Cognitive Passenger Selection for Mass Transit Safety
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Solution Overview
Problem
Mass transit operators face challenges in ensuring that passengers are appropriately allocated to seats with specific restrictions, such as exit-row seats, as they cannot determine if passengers are willing and able to perform the required duties during emergency situations, potentially compromising safety.
Innovation Solution
A cognitive-based passenger selection system uses machine learning models to assess both physical and non-physical attributes of passengers, such as fear of heights or physical fitness, to intelligently allocate them to seats that match their capabilities, ensuring compliance with predefined rules and enhancing safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If employees manually allocate passengers to seats before departure, then seat allocation can be completed, but the employees cannot determine whether passengers are willing and able to perform duties associated with restricted seats
Solution Approach 1:
The patent replaces manual employee assessment with an automated computer-based system that uses machine learning models to evaluate passenger attributes. The system automatically processes passenger data from multiple sources, applies rules associated with restricted seats, and generates seat allocations without requiring employees to manually assess passenger capabilities.
Solution Approach 2:
The system enables passengers to effectively self-assess their suitability for restricted seats through the automated evaluation process. By analyzing passenger attributes and comparing them against seat requirements, the system allows passengers to be automatically classified as suitable or unsuitable for specific seats without direct employee intervention.
2Productivity
If employees allocate passengers to restricted seats manually, then allocation can be done, but the safety of all persons on board may be at risk during emergency situations
Solution Approach 1:
The system performs preliminary assessment of passenger suitability for restricted seats before final seat allocation is made. By evaluating passenger attributes against seat requirements in advance and generating a suitability score, the system ensures that only passengers meeting the criteria are allocated to restricted seats, preventing unsafe allocations before they occur.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously analyzing passenger attributes and comparing them against updated rules and requirements for restricted seats. The machine learning model learns from past allocations and outcomes, refining its assessment accuracy over time to improve the reliability of passenger suitability determinations.
Data Source
AI summary
Aspects of the invention perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.