a

Agent-Based Modeling 

and Simulation 

Workshop 2010

The Exploratory Advanced Research Program
Foreword

The Federal Highway Administration’s (FHWA) Office of Operations R&D and the Exploratory Advanced 

Research Program Team hosted a workshop in May 2010 as part of an ongoing effort to examine the area 

of Agent-Based Modeling and Simulation (ABMS) and its application in transportation research. The 

workshop successfully brought together experts from various fields outside of transportation to share 

their knowledge and experience in applying ABMS to study topics such as consumer behavior in the market place, the impact of certain policies on Hawaii’s fisheries, and predicting the spread of epidemics. The 

workshop also connected FHWA technical experts with those from academia, national laboratories, and 

other sources conducting relevant ABMS research.

Through the distribution of this workshop summary report, it is our hope that the information and 

thoughts shared at the meeting will help shape the scope of FHWA’s further investment in modeling and 

simulation research through open solicitation, interagency agreements, or other appropriate mechanisms. 

Michael F. Trentacoste

Associate Administrator

Office of Research, Development, and Technology

Federal Highway Administration

Notice

This document is disseminated in the interest of information exchange under the sponsorship of the Department of Transportation. The United States Government assumes no 

liability for its contents or use thereof. This report does not constitute a standard, specification, or regulation.

The United States Government does not endorse products or manufacturers. Trade 

and manufacturers’ names appear in this report only because they are essential to the 

object 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 image: © iStockphoto.com/zennie
Technical Report Documentation 

i

1. Report No.

FHWA-HRT-11-036

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

4 Title and Subtitle

Agent-Based Modeling and Simulation Workshop Summary Report

5 Report Date

July 2011

6 Performing Organization Code

7. Author(s)

Office of Operations R&D

Federal Highway Administration

8 Performing Organization Report 

No. 

9 Performing Organization Name and Address

Woodward Communications, Inc.

1420 N Street, NW, Suite 102

Washington, D.C. 20005

10 Work Unit No. (TRAIS)

11 Contract or Grant No.

DTFH61-09-F-00027

12 Sponsoring Agency Name and Address

Office of Operations R&D

Federal Highway Administration

U.S. Department of Transportation

1200 New Jersey Avenue, SE

Washington, DC 20590

13 Type of Report and Period 

Covered

Summary Report

14 Sponsoring Agency Code

15 Supplementary Notes

FHWA Technical Point of Contact: C. Y. David Yang, Office of Operations R&D

16 Abstract

The  report  summarizes  a  workshop  held in  May  2010  as  part  of  an  ongoing  effort  to  examine  Agent-­‐‐Based  Modeling  

and  Simulation  and  its application  in  transportation  research.  

Part   One   focuses   on   the   following   presentations:   Agent-­‐‐Based   Simulation   and   Modeling:   Identification   of  

Breakthrough   Research   for  Highway   Transportation,   Computer   Simulation   for   Transportation   Studies  – A    Brief  

History;   Overview   and   Development   of   Agent-­‐‐Based   Modeling   and   Simulation; Agent-­‐‐Based   Modeling   with  

Repast   Simphony   Including   a   Consumer   Products   Modeling   Example; Using   Pattern-­‐‐Oriented   Modeling   in  

Developing   the  Agent-­‐‐Based  Model   of  Hawaii’s  Longline   Fishery;  Predicting  Pandemic  Disease   Spread  in  Urban  

Environments  With  Agent-­‐‐Based  Simulation,  Agent-­‐‐Based  Modeling  of  Transportation  Systems.  

Part  Two  of  the  report  summarizes  the  group  breakout  discussions.

17 Key Words

Agent-Based Modeling Simulation (ABMS), Computer 

Simulation, Transportation Systems

18 Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

19 Security Classification 

(of this report)

Unclassified

20 Security Classification 

(of this page) 

Unclassified

21 No. of pages

24

22 Price

Form DOT F 1700.7 (8-72)
ii

Following a welcome from Michael 

Trentacoste, Associate Administrator for 

Federal Highway Administration (FHWA) 

and Director of the Turner-Fairbank Highway 

Research Center, the Agent-Based Modeling 

and Simulation (ABMS) Workshop began with 

a presentation by David Kuehn, Exploratory Advanced Research (EAR) Program Manager. 

Kuehn gave an overview of the EAR Program 

and its current status, explained the steps of the 

research process, and commented on the high 

value the program places on partnerships and collaboration. Kuehn explained that the goal of the 

workshop was to gain insight into how other fields 

of study utilize agent-based modeling. 

Next, David Yang of FHWA’s Office of Research 

and Development gave a brief historical perspective 

on the significant developments of computer simulation used in transportation studies—highlighting 

the link between computer programming languages, 

transportation theories, and analytical tools.

Argonne National Laboratory’s Charles Macal 

continued the workshop with an overview of 

agent-based modeling and its development. A 

relatively new approach to modeling systems, 

agent-based models are useful in analyzing and 

understanding how people’s behavior may affect 

factors such as pedestrian movement, transportation, and traffic. Macal defined characteristics and 

features of agents, and offered nine suggestions on 

when agent-based modeling is an appropriate 

methodology.

