Counterflow model for agent-based simulation of crowd dynamics

Simo Heliövaara (Corresponding Author), Timo Korhonen, Simo Hostikka, Harri Ehtamo

    Research output: Contribution to journalArticleScientificpeer-review

    67 Citations (Scopus)

    Abstract

    Agent-based crowd models describe pedestrians as autonomous interacting agents. Current models take into account the physical contact forces occurring in a crowd, but the description of many behavioural actions is still a challenge. This paper presents a model for agents’ behaviour in counterflow situations, where they try to avoid collisions with oncoming agents. In the model, the agents observe the walking directions of the agents in front of them and choose their own actions accordingly. We implement the model to the widely used social force model, which describes the motion of each agent in a Newtonian manner. Nevertheless, the basic idea of the counterflow model can be used with various modelling platforms. We study the effects of the model’s parameters with Monte Carlo simulations and justify our selection of their values. Simulation results are compared with previously published experimental data and the results match well.
    Original languageEnglish
    Pages (from-to)89-100
    Number of pages11
    JournalBuilding and Environment
    Volume48
    DOIs
    Publication statusPublished - 2012
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    simulation
    pedestrian
    Autonomous agents
    walking
    collision
    contact
    modeling
    Values

    Keywords

    • evacuation
    • counterflow
    • FDS + Evac
    • crowd dynamics

    Cite this

    Heliövaara, Simo ; Korhonen, Timo ; Hostikka, Simo ; Ehtamo, Harri. / Counterflow model for agent-based simulation of crowd dynamics. In: Building and Environment. 2012 ; Vol. 48. pp. 89-100.
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    Counterflow model for agent-based simulation of crowd dynamics. / Heliövaara, Simo (Corresponding Author); Korhonen, Timo; Hostikka, Simo; Ehtamo, Harri.

    In: Building and Environment, Vol. 48, 2012, p. 89-100.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Counterflow model for agent-based simulation of crowd dynamics

    AU - Heliövaara, Simo

    AU - Korhonen, Timo

    AU - Hostikka, Simo

    AU - Ehtamo, Harri

    PY - 2012

    Y1 - 2012

    N2 - Agent-based crowd models describe pedestrians as autonomous interacting agents. Current models take into account the physical contact forces occurring in a crowd, but the description of many behavioural actions is still a challenge. This paper presents a model for agents’ behaviour in counterflow situations, where they try to avoid collisions with oncoming agents. In the model, the agents observe the walking directions of the agents in front of them and choose their own actions accordingly. We implement the model to the widely used social force model, which describes the motion of each agent in a Newtonian manner. Nevertheless, the basic idea of the counterflow model can be used with various modelling platforms. We study the effects of the model’s parameters with Monte Carlo simulations and justify our selection of their values. Simulation results are compared with previously published experimental data and the results match well.

    AB - Agent-based crowd models describe pedestrians as autonomous interacting agents. Current models take into account the physical contact forces occurring in a crowd, but the description of many behavioural actions is still a challenge. This paper presents a model for agents’ behaviour in counterflow situations, where they try to avoid collisions with oncoming agents. In the model, the agents observe the walking directions of the agents in front of them and choose their own actions accordingly. We implement the model to the widely used social force model, which describes the motion of each agent in a Newtonian manner. Nevertheless, the basic idea of the counterflow model can be used with various modelling platforms. We study the effects of the model’s parameters with Monte Carlo simulations and justify our selection of their values. Simulation results are compared with previously published experimental data and the results match well.

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