Geospatial Premium Allocation for Dynamic Transportation Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional on-demand transportation-network systems face inefficiencies due to static computational models that inaccurately distribute premiums to transportation providers, leading to decreased request and acceptance rates, as they fail to account for dynamic changes in request volumes and provider availability.
Innovation Solution
A transportation matching system that determines geospatial scores and generates geospatial-based-proportion metrics for provider devices, integrating location data to dynamically allocate transportation vehicles across geocoded areas, using geocode-specific and regional repositories to balance premiums and incentivize accurate vehicle flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static computational models are used to match requests with transportation providers, then the system structure is simple and easy to implement, but the accuracy of premium allocation and provider matching deteriorates 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 provider location data, request volume, and geographic distribution. The system dynamically recalculates premiums as providers move and conditions change, resolving the contradiction by accepting increased computational complexity to achieve accurate real-time premium allocation.
Solution Approach 2:
The system implements feedback loops where premium calculations are continuously refined based on actual provider responses, location changes, and request fulfillment outcomes. This feedback mechanism allows the system to adapt premium allocations in real-time, improving accuracy during high-volume periods while maintaining a manageable computational structure through iterative adjustments.
2Ease of manufacture
If separate computational models are used for premiums and matching, then each model can be optimized independently, but the overall system accuracy deteriorates due to lack of integrated accounting
Solution Approach 1:
The patent merges the premium computational model and the matching computational model into a unified integrated system. This single computational framework simultaneously handles both premium calculation and provider-request matching, ensuring consistent accounting across all operations. The integration eliminates the accuracy deterioration caused by separate models while maintaining optimization flexibility through modular sub-routines within the unified system.
3Productivity
If premiums are tied to specific times and locations, then providers are incentivized to reach target areas, but provider rejection of matches increases due to unpredictable travel times during high-volume periods
Solution Approach 1:
The system dynamically adjusts premium allocations based on real-time provider location data and changing travel conditions. Instead of rigid time-based premiums, the system calculates premiums based on actual provider proximity to request locations and adjusts in real-time as conditions change. This dynamic approach maintains incentive effectiveness while adapting to unpredictable travel times, reducing provider rejection rates.
Solution Approach 2:
The patent changes the parameters used for premium calculation from fixed time-based metrics to dynamic spatial and temporal parameters that account for provider location, request volume, and travel conditions. By changing these parameters in real-time, the system maintains effective incentives while accommodating provider flexibility and reducing match rejections during high-volume periods.
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.