Exploratory Advanced 

Research Program

Multidisciplinary Initiative to Create and 

Integrate Realistic Artificial Datasets 

Research Summary Report
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Cover Photo Credits: 

All images source: FHWA.
1. Report No. 

FHWA-HRT-23-058

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

4. Title and Subtitle 

Multidisciplinary Initiative to Create and Integrate Realistic 

Artificial Datasets

5. Report Date 

June 2023

6. Performing Organization Code

7. Author(s) 

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

9648), H. Brown (ORCID: 0000-0003-1473-901X), P. Savolainen (ORCID: 

0000-0001-5767-9104), V. Shankar (ORCID: 0000-0002-6671-2268), B. 

Balakrishnan (ORCID: 0000-0002-0994-0213), Y. Shang ORCID: (0000-0001-

7771-4034), S. Chakraborty (ORCID: 0000-0003-2022-1735), Y. Adu-Gyamfi 

(ORCID: 0000-0002-1924-9792), C. Li (ORCID: 0000-0002-3237-1477), K. Aati 

(ORCID: 0000-0001-8834-7735), S. Lima, Y. Huang (ORCID: 0000-0002-7346-

5293), A. Mussah (ORCID: 0000-0002-1084-5598), J. Hopfenblatt

8. Performing Organization Report 

No.

9. Performing Organization Name and Address 

University of Missouri-Columbia

E2509 Lafferre Hall

Columbia, MO 65211

Schatz Publishing Group

11950 W. Highland Ave.

Blackwell, OK 74631

10. Work Unit No.

11. Contract or Grant No. 

693JJ31950023

12. Sponsoring Agency Name and Address

Office of Corporate Research, Technology, and Innovation 

Management

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101 

13. Type of Report and Period 

Covered

Research Summary Report

September 20, 2019–March 19, 2022

14. Sponsoring Agency Code

HRTM-30

15. Supplementary Notes

The Contracting Officer’s representative was Yusuf Mohamedshah 

(HRSO-2; ORCID: 0000-0003-0105-5559).

16. Abstract

Safety models traditionally focus on crash frequencies or rates and do not always reflect the underlying causes of the crashes. 

Even models that accurately estimate crash measures and consist of multiple causal factors may not explain all causal 

relationships. Yet understanding what causes crashes is crucial to developing countermeasures and ensuring safety. To expand 

traditional modeling practices and results, the Federal Highway Administration’s Exploratory Advanced Research Program 

sponsored a project to develop a framework that would generate realistic artificial datasets (RADs) that mimic the known causal 

relationships between contributing factors and crashes. The researchers applied the framework to generate RADs for ramp 

terminals and speed change lane facilities at diamond interchanges. They also developed web-based software to provide easy 

access to the RADs, so other researchers could use them to test their models. The software contains 196 pregenerated datasets 

and the option to submit custom data requests. The framework is generic, so it can be used to generate RADs for other types of 

facilities. The researchers evaluated and compared the performance of different models using a model evaluation rubric. They 

also used RADs to evaluate new behavioral and roadway countermeasures by generating virtual reality simulation testbeds for 

crashes and near-crashes occurring at interchanges. They used safety-critical events recorded in the second Strategic Highway 

Research Program Naturalistic Driving Study to build the testbeds. In addition, the research team developed a graphical user 

interface to facilitate the testbeds for left-turn and speed change lane crashes. Virtual reality provides an engaging platform for 

evaluating countermeasures and educating the public about interchange crashes, which can help achieve the U.S. Department 

of Transportation’s goal of zero roadway fatalities. 

17. Key Words

Crashes, realistic artificial data, machine-learning models, safety, 

simulation testbed, synthetic data, visualization

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

https://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. 

