人々の流動を計測し行動モデルと組合せて推定する。
Estimating People Flow in Combination of Sensing and Behavior Modeling

Total concept of People Flow Project(「人の流れプロジェクト」全体に関する研究)

  • People Flow Project
    (人の流れプロジェクト)

  • PFLOW: Reconstruction of people flow by recycling large-scale fragmentary social survey data
    (断片的な時空間データを利用した大規模な人の流動の再現)

    • Poster (Japanese)
    • Main paper(s)
      Yoshihide Sekimoto, Ryosuke Shibasaki, Hiroshi Kanasugi and Tomotaka Usui, Yasunobu Shimazaki, PFLOW: Reconstruction of people flow recycling large-scale social survey data, IEEE Pervasive Computing, Vol.10, No.4, pp.27-35, Oct.-Dec. 2011.
      Yoshihide Sekimoto, Atsuto Watanabe, Toshikazu Nakamura, Hiroshi Kanasugi, Tomotaka Usui, Combination of spatio-temporal correction methods using traffic survey data for reconstruction of people flow, Pervasive and Mobile Computing Journal, Elsevier, Vol.9, pp. 629-642, (Impact factor: 1.25 in 2011), 2013. DOI
  • A Study on Fundamental Technologies for Developing People Flow Dataset
    (大規模な人々の流動データセット整備へ向けた解析基盤技術の検討)

    • Poster (English) (Japanese)
    • Main paper(s)
      関本義秀, 薄井智貴, 金杉洋, 増田祐介, 都市空間における効率的な動線解析の共通基盤のあり方について, 土木学会論文集F3(土木情報学), Vol.67, No.2(特集号), pp.170-180, 2011. PDF
  • Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas
    (OPEN PFLOW:都市部における典型的な人々の流動のためのオープンなデータセットの作成と評価)

    • Poster (Japanese)
    • Main paper(s)
      Takehiro Kashiyama, Yanbo Pang, and Yoshihide Sekimoto, Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas, Transportation Research Part C., Elsevier, Vol. 85, December 2017, pp.249-267.

Data assimilation method approach(データ同化手法を用いた研究)

  • Real-Time Prediction of People’s Movement under Disaster Situations using Particle Filter
    (災害時にリアルタイムで高精度な人流推定を行えるパーティクルフィルタの提案)

    • Poster (Japanese)
    • Main paper(s)
      Akihito Sudo, Takehiro Kashiyama, Takahiro Yabe, Hiroshi Kanasugi, Xuan Song, Tomoyuki Higuchi, Shin’Ya Nakano, Masaya Saito and Yoshihide Sekimoto, Particle Filter for Real-time Human Mobility Prediction following Unprecedented Disaster, The 24th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2016), San Francisco, 2016.
      Yoshihide Sekimoto, Akihito Sudo, Takehiro Kashiyama, Toshikazu Seto, Hideki Hayashi, Akinori Asahara, Hiroki Ishizuka and Satoshi Nishiyama, Real-time people movement estimation in large disasters from several kinds of mobile phone data, The 5th International Workshop on Pervasive Urban Applications (PURBA2016) in conjunction with ACM UbiComp 2016, Heidelberg, Germany, 2016.
  • Real-Time Prediction of People’s Movement under Disaster Situations using Particle Filter
    (パーティクルフィルタを用いた災害時におけるリアルタイムな人流推定手法)

    • Poster (Japanese)
    • Main paper(s)
      矢部貴大, 関本義秀, 樫山武浩, 金杉洋, 須藤明人, パーティクルフィルタを用いた災害時におけるリアルタイムな人流推定手法, 交通工学論文集, Vol.2, No.2, pp.A_19-A_27, 2016.2.
  • Estimation of People Flow from Hourly Mesh Population Data by Revision of Route Choice Probability
    (時空間メッシュ集計データを用いたデータ同化手法による人流推定)

    • Poster (Japanese)
    • Main paper(s)
      若生凌, 関本義秀, 金杉洋, 柴崎亮介, 時空間メッシュ集計データを用いた同化手法による人流推定, 第23回地理情報システム学会講演論文集, Vol.23, CD-ROM, 2014.
  • Estimation of Usual People Flow Using Particle Filter
    (パーティクルフィルタを用いた平時の流動の推定)

