EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES  |  a

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Exploratory Advanced 

Research Program

The Role of Artificial 

Intelligence and 

Machine Learning

in Federally 

Supported Surface 

Transportation 

Initiatives
b  |  EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES 

Notice 

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

Department of Transportation in the interest of information exchange. 

The U.S. Government assumes no liability for the use of the information 

contained in this document. This report does not constitute a standard, 

specifcation, or regulation. 

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

Trademarks or manufacturers’ names appear in this document 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. 

Photo credits 

Please see back cover for copyright credits for all the decorative photos 

appearing throughout the document. 
Contents

Introduction 2 

What Is Artifcial Intelligence? 3 

What Is Machine Learning? 3 

EAR Program’s Involvement in R&D for 

Artifcial Intelligence and Machine Learning 6 

Making Sense of Big Data 8 

Using Video Analytics to Help Analyze Driver Behavior 10 

Additional Opportunities and Challenges for Artifcial 

Intelligence and Machine Learning Research 12 

Conclusion 12 

EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES  |  1
Introduction 

When people hear the phrase “artifcial intelligence,” they might 

think of robots and machines that perform efciently and quickly 

some of the functions that humans do, such as translating signs in 

a foreign language, or of robotic dogs that comfort older adults. When defned 

within the context of the transportation sector, artifcial intelligence conjures up 

futuristic images of cars that can drive by themselves, taking into account 

factors such as speed, the distances of nearby cars, and weather conditions. 

Artifcial intelligence within transportation does include highly automated 

vehicles. Further, the use of artifcial intelligence through enabling computers to 

digest and analyze large amounts of data and form conclusions—a process 

known as machine learning—can provide broad public benefts to transportation 

in numerous ways. It can improve trafc fows at individual signalized 

intersections, along specifc routes as part of integrated corridor management, 

or can support human decisionmaking processes in a Trafc Management 

Center for various tasks, including, for example, incident detection and 

management, trafc demand prediction, and detouring corridor signal control. 

Artifcial intelligence can also facilitate trafc safety through monitoring realtime trafc and weather conditions and by sending those data to trafc signals 

and to platoons of partially or fully automated vehicles. It can discern and 

anticipate how drivers might react under certain trafc situations through 

reviewing naturalistic driving study video data or by processing data and 

providing information to travelers with disabilities to provide assistance in trip 

planning and increased situational awareness while traveling. 

FHWA test vehicles simulate a “platoon” of vehicles using cooperative adaptive cruise control. Source: FHWA. 

2  |  EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES 
What Is Artificial Intelligence? 

Artifcial intelligence consists of the “thoughts” and conclusions that computers make after receiving 

data inputs. If human intelligence uses the brain to receive, store, and analyze information, artifcial 

intelligence uses various technologies to also receive, store, and analyze information. 

In transportation, artifcial i ntelligence e ncompasses a w ide a rea o f t echnology, f rom A dvanced 

Driver Assistance Systems to predictive traffic modeling and control systems. 

What Is Machine Learning? 

Machine learning is a branch within artifcial intelligence in which computers use programming as a 

jumping-of point to create their own processes to analyze vast amounts of data. The machines 

themselves develop processes and algorithms to take these data and compute observations, trends, 

and conclusions about the data. 

Within the transportation sector, machine learning technologies can be useful in fnding data trends. 

For instance, machine learning can consist of ingesting great amounts of trafc data; analyzing those 

data with other data, such as weather conditions, trafc volume, and vehicular speeds; and then 

developing observations that might help trafc engineers make certain intersections safer. 

The Exploratory Advanced Research (EAR) Program of the Federal Highway 

Administration (FHWA) is exploring the development of artifcial intelligence and 

machine learning technology within the surface transportation sector. By working 

with universities, industry, and Government conducting cutting-edge research in 

these felds, FHWA ultimately seeks to make surface transportation safer and more 

efcient. 

The U.S. Department of Transportation (DOT) has a keen interest in developing 

technologies that promote trafc safety and improve efciency. Those technologies 

include helping the transport of people or cargo to run more smoothly. 

