ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLII-3-W10-613-2020 RESEARCH ON GIS MASSIVE TRAFFIC DATA ANALYSIS PLATFORM BASED ON HADOOP Liu J. Y. 1 Yang C. Y. 1 Wang P. 2 Ya Y. Z. 1 Modern Educational Technology Center, Guilin University of Technology, No. 12 Jiangan Road, Guilin City, China Network Information Center, Guangxi Normal University, No. 15 Yucai Road, Guilin City, China 07 02 2020 XLII-3/W10 613 621 Copyright: © 2020 J. Y. Liu et al. 2020 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLII-3-W10/613/2020/isprs-archives-XLII-3-W10-613-2020.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-3-W10/613/2020/isprs-archives-XLII-3-W10-613-2020.pdf

In view of the limitations of storage and calculation of mass traffic data in traditional GIS platform, this paper uses efficient and scientific technical means to analyze the data, and proposes a Hadoop-based GIS mass traffic data analysis platform. The platform uses MapReduce as a distributed computing programming model to analyze massive data for urban traffic decision-making, and uses HDFS distributed file storage framework to store and manage massive traffic data at TB level or even PB level. Finally, the results are displayed by using geographic information system spatial visualization technology, and the impact of the data volume and the number of nodes in the cluster on the calculation time-consuming is analyzed and compared. The experimental results show that the use of distributed multi-node cluster can effectively improve the storage and computing efficiency of massive traffic data, and greatly accelerate the total task scheduling time.