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Publication Number:  FHWA-HRT-13-054    Date:  November 2013
Publication Number: FHWA-HRT-13-054
Date: November 2013


The Exploratory Advanced Research Program

A Primer for Agent-Based Simulation and Modeling in Transportation Applications

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Agent-based modeling and simulation (ABMS) methods have been applied in a spectrum of research domains. This primer focuses on ABMS in the transportation interdisciplinary domain, describes the basic concepts of ABMS and the recent progress of ABMS in transportation areas, and elaborates on the scope and key characteristics of past agent-based transportation models, based on research results that have been reported in the literature. Specifically, the objectives of this primer are to explain the basic concept of ABMS and various ABMS methodologies scoped in the literature, review ABMS applications emerging in transportation studies in the last few decades, describe the general ABMS modeling frameworks and commonly shared procedures exhibited in a variety of transportation applications, outline the strength and limitation of ABMS in various transportation applications, and demonstrate that ABMS exhibits certain comparable modeling outcomes compared to classical approaches through a traveler's route choice decisionmaking process example.

The target audiences of this primer are researchers and practitioners in the interdisciplinary fields of transportation, who are specialized or interested in social science models, behavioral models, activity-based travel demand models, lane use models, route choice models, human factors, and artificial intelligence with applications in transportation.

Monique R. Evans
Director, Office of Safety Research and Development

Debra S. Elston
Director, Office of Corporate Research, Technology, and Innovation Management



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.


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Technical Report Documentation Page

1. Report No.


2. Government Accession No. 3 Recipient's Catalog No.
4. Title and Subtitle

A Primer for Agent-Based Simulation and Modeling in Transportation Applications

5. Report Date

November 2013

6. Performing Organization Code
7. Author(s)

Hong Zheng, Young-Jun Son, Yi-Chang Chiu, Larry Head, Yiheng Feng, Hui Xi, Sojung Kim, Mark Hickman

8. Performing Organization Report No.


9. Performing Organization Name and Address

University of Arizona, 1401 E University Blvd, Tucson, AZ 85721

10. Work Unit No. (TRAIS)

11. Contract or Grant No.


12. Sponsoring Agency Name and Address

Office of Safety Research and Development
and Office of Corporate Research, Technology, and Innovation Management
Federal Highway Administration
6300 Georgetown Pike
McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Report, May 2011-May 2014

14. Sponsoring Agency Code


15. Supplementary Notes

FHWA Agreement Officer's Technical Representative (AOTR): Dr. C. Y. David Yang, Office of Safety R&D

16. Abstract

Agent-based modeling and simulation (ABMS) methods have been applied across a spectrum of domains within transportation studies. Different paradigms for ABMS in transportation exist; in general, ABMS has strong roots in the individual-based travelers' model in the activity-based travel demand domain. In the distributed system domain, ABMS is commonly seen as a method, known as multiagent systems, for a distributed autonomous system. Recently, transportation-related applications leveraging ABMS have continued to grow. This report attempts to clarify the concept of ABMS and summarize variant paradigms that have been studied in the transportation field. It will do this by distinguishing similarities or differences of the specified problems, model capabilities, strengths and weaknesses of ABMS scoped in different applications, and through a comprehensive review of ABMS approaches that have been seen in transportation studies.

The report also seeks to connect the individual-based ABMS with the transportation problems viewed in the social science paradigm. This is achieved by trying to apply ABMS characterized by social science rules to study behavioral decisions of individual travelers. This exploratory study is demonstrated in an example of travelers' route choice decisions, which features a bottom-up, rather than a conventional top-down, approach to formulate the mechanism of an individual traveler's complex route choice behavioral process as a collaborative and reactive result of the traveler's mindset and the network environment integrated in an ABMS.

17. Key Words

Agent-based modeling and simulation, transportation, route choice, transportation planning.

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)


20. Security Classification
(of this page)


21. No. of Pages


22. Price


Form DOT F 1700.7 Reproduction of completed page authorized


SI* (Modern Metric) Conversion Factors





List of Symbols

> Greater than
Less than or equal to
Not equal to
Proportional to
Summation symbol
Product symbol
an,a0n,a1n,a2n,ajn,ain Dirichlet distribution parameter set
β Beta function
dn Vector of minimum travel time variables
djn Binary variable, equal to 1 if the traveler perceives that the jth route takes the minimum travel time (TTnmin) on the nth day, and 0 otherwise
e Natural exponential constant, equal to 2.71828
E Expectation
ε Route Choice Threshold
Element of
f Function
fr Proportational value of travelers who replan their trips in TRANSIMS
g Data likelihood function
j Route
n Day
θ Gumbel distribution parameter
pin Subjective probability that the ith route takes the minimum travel time on the nth day
pn Vector of subjective probabilities
si Number of iterations of the outer-loop in DaySim
TTnj Travel time of jth route on nth day
TTnmin Minimum travel time of all routes on nth day
yjn Perception error term


List of Acronyms and Abbreviations

2D two dimensional
3D three dimensional
ABMS agent-based modeling and simulation
ABS agent-based simulation
ABSS agent-based social simulation
ACT-R Adaptive Control of Thought-Rational
aDAPTS Agent-based Dynamic Activity Planning and Travel Scheduling
AI artificial intelligence
API application programming interface
BBN Bayesian belief network
BDI belief-desire-intention
BPR Bureau of Public Roads
CARTESIUS Coordinated Adaptive Real-Time Expert System for Incident Management in Urban Systems
CAS complex adaptive systems
CEMDAP Comprehensive Econometric Microsimulator for Daily Activity Travel Patterns
CEMSELTS Comprehensive Econometric Microsimulator for Socioeconomics, Land Use, and Transportation System
CTMRGS Cooperative Traffic Management and Route Guidance System
DAI distributed artificial intelligence
DaySim Person Day Activity and Travel Simulator
DTA dynamic traffic assignment
DYNASMART-P Dynamic Network Assignment-Simulation Model for Advanced Roadway Telematics (Planning version)
DynusT Dynamic Urban Systems for Transportation
EMME equilibre multimodal, multimodal equilibrium
FIPA Foundation for Intelligent Physical Agents
GA genetic algorithm
GMU George Mason University
HOV high-occupancy vehicle
HUT Helsinki University of Technology
ILUTE Integrated Land Use, Transportation, Environment
ISP information service provider
MABS multi-agent based simulation
MALTA Multiresolution and Loading of Transportation Activities
MAS multi-agent systems
MASON Multiagent Simulator of Neighborhoods
MATSim Multi-Agent Transport Simulation Toolkit
MITSIM MIcroscopic Traffic SIMulator
NASA National Aeronautics and Space Administration
NP-hard Non-deterministic Polynomial-time hard
O-D origin-destination
OpenAMOS Open Activity-Mobility Simulator
PCATS Prism Constrained Activity Travel Simulator
PopGen population generator
PopSyn population synthesizer
Repast Recursive Porous Agent Simulation Toolkit
RL reinforcement learning
SAAS social aspects of agent systems
SACSIM Sacramento Activity-Based Travel Demand Simulation Model
SimAGENT Simulator of Activities, Greenhouse Emission, Networks, and Travel
SOCSIM social simulation
SOV single-occupant vehicle
TASHA Travel Activity Scheduler for Household Agents
TRACK-R TRaffic Agent City for Knowledge-Based Recommendation
TRANSIMS Transportation Analysis and Simulation System
TRYS Tráfico, Razonamiento y Simulación
TRYSA2 Tráfico, Razonamiento y Simulación Autonomous Agents
UE user equilibrium


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