(of this page)

Unclassified

21. No. of Pages 

16

22. Price

N/A

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

Technical Report Documentation Page

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TABLE OF CONTENTS

Introduction 1

Project Overview 3

Literature Review 4

Developing the Framework 5

Identify Contributing Factors at the Selected Interchange Facility 5

Establish the Cause–Effect Relationships 5

Generate the Crash Data 6

Applying RAD to Crash Prediction Models 6

Software Development and Use 8

Simulation Testbeds 9

Conclusions 11

Acknowledgments 12

References 12

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LIST OF FIGURES

Figure 1. Map. Example of a diamond interchange with two ramp terminals. 1

Figure 2. Map. Real-world example of an entrance SCL. 2

Figure 3. Graphic. Components of speed change lanes at an intersection 

 (Sun, Edara, Brown, et al. 2016). 6

Figure 4. Screenshot. Landing page with main menu options in the RAD software. 8

Figure 5. Diagram. RAD software request process. 8

Figure 6. Diagram. Left-turn crash event. 9

Figure 7. Diagram. Left-turn near-crash event. 9

Figure 8. Graphic. Example of a highway and an overpass structure. 10

Figure 9. Screenshot. User menu showing three visualization options. 10

LIST OF TABLES

Table 1. Overall model performance scores of the statistical and machine-learning models. 7

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Crash prediction models are vital to 

transportation safety decisionmaking. 

Models provide the basis for developing 

countermeasures to reduce crashes. However, 

traditional safety models focus on crash frequencies 

and rates and not on the cause-and-effect 

relationships that lead to crashes.

The U.S. Department of Transportation (USDOT) 

recognizes the importance of using data-driven safety 

analysis (DDSA) models to improve transportation 

safety. USDOT’s Strategic Plan 2022–2026 

emphasizes the importance of using DDSA methods 

as part of the Safe System Approach, which includes 

roadway countermeasures, behavioral interventions, 

enforcement, vehicle safety features, and emergency 

medical care to prevent crashes and minimize injuries 

when crashes occur (USDOT 2022). 

Introduction

To address the need to incorporate causal relationships 

in crash prediction models, the Federal Highway 

Administration’s (FHWA) Exploratory Advanced 

Research (EAR) Program supported a project titled 

“MIMIC: Multidisciplinary Initiative on Methods to 

Integrate and Create Artificial Realistic Data.” The 

project aimed to develop a framework to generate 

realistic artificial datasets (RADs)—datasets containing 

computer-generated data rather than real-world 

information—for interchange facilities to test how well a 

crash prediction model reflects the underlying causeand-effect relationships. A team of researchers from the 

University of Missouri, Iowa State University, and Texas 

Tech University applied statistical and machine-learning 

methods to generate RADs to mimic the relationships 

between contributing factors and crashes and test how 

well a given model performs. 

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

section). Figure 1. Map. Example of a diamond interchange with two ramp terminals.

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RADs can aid highway research in the following ways:

• Assess a new crash estimation method.

• Compare methods to analyze alternatives.

• Conduct human factors evaluation of 

behavioral and roadway countermeasures.

Advancing RADs also will enhance FHWA’s efforts to 

encourage practitioners to apply DDSA methods to 

safety decisionmaking through programs such as 

Every Day Counts (EDC) by expanding the number 

of tools available for safety analysis (FHWA 2022a). 

EDC is a State-based model that identifies and rapidly 

deploys proven but underutilized innovations that 

make the Nation’s transportation system adaptable, 

sustainable, equitable, and safer.

The researchers applied the RAD-generation 

framework to two types of crashes occurring at 

diamond interchanges: ramp terminal left-turn crashes 

(figure 1) and speed change lane (an uncontrolled 

terminal between a ramp and a freeway) crashes 

(figure 2). The framework is generic and can be used to 

generate RADs for work zones, innovative geometric 

designs, bicyclist/pedestrian lanes and crossings, and 

other facilities. The researchers developed web-based 

software to access the datasets.

The researchers developed a rubric to evaluate and 

compare the performance of the models. They applied 

the rubric using statistical and machine-learning 

methods to each of the developed models. The tests 

evaluated six criteria, including the ability of a model to 

explain the cause–effect relationship between an 

independent variable and the outcome.

