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Customer Success Stories

Korea Railroad Corporation

Following the establishment of a big data platform (TeraONE™) at the Korea Railroad Corporation in 2018, it has established a foundation for data sharing and transaction in connection with the transportation platform of the Korea Information Society Agency (NIA).

Challenges

  • Activating the utilization and distribution of land/transportation big data and establishing a foundation for data transactions
  • Data collection, purification, and storage establishment to produce high utilization railway big data such as train operation information, passenger and wide-area transportation analysis, etc
  • Enabling mobility data opening and sharing by establishing links with NIA transportation platforms

Solutions

  • Big Data Integration Platform Infrastructure Expansion
  • Selection of new data linkage targets and establishment of external data API expansion for the gradual expansion of data
  • Improve big data analytics and perform new tasks
  • Development of Data Interconnection with NIA Transportation Platform

Benefits

  • Incremental data growth through collection and association of big data integration platforms
    • Selection of new data linkage targets for future use, such as wide-area railway information, railway operation information, maintenance history, and real-time train location information
    • Extend external data APIs to correlate with external factors
    • Establishment of a quality measurement system for data held data
  • Data Interconnection with NIA Transport Platform
    • Extracting and generating data sets to be opened and distributed on NIA transportation platforms such as mobility information and railway statistics using railway transportation and train location information
    • Transfer the generated data set to the open server and use a dedicated line between transportation platforms to the transportation platform collection server
  • Improve big data analytics and perform new tasks
    • Improvement of demand forecasting based on analysis of reservation and ticketing behavior and log analysis
    • Demand Analysis by Wide Area Railway Route/Section/Day/Time Zone/External Factors
    • Correlation analysis between vehicle failure and maintenance history and optimization of maintenance resource utilization

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