Large Scale Human Mobility Prediction in Real-time using High Performance Particle Filter

• SUDO Akihito

  東京大学生産技術研究所

• KASHIYAMA Takehiro

  東京大学生産技術研究所

• YABE Takahiro

  東京大学大学院工学系研究科

• HIGUCHI Tomoyuki

  統計数理研究所

• NAKANO Shin'ya

  統計数理研究所

• SAITO Masaya

  統計数理研究所

• SEKIMOTO Yoshihide

  東京大学生産技術研究所

Bibliographic Information

Other Title

• 災害時におけるリアルタイムな広域人流推定のための高精度な粒子フィルタの提案

Abstract

Real-time estimation of human mobility after a massive disaster plays a crucial role in disaster relief. Traffic condition estimation with mobile phone data using data assimilation techniques recently becomes attractive research field. One of the researches studies the real-time estimation of human mobility following a massive disaster using data assimilation techniques. However, the target area of the previous study is narrower area than the inside of the Yamanote line. Therefore, we aim to estimate the real-time human mobility following a massive disaster in the area covering all of the Kanto region. In order to reduce the process time, we employ the fast algorithm to calculate the earth mover's distance (EMD). Moreover, the result of EMD is converted to a bipartite graph. In the experiment, we evaluate the computational cost and accuracy using real GPS data.

Journal

• JSTE Journal of Traffic Engineering

  JSTE Journal of Traffic Engineering 3 (2), A_76-A_83, 2017

  Japan Society of Traffic Engineers

Keywords

• Disaster management
• Human mobility estimation
• Mobile phone data
• Particle filter

Details

• CRID: 1390001205759592704
• NII Article ID: 130005312717
• DOI: 10.14954/jste.3.2_a_76
• ISSN: 21872929
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed