Geocoded Provider Allocation With Iterative Matching Constraints
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Solution Overview
Problem
Conventional on-demand transportation-network systems face inefficiencies in matching transportation requests with providers during volatile or high-volume time periods due to static computational models that fail to adjust for varying request volumes and provider inducements, leading to decreased request and acceptance rates.
Innovation Solution
A transportation matching system iteratively adjusts the allocation of transportation providers across geocoded areas based on a transportation-value metric, incorporating constraints such as neighborhood, provider-target, and allocation limits, until reaching a convergence or time threshold, thereby optimizing the allocation for improved efficiency and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static computational models are used to match requests with transportation providers, then the system structure is simple and easy to implement, but the system cannot efficiently match requests with providers during volatile or high-volume time periods
Solution Approach 1:
The patent applies dynamics by transitioning from static computational models to dynamic iterative models that continuously adjust transportation provider allocations based on real-time request volumes and provider responses. The system iteratively adjusts allocations across geocoded areas until convergence or time threshold, enabling adaptive response to volatile conditions while maintaining manageable complexity through structured iteration.
Solution Approach 2:
The patent changes key parameters including transportation provider allocation distributions, inducement levels, and time thresholds dynamically. The iterative process modifies these parameters based on observed matching rates and request patterns, allowing the system to optimize performance during high-volume periods without requiring complete model redesign.
2Measurement precision
If separate computational models are used for inducements and matching, then the system is easier to implement and maintain, but the accuracy of providing marginal incentives to transportation providers decreases
Solution Approach 1:
The patent merges previously separate computational models for inducements and matching into a unified iterative framework. This integration allows the system to simultaneously optimize both matching accuracy and inducement effectiveness by considering their interdependencies, achieving higher precision in determining marginal incentives while maintaining computational tractability through shared data structures and convergence criteria.
3Productivity
If static computational models are used, then the system is simpler to operate, but the rate at which people request transportation and providers accept requests decreases during high-volume periods
Solution Approach 1:
The patent implements feedback mechanisms where the iterative model continuously observes request volumes, provider acceptance patterns, and matching rates, then adjusts allocations and inducements accordingly. This closed-loop system automatically responds to changing conditions, maintaining high acceptance rates during volatile periods without requiring manual intervention, thus preserving ease of operation while boosting productivity.
4Adaptability or versatility
If inducements are provided rigidly based on static models, then the system is easier to implement, but the system does not account for variability of provider inducements to travel to target areas
Solution Approach 1:
The patent applies dynamics to inducement distribution by making allocation decisions adaptive rather than fixed. The iterative model adjusts inducement levels based on real-time observations of provider behavior, travel patterns, and area-specific conditions, allowing the system to flexibly respond to variability in provider responses while maintaining a structured implementation framework that prevents excessive complexity.
Data Source
AI summary
This disclosure describes methods, non-transitory computer readable media, and systems that can iteratively adjust an allocation of transportation providers across geocoded areas for a time period until identifying a final allocation of transportation providers corresponding to an improved transportation-value metric for the geocoded areas. By iteratively adjusting both a transportation-value metric and a transportation-provider allocation across geocoded areas, the disclosed systems intelligently generate a final allocation of transportation providers that increases a transportation efficiency reflected in the transportation-value metric. The disclosed systems' iterative approach to adjusting a transportation-provider allocation across geocoded areas can improve the efficiency and flexibility of transportation-network systems in allocating transportation providers and can integrate constraints for provider allocations, neighboring geocoded areas, provider inducements, and projected transportation requests within a unified computational model.