Train Deviation Propagation Analysis via Multi-Layer Coupling
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Solution Overview
Problem
Current methods for analyzing train operation data in rail transit systems primarily focus on performance indicators like punctuality and delay, without adequately addressing the complex multi-layer coupling relationships that affect train operation deviation propagation.
Innovation Solution
A multi-layer coupling relationship-based recognition method that uniformly screens and recognizes effective train event time sequences, extracts relevant train activity data, constructs coupling relationship groups, and analyzes the propagation rules of train operation deviations in space-time ranges to optimize real-time operation adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional performance indicator analysis methods are used for train operation data, then the analysis process is simple, but the analysis precision and ability to identify deviation propagation conditions is insufficient
Solution Approach 1:
The patent segments the train operation data analysis into multiple layers: basic information layer, coupling relationship layer, and deviation propagation layer. Each layer processes specific aspects of the data independently, allowing complex multi-train coupling relationships to be analyzed systematically without overwhelming complexity in a single monolithic system.
Solution Approach 2:
The patent introduces a multi-dimensional analysis framework that examines train operation deviations across multiple dimensions simultaneously: time dimension (deviation propagation over time), space dimension (deviation propagation across different stations and trains), and coupling relationship dimension (interactions between multiple trains). This dimensional expansion enables comprehensive identification of propagation conditions that single-dimension methods cannot detect.
2Adaptability or versatility
If single-train delay analysis methods are used, then the analysis is straightforward, but the complexity of multi-train coupling relationships cannot be handled
Solution Approach 1:
The patent creates a universal coupling relationship analysis framework that can handle various types of train interactions (overtaking, meeting, parallel running, station operations) through a unified set of rules and algorithms. The system identifies coupling relationships between multiple trains and applies consistent analysis methods across different scenarios, making the system adaptable to diverse multi-train situations without requiring separate specialized methods for each case.
3Productivity
If comprehensive train operation data is collected and analyzed, then the data utilization is high, but the data processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential features and coupling relationships from the comprehensive train operation data that are relevant to deviation propagation analysis. Instead of processing all raw data, the system identifies and extracts key parameters such as train identifiers, timing information, spatial relationships, and coupling relationship types, significantly reducing the data volume requiring intensive processing while maintaining analysis quality.
Solution Approach 2:
The patent performs preliminary data preparation and organization before the main deviation propagation analysis. Train operation data is pre-processed to establish basic coupling relationships and organize timing-spatial information in advance, so that when deviation propagation conditions need to be identified, the system can work with pre-organized data structures rather than raw comprehensive data, improving processing efficiency.
Data Source
Figure 1~2
AI summary
The present invention relates to a multi-layer coupling relationship-based train operation deviation propagation condition recognition method, where the method includes the following steps: (1) recognizing an effective train event time sequence, including an arrival event and a departure event of a train at each passing station; (2) uniformly extracting train activity data, including a stop activity, a section operation activity, a turn-back activity, and an arrival or departure interval activity; (3) constructing coupling relationship groups between a train event and a train activity and between train activities; and (4) performing statistics on changes of train operation deviation in each relationship group, and outputting a respective distribution function and a time-space distribution visualized result. Compared with the prior art, the present invention has the advantages of being practical, automatic recognition, feedback optimization, and the like.