A hybrid gravity and route choice model to assess vector traffic in large-scale road networks

  • Author(s) / Creator(s)
  • Human traffic along roads can be a major vector for infectious
    diseases and invasive species. Though most road traffic is
    local, a small number of long-distance trips can suffice to
    move an invasion or disease front forward. Therefore,
    understanding how many agents travel over long distances
    and which routes they choose is key to successful
    management of diseases and invasions. Stochastic gravity
    models have been used to estimate the distribution of trips
    between origins and destinations of agents. However, in
    large-scale systems, it is hard to collect the data required to
    fit these models, as the number of long-distance travellers is
    small, and origins and destinations can have multiple access
    points. Therefore, gravity models often provide only relative
    measures of the agent flow. Furthermore, gravity models
    yield no insights into which roads agents use. We resolve
    these issues by combining a stochastic gravity model with a
    stochastic route choice model. Our hybrid model can be
    fitted to survey data collected at roads that are used by
    many long-distance travellers. This decreases the sampling
    effort, allows us to obtain absolute predictions of both
    vector pressure and pathways, and permits rigorous model
    validation. After introducing our approach in general terms,
    we demonstrate its benefits by applying it to the potential
    invasion of zebra and quagga mussels (Dreissena spp.) to
    the Canadian province British Columbia. The model yields
    an R2
    -value of 0.73 for variance-corrected agent counts at
    survey locations.

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  • Type of Item
    Article (Published)
  • DOI
  • License
    Attribution-NonCommercial 4.0 International