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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations

 
REPORT
This report is an archived publication and may contain dated technical, contact, and link information
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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

CONCLUSIONS

ABMS has been widely applied in a spectrum of disciplines, including, but not limited to, ecology, biology, business, economic science, computer simulation, social sciences, political science, policy, and military studies. The cognition and knowledge of ABMS and the recognition of applications continue to expand in step with its rapid development.

This primer reviews and summarizes the ABMS approaches that have been studied in the transportation paradigm in the past few decades and thus presents and depicts the concept of ABMS as scoped in the literature. Those applications fall primarily into two methodological domains: individual-based models that study personal transportation-related activities and behavior, and system and computational methods, known as MAS, to study a collaborative and reactive transportation system by modeling autonomous decisionmaking by a collection of subsystem entities called agents. In a non-trivial review effort, the authors offer the summary that the former is closely related to models for activity-based travel demand and land use, whereas the latter is typically scoped as a powerful technique for simulating dynamic complex systems to observe emergent behavior.

Another goal of this primer is to promote the understanding that the traditional transportation behavioral models could be viable for use within ABMS. This effort is demonstrated in the travelers' route choice decisionmaking process in this primer. In chapter 6, a behavioral model is established in a bottom-up framework and tries to formulate the mechanism of a traveler's complex route choice behavioral process as a collaborative and reactive result of users' mindset and the network environment. The authors hope that this ABMS modeling paradigm demonstrates that one can flexibly predict travelers' behavioral actions in response to real-time information and sudden changes in the network environment and that it is plausible to observe emergent behavior as a stimulus to a new environment setup.

 

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