“It is such an exciting time to be in the transportation space today. We’re witnessing 

a growing torrent of technological advances and new concepts that could 

fundamentally alter the transportation landscape in the coming decades—maybe 

even sooner,” said Derek Kan, Under Secretary of Transportation for Policy at DOT’s 

John A. Volpe National Transportation Center, an agency that provides 

multidisciplinary and cross-modal expertise on emerging transportation issues. 

Kan’s remarks were part of Volpe’s 2018 speaker series on Transportation in the 

Age of Artifcial Intelligence and Predictive Analytics. 

EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES  |  3
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For instance, new sensors create large amounts of multimodal surface 

transportation data, and technologies can be developed to fnd synergies 

among all these datasets so that trafc engineers and public planners can 

better understand what variables may bring about trafc crashes or injuries. 

Eforts to increase the use of artifcial intelligence and machine learning in 

federally supported surface transportation research are part of wider plans to 

develop and refne the Federal Government’s role in developing technologies 

related to artifcial intelligence and machine learning. There is a need to clarify 

how the Federal Government should invest in research and development 

(R&D) within the realm of artifcial intelligence, since the R&D landscape 

includes not only the Federal Government but also nonprofts and privatesector industries. 

In October 2016, the National Science and Technology Council, which on 

behalf of the White House coordinates science and technology policy across 

the various Federal departments that support R&D, published The National 

Artifcial Intelligence Research and Development Strategic Plan. The plan 

outlined how Federal Government 

agencies should prioritize federally 

funded R&D in artifcial intelligence. It 

provided a framework for Federal 

agencies to decide how to proceed 

with R&D in such a way that addresses 

long-term technical and societal 

challenges while also honoring 

individual agencies’ budgets, 

capabilities, and missions. 

The plan laid out seven strategies in 

determining how to prioritize 

research in artifcial intelligence. 

Some of these strategies resonate 

with the objectives of the EAR 

Source: National Science and Technology Council. Program. One strategy was 

4  |  EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES 
developing methods for human–artifcial intelligence systems collaboration to 

optimize performance. Another strategy was ensuring the safety and security of 

artifcial intelligence systems so that operations occur in a well-defned, reliable, 

and safe manner. Two other strategies were developing shared public datasets and 

environments for artifcial intelligence training and testing, and measuring and 

evaluating artifcial intelligence technologies through standards and benchmarks. 

The Federal Government will continue to revise its plans on how to support R&D 

eforts for artifcial intelligence and machine learning as discoveries in both the 

public and the private sectors advance what technologies are available. 

Typical use of a two-dimensional light detection and ranging radar (LIDAR) to aid positioning in urban areas. Source: FHWA. 

EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES  |  5
EAR Program’s Involvement in R&D for Artifcial Intelligence and Machine Learning 

FHWA EAR Program investments help realize The National Artifcial Intelligence Research 

and Development Strategic Plan through enabling longer term research that might not 

have immediate or clear return on investment so might not necessarily be compatible with 

private industry pursuits. 

Artifcial intelligence for use in transportation can beneft 

society in multiple ways. One way is to augment data 

from structural health monitoring of highway assets such 

as bridges, which can also lead to reduced repair and 

reconstruction costs, not to mention the general public’s 

increased confdence in safe structures. Another way is to 

improve network mobility, which can lead to more 

efcient trafc fows and supply chain logistics fows, as 

well as potentially reducing transportation-related 

emissions. 

FHWA’s EAR Program has recently supported 

two research areas to develop technologies 

associated with artifcial intelligence and 

machine learning. One area is the collection of 

large amounts of trafc data, including safety 

data, to spot trends and identify relationships 

between seemingly disparate data streams. 

The second area is the development of video 

analytics research to help determine driver 

behavior in various driving scenarios. 

6  |  EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES 
EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES  |  7
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Making Sense of Big Data 

I

n the area of data collection and analysis, FHWA’s EAR Program recently supported research 

performed by two entities, Calspan-University of Bufalo Research Center (CUBRC), a systems 

integration research organization, and the Palo Alto Research Center (PARC), an R&D frm. 

Both research projects sought to process massive amounts 

of transportation-related data—sometimes referred to as 

big data—from structured, semistructured, and unstructured 

datasets. 

