Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-23-015 JANUARY 2023 

MIMIC—Multidisciplinary Initiative 

on Methods to Integrate and Create 

Realistic Artificial Data
FOREWORD

Data-driven safety analysis models help State and local agencies quantify safety data, identify 

high-risk roadway features, and predict the effects of proposed safety measures. However, even 

when a model performs well overall, it may not accurately represent the interactions between 

variables for a specific location or crash because the underlying relationships in the real world 

are unknown. One proposed solution is to generate realistic artificial datasets (RADs) with 

predetermined safety relationships built into them. Because these relationships are known, the 

RAD can serve as a testbed, revealing how well a model reflects those underlying cause-andeffect relationships.

This study describes the development of RAD for ramp terminals and speed change lanes at 

diamond interchanges. A web-based software was developed under the Federal Highway 

Administration’s Exploratory Advanced Research Program. The software provides the ability to 

generate RAD for multiple years and locations as well as access to pregenerated datasets. This 

report will be of interest to academics and researchers developing crash modification functions 

and statistical models to determine how the models best represent real-world relationships.

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

FHWA-HRT-23-015 

4. Title and Subtitle 5. Report Date 

MIMIC—Multidisciplinary Initiative on Methods to Integrate and Create 

Realistic Artificial Data. 

January 2023

6. Performing Organization Code 

7. Author(s) 8. Performing Organization Report No. 

Edara, P. (0000-0003-2707-642X), Sun, C. (0000-0002-8857-9648), Brown, 

H. (0000-0003-1473-901X), Savolainen, P. (0000-0001-5767-9104), Shankar, 

V. (0000-0002-6671-2268), Balakrishnan, B. (0000-0002-0994-0213), 

Shang, Y. (0000-0001-7771-4034), Chakraborty, S. (0000-0003-2022-1735), 

Adu-Gyamfi, Y. (0000-0002-1924-9792), Li, C. (0000-0002-3237-1477), Aati, 

K. (0000-0001-8834-7735), Lima, S., Huang, Y. (0000-0002-7346-5293), 

Mussah, A. (0000-0002-1084-5598), Hopfenblatt, J. 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

University of Missouri-Columbia

E2509 Lafferre Hall

Columbia, MO 65211

11. Contract or Grant No. 

693JJ31950023 

12. Sponsoring Organization Name and Address 13. Type of Report and Period Covered 

United States Department of Transportation

Federal Highway Administration

HSA Room #E71-324 

1200 New Jersey Avenue SE 

Washington, DC 20590

Final Report; September 2019–October 2022

14. Sponsoring Agency Code 

HRSO-2 

15. Supplementary Notes 

This project was supported by the Exploratory Advanced Research (EAR) Program. EAR Program oversight was provided by 

David Kuehn and Jim Shurbutt. Yusuf Mohamedshah served as Federal Highway Administration (FHWA) Contracting Officer’s 

Technical Manager. Additional project guidance was provided by FHWA staff members Carol Tan and Ana Maria Eigen. The 

project’s technical advisory committee consisted of Dean Kanitz (Michigan Department of Transportation (DOT)), Ida van 

Schalkwyk (Washington State DOT), Ray Shank (Missouri DOT), and John Miller (FHWA Missouri Division). 

16. Abstract 

Traditional safety modeling efforts primarily focus on accurately estimating crash frequencies or rates. The true relationships 

between crashes and potential causal factors are not always easily discernible from safety models. While a model consisting of 

multiple causal factors may produce accurate estimates of crash measures, it may not accurately explain all causal relationships. 

Knowing the true cause-and-effect relationships is important while choosing countermeasures to address safety problems. This 

Exploratory Advanced Research Program project developed a framework to generate realistic artificial data (RAD) datasets that 

mimic the known causal relationships between contributing factors and crashes. The proposed framework is generic and can be 

used to generate RAD for other facilities, such as work zones, bicycle/pedestrian facilities, innovative geometric designs, etc. The 

framework was applied to generate RAD for ramp terminals and speed change lane facilities at diamond interchanges. A 

web-based software was developed to provide easy access to the RAD dataset. The software provides 196 pregenerated datasets 

and the option to request custom datasets. Sample RAD datasets were used to test negative binomial and a suite of machine 

learning models. A model evaluation rubric was developed to evaluate and compare the performance of different models.

Additionally, this project developed a second type of RAD dataset—the virtual reality (VR) simulation testbeds for crashes and 

near-crashes occurring at interchanges. Driving simulator studies offer another source of RAD for evaluating new behavioral and 

roadway countermeasures. The testbeds were developed using safety critical events recorded in the Strategic Highway Research 

Program 2 Naturalistic Driving Study data. VR offers an engaging visualization platform to educate the public about interchange 

crashes and to evaluate different countermeasures. These interventions are well aligned with the USDOT’s National Roadway 

Safety Strategy’s Safe System Approach of considering an overlapping set of safety measures—roadway countermeasures, 

behavioral interventions, enforcement, vehicle safety features, and emergency medical care—to achieve zero roadway fatalities. 

17. Key Words 18. Distribution Statement 

Crashes, realistic artificial data, safety, synthetic data, visualization, simulator 

testbeds

No restrictions. This document is available to 

the public through the National Technical 

Information Service, Springfield, VA 22161.

http://www.ntis.gov

19. Security Classification (of this 

report)

20. Security Classification (of this 

page) 

21. No. of Pages 

69

22. Price 

NA

Unclassified. Unclassified.

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

TABLE OF CONTENTS

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

CHAPTER 1. BACKGROUND ................................................................................................... 7

Project Motivation .................................................................................................................. 7

Project Overview ..................................................................................................................... 8

Study Scope.............................................................................................................................. 9

CHAPTER 2. LITERATURE REVIEW .................................................................................. 11

Synthetic Data ....................................................................................................................... 11

Generation of Synthetic Data ............................................................................................ 11

Evaluation of Synthetic Data ............................................................................................ 11

Applications of Synthetic Data ......................................................................................... 12

Interchange Safety Modeling ............................................................................................... 13

State of the Practice Review ............................................................................................. 13

Crash Modeling Methods and CMFs ................................................................................ 15

CHAPTER 3. METHODOLOGY ............................................................................................. 17

Selection of Independent Variables ..................................................................................... 17

Establishment of Cause–Effect Relationships .................................................................... 21

Generation of Crash Data .................................................................................................... 21

CHAPTER 4. LT CRASHES AT INTERCHANGE RAMP TERMINALS ......................... 23

CHAPTER 5. SCL CRASHES AT INTERCHANGES .......................................................... 27

CHAPTER 6. MODEL EVALUATION RUBRIC .................................................................. 29

Descriptive Analysis .............................................................................................................. 29

Model Selection ..................................................................................................................... 29

Training and Testing ............................................................................................................ 29

Overall Model Performance................................................................................................. 30

Model Inference .................................................................................................................... 30

CHAPTER 7. MODEL TESTING USING RAD DATASETS ............................................... 31

Team 1 Models ...................................................................................................................... 31

Team 2 Models ...................................................................................................................... 31

Team 3 Models ...................................................................................................................... 32

Evaluation of Models ............................................................................................................ 33

CHAPTER 8. SOFTWARE DEVELOPMENT ....................................................................... 35

RAD Software Development ................................................................................................ 35

Web Development ................................................................................................................. 35

User Authentication .......................................................................................................... 36

Custom RAD Request Handler ......................................................................................... 37

Running Time Estimator ................................................................................................... 37

Multijob Scheduler............................................................................................................ 38

Email Notification............................................................................................................. 38

Download Pregenerated RAD ........................................................................................... 38

VR Animation and Simulator Testbeds ............................................................................ 38
iv

CHAPTER 9. SIMULATOR TESTBED DEVELOPMENT ................................................. 41

CHAPTER 10. CONCLUSIONS AND CONSIDERATIONS FOR FUTURE RESEARCH

....................................................................................................................................................... 49

APPENDIX. DESCRIPTIVE STATISTICS OF SAMPLE RAD DATA .............................. 51

ACKNOWLEDGMENTS .......................................................................................................... 55

REFERENCES ............................................................................................................................ 57
v 

LIST OF FIGURES

Figure 1. Screenshot. Landing page with main menu options in the RAD software. ..................... 3

Figure 2. Graphic. Software development workflow. ..................................................................... 3

Figure 3. Screenshot. Main landing page of the simulator testbed user interface. ......................... 5

Figure 4. Screenshot. User menu showing three visualization options. ......................................... 5

Figure 5. Map. Example diamond interchange with two ramp terminals. ...................................... 9

Figure 6. Graphic. Entrance SCLs.(5) ............................................................................................ 10

Figure 7. Map. Real-world example of an entrance SCL. ............................................................ 10

Figure 8. Graphic. Components of SCLs at an interchange.(7) ..................................................... 19

Figure 9. Screenshot. Example of process for spatially locating crashes. .................................... 20

Figure 10. Graphs. Generated crash data distribution and observed distribution. ........................ 22

Figure 11. Screenshot. Files in RAD folder. ................................................................................. 23

Figure 12. Graphic. Software development workflow.................................................................. 35

Figure 13. Screenshot. User authentication page. ......................................................................... 36

Figure 14. Screenshot. Landing page with main menu options in the RAD software. ................. 36

Figure 15. Screenshot. Custom RAD request handler. ................................................................. 37

Figure 16. Screenshot. Running time estimator for custom query. .............................................. 37

Figure 17. Screenshot. Window to download pregenerated RAD. ............................................... 38

Figure 18. Screenshot. Web page for VR animation and simulator testbeds. .............................. 39

Figure 19. Graphic. Four-step process to develop testbeds. ......................................................... 41

Figure 20. Graphic. LT crash event. ............................................................................................. 42

Figure 21. Graphic. LT near-crash event. ..................................................................................... 42

Figure 22. Graphic. Near-crash event within entrance SCL.

(5) ..................................................... 43

Figure 23. Graphic. Crash event within exit SCL on freeway lane.(5) .......................................... 43

Figure 24. Graphic. Crash event within exit SCL on deceleration lane.

(5) ................................... 43

Figure 25. Graphic. Drawing vehicle trajectories in a computer-aided design program. ............. 44

Figure 26. Graphic and Photographs. Extracting roadway signs from NDS videos..................... 45

Figure 27. Graphic. Example of a highway and an overpass structure......................................... 45

Figure 28. Screenshot. Simulating vehicles in a simulation-optimized runtime build. ................ 46

Figure 29. Screenshot. Main landing page of the simulator testbed user interface. ..................... 46

Figure 30. Screenshot. User menu showing three visualization options. ..................................... 47

Figure 31. Screenshot. Aerial view of an SCL crash. ................................................................... 47

Figure 32. Screenshot. SCL crash shown in 360-degree view. .................................................... 47
vi 

LIST OF TABLES

Table 1. Criteria and scores for model evaluation. ......................................................................... 2

Table 2. Variables used in the safety and operational performance analysis of interchange 

facilities. .............................................................................................................................. 14

Table 3. Interchange-related studies listed in CMF Clearinghouse. ............................................. 16

Table 4. Contributing factors for LT crashes at entrance ramp. ................................................... 17

Table 5. Contributing factors for SCL crashes. ............................................................................ 18

Table 6. Variables in the RAD dataset for LT crashes at ramp terminals. ................................... 23

Table 7. Additional variables specific to individual crashes for LT crashes at ramp 

terminals. ............................................................................................................................. 25

Table 8. Variables in the RAD dataset for SCL facilities. ............................................................ 27

Table 9. Additional variables specific to individual crashes. ....................................................... 28

Table 10. Criteria and scores for model evaluation. ..................................................................... 29

Table 11. Evaluation of the statistical and machine learning models developed using RAD 

datasets. ............................................................................................................................... 33

Table 12. Descriptive statistics of crash frequency (RAD with 400 LT crash sites). ................... 51

Table 13. Descriptive statistics of crossroad AADT (RAD with 400 LT crash sites). ................. 51

Table 14. Descriptive statistics of left-turn AADT (RAD with 400 LT crash sites). ................... 51

Table 15. Descriptive statistics of independent variables (RAD with 400 LT crash sites). ......... 51

