An Online Adaptive Boosting Ensemble Approach to Human Mobility Prediction at a Metropolitan Scale

2021 年度 実施状況報告書

• 研究課題/領域番号: 20K19782
• 研究機関: 東京大学
• 研究代表者: FAN ZIPEI 東京大学, 空間情報科学研究センター, 特任講師 (70835397)
• 研究期間 (年度): 2020-04-01 – 2023-03-31
• キーワード: Mobility Prediction / Ensemble Model

研究実績の概要

I finished the development of the online adaptive ensemble mobility prediction model in the proposal. For the irregularity simulation part, I have submitted three journal papers to the top transportation journal IEEE TITS (under review), the top visualization journal IEEE TVCG and one ACM TSAS. For the online ensemble part, I have submitted a journal paper to the top data mining journal IEEE TKDE, and now it is under minor revision. In addition, one paper on the real time implementation on the mobility prediction system and some analysis on the irregular events is being prepared to submit to the top conference ACM sigspatial.

現在までの達成度 (区分)

2: おおむね順調に進展している

理由

I have finished the planned irregular simulation, online ensemble and real time system implementation.

今後の研究の推進方策

Finalizing the paper writing, and deployment on the real streaming data.

次年度使用額が生じた理由

Finalizing the paper writing and attending conferences (registration fees and travel expenses), and some other additional experiments with extra devices.

研究成果

• [雑誌論文] EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control (2022)
  • 著者名/発表者名: Yang Chuang、Zhang Zhiwen、Fan Zipei、Jiang Renhe、Chen Quanjun、Song Xuan、Shibasaki Ryosuke
  • 雑誌名: IEEE Transactions on Visualization and Computer Graphics 巻: 0 ページ: 1～1
  • DOI: 10.1109/TVCG.2022.3165385
  • 査読あり / 国際共著
• [雑誌論文] Human Mobility-based Individual-level Epidemic Simulation Platform (2022)
  • 著者名/発表者名: Fan Zipei、Yang Chuang、Zhang Zhiwen、Song Xuan、Liu Yinghao、Jiang Renhe、Chen Quanjun、Shibasaki Ryosuke
  • 雑誌名: ACM Transactions on Spatial Algorithms and Systems 巻: 8 ページ: 1～16
  • DOI: 10.1145/3491063
  • 査読あり / 国際共著
• [雑誌論文] Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System (2022)
  • 著者名/発表者名: Jiang Renhe、Cai Zekun、Wang Zhaonan、Yang Chuang、Fan Zipei、Chen Quanjun、Song Xuan、Shibasaki Ryosuke
  • 雑誌名: ACM Transactions on Intelligent Systems and Technology 巻: 13 ページ: 1～24
  • DOI: 10.1145/3472300
  • 査読あり / 国際共著
• [雑誌論文] Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction (2021)
  • 著者名/発表者名: Wang Zhaonan、Jiang Renhe、Cai Zekun、Fan Zipei、Liu Xin、Kim Kyoung-Sook、Song Xuan、Shibasaki Ryosuke
  • 雑誌名: Proceedings of the 30th ACM International Conference on Information & Knowledge Management 巻: - ページ: -
  • DOI: 10.1145/3459637.3482482
  • 査読あり / 国際共著
• [雑誌論文] Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19 (2021)
  • 著者名/発表者名: Jiang Renhe、Wang Zhaonan、Cai Zekun、Yang Chuang、Fan Zipei、Xia Tianqi、Matsubara Go、Mizuseki Hiroto、Song Xuan、Shibasaki Ryosuke
  • 雑誌名: Joint European Conference on Machine Learning and Knowledge Discovery in Databases 巻: - ページ: 319～334
  • DOI: 10.1007/978-3-030-86514-6_20
  • 査読あり / 国際共著
• [雑誌論文] The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting (2021)
  • 著者名/発表者名: Deng Jinliang、Chen Xiusi、Fan Zipei、Jiang Renhe、Song Xuan、Tsang Ivor W.
  • 雑誌名: ACM Transactions on Knowledge Discovery from Data 巻: 15 ページ: 1～25
  • DOI: 10.1145/3450528
  • 査読あり / 国際共著
• [雑誌論文] FedVoting: A Cross-Silo Boosting Tree Construction Method for Privacy-Preserving Long-Term Human Mobility Prediction (2021)
  • 著者名/発表者名: Liu Yinghao、Fan Zipei、Song Xuan、Shibasaki Ryosuke
  • 雑誌名: Sensors 巻: 21 ページ: 8282～8282
  • DOI: 10.3390/s21248282
  • 査読あり / オープンアクセス / 国際共著
• [学会発表] Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction (2021)
  • 著者名/発表者名: Wang Zhaonan
  • 学会等名: the 30th ACM International Conference on Information & Knowledge Management
  • 国際学会
• [学会発表] Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19 (2021)
  • 著者名/発表者名: Jiang Renhe
  • 学会等名: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
  • 国際学会