Exploratory Advanced Research . . . Next Generation Transportation Solutions

•	 What is the magnitude of difference in driving 

rules among different drivers? 

•	 Do the rules change after certain experiences? 

For example, does a driver change the way he 

or she drives after being involved in an 

incident? 

•	 What is the impact of an incident on the system 

performance? What is the effect of drivers’ interaction during incidents?

Capturing Driver Behavior

This study is developing “intelligent agents” that 

can encapsulate individual driver decisions in response to varying traffic situations. The developed 

agents are designed to learn drivers’ temporal actions for any given traffic state retrieved from a 

naturalistic driving database. These driving rules 

of the agents will be coded in a computer simulation environment to test and study the collective 

effects of the learned behaviors with multiple drivers and under different situations. 

Learning Approach

As mathematical formulation alone is not adequate 

in predicting changes in acceleration rate, or the 

time it takes a driver to initiate the transformation 

from perception to reaction, this project uses artificial intelligence to model and predict driver behavior. The study uses reinforcement learning, a novel 

and successful area of artificial intelligence, to 

tackle how an independent agent that senses and 

acts on its environment can learn to choose logical 

actions to reach its long-term goals. This method 

allows the agent to keep learning from observations, actions conducted, and rewards received. 

“A driver action at any time depends on their 

perception of the surrounding environment.” says 

David Yang at FHWA. “A driver will initiate a sequence of actions, from acceleration and deceleration, to steering input, all of which are dependent 

on a given set of initial and final conditions, such 

The Exploratory Advanced Research Program Fact Sheet

A Behavioral Background 

Current literature that has detailed the characterization of driver behavior is limited. The research that 

does exist is typically limited to specific locations 

and scope. The majority of traffic modeling and parameter calibration research assumes somewhat 

similar driving conditions and behavioral sets for all 

of the drivers’ population; differences in drivers’ actions are merely represented by drawing samples 

from statistical distributions assigned to each driver 

type. This approach does not capture or predict individual driver actions that reflect the effects of various situational and environmental factors.

The study aims to answer a number of questions 

related to driver and traffic performance:

•	 What are the driving rules during normal and abnormal driving conditions?

Existing traffic analysis and simulation tools cannot effectively model 

drivers’ ability to recognize and respond 

to their environment with behavior that 

varies depending upon the encountered 

driving situation. “Driver Behavior in 

Traffic,” an Exploratory Advanced 

Research (EAR) Program project, aims 

to characterize driver behavior using 

naturalistic driving data and agentbased modeling techniques. This project 

was awarded by the Federal Highway 

Administration (FHWA) in 2009, and is 

being conducted by Virginia Tech 

University in partnership with PTV 

America and the Virginia Transportation 

Research Council.

Modeling Driver Characteristics 

Driver Behavior In Traffic
as target speed, car following distance, or road geometry. This research will help us to better understand driver behavior so we can effectively predict 

the next driver action for a given situation.”

Future Work

At the conclusion of this project, agents will be developed to mimic realistic driver behavior in various 

driving scenarios. After verification and validation of 

the developed agents, an abstraction of their 

learned “driving rules” will be embedded in a microscopic traffic simulation tool, VISSIM. 

It is expected that analyzing trained agent characteristics will provide the transportation community with innovative methods for developing more accurate and sensitive traffic simulation models. It 

could also lead to future research developing new 

generations of traffic simulation tools that can accurately capture driver behavior in complex traffic 

situations. 

Learn More

For more information on this EAR Program project, 

contact David Yang, FHWA Office of Operations 

Research and Development, at 202-493-3284 

(email: david.yang@dot.gov).

What Is the Exploratory 

Advanced Research Program?

FHWA’s Exploratory Advanced Research (EAR) 

Program focuses on long-term, high-risk research with a high payoff potential. The program addresses underlying gaps faced by applied highway research programs, anticipates 

emerging issues with national implications, 

and reflects broad transportation industry 

goals and objectives. 

To learn more about the EAR Program, visit 

the Exploratory Advanced Research Web site 

at www.fhwa.dot.gov/advancedresearch. 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. For additional information, contact 

David Kuehn at FHWA, 202-493-3414 (email: 

david.kuehn@fhwa.dot.gov), or Terry Halkyard 

at FHWA, 202-493-3467 (email: terry.halkyard@

fhwa.dot.gov). 

Publication No. 

FHWA-HRT-10-070

HRTM-04/8-10(1M)E

Modeling Driver Characteristics 

Driver Behavior In Traffic

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©Olivier Le Queinec, 

Dreamstime.com

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Example of a sudden lane change