Table 16. Descriptive statistics of crash frequency (RAD with 400 SCL sites). .......................... 52

Table 17. Descriptive statistics of freeway AADT (RAD with 400 SCL sites). .......................... 53

Table 18. Descriptive statistics of ramp AADT (RAD with 400 SCL sites). ............................... 53

Table 19. Descriptive statistics of ramp truck AADT (RAD with 400 SCL sites)....................... 53

Table 20. Descriptive statistics of roadway geometric variables (RAD with 400 SCL sites). ..... 53

Table 21. Descriptive statistics of other independent variables (RAD with 400 SCL sites). ....... 54
vii 

LIST OF ABBREVIATIONS

3D three dimensional

AADT annual average daily traffic

AASHTO American Association of State Highway and Transportation Officials

ADT average daily traffic

AIC Akaike information criterion

ARD artificial realistic data

BIC Bayesian information criterion

CMF crash modification factor

CNN convolutional neural network

DDSA data-driven safety analysis

DOT department of transportation

EDC Every Day Counts 

FHWA Federal Highway Administration

EAR Exploratory Advanced Research

GAN generative adversarial networks

HSIS Highway Safety Information System

HSM Highway Safety Manual

KNN k-nearest neighbor 

LT left turn

MSE mean squared error 

NCHRP National Cooperative Highway Research Program 

NDS Naturalistic Driving Study

PDO property damage only 

RAD realistic artificial data

RSE relative squared error 

SCL speed change lane

SHRP Strategic Highway Research Program

SPF safety performance function

SVM support vector machine 

USDOT U.S. Department of Transportation

VR virtual reality

1 

EXECUTIVE SUMMARY

Data-driven methods are an important component of transportation safety decisionmaking. The 

U.S. Department of Transportation’s (USDOT) Strategic Plan (2022–2026) stresses the 

importance of using data-driven methods as part of the overall Safe System Approach toward 

achieving zero roadway fatalities.(1,2)

 These methods typically require analytical evaluation of 

predicted and expected crashes based on geometric and traffic characteristics and other 

contributing factors. One tool that could facilitate this evaluation is realistic artificial data 

(RAD).(3,1) RAD can be beneficial to highway safety research in several ways: assessing a new 

crash estimation method, comparing methods to analyze alternatives, and conducting human 

factors evaluation of behavioral and roadway countermeasures. Advancing RAD will also 

enhance the Federal Highway Administration’s (FHWA) efforts to encourage practitioners to 

apply data-driven methods to safety decisionmaking through programs such as Every Day 

Counts by expanding the number of tools available for safety analysis.(4)

Although artificial data have been used in many diverse applications, such as security, image 

processing, surveys, cancer genomics, infrared spectroscopy, and geography, their use in 

transportation has been limited. The main goal of this Exploratory Advanced Research (EAR) 

Program project was to develop a framework to generate RAD for interchange facilities and to 

generate datasets using that framework. Even though interchanges are ubiquitous in our 

transportation network and carry significant traffic volumes, accurate crash data for such 

facilities are lacking throughout the United States. The scope of this project involves generating 

RAD for two types of crashes occurring at diamond interchanges—ramp terminal left-turn (LT) 

crashes and speed change lane (SCL) crashes.

The data generation framework consists of three main steps. The first step identifies a set of 

contributing factors at the selected interchange facility (e.g., SCL, ramp terminal). Roadway, 

traffic, and driver contributing factors were synthesized from the literature from each selected 

facility. Sampling distributions were generated for each of the factors using observed data from 

Washington and Missouri. The RAD for these factors were then generated by repeatedly 

sampling the distributions for a given sample size (e.g., 500 sites). Data from other States were 

also considered. Highway Safety Information System data for interchange crashes were obtained 

for a 5-yr period. Data from Washington were the most complete and recent for the purposes of 

developing RAD, although data from California, Illinois, Maine, and Minnesota were also 

reviewed. In addition to Washington, interchange safety data from Missouri were also used. The 

Missouri data were acquired from Missouri DOT’s Transportation Management System as part 

of a recently completed Highway Safety Manual (HSM) calibration project.(5,6,7)

The second step of the data generation process establishes the effect of each contributing factor 

on crash frequency. This information was also synthesized from published literature, HSM, and 

the Crash Modification Factors Clearinghouse.(8)

 When no reliable information was available for 

a particular variable, assumptions were made based on analyzing observed data from 

Washington and Missouri.

The third step of the data generation process quantifies the combined effect of all contributing 

factors on crash frequency. This quantification was done in two stages. First, the research team 
2 

estimated the composite crash measure for a given site based on its roadway and traffic 

characteristics. They considered both individual effects of each factor and interaction effects 

between two or more factors in generating the composite measure. A site with a higher 

composite crash measure was likely to experience a higher crash frequency. In the second stage, 

the researchers converted the composite crash measure to realistic crash frequency (i.e., counts) 

using observed crash data. This conversion was done using a hierarchical Poisson approach, with 

parameters optimized for each level of the hierarchy using observed crash data. The research 

team adjusted the generated crash data distribution parameters to match the overall distributional 

shape and crash counts at individual sites. Once the crash counts were generated, they used the 

crash severity distributions to subdivide the overall crash counts into fatal, injury, and property 

damage only crashes. In addition to crash severity, crash-specific factors pertaining to the driver 

(e.g., distraction, age, gender), vehicle type, and roadway (e.g., road condition at the time of 

crash) were also generated for each crash.

The researchers developed a model evaluation rubric to evaluate the performance of models 

developed using RAD with a scoring system of 0–100. Table 1 shows the six criteria and the 

maximum points assigned to each model. Because the primary goal of RAD is to evaluate the 

ability of models to accurately estimate the cause–effect relationships, model inference is 

weighed more than other criteria.

Table 1. Criteria and scores for model evaluation.

Criteria Points

Descriptive analysis of data 10

Model selection 10

Training and testing data 10

Overall prediction accuracy 20

Model inference 50

Total score 100

The RAD datasets generated for LT and SCL facilities were used to test crash prediction models. 

Two teams estimated statistical models, while one team developed a series of machine learning 

models. Statistical models include various forms of negative binomial regression, whereas the 

machine learning models ranged from a simple ridge regression model to a complex deep 

learning model TabNet.(9)

 The model evaluation rubric was applied to the models developed by 

the three teams. All teams provided basic descriptive statistics of the RAD datasets. Overall 

scores (out of 100) ranged between 72 and 91, with the main difference in performance 

appearing in the model inference criteria.

To facilitate the use of RAD, a web-based software was developed to provide access to RAD 

datasets. Figure 1 is a screenshot of the RAD website’s homepage showing the three types of 

data that are available for each of the two facility types. Figure 2 shows the workflow of the 

software. The users submit a RAD data request to the web server. The web server will call the 

RAD generator to produce a set of RAD datasets. Depending on the type of request, either a 

pregenerated dataset or a custom dataset will be produced. For custom datasets, an email 

notification with the download URL for the generated data will be sent to the user-provided 

email.
3 

Source: FHWA. 

SHRP = Strategic Highway Research Program; NDS = Naturalistic Driving Study. 

Figure 1. Screenshot. Landing page with main menu options in the RAD software.

Source: FHWA. 

Figure 2. Graphic. Software development workflow.

The second type of RAD datasets developed in this project are the virtual reality (VR) simulation 

testbeds for crashes and near-crashes occurring at interchanges. The testbeds were developed 

using a four-step process: 

1. Analyze Strategic Highway Research Program (SHRP)2 Naturalistic Driving Study 

(NDS) videos. 

2. Develop crash diagrams. 

3. Create three-dimensional (3D) modeling of roadway and environment. 

4. Reconstruct crash in VR. 
4 

Step 1 involves obtaining and analyzing videos of safety-critical events occurring at 

interchanges. This step was accomplished using SHRP2 NDS data. A total of 114 crash and 

near-crash events involving left-turning vehicles and 310 events occurring on SCLs were 

evaluated to develop the testbeds. Both video and kinematic data for safety-critical events were 

used for reconstruction.

The second step of the crash reconstruction process involves crash diagramming. This task 

entails drawing detailed trajectories of vehicles involved in the crash event. After drawing the 

trajectories, road signs are generated. Signs similar to those observed in the crash videos were 

generated because the actual crash locations were withheld due to privacy concerns. 

After extracting the vehicle trajectories and basic signage from the NDS videos, the third step 

involves creating the roadway and the environment using 3D modeling tools. Coded roadway 

elements include travel lanes, shoulders, medians, barriers, terrain, overpasses, pavement 

markings, etc. Environment elements include signage, overall lighting, and foliage next to the 

highway.The fourth and final step in the testbed development process involves creating a crash 

simulation. Vehicle information and the trajectories extracted in the first step were overlaid on 

top of the roadway and environment elements created in the second and third steps to reconstruct 

the crash. A commonly used simulation engine was used to create the testbeds.

The researchers created a graphical user interface to facilitate the use of simulator testbeds 

created for LT and SCL crashes. Figure 3 shows a screenshot of the homepage of the user 

interface. A user has three visualization options (as shown in figure 4):

• An aerial view is a recreated animation of a crash.

• The 360-degree view places the user in the driver’s seat of the subject vehicle and 

provides the driver’s perspective of the crash.

• The test-drive view is similar to the 360-degree mode, with the exception that the user 

actively controls the vehicle.

Although aerial and 360-degree views are not interactive, the test drive mode gives control to the 

user to drive through the scenario and react to the conditions that led to a crash. 
5 

Source: FHWA. 

Figure 3. Screenshot. Main landing page of the simulator testbed user interface. 

Source: FHWA. 

Figure 4. Screenshot. User menu showing three visualization options. 

In summary, this EAR Program project developed synthetic datasets for interchange facilities for 

the first time. A three-step framework was developed to generate RAD. The developed datasets 

were then used by state-of-the-art statistical and machine learning approaches for modeling crash 

frequency and to ascertain the cause-and-effect relationships. A web-based software was 

developed to provide easy access to the RAD datasets. The software provides 196 pregenerated 

datasets and the option to submit custom data requests. The RAD dataset is provided in a 

spreadsheet format similar to the safety datasets obtained from State DOTs. The proposed 

framework is generic and can be used to generate RAD for other facilities, such as work zones, 

bicyclist/pedestrian facilities, innovative geometric designs, etc.

This project also extended the idea of RAD by generating realistic simulation testbeds using 

NDS data. The VR RAD testbeds were developed with two intended purposes. First, the VR

animations of crashes and near-crashes can be used for driver education. Because the testbeds 

were developed using actual crashes documented in the NDS, they provide a realistic experience 

that is more engaging. For example, outreach activities targeted at teen drivers can use the 

animations to help provide a realistic, immersive experience of a crash and to encourage safe 

driving practices in such circumstances. Lower hardware costs, a younger workforce, and 
6 

investments from technology companies in improving VR experience are all significant reasons 

to believe that the transportation industry will increasingly embrace VR-enabled training and 

education. 

A second purpose served by the VR testbeds is to assist with human factors research. For 

example, a driving simulator platform can readily use the testbeds to test the performance of 

safety countermeasures, such as in-vehicle driver information systems, roadside dynamic 

message signs, collision avoidance systems, etc. This purpose is well aligned with the USDOT’s 

National Roadway Safety Strategy’s Safe System Approach, which considers an overlapping set 

of safety measures: roadway countermeasures, behavioral interventions, enforcement, vehicle 

safety features, and emergency medical care.
7 

CHAPTER 1. BACKGROUND

Data are critical to understanding crash causation, which leads to the optimization of safety 

countermeasures. The Federal Highway Administration (FHWA) has been an active proponent 

of data-driven methods for safety decisionmaking. Through the Every Day Counts (EDC) 

program (EDC-3 and EDC-4), FHWA has been encouraging practitioners to apply a data-driven 

safety analysis (DDSA) approach to safety decisionmaking.(4) The DDSA advocates for a new 

line of thinking that relies on predicted and expected safety values using statistical methods. 