PARC’s project researchers developed automated methods 

to integrate information from large unrelated datasets. For 

the project “Merging Information from Disparate Sources 

to Enhance Trafc Safety,” the PARC researchers 

established machine learning tools to process data from 

several datasets, including the second Strategic Highway 

Research Program (SHRP 2), which included a naturalistic 

driving study (NDS) that provided trip summary records of 

more than 36,000 baseline driving events, and another 

dataset with information describing the physical 

characteristics of the most frequently traveled roadway 

sections. The frm also processed data from additional 

datasets, including Clarus roadway-weather data and video 

logs, and data revealing video, radar, and still photography 

information from Chicago intersections. The machine 

learning tools developed from processing and analyzing 

these datasets together enabled researchers to detect 

safety issues that might not have been so readily spotted 

otherwise. 

CUBRC’s project researchers developed a layered 

infrastructure to ingest, store, analyze, and display 

information. For the project “Knowledge Discovery in 

Massive Transportation Datasets,” the researchers 

produced the Transportation Research Informatics Platform 

(TRIP), a dashboard that provides users with a way to see 

streaming data and historical data of trafc in Seattle, 

Washington. The data include information on crashes, 

trafc volumes, roadway characteristics, weather and 

roadway surface conditions, and work zones and trafc 

laws. 

CUBRC developed the TRIP Interface to allow researchers to access accident information and overlay other search terms such as weather types, accident types, collision 

severity, and location. © 2017 CUBRC, Basemap © MapQuest. 

8  |  EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES 
Big Data 

Next Exit 

EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES  |  9
Using Video Analytics to Help Analyze Driver Behavior 

Promoting trafc safety has also been the ultimate goal for the EAR Program’s support of video analytics research and the pursuit to establish baselines for high-risk driving behavior. 

Researchers from multiple universities and organizations have been examining ways to develop 

machine learning tools to process the vast amount of video data available in the SHRP 2 NDS data. 

These data consist of 1.2-million hours of video data 

collected from the vehicles of approximately 3,000 

volunteers going about their regular activities. The 

vehicles of the volunteers were equipped with four 

cameras, a global positioning system popularly known as 

GPS, and other sensors. By analyzing the NDS data, 

researchers hope to understand the correlations among 

driver behavior, road design, trafc, and other factors. 

There have been multiple video analytics approaches to 

these massive datasets. At Carnegie Mellon University, 

researchers developed machine learning tools to process 

and analyze the NDS data. They developed learning 

algorithms that could parse out important data from less 

important data as well as recognize and cull together 

desired factors. The tools address ways to automate the 

interpretation of ambiguous video data by building a 

sequence of context-dependent predictions. Additional 

video analytics projects at Carnegie Mellon included the 

development of an automated real-time system to analyze 

drivers’ emotional states and determine driver distraction 

or fatigue, and the creation of an automated image 

distorting technique that could mask faces while still 

preserving facial behaviors. 

SRI International developed a comprehensive coding 

system, known as DCode, that could assist researchers 

wanting to analyze SHRP 2 data. The coding system allows 

researchers to extract driver behavior features, such as 

head pose, facial expressions, and gestures of holding a 

mobile phone. Data on the surrounding environment are 

also available. The organization also produced an image 

distortion technique that could mask faces while also 

preserving characteristics such as facial expressions and 

head pose. 

Researchers at the University of Wisconsin–Madison 

developed automated and semi-automated video coding 

as a way to develop an open software platform that could 

ultimately quantify driver distraction and engagement. 

The platform would enable feature extraction, behavior 

characterization, and visualization of SHRP 2 data. And at 

Oak Ridge National Laboratory, researchers developed 

calibration and measurement techniques to help the 

broader community of researchers wanting to work with 

NDS data. 

Because the amount of data is so vast, there are still 

plenty of research opportunities to analyze the SHRP 2 

data and NDS data. FHWA is planning further study on 

developing contextual data to aid in the behavior analysis 

of video data, creating reduced datasets that avoid 

privacy concerns, and refning data security methods. 

Researchers at 

the University of 

Wisconsin–Madison 

developed an 

interface that allows 

the viewer to see 

cabin video and 

roadway video 

simultaneously. 

© Regents of 

the University of 

Wisconsin–Madison. 

