source: FHWA.

Figure 28. Graphic. DDI ramp terminal.
33 

Source: FHWA. 

Figure 29. Graphic. Roundabout ramp terminal.

FREEWAY RAMP TERMINAL TYPE

This project considered two forms of merging and diverging movements: speed change lanes 

(acceleration and deceleration lanes) and lane adds/drops. Speed change lanes may be of the 

parallel or tapered form and are introduced near the interchange to facilitate the changes in speed 

required to navigate the ramp and the mainline. Both parallel and tapered speed change lanes are 

introduced or removed from the mainline using a taper. Lane adds and lane drops are auxiliary 

lanes that are added after the entrance of an on-ramp or removed from the freeway mainline at an 

exit ramp. In the cases of multilane ramps, there may be both a speed change lane and a lane add 

or lane drop. The project team further identified locations of any lane adds or lane drops within 

an interchange if not directly associated with an entrance or exit ramp. Lane adds and drops are 

provided at interchanges periodically and are often correlated with interchange type. 

The project team also identified crossroad ramp terminals with merging or diverging movements. 

FREEWAY RAMP TYPE

While there are many labels and definitions for ramps, the project team consolidated ramps into 

three standard forms for this project:

• Diamond ramp: A direct ramp between the crossroad ramp terminal to the mainline; it 

may include some curvature on the ramp. 
34 

• Loop ramp: A looping connection between a crossroad ramp terminal and the mainline; it 

includes a single or compound curve that results in a roughly 180-degree change in 

direction for the vehicle. The crossroad ramp terminal may be controlled or free flowing. 

• Outer connection: This direct, free-flowing connection between the crossroad and the 

mainline is typically located on the outer portion of the interchange. The crossroad ramp 

terminal is usually free flowing. 

Figure 30 provides an aerial image of an interchange that contains all three ramp types. The 

southwest and southeast quadrants of the interchange contain diamond ramps and loop ramps, 

while the northeast quadrant contains an outer connection ramp. 

© 2021 Google® EarthTM. 

Figure 30. Photo. Aerial view of an interchange containing diamond ramps, loop ramps, 

and direct connection ramps.(10) 

DATA COLLECTION PRIORITIZATION

The project team collected data at existing interchanges of the types described in the Crossroad 

Ramp Terminal Types and Traffic Control, Freeway Ramp Terminal Type, and Freeway Ramp 

Type sections. The project prioritized States for inclusion in the dataset based on several factors, 

including the following: 

• Geographic diversity: The project team identified States from across the United States to 

generalize the results.
35 

• Large sample sizes: By choosing States with large sample sizes of the interchange types, 

the project team reduced the number of States needed for the project. 

• Wide-scale implementation: The project team chose States with wide-scale 

implementation of any interchanges of the configuration described previously in figure 1. 

• Available and integrable data: The project team chose States with easy-to-work-with 

roadway, traffic, and crash data. 

• Accessible ramp traffic and crash data. Not every State has ramp data (particularly traffic 

data) available. While geometrics are less important for planning-level analysis, traffic 

data are the most important predictor of crash frequency and should be incorporated at all 

levels of interchange analysis.

Table 8 summarizes the States from which the project team collected data for each interchange 

configuration. The table provides the sample size collected within each State. The project team, 

to the extent possible, attempted to balance data collection across States and interchange types by 

State. However, for less common interchange configurations, such as DDIs, the project team 

collected data from all available locations that had at least 2 yr of data. 

Table 8. Summary of number of responses by State for interchange data collection. 

Interchange 

Configuration Arizona Missouri

North 

Carolina Ohio Utah Total 

Conventional 

diamond 11 11 9 13 19 63 

CD 4 0 3 5 3 15 

TDI 7 7 2 4 3 23 

DDI 0 12 3 2 7 24 

SPDI 13 9 7 3 16 48 

Parclo A2 0 3 5 2 0 10 

Parclo A4 0 1 7 7 1 16 

Parclo B2 2 1 4 3 1 11 

Parclo B4 0 0 6 2 0 8 

Parclo AB2 4 9 11 9 2 35 

Parclo AB4 2 0 2 5 1 10 

DATA COLLECTION PROCESS

The data collection process consisted of the following: 

• Identify interchange and document the location of key interchange components. The 

project team identified the locations of the following components at each interchange: 

o Freeway. 

o Crossroad. 

o Ramp segment. 

o Crossroad and freeway ramp terminals.
36 

• Download the linear referencing system (LRS) for each State.

• Collect and integrate data from available roadway inventory databases, including key 

cross-section components and traffic volume data.

• Validate roadway inventory data and collect supplemental data using online satellite and 

street-level imagery to assist in data collection. Additionally, use geographic information 

system (GIS) software for supplemental data collection.

• Download crash files for all States from the Highway Safety Information System (HSIS) 

or State database, DOT GIS maps, and directly from the State’s DOT office.(14)

• Integrate crash data using the LRS for interchange areas. 

The following subsections provide further details for each step in the process, discussion of any 

challenges observed, and steps taken to overcome those challenges.

Identify Interchange Locations and Type

The project team first conducted a demonstration using North Carolina’s data to identify the 

location and type of interchange and establish an approach to building an interchange area in 

GIS. The demonstration aimed to determine how much effort was required to find interchanges 

other than diamond and to develop a proof-of-concept for automating the process of building the 

interchange area.

The purpose of building an interchange area in GIS was to flag crashes mapped to GIS occurring 

within the interchange area since interchanges consist of multiple facilities. For example, a 

standard diamond interchange consists of the freeway section, the crossroad section, and four 

individual ramps. These sections would be identified as six distinct facilities within a roadway 

inventory with six distinct beginning and ending points for pulling crash statistics. On a large 

scale, identifying and pulling all crashes for an interchange would require a great deal of effort. 

Using an automated approach, the project team could spend more time identifying interchanges 

and capturing supplemental data. 

Additionally, the North Carolina DOT provided a web-based application with interchanges 

mapped and classified by type and construction year. The project team exported these data to 

identify interchanges meeting certain criteria (e.g., opened to traffic for at least two of the study 

years). Additionally, the project team filtered the pool of interchanges to only those included in 

the study design to identify potential candidates for data collection. The demonstration effort 

showed that staff could efficiently identify and determine interchange types and that the GISbased approach would work well for merging data, including crash data. The demonstration effort 

showed that annual crash frequency, for interchanges as a whole, was relatively consistent from 

year to year. Based on the results of the demonstration effort, the project team moved forward 

with data collection for all five States.
37 

LRS Download

The data collection process included downloading the LRS for all five States in the study area to 

GIS software. The team could then visually connect the data in a GIS platform to a base map that 

served as a merging point for crash data. The project team identified interchange areas with the 

following criteria:

• Located 1,500 ft from the furthest painted gore on each side of the crossroad.

• Located 100 ft from the ramp curb return on the outside of each crossroad ramp terminal.

The project team used GIS to manually drop points and identify the interchange. Once each side 

of the freeway, crossroad, and ramps was identified, the team took the following steps to create a 

model to draw lines to connect the points: 

1. Locate features along routes: 

a. Input route features: State LRS. 

b. Input features: Interchange points. 

c. Search radius: 10 ft. 

d. Route identifier field: RID. 

e. Event type: Point. 

2. Add fields: 

a. Field name: From_Milepost. 

b. Field type: Double. 

c. Field name: To_Milepost. 

d. Field type: Double. 

3. Calculate field: 

a. Field name: From_Milepost. 

b. Expression: From_Milepost = !MEAS!. 

4. Calculate field: 

a. Field name: To_Milepost. 

b. Expression: To_Milepost = !MEAS!. 

5. Make route event layer: 

a. Input route features: State LRS. 

b. Route identifier field: Unique route identity (ID) for the routes in the State. 

c. Input event table: Table output from step 4 of the model. 

d. Route identifier field: RID. 

e. Event type: Line. 

f. From_Measure field: From_Milepost. 

g. To_Measure field: To_Milepost. 

6. Copy features: Copy the table output from step 5. 
38 

7. Dissolve: 

a. Dissolve fields: RID, interchange ID. 

b. Statistics fields: From_Milepost: Minimum. 

c. To_Milepost: Maximum. 

8. Make route event layer: 

a. Input route features: State LRS. 

b. Route identifier field: Unique route ID for the routes in the State. 

c. Input event table: Table output from step 7 of the model. 

d. Route identifier field: RID. 

e. Event type: Line. 

f. From_Measure field: MIN_From_Milepost. 

g. To_Measure Field: MIN_From_Milepost. 

9. Copy features: Copy the output from step 8. This output is a line file that displays the 

interchange area of study.

The project team identified several issues while running this model. The primary issue was that 

not all lines were drawn when running the model, which resulted from one of the following 

reasons:

• The LRS centerline ended between the points, or multiple LRS centerlines were in the 

interchange area. To overcome this problem, the project team added multiple points and 

reran the model to draw the lines or trace over the LRS and extend the line to the end of 

the study area.

• The LRS had multiple lines drawn on top of each other:

o In Missouri and Arizona, the State LRS file had multiple lines drawn on top of each 

other for co-routes. For example, U.S. 40 and I–70 were drawn on top of each other.

o To solve this issue, the project team highlighted the route, exported the route to a new 

layer, deleted one of the routes so that the model was identified, and drew a line for a 

single route only.

• For routes that contained a roundabout, the model had to rerun multiple times to draw the 

roundabout, or parts of the roundabout had to be manually traced and drawn.

Figure 31 shows an example interchange area mapped in GIS software after running the LRSbased model. While the freeway/ramp gores are not aligned on each side of the crossroad, the 

interchange area includes the same length in both directions of the freeway. The interchange area 

includes the entire freeway section within the identified bounds, all four ramps, and the entire 

length of the crossroad within the identified bounds.
39 

Original map: © 2022 ESRITM, NC CGIA, Maxar. Modifications by FHWA (see Acknowledgments section).

Figure 31. Photo. Example interchange area in GIS.

(15)

Once all the interchange areas were drawn, the project team identified overlaps between adjacent 

interchange areas. In these cases, the project team identified the halfway point between the 

overlap and moved the interchange lines to avoid overlap. Table 9 indicates the interchanges with 

overlap and the extent of the overlap.

Table 9. Interchanges that overlapped.

State Interchange ID Overlap (ft) Halfway (ft)

Arizona

AZ 2 and AZ 23 605.36 302.68

AZ 11 and AZ 12 150.48 75.24

AZ 12 and AZ 13 302.57 151.285

AZ 32 and AZ 33 782.85 391.425

AZ 35 and AZ 36 2,097.17 1,048.585

AZ 36 and AZ 40 2,354.78 1,177.39

AZ 40 and AZ 37 2,092.21 1,046.105

Utah UT 123 and UT 124 1,320.5 660.25

UT 130 and UT 306 745.61 372.805

Ohio OH 40 and OH 41 1,328.52 664.26

Missouri MO 319 and MO 320 1,190.40 595.20

MO 863 and MO 864 349.1 174.55

Collect and Integrate Roadway Inventory Data 

The project team began the data collection and integration process by mapping existing roadway 

inventory data with freeways, segments, and ramps when available. General data elements 

collected from roadway inventory included functional class, number of through lanes, median 

type, AADT, and speed limit. The project team integrated LRS data based on segment IDs and 

mileposts, collected data for each facility component, and consolidated data as needed. For 

example, the project team collected each ramp’s AADT volume and then aggregated the values 

into entrance ramp AADT, exit ramp AADT, and total AADT.
40 

The project team used FHWA’s HSIS to download roadway and traffic data for North Carolina 

and Ohio facilities. The project team worked with Arizona, Missouri, and Utah DOTs to collect 

roadway and traffic data for facilities in these States.

