書誌事項
- タイトル別名
-
- Partitioning a road network for distributed processing for large-scale traffic flow prediction
- ダイキボ コウツウリュウ ヨソク ノ ブンサン ショリ ノ タメ ノ ドウロ ネットワーク ブンカツ シュホウ
この論文をさがす
抄録
<p>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. </p>
収録刊行物
-
- 人工知能学会研究会資料 人工知能基本問題研究会
-
人工知能学会研究会資料 人工知能基本問題研究会 117 (0), 08-, 2021-09-20
一般社団法人 人工知能学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390852405229192960
-
- NII論文ID
- 130008090658
- 40022709511
-
- NII書誌ID
- AA11977943
-
- ISSN
- 24364584
-
- NDL書誌ID
- 031737843
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- NDL
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用可