Train Tracking Module for Unexpected Track Occupancy Detection
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Solution Overview
Problem
Current railway dispatch systems face challenges in accurately detecting track occupancy, particularly in areas with limited in-track detection devices, leading to potential safety issues and inefficiencies in train operations.
Innovation Solution
A train tracking system that utilizes a train tracking module configured with computer executable instructions and a processor to receive information from multiple input sources, determine a train's position within a track map, and employ a learning algorithm to detect unexpected track occupancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dispatch systems are used with limited in-track detection devices, then device complexity is reduced, but measurement precision of track occupancy deteriorates
Solution Approach 1:
The system divides track occupancy detection into multiple segments by combining data from different sources: GPS tracking provides continuous position data, while in-track detection devices provide verification at specific locations. This segmentation allows the system to maintain high measurement precision without requiring detection devices along the entire track.
Solution Approach 2:
The dispatch system is enhanced to serve multiple functions: it not only manages train scheduling and routing but also performs track occupancy detection by integrating GPS data from trains with data from in-track detection devices. This multi-functionality improves measurement precision without adding dedicated detection infrastructure.
2Reliability
If multiple input sources are integrated for train tracking, then reliability of track occupancy detection is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where GPS position data and in-track detection device data are continuously cross-validated. When discrepancies are detected, the system adjusts tracking accuracy assessments and can trigger alerts. This feedback loop enhances reliability by ensuring consistent detection across multiple data sources.
Solution Approach 2:
The patent combines GPS tracking data from trains with data from in-track detection devices into a unified tracking system. By merging these complementary data sources, the system achieves higher reliability in track occupancy detection than either source could provide alone, while managing complexity through integrated processing.
3Measurement precision
If learning algorithms are used to detect unexpected occupancy, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The learning algorithm does not continuously process all data at full computational intensity. Instead, it applies partial processing by focusing computational resources on analyzing discrepancies between GPS-tracked positions and expected positions, rather than continuously analyzing all movement data. This selective approach improves detection precision while managing energy consumption.
Data Source
AI summary
A train tracking system includes a train tracking module, a plurality of input sources providing train related information, and one or more interface(s) associated with the train tracking module, wherein the train tracking module is configured via computer executable instructions and through operation of a processor to receive the train related information from the plurality of input sources via the one or more interface(s), determine a position of a train within a train track map, the position of the train corresponding to a track location of the train in a track network, and wherein the train tracking module further comprises a learning algorithm, the train tracking module being configured to, utilizing the learning algorithm, determine an unexpected occupancy of the track location.