Roadway Inventory Validation and Supplemental Data Collection

The project team used satellite and street-level imagery, along with State video logs, to validate 

roadway inventory data and collect supplemental data outlined in the project’s data collection 

plan. Street-level imagery and video log files allowed the project team to verify the presence and 

amount of features (e.g., number of lanes on the major road), while Google Earth allowed the 

project team to verify the presence of features and to conduct measurements when necessary. 

Additionally, the project team took measurements in GIS software to support data collection as 

needed.

Figure 32 provides an example of the data collection process, including identification of the 

primary and secondary directions. This process facilitated data collection in both travel directions 

separately. The project team postprocessed directional data to determine the values of data 

elements in the crash frequency model interchange database.

In the United States, mileposts traditionally increase in the eastern direction for east-west routes 

and in the northern direction for north-south routes. In figure 32, this mainline is primarily an 

east-west route; therefore, the eastern direction is identified as the primary direction and the 

western direction as the secondary direction. For each interchange, the project team used route 

numbering to identify the primary direction (even-numbered routes are primarily east-west, oddnumbered routes are north-south) and guide the data collection. 

Figure 32 provides an example where the entrance ramps have a parallel merge (PM), and the 

exit ramps have a parallel diverge and taper diverge. These features were individually coded into 

the data collection dataset and then processed in the analytical database to read as PMs and 

variable diverges. Additionally, figure 32 provides an overview of data collection by component, 

noting the presence of loop ramps and diamond ramps.
41 

Original map: © 2021 Google® EarthTM. Modifications by the authors (see Acknowledgments section).

Figure 32. Graphic. Sample component breakdown of a parclo AB2 interchange.(10)

Table 10 through table 12 provide an overview of the following:

• Table 10: Data collection elements collected for ramp segments. 

• Table 11: Freeway and crossroad segments. 

• Table 12: Crossroad ramp terminals.

Ramp data, in particular, were collected at the individual ramp level and aggregated into 

combined measures in the analysis dataset. As noted previously within the Collect and Integrate 

Roadway Inventory Data section, the project team looked at total entrance, total exit, and total 

ramp AADTs rather than individual ramp AADTs. The project team also computed the 

coefficient of variation (COV) for ramp AADTs to look at the impact of flow differential among 

ramps and its impact on safety performance.
42 

Table 10. Data elements for ramp segments.

Category Attribute Description

General

State Indicator for State

Interchange ID Unique interchange identifier

GPS_loc GPS latitude and longitude

Ramp_type Indicator for entrance or exit ramp

Satellite 

imagery

street-level 

imagery

GIS software

Ramp_length Ramp length in miles

Ramp_fr_type Ramp freeway terminal type (parallel or taper)

Ramp_side Indicator for ramp side (left or right)

Ramp_meter Indicator for presence of ramp meter 

Ramp_lanes Number of ramp through lanes

State agency 

LRS Ramp_AADT Ramp AADT

GPS = Global Positioning System.
43 

Table 11. Data elements for freeway and crossroad segments.

Category Attribute Description

General

State Indicator for State 

Interchange_ID Unique interchange identifier

Freeway_ID Freeway route number

Crossroad_ID Crossroad name or route number

GPS_loc GPS latitude and longitude

Config Interchange configuration

Area_type Urban or rural area type

Satellite 

imagery

street-level 

imagery

GIS software

Int_skew Skew angle between freeway and crossroad

Xr_loc Overpass or underpass crossroad location

Ml Indicator for presence of a managed lane

Fr_len Freeway length in interchange area

Quadrants Interchange number of quadrants (two or four)

Parclo_T Parclo type (A, B, or AB)

Xr_len Crossroad length in interchange area

Term_len Length between crossroad terminal midpoints

Cd_rd Indicator for presence of a collector-distributor road

Min_adj_int_Xr Minimum adjacent crossroad-to-crossroad distance 

Avg_adj_int_Xr Average adjacent crossroad-to-crossroad distance

Min_adj_int_gore Minimum adjacent gore-to-gore distance

Avg_adj_int_gore Average adjacent gore-to-gore distance

Min_adj_sig_int_ Minimum adjacent terminal to signal distance

Avg_adj_sig_int Average adjacent terminal to signal distance

Weave_ind Indicator for weaving section at interchange boundary

Ln_add_drop Indicator for lane add or drop presence

Adj_land_use Primary adjacent land use

Xr_sidewalk Indicator for presence of sidewalk on crossroad

Xr_bike Indicator for presence of bicycle facility on crossroad

Light Indicator for presence of interchange lighting

Cons_Yr Indicator for construction by year at interchange

State agency 

LRS

Free_fun_class Freeway functional classification

Xr_fun_class Crossroad functional classification

Fr_lanes Number of freeway through lanes

Xr_lanes Number of crossroad through lanes

Fr_med_type Freeway median type 

Fr_AADT Bidirectional freeway AADT

Xr_AADT Bidirectional crossroad AADT

Fr_PSL Freeway posted speed limit

Xr_PSL Crossroad posted speed limit
44 

Table 12. Data elements for crossroad ramp terminals.

Category Attribute Description

General

State Indicator for State

Interchange ID Unique interchange identifier

GPS_loc GPS latitude and longitude

Xr_term_ID Unique crossroad ramp terminal identifier

Xr_term_type Crossroad ramp terminal type

Satellite 

imagery

street-level 

imagery

Xr_term_ctrl Crossroad ramp terminal traffic control type (signal, 

all-way stop, ramp stop, yield, roundabout, no control)

Xr_term_lt_sig Crossroad ramp terminal left-turn signalization type 

(protected, permitted, protected/permitted, N/A)

Xr_term_rt_ctrl Crossroad ramp terminal right-turn control (signal, 

stop, yield, no control)

Xr_term_rt_chan Indicator for crossroad ramp terminal right-turn 

channelization

Xr_term_lt_lane Crossroad ramp terminal crossroad left-turn lanes

Xr_term_rt_lane Crossroad ramp terminal crossroad right-turn lanes

Xr_term_ramp_lt Crossroad ramp terminal ramp left-turn lanes

Xr_term_ramp_rt Crossroad ramp terminal ramp right-turn lanes

Xr_term_nonramp Indicator for nonramp crossroad ramp terminal leg

Xr_term_dway Number of driveways within 250 ft of crossroad ramp 

terminal on outside legs

Xr_term_acc_ln Presence of acceleration lane at crossroad ramp 

terminal

Xr_term_decel_ln Presence of deceleration lane at crossroad ramp 

terminal

Xr_term_cr_wlk Presence of painted crosswalk at crossroad ramp 

terminal

Xr_term_ped_ln Number of lanes crossed by pedestrians on crossroad

Xr_ramp_ped_ln Number of lanes crossed by pedestrians on ramps

Xr_term_ped_sig Presence of pedestrian signals at crossroad ramp 

terminals

Xr_term_ped_rt Number of uncontrolled right-turns conflicting with 

pedestrian crossings

Xr_term_frontage Indicator for frontage road

State agency 

LRS Xr_term_nr_aadt Crossroad ramp terminal nonramp leg AADT

N/A = not applicable.

Crash Data Download

As with the roadway inventory and traffic data, the project team used FHWA’s HSIS to 

download crash data for North Carolina and Ohio facilities.(14)

 Arizona, Missouri, and Utah 

DOTs provided crash data for facilities in their States. The project team collected 2014 through 

2020 crash data for Arizona; 2016 through 2020 data for Missouri, North Carolina, and Ohio; and 
45 

2013 through 2020 for Utah. The project team integrated and used the most recent 5-yr (2016 

through 2020) data for each State.

The project team used satellite and street-level historic imagery at each site to confirm the 

interchange existed in its present form for the entire duration of the study period.(10) If an 

interchange had undergone construction during the study period, the years of construction activity 

were dropped from the dataset. The project team removed any interchanges from the dataset if at 

least 2 full yr of data were not available. Additionally, for newer interchange configurations (such 

as the DDI), the interchange was included if it was open for at least 2 yr during the study period.

The project team grouped crashes into KABC crashes and PDO crashes for crash frequency and 

severity model development. The project team classified crashes based on the KABCO scale, 

defining K, A, B, or C as “fatal and injury” and O as “PDO” crashes.

Crash Data Integration

Once all the interchange lines were drawn, the project team integrated crash data. Crash data 

came as spreadsheet data files and GIS point files. The following were the steps taken for each of 

the crash files:

• GIS point files—Perform a spatial join: 

o Target features: Crash file. 

o Join features: Interchange line file. 

o Output feature class: State_Crash.

o Join operation: Join one-to-one. 

o Uncheck the box “Keep All Target Features.” 

o Match option: Intersect. 

o Search radius: 20 ft. 

• Excel files: 

o Make route event layer: 

 Input route features: State LRS. 

 Route identifier field: Unique State route identifier. 

 Input table: Crash file. 

 Route identifier field: Unique State route identifier. 

 Event type: Point. 

 Measure field: Milepost field in crash file. 

 Layer name: State crashes. 

o Perform a spatial join. 

Figure 33 provides a sample interchange with crash points merged to the interchange area. 

Arizona, Ohio, and Utah all had crashes reported on both inventory and noninventory directions

of the road and onramps. Missouri’s crashes were all associated with the interchange’s latitude

and longitude. North Carolina only reported crashes on the inventory side of the roadway. 

Crashes were not represented on ramps or the noninventory side. Figure 34 provides an example 

graphic of an interchange in North Carolina where crashes are only represented on the inventory 

side of the roadway. This situation did not cause data collection issues since the database treated 

each interchange as the unit of observation rather than the component of the interchange.
46 

Original map: © 2022 ESRITM, NC CGIA, Maxar. Modifications by FHWA (see Acknowledgments section).

Figure 33. Photo. Interchange with crash points.(15)

Original map: © 2022 ESRITM, NC CGIA, Maxar. Modifications by FHWA (see Acknowledgments section).

Figure 34. Photo. Interchange showing crashes on the inventory side of the roadway.(15)

The project team encountered some issues merging the GIS crash files. For example, some States 

had crashes that were located more than 20 ft away from the roadway’s centerline. The project 

team had to add these crashes into the crash file. In North Carolina, two interchanges had crashes 

reported on both the inventory and noninventory side of the roadway. The interchanges that had 

noninventory crashes were NC 273 and NC 948. In addition, NC interchange 560 had one crash 

reported on a ramp.

DATABASE SUMMARY

This section summarizes the data assembled for estimating planning-level predictive models for 

interchanges by type. As noted, the project team originally anticipated developing separate 

databases for each interchange configuration, but this would have led to sample size issues and 

possible counterintuitive results for safety performance effects for features common to multiple 

interchange configurations. The crash frequency database consists of one master database file 

with separate outcome variables for KABC crashes and PDO crashes.