Many States now use DDSA approaches (75 percent per FHWA EDC website) to strategically 

invest in systemic treatments that target specific crash types rather than chasing high-crash 

locations and addressing them piecemeal. The U.S. Department of Transportation’s (USDOT) 

Research, Development, and Technology Strategic Plan (2018–2022) further reinforced the 

importance of reliable data and effective analytical tools in achieving the strategic safety goal of 

zero fatalities.(1) Specifically, the safety data initiative of the systemic safety approach “seeks to 

develop new and integrated data sources, analysis, and visualization techniques to enhance our 

understanding of crash risk and our ability to mitigate it.”(1)

PROJECT MOTIVATION

As described by Hauer, artificial realistic data (ARD) dataset can be a useful tool for research on 

highway safety.(3) Specifically, three areas where the use of ARD could be beneficial are: 

determining a sample size or assessing a new estimation method, comparing methods to analyze 

alternatives, and evaluating ways to generate multivariate models. Some of the common 

challenges in developing safety models include difficulties in identifying causal relationships in 

the data; the use of average values for variables, variable errors, missing variables, and complex 

variable dependencies, and the use of simple mathematical functions. ARD offers an innovative 

way to address these challenges by developing models that not only accurately predict crash 

frequency but also accurately explain the cause-and-effect relationships between crashes and the 

independent variables.

Council et al. led the first ARD effort sponsored by FHWA to examine the performance of 

different modeling methods for cross-sectional studies.1 Using Highway Safety Information 

System (HSIS) data from Washington, a dataset was created consisting of 2,400 mi of 

homogenous segments of 0.02 mi each. Crashes were assigned to each of the segments based on 

certain known causal relationships. The case study examined single-vehicle-lane-departure 

crashes occurring on rural two-lane roadways. The crash and roadway data were then provided to 

a modeler not privy to the assumed causal relationships. The modeler was tasked with estimating 

regression models and deriving the causal relationships. The model results were then checked 

against the assumed relationships. This effort is one of the few endeavors in transportation to 

create synthetic data for safety modeling.

1

Council, F., E. Hauer, B. Lan, D. Harwood, and R. Srinivasan. 2017. Use of “Artificial Realistic Data” (ARD) 

to Assess the Performance of Cross-Sectional Analysis Methods in Capturing Causal Relationships Between 

Individual Roadway Attributes and Safety. Unpublished Report. Washington, DC: Federal Highway Administration.
8 

PROJECT OVERVIEW

In this Exploratory Advanced Research (EAR) Program project, this initial effort by Council et 

al. was extended by developing synthetic datasets for interchange facilities.2 Specifically, 

datasets were generated for crashes occurring at ramp terminals and speed change lanes (SCLs) 

of diamond interchanges. Diamond interchanges are one of the highly prevalent designs in the 

United States. These realistic artificial data (RAD) datasets were then used by state-of-the-art 

statistical and machine learning approaches for modeling crash frequency and to ascertain the 

cause-and-effect relationships. A web-based software was developed to easily access the RAD 

datasets. The software provides 196 pregenerated datasets and the option to submit custom data 

requests. The RAD dataset is provided in a spreadsheet format similar to the safety datasets 

obtained from State DOTs.

RAD datasets can be used to test the performance of different safety modeling approaches. For 

example, if a modeler estimated different forms of statistical models using a crash dataset from a 

particular State DOT, the different models can only be compared using overall goodness-of-fit 

measures (e.g., prediction accuracy, likelihood value). Since the ground truth cause–effect 

relationships between independent and dependent variables are seldom known, the models 

cannot be compared by their ability to extract the true cause–effect relationships. RAD datasets, 

on the other hand, are created using cause–effect relationships that were established using 

literature reviews, subject matter expert interviews, and observed safety data from Washington 

and Missouri. Thus, different models can be compared based on overall goodness of fit as well as

on model inference, that is, the model estimated cause–effect relationships versus the ground 

truth (assumed) relationships. If a model can satisfactorily extract these relationships from RAD 

data, the user can confidently apply that model to real data (e.g., from a State DOT) and generate 

reliable crash modification factors (CMFs).

This project also expands the idea of RAD by generating realistic simulation testbeds using 

Strategic Highway Research Program (SHRP)2 Naturalistic Driving Study (NDS) data. The 

virtual reality (VR) RAD testbeds were developed with two intended purposes. First, the VR

animations of crashes and near-crashes can be used for driver education. Since the testbeds were 

developed using actual crashes documented in the NDS, they provide an immersive, realistic 

experience that is engaging. For example, outreach activities targeted at teen drivers can use the 

animations to help provide a realistic, immersive experience of a crash and to encourage safe 

driving practices in such circumstances. Lower hardware costs, a younger workforce, and 

investments from technology companies in improving the VR experience are all significant 

reasons to believe that the transportation industry will increasingly embrace VR-enabled training 

and education. 

The second purpose of developing the VR testbeds of safety-critical events is to assist with 

human factors research to improve interchange safety. For example, a driving simulator platform 

can readily use the testbeds to test the performance of safety countermeasures, such as in-vehicle 

driver information systems, roadside dynamic message signs, collision avoidance systems, etc.

2

Council, F., E. Hauer, B. Lan, D. Harwood, and R. Srinivasan. 2017. Use of “Artificial Realistic Data” (ARD) 

to Assess the Performance of Cross-Sectional Analysis Methods in Capturing Causal Relationships Between 

Individual Roadway Attributes and Safety. Unpublished Report. Washington, DC: Federal Highway Administration.
9 

This purpose is well aligned with the USDOT’s National Roadway Safety Strategy’s Safe 

System Approach, which considers an overlapping set of safety measures: roadway 

countermeasures, behavioral interventions, enforcement, vehicle safety features, and emergency 

medical care. 

STUDY SCOPE

This project focused on two facilities of a diamond interchange. The first facility is the ramp 

terminal. Figure 5 shows a diamond interchange with two ramp terminals (one on the south side 

and one on the north side). Each site refers to one ramp terminal. The crash type of interest is the 

multivehicle crash occurring between vehicles turning left onto the entrance ramp of the freeway 

(shown as a left-pointing, curved arrow in the diagram) and the oncoming through vehicle on the 

crossroad (shown as a downward-pointing, straight arrow). In the RAD dataset, there is no 

spatial correlation between consecutively numbered sites.

Original photo: Imagery © 2020 Maxar Technologies, map data © 2020 Google®. Modifications by FHWA (see 

acknowledgments section).

Figure 5. Map. Example diamond interchange with two ramp terminals. 
10 

The second interchange facility is the freeway SCL. An SCL facility is an uncontrolled terminal 

between a ramp and a freeway. The schematic in figure 6 shows an entrance SCL measured from 

the gore point to the end of the taper.(5) Figure 7 shows a real-world example of an entrance SCL

segment.

© 2014 American Association of State Highway and Transportation Officials. 

Figure 6. Graphic. Entrance SCLs.(5)

Original photo: Imagery © 2022 Maxar Technologies, Map data © 2022 Google®. Modifications by FHWA (see 

acknowledgments section).

Figure 7. Map. Real-world example of an entrance SCL. 
11 

CHAPTER 2. LITERATURE REVIEW

The research team reviewed literature from two different domains. First, studies documenting the 

development and use of synthetic data were reviewed. Second, due to the focus of this project on 

interchange safety, literature pertaining to the understanding of crash causation at interchanges 

was examined. While the synthetic data review provided information on available data 

generation methods, the interchange safety review helped to obtain information on the key 

independent variables, their impact on crash frequency, and the state-of-the-practice crash 

prediction models.

SYNTHETIC DATA

Although the concept of artificial or synthetic data is new in transportation, its use has been 

demonstrated in other disciplines. A literature review revealed studies have demonstrated the 

successful development, evaluation, and application of artificial datasets.

Generation of Synthetic Data

Probabilistic models and deep generative models are two main methods for synthetic data 

generation. Probabilistic models focus on mimicking the structure and distribution of real data, 

whereas deep generative models focus on replicating the structure of real data and the 

information it contains. The Bayesian network and the Markov model are two popular 

probabilistic models, and deep generative models include variational autoencoder and generative 

adversarial networks (GAN). In Ping et al., a synthetic data generation tool called 

DataSynthesizer was proposed using the Bayesian network.(10)

 Zhang et al. proposed a Bayesian 

network to generate synthetic high-dimensional data.(11) The Markov chain approach can be 

applied to generate temporal synthetic data, such as solar states for a smart grid.(12) Islam et al. 

presented a data augmentation technique to reproduce crash data.(13)

 GAN have been used to 

generate synthetic health data and sensor data.(14,15) A method called TGAN was proposed by Xu 

and Veeramachaneni to synthesize tabular data using GAN.

(16)

Ichim provided an overview of several methods of generating synthetic data, such as probability 

distribution, Latin hypercube sampling, information preserving statistical obfuscation, data 

shuffling, and multiple imputations.(17)

 The author proposed the use of a quantile-based bootstrap 

strategy and tested it using survey data. Bootstrapping has also been used in other studies by 

Barth et al., Jia and Culver, and Thanathamathee and Lursinsap (2013).(18–20) Other methods that 

have been used include Hadoop, convolutional neural network (CNN), genetic algorithm, 

Bayesian Hierarchical model, and n-spheres.(21–25)

Evaluation of Synthetic Data

The quality of synthetic data is critical to its widespread adoption. One straightforward method

to evaluate the quality of synthetic data is to compare the distribution of each variable with the 

original dataset, but this method does not consider joint distributions of variables. To overcome 

the limitations, the synthetic and original datasets can be compared by visualizing the joint 

distribution of high-dimension data with dimension-reduction techniques. The relative 

performance of two machine learning algorithms on the synthetic dataset and the original dataset 
12 

can be used to measure the quality of synthetic data. A good synthetic dataset should preserve 

the same relative performance as the original dataset.(26)

In this project, a rubric was developed to evaluate the synthetic datasets that were developed. A

rubric contains three main components: evaluative criteria; quality definition for those criteria at 

different levels, and a scoring strategy.(27) The goal is to create a rubric grading system to rank 

different models based on their performance, which will be helpful for modelers to revise and 

improve their models. Standard rubrics are created based on expert review and general rules of 

thumb.(28)

Applications of Synthetic Data

Patki et al. developed and used a synthetic data vault to create synthetic data for five datasets.(29)

A crowdsourced experiment was then performed in which data scientists were asked to create 

predictive models with both the synthetic data and real data. The results showed that there was 

no significant difference in the models developed from the synthetic data and real data. Carlucci 

et al. created a synthetic database of depth images, and experiments to test the database on two 

publicly available object datasets showed that the features obtained from processing the synthetic 

data were stronger.(22) In a research study, Soltana et al. developed and tested an approach to 

generate synthetic test data using synthetic data for citizens’ records in a public administration 

system.(30) The case study demonstrated that the results met the criteria for both logical validity 

and statistical representation.

von Neumann-Cosel et al. demonstrated the successful application of synthetic datasets in a 

simulated environment through research in which a lane-tracking algorithm was tested.(31) The 

use of synthetic images was innovative as lane-tracking algorithms are usually evaluated with 

real camera data and then verified using ground truth data. The study found that specific output 

parameters of image processing algorithms could be tested using synthetic images. The 

implementation of the simulation environment to test the lane-tracking algorithm allowed the 

process for investigating various scenarios to be automated, thus reducing the required extent of 

testing on actual roads. 