Executive Summary 

Michael North, also of Argonne, proceeded to 

showcase Repast Simphony and explained the capabilities and system requisites of this free and 

open-source software for agent-based modeling. 

He also provided multiple, non-transportation examples of agent-based modeling studies, including 

a successful effort in a consumer product market. 

The practice of longline fishing in Hawaii was 

the next example, presented by Run Yu of University of Hawaii and Apple Inc. By illustrating an 

agent-based model using pattern-oriented modeling, Yu explained how this research method is useful in evaluating policy and the impact of regulations on Hawaii’s fishery. 

Following this, the University of Toronto’s Dionne 

Aleman highlighted how using an agent-based 

simulation can aid public health officials in preparing a population for a pandemic disease. Information gleaned can aid officials in planning for public 

information campaigns, vaccine supply needs, and 

healthcare facility utilization.

Finally, William Rand of the University of Maryland discussed a transportation-related study that 

used an agent-based model. He also explained the 

key attributes of NetLogo, a tool often used for 

agent-based modeling and simulation.

The remainder of the 1-day workshop included small group discussions on three critical topics related to ABMS: technical gaps to overcome, 

challenges of incorporating ABMS in transportation, and potential applications of ABMS in 

transportation. 
iii

Table of Contents

INTRODUCTION iv

PART ONE: PRESENTATIONS  1

1. Agent-Based Simulation and Modeling: Identification of Breakthrough 

Research for Highway Transportation 2

2. Computer Simulation for Transportation Studies—A Brief History 3

3. Overview and Development of Agent-Based Modeling and Simulation 4

4. Agent-Based Modeling With Repast Simphony Including a Consumer 

Products Modeling Example 6

5. Using Pattern-Oriented Modeling in Developing the Agent-Based Model 

of Hawaii’s Longline Fishery 8

6. Predicting Pandemic Disease Spread in Urban Environments With 

Agent-Based Simulation 10

7. Agent-Based Modeling of Transportation Systems 12

PART TWO: GROUP DISCUSSION 15

Key Technical Gaps to Overcome 16

Challenges of Incorporating ABMS in Transportation 17

Potential Applications of ABMS in Transportation 18

APPENDIX A: AGENDA 21

APPENDIX B: KEY CONTACT INFORMATION 23

APPENDIX C: REFERENCES inside back cover
iv

On May 4, 2010, at the Turner-Fairbank Highway Research Center in McLean, 

VA, the Federal Highway Administration’s (FHWA) Exploratory Advanced 

Research (EAR) Program convened a 1-day workshop on Agent-Based Modeling and Simulation (ABMS). 

A panel of five speakers presented tools, methods, and concepts related to ABMS. 

These presentations, including answers to any additional questions raised, are incorporated into part one of this report. Following the presentations, these speakers and representatives from academia, the transportation industry, and other professional areas 

specific to agent-based modeling, discussed applications to transportation, knowledge 

gaps, and barriers to implementation. These small group discussions are summarized in 

part two of this report.

The workshop was part of a targeted effort to convene special workshops and symposia and invite noteworthy speakers on groundbreaking topics for brainstorming future 

trends and research gaps. 

This report is being distributed to the workshop participants, as well as to the broader highway community.

Introduction
Part One

Presentations
2

FHWA

ADVANCED

RESEARCH

PLAN

BASIC

RESEARCH

FHWA,

NCHRP,

State DOT,

UTC,

Industry

Universities/

Centers of

Excellence

National and

International

Transportation

Institutes

National and

International

Laboratories

Exploratory

Advanced

Research

National

Science

Foundation

Advanced

Transportation

Research at DOT,

DOE, DOD, EPS,

etc.

APPLIED

RESEARCH

FHWA

Mission Oriented

Advanced 

Research

EAR Program Funding

In 2005, the EAR Program received approximately 

$10-11 million in funding to focus on long-term 

needs of the larger highway community, address 

persistent research gaps, and utilize ideas from 

basic research in different industries and sectors 

to match highway research questions and needs. 

There have been four cycles of solicitations, 

resulting in 32 awards—9 of which are in process 

now. Most projects have some level of funding 

match, with about $29 million in Federal funding currently involved in projects. These projects 

are generally between 2 and 4 years in duration, 

and FY 2010 will see a full complement of EAR 

Program projects running, with some nearing 

completion. 

1. Agent-Based Simulation and Modeling: 

Identification of Breakthrough Research 

for Highway Transportation

David Kuehn, EAR Program Manager, FHWA

202-493-3414 (email: david.kuehn@dot.gov)

Advanced Research Process

The EAR Program process begins with an initial 

stage investigation, which has included more than 

40 workshops, literature reviews, and frequent 

scanning—all designed to help identify the best opportunities from different fields and areas of science and engineering. Not all initial stage research 

is selected to move to the next stage. 

