Reconstruction of People Flow in Areas of Incomplete Data Availability

被引：1

作者：

Xu, Yongwei ^{[1
]}

Shibasaki, Ryosuke ^{[1
]}

Shao, Xiaowei ^{[2
]}

机构：

[1] Univ Tokyo, Ctr Spatial Informat Sci, Kashiwa, Chiba, Japan

[2] Univ Tokyo, Earth Observat Data Integrat & Fus Res Initiat, Meguro Ku, Tokyo, Japan

来源：

2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS | 2015年

关键词：

people flow; data assimilation; incomplete areas; SOCIAL FORCE MODEL; JAMMING TRANSITION; ASSIGNMENT; SIMULATION; DYNAMICS;

D O I：

10.1109/ITSC.2015.183

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

Data Assimilation is a technique that synthesizes information from a dynamic (numerical) model and observation data. To reconstruct people flow in areas that are partially invisible to sensors, we assess three data assimilation methods: Kalman filter, 3DVAR, and particle filter. While most studies focus on individual -based analysis, in this study, we process the movement of people using a dynamic continuum flow theory. We derive the dynamic model of people flow and numerically solve it using the data assimilation method. Our proposed method is validated in 1D and 2D simulation experiments and on real tracking data.

引用

页码：1104 / 1110

页数：7

共 50 条

[11] Comparison of wireless optical communication availability data and traffic data [J].

Nadeem, F. ;

Geiger, B. ;

Henkel, M. ;

Leitgeb, E. ;

Awan, M. S. ;

Gebhart, M. ;

Kandus, G. .

2009 IEEE VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-5, 2009, :3080-+

[12] Reconstruction of large-scale flow structures in a stirred tank from limited sensor data [J].

Mikhaylov, Kirill ;

Rigopoulos, Stelios ;

Papadakis, George .

AICHE JOURNAL, 2021, 67 (10)

[13] Predicting Pedestrian Flow: A Methodology and a Proof of Concept Based on Real-Life Data [J].

Davidich, Maria ;

Koester, Gerta .

PLOS ONE, 2013, 8 (12)

[14] Combination of data replication and scheduling algorithm for improving data availability in Data Grids [J].

Mansouri, Najme ;

Dastghaibyfard, Gholam Hosein ;

Mansouri, Ehsan .

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2013, 36 (02) :711-722

[15] Development of a Reinforcement Learning based Agent Model and People Flow Data to Mega Metropolitan Area [J].

Pang, Yanbo ;

Kashiyama, Takehiro ;

Sekimoto, Yoshihide .

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, :3755-3759

[16] Characterising flow regimes in a semi-arid region with limited data availability: The Nil Wadi case study (Algeria) [J].

De Girolamo, Anna Maria ;

Drouiche, Abdelmalek ;

Ricci, Giovanni Francesco ;

Parete, Giuseppe ;

Gentile, Francesco ;

Debieche, Taha-Hocine .

JOURNAL OF HYDROLOGY-REGIONAL STUDIES, 2022, 41

[17] DNN modeling of partial differential equations with incomplete data [J].

Churchill, Victor ;

Chen, Yuan ;

Xu, Zhongshu ;

Xiu, Dongbin .

JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 493

[18] Crystal structure prediction supported by incomplete experimental data [J].

Tsujimoto, Naoto ;

Adachi, Daiki ;

Akashi, Ryosuke ;

Todo, Synge ;

Tsuneyuki, Shinji .

PHYSICAL REVIEW MATERIALS, 2018, 2 (05)

[19] Effects of Missing Data Methods in SEM Under Conditions of Incomplete and Nonnormal Data [J].

Li, Jian ;

Lomax, Richard G. .

JOURNAL OF EXPERIMENTAL EDUCATION, 2017, 85 (02) :231-258

[20] Optimal Control for Fractional Diffusion Equations with Incomplete Data [J].

Mophou, Gisele .

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2017, 174 (01) :176-196

← 1 2 3 4 5 →