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Cell Phone Data and 

Travel Behavior Research 

The Exploratory Advanced Research Program

SYMPOSIUM SUMMARY REPORT • February 12, 2014
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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.

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.

Cover photo illustration: 

© Inrix, Inc., EPS10.com and Comparativa, Flickr.com
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Technical Report Documentation Page
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Table of Contents

Introduction

Opening Remarks

Data Coverage, Resolution, and Characteristics 

Presentations

 Bill King, AirSage

 Daniel Rolf and Keith Hangland, HERE

 Rick Schuman, INRIX

 Nick Cohn, TomTom

Major Themes Discussed

Key Takeaways

Agency and Academic Experience Using Cell Data

Presentations

 Cynthia Chen, University of Washington

 Xiaowei Xu, University of Arkansas

 Elizabeth Birriel, Florida Department of Transportation

 Camille Kamga and Anil Yazici, City University of New York

 Andrew Rohne, Ohio–Kentucky–Indiana Regional Council 

of Governments

 Krishnan Viswanathan, CDM Smith

Major Themes Discussed

Key Takeaways

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Linking Trip Information to Behavior Data

Presentations

 Lei Zhang, University of Maryland

 Billy Bachman, Westat

 Shan Jiang, Massachusetts Institute of Technology

Major Themes Discussed

Key Takeaways

Relating Census Block and American Community Survey 

County Data, Demographic, and Economic Data to 

Derived Trip Data

Presentation

 Ben Pierce, Battelle

Major Themes Discussed

Key Takeaways

Panel Summary and Next Steps

Major Themes Discussed

Key Takeaways

Appendix A: Agenda

Appendix B: Attendees

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List of Figures

Figure 1. AirSage pinpoints a user’s location.

Figure 2. Cellular analytics illustrate where a region’s visitors 

originated and highlights areas of demand.

Figure 3. Clustering algorithm developed to represent individuals 

in a complex system.

Figure 4. Taxi data used to determine demand for taxis during 

inclement weather.

Figure 5. AirSage cellphone data sample.

Figure 6. Road usage based on cell phone data linked with census 

tracts to illustrate congestion exposure.

Figure 7. Example of a large national survey with sample sizes in the 

tens of thousands.

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List of Acronyms and Abbreviations

General Terms

EAR Exploratory Advanced Research

FHWA Federal Highway Administration

FDOT Florida Department of Transportation

GPS Global Positioning System

OD origin–destination

NHTS National Household Travel Survey

TRB Transportation Research Board
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Introduction

On February 12, 2014, the Federal 

Highway Administration’s (FHWA's) 

Office of Highway Policy Information, 

with the Exploratory Advanced Research (EAR) 

Program, sponsored a Cell Phone Data and Travel 

Behavior Research Symposium. The symposium 

brought together data providers in private 

industry, researchers in academic fields, and 

other professionals from both public agencies 

and private businesses and institutes to explore 

opportunities and challenges using cellular 

location data for national travel behavior analysis. 

The authors of this document summarize the key 

themes discussed during the symposium held 

at the Transportation Research Board’s (TRB's) 

Keck Center in Washington, DC.

Discussions centered on the availability of 

cellular data and the common types of 

licensing agreements, applications of 

cellular data and how it can be leveraged, 

fusion of cellular data in terms of 

merging it with other data sources, and 

validation of cellular data to determine 

accurate and meaningful results. Particular 

focal points included applications and 

limitations of land-use models and data 

and using surveys in conjunction with 

cellular location data to facilitate accuracy 

and precision. 

FHWA sponsored this symposium in 

cooperation with the TRB Travel Survey 

Methods Committee, Special Task Force on 

Data for Decisions and Performance Measures, 

and Task Force on Understanding New 

Directions for the National Household 

Travel Survey (NHTS).
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Opening Remarks

The symposium began with representatives 

of the sponsoring offices providing a brief 

introduction to their interests in cellular 

data. David Winter, Director of FHWA’s Office 

of Highway Policy Information, began by 

stating that cellular data is a very important 

emerging field. He noted that cellular data 

can help provide reliable data for national 

decisionmaking and that the critical next 

steps include how best to integrate and 

utilize cellular data with current methods. 

Winter explained that the Office of Highway 

Policy Information has worked with TRB 

and others on several projects, such as the 

NHTS task force. According to Winter, this 

type of collaboration will be useful moving 

forward as researchers explore emerging 

areas, such as cellular data.