The next step in the process is solicitation and a 

search for the best and brightest teams, with particular importance placed on collaboration. Merit 

review is used in the initial evaluation of proposals and throughout the research projects, and 

stakeholders representing academia, government, 

industry, and international interests are involved 

from beginning to end in communicating results. 

Program Goals

The topic areas of greatest interest to the EAR Program are integrating highway system concepts, 

nanoscale research, human behavior and travel 

choices, new technology and advanced policies for 

energy and resource conservation, information sciences, breakthrough concepts in material science, 

and technology for assessing performance. 

The hidden gems of the EAR Program are the 

partnerships created in the various stages of the research. The stakeholder involvement, the networking, and the sharing of ideas—all these aspects increase the knowledge base in advance of published 

papers and formal reports. The EAR Program is 

looking for original ideas and concepts that are going to advance the science and engineering of highway transportation. It is trying to accelerate the 

capacity of the industry to think about tough problems, and through innovative research and collaboration, attempt to solve them. 

What is EAR?

Diagram shows how EAR responds to the gap between basic and 

applied research.

Source: FHWA
3

2. Computer Simulation for Transportation Studies—

A Brief History

David Yang, Ph.D., Office of Operations R&D, FHWA

202-493-3284 (email: david.yang@dot.gov)

Development of Computer and 

Programming Language

There have been several key developments in traffic 

simulation history, starting with the Monte Carlo 

method. This commonly used simulation method 

in traffic and transportation can be traced back to 

the 18th century.1

 The unveiling of the first general 

computer was another milestone in the 1940s, 

the same decade that saw the first formulation of 

a Monte Carlo computation for an electronic 

computing machine.2 In the 1950s, IBM launched 

FORTRAN, a useful programming language.3 The 

PC era began in the 1980s and by the mid-1990s, 

Web-based simulations began. All of these developments are useful reference points in the evolution of simulation used in transportation. 

Development of Simulation for 

Various Modes of Transportation

An important development of simulation tools for 

transportation research was the use of flight training devices, developed in the late-1920s, with 

American Airlines becoming the first airline to use 

a training simulator in the 1930s.4 The availability 

of simulation for rail and ships began to develop in 

the 1970s; and then in the late-1990s and early2000s, the National Advanced Driving Simulator 

was developed to study driver behavior. In more 

recent years, the Federal Railroad Administration 

installed an advanced train simulator.

Development of Simulation for 

Traffic Analysis

Bruce Greenshields was the first to use mathematical equations to study highway capacity in 

the 1930s.5 Shortly after, William Adams used 

probability theory to describe traffic flow. In the 

early-1950s, intersection simulation work was 

being conducted, as well as a University of California study on highway simulation. Since the 

1960s, many simulation tools have been developed 

and used to examine various transportation and 

traffic events. Examples include the Model of 

Metropolis and TRANS (1960s); CORSIM and the 

Texas Model (1970s); INTRANS, INTEGRATION, 

and AMSUN2 (1980s); TRANSIMS, VISSIM, and 

PARAMICS (1990s); and NGSIM (2000s). 

Developing Existing Tools

While all of these historical milestones are important to the type of models and simulation tools now 

used to study issues related to traffic and transportation, it is expected that this workshop will help 

determine how to improve upon these tools. Although these tools do help researchers model driver behavior, they do not do so at the level required 

and, in particular, offer no insight into the driver’s 

decisionmaking process. Looking at methodology 

used in areas of study outside of transportation—

such as agent-based methodology—may help improve transportation simulation models.

A timeline of computer simulation.

Source: FHWA

Monte 

Carlo

TRANS

INTRAS

INTEGRATION

AMSUN2

TRANSIMS

VISSIM

PARAMICS

CORSIM

TEXAS

1960 1970 1980 1990 2000 2010

Next step for 

simulation in 

transportation?

NGSIM

DYNASMART-P
4

Introduction to Agent-Based 

Modeling

Agent-based modeling is a relatively new approach 

to modeling systems comprised of autonomous and 

interacting agents, and there are a growing number 

of agent-based applications in a variety of fields. 

Agent-based modeling can be used to study such 

phenomena as consumer choice, the spread of epidemics, and behavioral economics in stock market 

analyses, for example, and it can also be used to understand how people’s behavior affects activities like 

pedestrian movement, transportation, and traffic. 

3. Overview and Development of 

Agent-Based Modeling and Simulation

Charles Macal, Ph.D., Argonne National Laboratory

630-252-3767 (email: macal@anl.gov)

Emerging Order

Agents are decentralized, suggesting there is no central authority. They have behaviors and they operate 

according to what those behaviors dictate they 

should do. Although agents are autonomous and act 

independently, they also interact with other agents. 

This does not mean every agent in a simulation interacts with every other agent all the time, but their 

environment is dynamic and local. 

One reason there is such interest in agent-based 

modeling is the notion of “emergence”: agentbased models often lead to the emergence of order, 

patterns, or structures—aspects not originally put 

into the model. Agent-based modeling also shows 

what, where, when, and how system-level consequences emerge from the individual level, or migrate from macro to micro and vice versa. 

