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Federal Highway Administration Research and Technology
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REPORT |
This report is an archived publication and may contain dated technical, contact, and link information |
Publication Number: FHWA-HRT-13-054 Date: November 2013 |
Publication Number: FHWA-HRT-13-054 Date: November 2013 |
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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 |
Debra S. Elston |
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Technical Report Documentation Page
1. Report No.
FHWA-HRT-13-054 |
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 |
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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.
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9. Performing Organization Name and Address University of Arizona, 1401 E University Blvd, Tucson, AZ 85721 |
10. Work Unit No. (TRAIS) |
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11. Contract or Grant No. DTFH61-11-H-00015 |
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12. Sponsoring Agency Name and Address
Office of Safety Research and Development |
13. Type of Report and Period Covered
Final Report, May 2011-May 2014 |
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14. Sponsoring Agency Code HRTM-30 |
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15. Supplementary Notes FHWA Agreement Officer's Technical Representative (AOTR): Dr. C. Y. David Yang, Office of Safety R&D |
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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. |
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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. |
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19. Security Classification Unclassified |
20. Security Classification Unclassified |
21. No. of Pages 75 |
22. Price N/A |
Form DOT F 1700.7 | Reproduction of completed page authorized |
SI* (Modern Metric) Conversion Factors
> | 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 |
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 |