David Kuehn, EAR Program Manager, explained 

that the EAR Program seeks out advances in 

science to test possible highway applications. He 

noted that the program works to reach out to the 

community, not only to determine what areas 

are on the cutting edge, but also to improve 

research outcomes through peer and panel 

reviews. Kuehn also highlighted that methods 

for applying cellular data to highway 

transportation can be viewed as high-risk, 

high-reward propositions. Cellular data aligns 

with many of the EAR Program focus areas 

and can contribute to the long-term goal 

of better system investment and better 

system operations. 

Tianjia Tang, Travel Monitoring and Surveys 

Team Leader in the Office of Highway 

Policy Information, explained that research 

on applying cell data will assist with 

determining why, when, and how people 

travel in a timely and comprehensive manner, 

which is scientifically and statistically 

significant. Tang stated that a primary goal 

would be to determine individual trips, 

roadway usage, and the reason for the trip 

on a nationwide scale. 
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Data Coverage, Resolution, and Characteristics

I n this session, industry representatives provided 

a brief overview of their companies and the 

types of data they collect and are able to 

provide. Symposium participants then discussed 

challenges relating to privacy and modes. 

Presentations

Bill King, AirSage

Bill King of AirSage explained that the company 

is a data provider with contracts in place with two 

major cell phone carriers. King highlighted how 

AirSage is able to pinpoint a user’s location by 

triangulating between multiple towers, as shown 

in figure 1. He noted users are anonymous, but 

AirSage is able to link to demographics at the 

census tract level. AirSage is also able to make 

inferences about users’ home and work locations 

within a dataset of extrapolated person trips. 

King stated that the data can be licensed to 

end users for a specific purpose.

Daniel Rolf and Keith Hangland, HERE

Daniel Rolf and Keith Hangland of HERE 

informed symposium participants that, 

although originally a mapping company, 

HERE now uses probe data collected from 

different devices by using applications 

or cell phones. Rolf and Hangland noted 

that HERE is not only collecting and 

selling probe data but is also using it. 

They explained that HERE data power 

freight and trucking industries and are 

also beginning to be used for government 

planning activities. Rolf and Hangland told 

participants that data can also be used to 

analyze traffic movement and efficiency to 

solve engineering and planning issues. 

Rick Schuman, INRIX

Rick Schuman of INRIX stated that the 

primary business for INRIX is real-time 

congestion or traffic movement data based 

on 25–30 percent of travelers, as shown in 

figure 2. Schuman noted that this can be 

obtained in real time or through a record or 

catalog. He explained that INRIX provides 

processed data, which is more usable for 

most buyers than raw data. Schuman told 

symposium participants that there is also 

an important distinction between metrolevel data (generally associated with a 

project) and national-level data (generally 

Figure 1. AirSage pinpoints a user’s location. associated with a product). © AirSage
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Nick Cohn, TomTom

Nick Cohn of TomTom informed 

symposium participants that one feature 

of TomTom’s data is origin–destination 

(OD). Cohn noted that, like other 

providers, available data are a sample of 

the total population. He explained that 

sample sizes and local data availability 

vary greatly, making it difficult to 

standardize a national product. Cohn 

highlighted that TomTom gathers Global 

Positioning System (GPS) data from 

vehicle movements used for both speed 

and OD. He stated that TomTom uses OD 

data, along with mapping and shape files, 

and makes data available through licenses. 

TomTom also creates customized datasets 

for specific customers and their needs. 

Major Themes Discussed

The symposium participants discussed 

several major themes during this session, 

as follows:

• Connecting data and behavior. Although 

land-use data are helpful and available at a 

city level, in terms of making connections 

between the location data and user 

behavior, there is currently no such 

nationwide map. 

• Identifying needs. The financial and 

advertising industries already use OD 

analytics but have different needs in terms 

of precision and bias. For the transportation 

industry, it should be established what 

margin of error is acceptable in terms of 

deriving behavior. 

• Confirming data usage. Licensing differs 

for educational facilities, researchers, 

government, and commercial entities who 

want to use available data.

• Making data available. There are privacy 

issues in terms of which aspects of data 

are available. 

• Determining mode. The ability to 

determine the mode (e.g., car, bus, cycling, 

and walking) varies depending on location 

and context as well as the acceptable 

margin of error.

