Study of Timetable Optimization with KPI Causal Relationship Estimation

• Item type: Symposium(1)
• 公開日: 2024-06-19
• タイトル: Study of Timetable Optimization with KPI Causal Relationship Estimation
• タイトル / 言語: en
• タイトル: Study of Timetable Optimization with KPI Causal Relationship Estimation
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: 株式会社日立製作所 サービスシステムイノベーションセンタ
• 著者所属: 株式会社日立製作所 サービスシステムイノベーションセンタ
• 著者所属: 株式会社日立製作所 サービスシステムイノベーションセンタ
• 著者所属: 株式会社日立製作所 社会システム事業部
• 著者所属(英): en
• 著者所属(英): Center for Digital Services, R&D Group, Hitachi, Ltd.
• 著者所属(英): en
• 著者所属(英): Center for Digital Services, R&D Group, Hitachi, Ltd.
• 著者所属(英): en
• 著者所属(英): Center for Digital Services, R&D Group, Hitachi, Ltd.
• 著者所属(英): en
• 著者所属(英): Social Infrastructure Information Systems Division, Social Infrastructure Systems Business Unit, Hitachi, Ltd.
• 著者名: 王, 偉; 前川, 勇樹; 手島, 久典; 橋本, 祐樹
• 著者名(英): Wang, Wei; Yuki, Maekawa; Hisanori, Teshima; Yuki, Hashimoto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: To ensure sustainable profits and environmentally friendly operations, railway operators need a more efficient and reactive planning approach and technology to deal with the changing demand and green quality that has been expected as carbon neutral. Key Performance Indicator(KPI) analyzer is one of cornerstones of timetable optimization based on KPIs causal relationship analysis. In this report, KPI analyzer performance such as causal estimation accuracy was evaluated with open data from subway. The timetable KPI causal estimation accuracy improved from 73% to 87% with open weather data and pre-set formula method. As example of KPI analyzer for timetable optimization, energy consumption optimization was evaluated with simulated energy consumption data for subway train. We found that for some of the trips, the energy saving could be up to 15%.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: To ensure sustainable profits and environmentally friendly operations, railway operators need a more efficient and reactive planning approach and technology to deal with the changing demand and green quality that has been expected as carbon neutral. Key Performance Indicator(KPI) analyzer is one of cornerstones of timetable optimization based on KPIs causal relationship analysis. In this report, KPI analyzer performance such as causal estimation accuracy was evaluated with open data from subway. The timetable KPI causal estimation accuracy improved from 73% to 87% with open weather data and pre-set formula method. As example of KPI analyzer for timetable optimization, energy consumption optimization was evaluated with simulated energy consumption data for subway train. We found that for some of the trips, the energy saving could be up to 15%.
• 書誌情報: マルチメディア，分散，協調とモバイルシンポジウム2024論文集; 巻 2024, p. 1707-1713, 発行日 2024-06-19
• 出版者 / 言語: ja
• 出版者: 情報処理学会