Agent Features

An agent-based model consists of four features: a set 

of agents defined by the user; a set of agent relationships, also defined by the user; an environment in 

which the agents live, such as infrastructure and 

roads; and a framework for simulating all these 

agent behaviors and interactions. Agent-based modeling begins and ends with the agent perspective, 

such as how a driver would behave in different situations. 

When running a simulation, there are five essential features for an agent to have: it is self-contained; it is autonomous or self-directed; it senses 

information and translates it into actions; it has a 

state that varies over time; and it is social and has 

dynamic interactions with other agents. There are 

other features often seen in agents, but they are not 

essential to a good model.

ABMS Development 

Agent Environment Design 

Model Architectural Design 

Prototyping 

Verification and Validation 

Implementation 

Agent and Agent Rule Design 

ABMS Development 

Agent Environment Design 

Model Architectural Design 

Prototyping 

Verification and Validation 

Implementation 

Agent and Agent Rule Design 

Diagram shows the steps to performing agent-based 

modeling.

Image courtesy of Charles Macal, Argonne Natl. Lab.
5

Model Scenarios

There are different typologies in terms of how 

agents interact spatially, and agent-based models 

can often look different. People are working on 

larger scales now, using realistic maps and Geospatial Information Systems (GIS), and agents are connected through networks. According to Macal, 

most of the original agent-based models, including 

traffic models, were based on cellular automata—a 

field developed by John von Neumann in the 1940s 

looking at self-replicating machines.6

There are nine top scenarios to consider when 

deciding whether or not to use agent-based simulation (see box). When using a model, it is always very 

important to calibrate as much as possible based on 

available empirical data. In the future it is anticipated that people may even design data collection specifically for the requirements of an agent-based 

model. For now, there is a definite need to set up a 

standard for calibration and validation of agentbased models before people can begin to feel comfortable using them for real-world decisionmaking. 

Top Nine Scenarios for 

Agent-Based Simulation

9. When agents need to learn, adapt, or change their 

behavior.

8. When agents need to have dynamic relationships 

with other agents.

7. When agents engage in strategic behavior.

6. When it is important that agents form organizations.

5. When agents need a geospatial component to their 

behaviors.

4. When the past is no predictor of the future.

3. When scale-up to arbitrary levels is important.

2. When process structural change needs to be a result 

of the model.

1. When the problem has a natural representation as 

agents.

A screen capture from NetLogo.

Wilensky, U. (1997). NetLogo Wolf Sheep Predation model. 

Evanston, IL: Center for Connected Learning and Computer-Based 

Modeling, Northwestern University.
6

Open-Source Software

Repast is a free, open-source, agent-based modeling 

and simulation library that can be used as a software tool to write agent-based models. Repast 

stands for Recursive Porous Agent Simulation Toolkit and has a variety of tools built in or connected to 

the system for statistics, scientific visualization, 

data mining, and spreadsheets. Once a model is run, 

it is possible to immediately analyze results, and execute the model using a variety of experimental designs. One aspect of the system enables specific goal 

seeking; using a predefined objective function it can 

compare two different results from the model and 

improve parameters to get better results.

4. Agent-Based Modeling with Repast Simphony 

Including a Consumer Products Modeling Example

Michael North, Ph.D., Argonne National Laboratory

630-252-6234 (email: north@anl.gov)

Repast Features

There are many different ways to write models using Repast: with flowcharts, written directly in 

Java, or using a tool called Groovy. For example, 

Repast is a powerful, flexible way to write models, 

allowing multiple paths from conception to analysis. In addition to the selection of different tools in 

the system, one exciting feature is the application’s 

full geographical information capability in 3-D. 

Various government agencies are making more and 

more data available to enhance the GIS capabilities.

The system also features a neural network tool, 

called Java Object Neural Engine, which can give 

individual agents or groups a neural network to 

create patterns and perform pattern 

recognition. Additionally, there are 

built-in tools for integrating other 

types of models into the system. For 

example, it would be possible to 

model the flow of traffic and then 

model driver behavior and decisionmaking when it starts to rain.

Developing Software

The next software release is due out 

in early fall 2010 and will include the 

ReLogo system as a major upgrade. 

ReLogo has two functions: to write 

models using simple Logo semantics, 

and to convert NetLogo models. In 

summer 2010, the Repast Exascale 

System will be released online. This 

is designed for use in some of the 

ReLogo automatically converts NetLogo models to Repast Simphony.

Image courtesy of Michael North, Argonne Natl. Lab.
7

world’s fastest computers and uses the C++/MPI 

component of the core feature set. 

Non-Transportation Agent-Based 

Modeling Examples

Forecasting Environmental Resilience in Arctic 

Landscapes—This study looks at the impact of human water use. For example, if people are told there 

is a shortage of water, will they reduce water consumption? 

