Travel Itinerary Optimization Engine for Carbon Emission Reduction
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Solution Overview
Problem
The resurgence of commuting and travel post-COVID-19 pandemic increases carbon-dioxide emissions, posing a challenge to climate change mitigation efforts, while in-person social interaction remains essential for human well-being.
Innovation Solution
A computing resource optimization engine that receives account and location identifiers, generates travel queries to connect individuals for co-location, determining an intersection score to optimize travel itineraries and reduce the need for transportation usage through collaboration platforms and online booking engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If travel restrictions are lifted and regular commuting resumes, then in-person social interaction and workforce productivity are improved, but carbon-dioxide emissions increase
Solution Approach 1:
The system merges travel planning with collaboration matching by integrating itinerary data from multiple sources and automatically identifying collaborators who will be co-located. This combines previously separate functions (travel booking and collaboration scheduling) into a unified system that optimizes both simultaneously.
Solution Approach 2:
The system implements feedback loops where travel itineraries are continuously monitored and used to update collaboration opportunities. The intersection score calculation provides feedback on potential co-location events, which then informs future travel recommendations and collaboration scheduling decisions.
2Object-generated harmful factors
If travel itineraries are optimized to maximize co-location opportunities, then carbon-dioxide emissions are reduced, but system complexity increases
Solution Approach 1:
The system segments the complex optimization problem into distinct modular components: itinerary data collection from multiple sources, collaborator identification through platform queries, intersection score calculation based on time and location overlap, and opportunity metric generation. Each module handles a specific aspect of the problem independently.
Solution Approach 2:
The system introduces intermediary computational layers including the intersection score metric and opportunity metric that mediate between raw itinerary data and final travel recommendations. These intermediaries simplify the decision-making process by pre-processing and synthesizing complex multi-source data into actionable insights.
3Measurement precision
If multiple data sources are integrated to improve collaboration matching accuracy, then co-location opportunities are maximized, but information processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant data elements from multiple sources needed for collaboration matching, such as location timestamps and participant identifiers. It filters out extraneous information while maintaining the precision required for accurate co-location detection and intersection score calculation.
Data Source
AI summary
The present specification provides, amongst other things, a novel resource optimization engine. In one example system, a plurality of collaboration platforms and travel booking engines and client devices are provided that connect to the optimization engine. The plurality of collaboration platforms manage the accounts of client devices that collaborate. The system includes an optimization engine configured to optimize travel and scheduling itineraries between different collaborators.