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Data Assimilation for Agent-Based Models

Author

Listed:
  • Amir Ghorbani

    (Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia)

  • Vahid Ghorbani

    (Integrated Engineering, Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Gyeonggi-do, Republic of Korea)

  • Morteza Nazari-Heris

    (College of Engineering, Lawrence Technological University, Southfield, MI 48075, USA)

  • Somayeh Asadi

    (Department of Architectural Engineering, Pennsylvania State University, University Park, State College, PA 16802, USA)

Abstract

This article presents a comprehensive review of the existing literature on the topic of data assimilation for agent-based models, with a specific emphasis on pedestrians and passengers within the context of transportation systems. This work highlights a plethora of advanced techniques that may have not been previously employed for online pedestrian simulation, and may therefore offer significant value to readers in this domain. Notably, these methods often necessitate a sophisticated understanding of mathematical principles such as linear algebra, probability theory, singular value decomposition, optimization, machine learning, and compressed sensing. Despite this complexity, this article strives to provide a nuanced explanation of these mathematical underpinnings. It is important to acknowledge that the subject matter under study is still in its nascent stages, and as such, it is highly probable that new techniques will emerge in the coming years. One potential avenue for future exploration involves the integration of machine learning with Agent-based Data Assimilation (ABDA, i.e., data assimilation methods used for agent-based models) methods.

Suggested Citation

  • Amir Ghorbani & Vahid Ghorbani & Morteza Nazari-Heris & Somayeh Asadi, 2023. "Data Assimilation for Agent-Based Models," Mathematics, MDPI, vol. 11(20), pages 1-25, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4296-:d:1260129
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    References listed on IDEAS

    as
    1. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    2. Neema Nassir & Mark Hickman & Zhen-Liang Ma, 2015. "Activity detection and transfer identification for public transit fare card data," Transportation, Springer, vol. 42(4), pages 683-705, July.
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