The project team separately arranged the data into an SDF development dataset. This dataset 

treats the crash as the outcome of interest and interchange features, including configuration, as 

input variables for the CPM. This dataset used the same data as the crash frequency database, and 

the resulting models can be used together to predict crash frequency at the interchange level for 

specific crash severity.
47 

Table 13 provides summary data, including AADT range and number of KABC and PDO crashes 

by interchange configuration. Not all interchange configurations had sufficient sample sizes to 

estimate separate predictive models. Therefore, a pooled approach was taken, using a minimum 

of 300 crashes per year. This approach is consistent with the recommendation by Srinivasan and 

Bauer to include at least 300 crashes per year for the crash type and severity category of 

interest.(16)

 

Table 13. Summary data for geometric, operational, and crash data. 

Int. Config. Freeway AADT 

Range

Crossroad 

AADT Range

Entrance 

Ramp AADT

Exit Ramp 

AADT

KABC 

Crashes

PDO 

Crashes

Diamond 5,000–210,000 350–40,500 100–33,400 125–24,500 2,139 7,157

CD 23,100–236,000 11,000–52,900 6,800–25,500 4,250–22,500 1,246 4,278

TDI 17,000–207,300 3,200–55,000 4,000–36,500 4,500–36,700 4,552 13,659

DDI 29,000–191,000 2,000–47,000 2,000–38,500 2,000–45,000 4,037 10,883

SPDI 21,000–261,000 3,700–64,000 3,100–70,000 3,200–75,000 11,964 38,102

Parclo A2 6,400–115,300 1,500–30,615 650–9,400 1,300–21,800 673 1,283

Parclo A4 46,181–135,000 12,000–68,000 10,200–34,400 9,300–39,600 1,032 3,663

Parclo B2 7,298–123,000 150–32,000 35–14,800 35–12,400 744 1,789

Parclo B4 23,900–144,000 1,200–67,500 4,900–32,200 4,300–31,000 555 1,855

Parclo AB2 5,500–300,000 200–51,500 200–29,200 200–24,600 3,144 9,841

Parclo AB4 22,000–132,300 9,000–57,000 5,600–27,600 5,500–27,200 423 1,253

Int. Config. = interchange configuration.

Table 14 provides an overview of the summary statistics for continuous variables for geometric, 

operational, and crash data. The summary statistics include the average (or mean), standard 

deviation, minimum, and maximum values for each characteristic. The project team used 

summary statistics for the initial screening of elements for potential outliers in the dataset. The 

project team further scrutinized or removed sites with unknown or extreme values from the 

dataset.
48 

Table 14. Summary information for geometric, operational, and crash data. 

Variable Average St. Dev. Minimum Maximum

Interchange skew angle (degrees) 12.84 14.13 0 60 

Freeway through lanes (both directions)

(No.) 5.84 2.11 4 12 

Crossroad through lanes (both directions) 

(No.) 3.77 1.30 2 6 

Freeway AADT (No.) 79,612 59,593 5,028 300,000 

Crossroad AADT (No.) 20,500 15,617 168 68,000 

Freeway posted speed limit (mph) 65.19 5.63 35 75 

Crossroad posted speed limit (mph) 42.02 9.02 20 65 

Interchange length along freeway (miles) 0.57 0.19 0.1 1.15 

Interchange length along crossroad (miles) 0.21 0.09 0.07 0.59 

Crossroad ramp terminal separation (miles) 0.15 0.09 0 0.8 

Adjacent crossroad minimum distance 

(miles) 1.89 1.44 0.36 9.65 

Adjacent crossroad average distance (miles) 2.52 1.78 0.51 10.45 

Adjacent gore minimum distance (miles) 1.38 1.41 0.13 8.97 

Adjacent gore average distance (miles) 2.00 1.76 0.17 9.74 

Adjacent intersection minimum distance 

(miles) 3.83 37.98 0.05 550 

Adjacent intersection average distance 

(miles) 5.82 47.53 0.05 710 

Combined exit ramp length (miles) 0.61 0.21 0.19 1.46 

Combined entrance ramp length (miles) 0.64 0.25 0.20 1.91 

Combined entrance ramp AADT 13,400 11,229 33 69,985 

Combined exit ramp AADT 13,383 11,210 15 74,985 

Driveway access points (No.) 0.69 1.27 0 7 

Maximum number of lanes crossed by 

pedestrians 4.82 2.48 0 10 

Pedestrian right-turn conflicts (No.) 0.48 1.06 0 7 

Interchange area (mi2) 0.12 0.07 0.01 0.42 

Fatal and injury crashes per year 29.00 44.23 0 228.60 

PDO crashes per year 89.13 140.66 0 746.80 

St. Dev. = standard deviation; No. = Number of. 

Table 15 provides an overview of the summary data for frequency variables included in the final 

dataset. The project team used these variables primarily as indicators in the dataset to describe the 

presence or absence of features or to distinguish between geometric design characteristics. The 

project team used indicator frequencies to the extent possible to better understand the sample size 

underlying the coefficients estimated in the model to attempt not to include factors with very 

small samples. In some cases, continuous data provided in table 14 were converted to categorical 

variables when indicated by preliminary data analysis. For example, the project team identified 
49 

that a skew angle of 30 degrees or greater was associated with a difference in safety performance 

relative to interchanges with skew less than 30 degrees.

Table 15. Summary information for categorical data. 

Category Characteristic

Percent 

Frequency

Ramp meter

Present 13.69 

Not present 86.31 

Nonramp leg at terminal Present 7.22 

Not present 92.78 

Crossroad ramp terminal control 

All-way stop 0.40 

Ramp stop 22.81 

Roundabout 6.84 

Signal 69.58 

Yield 0.38 

Area type 

Rural 23.19 

Urban 76.81 

Crossroad location Overpass 56.65 

Underpass 43.35 

Weaving section at interchange area boundary Present 14.45 

Not present 85.55 

Frontage road Present 9.20 

Not present 90.80 

Sidewalk Present 45.63 

Not present 54.37 

Lighting Present 80.61 

Not present 19.39 

Freeway median type 

Rigid barrier 41.06 

Semirigid barrier 23.57 

Flexible barrier 12.17 

Depressed/grass 23.20 

Collector distributor road Not present 96.96 

Present 3.04 

Managed lane 

Not present 85.17 

Present 14.83 

51 

CHAPTER 5. PREDICTIVE MODEL DEVELOPMENT

INTRODUCTION

This chapter describes the development of the planning-level CPMs and the overall findings for 

interchange safety performance by configuration. It includes an overview of the methods 

considered and employed for developing statistical models, the final variables included in the 

CPMs (and their interpretations), and the final models recommended for assessing the safety 

performance of interchange configurations and associated severity distributions.

The CPMs in this chapter may be used to predict crash frequency for an interchange area as 

defined in chapter 3. The CPM predicts all crashes within the interchange area and does not 

provide a breakdown by facility component (i.e., ramp terminals, ramps, freeway segment, or 

crossroad segment). The CPM includes an SPF, a set of AFs, a calibration factor, and an SDF. 

The project team designed this methodology to be consistent with the project design-level 

approach provided in Part C of the HSM.(3)

 While the HSM Part C focuses on individual designlevel components, this application focuses on planning-level inputs and interchange area crash 

frequency and severity analysis. 

The interchange area may not be the same size for all alternatives considered. For a fair 

comparison, alternative analysis should consider the largest interchange area or focus on the 

interchange influence area as the baseline for all analyses. The HSM Part C predictive method 

can supplement this analysis for other components of the interchange influence area or for the 

difference in interchange area along the freeway mainline or crossroad.(3)

MODELING APPROACH

The project team worked with FHWA to decide on a model form to fit within the project 

constraints and yield the most reliable predictions possible. Since the CPM includes a pooled set 

of interchanges across varying configurations, the model development databases include at least 

300 crashes per year, which adheres to recommendations by Srinivasan and Bauer.

(16)

Additionally, because the project team was developing SDFs, there was less of a need to develop 

separate CPMs for each crash severity level. 

The project team began by developing the KABC crash predictive model and then used those 

results to develop a PDO crash predictive model. Once the project team completed the KABC

model, they tested the AFs in the PDO crashes model. The project team further assessed the 

merits of predictors not included in the KABC model, particularly those expected to influence 

PDO crash frequency.

As indicated in chapter 3, the project team used VIEDA, as outlined in The Art of Regression 

Modeling in Road Safety, to select and include variables in the predictive model.(11)

 The project 

team retained variables, or AFs, in the KABC and PDO models if the VIEDA identified a 

relationship with safety outcomes and if the magnitude of effect was consistent with intuition. 

The project team did not hold to a strict confidence level when assessing statistical significance 

for inclusion in the models. 
52 

Due to the overall sample size of data collected for CPM development, the project team could not 

use a hold-out process for model validation. Ideally, the project team would have withheld data 

from one State to evaluate model fit and transferability. However, since such a broad range of 

interchange configurations was gathered, the project team had too few sites to withhold for any 

one State. Further, using a 70-percent training and 30-percent testing dataset for calibration was 

considered but deemed impractical due to overall sample sizes. Therefore, the project team relied 

on model fit statistics and CURE plots to assess the validity of the models.

STATISTICAL ANALYSIS METHODS

The current state of the practice for developing statistical road safety models is to assume a loglinear relationship between expected crash frequency and site characteristics. Generalized linear 

modeling techniques were applied to develop the models, and a log-linear relationship was 

specified using a negative binomial error structure. The negative binomial error structure allows 

for overdispersion that is often present in crash data (i.e., the variance is greater than the mean). 

Typically, in statistical road safety modeling, this “additional dispersion” is specified as a 

dispersion parameter multiplied by the expected number of crashes squared (NB-2 model). 

The models considered for this analysis were structured as shown in figure 35. 

Figure 35. Equation. Structure of prediction models.

Where:

λi = expected number of crashes for the ith observation, with an observation in this analysis 

being a segment year. 

Xi = a matrix of explanatory variables associated with λi, including traffic volume (AADT), 

geometric design features, and operational characteristics. 

β = a vector of parameters to be estimated that quantify the relationships between the 

explanatory variables and λi. 

εi = a disturbance term, where exp(εi) is gamma-distributed, with a mean equal to one and 

variance equal to αi. 

The NB-2 model’s variance in the expected number of crashes is then written as λi + αiλi

2

. The 

base SPFs and AFs are inferred from the resulting NB-2 model. 

FATAL AND INJURY CRASH FREQUENCY PREDICTION MODEL

This section describes the development of predictive model equations, based on KABC crash 

data, in the following five subsections: 

• The Model Development subsection describes the structure of the predictive equations as 

used in the regression analysis. 

• The Model Estimation subsection describes the regression statistics for each of the 

estimated equations (i.e., the proposed safety prediction models). 
53 

• The Model Evaluation subsection briefly describes the model fit parameters used to assess 

model validity. 

• The Estimated AFs subsection describes the estimated AFs. 

• The Sensitivity Analysis subsection provides a sensitivity analysis of the predictive model 

equations over the applicable range of AADT. 

Model Development

The regression model predicts KABC crash frequency for the interchange area. The generalized 

form of the CPM shows all the AFs in the model. Indicator variables determine which AFs are 

applicable to each observation in the dataset based on the associated interchange configuration 

type. 

