Geospatial Scoring for Dynamic Provider Premium Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional on-demand transportation-network systems use static computational models that fail to accurately match transportation requests with providers during volatile or high-volume time periods, leading to inaccurate premium distribution and decreased request and acceptance rates.
Innovation Solution
A transportation matching system that determines geospatial scores and generates geospatial-based-proportion metrics for provider devices, integrating location data and cumulative metrics to dynamically allocate transportation vehicles across geocoded areas, providing proportional premiums based on actual transportation events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static computational models are used to match requests with providers, then the system structure is simple, but the matching accuracy decreases during volatile or high-volume time periods
Solution Approach 1:
The patent transitions from static computational models to dynamic computational models that continuously adjust premium calculations based on real-time geospatial data, transportation events, and provider locations. This allows the system to adapt to volatile or high-volume time periods while maintaining accurate matching between requests and providers.
Solution Approach 2:
The system changes key parameters from fixed static values to dynamic variables including geospatial scores, cumulative transportation events, provider device locations, and time-dependent factors. These parameter changes enable the computational model to accurately reflect real-time conditions and improve matching precision during varying demand conditions.
2Ease of operation
If premiums are tied to specific times and locations, then provider incentives are clear, but providers reject matches with unpredictable travel times or during high volume periods
Solution Approach 1:
The system implements feedback loops where premium calculations continuously incorporate real-time data about provider locations, transportation events, geospatial scores, and cumulative metrics. This feedback mechanism allows providers to see accurate, up-to-date premium information that reflects actual conditions, reducing rejections due to unpredictable travel times or high volume periods.
Solution Approach 2:
The system performs preliminary calculations of geospatial scores and cumulative transportation events before matching requests with providers. This allows the system to pre-determine accurate premium values and provider incentives based on current conditions, enabling providers to make informed decisions about accepting matches without uncertainty about travel times or demand levels.
3Ease of manufacture
If separate computational models are used for premiums and matching, then model development is simplified, but the accuracy of marginal incentives decreases
Solution Approach 1:
The patent merges previously separate computational models for premium calculation and request-provider matching into a unified computational model. This integrated model simultaneously considers geospatial scores, cumulative transportation events, provider locations, and matching criteria, resulting in accurate marginal incentives that properly reflect both premium allocation and matching quality.
4Ease of manufacture
If static computational models are used, then system implementation is straightforward, but premium allocation becomes inaccurate during high demand periods
Solution Approach 1:
The system replaces static computational models with dynamic models that continuously update premium allocations based on real-time geospatial data, transportation events, provider locations, and cumulative metrics. This dynamic approach maintains straightforward implementation through standardized computational processes while achieving accurate premium allocation during high demand periods by adapting to changing conditions.
Data Source
AI summary
This disclosure describes methods, non-transitory computer readable media, and systems that can determine locations and geospatial scores for pick-up events and other transportation events relative to geocoded areas at a given time period and generate geospatial-based proportion metrics for provider devices corresponding to such transportation events as proportional premiums for performing the events. For example, the systems determine locations of provider devices across geocoded areas for a particular time period. The systems can generate geospatial scores for transportation events of the provider devices occurring within the time period based on the specific locations and times of the events relative to the geocoded areas. Based on the geospatial scores and cumulative metrics accounting for such transportation events relative to the geocoded areas, the systems can generate geospatial-based-proportion metrics corresponding to such events for a graphical display on provider devices.