Flight Occupancy Clustering for Lower Per-Passenger Emissions
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Solution Overview
Problem
Flying on flights that are not full leads to higher per-passenger carbon emissions due to inefficient fuel consumption and increased operational flights, exacerbating greenhouse gas emissions and contributing to aviation's carbon footprint.
Innovation Solution
A system that categorizes flights into clusters based on occupancy and sends messages to passengers to move to other flights, using AI to predict ticket relinquishment and incentives, ensuring full or near-full flight capacity, thereby reducing emissions by 20% per passenger.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If airlines operate underbooked flights to maintain schedules and retain airport slots, then flight schedule reliability is improved, but greenhouse gas emissions per passenger increase
Solution Approach 1:
The system implements a feedback mechanism by continuously monitoring flight occupancy levels and using AI predictions to identify passengers who might relinquish their tickets. This feedback loop enables dynamic adjustment of marketing efforts to optimize both schedule reliability and emissions reduction.
Solution Approach 2:
The system acts as an intermediary between airlines and passengers by facilitating the transfer of tickets from passengers who will not travel to the marketing system, which then promotes these seats to potential travelers. This intermediary role enables efficient seat reallocation without disrupting flight schedules.
2Productivity
If airlines operate more flights to accommodate the same number of passengers, then passenger service capacity is improved, but total emissions increase
Solution Approach 1:
The system merges underutilized capacity from multiple flights by identifying and transferring tickets across different flight options. This consolidation allows airlines to maintain passenger service capacity while reducing the total number of flights needed, thereby lowering total emissions.
Solution Approach 2:
The system introduces dynamic seat allocation by enabling real-time transfer of tickets between flights based on occupancy levels and passenger behavior predictions. This dynamic approach allows flexible optimization of flight utilization without compromising service capacity.
3Adaptability or versatility
If flights operate below full passenger capacity, then operational flexibility is improved, but per-passenger fuel consumption increases
Solution Approach 1:
The system changes the parameter of flight occupancy by actively managing seat allocation through ticket transfers. By adjusting occupancy levels toward full capacity while maintaining operational flexibility, the system reduces per-passenger fuel consumption without sacrificing adaptability.
Data Source
AI summary
The system obtains multiple attributes associated with a transportation, where the multiple attributes include a time remaining until departure, occupancy associated with the transportation, and an indication of future increase in him occupancy. Based on the multiple attributes, the system categorizes the transportation into multiple clusters. A first cluster is expected to be over full. A second cluster is expected to have the number of users match the number of seats. A third cluster is likely to have the number of users exceed the number of seats. A fourth cluster is expected to be partially empty. Upon categorizing the transportation into the first, second, or third cluster, the system determines the fourth transportation and increases the number of users associated with the fourth transportation by sending a message to a user associated with the transportation including a request to move the user to the fourth transportation.