Synthetic datasets have also been implemented successfully in various applications in civil 

engineering. Jia and Culver investigated several synthetic flow generation methods to develop 

flow records for hydrological calibration to address the challenges created by the limited 

availability of historical flow data.(19) The methods were tested using a case study at Buck 

Mountain Run in Albemarle, VA. The results showed that the best combination of methods was 

the bootstrapped artificial neural network for low- and medium-flow predictions and a modified 

drainage area ratio for the highest 10 percent of synthetic flows. In another study, Naess and 

Claussen used synthetic data to assess the performance of different estimators for the prediction 

of values for long return periods such as wind loads.(32) Sakshaug and Raghunathan used a 

Bayesian Hierarchical method to generate synthetic datasets for estimating small areas.(24)
13 

INTERCHANGE SAFETY MODELING

State of the Practice Review

Freeway interchanges consist of freeway segments, SCLs, entrance and exit ramp segments, and 

ramp terminals (intersections). Given the important role freeways play in carrying high traffic 

volumes, the safety of interchanges is an important concern. American Association of State 

Highway and Transportation Officials’ (AASHTO) Highway Safety Manual (HSM) was updated 

in 2014 to provide safety performance functions (SPFs) and CMFs for different freeway 

facilities, including interchanges.(5) However, there are several important limitations related to 

the use of SPFs and CMFs.(33) The first practical limitation is the amount of data required for 

calibration. For example, the predictive method for basic freeway segments in the HSM requires 

a total of 14 data elements, whereas the method for ramps requires 10 elements. Unfortunately, 

collecting and analyzing data for several of these elements require a high level of effort, as noted 

in National Cooperative Highway Research Program (NCHRP) Project 17-45 (e.g., length and 

radii of horizontal curves, length of/offset to median barriers, clear zone width), which can 

inhibit the effective utilization of these tools.

(34)

 The authors noted challenges in transferability of 

models to other contexts, which may be reflective of differences in geometric characteristics in 

the areas where these studies have been conducted. In addition, a recent meta-analysis of SPFs 

for freeway merge and diverge areas found that existing research in this area has been largely 

inconsistent.(35) For example, the effect of deceleration length on safety was reported to be 

significant in some studies and insignificant in others. Collectively, these results reinforce 

another limitation noted in the HSM in that the SPFs must be calibrated to reflect local driver 

populations, conditions, and environments. 

Another limitation to the use of SPFs and CMFs is specific to the ramp terminal facility type. A 

review of the literature for on-ramp terminal crashes shows that most studies are constrained by 

the lack of crash data specific to this type of freeway geometric element. As such, several studies 

rely on simulations to address the situations of analyzing scenarios that help improve safety and 

traffic flow through these sections.(36,37)

Determining whether a crash should be located on a ramp terminal is not as straightforward as it 

seems. The NCHRP 17-45 project, which influenced the production of the HSM chapter on 

freeway interchanges, includes extensive discussions of the process and criteria for identifying 

interchange ramp-terminal crashes.(34) In the analysis of this study to generate RAD, the crashes 

utilized were collected from Missouri and Washington, which have both taken extensive 

measures to locate and identify such crashes. Washington, in their reporting of freeway crashes 

for the HSIS database, included an intersection-related variable that makes the process more 

manageable. Missouri is also able to provide on-ramp terminal crash data at specifically selected 

locations due to a previously completed research study.(6,7) This project overcame a tremendous 

data challenge by using Washington and Missouri ramp terminal crash data. 

A few studies have attempted to create localized SPFs for interchange ramp terminals by locating 

and analyzing crashes within specific study areas for specific States.(38–40)

 An SPF is a calibrated 

relationship between collision frequency, traffic volume, and other characteristics of a site.(40)

Typically, there are several variables employed in these analyses, which are justified by 

extensive literature in the transportation safety field. These variables include those components 
14 

influenced by factors such as traffic volume (exposure), roadway geometry, and traffic signal 

timing, as described in the literature.

Some of the most common variables utilized in the existing literature (table 2) include annual 

average daily traffic (AADT) and average daily traffic (ADT) values, clearance intervals as a 

factor for traffic signal timing, and roadway geometric elements such as roadway surface type, 

number of lanes, median type and width, and shoulder widths. 

Table 2. Variables used in the safety and operational performance analysis of interchange 

facilities.

Independent variable Studies that feature the selected variable 

Segment length Parajuli et al.;* Le and Porter; Park, Fitzpatrick, and Lord; Claros, 

Edara, and Sun (2017).(40–43)

Speed limit Wang, Qin, and Noyce;* Chen et al. (2011a); Bonneson and 

Zimmerman; Fang, Elefteriadou, and Elias; Claros, Edara, and 

Sun (2016).(38,44–47)

Number of lanes Elefteriadou et al.; Parajuli et al.;* Le and Porter; Claros, Edara,

and Sun (2017); Chen at al. (2011a); Claros, Edara, and Sun 

(2016); Wang et al.; Chen et al. (2011b)(37,40,41,43,44,47–49) 

ADT Elefteriadou et al.; Torbic et al.; Le and Porter; Park, Fitzpatrick, 

and Lord; Chen et al. (2011b), Liu et al.(37,39,41,42,49,50) 

AADT Wang, Qin, and Noyce;* Parajuli et al.;* Claros, Edara, and Sun

(2017); Chen et al. (2011a); Claros, Edara, and Sun (2016); Wang 

et al.(38,40,43,44,47,48) 

Surface type Chen et al. (2011b); Liu et al.(49,50) 

Median width Park, Fitzpatrick, and Lord; Claros, Edara, and Sun (2017); Wang 

et al.(42,43,48)

Lane width Fang, Elefteriadou, and Elias(46)

Shoulder width Park, Fitzpatrick, and Lord(42)

Terminal spacing Wang, Qin, and Noyce;* Claros, Edara, and Sun (2016)(38,47)

Signal timing Elefteriadou et al.; Wang, Qin, and Noyce;* Bonneson and 

Zimmerman; Fang, Elefteriadou, and Elias(37,38,45,46) 

Traffic control type Torbic et al.;* Claros, Edara, and Sun (2016 and 2017)

(39,43,47)

Interchange 

configuration 

Torbic et al.;* Parajuli et al.;* Fang , Elefteriadou, and Elias; 

Claros, Edara, and Sun (2016).(39,40,46,47) 

*Studies used in the generation of SPFs for interchange ramp terminals. 

In an extensive study on the safety performance of ramp terminals, Parajuli et al. collected data 

from 380 ramp terminals in Ontario, QC, Canada.(40) In that study, six different 
15 

ramp-terminal-specific SPFs were developed, accounting for the factors of geometry type, traffic 

control type, and crash severity level.

Wang, Qin, and Noyce presented interesting insights into the effects of yellow or all-red interval 

timing on crash safety at interchange ramp terminals, as well as terminal spacing and exclusive 

right turn phases.(38) Their study suggested that the crash frequency will increase with the 

deficient yellow or all-red intervals and with the increase in terminal spacing.

Crash Modeling Methods and CMFs

Many statistical, machine learning, and deep learning methods have been applied in the study of 

crash modeling. For statistical methods, generalized linear model, Poisson, and zero inflated 

negative binomial are often employed to model crash frequency.(51) The Poisson distribution has 

the advantage of simulating the unobserved heterogeneity for a smaller dataset, whereas the 

negative binomial distribution can simulate a dataset with a lot of zeros and a long tail.(52,53) The 

Poisson-Gamma model can reflect the skewness of the data and be more tunable with a gamma 

prior.(54)

 In this EARP project, a modified version of the Poisson-Gamma model is applied to 

generate the synthetic data.

Iranitalab and Khattak applied various machine learning methods for predicting crash severity, 

including k-nearest neighbor (KNN), support vector machine (SVM), decision tree, and random 

forest.(55) For deep learning methods, Huang et al. used CNN for highway crash detection and 

risk estimation.(56) A long short-term memory–CNN based model was proposed to predict 

real-time crash risk on arterials.(57)

The CMF Clearinghouse is an online repository of CMFs for various types of facilities. The 

research team queried CMFs for interchange-related facilities. The CMF Clearinghouse also cites 

the studies from which a given CMF is derived. Table 3 provides a summary of these studies, 

including the independent variables used in the model and the data used for model development. 

Table 3 lists seven studies. The State data from these studies include Florida, California, 

Washington, and Texas; Florida data were used by five of the seven studies. 
16 

Table 3. Interchange-related studies listed in CMF Clearinghouse.

Study Title Authors Data General Variables (Number and Types)

Safety Evaluation of Geometric 

Design Criteria for Spacing of 

Entrance–Exit Ramp Sequence and 

Use of Auxiliary Lanes

Le and Porter(41) Digital mapping and satellite imaging applications, 

primarily Google® EarthTM and Google MapsTM; 

the online interchange database available through the 

Washington State DOT Interchange Viewer; HSIS 

database.

21 variables: AADT, segment length, high occupancy 

vehicle indicator, crash count (by types), California

interstate indicator, Washington interstate indicator. 

Evaluating the Effects of Freeway 

Design Elements on Safety

Park, Fitzpatrick, 

and Lord(42)

Texas DOT geometric database and Texas crashes 

electronic database. Crash data for 5 yr (1997–2001). 

10 variables: number of lanes, ADT, segment length, 

shoulder width (inside and outside), lane width, 

median width, barrier presence indicator, curvature, 

on-ramp density, crash counts (no PDO).

Selecting Optimal Deceleration 

Lane Lengths at Freeway Diverge 

Areas Combining Safety and 

Operational Effects

Chen, Zhou, and 

Lin(58)

Three-year crash data (2004–2006) obtained from the 

crash database maintained by Florida DOT. Freeway 

segments selected from the Florida Highway System.

6 variables: deceleration lane length, total crash 

count, average crash frequency, crash index, average 

percentage of severe crashes, delay. 

How Lane Arrangements on 

Freeway Mainlines and Ramps 

Affect Safety of Freeways with 

Closely Spaced Entrance and Exit 

Ramps

Liu et al.(50) Three-year crash data (2004–2006) obtained from the 

crash database maintained by Florida DOT. Only 

crashes that occurred on the deceleration lanes were 

included in the study.

10 variables: number of lanes, ADT, posted speed 

limit (indicator), right shoulder width, arrangements 

(indicator), road surface condition, land type, road 

surface type, right shoulder type. 

Safety Evaluation of 

Truck-Related Crashes at Freeway 

Diverge Areas 

Wang et al.(48) Truck-related crash data collected at selected freeway 

exit ramp segments in the State of Florida.

Geometric data and traffic data were collected from the 

Florida Roadway Characteristics Inventory database.

12 variables: injury severity (KABCO)(59)

, 

deceleration length, number of lanes, shoulder width, 

median width, speed limit, speed limit difference 

between highway and ramp, curve (indicator), grade 

(indicator), AADT, truck AADT, ramp AADT. 

Identifying Crash Distributions and

Prone Locations by Lane Groups at 

Freeway Diverging Areas

Chen et al.(44) Interstate freeways in Florida; 3 yr (2004–2006) of crash 

data for 326 sites. Total of 7,872 crashes were reported 

with average value of 4.78, 12.82, 10.23, and 15.41 

crashes per year. 

10 variables: crash types, number of freeway lanes, 

lane unbalanced exit ramp (indicator), number of 

lanes on exit ramp, deceleration length, freeway ADT, 

exit ramp ADT, right shoulder type. 

Operational and Safety 

Performance of Left-Side 

Off-Ramps at Freeway Diverge 

Areas

Zhou et al.(60) Crash data collected at 74 freeway segments in Florida, 

with 11 sites for left-side off-ramps and 63 sites for the 

right-side off-ramps.

8 variables: left-side off-ramp (indicator), log AADT 

on Freeway, log AADT on ramp, crash counts, 

number of lanes, posted speed limit, ramp length, 

deceleration lane length. 

PDO = property damage only.
17

CHAPTER 3. METHODOLOGY

SELECTION OF INDEPENDENT VARIABLES

The first step of the data generation process identifies a set of factors expected to contribute to 

crash occurrence at the selected interchange facility (e.g., SCL, ramp terminal). This step was 

guided by the literature reviewed in chapter 2. Roadway, traffic, and driver contributing factors 

were synthesized from the literature. Table 4 lists the 21 factors identified for left-turn (LT) 

crashes involving crossroad vehicles turning left onto the entrance ramp to the freeway. The 

factors include exposure (traffic), signal control, geometric design, environment (e.g., lighting), 

and driver characteristics. Table 5 shows the 19 factors for SCL crashes. The SCL factors were 

similar to LT factors, except for signal control.

Table 4. Contributing factors for LT crashes at entrance ramp. 

