Exploratory Advanced Research . . . Next Generation Transportation Solutions

“The outcome for our project would be a 

dataset that other researchers or 

practitioners who work for State 

transportation agencies, local municipalities, 

and so forth, can all use...to quickly find out 

if a method is the best at teasing out those 

cause/effect relationships or if it’s only good 

enough for accurately predicting the overall 

crash rates or crash frequencies,” explained 

Praveen Edara, principal investigator from 

the University of Missouri. 

The researchers selected urban interchanges 

as a representative facility type for the 

project because these roadway features lack 

accurate crash data and are overrepresented 

in real-world crash data, with more than 50 

percent of fatal or injury crashes involving 

intersections.1 They are also using deidentified data from the second strategic 

highway research program naturalistic 

driving study database, which includes over 

a million hours of video data, to build a 

minimum of three simulator testbeds 

representative of urban interchanges under 

varying conditions (e.g. daytime, nighttime, 

angle crash with left-turning vehicle onto a 

freeway). 

The team is documenting its approach to 

generating the datasets so future 

researchers can apply those methods to 

create RADs for other facility types, such as 

work zones, alternative intersections, or 

pedestrian facilities.

RAD Tool for Multi-Level Applications 

In a collaboration between the University of 

Connecticut and the University of Central 

Florida, researchers are building a RAD tool 

for generating datasets for all facility types 

using different input combinations. The tool 

will be customizable and capable of 

generating datasets at both the macroscopic 

and microscopic levels. 

The macroscopic level is suited for singlestep data models, such as those currently 

used in transportation safety analysis. The 

microscopic level can compare advanced 

research models and consider complex 

factors like human behavior as well as 

Crashes occur because of complexinteractions between multiple 

variables, including driver behavior, 

environmental context, roadway 

design, and vehicle dynamics. Datadriven safety analysis (DDSA) 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 (RAD) with predetermined 

safety relationships built into them. 

Since these are known, the RAD can 

serve as a testbed, revealing how well a 

model reflects those underlying cause 

and effect relationships.

Two projects supported by the 

Federal Highway Administration’s 

(FHWA) Exploratory Advanced 

Research (EAR) Program are 

generating RADs. The researchers 

working on “Multidisciplinary Initiative 

on Methods to Integrate and Create 

Artificial Realistic Data” are 

developing open-source datasets for 

urban interchanges. For “Development 

and Application of a Disaggregate 

Artificial Realistic Data Generator for 

Computationally Testing Safety 

Analysis Methods,” the researchers are 

building a RAD-generating framework.

RAD Using Simulator Data and 

Machine Learning

A University of Missouri research team 

is working with colleagues from Iowa 

State University and Texas Tech 

University to apply statistical and 

machine learning methods to generate 

open-source RADs capable of 

revealing how well a given model 

performs. 

The Exploratory Advanced Research Program Fact Sheet

Realistic Artificial Datasets:

Objective Evaluation of Data-Driven Safety Analysis Models
accommodate major changes in 

transportation, such as the inclusion of 

autonomous vehicles on roadways. 

“I really feel that we are reaching the limits 

of what we can do with just road 

characteristics,” explained John Ivan, 

principal investigator from the University of 

Connecticut. “Because that’s not the major 

variation or causality for crashes occurring. 

The human factor is much bigger...so what 

this is going to do is allow approaches that 

start to get at driver behavior and driver 

characteristics.” 

To show proof of concept, the research team 

will generate two case studies: one focused 

on vehicle crashes on segments and one 

focused on vehicle and pedestrian crashes 

at intersections. 

Informing Best Practices for DDSA

The development of RAD tools is key to the 

FHWA goal of expanding the adoption of 

DDSA as it will enable users to objectively 

select the best methods for their data. “In 

the future, when any transportation 

researcher comes up with a model, they will 

not have to scratch their head to determine 

whether their models are working well or 

not,” said Yusuf Mohamedshah, data analyst 

with the FHWA Office of Safety Research 

and Development. “They can run their 

model across either of these RADs and 

look at the outputs to figure out how well 

their models are doing.” RAD generation 

leverages leading-edge machine learning 

algorithms to develop robust tools suited 

for current safety analysis approaches as 

well as emerging and exploratory 

transportation models.

Learn More 

For more information about these 

projects, contact Yusuf Mohamedshah, 

FHWA Office of Safety Research and 

Development at (202) 493-3464 (email: 

yusuf.mohamedshah@dot.gov). 

Publication No. 

FHWA-HRT-20-047

HRTM-30/08-20(WEB)E

Realistic Artificial Datasets:

Objective Evaluation of Data-Driven Safety Analysis Models 

Photo credit, page 1: 

Source: FHWA. Mid-block 

crosswalks facilitate 

non-intersection 

crossings, providing safety 

benefits for pedestrians 

and drivers.

What Is the Exploratory 

Advanced Research Program?

The EAR Program addresses the need 

for longer term, higher risk research 

with the potential for transformative 

improvements to transportation 

systems. The EAR Program seeks to 

leverage advantages in science and 

engineering that could lead to 

breakthroughs for critical, current, 

and emerging issues in highway 

transportation by experts from 

different disciplines who have the 

talent and interest in researching 

solutions and might not do so without 

EAR Program funding.

To learn more about the EARProgram,

visit www.fhwa.dot.gov/

advancedresearch. The website 

features information on research 

solicitations, updates on ongoing 

research, links to published materials, 

summaries of past EAR Program events,

and details on upcoming events. 

EXPLORATORY ADVANCED RESEARCH

© University of Missouri. 

RAD tools are 

customizable and use 

variable inputs, such as 

driver behavior, 

environmental context, 

and roadway design to 

generate safety analysis 

datasets that help 

researchers better 

understand underlying 

safety relationships.

1

 Federal Highway Administration. (2019). “Intersection Safety.” (webpage) Available online: https://highways.dot.gov/research/research-programs/safety/

intersection-safety, last accessed April 27, 2020.