主催: 人工知能学会
会議名: 第117回 人工知能基本問題研究会
回次: 117
開催地: オンライン
開催日: 2021/09/29
p. 08-
Recently, many cars and road infrastructures have collected traffic data. Furthermore, traffic flow prediction using these data has been the focus of many studies. Traffic flow prediction is useful in avoiding traffic jams and suggesting an efficient route. However, large-scale traffic flow prediction takes much execution time. This paper proposes a method of partitioning a road network for distributed processing for large-scale traffic flow prediction. Our method consists two steps : (1) Partitioning the process of training models; (2) Selecting input data for each model. Our experimental evaluation shows that the method successfully reduces execution time. Too much input data does not improve prediction accuracy. Moreover, some input data is unrelated to distance between roads.