In addition to establishing RADs in a traditional table 

format and then developing the underlying models, the 

research team developed a simulation testbed. They 

used safety-critical events recorded in the second 

Strategic Highway Research Program (SHRP2) 

Naturalistic Driving Study (NDS) to create the testbeds 

(Virginia Tech Transportation Institute 2020). Although 

not a true RAD, the simulation testbed allowed the 

researchers to create variables for a situation, run a 

virtual-reality simulation model for crashes and 

near-crashes at interchanges, and evaluate new 

behavioral and roadway countermeasures. Virtual 

reality provides an engaging visualization platform for 

examining human factors and countermeasures in 

crash scenarios and educating the public about 

interchange crashes.

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

section). Figure 2. Map. Real-world example of an entrance speed change lane.

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Project Overview

Currently, researchers collect data and develop models to predict the likelihood of crashes or 

the relationship of crash occurrence to a geometric feature of the highway, such as traffic, 

number of lanes, or shoulder width. The researchers then test the model to determine how well it 

predicts crashes.

When testing the model’s validity, researchers typically 

randomly select a part of the original data used to 

develop the model and perform a type of analysis, 

such as statistical analysis. Although this method is 

the best practice right now, by using the same data to 

develop and test the model, the results provide a very 

high relationship factor. 

Modelers estimating different forms of statistical 

models can only compare them using overall 

goodness of fit measures, such as prediction accuracy 

and likelihood value. They cannot compare the models 

based on their ability to extract cause–effect 

relationships because the cause–effect relationships 

between independent and dependent variables are 

seldom known. RADs provide an effective tool for 

testing a model’s predicted outcome because RADs 

are created using assumed cause–effect relationships. 

The research team developed RADs in two ways:

1. Tabular form—The traditional way of looking 

at the dataset in a table and developing the 

underlying models.

2. Simulation testbed—Researchers use RADs 

to create the variables for their situation and 

run a simulation model. 

The research team chose to work with urban 

interchanges as a representative facility type because 

accurate crash data is lacking for these roadway 

features, and they are overrepresented in real-world 

crash data, with more than 50 percent of fatal or injury 

crashes involving intersections (FHWA 2019). 

Specifically, they developed RADs for ramp terminal 

left-turn crashes and speed change lane crashes at 

diamond intersections. 

The first step the researchers undertook was a 

literature review.

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Literature Review

The research team reviewed the literature on developing and using synthetic, or 

realistic artificial, data and understanding the causes of crashes at interchanges. 

Studies documenting synthetic data development and use provided information on 

available data-generation methods. Literature on crash causation at interchanges 

helped provide information on key independent variables, their impact on crash 

frequency, and state-of-the-practice crash prediction models.

Although new to transportation, synthetic data has 

been used in other disciplines. The researchers 

reviewed studies that demonstrated the successful 

development, evaluation, and application of artificial 

datasets. For this project, the researchers developed 

rubrics using RADs to rank the different models 

based on their performance. The goal was to create 

a rubric grading system to rank different models 

based on their performance to help modelers revise 

and improve their models. 

The researchers also reviewed the literature on ramp 

terminal crashes. They found that most studies are 

constrained by the lack of crash data based on 

different types of freeway interchanges, such as 

speed change lanes, entrance and exit ramp 

segments, ramp terminals (intersections), and 

freeway segments. As a result, several studies relied 

on simulations to analyze scenarios that could 

improve safety and traffic flow through these highway 

sections (Portera and Bassani 2021; Elefteriadou et 

al. 2005). The researchers in this study also turned to 

simulations to test safety countermeasures. 

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Once the literature review was complete, the researchers developed the data-generation 

framework. They developed RADs for crashes at ramp terminals and speed change 

lanes at diamond interchanges, which are highly prevalent on U.S. roadways. Then they 

applied statistical and machine-learning approaches to the RADs to model crash frequency 

and ascertain the cause–effect relationships. The researchers used the following three steps 

to develop the framework.