    • Main paper(s)
      中村敏和, 関本義秀, 薄井智貴, 柴崎亮介, パーティクルフィルターを用いた都市圏レベルの人の流れの推定手法の構築, 土木学会論文集D3(土木計画学), Vol.69, No.3, pp.227-236, 2013. PDF
  • People Movement Estimation Using Sparse CDR Data
    (同化手法を用いた携帯基地局情報に基づく人の移動推定)

    • Poster (English) (Japanese)
    • Main paper(s)
      長谷川瑶子, 関本義秀, 金杉洋, 樫山武浩, 同化手法を用いたスパースな携帯基地局情報に基づく人の移動推定, 交通工学論文集, Vol.1, No.4, pp.A_9-A_17, 2015.4

Reinforcement based approach

  • Development of a people mass movement simulation framework based on reinforcement learning
    (強化学習に基づく人々の移動シミュレーションフレームワークの開発)

    • Poster (English)
    • Main paper(s)
      Yanbo Pang, Kota Tsubouchi, Takahiro Yabe, Yoshihide Sekimoto, Replicating Urban Dynamics by Generating Human-like Agents from Smartphone GPS Data (Poster paper), The 26th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2018), November 2018. Seattle, Washington, USA.

Sparse modeling approach(スパースモデリングを用いた研究)

  • Real-time prediction of people flow from mobile GPS data by sparse modelling
    (携帯GPSデータを用いたスパースモデリングによるリアルタイムな人の行動予測)

CDRs (Call Detail Records) data oriented approach(携帯基地局データを用いた研究)

  • Estimation of link traffic volume from sparse CDRs data: A case study of Dhaka
    (携帯電話通話履歴を用いたリンク交通量の推定~ダッカの事例~)

    • Poster (Japanese)
    • Main paper(s)
      関本義秀, 樫山武浩, 長谷川瑶子, 金杉洋, スパースな携帯電話通話履歴を用いたリンク交通量の推定~ダッカの事例, 交通工学論文集, Vol.1, No.4, pp.A_1-A_8, 2015.4
      Yoko Hasegawa, Yoshihide Sekimoto, Takehiro Kashiyama, Hiroshi Kanasugi, Transportation Melting Pot Dhaka: Road-link Based Traffic Volume Estimation from Sparse CDR Data, The 1st International Conference on IoT in Urban Space (Urb-IoT 2014), Rome, 2014. (Best Poster Award)
  • Analysis of People’s Behavior Using Call Detail Records
    (鉄道データとCDRを用いたリアルタイムの乗客位置分析)

    • Poster (English) (Japanese)
    • Main paper(s)
      Takuya Kanno, Yoshihide Sekimoto, Hiroshi Kanasugi and Ryosuke Shibasaki, Location estimation of real-time passengers:Using train object from timetable information created with crowdsourcing and CDRs, The 4th International Workshop on Pervasive Urban Applications 2015 (PURBA2015) in conjunction with UbiComp2015
  • Study on Personal Mobility Estimation Based on Cellular Network Data
    (携帯電話基地局通信履歴に基づく人の移動行動の推定可能性に関する研究)

    • Poster (Japanese)
    • Main papers(s)
      Hiroshi Kanasugi, Yoshihide Sekimoto, Mori Kurokawa, Takafumi Watanabe, Shigeki Muramatsu, Ryosuke Shibasaki, Spatiotemporal Route Estimation Consistent with Human Mobility Using Cellular Network Data, Proceedings of PerMoby2013 in conjunction with IEEE PerCom 2013, pp.267-272, 2013.

GPS data oriented approach(GPSデータを用いた研究)

  • Using Large-Scale, Long-Term GPS Data from Mobile Phones to Identify Transportation Modes and Analyze Mobility in the Tokyo Metropolitan Area
    (携帯電話による大規模・長期間のGPSデータを用いた、東京都市圏における交通モードの推定およびモビリティの分析)

    • Poster (English) (Japanese)
    • Main paper(s)
      Apichon Witayangkurn, Teerayut Horanont, Natsumi Ono, Yoshihide Sekimoto and Ryosuke Shibasaki, Trip Reconstruction and Transportation Mode Extraction on Low Data Rate GPS Data from Mobile Phone, CUPUM2013, CDROM, 2013.

Others(その他の研究)

  • Agent Simulation for Multi Mode City Wide Movement
    (マルチ移動モード対応のエージェントシミュレーション)