Variable Description

aadt AADT of the crossroad facility

left_turn_aadt AADT of LT movement onto the entrance ramp

presence_of_left_turn_lane Presence of LT lane on the crossroad

number_of_left_turn_lanes Number of LT lanes on the crossroad

signal_control_type Type of LT signal control scheme

functional_class Functional classification of the crossroad facility

jurisdiction Jurisdiction where the site is present

no_lanes Total number of lanes on the crossroad (in both directions)

terrain Terrain of the crossroad at the ramp terminal

horizontal_alignment Horizontal alignment of the crossroad at the ramp terminal

intersection_angle Intersection skew angle

median_presence Presence of median on the crossroad approach

channelization_presence Presence of LT channelization

speed_limit Speed limit on the crossroad approach (in miles per hour)

road_surface_cond Road surface condition when the crash occurred

light_condition Time of the day when the crash occurred

visibility Visibility when the crash occurred

gender Gender of the driver in the left-turning vehicle

age Age of the driver in the left-turning vehicle

distraction Whether the left-turning driver was distracted

vehicle_type Type of vehicle making the LT
18

Table 5. Contributing factors for SCL crashes. 

Variable Description

SCL_len Length of SCL segment (in miles)

Jurisdiction Jurisdiction where the site is present

No_Lane Total number of freeway lanes at the start of SCL segment (does 

not include acceleration lanes)

Terrain Terrain of the SCL segment

Horizontal_alignment Horizontal alignment of the SCL segment

Median_Width Width of the median (in feet)

Inside_Shoulder_Width Width of the shoulder on the left-hand side (in feet)

Outside_Shoulder_Width Width of the shoulder on the right-hand side (in feet)

Speed_Limit Speed limit on the SCL segment (in miles per hour)

Freeway_AADT AADT on the freeway (in one direction)

Ramp_AADT AADT on the entrance ramp

Ramp_Truck_AADT Annual average daily truck traffic on the entrance ramp

road_surface_cond Road surface condition when the crash occurred 

light_condition Time of the day when the crash occurred

visibility Visibility when the crash occurred

gender Gender of the driver at fault in the crash

age Age of the driver at fault in the crash

distraction Whether the driver was driving while distracted

vehicle_type Type of vehicle

Sampling distributions were generated for each of the factors reported in table 4 and table 5

using observed data from Washington and Missouri. When factors were correlated to each other 

(e.g., AADT and number of lanes, jurisdiction, and terrain), joint sampling distributions were 

generated. The RAD for these factors were then generated by repeatedly sampling the 

distributions for a given sample size (e.g., 500 sites). 

HSIS data for interchange crashes were obtained for a 5-yr period to help generate RAD. 

Washington was the most complete and recent (2013–2017) for the purposes of RAD, although 

data from California, Illinois, Maine, and Minnesota were also reviewed. In addition to 

Washington, interchange safety data from Missouri were also used in developing RAD. The 

Missouri data were acquired from Missouri DOT Transportation Management System database

as part of a recently completed HSM Calibration project.(6,7)

Interchange schematics and the milepost where the crash occurred were analyzed together to 

determine if a crash occurred in the SCLs. Crash reports do not indicate whether a crash was a 

SCL crash. Washington State DOT (WSDOT) publishes schematics of all interchanges and 

locations online.(61)

 For this project, drawings of 205 diamond interchanges were analyzed for 
19 

Washington. The interchange schematics provide further information regarding the crossroad 

segment and ramp terminals that made it possible to query all the crashes that occurred at the 

various possible locations.(61) A similar process was used to extract crash data from 75 diamond 

interchanges in Missouri. Google® Maps Road Application Programming InterfaceTM was used 

to extract speed limit values for the interchange sites.

Extracting LT crash data for ramp terminals was straightforward, whereas extracting crashes for 

SCL facilities involved additional steps. Figure 8 shows the four SCL facilities at a diamond 

interchange, two related to the exit and two related to the entrance.(7)

© 2016 Missouri DOT. Modifications: FHWA (see acknowledgments section).

Figure 8. Graphic. Components of SCLs at an interchange.(7)

The mileposts of the “gore” and “taper” points that demarcate the SCLs as a portion of the 

freeway segment are provided in the schematic drawings. To correctly identify the location of 

crashes as related to the SCL by direction and location, the direction of travel (increasing or 

decreasing milepost) and location of SCL (merging or diverging from the ramp) were also

extracted from the schematics. 

The next step in the data collection process involved locating the potential crashes that occurred 

at the SCLs. Geographic information systems (GIS) software(62) and HSIS shapefile crash data

were used to map all the crashes by using their geolocations. From the earlier process of locating 

ramp terminal crashes, the Global Positioning System coordinates of the ramp terminals gathered 

in the process were useful in defining a spatial boundary for filtering the most likely candidates 

of the SCL crashes. Using a 1-mi buffer from the ramp terminal geolocations, an initial filtering 

was done to reduce the number of potential crashes under study. An example of the process for 

spatially locating crashes is shown in figure 9. 
20 

© 2022 Esri, NGA, USGS, FEMA, King County, WA State Parks GIS, Esri, HERE, Garmin, SafeGraph, 

GeoTechnologies, Inc., METI/NASA, USGS, Bureau of Land Management, EPA, NPS, US Census Bureau, USDA

(see acknowledgments section). 

Figure 9. Screenshot. Example of process for spatially locating crashes. 

After the potential crashes were identified, the crash data were merged with location data, and a 

two-stage filter defining the location parameter of the crashes with respect to the SCLs was used 

to filter and select the crashes that occurred within the footprint of the SCLs. This process 

utilized the milepost, road inventory, and direction of travel variables to do the matching and 

selection.
21

The final processes involved aggregating the crashes by location to get the number of crashes 

that occurred at each SCL site. After this aggregation was performed, the SCL files were 

matched to the corresponding segment locations in the “roadlog” file in the same manner as was 

done for the crashes, by defining the spatial location and filtering the potential one-to-one 

matches. The result of this multistep process was the proper identification of the crashes that 

occurred on the speed changes lanes as distinct from the mainline and ramp crashes.

ESTABLISHMENT OF CAUSE–EFFECT RELATIONSHIPS

The second step of the data generation process establishes the effect of each contributing factor 

on crash frequency. This information was also synthesized from the literature reviewed in 

chapter 2, HSM, and the CMF Clearinghouse.(5,8) When no reliable information was available for 

a particular variable, cause–effect relationships were established based on analyzing observed 

safety data from diamond interchanges in Washington and Missouri. 

GENERATION OF CRASH DATA

The third step of the data generation process quantifies the combined effect of all contributing 

factors on crash frequency. This quantification was done in two stages. First, a composite crash 

score was estimated for a given site based on its roadway and traffic characteristics. Both 

individual effects of each factor and interaction effects between two or more factors were 

considered in generating the composite score. This score is transient and has no practical 

significance other than the fact that a site with a higher composite crash score is likely to 

experience a higher crash frequency. In the second stage, the composite crash score was 

converted to realistic crash frequency (i.e., counts) using observed crash data. This conversion 

was done using a hierarchical Poisson approach with parameters optimized for each level of the 

hierarchy using observed crash data. Figure 10 shows an example of the generated crash data 

distribution (12-A) and the observed distribution (12-B). The distribution parameters were 

adjusted to match the overall shape and crash counts at individual sites. Due to the prevalence of 

zero crash sites, it is challenging to identify the parameter values that help produce an accurate 

count of nonzero crashes. In the example shown in figure 10, the RAD process performs 

reasonably well at generating nonzero crashes, including the very low numbers of high-crash 

count sites.
22 

Source: FHWA. 

A. Generated crash data distribution.

Source: FHWA. 

B. Observed crash distribution. 

Figure 10. Graphs. Generated crash data distribution and observed distribution. 

Once the crash counts were generated, crash severity distributions were used to subdivide the 

overall crash counts into fatal, injury, and property damage only (PDO) crashes. The low crash 

count values per site did not allow for generating realistic data for all KABCO severities.(59)

 In 

addition to crash severity, crash-specific factors pertaining to the driver (e.g., distraction, age, 

gender), vehicle type, and roadway (e.g., road condition at the time of the crash) were also 

generated for each crash. 
23 

CHAPTER 4. LT CRASHES AT INTERCHANGE RAMP TERMINALS

RAD datasets were generated for LT crashes at interchange ramp terminals. The datasets are in a 

tabular format and include separate files for crash contributing factors, crash counts, and 

individual crash characteristics. A screenshot of the RAD folder is shown in figure 11. The 

“RAD_input_variables” contains data pertaining to geometric and traffic characteristics, whereas 

the “RAD_crash_data” contains crash data for each interchange site. In the example shown in 

figure 11, data for a 5-yr period (assumed to be 2013–2017) was generated. Characteristics of 

crashes occurring in a particular year are provided in the file with the corresponding year in the 

title (“2013_crash_characteristics” provides crash severity and driver and vehicle information for 

2013). Table 6 describes each of the 17 variables and their format in the input variable and crash 

datasets. Depending on the variable, its value could be numerical or categorical. Crash 

characteristics are described in table 7. The crash characteristics include driver, vehicle, road, 

environment, and crash. 

Source: FHWA. 

Figure 11. Screenshot. Files in RAD folder.

Table 6. Variables in the RAD dataset for LT crashes at ramp terminals. 

Variable Description Values

site id Unique identification number for 

each ramp terminal site

Numerical

aadt AADT of the Crossroad facility Numerical

left_turn_aadt AADT of LT movement onto the 

entrance ramp

Numerical
24 

Variable Description Values

presence_of_left_turn_lane Presence of LT lane on the crossroad Binary: Yes, No

number_of_left_turn_lanes Number of LT lanes on the crossroad Numerical

signal_control_type Type of LT signal control scheme Categorical:

PP—protected and 

permitted

PO—protected only 

FYA—permitted

functional_class Functional classification of the 

crossroad facility

Categorical: 

rural minor arterial 

(6) 

urban principal 

arterial (14)

urban minor arterial 

(16) 

jurisdiction Jurisdiction where the site is present Categorical:

urban, rural 

no_lanes Total number of lanes on the 

crossroad (in both directions) 

Numerical

terrain Terrain of the crossroad at the ramp 

terminal

Categorical:

level, rolling, 

mountainous 

horizontal_alignment Horizontal alignment of the 

crossroad at the ramp terminal

Categorical: 

tangent, curve

intersection_angle Intersection skew angle Categorical:

90 degrees, <90 

degrees

median_presence Presence of median on the crossroad 

approach 

Binary: 

yes, no 

channelization_presence Presence of LT channelization Binary: 

yes, no 

speed_limit Speed limit on the crossroad 

approach (in miles per hour) 

Numerical

Crashes per site per year Total number of crashes occurring at 

a site in a year

Numerical

Crashes per site per year by 

severity

Number of crashes by severity for 

each site in a year

Numerical
25 

Table 7. Additional variables specific to individual crashes for LT crashes at ramp 

terminals. 

Variable Description Values

crash_id Unique identification for each 

crash

N/A 

gender Gender of the driver in the leftturning vehicleBinary: 

female, male

age Age of the driver in the leftturning vehicle Categorical:

young age (<25 yr), middle age 

(25–65 yr), old age (>65 yr)

distraction Whether the left-turning driver 

was distracted 

Binary:

yes, no

vehicle_type Type of vehicle making the LT Binary:

passenger vehicle, truck 

road_surface_cond Road surface condition when 

the crash occurred 

Binary: 

dry, wet

light_condition Time of the day when the crash 

occurred

Binary:

day, night

visibility Visibility when the crash 

occurred

Binary:

clear, poor visibility

severity Crash severity Categorical:

FI (fatal, disabling, and minor injury)

PDO

date Date of the crash (mo/dd/year) N/A

27 

CHAPTER 5. SCL CRASHES AT INTERCHANGES

This chapter describes the RAD datasets generated for SCL facilities at diamond interchanges. 

The generated datasets are in a tabular format and include separate files for crash contributing 

factors, crash counts, and individual crash characteristics. The “RAD_input_variables” contains 

data pertaining to geometric and traffic characteristics, whereas the “RAD_crash_data” contains 

multivehicle crash data for each interchange site. Characteristics of crashes occurring in a 

particular year are provided in the file with the corresponding year in the title (e.g., 

“2013_crash_characteristics” provides crash severity, driver, and vehicle information for 2013). 