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EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN

Additional Opportunities and Challenges for Artifcial Intelligence 

and Machine Learning Research 

As FHWA’s EAR Program delves further into supporting research eforts for artifcial intelligence and machine learning, it seeks to advance research areas that have positive implications for 

surface transportation systems. 

There are two general categories of research for artifcial 

intelligence and machine learning. 

The frst general category is research that involves 

studying and analyzing collected datasets. This category 

encompasses the big data projects and the video 

analytics projects described previously. FHWA has 

access to video and sensor data from the SHRP 2 safety 

studies, and additional research can be made to sort and 

process the data to provide better insights not only into 

how and why crashes happen but also how and why 

drivers avoid crashes. Other research areas within this 

general category include development of forecasting 

and prediction tools, data validation to ensure quality 

control, and data discovery and integration for a variety 

of topics from freight movement to interpretation of 

signal data from non-destructive technologies measuring 

the conditions of structures and pavements. 

The second general category is developing tools to 

provide real-time operational support, such as the timing 

of trafc signals. This research category includes the 

development of real-time artifcial intelligence 

applications, which will require longer term exploration 

because of the advanced technologies these applications 

require. 

Another topic that benefts from real-time operational 

support is supporting the population with disabilities by 

providing artifcial intelligence and machine learning 

tools that can respond to travel and mobility needs, 

including those needs of passengers with disabilities 

who must travel across multiple transportation modes. 

A chest-mounted stereo camera and inertial measurement unit form part of a 

pedestrian navigation system developed by Auburn University. © Auburn University. 

12  |  

Conclusion 

FHWA’s EAR Program stands ready to provide continued support to 

artifcial intelligence and machine learning research, particularly as they 

can be applied to trafc safety. Given the ever-evolving nature of these 

disciplines, the opportunities to develop transportation-related tools that aid in 

the greater public good are almost as vast as the data available. 

EAR PROGRAM—THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 

FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES
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FEDERALLY SUPPORTED SURFACE TRANSPORTATION INITIATIVES  |  13

Getting Involved with the EAR Program 

To take advantage of a broad variety of scientifc 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. 

References 

Kan, D. (2018). Transportation in the Age of Artifcial Intelligence and Predictive Analytics: 

Kickof Event, John A. Volpe National Transportation Center. (video). Available online: 

https://www.youtube.com/watch?time_continue=6&v=yb4Ab_P84SE, last accessed 

October 12, 2018. 

National Science and Technology Council. (2016). The National Artifcial Intelligence Research 

and Development Strategic Plan. Available online: https://www.nitrd.gov/PUBS/national_ai_ 

rd_strategic_plan.pdf, last accessed October 12, 2018. The Networking and Information 

Technology Research and Development Program. (2016). “News.” Available online: https:// 

www.nitrd.gov/news/national_ai_rd_strategic_plan.aspx, last accessed October 12, 2018. 

Federal Highway Administration. (n.d.). “Breakthrough in Computer Vision for Highway 

Transportation Research,” Washington. DC. Available online: https://www.fhwa.dot.gov/ 

publications/research/articles/computervision_breakthrough.cfm, last accessed October 12, 

2018. 
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EAR Program Results 

The EAR Program strives to develop partnerships with the public and 

private sectors because the very nature of the EAR Program is to apply 

ideas across traditional felds of research and stimulate new approaches 

to problemsolving. The program bridges basic research (e.g., academic 

work funded by National Science Foundation grants) and applied 

research (e.g., studies funded by State departments of transportation). 

In addition to sponsoring EAR Program projects that advance the 

development of highway infrastructure and operations, the EAR 

Program is committed to promoting cross-fertilization with other 

technical felds, furthering promising lines of research, and deepening 

vital research capacity. 

Photo Credits for Decorative Images 

Cover: 

From left: 

Source: FHWA. 

Source: FHWA. 

Page 9: 

From top: 

© TMNCorp. 

Source: FHWA. 

© Auburn University. Source: FHWA. 

Page 6: 

From top: 

© SRI International. 

Page 11: 

From top: 

© 2017 CUBRC. 

© University of Nebraska Lincoln. 

© Drexel University. 

Source: FHWA. 

Source: FHWA. 

Page 7: 

From top: 

© Michigan State University. 

© Mechanical Simulation Corporation. 

© 2016 Texas A&M Transportation Institute. 

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