Key Takeaways 

The symposium participants noted several 

key takeaways during the discussion, 

as follows:

• Data are available from providers through 

specific licenses and agreements.

• OD trips can be determined; however, the 

reason for a trip and mode are less certain 

and have wider margins for error.

• Privacy issues weigh upon data availability 

to some degree.

Figure 2. Cellular analytics illustrate where a region’s 

visitors originated and highlights areas of demand.

© INRIX, Inc.
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n this next session, researchers from various agencies and academic institutions 

provided descriptions of the work they 

have been conducting and their uses of 

cellular data. Symposium participants then 

discussed challenges relating to sampling 

and dataset extrapolation. 

Presentations

Cynthia Chen, University of Washington

Cynthia Chen identified certain challenges 

and limitations associated with the use of 

cell data. These include difficulties with 

penetration rates and sample sizes (e.g., in 

some markets users have multiple phones), 

as well as the varying market shares of 

different carriers. Another challenge is 

oscillation between two cell towers and 

therefore being unable to determine an 

exact location. In a preliminary dataset of 

8,000 people, Chen was able to determine 

90 percent of homes and workplaces within 

a certain area. Current key challenges that 

she and her team are working on include 

validation of trips and population expansion. 

Xiaowei Xu, University of Arkansas

Xiaowei Xu conducted research that 

focuses on determining data clusters and 

associating individuals with groups of 

people or communities. He accomplished 

this through an algorithm that connects 

individuals’ social networking activities, as 

shown in figure 3. One specific example 

where Xu applied this algorithm was at 

college football games. Xu noted that 

challenges arise when cleaning and using 

data, as well as when finding a dynamic 

algorithm to enhance the result. Xu told the 

symposium participants that a major area 

to improve upon is to establish new 

methods for validating travel behavior by 

using cell data. 

Elizabeth Birriel, Florida Department of 

Transportation

Elizabeth Birriel explained that the Florida 

Department of Transportation (FDOT) has 

a desire to use cellular data to improve 

information used in 511 systems. These 

systems provide information about travel 

systems and travel routes. This information 

improvement was already achieved by 

using 2010 data, but it would be helpful to 

Agency and Academic Experience Using Cell Data

Figure 3. Clustering algorithm developed to 

represent individuals in a complex system.

© University of Arkansas
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continue to refine these systems through 

the use of more data as coverage was 

strong for the interstate highway system 

but spotty for arterial roadways. FDOT is 

also looking at how cellular data and 

automated vehicles can be leveraged with 

each other moving forward. 

Camille Kamga and Anil Yazici, City 

University of New York

Camille Kamga and Anil Yazici noted that 

they have used cellular data for mobility 

studies, specifically within New York City. 

One study involved using taxi data and 

determining that there is higher demand 

for taxis during inclement weather, as 

shown in figure 4. Data were also used to 

determine, as others have found, that the 

morning and afternoon peak periods are 

not symmetric. Further challenges and 

areas of research include determining the 

reason for these findings, for example, if 

changes in mobility are based on trip 

cancellations or mode shifts. 

Andrew Rohne, Ohio–Kentucky–Indiana 

Regional Council of Governments

Andrew Rohne used data from AirSage to 

attempt to model trips in his region of 

interest (greater Cincinnati, OH). In doing 

so, some challenges were identified 

regarding counts and capacities. Rohne 

compared his results using AirSage data to 

Ohio Department of Transportation and 

NHTS data to see how results matched and 

what the discrepancies were. Lessons 

learned and areas of future research include 

thinking through which data are available 

and what time of year the data would be 

most desirable for research purposes. Rohen 

also noted the possibility of incorporating 

cellular data in an NHTS expansion.

Krishnan Viswanathan, CDM Smith

Krishnan Viswanathan explained that he 

used data from AirSage to attempt to 

model trips in his region of interest 

(Wilmington, NC), as shown in figure 5. He 

derived various trips and examined 

Figure 4. Taxi data used to determine demand for taxis during inclement weather.

© City University of New York
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different studies on non-home, residential, 

and home-to-home–based trips. To better 

understand movement and trips, 

Viswanathan noted that a possible next step 

would be to recruit users to willingly provide 

location data and demographic information. 