Table 8 describes each of the 15 variables and their format in the input variable and crash 

datasets. Ten different crash characteristics are described in table 9. As can be expected, some of 

the input variables for SCLs are different from those reported for ramp terminals. The length of 

the SCL segment, inside and outside shoulder widths, freeway and ramp AADT, and truck 

volume are examples of variables unique to SCL datasets.

Table 8. Variables in the RAD dataset for SCL facilities. 

Variable Description Values

site id Unique identification number for each SCL

segment site

Numerical

SCL_len Length of SCL segment (in miles) Numerical

Jurisdiction Jurisdiction where the site is present Categorical:

urban, rural

No_Lane Total number of freeway lanes at the start of 

SCL segment (does not include acceleration 

lanes)

Numerical

Terrain Terrain of the SCL segment Categorical:

level, rolling

Horizontal_alignment Horizontal alignment of the SCL segment Categorical: 

tangent, 

curve

Median_Width Width of the median (in feet) Numerical

Inside_Shoulder_Width Width of the shoulder on the left-hand side (in 

feet)

Numerical

Outside_Shoulder_Width Width of the shoulder on the right-hand side 

(in feet)

Numerical

Speed_Limit Speed limit on the SCL segment (in miles per 

hour)

Numerical

Freeway_AADT AADT on the Freeway (in one direction) Numerical

Ramp_AADT AADT on the entrance ramp Numerical
28 

Variable Description Values

Ramp_Truck_AADT Annual average daily truck traffic on the 

entrance ramp

Numerical

Crashes per site per year Total number of crashes occurring at a site in a 

year

Numerical

Crashes per site per year by 

severity

Number of crashes by severity for each site in 

a year

Numerical

Table 9. Additional variables specific to individual crashes. 

Variable Description Values

crash_id Unique identification for each 

crash

N/A 

gender Gender of the driver at fault in 

the crash

Binary: 

Female, male

age Age of the driver at fault in the 

crash

Categorical:

young age (<25 yr), middle age 

(25–65 yr), old age (>65 yr) 

distraction Whether the driver was driving 

while distracted

Binary: 

yes, no 

vehicle_type Type of vehicle Binary: 

passenger vehicle, truck 

road_surface_cond Road surface condition when 

the crash occurred 

Binary: 

dry, wet 

light_condition Time of the day when the crash 

occurred

Binary: 

day, night 

visibility Visibility when the crash 

occurred

Binary: 

clear, poor visibility

severity Crash severity Categorical:

FI (fatal, disabling, and minor injury)

PDO

date Date of the crash (mo/dd/year) N/A
29 

CHAPTER 6. MODEL EVALUATION RUBRIC

This section presents a rubric that can be used to evaluate the performance of models developed 

using RAD. The score has a range of 0–100. Table 10 shows the six criteria and the maximum 

points assigned to each model. The six criteria try to capture different complexities in modeling; 

the composite score balances tradeoffs in the modeling process. The criteria and points were 

determined based on a review of literature. Model inference is weighed more than other criteria 

because the primary goal of RAD is to evaluate the ability of models to accurately estimate the 

cause–effect relationships.

Table 10. Criteria and scores for model evaluation. 

Criteria Points

Descriptive analysis of data 10

Model selection 10

Training and testing data 10

Overall prediction accuracy 20

Model inference 50

Total score 100

Grading for each criterion follows a rating procedure. The number of ratings can vary across the 

criteria and will be described next.

DESCRIPTIVE ANALYSIS

This criterion pertains to the calculation of basic summary statistics (e.g., mean, standard 

deviation), scatterplots of response variables and predictors, and correlation matrix. Rating 1, 

100 percent of the maximum score of 10 points, is assigned for providing the basic statistics. 

Rating 2, 70 percent of 10 points, is assigned for partial descriptive statistics. Rating 3, 

40 percent of 10 points, is assigned when very limited descriptive statistics are provided. The 

score for this criterion is computed by multiplying the percentage (based on rank) and 10 points 

(maximum possible score for the criterion).

MODEL SELECTION

The selection of independent variables for inclusion in the model is an important step in

developing a model. Use of a variable selection process and parameter tuning earns Rating 1 

(100 percent of total criterion score). Limited justification of the variable selection process earns 

Rating 2 (60 percent), and no justification earns Rating 3 (0 percent).

TRAINING AND TESTING 

It is a good practice to split the overall dataset into training and testing datasets to accurately 

assess model performance. A Rating 1 (100 percent of maximum possible score of 10 points) is 

assigned when a model performance on a testing dataset is provided. Additional goodness-of-fit 

measures using cross-validation data folding may also be provided. No points were assigned for 

models when they were not evaluated using a testing dataset. 
30 

OVERALL MODEL PERFORMANCE

Model performance can be reported using various goodness-of-fit measures. Examples include 

mean squared error (MSE), confusion matrix, and log-likelihood. Reporting a minimum of two 

measures of performance is recommended. Five ratings are established for this criterion based on 

MSE and confusion matrix. Rating 1 (100 percent of maximum possible score of 20 points) for 

MSE <1 or >90 percent accuracy of confusion matrix. Rating 2 (80 percent) for MSE <1.5 or 

>80 percent accuracy of confusion matrix. Rating 3 (70 percent) for MSE <2 or >70 percent

accuracy of confusion matrix. Rating 4 (50 percent) for MSE <2.5 or >60 percent accuracy of 

confusion matrix. Rating 5 (30 percent) for MSE <3 or >50 percent accuracy of confusion 

matrix.

MODEL INFERENCE

Model inference entails a model’s ability to explain the cause–effect relationship between an 

independent variable and outcome (e.g., crash frequency). As table 10 shows, the highest number 

of points (50) are assigned to this criterion. Count regression models can generate CMFs that 

explain the cause–effect relationship. Other surrogate measures (e.g., partial dependence values) 

may be used for machine learning models. Ratings are awarded based on the ratio of estimated 

CMF to true CMF. The ratings are as follows:

• Rating 1 (100 percent of total score) when the ratio is between 0.9 and 1.1. 

• Rating 2 (90 percent of score) when the ratio is either between 0.5 and 0.9 or between 1.1 

and 1.5. 

• Rating 3 (70 percent) when the ratio is either between 0.1 and 0.5 or between 1.5 and 1.9. 

• Rating 4 (50 percent) when the ratio is either smaller than 0.1 or greater than 1.9. 

• Rating 5 (30 percent) is awarded when the CMF values are computed but do not satisfy 

Rating 4. 
31 

CHAPTER 7. MODEL TESTING USING RAD DATASETS

To illustrate the use of RAD, sample RAD datasets generated for LT and SCL facilities were 

used to test the performance of crash prediction models. Two teams estimated statistical models, 

while one team developed a series of machine learning models. The teams developing the 

models were not privy to the RAD generation procedures to allow for an unbiased estimation of 

the cause–effect relationships. The detailed model parameters and results of each of the teams 

are provided as a separate document to FHWA. This chapter first summarizes the models 

developed by each team and then presents the evaluation results for the models using the rubric 

presented in chapter 6. 

TEAM 1 MODELS

A variety of models were tested, and the resultant models presented in this report represent the 

best models in terms of various fit criteria. Some unobserved heterogeneity was determined to 

exist in the dataset. A few parameters in the models were determined to be random as a result 

with statistically significant standard deviations. The level of unobserved heterogeneity appears 

to be limited, however, with no more than three parameters being determined to be random. The 

intercept in all three models appears to be random. This observed outcome indicates that a basic 

random effect is seen to be prevalent in all three datasets. Any unobserved heterogeneity over 

and above the random effect is typically attributed to the number of LT lanes and speed limit (for 

the freeway speed change dataset). This obtained result indicates that LT lanes appear to be 

influenced by stochastic effects that are not captured explicitly in the datasets. The speed limit 

effect is similar in that variations in speed limits are not sufficiently captured by speed limit 

indicators alone; instead, the indicators captured the underlying processes that represent a 

continuous distribution of unobserved effects that are at play with respect to speed limits. Such 

processes may be capturing the effect of driving behavior. 

The team presented findings on the statistical significance of the estimated coefficients, the 

marginal effects of the associated parameters, and the convergent goodness-of-fit measures, such 

as log-likelihood, Akaike information criterion (AIC), and the Bayesian information criterion 

(BIC).

TEAM 2 MODELS

A series of count data models were estimated to investigate the relationship between various 

site-specific factors and the number of head-on/LT crashes that occurred at each ramp terminal in 

the RAD. 

First, a series of simple negative binomial models were estimated where crashes were regressed 

against individual variables of interest. The initial analysis focused on understanding the 

relationship between crashes and AADT. In the research literature, this relationship is generally 

accounted for in one of two ways: AADT is logged and treated as an offset variable, with its 

parameter being constrained to be equal to one, or AADT is included as a covariate in 

logarithmic form. When treated as an offset, an explicit assumption is introduced that crashes 

will increase proportionately with traffic volume. In contrast, empirical data often show that 
32 

crashes tend to increase at a decreasing rate, with this effect often characterizing the effects of 

increasing congestion and lower speeds at higher ranges of volumes.

As noted in the previous paragraph, when treated as an offset, the parameter estimate on 

log(AADT) is constrained to be equal to one. In effect, this means that a 1-percent increase in 

traffic volume would be associated with a 1-percent increase in crashes.

The degree to which the effects of other variables change with respect to how AADT is 

introduced in the model was also of interest. Several design parameters of interest, such as the 

type of LT signal phasing that is introduced, the number of travel lanes on the crossroad, and the 

speed limit, among other factors, may be influenced by AADT. To this end, results from the 

following three negative binomial models are presented: 

• A series of models for crashes versus each individual variable of interest.

• A series of models for crashes versus each individual variable of interest, along with 

log(AADT) as an offset.

• A series of models for crashes versus each individual variable of interest, along with 

log(AADT) treated as a covariate.

Forecasting accuracy measures were calculated based on the difference between the predicted 

crashes and crashes provided in the data for the training dataset. The following measures have 

been included: the mean absolute deviation, the sum squared error, MSE, the root mean square 

error, and the standard deviation of errors. CMFs were presented for the statistically significant 

variables.

TEAM 3 MODELS

Seven machine learning models were developed to predict crash counts using the RAD dataset, 

ranging from a simple ridge regression model to a complex deep learning model TabNet.(9) The 

seven models are: ridge regression, KNN, SVM, decision tree, random forest, XGBoost,(63)

 and 

TabNet.(9) The first five models are commonly used machine learning models found in most 

introductory textbooks. XGBoost is an implementation of gradient-boosted decision trees. 

XGBoost is well-known for its speed and good performance in Kaggle (machine learning) 

competitions. It usually works well for structured or tabular data, which is a good match for the 

RAD dataset. TabNet is a deep learning model specifically designed for tabular data learning. It 

uses sequential attention to choose which features to reason from at each decision step and 

achieves state-of-the-art performance for tabular data modeling. TabNet uses masked 

self-supervised learning to learn representation for categorical columns and then uses them in the 

prediction to improve the results.

In this study, the crash count prediction was formulated as a regression problem since the range 

of crash counts is relatively large for a classification task and is not deterministic. For models 

that need a validation dataset, a portion of the training dataset was used for validation. 

The performance of machine learning models on testing dataset (20 percent of the entire dataset) 

were reported using MSE, relative squared error (RSE), and R-square. Partial dependence plots 
33 

were generated to extract the cause–effect relationships. Partial dependence shows the 

dependence between the target response and the input feature of interest, marginalizing over the 

values of all other input features. The partial dependence values were used to compute CMFs and 

95 percent confidence interval values for CMFs.

