Dynamic Provider Eligibility Model for Ride Matching
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Solution Overview
Problem
On-demand service matching systems face inefficiencies in matching providers and requestors due to the distributed and portable nature of both parties, leading to provider downtime, increased system resource usage, and canceled requests, as traditional geographic-based matching methods fail to account for the dynamic arrival times and readiness of users.
Innovation Solution
Implementing a dynamic provider eligibility model that uses estimated arrival times and travel times for both requestors and providers to efficiently match requests, optimizing the system by filtering providers based on proximity and availability, and predicting future provider availability to minimize downtime and resource wastage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If providers are matched to requestors based on closest geographic location, then matching speed is improved, but provider downtime increases and request cancellation rate increases
Solution Approach 1:
The patent applies dynamics by making the matching criteria adaptive rather than static. The system dynamically adjusts provider eligibility based on real-time factors including estimated arrival times, requestor readiness times, and provider availability windows. This dynamic approach allows the system to balance matching speed with minimizing provider downtime by selecting providers whose arrival aligns with requestor readiness, rather than simply selecting the geographically closest provider.
Solution Approach 2:
The system changes multiple parameters simultaneously to resolve the contradiction: it considers not just geographic distance but also temporal parameters (estimated arrival time, requestor readiness time, provider availability window). By changing from a single-parameter (distance) matching system to a multi-parameter system that includes time-based eligibility criteria, the system achieves both fast matching and minimized provider downtime.
2Device complexity
If providers are matched to requestors based on closest geographic location, then matching simplicity is improved, but request cancellation rate increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing provider availability windows and requestor readiness times before the actual matching occurs. This preliminary preparation allows the matching algorithm to quickly evaluate provider eligibility without complex real-time calculations, maintaining system simplicity while improving reliability by ensuring only compatible provider-requestor pairs are matched.
Solution Approach 2:
The patent introduces an intermediary eligibility assessment layer between the simple geographic matching and the final provider-requestor pairing. This intermediary layer evaluates temporal compatibility (whether provider arrival time aligns with requestor readiness) without significantly increasing system complexity. It acts as a mediator that filters out incompatible matches before final pairing, thereby reducing cancellation rates while maintaining operational simplicity.
3Loss of energy
If traditional geographic-based matching is used, then system resource usage is reduced, but provider availability utilization decreases
Solution Approach 1:
The system applies self-service by having providers indicate their own availability windows and having requestors indicate their readiness times. This self-provided information allows the matching system to efficiently evaluate compatibility without requiring complex real-time monitoring or coordination, minimizing system resource usage while maximizing provider utilization by accurately matching available providers with ready requestors.
Solution Approach 2:
The system performs partial matching by evaluating only the critical temporal parameters (arrival time vs. readiness time) rather than optimizing all possible matching criteria. This partial approach to temporal matching achieves sufficient provider utilization without the excessive computational resources that would be required for comprehensive multi-factor optimization, balancing resource efficiency with productivity improvement.
Data Source
AI summary
Embodiments provide techniques, including systems and methods, for determining matches of requestors and providers based on a dynamic provider eligibility model. For example, a request matching model uses an estimated arrival time for a requestor and estimated travel times for available providers to a pickup location to determine eligible providers for matching to a ride request. The matching model determines those providers that are far enough away from the request location to allow the requestor time to arrive at the pickup location without matching providers that are too far away, causing delay for the requestor and lowering the efficiency of the system by taking provider system resources from other service areas and increasing provider downtime upon matching. Additionally, embodiments provide more efficient matching processing leading to fewer canceled matched requests, fewer requests for a successful match, and fewer system resources necessary to meet requestor demand.