Major Themes Discussed

The symposium participants discussed several 

major themes during this session, as follows:

• Determining a timeframe. It is important 

while conducting research of this nature to 

determine what periods of the year will be 

most useful to analyze data, as data are 

typically purchased in monthly blocks. 

• Focusing on long-distance travel. Although 

it is possible to determine long-distance 

travel to some degree, it is generally not 

the focus of regional level analysis as the 

possible long-distance traveler simply 

moves though the region without stopping.

• Accessing different data sets. Data 

consists of the movement of people 

rather than trips in the traditional sense. It 

is possible to access data that are 

extrapolated at the census tract level or 

to have unextrapolated data sets, such as 

device movements and a mobile survey. 

Key Takeaways

The symposium participants noted 

several key takeaways during the 

discussion, as follows:

• Validating results is key to determining the 

usefulness and meaningfulness of data.

• An important challenge moving forward is 

linking data to behavior through 

demographic information.

• Although it is possible to determine 

long-distance travel and mode based 

on duration, location, and land use, it 

remains inexact. 

Figure 5. AirSage cellphone data sample.

© AirSage
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n this session, researchers from various 

agencies and academic institutions provided descriptions of the work that they have 

been conducting and their uses of linking cellular and other mobile data with behavior and 

environment data. Symposium participants 

then discussed challenges relating to land 

use and margins of error. 

Presentations

Lei Zhang, University of Maryland

Lei Zhang’s focus has been to research trip 

purpose by using passively collected data 

and by attaining varying trip characteristics 

and details depending on the model. Zhang 

linked the 1995 American Travel Survey and 

cell data for urban trips. Zhang found that 

the most difficult types of trips to categorize 

were personal in nature, because there is 

less of a pattern. His model utilized land use, 

time of day, and duration and works fairly 

well across regions. Zhang noted that a 

useful next step would be linking the data in 

terms of a time series. 

Billy Bachman, Westat

Billy Bachman focused on what can be 

learned from GPS data in conjunction with 

travel surveys. He highlighted that 

developing the link to behavior is important 

for building forecasts and determining 

future movement. A challenge that Bachman 

noted included ensuring that the data 

stream is uninterrupted or does not contain 

missing data. This is a specific area in which 

cellular data could prove to be beneficial. 

Integrating cellular data into traditional 

survey approaches could be particularly 

beneficial in filling in gaps and answering 

lingering questions.

Shan Jiang, Massachusetts Institute of 

Technology

Shan Jiang worked on extracting data from 

cellular devices to link trips to activity. This 

included looking at possible nodes for daily 

travel, which typically consisted of home 

and work. In addition, Jiang linked the data 

to census tract data with the goal of 

determining the most heavily impacted 

roadways in terms of traffic to target 

congestion reduction, as shown in figure 6. 

Jiang noted that a key challenge is 

validating cell phone inferred results with 

survey and GPS data. 

Linking Trip Information to Behavior Data

Figure 6. Road usage based on cell phone data linked 

with census tracts to illustrate congestion exposure.

© Wang, P., Hunter, T., Bayen, A.M., Schechtner, K., & González, M.C. (December 2012). Understanding Road Usage Patterns in Urban Areas. Scientific Reports.
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Major Themes Discussed

The symposium participants discussed 

several major themes during this session, 

as follows:

• Improving accuracy. Cellular data are 

generally less accurate than GPS-tracking 

data in terms of position. Cellular data 

focuses mainly on travel among larger 

zones and monitors the handoff among 

different cell coverage areas, whereas GPS 

data can pinpoint specific OD locations.

• Validating results. Multiple data along the 

same paths assist in validating results by 

using cellular data. 

• Processing issues. Pre-processing of cell 

data by the telecommunication companies 

that collect the data and post-processing 

by data providers can leave artifacts that 

limit use for understanding travel behavior.

• Developing a nationwide standard. Land-use 

models and data are extremely helpful, but 

specificity and configuration varies from 

region to region, and there is no nationwide 

standard land-use model. 

Key Takeaways

The symposium participants noted 

several key takeaways during the 

discussion, as follows:

• Land-use modelling is an important aspect 

of using cellular data but is inconsistent 

across regions or nationwide.

• A key challenge moving forward will be 

linking data from cellular devices to make 

it more meaningful.

• Validating results and determining the 

margin of error that one is willing to accept 

is critical.
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n this session, Ben Pierce from Battelle 

presented on reducing bias and variance 

in sampling by linking cellular data 

with travel surveys and other datasets. 

