Transportation Matching Cache for Instant Request-Provider Assignment
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Solution Overview
Problem
Dynamic transportation matching systems face inefficiencies in matching transportation requestors with providers, particularly when handling large volumes of requests and updates, leading to computational challenges and delayed or inaccurate estimated times of arrival (ETAs).
Innovation Solution
Implementing a greedy algorithm for immediate matching and precomputing/caching evaluations of matching schemes to exclude certain providers, using a linear program with a perturbation term to optimize and cache provider availability values, allowing for instant matching and accurate ETA calculations without significant additional computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a dynamic transportation matching system matches batches of requestors and providers at intervals to improve computational efficiency, then computing resource consumption is reduced, but matching delays occur for new requests arriving between intervals
Solution Approach 1:
The system pre-computes and caches provider availability values by solving a linear program that excludes each provider in turn before matching new requests. This preliminary action allows immediate matching of new requests without waiting for the next batch interval, eliminating matching delays while maintaining computational efficiency through cached results
Solution Approach 2:
The matching process is segmented into two distinct phases: (1) batch processing at intervals to compute and cache provider availability values for computational efficiency, and (2) immediate matching for new requests using the cached values. This segmentation allows the system to optimize for both computational efficiency and responsiveness to new requests
2Device complexity
If a naive matching algorithm is used to simplify the matching process, then implementation complexity is reduced, but matching optimality deteriorates
Solution Approach 1:
The system performs preliminary optimization by solving a linear program to compute provider availability values that reflect optimal matching considerations. This pre-computed optimality information is then cached and used during immediate matching, allowing the system to achieve optimal or near-optimal matching results without complex real-time optimization algorithms
Solution Approach 2:
The system transforms the complex matching optimization problem into a simplified form by changing parameters: instead of optimizing full requestor-provider matches in real-time, it computes provider availability values (a transformed parameter) that encapsulate optimality information. This parameter transformation allows simple greedy matching to achieve near-optimal results
3Measurement precision
If all potential requestors are included in the global matching problem to improve matching accuracy, then matching precision is improved, but computational feasibility deteriorates
Solution Approach 1:
The system extracts and caches provider availability values from the global matching problem before processing new requests. By pre-computing and storing these availability values for all providers, the system captures the effects of all potential requestors without including them all in the real-time matching computation, maintaining accuracy while ensuring computational feasibility
Solution Approach 2:
The system performs preliminary computation of provider availability values that incorporate information about all potential requestors. This pre-computation phase handles the computationally intensive task of considering all requestors, while the subsequent immediate matching phase uses cached results to achieve fast, accurate matching without re-processing all requestors
Data Source
AI summary
The disclosed computer-implemented method may include instant and optimized matching of transportation requesters with transportation providers by precomputing and caching evaluations of matching schemes between existing requests and available providers, where each of the matching schemes excludes one of the available providers. The possibility of matching any new request to a given provider may then be evaluated according to the cached results of the matching scheme that excluded that provider along with an evaluation of matching the new request to the provider, which may be a computationally easy problem. Thus, the new request may be matched to a provider instantly without waiting for the next iteration of solving the global matching problem. Matching requestors and providers in this way may also improve the accuracy of estimated time of arrival information provided to requestors. Various other methods, systems, and computer-readable media are also disclosed.