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A hybrid gravity and route choice model to assess vector traffic in large-scale road networks
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- Author(s) / Creator(s)
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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. -
- Date created
- 2020-01-01
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- Subjects / Keywords
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- Type of Item
- Article (Published)