Symposium participants then discussed 

challenges relating to changing methods 

based on emerging technologies. 

Presentation

Ben Pierce, Battelle 

Ben Pierce of Battelle began his presentation 

with an exercise to demonstrate the law of 

large numbers. This law indicates that as a 

group, it is possible to get close to the right 

answer, and as the size of the group 

increases, the closer to the answer the 

group will get. Pierce explained that 

statistics can measure variance in large 

surveys, as shown in figure 7; however, bias 

also needs to be considered. Pierce 

recommended taking an unbiased but 

imprecise survey and using precise cellular 

data to calibrate it. He noted that, even if 

cellular data are biased, it may still be 

necessary to accept a certain level of bias 

to reduce the mean squared error. 

Major Themes Discussed

The symposium participants discussed the 

following major themes after the presentation:

• Reconciling improvements. A key 

challenge moving forward is attempting to 

reconcile technological improvements 

with changing methods and how 

information is collected and analyzed.

• Reducing uncertainty. It is possible to 

reduce the level of uncertainty within 

the results. 

Key Takeaways

During the discussion, the symposium 

participants agreed that leveraging and 

reconciling surveys and cellular data will be 

helpful in determining a broader picture of 

what is happening.

Relating Census Block and American Community Survey 

County Data, Demographic, and Economic Data to 

Derived Trip Data 

Figure 7. Example of a large national survey with sample sizes in the tens of thousands.

© Battelle
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This session involved general discussion 

regarding the key trends touched upon 

throughout the day.

Major Themes Discussed

The participants discussed the following 

overall themes:

• Utilizing data. Symposium participants 

noted that there remains tremendous 

value in using data despite the limitations. 

Several models and algorithms are 

transferable between GPS and cellular 

location data, which allows for some ease 

of use. 

• Finding a balance. Some type of balance 

between traditionally collected data and 

emerging data is the best way to focus 

research efforts moving forward to answer 

national policy questions. 

• Developing standards. It would be helpful 

to develop a common or standard 

language when discussing emerging issues 

and when differentiating among the 

various types of data streams (e.g., GPS or 

cellular, phone on or off). 

• Working with coverage differences. 

Coverage in rural areas is available as long 

as there is service, but it may be less 

precise than in urban areas. Sampling 

issues and discrepancies occur not only in 

rural and urban areas, but also in terms of 

different types of carriers and areas of 

higher or lower income. 

Key Takeaways

The participants noted several final 

takeaways from the symposium, as follows:

• Participants noted that different data are 

better. As researchers continue to gain a 

better understanding of mobility, traffic 

patterns, congestion, and behavior, a 

variety of data can increase understanding 

with accuracy and precision. 

• For cell data to support highway policy 

information at the national level, standard 

product outputs can increase the value 

over ad hoc outputs designed for 

multiple projects. 

• Although cellular data are available from 

various providers, acquiring data can be 

costly, and the licensing agreements can 

limit the use of the data. Fusing the 

cellular data with demographic information, 

census tract data, travel surveys, or other 

data will be a critical next step in 

determining travel behavior and 

developing applications for cellular data. 

Although there are certain privacy issues 

and other challenges that will need to be 

addressed over time, this data fusion will 

help to unlock further advantages of 

cellular data. 

Panel Summary and Next Steps
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Appendices
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FHWA Cell Phone Data and Travel Behavior Research Symposium 

February 12, 2014

8:30–8:45 a.m. Welcome and Introductions

 David Winter, Federal Highway Administration, 

 Office of Highway Policy Information

 Tom Palmerlee, Transportation Research Board

 Overview of Exploratory Advanced Research Program

 David Kuehn, Federal Highway Administration, 

 Exploratory Advanced Research Program

8:45–9 a.m. Issues to Resolve and Understand

 Why Are We Gathering?

