Rail Vehicle Event Synthesis for High-Risk Geolocation Prediction
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Solution Overview
Problem
Rail vehicles lack effective systems to detect and analyze geolocations where events such as collisions or near-collisions occur, relying on surveillance cameras that are not integrated with mechanical or safety subsystems, leading to a lack of real-time data for preventing future incidents.
Innovation Solution
A system that includes processors, computing systems, and sensors to collect and analyze data from rail vehicle event recorders and review systems, identifying geolocations with elevated likelihoods of events by normalizing parameters like frequency and severity, and predicting future event locations for proactive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surveillance cameras are used for monitoring interior passenger compartments, then passenger safety surveillance is improved, but the ability to detect mechanical and safety subsystem events is lost
Solution Approach 1:
The camera system is enhanced to serve multiple functions: it continues to monitor interior passenger compartments while also detecting mechanical and safety subsystem events through integration with the train's event recorders and sensors. This multi-functional approach allows a single camera network to address both passenger safety and mechanical safety concerns.
Solution Approach 2:
The patent merges the surveillance camera system with the mechanical and safety subsystem event detection systems by integrating camera feeds with data from event recorders, sensors, and other train subsystems. This consolidation creates a unified monitoring system that captures both interior and mechanical event data.
2Device complexity
If cameras are not connected to mechanical and safety subsystems, then system simplicity is maintained, but real-time event detection and prevention capability deteriorates
Solution Approach 1:
The patent introduces an intermediary processing system that receives data from both the camera system and mechanical/safety subsystems, consolidates this information, and analyzes it for event detection. This intermediary layer enables real-time analysis without requiring direct complex integration between all subsystems.
Solution Approach 2:
The system implements feedback mechanisms where detected events and analyzed patterns are immediately communicated back to operators and control systems. This real-time feedback enables proactive response to potential safety issues, transforming passive recording into active prevention capability.
3Ease of operation
If video information is reviewed manually after offloading, then operational simplicity is maintained, but time for event analysis and prevention increases
Solution Approach 1:
The system performs self-service analysis by automatically detecting events, identifying patterns, and generating alerts without requiring manual review of all video footage. The automated event detection system processes data continuously and independently, freeing operators for higher-level analysis and response actions.
Solution Approach 2:
The system performs preliminary analysis and filtering of event data in real-time, identifying and isolating potential events before they require manual review. This preliminary action reduces the volume of data needing human attention and enables faster response to critical events.
4Device complexity
If no geolocation analysis system is implemented, then system complexity is minimized, but ability to identify high-risk locations and prevent future events is lost
Solution Approach 1:
The geolocation analysis system segments the rail network into distinct locations and zones, analyzing event patterns specific to each segment. This segmentation enables targeted analysis of high-risk locations and allows the system to identify patterns at the local level while maintaining overall system simplicity.
Data Source
AI summary
This disclosure relates to a system configured to identify geolocations in a rail network where rail vehicle events are likely to occur. In some implementations, the system may include one or more of a processor, a computing system, electronic storage, external resources, and/or other components. The system may be configured to illustrate the geolocations in the rail network where rail vehicle events are likely to occur on a map of the rail network, predict geolocations in the rail network where rail vehicle events will likely occur, generate coaching information based on the identified geolocations, and/or perform other actions.