The generalized form consists of AFs with indicator or continuous variables used to determine 

when the AF is applicable or applicable to a specific extent. For example, the generalized form of 

the area type AF includes an indicator variable that is used to determine when the subject 

interchange is applicable. The number of left-turn lanes on crossroad approaches to crossroad 

ramp terminals is considered a base condition of zero-turn lanes.

Figure 36 through figure 46 describe the generalized regression model.

Figure 36. Equation. Generalized KABC CPM.

with

Figure 37. Equation. KABC SPF.

Figure 38. Equation. Freeway AF for five and six lanes. 

Figure 39. Equation. Freeway AF for more than six lanes. 
54 

Figure 40. Equation. Crossroad AF for more than four lanes. 

Figure 41. Equation. Area type AF. 

Figure 42. Equation. Interchange skew AF.

Figure 43. Equation. Nearby gore AF. 

Figure 44. Equation. Managed-lane AF.

Figure 45. Equation. Crossroad left-turn lanes AF.

Figure 46. Equation. Ramp volume COV AF.

Where:

AADTfr = freeway bidirectional AADT volume (vehicles/day). 

AADTr = total ramp AADT volume (vehicles/day). 

AADTxr = crossroad bidirectional AADT volume (vehicles/day). 

AFat,kabc = AF for area type for KABC crashes. 

AFcvrvol,kabc = AF for COV for ramp volumes for KABC crashes. 

AFfr56,kabc = AF for five or six freeway through lanes for KABC crashes. 

AFfr>6,kabc = AF for more than six freeway through lanes for KABC crashes. 

AFgore,kabc = AF for close gore-to-gore interchange spacing for KABC crashes. 

AFml,kabc = AF for managed lane presence for KABC crashes. 

AFskew,kabc = AF for freeway and crossroad skew for KABC crashes. 

AFxr>4,kabc = AF for more than four crossroad through lanes for KABC crashes. 

AFxrlt,kabc = AF for the number of crossroad left-turn lanes for KABC crashes. 

bi = regression coefficient for condition i. 

CVrvol = COV for ramp volumes. 
55 

Iat = indicator variable for area type (1 = urban; 0 otherwise). 

IDDI = indicator variable for DDI interchange configuration (1 = DDI; 0 otherwise). 

Ifr56 = indicator variable for five or six freeway through lanes (1 = yes; 0 otherwise). 

Ifr>6 = indicator variable for more than six freeway through lanes (1 = yes; 0 otherwise). 

Igore = indicator variable for gore-to-gore spacing with nearest adjacent interchange less than 

0.5 mi (1 = yes; 0 otherwise). 

Iml = indicator variable for the presence of managed lane(s) in one or both directions (1 = yes; 

0 otherwise). 

IParclo = indicator variable parclo interchange configuration (1 = parclo; 0 otherwise). 

IParcloA = indicator variable parclo type A interchange configuration (1 = parclo A; 

0 otherwise). 

IRD = indicator variable for roundabout diamond interchange configuration (1 = roundabout 

diamond; 0 otherwise). 

Iskew = indicator variable for skew angle between freeway and crossroad greater than 

30 degrees (1 = yes; 0 otherwise). 

ISPDI = indicator variable for SPDI interchange configuration (1 = SPDI; 0 otherwise). 

ITDI = indicator variable for TDI interchange configuration (1 = TDI; 0 otherwise). 

Ixr>4 = indicator variable for more than four crossroad through lanes (1 = yes; 0 otherwise). 

Infr = number of freeway through lanes. 

Inxr = number of crossroad through lanes. 

LTxr = number of left-turn lanes on crossroad approaches. 

Np,kabc = predicted average KABC crash frequency (crashes/year). 

Nspf,kabc = predicted average KABC crash frequency for an interchange with base conditions 

(crashes/year). 

y = study period (years). 

The regression equations include the base condition for the attribute. The base conditions were 

established as those most commonly occurring in the study sites and included the following:

• Interchange configuration: Conventional diamond or CD interchange. 

• Number of freeway through lanes: Four. 

• Number of crossroad through lanes: Two, three, or four. 

• Area type: Rural. 

• Interchange skew: <30 degrees.

• Minimum gore-to-gore distance to nearby interchange: ≥0.50 mi.

• Managed lanes in one or both directions: Not present. 

• Ramp AADT COV: 0. 

• Crossroad number of turn lanes at crossroad ramp terminals: 0. 

Model Estimation

As indicated in the Model Development section, the project database included all interchange 

configurations in a pooled dataset. With this approach, the regression model was used to develop 

figure 36 through figure 46 from a single equation. The coefficients for variables specific to each 

interchange configuration are pulled from the model. Variables not specific to an interchange 

configuration are estimated with data from all configurations. This approach constrains the 

impacts of these variables to be the same across interchange configurations.
56 

Table 16 provides the generalized negative binomial regression model results. The final model 

includes 261 interchanges in the pooled dataset. The model fit statistics indicate an improvement 

in the overall fit by including the associated predictors.

Table 16. Final predictive model and parameters for KABC crash frequency.

Variable Description Value

Standard 

Error z-statistic

b0,kabc Interchange constant −6.814 0.658 −10.35* 

b1,kabc

Freeway volume per lane interaction with 

ramp volume 0.376 0.046 8.15* 

b2,kabc Crossroad volume per lane 0.189 0.072 2.63*

b3,kabc DDI main effect −0.083 0.150 −0.55

b4,kabc Parclo interchange main effect 0.158 0.104 1.52

b5,kabc Parclo type A interchange additive effect −0.221 0.132 −1.67

b6,kabc SPDI main effect −5.563 2.088 −2.66*

b7,kabc TDI main effect −3.064 1.573 −1.95*

b8,kabc

Roundabout diamond interchange main 

effect −0.267 0.164 −1.63

b9,kabc

Interaction between SPDI and freeway 

volume per lane 0.214 0.122 1.76

b10,kabc

Interaction between TDI and crossroad 

volume per lane 0.362 0.183 1.97*

b11,kabc

Interaction between SPDI and crossroad 

volume per lane 0.151 0.166 0.91

bfr56,kabc Freeway five or six through lanes 0.363 0.101 3.59*

bfr>6,kabc Freeway with more than six through lanes 0.744 0.125 5.93*

bxr>4,kabc Crossroad more than four through lanes 0.227 0.091 2.49*

bat,kabc Urban area type 0.367 0.118 3.12*

bgore,kabc

Minimum nearby gore distance within 

0.5 mi 0.206 0.128 1.61

bml,kabc Managed lane presence 0.282 0.143 1.97*

bxrlt,kabc Crossroad left-turn lanes −0.056 0.048 −1.19

bcvrvol,kabc Ramp volume variability −0.299 0.148 −2.01*

bskew,kabc Interchange skew angle 0.235 0.100 2.34*

*Significant at 0.05 level.

Overdispersion parameter: 0.242.

Model Evaluation 

The project team evaluated the validity of the models through CURE plots to maximize the 

sample size used for model development. Figure 47 provides the CURE plot for KABC crashes. 

As shown, the CUREs fall within the confidence intervals for the entire range of predicted 

crashes. The CUREs generally oscillate about zero, indicating minimum bias for any range of 

predicted crash frequency. Overall, the CURE plot indicates a good fit between the predicted and 

observed data. 
57 

Source: FHWA. 

Figure 47. Graph. CURE plot for KABC crashes.

Estimated AFs

The project team estimated several AFs in conjunction with the SPFs using KABC crash data. 

Collectively, they describe the relationship between various factors and crash frequency. This 

section describes the AFs, including the range of applicability.

Freeway Through Lanes

Figure 48 and figure 49 provide the number of freeway through lanes AFs. 

Figure 48. Equation. Freeway with five and six lanes KABC AF value. 

Figure 49. Equation. Freeway with more than six lanes KABC AF value.

The base condition is four freeway through lanes. The AF shows an expected 43.8-percent 

increase in KABC crashes when five or six freeway through lanes are present. The AF shows an 
58 

expected 110.4-percent increase in KABC crashes when more than six freeway through lanes are 

present.

Crossroad Through Lanes 

Figure 50 provides the number of crossroad through lanes AF. 

Figure 50. Equation. Crossroad through lanes KABC AF value.

The base condition is four or fewer crossroad through lanes. The AF shows an expected 

25.5-percent increase in KABC crashes when five or more crossroad through lanes are present.

Area Type

Figure 51 provides the area type AF.

Figure 51. Equation. Area type KABC AF value.

The base condition is a rural area type. The AF shows an expected 44.3-percent increase in 

KABC crashes when the interchange area type is urban.

Nearby Adjacent Interchange Gore

Figure 52 provides the nearby adjacent interchange gore AF.

Figure 52. Equation. Nearby gore KABC AF value.

The base condition is no adjacent interchange gores within 0.5 mi of the subject interchange’s 

gore locations. The AF shows an expected 22.9-percent increase in KABC crashes when at least 

one adjacent interchange gore is within 0.5 mi of the subject interchange’s gore location. 

Managed Lane 

Figure 53 provides the managed lane AF.

Figure 53. Equation. Managed lane KABC AF value.
59 

The base condition is no managed lanes present within the interchange area. The AF shows an 

expected 32.6-percent increase in KABC crashes when at least one managed lane is present 

within the interchange area.

Skew Angle

Figure 54 provides the skew angle AF.

Figure 54. Equation. Interchange skew angle KABC AF value.

The base condition is no skew between the freeway mainline and crossroad. The AF shows an 

expected 26.5-percent increase in KABC crashes when there is a skew angle between the freeway 

mainline and crossroad that is at least 30 degrees.

Crossroad Left-Turn Lanes

Figure 55 provides the crossroad left-turn lanes AF. 

Figure 55. Equation. Crossroad number of left-turn lanes KABC AF value. 

The base condition is no left-turn lanes present on the crossroad. The AF is applicable from zero 

to seven crossroad left-turn lanes. The AF ranges from a 0-percent reduction to a 32.5-percent 

reduction in KABC crashes when there are seven left-turn lanes combined for all crossroad ramp 

terminal approaches on crossroad approaches. 

Ramp Volume Variability 

Figure 56 provides the ramp volume variability AF.

Figure 56. Equation. Ramp AADT COV KABC AF value. 

The base condition is no variability in ramp volumes. The COV is computed by taking the 

standard deviation of ramp volumes and dividing by the average ramp volume. The AF is 

applicable from a COV from 0 to 1.15. The AF ranges from a 0-percent reduction to a 

29.1-percent reduction in KABC crashes when the COV is 1.15. 

Sensitivity Analysis

The project team conducted a sensitivity analysis for the base SPF for three different scenarios. 

SPFs are sensitive to freeway AADT, crossroad AADT, ramp AADT, number of freeway 

through lanes, number of crossroad through lanes, and interchange configuration. The purpose of 
60 

the sensitivity analysis was to compare base predictions for each interchange configuration given 

a set of inputs to determine how the input characteristics influence the relative comparison of 

predicted safety performance. For the first scenario, the project team assumed the freeway had 

four through lanes, the crossroad had two through lanes and an AADT of 5,000 vehicles per day, 

and the ramps had a combined total of 5,000 vehicles per day. For the second scenario, the 

crossroad AADT was changed to 15,000 vehicles per day. For the third scenario, the crossroad 

AADT was 55,000 vehicles per day (and four travel lanes), and the total combined ramp AADT 

was 55,000 vehicles per day. Figure 57 through figure 59 plot the predicted crash frequency as a 

function of freeway AADT, assuming the applicable ranges for each interchange configuration. 