Investigating Socio-Cultural and Genetic Process 

Interactions—This study allows researchers to study 

the effects of migration and social choices that influence long-term population genetics in Indonesia. 

Red-Cockaded Woodpecker—The military is using this study to help them judiciously protect an 

endangered species living on bases in the southwestern United States, yet still continue necessary 

base operations. 

Consumer Market Modeling (Virtual Lab)—This 

very detailed, successful model has been applied by 

Proctor and Gamble to two consumer product categories and has resulted in better decisionmaking 

and cost savings. 

Numerous examples are provided by other fields 

of study, including energy systems, emergency response, social modeling, and chemical production. The failure of one infrastructure and how 

that may ripple and impact other areas is one area 

of interest; Repast could be used to investigate if a 

failure in electric power would propagate into gas 

infrastructure, for example.

Screen capture of Repast Simphony software.

Image courtesy of Michael North, Argonne Natl. Lab.
8

Hawaii’s Longline Fishery

In 2009, the National Marine Fishery Service provided funding for a study of Hawaii’s longline 

fishery using agent-based modeling. The study is 

ongoing and uses pattern-oriented modeling. 

Environmental groups have concerns about the 

longlines due to the shallow water and the potential for protected animals to be harmed or killed, 

particularly sea turtles. Lawsuits have been filed, 

resulting in temporarily closed areas and new industry-wide parameters being set. Complaints 

5. Using Pattern-Oriented Modeling in Developing the 

Agent-Based Model of Hawaii’s Longline Fishery

Run Yu, Ph.D., University of Hawaii/Apple Inc.

808-956-8819 (email: run@hawaii.edu)

from both sides of the debate continue, but longline fishing remains the most important fishery in 

Hawaii. 

Identifying Patterns

The goal of this project was to develop a simulation 

model for Hawaii’s longline fishery, simulating 

fishing behaviors. It was necessary to identify the 

patterns and develop a model to reproduce them, 

then test and validate. The research team found, 

through literature reviews and preliminary surLongline fishing vessels, part of the most important fishery in Hawaii.

Source: Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA
9

A screen capture showing the agent-based model 

of Hawaii’s Longline Fishery.

Image courtesy of Run Yu and PingSun Leung.

veys, that only a few fishermen actually make money and that there is a social network of fisherman 

based on ethnicity. Fortunately, there was already 

significant data about human fishing activities to 

start a study, owing to Federal regulations requiring that fishermen keep a detailed logbook. 

Developing a Prototype 

Simulation Model

Three patterns were recognized from the data: 

entry and exit; quarterly fishing efforts; and spatial fishing efforts. These patterns determined the 

following decisions to be simulated in a prototype 

model: when the fisherman wants to enter or exit 

the fisheries; whether to fish tuna or swordfish; 

where to fish; and when to return. The patterns 

also reveal learning opportunities that can influence future decisions. 

The prototype model featured 120 vessels and 

one by-catch species, the loggerhead turtle. Both 

tuna and swordfish trips were included, with three 

species of fish. A daily catch index and three management options were also factors. 

Implementing the Model

The model was used to evaluate policies and regulatory options and the resulting impacts on the fishery. 

The fish were modeled using standard catch per unit 

effort (CPUE) data, so there are no real or artificial 

fish in the model at this stage; however, an agentbased model of the fish population is being considered. The study is ongoing, and plans are in motion 

to calibrate the model against more patterns and to 

test alternative decision rules. Researchers also plan 

to look more at communication within the social 

networks and other management options. 
10

Learning from the H1N1 Pandemic

The recent concern over the spread of the H1NI 

flu prompted study of pandemic disease spread 

for the Greater Toronto Region using agent-based 

simulation. The predicted spread of H1N1 was not 

as bad as expected, and the simulation yielded 

some valuable information for public health officials. In particular, it was found that there are differences in the spread of disease in rural and urban populations, and the need for better planning 

is apparent. 

In considering the best way to study the spread 

of pandemic disease to prepare for the future, it 

was determined that the goals were to minimize 

the disruption to daily life, the duration of the outbreak, the number of people infected, the number 

of deaths, and the geographic spread. How to 

achieve these goals prompted scrutiny of the effect of resources (i.e., vaccines, personal protective equipment, staffing) and policies (i.e., school 

closings, public information campaigns, media). 

6. Predicting Pandemic Disease Spread in Urban 

Environments With Agent-Based Simulation

Dionne Aleman, Ph.D., University of Toronto

416-953-3278 (email: aleman@mie.utoronto.ca)

Implementing Models

In the past, decisions were based primarily on intuition, experience, and existing disease-spread 

models. Homogeneous mixing models, though 

widely used, are not considered easy to calculate 

and do not take into account different environments and scenarios. Non-homogeneous mixing 

models are not ideal either, due to their complexity and limited ability to track data. A simulation 

model was chosen for this study due to the simplicity and flexibility it afforded. 