 Tianjia Tang, Federal Highway Administration, 

 Travel Monitoring and Surveys Team

9–10:15 a.m. Data Coverage, Resolution, and Characteristics

 Moderator: Brad Gudzinas, Federal Highway Administration, 

 Travel Monitoring and Surveys Team

• Bill King, AirSage

• Daniel Rolf, HERE

• Keith Hangland, HERE

• Rick Schuman, INRIX

• Nick Cohn, TomTom

 Discussion 

10:15–10:30 a.m. Break 

10:30 a.m.–12 p.m. Agency and Academic Experience Using Cell Data

 Moderator: Guy Rousseau, Atlanta Regional Commission

• Cynthia Chen, University of Washington

• Xiaowei Xu, University of Arkansas

• Elizabeth Birriel, Florida Department of Transportation

• Camille Kamga, City University of New York, 

Transportation Research Center

• Anil Yazici, City University of New York, Transportation 

Research Center

• Andrew Rohne, Ohio–Kentucky–Indiana Regional Council of Governments

• Krishnan Viswanathan, CDM Smith

 Discussion

Appendix A: Agenda
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12–1 p.m. Lunch

1–2:15 p.m. Linking Trip Information to Behavior Data

• Deriving Trip Data From Cellular Data 

	 	 	 • Cellular Data, Social-Demographic, and Economic Aspects of the

Data Generators

 Moderator: Cynthia Chen, University of Washington

• Lei Zhang, University of Maryland

• William (Billy) Bachman, National Cooperative Highway Research 

Program 08-89: Applying GPS Data to Understand Travel Behavior

• Shan Jiang, Massachusetts Institute of Technology

 Discussion

2:15–3:30 p.m. Relating Census Block and American Community Survey County 

Data, Demographic, and Economic Data to Derived Trip Data

 • Non-Probability-Based Data for Universe Coverage

 Moderator: Stacey Bricka, Texas A&M Transportation Institute

 • Ben Pierce, Battelle

3:30 p.m. Panel Summary and Next Steps

 Brad Gudzinas, Federal Highway Administration, 

Travel Monitoring and Surveys Team

 Adjourn 
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Name Organization

William Bachman Westat

Peter Bang Federal Highway Administration

Elizabeth Birriel Florida Department of Transportation

Stacey Bricka Texas A&M Transportation Institute

Greg Bucci U.S. Department of Transportation Volpe Center 

Cynthia Chen University of Washington

Nick Cohn TomTom

Gareth Coville The MITRE Corporation

Vivian Daigler Nustats

Elenna Dugundji Netherlands e-Science Center and 

 University of Amsterdam

Daniel Fisher AirSage

Rhett Fussell Parsons Brinckerhoff

Deepak Gopalakrishna Battelle

Ryan Gordon RTI International

Sarah Griffith Nustats

Brad Gudzinas Federal Highway Administration

Keith Hangland HERE

Ed Hard Texas A&M Transportation Institute

Ryan Hicks Boston Region Metropolitan Planning Organization

Weimin Huang Cambridge Systematics

Shan Jang Massachusetts Institute of Technology

Appendix B: Attendees
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Name Organization 

Camille Kamga City University of New York, 

 Transportation Research Center

Nicole Katsikides Federal Highway Administration

Bill King AirSage

Anurag Komanduri Cambridge Systematics

David Kuehn Federal Highway Administration

Michelle Lee Resource Systems Group

Huan Li Mygistics

Yangwen Liu Virginia Department of Transportation

Nancy McGuckin Consultant

Jasmy Methipara Macrosys

Rolf Moeckel University of Maryland

Elaine Murakami Federal Highway Administration

Dan Murray American Transportation Research Institute

Thomas Palmerlee Transportation Research Board

Julie Parker Bureau of Transportation Statistics

Ben Pierce Battelle

Eric Pihl Federal Highway Administration

Jim Powers U.S. Department of Transportation Volpe Center

Andrew Rohne Ohio-Kentucky-Indiana Regional Council of Governments

Daniel Rolf HERE

Guy Rousseau Atlanta Regional Commission

Hossain Sanjani Bureau of Transportation Statistics

Jean-Daniel Saphores University of California, Irvine

Rick Schuman INRIX
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Name Organization 

Tianjia Tang Federal Highway Administration 

Rich Taylor Federal Highway Administration

Krishnan Viswanathan CDM Smith

David Winter Federal Highway Administration

Xiaowei Xu University of Arkansas 

Anil Yazici City University of New York, 

 Transportation Research Center

Lei Zhang University of Maryland
Office of Corporate Research, Technology, 

and Innovation Management

6300 Georgetown Pike

McLean, VA 22101-2296

Publication No. FHWA-HRT-14-060

HRTM-30/07-14(1M)E