IDENTIFY CONTRIBUTING FACTORS AT THE 

SELECTED INTERCHANGE FACILITY 

In step one, the researchers identified the contributing 

factors for each selected facility, including roadway, 

traffic, and driver characteristics. They generated 

sampling distributions for each factor using observed 

data from the States of Washington and Missouri. The 

Missouri data was acquired from the Missouri 

Department of Transportation’s (DOT) Transportation 

Management Systems database as part of a recently 

completed Highway Safety Manual calibration project 

(Sun, Brown, Edara, et al. 2013). By repeatedly 

sampling the distributions for a given sample size 

(e.g., 500 sites), the researchers generated realistic 

artificial data for these factors. They also obtained 

several States’ data from FHWA’s Highway Safety 

Information System on interchange crashes for a 

5-yr period. While the researchers reviewed data from 

California, Illinois, Maine, and Minnesota, they found 

the data from Washington were the most complete and 

recent (2013–2017) for developing the RADs.

While extracting left-turn crash data for ramp 

terminals was straightforward, extracting crashes for 

speed change lane facilities was more involved. 

Figure 3 shows the four speed change lane facilities 

at a diamond interchange, two related to the exit and 

two related to the entrance. Crash reports do not 

indicate if a crash was a speed change lane crash, so 

the researchers analyzed interchange schematics 

and the milepost where the crash occurred to 

determine if a crash occurred in the speed change 

lanes. The researchers analyzed 205 of the 

Washington State DOT’s (WSDOT) diamond 

interchange drawings. The WSDOT’s schematics 

(published online) also provided information on the 

location of the speed change lane, the direction of 

travel, and the location of crashes related to the 

speed change lane by direction and location (WSDOT 

2016). They used a similar process to extract crash 

data from 75 diamond interchanges in Missouri.

The researchers located the potential crashes that 

occurred at the speed change lanes and used 

geographic information systems software and HSIS 

shapefile crash data to map the crash geolocations. 

Then they merged the potential crash data with the 

location data. Finally, they aggregated the crashes by 

location to get the number of crashes that occurred at 

each speed change lane site. This process 

distinguished the crashes that occurred on the speed 

change lanes from the mainline and ramp crashes.

ESTABLISH THE CAUSE–EFFECT RELATIONSHIPS 

In step two, the researchers established the 

cause–effect relationship for each significant factor 

that influences crash frequency, synthesizing 

information from published literature, the Highway 

Safety Manual, and the Crash Modification Factor 

(CMF) Clearinghouse (American Association of State 

Developing the Framework

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Highway and Transportation Officials 2010; FHWA 

2022b). When no reliable information was available for 

a particular variable, the researchers made assumptions 

based on observed data from Washington and Missouri. 

GENERATE THE CRASH DATA 

For the third and final step, the researchers combined 

the effect of all contributing factors on crash frequency 

and estimated a composite crash measure for a given 

site based on its roadway and traffic characteristics. In 

generating the composite score, they considered the 

individual effects of each factor and the interaction 

effects between two or more factors. The composite 

crash score was converted to realistic crash frequency 

(i.e., counts) using observed crash data and a 

hierarchical Poisson approach, with parameters 

optimized for each level of the hierarchy. The 

researchers then adjusted the generated crash data 

distribution parameters to match the individual sites’ 

overall distributional shape and crash counts. 

Once the crash counts were generated, the 

researchers categorized them as fatal, injury-causing, 

or property damage-only crashes. In addition to 

severity, they also generated characteristics about the 

driver (e.g., level of distraction, age, gender), vehicle, 

and roadway (e.g., road condition) for each crash.

APPLYING RAD TO CRASH PREDICTION MODELS

The RADs generated for left-turn and speed change 

lane facilities were used to test crash prediction 

models. The researchers used the developed datasets 

with state-of-the-art statistical and machine-learning 

approaches to model crash frequency and ascertain 

the cause–effect relationships. The researchers 

developed a rubric to evaluate the performance of 

models developed using RADs. The rubric had a 

scoring system of 0–100. Six criteria were evaluated to 

capture different complexities in modeling, including:

• Data descriptive analysis.

• Model selection.

• Data training and testing.

• Prediction accuracy.

• Model inference.

© 2016 Missouri DOT. 

Modifications by FHWA 

to show the gore point, 

speed change, diverging, 

and merging lanes.

Figure 3. Graphic. 

