Abstract: An estimation method executed by a computer, the estimation method includes estimating a fouling degree of a target ship based on an accumulation period since a most recent maintenance on the target ship; selecting, as training data, one piece of data that has a fouling degree that resembles the estimated fouling degree, from among a plurality of pieces of data, when generating a machine learning model for estimating an amount of fuel consumption due to navigation; and generating the machine learning model based on the selected training data.

Abstract: A navigation support method executed by a computer includes: classifying vessel voyage data according to each meteorological and hydrographic condition; calculating characteristic distribution of vessel maneuvering for each meteorological and hydrographic condition, using the vessel voyage data that has been classified; extracting a plurality of vessel maneuvering patterns from the characteristic distribution of vessel maneuvering that has been calculated for each meteorological and hydrographic condition, and aggregating the vessel voyage data for each of the vessel maneuvering patterns; and generating a learning model for each of the vessel maneuvering patterns from the vessel voyage data aggregated for each of the vessel maneuvering patterns, using meteorological and hydrographic actual data as an explanatory variable and vessel performance as an objective variable.

Abstract: An estimation method executed by a computer, the estimation method includes estimating a fouling degree of a target ship based on an accumulation period since a most recent maintenance on the target ship; selecting, as training data, one piece of data that has a fouling degree that resembles the estimated fouling degree, from among a plurality of pieces of data, when generating a machine learning model for estimating an amount of fuel consumption due to navigation; and generating the machine learning model based on the selected training data.

Abstract: A people flow simulation apparatus includes a memory configured to store incentive information and effect characteristic information in association with a plurality of person models, the incentive information indicating incentive provided for each of the plurality of person models, the effect characteristic information indicating each of characteristics of effects that the incentive has on each of the plurality of persons models, and a processor coupled to the memory and the processor configured to calculate probabilities with which a first person model goes to each of a plurality of places on the basis of first incentive information and first effect characteristic information associated with the first person model included in the plurality of person models, and select, from among the plurality of places, a first place as a destination to which the first person model goes in accordance with the calculated probabilities.