To provide a program, method and device which build a machine learning model for high accuracy traffic simulation using a method expressing multiple traffic states as discriminable traffic demands.SOLUTION: In a machine learning device 10, a machine learning unit 20 includes: a first generation unit 22 which generates, based on route information indicating travel status of each of a plurality of mobile bodies in a specific geographical range at each of a plurality of points of time, traffic flow information indicating the number of mobile bodies located in route sections within the specific geographical range for each of the plurality of points of time; and a training unit 24 which trains a machine learning model for deriving a degree of traffic congestion corresponding to the traffic flow information, using training data that includes the generated traffic flow information as input feature values and includes information indicating a degree of traffic congestion in the specific geographical range at a point of time corresponding to the traffic flow information as label information.SELECTED DRAWING: Figure 6
【課題】多様な交通状態を区別可能な交通需要の表現方法を用いて、高精度な交通シミュレーションの機械学習モデルを構築するプログラム、方法及び装置を提供する。【解決手段】機械学習装置10において、機械学習部20は、所定の地理範囲における複数の移動体それぞれの移動状況を複数の時点毎に示した経路情報に基づいて、複数の時点毎の所定の地理範囲内にある経路区間それぞれに位置する移動体の数を示す交通流情報を生成する第1生成部22と、生成された交通流情報を入力特徴量とし、交通流情報に対応する時点における所定の地理範囲の交通の混み具合を示す情報をラベル情報とした訓練データを用いて、交通流情報に応じた交通の混み具合を導出する機械学習モデルを訓練する訓練部24と、を備える。【選択図】図6
MACHINE LEARNING PROGRAM, METHOD, AND DEVICE
機械学習プログラム、方法、及び装置
2024-12-17
Patent
Electronic Resource
Japanese
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