Components of speed 

change lanes at an 

intersection (Sun, Edara, 

Brown, et al. 2016).

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To allow for unbiased evaluation of the cause–effect 

relationships, three teams whose members were 

unaware of the RAD-generation procedures estimated 

the models. Two teams worked with statistical models, 

and one team developed a series of machine-learning 

models. The evaluation rubric was applied to each of 

the developed models.

The results showed the two statistical models 

performed similarly, while the machine-learning models 

outperformed the statistical models, especially in the 

model inference criterion. The CMFs in the 

machine-learning models were closer to the true 

CMFs, thus explaining the assumed cause–effect 

relationships. The machine-learning models performed 

better on the speed change lane RADs than on the 

ramp terminal dataset. One possible reason the 

machine-learning models performed better is their 

ability to capture the nonlinear relationships between 

crash frequency and the independent variables. Table 

1 shows the evaluation of the statistical and machinelearning models developed using the RADs.

One other use of a 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 speed change lanes—Study A is using 

observed data from State A, and Study B is using 

observed data from State B. How can researchers 

accurately assess the CMFs generated from the two 

studies? How can they determine which CMFs explain 

the true cause–effect relationships between crash 

frequency and the corresponding input variables (e.g., 

freeway annual average daily traffic, speed change 

length)? RAD can help answer these questions. The 

research teams conducting the two studies apply their 

modeling approaches to the same RAD. The resulting 

models can be compared by performing 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 to the known CMFs (i.e., those used to 

develop RAD). The modeling approach that performs 

the best on the RAD is more likely to explain the true 

cause–effect relationship in observed data. Thus, in 

the example, if the performance of the model 

developed in Study A on the RAD 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 speed change lanes already estimated 

using data from different 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

Table 1. Overall model 

performance scores 

of the statistical and 

machine-learning models.

LT = left turn; SCL = speed change lane.

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Software Development and Use

The researchers developed web-based software to provide access to the RADs and facilitate 

their use. The software includes a landing page (figure 4) for entering real-time user requests 

to the server and a RAD generator. The software consists of 196 pregenerated datasets in a 

spreadsheet format similar to the safety datasets provided by State DOTs. The software also 

allows the user to submit custom data requests. 

The pregenerated RADs are available for 16 

combinations, based on the number of sites and years. 

For each combination, five different pregenerated 

datasets are available. To download a pregenerated 

RAD, users select the number of sites and years they 

want and then click Download. The system randomly 

provides one dataset from the five pregenerated 

datasets for the chosen combination. 

Figure 5 shows the process when a user submits a 

custom RAD request. First, a user enters the number of 

sites, the number of years of the RAD, and an email 

address to receive the dataset once it is generated. 

After the RAD request is submitted, the RAD generator 

creates the dataset. When the request has finished 

processing, the user receives an email notification that 

the RAD is ready and can be downloaded through a 

provided link. 

Source: FHWA. Figure 4. Screenshot. Landing page with main menu options in the RAD software.

Source: FHWA. 

Figure 5. Diagram. RAD software request process.

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The researchers extended the use of the RADs to generate realistic simulation testbeds for 

crashes and near-crashes occurring at interchanges to evaluate new behavioral and roadway 

countermeasures. They created variables for a situation and ran the virtual-reality simulation 

models. To develop the testbeds, the researchers used the following four-step process:

Simulation Testbeds

• Obtain and analyze videos of safety-critical 

events. The researchers obtained de-identified 

data from the SHRP2 NDS database for 41,479 

crash or near-crash events. By performing a 

series of data reductions, they identified 114 

crash and near-crash events involving left-turning 

vehicles and 310 speed change lane events. Of 

the 310 speed change lane events, 179 occurred 

on entrance speed change lanes and 131 on exit 

speed change lanes. The event information 

included time of day, weather, vehicle status, 

vehicle trajectory, surrounding traffic, road 

conditions, signage, and pavement markings.

• Diagram crashes. The researchers then 

diagrammed crash events, drawing trajectories of 

vehicles involved in each crash (figure 6 and 

figure 7). They also generated road signs for the 

simulator based on signs observed in the videos. 

