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 people count estimation apparatus includes an information management unit configured to at least manage road network data which is spatial information data regarding a road network formed to include a plurality of observation points and a road connecting the observation points, an observation point list which is a list of the observation points, and the numbers of people passing per unit time at the observation points in each direction of the road, a coefficient calculation unit configured to identify, from the observation point list, a first observation point where the number of people passing in each direction is not measured among the plurality of observation points, identify a second observation point where the number of people passing in each direction is measurable and which is connected to the first observation point by the road among the plurality of observation points, and calculate a weight that an observation value per time period at the second observation point contributes to the number of peopl

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: An information provision device includes: a simulation execution unit that executes a simulation of a movement of moving bodies on multiple routes from a point of departure to a destination on a basis of setting data that accounts for different means of transportation; a simulation result analysis unit that obtains, as a simulation result, information related to a number and a density of the moving bodies on each of the routes from the point of departure to the destination on a basis of a result of the simulation execution unit executing the simulation; and an information provision unit that provides information based on the simulation result obtained by the simulation result analysis unit.

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).