EVALUATION OF MODELS

The model evaluation rubric proposed in chapter 6 was applied to the models developed by the 

three teams. All teams provided basic descriptive statistics of the RAD datasets. Samples of these 

statistics can be found in the appendix. Thus, all teams received the maximum score of 10 points 

on this criterion. Model selection criterion entails justification of the inclusion of independent 

variables in the crash frequency models. When sufficient details were provided on the variable 

selection process and the tuning of parameters, a score of 10 was assigned. A score of 8 was 

assigned to some models when there was missing information on variable selection. The next 

criterion of training and testing dataset involved not using the entire RAD dataset for model 

estimation. A test dataset not used for model estimation can provide a good evaluation of any 

model overfitting. Teams receiving the data were asked to divide the dataset into training and 

testing datasets for a robust evaluation. Thus, all models received a score of 10. Overall model 

performance evaluation involves assessing the ability of the models to accurately predict the 

crash frequency for a site. The goodness-of-fit measures reported varied between statistical and 

machine learning models. Statistical models reported AIC, BIC, and likelihood values, whereas 

machine learning models reported MSE and RSE values. Overall model performance scores are 

shown in table 11. Finally, the ability of a model to explain the cause–effect relationship between 

an independent variable and the outcome (i.e., crash frequency) was evaluated. Marginal effects 

and CMFs were provided for some statistical models. Partial dependence plots and 

pseudo-CMFs were provided for the machine learning models. The scores for this criterion 

ranged between 30 and 45 for the different models. 

Table 11. Evaluation of the statistical and machine learning models developed using RAD 

datasets. 

Criteria

Maximum

Score

Team 1 

LT

Team 2 

LT

Team 3 

LT

Team 1 

SCL

Team 2 

SCL

Team 3 

SCL

Descriptive statistics 10 10 10 10 10 10 10

Model selection 10 8 8 10 8 8 10

Training and testing data 10 10 10 10 10 10 10

Overall model performance 20 14 16 16 14 14 16

Model inference 50 30 30 35 35 35 45

Total score 100 72 74 81 77 77 91

In summary, the statistical models developed by Teams 1 and 2 had similar performances. The 

machine learning models developed by Team 3 outperformed the statistical models, especially in 

the model inference criterion. The CMFs computed from Team 3 models were closer to the true 

CMFs, thus explaining the assumed cause–effect relationships. The performance of machine 

learning models on the SCL RAD dataset was found to be better than their performance on the 

ramp terminal dataset. One possible reason for the superior performance of machine learning 
34 

models over count regression models is their ability to better capture the nonlinear relationships 

between crash frequency and the independent variables.

One other use of RAD is to allow researchers to compare the performance of different models 

estimated using different observed data. Consider, for example, two studies developing crash 

frequency models for SCLs—Study A is using observed data from State A, and Study B is using 

observed data from State B. How to accurately assess the CMFs generated from the two studies? 

How to determine which CMFs explain the true cause–effect relationships between crash 

frequency and the corresponding input variables (e.g., freeway AADT, SCL length)? RAD can 

help answer these questions. The research teams conducting the two studies apply their modeling 

approaches to the same RAD dataset. The rubric offered in chapter 6 can be used for comparing 

the model performance along with statistical tests such as goodness of fit, marginal effects, 

variable variance, etc. Alternatively, the comparison may also be made by only using the model 

inference criterion, that is, comparing the CMFs generated using the RAD dataset to the known 

CMFs (i.e., those used to develop RAD). The modeling approach that performs the best on the 

RAD dataset is more likely to explain the true cause–effect relationship in observed data. Thus, 

in the above example, if the performance of the model developed in Study A on the RAD dataset 

is better than the performance of the model developed in Study B, Study A is likely to also 

produce more reliable CMFs when using observed data. This type of testing was not conducted 

in this project due to the lack of readily available models (and CMFs) for interchange ramp 

terminals and SCLs already estimated using data from different datasets.
35 

CHAPTER 8. SOFTWARE DEVELOPMENT

RAD SOFTWARE DEVELOPMENT

The software development of this project includes the implementation of a RAD generator and 

the website that can serve real-time user requests. Figure 12 shows the workflow of the software. 

The users submit a RAD request to the web server. The web server will call the RAD generator 

to produce a RAD dataset. Depending on the type of request, either a pregenerated dataset or a 

custom dataset will be generated. For custom datasets, an email notification with the download 

URL for the generated data will be sent to the user-provided email.

Source: FHWA. 

Figure 12. Graphic. Software development workflow.

WEB DEVELOPMENT

For web development, HTML5, CSS, and JavaScript programming languages are used for 

front-end user interface implementation, and PHP scripting language is used in the backend to 

provide web services.

The website developed in this project includes the following seven functionalities: 

1. User authentication. 

2. Custom RAD request handler. 

3. Running time estimator. 

4. Multijob scheduler. 

5. Email notification. 
36 

6. Pregenerated RAD downloads.

7. VR animation and simulator testbeds.

User Authentication

To ensure only authenticated users have access to the RAD datasets, a password authentication 

method is used. Anyone who wants access to the website will be redirected to the login page and 

must enter their credentials to proceed. Figure 13 shows a screenshot of the login page. The 

landing page after logging into the website is shown in figure 14. 

Source: FHWA. 

Figure 13. Screenshot. User authentication page.

Source: FHWA. 

Figure 14. Screenshot. Landing page with main menu options in the RAD software.
37 

Custom RAD Request Handler

After logging into the website, the user will have the option to submit custom RAD requests to 

the server. The users need to enter three input parameters (see figure 15): the number of sites, the 

number of years of RAD, and an email address to receive the dataset once generated. Once the 

RAD request is submitted to the web server, the RAD request handler calls the RAD generator to 

create the dataset.

Source: FHWA. 

Figure 15. Screenshot. Custom RAD request handler.

Running Time Estimator

After the users submit their RAD request, the running time estimator will provide a notification 

with the estimated time needed to complete the job to the users on the website. If the users think 

the job will take too long, they can click the cancel button in the popup window to cancel the 

RAD request and modify the input parameters to resubmit a new request, which takes less time, 

or the user can click the OK button in the popup window to proceed if they are satisfied with the 

estimated running time. Figure 16 shows an example of the popup window message for a custom 

query. 

Source: FHWA. 

Figure 16. Screenshot. Running time estimator for custom query.
38 

Multijob Scheduler

There could be multiple users submitting requests at the same time, or one user may submit 

different requests within a short timeframe. These situations could result in heavy workload for 

the server and increase the waiting time for users. To solve this problem, a multijob scheduler 

has been implemented on the server side to facilitate the speed of this RAD software. The 

multijob scheduler uses multithreading and can handle five requests simultaneously. The 

schedular uses a first come, first serve mechanism to process jobs. In the future, if the number of 

requests increases to a situation that the current multijob scheduler could not handle, the server 

capabilities can be enhanced to address the increasing demand.

Email Notification

The email notification function allows the users to receive an email stating their requests have 

finished, and they can download the RAD through the URL provided in the email. This function 

is convenient because it eliminates wait times and even allows the submission of multiple RAD 

data requests.

Download Pregenerated RAD

In addition to submitting custom RAD requests, users can also download pregenerated RAD 

datasets. There are pregenerated RAD datasets for 16 combinations of number of sites and years. 

For each combination, five different pregenerated datasets are provided. To download a 

pregenerated RAD, the users select the number of sites and years they want and then click 

download, and they will randomly get one dataset from the five pregenerated datasets for the 

chosen combination. Figure 17 shows the pregenerated RAD window where the users select the 

two input parameters.

Source: FHWA. 

Figure 17. Screenshot. Window to download pregenerated RAD. 

VR Animation and Simulator Testbeds

This web page includes crash recreation animation software and data files for different scenarios. 

A screenshot of the web page is shown in figure 18. 
39 

Source: FHWA. 

Figure 18. Screenshot. Web page for VR animation and simulator testbeds. 

41 

CHAPTER 9. SIMULATOR TESTBED DEVELOPMENT

This chapter presents the approach used to develop VR simulation testbeds for crashes and 

near-crashes occurring at interchanges. These testbeds serve two main purposes. First, they can 

be used as a crash visualization tool for public education. Second, the testbeds will enable safety 

researchers to evaluate human factors countermeasures (behavioral, technology, etc.) to improve 

interchange safety. The testbeds were developed using a four-step process shown in figure 19. 

Source: FHWA. 

Figure 19. Graphic. Four-step process to develop testbeds. 

Step 1 involved obtaining and analyzing videos of safety-critical events occurring at 

interchanges. This step was accomplished using SHRP2 NDS data. The NDS dataset was 

obtained for all junction-related crash, near-crash, and baseline events. The dataset consisted of 

41,479 events. To identify the LT and SCL events, a series of data reductions were applied to 

filter the data based on precipitating event, event nature, traffic control, and event severity. The 

data reduction yielded 114 crash and near-crash events involving left-turning vehicles and 310 

events occurring on SCLs. Forward-view videos were reviewed for each of the 114 events to 

determine the relative location of the events within the interchange footprint (i.e., crossroad, 

SCL, ramp segment, etc.). Two locations were of particular interest in this study—crossroad 

crashes involving left-turning vehicles and SCL crashes. Figure 20 and figure 21 show 

schematics of an LT crash occurring on the crossroad. The subject vehicle could be either the 

vehicle turning left onto the entrance ramp or the oncoming through vehicle. 
42 

Source: FHWA.

Figure 20. Graphic. LT crash event. 

Source: FHWA.

Figure 21. Graphic. LT near-crash event. 

Of the 310 events occurring on SCLs, 179 occurred on entrance SCLs and 131 on exit SCLs. The 

NDS time series data provide event data at 0.1-s intervals, including the distance between 

follower and leader, relative velocity, and headway. These data were used to manually validate 
43 

and further filter the events occurring on entrance and exit SCLs. Figure 22, figure 23, and figure 

24 show schematics of three types of SCL events that were reconstructed: near-crash at entrance 

SCL, crash at exit SCL on freeway, and crash at exit SCL on deceleration lane. 

Original graphic: © 2014 AASHTO. Modifications: FHWA (see acknowledgments section).

Figure 22. Graphic. Near-crash event within entrance SCL.

(5)

Original graphic: © 2014 AASHTO. Modifications: FHWA (see acknowledgments section).

Figure 23. Graphic. Crash event within exit SCL on freeway lane.(5)

Original graphic: © 2014 AASHTO. Modifications: FHWA (see acknowledgments section).

Figure 24. Graphic. Crash event within exit SCL on deceleration lane.

(5)

Both video and kinematic data for safety-critical events were used for reconstruction. The event 

information included time of day, weather, vehicle status, vehicle trajectory, surrounding traffic, 

road conditions, signage, and pavement markings. 
44 

The second step of the crash reconstruction process involves crash diagramming. This task 

entails drawing detailed trajectories of vehicles involved in the crash event. Figure 25 shows an 

example of trajectories of two vehicles, V1 and V2. 

Source: FHWA. 

Figure 25. Graphic. Drawing vehicle trajectories in a computer-aided design program. 

After drawing the trajectories, the road signs are generated. Due to privacy concerns, the actual 

locations of the crashes were not available for the NDS dataset. Signs similar to those observed 

in the crash videos were generated because the actual crash locations are not known. Figure 26

shows an example of this step.
45 

Source: FHWA. 

A. Plan view of vehicle trajectories and overhead sign. 

© 2022 Virginia Tech Transportation Institute. 

B. Screenshots from NDS videos. 

Figure 26. Graphic and Photographs. Extracting roadway signs from NDS videos. 

After extracting the vehicle trajectories and basic signage from the NDS videos, the third step 

involves creating the roadway and the environment using three-dimensional (3D) modeling tools. 

Coded roadway elements include travel lanes, shoulders, medians, barriers, terrain, overpasses, 

pavement markings, etc. Figure 27 provides an example of a highway and an overpass structure 

created during this step. Environment elements include signage, overall lighting, and foliage next 

to the highway. 

Source: FHWA. 

Figure 27. Graphic. Example of a highway and an overpass structure. 

The fourth and final step in the testbed development process involves creating a crash 

simulation. Vehicle information and the trajectories extracted in the first step are overlaid on top 
46 

of the roadway and environment elements created in the second and third steps to reconstruct the 

crash. A commonly used simulation engine was used to create the testbeds. Figure 28 shows a 

screenshot of simulating a car in the software. Scripts were written to add background traffic to 

the simulation. 

Source: FHWA. 

Figure 28. Screenshot. Simulating vehicles in a simulation-optimized runtime build. 

