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Railway Traffic Control

Railway Traffic Control

Train Management System

Advanced train operation management technologies that automate railway control tasks and enhance customer service by leveraging AI, big data, and other Fourth Industrial Revolution technologies

System Configuration

Development Strategy

  • Enhancing controller convenience and efficiency through optimized train schedule support
  • Maximizing control efficiency with integrated operation management and controller support
  • Enabling optimal decision-making with a real-time driver support system
  • Minimizing train schedule changes to ensure passenger safety and convenience
  • AI-powered micro-simulation for optimized train operations
  • Data-driven schedule generation for optimal train operations
  • Enhanced system verification for optimized train schedule changes
  • Supporting controllers with predictive train operations and data-driven solutions
  • Real-time and predictive insights for safe and optimal train operations
  • Systematic management with data verification, conflict resolution, and operational rule control
  • Predicting train operations and resolving conflicts through data-driven simulations

Our Technology Expertise

  Automatic Conversion of Operation Plans


  • Automatically gathers and validates the basic schedule to produce an optimized schedule while minimizing controller involvement
  • Real-time error detection and optimal schedule suggestion when requesting schedule changes based on various situations.
  • By providing virtual simulation and decision support for schedule adjustments, such as creating, modifying, and deleting offline schedules, even non-specialists can handle scheduling tasks effectively
  • Supports controller decision-making by simulating and validating schedule changes arising from the addition of temporary trains

  Decision Support

      Accurately predicts train locations based on learned train operation information and presents decision-making solutions

      for detecting and resolving conflicts.


  • After collecting train operation performance information, inspect for any abnormalities.
  • After collecting train operation performance information, analysis of delay rates and delay statistics.
  • Predicting the location and route of running trains by time unit
  • Creating optimized patterns through driving pattern analysis
  • Inquiry of section speed by train based on railway conflict detection and resolution data

  Railway Conflict Detection and Resolution System

      Conflict detection based on real-time operation information and conflict resolution based on repeated simulations


  • Conflict detection that predicts the expected location and expected route of the conflict
  • Simulate comprehensive solutions from initial conflict to serial conflicts
  • Priority analysis by train type/class with route-specific weighting
  • Analysis of the cause of detection based on conflict data input

  Micro Simulation

      Simulations leverage AI-based prediction and optimization technologies using collected route and train data.


  • Based on information gathered from railway operators, virtual tracks and trains are generated
  • Event-driven simulations using validated railway network data
  • Virtualizes railway networks, facilities, related systems, and schedule information for digital twin–based simulations

  Driver Support System


  • Provides detailed information on preceding and following trains using real-time train operation information.
  • Provides operational status information, allowing drivers to monitor operation plan and delays
  • Provides drivers with a map-based monitoring screen using real-time operational data
  • Provides notification and alert information, and priority transmission of emergency messages.

Key Benefit

  • Improve the efficiency of control work by verifying the train operation plan information of the Korea National Railway and various operators and automatically converting it into a schedule operated by the control system
  • The effect of minimizing delays in advance by handling railway conflict through real-time and advance simulation of various train operation situations.
  • Leading effect in the railway control field by introducing AI/big data of the 4th industrial revolution into the control system
  • Cost reduction and increased operation density through analysis of improved punctuality and energy saving in train operation
  • Improve on-site response capabilities and minimize the risk of accidents through rapid decision-making in the event of an unusual occurrence.
  • Reduce human errors through automation of control tasks and continuous performance improvement through the introduction of AI