Train Control Timetable Updates Through Vehicle-Side Driving Profiles

The patent segments the timetable calculation process into two distinct parts: a fast pre-calculation phase that computes baseline travel times using simplified assumptions, and a subsequent optimization phase that refines these results using actual measured parameters. This segmentation allows the system to avoid performing complex calculations from scratch, thereby reducing overall computation time while maintaining accuracy.

The patent performs preliminary calculations of travel times using standard assumptions and average parameters before actual train operations begin. These pre-computed values serve as initial estimates that are later adjusted based on real-world measurements. This preliminary action eliminates the need to perform full complex calculations in real-time, significantly reducing computational burden during operation.

The patent implements a feedback mechanism where actual train operation data (acceleration, braking, resistance parameters) is continuously measured and fed back into the calculation system. This feedback allows the system to dynamically adjust travel time predictions and speed profiles based on real-world performance, eliminating the need for conservative uniform allowances while maintaining timetable reliability through data-driven adjustments.

The patent changes the approach from using fixed uniform time allowances to using dynamically adjusted parameters based on actual measurements. By continuously updating resistance parameters, mass values, and performance characteristics based on real train data, the system can optimize speed profiles without applying blanket speed reductions, thereby maintaining productivity while ensuring reliability.

The patent transitions from static averaged parameters to dynamic measured parameters. Instead of using fixed annual planning averages, the system continuously updates parameter values based on real-time sensor data from actual train operations. This dynamic approach maintains calculation simplicity while dramatically improving accuracy by reflecting actual traffic conditions rather than theoretical averages.

The system uses the train's own operational data to self-calibrate and improve its predictions. By measuring actual performance parameters during operation and using this data to refine future calculations, the system eliminates the need for external complex modeling while achieving high accuracy through self-generated real-world data.

The patent performs preliminary calculations of critical path trains and potential delay propagation scenarios before disruptions occur. When delays happen, the system can immediately apply pre-computed adjustment strategies to critical trains rather than performing full recalculation, thereby maintaining accuracy while minimizing additional time loss.

The patent applies different levels of calculation detail to different trains based on their criticality. Instead of uniformly recalculating all timetables, the system focuses computational resources only on critical path trains that actually affect overall schedule performance. This localized approach maintains accuracy for what matters while avoiding unnecessary calculations that would cause further delays.