Estimation apparatus, estimation method and estimation program
An estimation apparatus includes a memory; and a processor configured to execute: receiving spatiotemporal population data and a probability of movement between areas as input; constructing a collective graphical model (CGM) in a path graph for estimating a number of people who have moved between areas from the spatiotemporal population data and the probability of movement between areas; generating an instance of a minimum cost flow problem for performing MAP estimation on the constructed CGM; solving the instance of the minimum cost flow problem to estimate the number of people who have moved between areas at individual time steps; and outputting the estimated number of people who have moved between the areas at the individual time steps.
1 . An estimation apparatus comprising:
a memory; and
a processor configured to execute:
receiving spatiotemporal population data and a probability of movement between areas as input;
constructing a collective graphical model (CGM) in a path graph for estimating a number of people who have moved between areas from the spatiotemporal population data and the probability of movement between areas;
generating an instance of a minimum cost flow problem for performing maximum a posteriori (MAP) estimation on the constructed CGM;
solving the instance of the minimum cost flow problem to estimate the number of people who have moved between areas at individual time steps, wherein the instance of the minimum cost flow is solved by iterating a shortest path search and a flow update until a required amount of flow M is satisfied, wherein solving the instance of the minimum cost flow is based on having a minimum number of sample data; and
outputting the estimated number of people who have moved between the areas at the individual time steps.
2 . The estimation apparatus according to claim 1 , wherein the solving solves the instance of the minimum cost flow problem using a shortest path iteration method to estimate the number of people who have moved between areas at the individual time steps.
3 . An estimation method executed by a computer including a memory and a processor, the estimation method comprising:
receiving spatiotemporal population data and a probability of movement between areas as input;
constructing a collective graphical model (CGM) in a path graph for estimating a number of people who have moved between areas from the spatiotemporal population data and the probability of movement between areas;
generating an instance of a minimum cost flow problem for performing maximum a posteriori (MAP) estimation in the constructed CGM;
solving the instance of the minimum cost flow problem to estimate the number of people who have moved between areas at individual time steps, wherein the instance of the minimum cost flow is solved by iterating a shortest path search and a flow update until a required amount of flow M is satisfied, wherein solving the instance of the minimum cost flow is based on having a minimum number of sample data; and
outputting the estimated number of people who have moved between areas at the individual time steps.
4 . The estimation method according to claim 3 , wherein, in the solving solves the instance of the minimum cost flow problem using a shortest path iteration method to estimate the number of people who have moved between areas at the individual time steps.
5 . A non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer to execute an estimation process comprising:
receiving spatiotemporal population data and a probability of movement between areas as input;
constructing a collective graphical model (CGM) in a path graph for estimating a number of people who have moved between areas from the spatiotemporal population data and the probability of movement between areas;
generating an instance of a minimum cost flow problem for performing maximum a posteriori (MAP) estimation in the constructed CGM;
solving the instance of the minimum cost flow problem to estimate the number of people who have moved between areas at individual time steps, wherein the instance of the minimum cost flow is solved by iterating a shortest path search and a flow update until a required amount of flow M is satisfied, wherein solving the instance of the minimum cost flow is based on having a minimum number of sample data; and
outputting the estimated number of people who have moved between areas at the individual time steps.