Train Travel Prediction Using Precomputed Timetables
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Solution Overview
Problem
Current train travel prediction methods by station unit lack accuracy due to not considering the train's moving state, leading to errors in arrival-departure time intervals, and result in low precision with increased calculation time.
Innovation Solution
A train travel prediction device that includes a required time storage unit for recording a pre-created timetable based on train simulation data, indicating time differences between last station departure and next station departure times, and an operation prediction unit that uses this information to create a prediction schedule without increasing calculation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If train moving state is not accurately considered in station unit prediction, then calculation time is short, but prediction precision deteriorates due to errors in arrival-departure time intervals
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing required time information for various train moving conditions in a database before actual prediction operations. The required time database stores pre-computed time intervals under different scenarios (normal operation, delayed departure, early arrival, etc.), allowing the prediction system to simply query and add these pre-computed values during operation, thus achieving fast calculation without sacrificing precision.
Solution Approach 2:
The patent implements dynamics by making the required time values adaptive rather than fixed. The system dynamically selects appropriate required time values from the database based on actual train moving conditions (whether the train is running early, on schedule, or delayed). This dynamic selection allows the system to adapt to different operational scenarios while maintaining both speed and accuracy.
2Ease of manufacture
If minimum arrival-departure time interval is determined as predetermined value, then calculation is simplified, but error occurs in accordance with actual train operation
Solution Approach 1:
The patent applies parameter changes by transforming the fixed predetermined time interval into variable required time values that change based on train moving conditions. The system stores multiple required time parameters in the database corresponding to different operational states (normal, delayed, early arrival, etc.), and selects the appropriate parameter based on actual conditions. This changes the time interval parameter dynamically while keeping the calculation process simple through database lookup.
Solution Approach 2:
The patent implements local quality by providing different required time values for different local conditions (different moving states of trains). Instead of using a single uniform time interval for all situations, the system stores and applies specific time values tailored to each operational scenario, ensuring local optimization for each condition while maintaining overall system simplicity.
Data Source
AI summary
A train travel prediction device includes a required time database that records a station-to-station required timetable created in advance between stations and indicating a relation of a time difference between a last station departure time of a target train and a next station departure time of a precedent train with respect to a required time of the target train to a next station by the use of a train simulation on the basis of a train moving condition and an operation prediction unit that creates a prediction schedule on the basis of information of the required time acquired for each target train during a prediction period by referring to the station-to-station required timetable recorded in the required time database on the basis of information of a train schedule and a train arrival-departure time.