Dynamic Travel Transaction System for Fair Pricing
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Solution Overview
Problem
Conventional fixed fee systems for services like public transportation fail to accurately represent the value provided to customers due to factors such as congestion, travel time, and passenger load, leading to overcharging or undercharging.
Innovation Solution
Implementing dynamic travel transactions that utilize real-time data from sensing devices, including user device data and travel variability data, to adjust fees based on parameters like vehicle capacity, traffic, and route changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed fee system is used for public transportation, then the service provider can simplify pricing and collection processes, but the fee inaccurately represents the actual service quality and conditions provided to customers
Solution Approach 1:
The patent implements dynamic pricing by transitioning from fixed fees to variable fees that automatically adjust based on real-time travel conditions. The system monitors parameters such as travel time, congestion levels, and route deviations, then dynamically modifies the fee structure to reflect actual service quality, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where travel data collected during transit (including traffic conditions, vehicle capacity, and route adherence) is fed back into the pricing model. This feedback loop enables the pricing system to continuously adapt and accurately represent service quality without requiring complex manual adjustments.
2Measurement precision
If real-time dynamic pricing is implemented based on travel variability data, then the fee accurately represents service quality, but the system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing pricing algorithms and data collection frameworks before dynamic pricing is activated. Travel variability parameters are predefined and monitored in advance, allowing the system to quickly compute accurate fees without requiring complex real-time decision-making, thus reducing overall system complexity.
Solution Approach 2:
The pricing system operates autonomously by automatically collecting travel data, computing variability metrics, and determining adjusted fees without human intervention. This self-service capability simplifies the overall system architecture by eliminating the need for manual pricing adjustments and complex administrative interfaces.
3Measurement precision
If comprehensive travel variability data is collected during vehicle operation, then accurate dynamic pricing can be achieved, but memory and storage burdens increase
Solution Approach 1:
The system extracts and retains only the essential travel variability parameters needed for accurate pricing (such as travel time deviations, congestion levels, and route changes) while discarding redundant data. This selective extraction approach maintains measurement precision by keeping critical data while significantly reducing overall storage requirements.
Solution Approach 2:
The system implements partial data collection by focusing on specific key parameters rather than capturing all possible travel data. By collecting only the necessary subset of variability data required for dynamic pricing calculations, the system achieves accurate pricing representation without the storage burden of comprehensive data collection.
4Measurement precision
If real-time processing of travel data is performed to generate dynamic fees, then accurate pricing is achieved, but computational strain on the system increases
Solution Approach 1:
The system performs preliminary computations by pre-calculating pricing algorithms and data processing pipelines before real-time operation. Travel variability metrics are predefined and computation-efficient formulas are established in advance, allowing the system to generate accurate dynamic fees with minimal real-time computational strain and energy consumption.
Data Source
AI summary
Methods, apparatuses, and computer program products are disclosed for providing dynamic travel transactions. An example computer-implemented method includes receiving first device data of a first user device associated with a first user. The first device data includes first trip data of a vehicle and the first user. The example method further includes receiving travel variability data associated with the vehicle that includes one or more vehicle operating parameters that vary during travel of the vehicle. The example method also includes generating a travel transaction based upon the first device data of the first user device and the travel variability data associated with the vehicle. In some instances, the method includes determining a base transaction based upon the first device data that's effectuated at a first time and determining a modification to the base transaction based upon the travel variability data that's effectuated at a second time.