International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 613–621, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-613-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 613–621, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-613-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

RESEARCH ON GIS MASSIVE TRAFFIC DATA ANALYSIS PLATFORM BASED ON HADOOP

J. Y. Liu1, C. Y. Yang1, P. Wang2, and Y. Z. Ya1 J. Y. Liu et al.
  • 1Modern Educational Technology Center, Guilin University of Technology, No. 12 Jiangan Road, Guilin City, China
  • 2Network Information Center, Guangxi Normal University, No. 15 Yucai Road, Guilin City, China

Keywords: Traffic data, GIS, Hadoop, MapReduce

Abstract. 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.