書誌事項
- タイトル別名
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- Trip time prediction via Context-driven Neural ODE using vehicle trajectory data
抄録
<p>Trip time prediction (TTP) have great importance in traffic analysys and management. Existing research related to traffic forecasting, including traffic speed, enable TTP. However, these studies mainly use aggregated data such as vehicle detectors which cause intergration error. Trip time is realised by the integration on predicted speed, but the accuracy of the actual trip time, which can be gathered by vehicle trajectory data, is not guaranteed. In this research, we propose the extension of Neural ODE which can minimise integration error. The proposed method can learn velocity field from ETC2.0 probe data that is a type of vehicle trajectory data. The experiment result using artificial dataset and large scale dataset shows superiority and learning stability of proposed method.</p>
収録刊行物
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- ロボティクス・メカトロニクス講演会講演概要集
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ロボティクス・メカトロニクス講演会講演概要集 2023 (0), 2A2-D06-, 2023
一般社団法人 日本機械学会
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詳細情報 詳細情報について
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- CRID
- 1390298919731428480
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- ISSN
- 24243124
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可