Abstract: An estimation method according to an embodiment is an estimation method that estimates a MAP solution of a CGM on a path graph, in which a computer executes: an input procedure that receives, as inputs, aggregate data and potentials of the CGM on the path graph; an estimation procedure that uses the aggregate data and the potentials to solve a MAP estimation problem of the CGM by a technique based on discrete DC programming and calculates a MAP estimation solution; and an output procedure that outputs the MAP estimation solution.

Abstract: 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.

Abstract: An optimization method according to one embodiment which is executed by a computer, the method including: an input procedure of inputting an acceptance probability function indicating a probability that each participant participating in a crowdsourcing market will accept a price, and a matching value indicating a value when each resource in the crowdsourcing market is allocated to each participant; a formulation procedure of formulating a first optimization problem for determining an optimal price that maximizes a profit of a provider of the crowdsourcing market using the acceptance probability function and the matching value; and an optimization procedure of calculating the optimal price by solving the first optimization problem according to a feature of the acceptance probability function.

Abstract: Disclosed is a time-specific area population estimation method executed by a computer. The method includes estimating a time-specific interareal movement probability, based on observed time-specific population in an area and a set of candidate areas for a movement from the area in a unit time; and estimating a population in the area at a time at which no observation is performed by using a cost function learned in the estimating of the time-specific interareal movement probability.

Abstract: An optimization method according to an embodiment is executed by a computer, the method including: receiving a participation probability function of each of groups participating in a two-sided market, the two-sided market including a first side and a second side, a total number of participants included in the group, a maximum number of participants on the second side with whom the participants can perform a transaction, a set of combinations of groups that can perform a transaction between the two sides, formulating, using the participation probability function, the total number, the maximum number, and the set of combinations, a first optimization problem for determining an optimal price for a participation fee that maximizes a profit of an intermediary of the two-sided market and a number of transactions between the participants, and calculating the optimal price by solving the first optimization problem according to a characteristic of the participation probability function.

Abstract: An object is to make it possible to accurately estimate the number of movements at a high speed. A generation unit (130) generates an optimization problem for estimating, with respect to each of a plurality of areas, the number of observation targets that move from the area to each of the other areas at each observation time point, as the number of movements, based on observation values and movement probabilities, the observation values indicating, with respect to each area, the number of observation targets that are present in the area at each observation time point, and the movement probabilities indicating, with respect to each area, probabilities of movement from the area to the other areas at each observation time point. A first estimation unit (140) estimates the number of movements by solving the generated optimization problem using a Sinkhorn-Knopp algorithm. A second estimation unit (160) estimates the movement probabilities based on the observation values and the estimated number of movements.

Abstract: The present disclosure enables vehicle dispatch in consideration of individual differences of each orderer for a price and a required time by a computer executing an input procedure to input parameters for a distance matrix relating to a distance between a taxi and an orderer giving a taxi dispatch order, a travel distance for an order, an opportunity cost parameter for a taxi driver, and an acceptance probability function of the orderer, and a calculation procedure to calculate a price and a required time to be presented to the orderer by solving an optimization problem formulated using the parameters.

Abstract: A device estimates a number of people moving between the areas of people by building a problem based on a population in each of areas at each of time points and a probability of movement between predetermined areas in a directed graph. The directed graph includes vertices that correspond to the areas and edges that correspond to movement paths between the areas. A cost function for each edge determined from the probability of movement satisfies a constraint of discrete convexity representing a monotonous increase in change of a function value. The device estimates the number of people moving by computing the problem using a predetermined algorithm and estimating a probability of movement between the areas at each of the time points by minimizing a cost for the problem. The device repeats the estimating until satisfying a predetermined condition.

Abstract: An intersection determination is made regarding whether or not a traffic route included in map information intersects a boundary between areas on a basis of the traffic route and an area shape between areas that are adjacent in the map information, the intersection determination being made for each pair of adjacent areas. On a basis of a weight determined for the boundary in a case where the intersection determination is made that no intersection exists, a movement cost graph expressing a movement cost for each area that accounts for the weight is constructed. A corrected shortest distance between areas is calculated on a basis of the movement cost graph. A human mobility between areas is estimated for each timestep, the timestep being the length of a predetermined time interval, according to a predetermined function for estimating the human mobility between areas on a basis of the corrected shortest distance between areas and a population of each of the areas at different predetermined times.

Abstract: A destination of a user is predicted with high accuracy while the user is moving regardless of whether past movement trajectory data of the user does not exist or the past movement trajectory data of the user exists.

Abstract: A solution to an optimization problem can be obtained while reducing the amount of computation. An optimization problem reformulating unit 130 reformulates an optimization problem as a processing target into an optimization problem in which a constraint on a discrete variable and a constraint on a continuous variable are separated. A discrete variable optimizing unit 160 optimizes the discrete variable in the reformulated optimization problem while fixing the continuous variable at a certain point. A continuous variable optimizing unit 180 optimizes the continuous variable in the reformulated optimization problem while fixing the discrete variable to a certain point. A link changing unit 200 changes a link coefficient representing the influence of a term in which the discrete variable and the continuous variable are multiplied by each other in the reformulated optimization problem.

Abstract: A human migration number can be estimated as an integer value with high accuracy without depending on the size of an area. A human migration number between areas at each time is estimated on the basis of the population of each of the areas at the time under a constraint that the population of each of the areas at each time and a human migration number between the areas at the time are in a predetermined relation and that the human migration number between the areas at the time is an integer value in order to maximize a likelihood function expressed using the population of each of areas at each time, the human migration number between the areas at the time, and the migration probability between the areas, the migration probability between the areas is estimated on the basis of the estimated human migration number between the areas at the time in order to maximize the likelihood function, and the estimation processing is repeated until a predetermined condition is satisfied.

Abstract: The probability of migration and the number of migrating persons with high accuracy and a small amount of calculation can be estimated even when migrations to areas other than adjacent areas are taken into consideration. A parameter estimation unit 120 estimates a first parameter indicating the likelihood of departure from the area to the other area and a second parameter indicating the likelihood of gathering of persons in the area for each of the plurality of areas, a third parameter indicating an influence on the probability of migration of a distance between the areas, and the number of migrating persons from the area to each of the other areas for each of the plurality of areas on the basis of the demographic information. A migration probability calculation unit 170 calculates the probability of migration from the area to each of the other areas for each of the plurality of areas on the basis of the first parameter, the second parameter, and the third parameter.