The classic SIR model was employed—where 

everyone is classified as Susceptible, Infected, or 

Recovered. People were the agents, with unique 

probabilities of becoming infected, and the probabilities were interrelated, allowing for infinite 

customizability and better bookkeeping. The 

whole collection was translated to a simple equation that defined the entire model. 

An individual’s state of susceptible (S), infected (I), or recovered/removed 

(R) can be thought of as a Markov Chain, where a system fluctuates 

probabilistically between states according to the arcs. 

© Dionne M. Aleman

What happens in the model

➤ Transition probabilities change in each time period

Markov chain for each person

S I R
11

Multiple Simulation Scenarios

With the simulation it was possible to model infection and death rates according to individual 

age, public transportation use, transmission within households, healthcare worker exposure, 

schools and daycare facilities, long-term care 

facilities, workplaces, effects of media information, and mitigation strategy effectiveness. Some 

drawbacks to agent-based simulation emerged: it 

is very time and resource intensive, and for this 

example, there was limited availability of reliable 

data. This is a common problem for all complexsystems models but especially so in this scenario 

when trying to assess people’s behavior and not 

knowing whether they are staying in homes or 

going out.

No commercial simulation software was used 

for the study—instead, the model was written in 

pure C++ to facilitate parallelization and memory 

management. Particular interest was given to the 

effects of public service announcements, vaccination rates, and the availability of healthcare services. 

Because of concerns about the confidence level of 

the results, sensitivity analysis was carried out and 

multiple simulations were run, with variables for 

timing and the percentage of people staying at 

home. There was 99 percent confidence in the comparisons of viral load rates for the number infected 

and the number dead, and also in the conclusion 

that including healthcare makes a difference. 

Key to Success

Agent-based simulation was a great choice to study 

pandemic disease spread and demonstrates the advantage of being able to test multiple scenarios to a 

very high degree of confidence. Reliable data are 

the key to success, but simulation models are still 

better used as “what-if” machines to compare relative outcome differences across various scenarios, 

rather than as crystal balls.

0 250 500 750 1000 1250 1500 1750 2000 2250 2500

Number of simulations

300

250

200

150

100

50

0

Dead individuals at end of simulation

Graph shows that with 50 percent 

of the population engaging in the 

simplistic social distancing behavior 

of staying home when infected, the 

number of deaths after 60 days of 

the simulated outbreak is skewed 

to relatively small values.

© Dionne M. Aleman
12

Patterns of Behavior

A flock of birds provides a useful example of how 

individuals interacting come together to form emergent patterns of behavior. That same notion of local, 

individual action giving rise to emergent properties 

can be used to create interesting behaviors within 

human population models. In the flock example, the 

birds follow three simple rules: cohere and move toward the center of your flockmates; align and move 

in the same direction as your flockmates; or avoid 

and do not get too close to any of your flockmates. 

Agent-based models are easy to build and construct, and this flock scenario is a good example 

that shows interesting behavior from simple rules 

that govern individuals. From this 2D example, it is 

possible to see how underlying rules can be extrapolated by observing behavior. The most important 

7. Agent-Based Modeling of Transportation Systems

William Rand, Ph.D., University of Maryland

301-405-7229 (email: wrand@umd.edu)

being the separation rule—overriding all other 

rules it tells the bird to turn if it is about to fly into 

another bird, something that can also be applied to 

cars in traffic simulations.

Tools for Understanding

There are many tools other than agent-based modeling that are useful for gaining understanding across 

disciplines and fields: others are social network 

analysis, network science, GIS, and machine learning. NetLogo is a very simple programming language 

developed for agent-based modeling. It is specifically intended to be a low-threshold, high-ceiling language and is very easy to program. NetLogo has several built-in tools for running experiments on 

agent-based models and collecting data. One is BehaviorSpace, allowing optimization of parameter 

settings—such as decreasing transit time to the minimum amount possible. Another, HubNet, allows for 

participatory simulations in scenarios where humans are involved in decisionmaking. A GIS extension allows for high-performance computing clusters and even simulation of cities. 

NetLogo in Action

The following examples show a selection of trafficrelated uses for NetLogo:

•	 Traffic systems—A sixth-grader wants to understand how and why traffic jams occur. 

•	 Commuter model—What is the effect of adding

capacity to a road system on commuter transit 

time?

•	 Transit location model—What is the optimal

location for transit stops relative to population 

density? 

A screen capture showing gridlock in NetLogo.

Wilensky, U., & Stroup, W. (1999). NetLogo HubNet Gridlock model. Evanston, 

IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University.
13

A flock of birds demonstrate how individuals can interact together and form emergent patterns of behavior.

© iStockphoto.com/Arpad Radoczy

In another example, Yandan Lu, a graduate student 

at the University of Illinois, Chicago, looked at worktrip modal balance using agent-based modeling. Lu 

asked the question, “At what point will people switch 

to a sustainable mode of transportation?” With a 

huge set of data, a machine-learning algorithm was 

used to extract rules. Lu demonstrated the model on 

the six-county Chicago metropolitan area and then 

looked at the impact of various policy interventions 

on the system. 