Source: FHWA. 

Figure 57. Graph. Predicted KABC crash frequency for scenario 1.
61 

Source: FHWA.

Figure 58. Graph. Predicted KABC crash frequency for scenario 2.

Source: FHWA.

Figure 59. Graph. Predicted KABC crash frequency for scenario 3.

The sensitivity analysis indicates that when crossroad and ramp volumes were lower (in this case, 

AADT of 5,000 vehicles per day), the SPDI had the lowest predicted crash frequency. This result 

is at the very low end of the range for which an SPDI or TDI was observed in terms of crossroad 

and ramp AADTs. The roundabout diamond and TDI had the next lowest predicted KABC crash 

frequency. In scenario 1, the parclo B or AB had the highest predicted KABC crash frequency. 
62 

For scenario 2, where the crossroad AADT was 15,000 vehicles per day, the predicted range of 

KABC crash frequency narrowed across configurations. Notably, the TDI and parclo B or AB in 

this scenario had very similar predicted KABC crash frequency at the top of the range. The 

roundabout diamond was at the low end of the predictive range, similar to SPDI. 

For scenario 3, where both the crossroad and total ramp AADTs were 55,000 vehicles per day, 

the predicted range of KABC crash frequency varied substantially. The roundabout diamond had 

the lowest predicted crash frequency for its entire applicable range. The TDI and SPDI predicted 

the highest KABC crash frequency depending on the freeway AADT range.

PDO CRASH FREQUENCY PREDICTION MODEL

This section describes the development of predictive model equations based on PDO crash data. 

It follows the format of the Fatal and Injury Crash Frequency Prediction Model section and 

consists of the same five subsections. 

Model Development

The proposed predictive model equations and the methods used to develop them were described 

in the Fatal and Injury Crash Frequency Prediction Model section. These equations were shown 

in figure 36 through figure 46. In this section, the subscript “kabc” changes to “pdo” in each 

equation. The base conditions in the KABC crash frequency models are also applicable to the 

PDO crash frequency model. 

For the base SPF, b10 applies to a DDI and is interacted with freeway volume per lane multiplied 

by ramp volume, b11 applies to a parclo and is interacted with freeway volume per lane multiplied 

by ramp volume, and b12 applies to a TDI and is interacted with freeway volume per lane 

multiplied by ramp volume.

Model Estimation

As indicated in the previous section, the project database included all interchange configurations 

in a pooled dataset. With this approach, the regression model was used to develop the equations 

in figure 36 through figure 46 from a single equation. The coefficients for variables specific to 

each interchange configuration are pulled from the model. Variables not specific to an 

interchange configuration are estimated with data from all configurations together. This approach 

constrains the impacts of these variables to be the same across interchange configurations.

Table 17 provides the generalized negative binomial regression model results. The final model 

includes 261 interchanges in the pooled dataset. The model fit statistics indicate an improvement 

in the overall fit by including the associated predictors.
63 

Table 17. Final predictive model and parameters for PDO crash frequency. 

Variable Description Value

Standard

Error z-statistic

b0,pdo Interchange constant −6.642 0.854 −7.78*

b1,pdo

Freeway volume per lane interaction with 

ramp volume 0.415 0.055 7.56*

b2,pdo Crossroad volume per lane 0.215 0.062 3.49*

b3,pdo DDI main effect 3.233 2.894 1.12

b4,pdo Parclo interchange main effect 1.244 1.014 1.23

b5,pdo Parclo type A interchange additive effect −0.202 0.129 −1.57

b6,pdo SPDI main effect −4.238 2.127 −1.99*

b7,pdo TDI main effect −2.918 2.337 −1.25

b8,pdo

Roundabout diamond interchange main 

effect −0.241 0.160 −1.50

b9,pdo

Interaction between SPDI and freeway 

volume per lane 0.208 0.106 1.95*

b10,pdo

Interaction between DDI and freeway 

volume per lane −0.177 0.146 −1.21

b11,pdo

Interaction between parclo and freeway 

volume per lane −0.061 0.054 −1.12

b12,pdo

Interaction between TDI and freeway 

volume per lane 0.142 0.119 1.20

bfr56,pdo Freeway five or six through lanes 0.317 0.099 3.20* 

bfr>6,pdo Freeway with more than six through lanes 0.746 0.126 5.90*

bxr>4,pdo Crossroad more than four through lanes 0.195 0.092 2.13*

bat,pdo Urban area type 0.232 0.13 2.05*

bgore,pdo

Minimum nearby gore distance within 

0.5 mi 0.193 0.129 1.50

bml,pdo Managed lane presence 0.234 0.143 1.63

bxrlt,pdo Crossroad left-turn lanes −0.038 0.05 −0.79

bcvrvol,pdo Ramp volume variability −0.206 0.145 −1.43

bskew,pdo Interchange skew angle 0.117 0.100 1.17

*Significant at 0.05 level. 

Overdispersion parameter: 0.260.

Model Evaluation 

The project team evaluated the validity of the models through CURE plots to maximize the 

sample size used for model development. Figure 60 provides the CURE plot for PDO crashes. As 

shown, the CUREs fall within the confidence intervals for nearly the entire range of predicted 

crashes. The CUREs generally oscillate about 0, indicating minimum bias for any range of 

predicted crash frequency. Overall, the CURE plot indicates a good fit between the predicted and 

observed data. 
64 

Source: FHWA. 

Figure 60. Graphic. CURE plot for PDO crashes.

Estimated AFs 

The project team estimated several AFs in conjunction with the SPFs using PDO crash data. 

Collectively, they describe the relationship between various factors and crash frequency. This 

section describes the AFs, including the range of applicability. 

Freeway Through Lanes

Figure 61 and figure 62 provide the number of freeway through lanes AFs. 

Figure 61. Equation. Freeway with five and six lanes PDO AF value.

Figure 62. Equation. Freeway with more than six lanes PDO AF value.

The base condition is four freeway through lanes. The AF shows an expected 37.3-percent 

increase in PDO crashes when five or six freeway through lanes are present. The AF shows an 

expected 110.9-percent increase in PDO crashes when more than six freeway through lanes are 

present.
65 

Crossroad Through Lanes 

Figure 63 provides the number of crossroad through lanes AF. 

Figure 63. Equation. Crossroad through lanes PDO AF value. 

The base condition is four or fewer crossroad through lanes. The AF shows an expected 

21.5-percent increase in PDO crashes when five or more crossroad through lanes are present.

Area Type

Figure 64 provides the area type AF.

Figure 64. Equation. Area type PDO AF value. 

The base condition is a rural area type. The AF shows an expected 26.1-percent increase in PDO

crashes when the interchange area type is urban.

Nearby Adjacent Interchange Gore

Figure 65 provides the nearby adjacent interchange gore AF.

Figure 65. Equation. Nearby adjacent interchange PDO AF value.

The base condition is no adjacent interchange gores within 0.5 mi of the subject interchange’s 

gore locations. The AF shows an expected 21.3-percent increase in PDO crashes when at least 

one adjacent interchange gore is within 0.5 mi of the subject interchange’s gore location. 

Managed Lane 

Figure 66 provides the managed lane AF.

Figure 66. Equation. Managed lane PDO AF value. 

The base condition is no managed lanes present within the interchange area. The AF shows an 

expected 26.4-percent increase in PDO crashes when at least one managed lane is present within 

the interchange area.
66 

Skew Angle

Figure 67 provides the skew angle AF.

Figure 67. Equation. Interchange skew angle PDO AF value.

The base condition is no skew between the freeway mainline and crossroad. The AF shows an 

expected 12.4-percent increase in PDO crashes when there is a skew angle between the freeway 

mainline and crossroad of at least 30 degrees.

Crossroad Left-Turn Lanes

Figure 68 provides the crossroad left-turn lanes AF. 

Figure 68. Equation. Crossroad number of left-turn lanes PDO AF value.

The base condition is no left-turn lanes present on the crossroad. The AF is applicable from zero 

to seven crossroad left-turn lanes. The AF ranges from a 0-percent reduction to a 23.4-percent 

reduction in PDO crashes when seven left-turn lanes are combined for all crossroad ramp 

terminal approaches on crossroad approaches. 

Ramp Volume Variability 

Figure 69 provides the ramp volume variability AF.

Figure 69. Equation. Ramp AADT COV PDO AF value.

The base condition is no variability in ramp volumes. The COV is computed by taking the 

standard deviation of ramp volumes and dividing by the average ramp volume. The AF is 

applicable from a COV from 0 to 1.15. The AF ranges from a 0-percent reduction to a 

21.1-percent reduction for PDO crashes when the COV is 1.15. 

Sensitivity Analysis

The project team conducted a sensitivity analysis for the base SPF for the same three scenarios as 

in the Fatal and Injury Crash Frequency Prediction Model section. Figure 70 through figure 72

provide the graphical representation of the SPF for the three scenarios. The sensitivity analysis 

indicates that when crossroad and ramp volumes are lower (in this case, 5,000 vehicles per day 

AADT), the SPDI had the lowest predicted crash frequency. The roundabout diamond and TDI 

had the next lowest predicted PDO crash frequency. In scenario 1, the parclo B or AB had the 

highest predicted PDO crash frequency. These results are consistent with the KABC findings.
67 

Source: FHWA. 

Figure 70. Graph. Predicted PDO crash frequency for scenario 1. 

Source: FHWA.

Figure 71. Graph. Predicted PDO crash frequency for scenario 2.
68 

Source: FHWA.

Figure 72. Graph. Predicted PDO crash frequency for scenario 3.

For scenario 2, where the crossroad AADT was 15,000 vehicles per day, the predicted range of 

PDO crash frequency narrowed across configurations. Notably, the TDI and parclo B or AB in 

this scenario had very similar predicted KABC crash frequencies at the top of the range. This was 

not the case for PDO crash frequency. Here, the TDI generally had a lower predicted crash 

frequency than most other interchange configurations. The SPDI remained the lowest in terms of 

predicted PDO crash frequency. 

For scenario 3, where both the crossroad and total ramp AADTs were 55,000 vehicles per day, 

the predicted range of PDO crash frequency varied substantially. In this case, the roundabout 

diamond and DDI had the lowest predicted crash frequencies across the freeway AADT range. 

The TDI and SPDI predicted the highest PDO crash frequency when the freeway AADT was 

higher than 60,000 vehicles per day. The parclo B or AB predicted the highest crash frequency 

when the freeway AADT was less than 60,000 vehicles per day. The results indicate the predicted 

crash frequency among interchange configurations is highly sensitive to all AADT inputs and 

ranges. Notably, this sensitivity applies to AADT per lane. For comparative purposes, the project 

team assumed four freeway lanes. At higher values of AADT, more lanes would be expected; 

however, the relative comparison across interchange configurations would remain the same since 

the multiplier for the number of lanes is applied uniformly across configurations. Additionally, 

since capacity varies among interchange configurations, AADT per lane sensitivity would be 

important when considering a different number of lanes for the interchange configuration. 
69 

CHAPTER 6. SDF DEVELOPMENT

This chapter describes the project team’s SDF development for interchange configurations 

included in this study. The SDF uses a predictive model for calculating the crash severity 

distribution. 