• Create the roadway and surrounding environment. 

The researchers used three-dimensional (3D) 

modeling tools to create the roadway and 

surrounding environment, including shoulders, 

travel lanes, medians, terrain, pavement 

markings, and overpasses, as well as lighting 

 and foliage (figure 8).

• Create the crash simulation. In the final step, the 

researchers reconstructed crashes by overlaying 

the vehicle information and trajectories on top of 

the roadway and environment elements.

Source: FHWA.

Figure 6. Diagram. Left-turn crash event.

Source: FHWA.

Figure 7. Diagram. Left-turn near-crash event.

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To make the simulator testbeds easy to use, the 

researchers created a graphical user interface that lets 

users choose from three different views (figure 9). The 

aerial view shows a recreated animation of a crash. 

The 360-degree view puts the user in the driver’s seat 

of the subject vehicle during the crash. The test-drive 

view also puts the user in the driver’s seat of the 

subject vehicle, but the user can actively control the 

vehicle. The aerial and 360-degree view options are 

noninteractive, but the test-drive view allows the user 

to control the scenario and react to conditions.

In addition to testing the models and safety 

countermeasures, the immersive experience provided 

by virtual-reality simulations of crashes and nearcrashes has two clear benefits. First, simulations can 

be used for driver education. For example, teen drivers 

can use simulations to practice safe driving practices. 

Second, human factors researchers can use the 

testbeds to evaluate safety countermeasures, such as 

in-vehicle information systems, collision avoidance 

systems, and dynamic message signs. This purpose is 

well aligned with USDOT’s Safe Systems Approach.

Source: FHWA.

Figure 8. Graphic.

Example of a highway and 

an overpass structure.

Source: FHWA.

Figure 9. Screenshot. 

User menu showing three 

visualization options.

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The RADs can be introduced in graduate courses at 

universities to encourage the application of new 

statistical and machine-learning approaches or 

variations of existing approaches. Since these 

datasets are ready to use, researchers do not 

have to expend effort obtaining, processing, and 

preparing the data for model development. A student 

competition organized by the Transportation 

Research Board or other professional transportation 

societies could further encourage using RADs to 

discover new modeling approaches. 

Improving interchange safety involves implementing 

effective behavioral and engineering countermeasures. 

The virtual-reality testbeds developed in the study from 

NDS crash videos offer a realistic and engaging way 

to support driver education and countermeasure 

evaluation studies. The testbeds also provide human 

factors researchers with a fully developed ramp 

terminal or a speed change lane section with signage 

and traffic. The modeler can easily add interventions to 

the testbed and conduct human factors evaluations. 

The developed testbeds are not dependent on a 

particular hardware device or system. They can be 

used across different visualization options, such as 

head-mounted devices, driving simulators, and 3D 

projection systems.

Conclusions

The researchers for this project developed two types of RADs to model the causal 

relationships of crashes at diamond interchanges. The first RADs used data compiled into 

tables, similar to what modelers use to conduct safety evaluation studies. Statistical and 

machine-learning models can be developed using these tabular RADs and their relative 

performance compared using the researchers’ rubric. While 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 using different measures of effectiveness. 

While this project demonstrated the proposed RAD 

framework for interchange facilities, 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, such 

as 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. In the future, virtual-reality testbeds could 

also be used to evaluate some behavioral and 

roadway countermeasures. For example, a driving 

simulator experiment can be set up using a testbed 

developed for speed change lanes, and the effect of 

different driver alert systems (e.g., in-vehicle, 

dynamic message signs) can be evaluated using 

study participants. 

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The original photograph in figure 1 (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 2 (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 speed change lane.

REFERENCES

American Association of State Highway and Transportation 

Officials. 2010. Highway Safety Manual. 1st ed. 

Washington, DC: American Association of State Highway 

and Transportation Officials.

Elefteriadou, L., C. Fang, R. Roess, and E. Prassas. 2005. 

“Methodology for Evaluating the Operational Performance 

of Interchange Ramp Terminals.” Transportation Research 

Record 1920, no. 1: 13–24.