Part Two

Group Discussion

For the afternoon session of the workshop, 

attendees were divided into three small groups, 

each tasked with making recommendations 

in a different area: key technical gaps to 

overcome, challenges of incorporating ABMS 

in transportation, and potential applications 

of ABMS in transportation. Following extensive 

discussion, each group summarized their 

findings and made recommendations.
16

Group 1 Identified Four Key Technical 

Gaps Facing Researchers:

Data Quality—Identified as the greatest challenge 

facing researchers, high-quality data are critical 

to creating a good model, but are expensive, timeintensive, and require sophisticated computer 

systems. 

Data Type—Knowing what type of data to collect 

presents another challenge to researchers, particularly if the data must integrate with other types of 

models.

Behavior—Behaviors can change over time, making the predictions of planning models more like 

guesses.

Logic—Humans are not logical systems but despite 

this, to model human behavior, logical mathematical equations need to be employed.

Technical Gap Conclusions

The discussion raised the issue that all models are 

essentially wrong but still remain useful for comparison. For example, when modeling a non-logical 

system, such as a human being, the only way to do 

so is with a computer using logical mathematical 

equations, which does not truly represent reality. 

Models should therefore be thought of as tools to 

infer underlying behavior patterns, offering useful 

insight but not specific numbers.

Data accuracy and availability were also discussed and the group concurred that time, effort, 

and money should always be invested in collecting 

as accurate data as possible. In certain situations a 

lack of data can be overcome by limiting the use of a 

simulation to hypothesis-testing only. To develop 

strong simulations from this, an expert input is necessary for an understanding of the underlying model structure. Adjusting behaviors in a model and 

identifying what a simulation is sensitive to is another important factor to consider.

One school of thought believes in “modeling 

for insights not numbers.” This is the idea that 

modeling offers comparative results and insights 

that can be taken out of a model and tested in the 

real world. Researchers often resort to the purely 

statistical characterization of situations to actual 

behavior, ignoring the intervening parts of how 

situations lead to attitudes and attitudes lead to 

behaviors.

In the transportation domain, there are at least 

three potential applications of agent-based techniques in transportation systems analysis: shortterm traffic operations for smaller scale purposes; 

long-term demand modeling for planning purposes; 

and interactions with other systems, such as energy, 

environment, etc. To collect data for these applications, it is important to look at the process of how a 

decision came about, rather than simply the end behavior. Simulators examining route choice are useful here, as are traditional survey methods, and also 

real-world observations on driving behavior.

Ultimately, with driver behavior data it is impossible to determine every situation under every set 

of conditions. Instead, key points must be connected to determine the driver behavior that may 

result.

Key Technical Gaps to Overcome
17

Group 2 Identified the Following 

Challenges Facing ABMS Researchers:

Credibility—One of the biggest challenges in incorporating ABMS in transportation is that ABMS is 

outside the traditional box and not considered part 

of current, traditional methods of traffic simulation. 

It is therefore critical to demonstrate the credibility 

of ABMS in transportation study.

Data—Ensuring access to proper data is another big 

challenge facing researchers, and it is important to 

clearly define the scope of project requirements.

Limitations—Not all transportation problems can 

be solved using ABMS; it is therefore important for 

limitations to be acknowledged.

Education—Although it will take time and effort, 

the transportation community must be educated on 

the benefits of using ABMS.

Incorporating ABMS Conclusions

The group established that one of the most important factors to establish ABMS credibility is acknowledging its limitations and appreciating that 

not all transportation problems can be addressed using the technique.

There has been much criticism and questioning 

in the economics field as to why ABMS was needed, when other tried and true research methods, 

similar to ABMS, were already in use. To counter 

this, the argument was made that using a new tool 

might provide new insight. Additionally, agentbased modeling is considered to be different from 

most aggregate modeling approaches, in that you 

can validate multiple levels of data. It was also stated that people have a duty to share knowledge with 

colleagues in the transportation community and 

begin to change the transportation curriculum.

A particular advantage of ABMS is the ability to 

describe the structure of a model to decisionmakers in terms of situations they already understand 

and have experience with. Progress is then quickly 

evident in the acceptance of ABMS in most domains. Although it does not replace all other techniques, it is a strong complementary process that 

can add to the portfolio of work.

The group concluded that making robust policy 

recommendations, regardless of the uncertainty of 

the underlying need, will assist in strengthening 

the case of using ABMS. Additionally, identifying a 

project area where ABMS may be helpful is another effective method to strengthen a case for using 

ABMS, demonstrate its value, and establish credibility. In other fields, anthropology and population 

domains have clearly demonstrated that a strong 

example can affect greater acceptance of ABMS. 

Challenges of Incorporating ABMS in Transportation
18

Group 3 Identified the Following 

Transportation Applications for ABMS:

•	 Prediction of driver behavior.

•	 Travel demand forecasting.

•	 IntelliDrive® implementation.

•	 Mode choice studies.

