Open AccessAnalysis of crowd flow characteristics based on multi-space scale and multi-source data fusion
Author(s) -
Ran Zhao,
Yan Wang,
Qiong Liu,
Daheng Dong,
Cuiling Li,
Yunlong Ma
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/5/052027
Subject(s) - computer science , big data , data science , tourism , scale (ratio) , data source , crowding , data mining , sensor fusion , geography , artificial intelligence , cartography , archaeology , neuroscience , biology
In the big data background, multi-source data fusion provides a very effective means for researchers to extract hidden and valuable information and knowledge from many heterogeneous data sources. Large-scale crowd movement is a complex process with big size of data coming from several sources such as safety risks of crowding and trampling in public places in the possible form of subways, railway stations, airports, tourist attractions, or large-scale exhibitions. The analysis of crowd flow characteristics among these high risk areas has also become a major research hotspot. Based on the multi-space scale concept and the available data source information, this paper proposes a technical framework of crowd flow characteristics analysis. Finally, a case study of crowd flow characteristics analysis is implemented to validate this technical freamwork. This study can provide a effective theoretical support and security warning for the public management department.