Analysts can use the SDF in conjunction with the KABC crash to predict crash frequency for 

specific levels of crash severity. Since SDFs include variables based on interchange geometry or 

operations, SDFs allow for the CPM’s severity predictions to be sensitive to specific features of 

interchanges or configurations. 

This chapter consists of two parts: a description of the proposed SDF model development and a 

description of the model parameters, including an assessment of the impact on crash severity.

BACKGROUND

The project team developed one SDF applicable for all interchange configurations. The model 

predicts the proportion of crashes by severity level. Specifically, the model predicts the 

proportion of K, A, B, and C crashes, given that a crash has occurred. In combination, these 

proportions predict KABC crash frequency disaggregated by individual severity level (in 

combination with the crash frequency models presented in chapter 5). This approach is intended 

to minimize the frequency-severity indeterminacy problem described by Hauer and is consistent 

with the methodology used for the freeway safety prediction model in chapter 18 of the HSM 

Supplement.(1)

DATABASE SUMMARY

This summary provides details on the range of data included in the database and provides some 

insight into the development of the predictive model form. The project team drew the data 

presented in this chapter from the same dataset used to develop the crash frequency models; 

however, the observational unit in this dataset is an individual crash rather than an interchange. 

The project team captured severity data for all five States included in the crash frequency dataset, 

which allowed them to include all States in the SDF database. The following sections provide an 

overview of the geometric and operational characteristics as well as the crash characteristics 

included in the SDF dataset. 

Geometric and Operational Characteristics

Table 18 summarizes the geometric and operational characteristics of the sites in the SDF 

database. The pooled dataset includes crash data from all interchange configurations in the crash 

frequency dataset from all five States.
70 

Table 18. Summary of SDF dataset geometric and operational characteristics. 

Variable Average Minimum Maximum 

Intersection skew angle (degrees) 12.27 0 60 

Freeway through lanes (both directions) (No.) 7.28 4 12 

Crossroad through lanes (both directions) (No.) 4.41 2 6 

Freeway AADT (No.) 132,054 5,028 300,000 

Crossroad AADT (No.) 29,112 168 68,000 

Freeway posted speed limit (mph) 61.95 35 75 

Crossroad posted speed limit (mph) 38.37 25 65 

Interchange length along freeway (miles) 0.51 0.1 1.15 

Interchange length along crossroad (miles) 0.16 0.07 0.59 

Crossroad ramp terminal separation (miles) 0.10 0 0.80 

Adjacent crossroad minimum distance (miles) 1.82 0.36 9.65 

Adjacent crossroad average distance (miles) 1.52 0.51 10.45 

Adjacent gore minimum distance (miles) 0.73 0.13 8.97 

Adjacent gore average distance (miles) 1.01 0.17 9.74 

Adjacent intersection minimum distance (miles) 15.17 0 550 

Adjacent intersection average distance (miles) 18.82 0.05 710 

Combined exit ramp length (miles) 0.59 0.19 1.46 

Combined entrance ramp length (miles) 0.56 0.20 1.91 

Combined entrance ramp AADT (No.) 18,946 33 69,985 

Combined exit ramp AADT (No.) 18,960 15 74,985 

Driveway access points (No.) 0.32 0 7 

Maximum lanes crossed by pedestrians (No.) 4.57 0 10 

Pedestrian right-turn conflicts (No.) 0.56 0 7 

Table 19 provides an overview of the categorical data for sites included in the SDF database. 

Note that while the same data are included in the crash frequency database, the data in table 18

and table 19 only include KABC crashes. In addition to differences in safety performance across 

locations, these data yield different averages and distributions of input elements for the crashbased dataset from the interchange-based dataset.
71 

Table 19. Summary of SDF information for categorical data.

Category Characteristic

Frequency

(percent)

Nonramp leg at terminal Present 1.83

Not present 98.17

Crossroad ramp terminal control 

All-way stop 0.13

Ramp stop 5.48

Roundabout 4.13

Signal 90.25

Yield <0.01

Area type 

Rural 6.14

Urban 93.86

Crossroad location Overpass 50.82

Underpass 49.18

Weaving section at interchange area boundary Present 24.69

Not present 75.31

Frontage road Present 11.18

Not present 88.82

Sidewalk Present 69.02

Not present 30.98

Lighting Present 96.30

Not present 3.70

Freeway median type 

Rigid barrier 76.63

Semirigid barrier 5.81

Flexible barrier 7.96

Depressed/grass 9.60

Collector distributor road Not present 93.26

Present 6.74

Managed lane

Not present 70.65

Present 29.35

Crash Characteristics

The project team translated 5 yr of crash data from the interchange database to the SDF database 

(unless data were removed due to construction). The study period included 2016–2020 for all 

States.

Table 20 provides an overview of the crash severity database by State and presents the data by 

individual severity level as well as for fatal and injury crashes combined (due to the sample size 

of fatal crashes (K) being fewer than 100 crashes per year). There is some variability among 

States; however, this variability is expected due to differences in reporting characteristics and 

interchange configurations and design across States. Using K and A crashes combined allowed 
72 

the project team to have a larger sample in each category for SDF development. Table 21

presents the crash severity data by interchange configuration.

Table 20. Summary of crash severity data by State. 

State K A B C KA Total 

KABC

Arizona 44 231 2,345 3,048 275 5,668 

Missouri 184 396 677 14,283 580 15,540 

North Carolina 38 78 583 1,930 116 2,629 

Ohio 14 84 685 764 98 1,547 

Utah 34 199 1,242 3,650 233 5,125 

Combined 314 988 5,532 23,675 1,302 30,509 

Table 21. Summary of crash severity data by interchange configuration. 

Interchange 

Configuration K A B C KA Total KABC

Diamond 44 103 420 1,572 147 2,139 

CD 20 52 437 737 72 1,246 

TDI 41 128 843 3,540 169 4,552 

DDI 58 134 381 3,464 192 4,037 

Parclo A 9 64 290 1,342 73 1,705 

Parclo B 22 52 227 998 74 1,299 

Parclo AB 47 143 545 2,832 190 3,567 

SPDI 73 312 2,389 9,190 385 11,964 

Combined 314 988 5,532 23,675 1,302 30,509 

SDF Model Development

This section describes the steps taken to develop the SDFs. The following subsections provide a 

description of the model form, an overview of the modeling approach, an overview of the 

statistical analysis methods, and a discussion of the findings.

Predictive Model Form

The severity of outcomes is defined by the probability of the outcome occurring. Figure 73

defines the probability of outcome m. 

Figure 73. Equation. Probability of outcome m. 

Where: 

Pm = probability of the outcome m. 

M = total number of possible outcomes modeled.
73 

Vi = deterministic component of outcome i. 

Vm = deterministic component of outcome m. 

In this case, the SDFs represent injury crash outcomes (K, A, and B), with C as the base scenario. 

Initial results indicated that crash sample sizes for fatalities (i.e., K) were small; therefore, K and 

A were combined for fatal and severe-injury crashes (KA). Analysts can differentiate K and A 

crashes through a proportion factor from the observed dataset. The probability for each outcome 

is shown in figure 74 through figure 77. 

Figure 74. Equation. Probability of KA severity outcome. 

Where:

PKA = probability of KA severity outcome. 

VB = deterministic component of B severity outcome. 

VKA = deterministic component of KA severity outcome. 

Figure 75. Equation. Probability of B severity outcome. 

Figure 76. Equation. Probability of C severity outcome. 

with

Figure 77. Equation. Deterministic component of outcome m. 

Where:

Csdf = local calibration factor. 

fm,i = severity AF for the relationship between severity m of traffic characteristics, geometric 

element, or traffic control feature i (i=to n). 

n = total number of severity AFs.

Vm,b = base deterministic component of outcome m. 

The deterministic component is a dimensionless number indicating the relative frequency of 

crashes associated with a specific severity level (i.e., K, A, B, or C), given that a crash has 

occurred. Severity level C is calculated as a total probability of 1.0 minus the assigned 

probabilities for KA- and B-level severities. The value Csdf is the local calibration factor agencies 
74 

can use to calibrate the model to their specific locations. The calibration procedure is provided in 

the HSM Part C, appendix A.(3)

Modeling Approach 

Pooled Dataset Approach 

As noted in the Crash Characteristics section, the project team opted to combine the SDF datasets 

into one pooled dataset since the sample proportions were relatively similar between interchange 

configurations. Additionally, this step was necessary due to small samples for more severe 

crashes for individual interchange configurations. The project team used the pooled dataset to 

estimate one regression model and used data for all interchange configurations. They then applied 

the coefficients to develop an SDF for each interchange configuration. As with the crash 

frequency models, some factors can be specific to interchange configuration, while others are 

common to all SDFs. The project team could then maximize the use of the sample dataset, 

particularly for individual interchange configurations with smaller sample sizes.

Model Development Process

The project team used a similar approach to the crash frequency modeling approach, prioritizing 

characteristics previously found to be related to crash severity for inclusion in the SDF. The 

project team added independent variables one at a time based on statistical significance, 

magnitude and direction of effect, and impact on other variables in the model. Since they used C 

(possible injury) crashes as the baseline, the project team entered variables for combined KA- and 

B-level injuries simultaneously. The magnitude and direction of effect for each severity category 

relative to the baseline and to each other were assessed. If the results were intuitive, the project 

team retained the variables. If the results indicated little to no difference in effect across adjacent 

severity categories, the project team constrained the coefficients to be the same. Otherwise, if the 

effect was not statistically or practically significant, the variable was dropped from the specific 

severity-level category.

Statistical Analysis Method

The project team used a multinomial logit model to analyze crash severity. This model allows for 

some variables to be constrained to have the same effect on each severity level while allowing 

other variables to have a fluctuating effect among levels. An investigation of the reliability of this 

approach has found that it tends to outperform the direction calibration of SPFs for each severity 

level.(12) This approach has been successfully applied to develop freeway predictive methods for 

the HSM.(7,18) For this research, the baseline is C-level injury crashes (but also considering 

combined K and A), and individual severity proportions are evaluated based on predictor 

variables.

SDF Prediction Model 

Model Development 

This subsection describes the regression model and the project team’s methods for estimating the 

model’s coefficients. The regression model is generalized to accommodate each interchange 
75 

configuration. The generalized form shows the variables included in the model. For some 

variables, the project team used indicator variables to determine when the corresponding AF was 

applicable. 

Figure 78 through figure 94 describe the regression model the project team calibrated using the 

severity data. 

Figure 78. Equation. Projected probability of severity outcome m. 

Figure 79. Equation. Projected deterministic component of KA crash severity.

Figure 80. Equation. Projected deterministic component of B crash severity.

Figure 81. Equation. Constant value for outcome m. 

Figure 82. Equation. Freeway AADT severity AF. 

Figure 83. Equation. Crossroad AADT severity AF. 

Figure 84. Equation. Freeway posted speed limit severity AF.

