IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-11440-4_25.html

Towards Microscopic Calibration of Pedestrian Simulation Models Using Open Trajectory Datasets: The Case Study of the Edinburgh Informatics Forum

In: Traffic and Granular Flow '17

Author

Listed:
  • Ruggiero Lovreglio

    (Massey University, School of Built Environment)

  • Charitha Dias

    (University of Tokyo, Institute of Industrial Science)

  • Xiang Song

    (Massachusetts Institute of Technology, Department of Civil and Environmental Engineering)

  • Lucia Ballerini

    (University of Edinburgh, Department of Neuroimaging Sciences)

Abstract

The investigation of crowd behaviours in normal and emergency situations has been greatly facilitated by various agent-based pedestrian models. Those models are built upon different assumptions in order to represent or mimic reality. One of the key challenges in pedestrian modelling is the verification of those assumptions or the best model specification by using existing datasets and suitable calibration approaches. This paper illustrates a case study where we calibrate different pedestrian model specifications with an open pedestrian trajectory dataset from Edinburgh Informatics Forum and select the best model according to various model selection criteria. Two floor field cellular automaton models with Euclidean and modified Euclidean distance metrics are presented for the static floor field. Our study shows that the modified Euclidean distance metrics can provide better fitting for the navigation environments without obstacles.

Suggested Citation

  • Ruggiero Lovreglio & Charitha Dias & Xiang Song & Lucia Ballerini, 2019. "Towards Microscopic Calibration of Pedestrian Simulation Models Using Open Trajectory Datasets: The Case Study of the Edinburgh Informatics Forum," Springer Books, in: Samer H. Hamdar (ed.), Traffic and Granular Flow '17, pages 215-223, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-11440-4_25
    DOI: 10.1007/978-3-030-11440-4_25
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-11440-4_25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.