FHWA. 2019. “Intersection Safety” (web page). https://

highways.dot.gov/research/research-programs/safety/

intersection-safety, last accessed April 27, 2020.

FHWA. 2022a. “Center for Accelerating Innovation: Every 

Day Counts” (web page). https://www.fhwa.dot.gov/innovation/everydaycounts/, last accessed October 19, 2022.

FHWA. 2022b. “Crash Modification Factors Clearinghouse” 

(website). https://www.cmfclearinghouse.org/, last accessed 

October 30, 2022.

Google®. 2022. Google® MapsTM, Mountain View, CA, 

obtained from: https://www.google.com/

maps/@38.9662619,-77.2979777,15z, last accessed 

October 25, 2022. 

Portera, A., and M. Bassani. 2021. “Experimental 

Investigation into Driver Behavior along Curved and 

Parallel Diverging Terminals of Exit Interchange Ramps.” 

Transportation Research Record 2675, no. 8: 254–267. 

https://doi.org/10.1177/0361198121997420, last accessed 

October 26, 2022.

Sun, C., H. Brown, P. Edara, B. Carlos, and K. Nam. 2013. 

Calibration of the Highway Safety Manual for Missouri. 

Report No. 25-1121-0003-177. Washington, DC: Research 

and Innovative Technology Administration. 

Sun, C., P. Edara, H. Brown, and C. Nemmers. 2016. Crash 

Location Correction for Freeway Interchange Modeling: 

Final Report. Report No. cmr 16-010. Jefferson City, MO: 

Missouri Department of Transportation.

USDOT. 2022. U.S. Department of Transportation Strategic 

Plan FY 2022–2026. Washington, DC: U.S. Department of 

Transportation. https://www.transportation.gov/sites/dot.

gov/files/2022-04/US_DOT_FY2022-26_Strategic_Plan.pdf, 

last accessed September 21, 2022. 

Virginia Tech Transportation Institute. 2020. “InSight Data 

Access Website: SHRP2 Naturalistic Driving Study” 

(website). https://insight.shrp2nds.us/, last accessed 

October 19, 2022.

WSDOT. 2016. “Interchange Viewer” (website). https://

www.wsdot.wa.gov/mapsdata/tools/InterchangeViewer/

default.htm, last accessed April 20, 2022.

ACKNOWLEDGMENTS

EAR PROGRAM—MULTIDISCIPLINARY INITIATIVE TO CREATE AND INTEGRATE 

REALISTIC ARTIFICIAL DATASETS—RESEARCH SUMMARY REPORT

12  |
Getting Involved with the EAR Program

To take advantage of a broad variety of scientific and engineering discoveries, the EAR 

Program involves both traditional stakeholders (State department of transportation 

researchers, University Transportation Center researchers, and Transportation Research 

Board committee and panel members) and nontraditional stakeholders (investigators from 

private industry, related disciplines in academia, and research programs in other countries) 

throughout the research process.

Learn More 

For more information, see the EAR Program website at https://highways.dot.gov/research/

exploratory-advanced-research. The site features information on research solicitations, 

updates on ongoing research, links to published materials, summaries of past EAR 

Program events, and details on upcoming events.
FHWA-HRT-22-026

HRTM-30/01-22(WEB)E

EAR Program Results

As a proponent of applying ideas across traditional research fields to 

stimulate new problem-solving approaches, the EAR Program strives to 

develop partnerships with the public and private sector. The program 

bridges basic research (e.g., academic work funded by National Science 

Foundation grants) and applied research (e.g., studies funded by State 

DOTs). In addition to sponsoring projects that advance the development 

of highway infrastructure and operations, the EAR Program is committed 

to promoting cross-fertilization with other technical fields, furthering 

promising lines of research, and deepening vital research capacity.

EXPLORATORY ADVANCED RESEARCH

FHWA-HRT-23-058

HRTM-30/06-23(Web)E

Recommended citation: Federal Highway Administration, 

Multidisciplinary Initiative to Create and Integrate Realistic Artificial 

Datasets (Washington, DC: 2023) https://doi.org/10.21949/1522003