Service Request Allocation via Trained Matching Model
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Solution Overview
Problem
Inefficient distribution of service requests in transportation O2O services, such as carpooling, leads to detours and low response rates, affecting service efficiency and resource utilization.
Innovation Solution
A method using a trained machine learning model to allocate service requests based on provider information, service request details, and real-time data, determining matching parameters to optimize route combinations and allocate requests to suitable service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple service requests are shared by the same service provider (carpooling), then resource utilization improves, but route efficiency deteriorates due to detours
Solution Approach 1:
The patent combines multiple service requests into a single route by identifying compatible requests that share common geographic areas or destinations. The system merges requests that can be served sequentially or simultaneously by the same provider, reducing total route length while maintaining resource utilization. This is achieved through compatibility assessment algorithms that evaluate spatial and temporal overlap between requests.
Solution Approach 2:
The system dynamically adjusts routing parameters based on real-time conditions, transforming fixed routes into flexible paths that accommodate multiple requests. By changing parameters such as pickup/dropoff sequences, routing preferences, and provider availability windows, the system optimizes the balance between resource utilization and route efficiency for carpooling scenarios.
2Productivity
If service requests are allocated efficiently using machine learning models, then service efficiency improves, but system complexity increases
Solution Approach 1:
The machine learning model operates autonomously to allocate service requests without requiring complex manual intervention or centralized control mechanisms. The system self-adjusts by continuously learning from historical data and real-time feedback, automatically optimizing allocations while managing its own complexity through adaptive algorithms that scale with data volume rather than requiring proportional increases in system infrastructure.
Solution Approach 2:
The patent replaces traditional rule-based or manually-controlled allocation mechanisms with machine learning-based intelligent systems. This substitution transforms complex decision-making processes from mechanical/manual operations into automated computational tasks, improving service efficiency while the complexity is managed through software rather than requiring complex physical or organizational structures.
3Measurement precision
If machine learning models are used to determine matching parameters, then allocation accuracy improves, but computational requirements increase
Solution Approach 1:
The system performs preliminary processing of service request data before it reaches the machine learning model, pre-filtering and organizing information to reduce the computational burden during actual matching operations. By preparing features, categorizing requests, and pre-assessing compatibility in advance, the system achieves high matching accuracy while minimizing the real-time computational energy required for model inference.
Data Source
AI summary
The present disclosure relates to systems and methods for providing Online-to-Offline services. The method may include obtain first information associated with a first service request having been allocated to a service provider and having been accepted by the service provider. The method may also include obtaining second information associated with a second service request initiated via an application executed by a second requester terminal. The method may also include determining a matching parameter based on the first information and the second information by using at least one trained matching model and determining whether the matching parameter is larger than a threshold. The method may also include transmitting data associated with the second service request based on a result of the determination that the matching parameter is larger than the threshold.