Abstract: A first learning unit (101) learns a difference model (111) for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points; a second learning unit (102) learns a prediction model (past) (112) for predicting variation of the monitoring target using the past monitoring data; a first generation unit (103) generates corrected past data using the difference model (111), by correcting a difference between the past monitoring data and the current monitoring data; and a third learning unit (104) learns a prediction model (current) (113) for predicting variation of the monitoring target using the current monitoring data, the difference model (111), the prediction model (past) (112), and the corrected past data, whereby variation of the monitoring targe

Abstract: The present invention can perform a human flow simulation in real time. An input unit (102) receives an input of passage information indicating passage through a point, a measurement unit (103) measures the number of passing people through the point by adding the received passage information, and a transmission unit (104) transmits the number of passing people measured by the measurement unit (103) at a predetermined time interval to a server that generates a parameter of a human flow simulation.

Abstract: A vector field setting unit (102) sets a vector field for providing virtual force for reproducing, in a region in which a moving body moves, movement of the moving body in a certain direction, and a simulation unit (104) simulates the movement of the moving body in the region, based on a movement model to which a parameter for simulating the movement of the moving body is set and the vector field set by the vector field setting unit, whereby the movement of the moving body is appropriately simulated even when the movement of the moving body is unusual.

Abstract: A path specific traffic volume estimation unit (130) estimates a path specific traffic volume corresponding to a traffic volume of observation objects at each time point at each of path candidates on the basis of observation value data that represents the number of the observation objects at each time point at each of a plurality of observation spots, observation spot specific reliability data that represents a reliability of the observation value data at each time point at each of the plurality of observation spots, and a path candidate list that represents a set of the path candidates through which the observation objects move.

Abstract: An estimation device (10) includes an input unit (30) and an estimation unit (32). A first observation value (40) for each of a plurality of observation areas (50), the first observation value being the number of presences (S) of persons who are observation targets at each of a plurality of observation times, and a second observation value (42) for each of a plurality of observation points (52) included in any one of the plurality of observation areas (50), the second observation value being the number of passages (C) of the persons at each of the plurality of observation times, are input to the input unit (30).

Abstract: An estimation device (10) includes an input unit (30) and an estimation unit (32). A first observation value (40) for each of a plurality of observation areas (50), the first observation value being the number of presences (S) of persons who are observation targets at each of a plurality of observation times, and a second observation value (42) for each of a plurality of observation points (52) included in any one of the plurality of observation areas (50), the second observation value being the number of passages (C) of the persons at each of the plurality of observation times, are input to the input unit (30). The estimation unit (32) optimizes an objective function based on the objective function in a simulation Sim, a constraint condition (G) satisfied between the first observation value (40) and the second observation value (42), the first observation value (40), and the second observation value (42) to estimate a parameter (Param_sim).

Abstract: An object is to make it possible to learn a prediction model for precisely predicting the number of passersby at a prediction time point. A learning unit 300 learns parameters of the prediction model with respect to each of a plurality of observation points based on learning data such that the number of passersby at the observation point at a prediction time point predicted using the prediction model matches the number of passersby at a time point corresponding to the prediction time point included in the learning data. The learning data includes, with respect to each observation point, a past time point of the observation point and the number of passersby at the observation point at the past time point.