Geocoded Provider Models for Dynamic Demand-Based Allocation
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Solution Overview
Problem
Existing on-demand transportation matching systems face inefficiencies and computational strain due to the inability to accurately redirect providers to areas with anticipated demand, often relying on outdated information and rigid, complex methods that result in delays and wasted resources.
Innovation Solution
A dynamic transportation matching system utilizing multiple machine-learning models, including an incremental provider model, provider allocation model, and personalized provider behavioral models, to identify and reallocate providers based on real-time data, generating customized interfaces to optimize provider relocation within a geocoded region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rigid and complex methods are used to allocate providers, then system structure is maintained, but provider reallocation accuracy deteriorates and computational efficiency decreases
Solution Approach 1:
The patent replaces conventional rigid mechanical allocation methods with machine learning models that dynamically predict provider需求和供给. The system uses behavioral models to forecast provider locations and demand patterns, substituting complex rule-based mechanics with data-driven predictive analytics that improve accuracy without proportionally increasing system complexity.
Solution Approach 2:
The system dynamically changes allocation parameters based on real-time data. Instead of fixed allocation rules, the patent adjusts provider distribution parameters continuously using machine learning predictions of demand and provider behavior, allowing the system to adapt to changing conditions while maintaining manageable complexity through parameter optimization.
2Productivity
If outdated information is used for provider allocation, then computational resources are conserved, but provider allocation efficiency deteriorates
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict future provider locations and demand patterns before actual allocation decisions are needed. The behavioral models forecast where providers will be and where demand will occur, allowing the system to pre-position providers optimally rather than reacting to outdated information, thus improving efficiency without excessive computational overhead.
Solution Approach 2:
The system incorporates feedback loops where machine learning models continuously learn from actual provider behavior and demand patterns. This feedback mechanism allows the system to refine predictions over time, improving allocation efficiency while computational resources are optimized through incremental learning rather than exhaustive real-time processing.
3Loss of time
If providers are not dynamically reallocated to areas with anticipated demand, then system simplicity is maintained, but requestor wait times increase and provider downtime increases
Solution Approach 1:
The patent replaces manual or rule-based provider allocation with automated machine learning models that predict demand and provider behavior. This automation uses behavioral forecasts to dynamically reposition providers before demand occurs, significantly reducing requestor wait times while the level of automation remains manageable through efficient model design and incremental implementation.
Data Source
AI summary
This disclosure describes a transportation matching system that manages the allocation of transportation providers by training and utilizing multiple machine-learning models to identify, allocate, and serve specific transportation providers with customized opportunities to relocate the transportation providers between geocoded areas in a geocoded region. For instance, the transportation matching system trains and utilizes an incremental provider model, a provider allocation model, and personalized provider behavioral models as well as a customized provider interface generator to satisfy anticipated transportation requests and improve transportation matching within a geocoded region.