The researchers created a graphical user interface to facilitate the use of simulator testbeds 

created for LT and SCL crashes. Screenshots of the user interface are shown in figure 29, figure 

30, figure 31, and figure 32. A user has three visualization options, as shown in figure 30. An 

aerial view is a recreated animation of a crash. The 360-degree view places the user in the 

driver’s seat of the subject vehicle and provides the driver’s perspective of the crash. The testdrive view is similar to the 360-degree mode, with the exception that the user actively controls 

the vehicle. Although the aerial and 360-degree views are not interactive, the test-drive mode 

gives control to the user to drive through the scenario and react to the conditions.

Source: FHWA. 

Figure 29. Screenshot. Main landing page of the simulator testbed user interface. 
47 

Source: FHWA. 

Figure 30. Screenshot. User menu showing three visualization options.

Source: FHWA. 

Figure 31. Screenshot. Aerial view of an SCL crash. 

Source: FHWA. 

Figure 32. Screenshot. SCL crash shown in 360-degree view. 

49 

CHAPTER 10. CONCLUSIONS AND CONSIDERATIONS FOR FUTURE RESEARCH

This project generated two types of RAD datasets for safety research. Tabular data similar to 

what modelers typically use to conduct safety evaluation studies are the first type of RAD. Users 

have the option to either download pregenerated datasets or to run custom queries for any 

number of sites and years. The chapter 6 proposed rubric enabled the chapter 7 development of 

statistical and machine learning models by using these tabular RAD datasets and making a 

comparison of their relative performance. The strength of different modeling methods in 

extracting the cause–effect relationships between the dependent and independent variables can 

be tested by comparing their performance with assumed cause–effect relationships. Although the 

rubric standardizes the evaluation process and accommodates the differences in performance 

measures (e.g., goodness of fit) across different modeling methods, it might still be challenging 

to compare methods by using different measures of effectiveness. To encourage the application 

of new statistical and machine learning approaches or variations of existing approaches, the RAD 

datasets can be introduced in graduate courses at universities. Since the datasets are ready to use 

(i.e., have gone through a quality assurance and a quality control process), researchers do not 

have to expend effort in obtaining the data, processing, and preparing them for model 

development. A student competition organized by the Transportation Research Board or other 

professional transportation societies could further encourage the use of RAD to discover new 

modeling approaches. 

The second type of RAD datasets generated in the study were the VR testbeds. These testbeds, 

developed using actual crash videos from NDS, offer a realistic and engaging way to support 

driver education and countermeasure evaluation studies. Improving interchange safety involves 

implementing effective behavioral and engineering countermeasures. The testbeds provide 

human factors researchers with a starting point (i.e., a fully developed ramp terminal or an SCL

section with signage and traffic). The modeler can easily add interventions to the testbed and 

conduct human factors evaluations. The developed testbeds are hardware agnostic and can be 

used across different visualization options—head-mounted devices, driving simulators, 

3D projection systems (e.g., CAVE (cave automatic virtual environment)). The use of VR

testbeds can be encouraged through a VR hackathon where users are asked to implement novel 

safety countermeasures, in the simulation, and evaluate their effectiveness.

This project demonstrated the proposed RAD framework for creating new RAD datasets for 

interchange facilities, although the same framework can be applied to generate artificial data for 

other roadway facilities. Of particular interest would be those facilities for which it is difficult to 

obtain accurate and complete real crash data. Examples of such facilities include work zones, 

alternative intersections (e.g., diverging diamond, J-turns), and bicycle facilities. The RAD 

software developed in this project can easily be extended to include data for additional facilities. 

Another direction for future research entails the application of VR testbeds to evaluate some 

behavioral and roadway countermeasures. For example, a driving simulator experiment can be 

set up using a testbed developed for SCLs, and the effect of different driver alert systems 

(e.g., in-vehicle, dynamic message signs) can be evaluated using study participants. 

51 

APPENDIX. DESCRIPTIVE STATISTICS OF SAMPLE RAD DATA

This appendix presents tables of descriptive statistics of crash frequency and various potential 

contributing factors for a sample RAD dataset of 400 sites and 5 yr. Table 12, table 13, table 14, 

table 15, and table 16 provide data pertaining to ramp terminal sites, whereas table 17, table 18, 

table 19, table 20, and table 21 provide data for SCL facilities.

Table 12. Descriptive statistics of crash frequency (RAD with 400 LT crash sites).

Statistic 2013 2014 2015 2016 2017

Mean 0.49 0.64 0.56 0.52 0.55

Median 0 0 0 0 0

Standard 

deviation

1.04 1.40 1.20 0.97 1.31

Minimum 0 0 0 0 0

Maximum 9 11 13 6 12

Table 13. Descriptive statistics of crossroad AADT (RAD with 400 LT crash sites).

Statistic 2013 2014 2015 2016 2017

Mean 15,776 16,091 16,413 16,742 17,076

Median 14,074 14,354 14,642 14,934 15,233

Standard 

deviation

2,959 3,018 3,078 3,140 3,203

Minimum 2,000 2,040 2,080 2,122 2,164

Maximum 40,000 40,800 41,616 42,448 43,297

Table 14. Descriptive statistics of left-turn AADT (RAD with 400 LT crash sites).

Statistic 2013 2014 2015 2016 2017

Mean 1,376 1,597.6 1,629.6 1,662.2 1,695.4

Median 1,566.8 1,403 1,431 1,459.5 1,489

Standard 

deviation

297 315 309 315 322

Minimum 160 163 166 169 173

Maximum 4,302 4,388 4,475 4,565 4,656

Table 15. Descriptive statistics of independent variables (RAD with 400 LT crash sites). 

Variable Description Values

presence_of_left_turn_lane Presence of LT lane on the 

crossroad

Yes—234 (58.5 percent)

No—166 (41.5 percent)

number_of_left_turn_lanes Number of LT lanes on the 

crossroad

0—166 (41.5 percent)

1—199 (49.8 percent) 

2—35 (8.7 percent)

signal_control_type Type of LT signal control 

scheme 

PP—132 (33 percent)

PO—91 (22.8 percent)
52 

Variable Description Values

FYA—177 (44.3 percent) 

functional_class Functional classification of 

the crossroad facility

Rural minor arterial—12 

(3 percent) 

Urban principal arterial—211 

(53 percent) 

Urban minor arterial—172 

(43 percent) 

jurisdiction Jurisdiction where the site is 

present

Urban—383 (95.7 percent)

Rural—17 (4.3 percent) 

no_lanes Total number of lanes on the 

crossroad (in both 

directions) 

2—126 (31.5 percent) 

4—221 (55.3 percent) 

6—53 (13.2 percent) 

terrain Terrain of the crossroad at 

the ramp terminal

Level—177 (44.2 percent)

Rolling—186 (46.5 percent) 

Mountain—37 (9.3 percent) 

horizontal_alignment Horizontal alignment of the 

crossroad at the ramp 

terminal

Tangent—293 (73.2 percent) 

Curve—107 (26.8 percent) 

intersection_angle Intersection skew angle 90 degrees—313 (78.2 

percent) <90 degrees—87 

(21.8 percent) 

median_presence Presence of median on the 

crossroad approach

Yes—207 (51.8 percent) 

No—193 (49.2 percent) 

channelization_presence Presence of LT

channelization

Yes—180 (45 percent) 

No—220 (55 percent) 

speed_limit Speed limit on the crossroad 

approach (in miles per hour) 

20 mph—10 (2.5 percent) 

25 mph—52 (13 percent) 

30 mph—30 (7.5 percent) 

35 mph—202 (50.5 percent) 

40 mph—38 (9.5 percent) 

45 mph—26 (6.5 percent) 

50 mph—23 (5.8 percent) 

55 mph—16 (4 percent) 

60 mph—3 (2.5 percent) 

Table 16. Descriptive statistics of crash frequency (RAD with 400 SCL sites).

Statistic 2013 2014 2015 2016 2017 

Mean 0.73 0.76 0.69 0.8 0.75 

Median 0 0 0 0 0 

Standard 

deviation 

2.93 2.73 2.85 3.92 2.67 

Minimum 0 0 0 0 0 

Maximum 12 13 13 16 13 
53 

Table 17. Descriptive statistics of freeway AADT (RAD with 400 SCL sites).

Statistic 2013 2014 2015 2016 2017

Mean 43,365 44,232 45,117 46,019 46,939

Median 36,237 36,961 37,700 38,545 39,224

Standard 

deviation

11,876 12,113 12,355 12,602 12,855

Minimum 2,076 2,117 2,159 2,203 2,247

Maximum 193,551 197,422 201,370 205,397 209,505

Table 18. Descriptive statistics of ramp AADT (RAD with 400 SCL sites).

Statistic 2013 2014 2015 2016 2017

Mean 5,957 6,076 6,198 6,322 6,448

Median 5,388 5,496 5,606 5,718 5,832

Standard 

deviation

1,204 1,228 1,253 1,278 1,303

Minimum 37 37 38 39 40

Maximum 18,565 18,936 19,315 19,701 20,095

Table 19. Descriptive statistics of ramp truck AADT (RAD with 400 SCL sites).

Statistic 2013 2014 2015 2016 2017

Mean 1,115.5 1,137.3 1,160 1,183.2 1,206.9

Median 754 769 784 799.5 815.5

Standard 

deviation

333 340 346 353 360

Minimum 3 3 3 3 3

Maximum 7,001 7,141 7,283 7,429 7,578

Table 20. Descriptive statistics of roadway geometric variables (RAD with 400 SCL sites).

Statistic

SCL Segment Length 

(mi)

Median 

Width (ft)

Inside 

Shoulder 

Width (ft)

Outside 

Shoulder 

Width (ft)

Mean 0.25 48.59 7.79 10.72

Median 0.27 50 7.29 10.67

Minimum 0.05 10.96 3.02 6.04

Maximum 0.67 188.95 22.98 15.79
54 

Table 21. Descriptive statistics of other independent variables (RAD with 400 SCL sites).

Variable Description Values

Jurisdiction Jurisdiction where the site is 

present

Urban—216 (54 percent)

Rural—184 (46 percent)

No_Lane Total number of freeway lanes at 

the start of SCL segment (does not 

include acceleration lanes)

1—15 (3.8 percent)

2—208 (52 percent) 

3—163 (40.7 percent) 

4—14 (3.5 percent)

Terrain Terrain of the SCL segment Level—269 (67.3 percent)

Rolling—131 (32.7 percent)

Horizontal_alignment Horizontal alignment of the SCL 

segment

Tangent—299 (74.7 

percent)

Curve—101 (25.3 percent)

Median Barrier Presence of a median barrier Yes—184 (46 percent)

No—216 (54 percent)

Median_Width Width of the median (in feet) 50 ft—10 (2.5 percent)

55 ft—19 (4.8 percent) 

60 ft—163 (40.8 percent) 

65 ft—21 (5.2 percent) 

70 ft—187 (46.7 percent)
55 

ACKNOWLEDGMENTS

The original photograph in figure 5 (Imagery © 2020 Maxar Technologies, map data © 2020 

Google®, which can be accessed at https://www.google.com/maps/@38.9441707,-

91.9411792,583m/data=!3m1!1e3) was modified by adding arrows to show turning movements. 

The original photograph in figure 7 (Imagery © 2020 Google, map data © 2021 Google®, which

can be accessed at https://www.google.com/maps/@38.944116,-

91.9364293,145m/data=!3m1!1e3) was modified by adding a rectangle and labels to show the 

SCL. 

The original graphic in figure 8 (© 2016 Missouri DOT) was modified by enhancing the 

hatching patterns. 

The map in figure 9 was created using ArcGIS® software by Esri. ArcGIS® and ArcMapTM are 

the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights 

reserved. For more information about Esri® software, please visit www.esri.com.

The original graphics in figure 22, figure 23, and figure 24 (© 2014 AASHTO) were modified by 

adding vehicles, vehicle paths, and labels. 

57 

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HRSO-2/01-23(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration,

MIMIC—Multidisciplinary Initiative on Methods 

to Integrate and Create Realistic Artificial Data 

(Washington, DC: 2023) https://doi.org/10.21949/1521958.