Figure 85. Equation. Crossroad posted speed limit severity AF.

Figure 86. Equation. Parclo type A or B severity AF.
76 

Figure 87. Equation. Parclo type AB severity AF.

Figure 88. Equation. SPDI, DDI, or TDI severity AF.

Figure 89. Equation. Roundabout diamond severity AF.

Figure 90. Equation. Nearby adjacent intersection severity AF. 

Figure 91. Equation. Nearby adjacent interchange gore severity AF. 

Figure 92. Equation. Pedestrian right-turn conflict severity AF.

Figure 93. Equation. Number of freeway lanes severity AF. 

Figure 94. Equation. Number of crossroad lanes severity AF.

Where:

bi = regression coefficient for condition i. 

Vb,m = base deterministic component for severity m.

fAADTfr,KA = AF for the relationship between freeway bidirectional AADT greater than or equal 

to 200,000 vehicles per day and KA severity. 

fAADTxr,KA = AF for the relationship between crossroad AADT greater than or equal to 

30,000 vehicles per day and KA severity. 

ffr65,m = AF for the relationship between freeway posted speed limit greater than or equal to 

65 mph and severity m. 

ffr_8l,m = AF for the relationship between freeways with eight or more lanes and severity m. 

fGORE25,KA = AF for the relationship between adjacent gore separation less than 0.25 mi and 

KA severity. 
77 

fINT10,m = AF for the relationship between adjacent intersection spacing being less than 

0.10 mi and severity m. 

fPAB,m = AF for the relationship between parclo type AB and severity m. 

fPAorB,m = AF for the relationship between parclo type A or B and severity m. 

fPED_RT,m = AF for the relationship between number of pedestrian crossings conflicting with 

right turns and severity m. 

fRDI,m = AF for the relationship between roundabout diamond and severity m. 

fSPDI_DDI_TDI,m = AF for the relationship between SPDI, DDI, or TDI and severity m. 

fxr45,m = AF for the relationship between crossroad posted speed limit greater than or equal to 

45 mph and severity m. 

fxr_41,m = severity AF for the relationship between crossroads with four or more lanes and 

severity m. 

Ifr200k = indicator for freeway bidirectional AADT greater than or equal to 200,000 vehicles 

per day.

Ifr65 = indicator for freeway posted speed limit greater than or equal to 65 mph. 

Ifr_8l = indicator for freeway having eight or more through lanes. 

IGORE25 = indicator for nearest adjacent interchange gore within 0.25 mi. 

IINT10 = indicator for nearest adjacent intersection within 0.10 mi. 

IPAB = indicator for the presence of a parclo type AB interchange. 

IPAorB = indicator for the presence of a parclo type A or B interchange. 

IRDI = indicator for the presence of a roundabout diamond interchange. 

ISPDI_DDI_TDI = indicator for the presence of an SPDI, DDI, or TDI interchange. 

Ixr45 = indicator for crossroad posted speed limit greater than or equal to 45 mph. 

Ixr_41 = indicator for crossroad having four or more lanes. 

Ixr30k = indicator for crossroad bidirectional AADT greater than or equal to 30,000 vehicles 

per day. 

NPED_RT = number of pedestrian crossings conflicting with right turns. 

Model Estimation

Table 22 presents the results of the SDF regression model. The table provides the regression 

coefficient and standard error for each variable by severity level in the resulting model. The 

z-statistic (for assessing statistical significance) was computed by dividing the coefficient by the 

standard error. As evidenced by the large sample size, many factors fit into the final SDF for 

combined KA and B crash severities. The project team focused on including factors 

differentiating the effects of interchange configuration. The base condition for this model is a 

C-level injury.
78 

Table 22. Summary of pooled data from SDF model.

Variable KA Coefficient

KA Standard 

Error B Coefficient

B Standard 

Error

b0,m −3.104* 0.078 −1.956* 0.067 

bAADTfr,KA −0.786* 0.098 N/A N/A

bAADTxr,KA −0.177* 0.063 N/A N/A

bfr65,m 0.870* 0.034 0.870* 0.034 

bxr45,m 0.231* 0.036 0.231* 0.036 

bfr81,m 0.483* 0.033 0.483* 0.033 

bxr41,m 0.177* 0.051 0.177* 0.051 

bGORE25,KA 0.302* 0.079 N/A N/A

bINT10,m 1.230* 0.251 1.230* 0.251 

bPED_RT,m 0.025 0.015 0.025 0.015 

bPAB,m −0.510* 0.062 −0.510* 0.062 

bPAorB,m −0.446* 0.067 −0.446* 0.067 

bRDI,m −0.848* 0.093 −0.848* 0.093 

bSPDI_DDI_TDI,m −0.745* 0.075 −0.518* 0.053 

*Indicates significant at 0.05 level. 

The coefficients fit into figure 81 through figure 94 as indicated by the applicable variable. In 

some cases, variables apply to specific interchange configurations, while others are generalizable 

to all interchange configurations. 

The results in table 22 indicate generally consistent effects for variables significantly associated

with KA and B crash severities. In other words, the coefficients for KA and B severity crashes 

were the same in most cases. The coefficient differed for the indicator for an SPDI, DDI, or TDI 

configuration. In that case, the coefficient indicated a larger reduction in the likelihood of a KA 

crash than a B crash compared to a C-crash baseline.

The results indicated freeway volumes greater than or equal to 200,000 vehicles per day were 

associated with a lower proportion of KA crash severity. The same was true for crossroad volume 

greater than or equal to 30,000 vehicles per day. The presence of an adjacent interchange gore 

within 0.25 mi of the subject interchange’s gore was associated with a higher probability of a KA 

crash severity. The model indicated that, compared to a baseline diamond or CD interchange, 

parclo AB, parclo A or B, roundabout diamond, SPDI, DDI, and TDI interchanges are associated 

with a lower probability of KA and B crash severities. The other factors indicated an increased 

probability of KA and B crash severities. 

While the project team estimated KA crash probabilities together as part of the SDF, KA crash 

probability may be split, assuming 24.1 percent of KA crashes are fatal.
79 

CHAPTER 7. CONCLUSIONS AND RECOMMENDATIONS

CONCLUSIONS

This project aimed to develop a planning-level safety prediction model to assess the predicted 

safety performance of interchange configurations to support IJRs. The project team developed the 

safety prediction model in a manner consistent with those developed for the HSM.(1) Specifically, 

the approach is consistent with that found in chapter 18 of the HSM Supplement. The CPM 

predicts KABC and PDO crash frequency separately at the interchange level. Additionally, the 

predictive method includes planning-level SDFs to assess the impact of features on crash 

severity.

The project team examined interchange types accounting for more than 75 percent of those 

considered in IJRs. This project included the following interchange configurations: 

• Diamond.

• CD.

• TDI.

• DDI.

• Roundabout diamond.

• SPDI.

• Parclo type A.

• Parclo type B.

• Parclo type AB.

The predictive method found no safety performance difference between a diamond and CD

interchange. These two configurations combined can be assumed as the base condition. The 

predicted crash frequency (for both KABC and PDO crashes) for interchange configurations 

relative to the base condition depends on the freeway volume, crossroad volume, combined ramp 

volumes, number of freeway lanes, and number of crossroad lanes. 

The predictive method adjusts for area type, interchange skew, the presence of a nearby 

interchange, the presence of managed lanes, variability in ramp volumes, and the number of leftturn lanes on the crossroad. 

For interchange crash severity, the base condition continued to be a diamond or CD interchange 

configuration. Crash severity was associated with interchange configuration; however, the project 

team found a similar severity distribution for SPDI, DDI, and TDI configurations. 

The SDFs further identified crash severity proportion to be associated with freeway AADT, 

crossroad AADT, freeway posted speed limit, crossroad posted speed limit, the presence of a 

nearby adjacent interchange, the presence of a nearby adjacent intersection on the crossroad, the 

number of pedestrian crossings conflicting with right-turning vehicles, number of freeway 

through lanes, and the number of crossroad through lanes. 
80 

The project team developed a basic implementation spreadsheet to support testing the 

relationships found in the CPM. The implementation spreadsheet does not identify if inputs are 

valid or within the range of data used to develop the predictive models. The implementation 

spreadsheet includes a prediction for all configurations simultaneously. The user can provide the

same inputs for all configurations or vary input parameters for each configuration when 

conducting a simultaneous test.

Additionally, the implementation spreadsheet provides a confidence interval. The variance for the 

confidence interval is calculated as shown in figure 95. 

Figure 95. Equation. Variance equation for the predictive model.

Where: 

Var(λi) = variance of crashes at the subject interchange.

E(λi) = expected crash frequency at the subject interchange.

α = overdispersion parameter.

The resulting variance for each prediction yielded comparative results that were not statistically 

different from each other in all cases. The 95-percent confidence interval typically encompasses a 

range from approximately zero to slightly more than double the predicted crash frequency for the 

interchange configuration prediction of interest. Therefore, even though there are practical 

implications of the CPM from one interchange configuration to another for a given set of input 

data, the results are not statistically significant.

Practitioners should exercise caution when making decisions based purely on safety prediction 

models. The purpose of the predictive model is to provide an estimate of safety performance, 

which should be considered along with other factors, such as cost, operational performance, and 

environmental constraints. Additionally, CPMs predict the long-term crash history of a location, 

given a set of input variables. Predictions from these models are more reliable when considering 

long-term crash frequency and additional factors that may make the location unique. Professional 

judgment should be exercised if interchange features are atypical or are outside the bounds of the 

data used to develop the models. Analysts should consult the database summary provided in 

chapter 4 to determine the bounds of data used to develop this CPM.

RECOMMENDATIONS FOR FUTURE EFFORTS

The predictive method developed in this research applies only to those interchange configurations 

described in chapters 2 and 3. The project team captured data for interchanges meeting the 

standard definitions of those configurations. For example, the project team did not capture data 

for sites with extra ramps, missing ramps, or direct-connection ramps. Future research should 

consider incorporating the safety effects of those features that make interchanges unique, as 

planners often look for unique solutions to operational or safety concerns. 

Future efforts should also consider expanding the dataset to more sites for analysis. Some 

configurations had smaller sample sizes, such as parclo B interchanges. Increasing the sample 
81 

size for each interchange configuration will improve the reliability of the CPM. The project team 

focused on collecting a broad set of attributes for inclusion in the predictive method. Future 

efforts could reduce the number of attributes considered to those most closely related to safety 

and focus on collecting data at more locations. 

Additionally, the predictive models should be expanded to include more interchange 

configurations. For example, double roundabout or dogbone interchanges have become more 

common, and there should be a sufficient sample to include their safety effects in the predictive 

model. Future work should also examine the role of signing and markings consistent with 

roadway design and lane configurations at exits, entrances, and reductions within and between 

interchanges. This project attempted to assess the effects of lane additions and lane drops, but the 

data did not include lengths of auxiliary lanes or associated markings and signage. 

83 

APPENDIX A: SURVEY PROVIDED TO DIVISIONS

Appendix A includes the survey provided to FHWA divisions to assess how many IARs are 

submitted per year and which interchange configurations were most commonly considered in 

IARs. Further, this survey distinguishes between systems and service interchanges.

