Journey Risk Index Aggregation for Health Threat Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current modeling techniques for assessing health threat risks in public transportation systems, such as airports and commercial flights, are limited in scope and accuracy, failing to account for the entire traveler's journey and the effectiveness of various control measures in reducing pathogen exposure, making it difficult to determine cost-effective measures that balance risk reduction with minimal disruption to operations and revenue.
Innovation Solution
A system and method that uses processors to determine a risk index for each node in a traveler's journey based on occupancy characteristics, control measures, and health threat prevalence, aggregating these indices to provide a comprehensive risk value and generate control signals for users, while also evaluating the adverse impacts of control measures to recommend cost-effective solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all available control measures are implemented to reduce pathogen exposure, then the risk of health threat is reduced, but the operational disruption and revenue loss increase significantly
Solution Approach 1:
The system dynamically adjusts control measure parameters (e.g., occupancy limits, social distancing requirements, mask mandates) based on real-time risk indices calculated from multiple data sources. This allows flexible optimization of the balance between health safety and operational efficiency without implementing all measures uniformly.
Solution Approach 2:
Instead of implementing all possible control measures, the system selects and applies only the necessary subset of measures based on calculated risk levels. This partial action approach reduces unnecessary operational disruption while maintaining adequate health protection.
2Reliability
If control measures are implemented to reduce pathogen exposure, then the risk of health threat is reduced, but the adverse impact on travelers and employees increases
Solution Approach 1:
The system continuously monitors risk indices and adjusts control measures accordingly, providing feedback loops that optimize the balance between health protection and minimizing adverse impacts on travelers and employees based on real-time conditions.
Solution Approach 2:
The system applies different control measure intensities to different locations and situations based on local risk assessments, rather than implementing uniform measures everywhere, thereby reducing unnecessary adverse impacts in low-risk areas.
3Device complexity
If known modeling techniques are used to assess health risk, then the analysis is simple, but the accuracy and scope are limited to single location or vehicle
Solution Approach 1:
The comprehensive risk assessment model divides the traveler's journey into multiple segments (pre-trip, in-transit, post-trip) and further segments locations into nodes with specific risk characteristics. This segmentation allows accurate multi-location risk assessment while maintaining manageable model complexity through modular architecture.
Solution Approach 2:
The system uses a universal risk index calculation framework that can be applied across multiple locations, transportation modes, and journey stages using the same core algorithms and data processing techniques, enabling accurate comprehensive assessment without proportionally increasing complexity.
Data Source
Figure 1
Figure 2
Figure 2
AI summary
A system and method include one or more processors configured to determine a risk index associated with a risk of exposure to a health threat for each node within a chain of nodes during a stage of a journey. The risk index for each node is based on occupancy characteristics of the node, a set of one or more control measures in effect to reduce the risk of exposure in the node, and a health threat prevalence in a jurisdiction associated with the node. The one or more processors are further configured to aggregate the risk indices for the nodes to determine a risk value associated with the stage of the journey, and to generate a control signal to notify a user of the risk value associated with the stage of the journey.