Group Conclusions

Although well-studied in terms of pedestrian traffic, evacuation models were suggested as an additional potential transportation application for 

ABMS. The group suggested it would be interesting to look at such a scenario within a vehicular 

traffic mode. Driver behavior is considered one of 

the missing pieces in the traffic and travel dimension simulations puzzle and an important area to 

develop. 

Another potential area the group put forward for 

investigation is vehicle purchasing trends. Currently, the main criteria for controlling the long-term 

emissions outcome is based on what type of vehicle 

a driver purchases, and this knowledge is based on 

past trends. A research project could determine the 

type of vehicle people purchase and how long they 

keep and use a vehicle. ABMS could offer new light 

in this area and assist planners by accounting for 

changing driver choices. It could also offer a new 

tool to measure the resulting impact of government 

policies and purchasing decisions.

Given that people have different definitions of 

ABMS, the group suggested that one or two projects need to be clearly identified as good examples 

that demonstrate the value of ABMS in transportation research. Ideally, it would be beneficial to find 

a project that other modeling methods cannot perform easily but an agent-based model could excel 

on. These projects should illustrate that ABMS is 

the best tool to yield information for some types of 

research questions.

Potential Applications of ABMS in Transportation
Appendices

21

Appendix A—Agenda

Agent-Based Modeling and Simulation Workshop

Turner-Fairbank Highway Research Center, McLean, VA, May 4, 2010

8:00 a.m. – 8:30 a.m. Introduction

Welcome

Michael Trentacoste, Associate Administrator for RD&T and Director of TFHRC

EAR Program Overview

David Kuehn, EAR Program Manager, FHWA

8:30 a.m.–8:45 a.m. Coffee Break

8:45 a.m.–9:00 a.m. Computer Simulation for Transportation Studies—A Brief History

David Yang, Ph.D., Office of Operations R&D, FHWA

9:00 a.m.–12:00 p.m. Overview and Development of Agent-Based Modeling and Simulation

Charles Macal, Ph.D., Argonne National Laboratory

Agent-Based Modeling With Repast Simphony Including a Consumer Products Modeling Example

Michael North, Ph.D., Argonne National Laboratory 

Using Pattern-Oriented Modeling in Developing the Agent-Based Model of Hawaii’s Longline Fishery

Run Yu, Ph.D., University of Hawaii/Apple Inc.

Predicting Pandemic Disease Spread in Urban Environments With Agent-Based Simulation

Dionne Aleman, Ph.D., University of Toronto

Agent-Based Modeling of Transportation Systems

William Rand, Ph.D., University of Maryland

12:00 p.m.–1:15 p.m. Lunch

Tour of Driving Simulator & Federal Outdoor Impact Laboratory

1:15 p.m.–2:30 p.m. Small Group Discussion

Group 1: Key Technical Gaps to Overcome

Facilitated by Randy VanGorder

Group 2: Challenges of Incorporating ABMS in Transportation

Facilitated by Deborah Curtis

Group 3: Potential Applications of ABMS in Transportation

Facilitated by David Yang

2:30 p.m.–4:00 p.m. Present Findings/Recommendations from Small Groups

Adjournment

23

Technical Information

C. Y. David Yang, Ph.D. 

Office of Operations R&D 

Turner-Fairbank Highway Research Center 

Federal Highway Administration

202-493-3284 

david.yang@dot.gov

EAR Program Information

David Kuehn

Exploratory Advanced Research Program

Turner-Fairbank Highway Research Center 

Federal Highway Administration

202-493-3414

david.kuehn@dot.gov

For more information visit www.fhwa.dot.gov/advancedresearch/

Appendix B—Key Contact Information

Appendix C—References

1. D. Goldsman, Richard E. Nance, and James R. Wilson. 2009. A Brief History of Simulation. In Proceedings of the 

2009 Winter Simulation Conference (M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, eds.), 

310–13. http://www.informs-sim.org/wsc09papers/028.pdf; p. 310.

2. B. W. Hollocks. 2008. Intelligence, Innovation and Integrity—KD Tocher and the Dawn of Simulation. Journal of 

Simulation 2(3), 128–137; p. 129.

3. Richard E. Nance and Robert G. Sargent. 2002. Perspectives on the Evolution of Simulation. Operations Research 

50(1), 161–172; p. 163.

4. Ray L. Page. “Brief History of Flight Simulation.” 2000. In SimTecT 2000 Proceedings, SimtechT 2000 Organizing 

and Technical Committee, Simulation Conference and Exhibition, Simulation Australia, Sydney. Available at 

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.132.5428&rep=rep1&type=pdf.

5. D. A. Henclewood. 2007. The development of a dynamic-interactive vehicle model for modeling traffic beyond the 

microscopic level. M.S. Thesis; http://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1073&context=theses; 

p. 22.

6. John Von Neumann. 1966. Theory of Self-Reproducing Automata (A. W. Burks, ed.), University of Illinois Press, 

Urbana.
Office of Operations R&D

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Publication No. FHWA-HRT-11-036

HRTM-30/07-11(1M)E