I. Background: FHWA is seeking to develop planning-level models and tools to predict 

crash frequency and severity for an existing or proposed service interchange. Model 

inputs will be limited to details that are generally known at the planning and conceptualdesign level and are expected to affect crash frequency and severity. The planning-level 

models will allow analysts to compare the potential safety performance effects of freeway 

access and interchange design decisions at the planning level.

Since the majority of IARs focus on a few specific interchange types, the scope of the 

planning-level models will focus on those that are most common. The purpose of this 

survey is to identify those subsets of interchange types that comprise at least 75 percent of 

those considered in IARs. 

II. Introductory question fields: 

A. Name. 

B. Job title. 

C. Email address. 

D. State. 

III. General information about requests: 

A. How many IARs does your State typically submit per year (see figure 96 for 

interchange configurations)? 

a. 0–5. 

b. 6–10. 

c. 11–15. 

d. 16–20. 

e. More than 20. 

B. Of those IARs, how many involve system interchanges (interchanges providing access 

from one freeway to another)? 

a. 0–2. 

b. 3–5. 

c. 6–8. 

d. More than 8. 

C. Can you estimate the percentage of service interchange IARs that are for rural 

interchanges (as opposed to urban/suburban interchanges)? 
84 

a. 0–25 percent. 

b. 26–50 percent. 

c. 51–75 percent. 

d. More than 75 percent. 

D. Approximately how many service interchange IARs per year involve a new 

interchange? 

a. 0–5. 

b. 6–10. 

c. 11–15. 

d. More than 15. 

E. Approximately how many service interchange IARs per year involve a 

reconfiguration of an existing interchange? 

a. 0–5. 

b. 6–10. 

c. 11–15. 

d. More than 15. 

F. Approximately how many service interchange IARs per year involve minor changes 

(such as modifying ramp terminal access control, adding lanes to an existing ramp, or 

relocating a ramp terminal to a different roadway)?

a. 0–5. 

b. 6–10. 

c. 11–15. 

d. 16–20. 

e. More than 20. 
85 

Source: FHWA.

Figure 96. Graphic. Interchange configurations considered in the survey.

G. Considering the interchange configuration provided in the figure 96 (note multiple 

examples of parclo configurations are provided for informative purposes, but many 

more variations exist in each category), please select from the list below (table 23) 

those interchange configurations that have been proposed in the IARs your office has 

reviewed within the last 3–5 yr. 
86 

Table 23. Annual IARs reviews by interchange configuration. 

Interchange 

Configuration

Uncommon 

or Rarely 

Considered

Sometimes 

Considered 

but Rarely 

Selected

Commonly 

Considered 

and 

Selected 

0-2 Times 

Per Yr

Commonly 

Considered 

and 

Selected 

3-5 Times 

Per Yr

Commonly 

Considered 

and 

Selected 

6-8 Times 

Per Yr

Commonly 

Considered 

and 

Selected 

More Than 

8 Times 

Per Yr

CLF interchange — — — — — — 

Conventional diamond 

interchange

— — — — — — 

CD interchange — — — — — — 

Tight urban diamond 

interchange

— — — — — — 

Single-point urban 

interchange (SPUI)

— — — — — — 

DDI — — — — — — 

Parclo–A (A2, A4, or 

variant)

— — — — — — 

Parclo–B (B2, B4, or 

variant)

— — — — — — 

Parclo–AB (or variant 

thereof)

— — — — — — 

DLT interchange — — — — — — 

SR — — — — — — 

Ramps to frontage 

road interchanges

— — — — — — 

Braided ramp 

interchanges

— — — — — — 

—Empty cell for the respondent to check.

H. If any interchange type(s) not listed in question 12 has been considered or selected in 

the last 3–5 yr, please list those type(s) here (and please provide an indication of how 

commonly considered or selected as with question 12). 

I. Does your State consider or select roundabouts as ramp terminals for appropriate 

interchange configurations listed in question 12? 

a. Does not commonly consider roundabouts.

b. Commonly considers roundabouts but has not installed many. 

c. Commonly installs roundabouts as ramp terminals at appropriate interchange 

configurations. 

J. Does your State consider the use of collector-distributor roads at interchanges?

IV. Followup: 

A. Would you be willing to provide more details to the research team through a followup 

phone call? 
87 

B. Can you provide the name, phone number, and email address of a State DOT contact 

who can provide details on available ramp traffic volume data?

C. Is there anything else you would like to tell us about IARs in your State? 

89 

APPENDIX B: ADDITIONAL SURVEY COMMENTS

Table 24 provides additional insights on IJRs (also known as IARs) for various States as 

indicated by respondents. In general, several respondents indicated that IJRs are seldom received 

in their States. Several States also indicated IJRs are not required for minor changes, and one 

State (Washington) noted that nontraditional interchange configurations are more commonplace. 

Additionally, several respondents specifically called attention to DDIs and single-point urban 

interchanges in their responses as something they would like to see in the model. 

Table 24. Additional comments on IJR (IAR) practices.

State Is there anything else you would like to tell us about IARs in your State?

Alabama

The type of interchange varies on project constraints. Separation of ramp termini is 

sometimes an issue, with the State asking to have multiple merge points. Roundabouts are 

becoming more common, but only using Highway Safety Improvement Program funding 

for most of them; they are not considered routinely.

Alaska I do not have much data about our access requests for the past 5 yr as I am new to the 

office, and we haven’t had any since I started.

California 

We are working with the State to develop a standard operating procedure (SOP) and 

guidelines on how to perform and submit IARs to FHWA. Our office updated our own 

SOP now that the State has adopted the two-point policy. Also, there are minor activities 

like adding a lane to a ramp, metering a ramp, or shifting the exit ramp to the same 

roadway that has been delegated to Caltrans, and we do not see.

Colorado We use a Minor Interchange Modification Request process for interchanges modifications 

that do not meet the requirement of needing an IAR. 

Delaware Due to limited interstate length in Delaware, we do not have very many interstate change 

requests, as a rule.

District of 

Columbia IARs are few and far between here in the District of Columbia.

Florida

Florida is operating under a programmatic agreement with the Florida DOT where the 

majority of interchange access review and approvals are delegated to the State. Therefore, 

we are less involved with interchange designs.

Hawaii Hawaii has not proposed any new interchanges in the past 3 yr.

Indiana

A few of the recent IARs have been handled through our programmatic review/approval 

process where the State does the review and determination of 

engineering/operational/safety acceptability.

Iowa

Iowa has a process that distinguishes between IJRs and Interchange Operations Reports 

(IORs). IORs document minor changes at interchanges that do not alter the interchange 

type or the number and type of access points. For this survey, Iowa’s IJRs were considered 

the equivalent of IAR’s discussed in the survey.

Kansas

Probably half of ours are modifications to existing interchanges. We have done several 

DDI retrofits, and we no longer require a full-blown [IAR] for those. Also, the same for 

roundabouts at the terminals; we don’t require full-blown [IAR]. If I would offer any 

advice for someone reviewing a [IAR] submittal, I would focus on operations. Safety is 

very closely tied to operations (i.e., poor operations leads to poor safety). I would also 

stress with everyone that even though the 20-yr design life is no longer a requirement, best 

practice should lead engineers to determine what year your level of service will begin to 

see [level of service] Fs and explore what future options there will be to improve it. 

Maryland They are called Interstate Access Point Approval.
90 

State Is there anything else you would like to tell us about IARs in your State?

Massachusetts

New interchanges are exceptionally rare. Modifications tend toward the minor, such as 

signalizing ramp terminals, extending acceleration lanes, or providing ramp widening to 

add turn lanes.

Michigan We are hopeful that DDIs and SPUIs will be given consideration.

Nebraska Very few IARs have been conducted in Nebraska in the past 5 yr.

New Jersey

In New Jersey, IARs recently submitted have primarily involved only modifications to 

existing interchanges. Configurations identified in question 12 represent existing and/or 

modified configurations.

New York The New York State DOT typically only submits 0–5 IARs per year for our review and 

approval. 

North Dakota

North Dakota does not fit your survey very well. FHWA receives about one IAR every 

other year. In the past 6 yr, IARs in North Dakota have only addressed new interchanges, 

not modifications to existing interchanges. There have been two IARs in the past 6 yr, one 

a modified SPUI that does not fit your matrix and a locked gate access with median 

crossovers for use in emergency flood conditions. 

Pennsylvania

We like diverging diamonds and roundabouts at terminals. They work well. We only 

process a few per year, and most are modifications to existing facilities. Our oversight of 

toll road interchanges is still a little vague. 

Rhode Island

In the past 5 yr, I recall only four IARs. Only one involved a new full interchange between 

an interstate and local road, and it was a (compact?) diamond interchange. The three others 

involve (currently in progress) modifying existing interstate interchanges, only one of 

which is an Interstate system-level interchange modification. These three involve 

improvements to existing partial interchanges via the addition of and/or modification of 

partial interchange configurations.

Vermont We (the Vermont FHWA Division Office) do not get very many IARs. I do not know of 

any in the 13 yr I have been in the Vermont FHWA Division Office.

Washington

The last four interchanges involved a three-level service interchange, a system-to-system 

three-level interchange, a half-diamond partial interchange, and a direct access highoccupancy vehicle Texas T. I use this as an example to demonstrate that nontraditional 

configurations are, in my limited experience, more the commonplace than anything else in 

the State of Washington. 

Wyoming

The Wyoming DOT (WYDOT) rarely designs/modifies a service interchange—less than 

one per year. WYDOT is currently designing a new system interchange (I–80/25) with a 

new service interchange, but funding will likely cause the project to be put indefinitely on 

hold.
91 

ACKNOWLEDGMENTS 

This report was prepared for the FHWA Office of Safety Research and Development under 

Contract DTFH61-16-D-00040. 

The project team gratefully acknowledges the participation of the following organizations for 

their assistance in this study:

• Arizona DOT.

• FHWA HSIS. 

• Missouri DOT.

• Utah DOT.

The maps in figure 6 and figure 32 in this document were modified. The original maps are the 

copyright property of Google® EarthTM and can be accessed from 

https://www.google.com/earth.

(10) The image overlays using arrows, labels, and/or other 

annotations were added to the maps by the authors. 

The maps in figure 31, figure 33 and figure 34 are the intellectual property of EsriTM and is used 

herein under license. Copyright © 2022 Esri and its licensors. All rights reserved. For more 

information about Esri software, please visit www.esri.com.

(15)

93 

REFERENCES

1. AASHTO. 2014. Highway Safety Manual Supplement. Washington, DC: American 

Association of State Highway and Transportation Officials. 

2. FHWA. 2010. Interstate System Access Informational Guide. Washington, DC: Federal 

Highway Administration. 

3. AASHTO. 2010. Highway Safety Manual. Washington, DC: American Association of 

State Highway and Transportation Officials.

4. FHWA. 2017. “Policy on Access to the Interstate System” (web page). 

https://www.fhwa.dot.gov/design/interstate/170522.cfm, last accessed December 7, 2022. 

5. Florida Department of Transportation. 2020. Interchange Access Request User’s Guide, 

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Recommended citation: Federal Highway Administration,

Safety Comparisons Between Interchange Types 

(Washington, DC: 2023) https://doi.org/10.21949/1521993