Large-Scale Agent-Based Models for Transportation Network Management under Unplanned Events

Large-Scale Agent-Based Models for Transportation Network Management under Unplanned Events

Yunjie Zhao (SUNY Buffalo, USA) and Adel W. Sadek (SUNY Buffalo, USA)
DOI: 10.4018/978-1-4666-8473-7.ch067
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Abstract

The focus of this chapter is on issues surrounding the development and applications of large-scale agent-based traffic models. Following a brief overview of Agent-Based Modeling and Simulation (ABMS) applications in transportation modeling, the chapter proceeds to describe the authors' continued efforts and experiences with the development, calibration, validation, and application of a regional agent-based traffic model of the Buffalo-Niagara metropolitan area. The model is developed using the TRansportation ANalysis SIMulation System (TRANSIMS), an open-source, agent-based suite of transportation models. A unique feature of the chapter is its focus on unplanned or extreme events, such as severe snowstorms and major incidents on the freeways, and how the models may be calibrated and applied under such situations. The chapter concludes by summarizing the main lessons learned from the Buffalo case study and providing suggestions for future research.
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Agent-Based Modeling In Transportation

ABMS is a modeling paradigm that aims at “describing a system from the perspective of its constituent units” (Bonabeau, 2002). It is a “bottom-up” modeling approach which starts with the individual components of the system (i.e. agents) and defines their potential interactions. The macro-level behavior of the system then results from the myriad interactions among its constituent units. Toroczkai and Eubank (2006) define an agent as an entity that has the following properties:

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