Dynamic Transportation Matching System for Time-Sensitive Requests
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Solution Overview
Problem
Conventional ride-sharing systems face issues with high system latency and inflexibility in matching requestors with providers, leading to increased wait times and rigid arrival time estimates, which are inadequate for time-sensitive requests.
Innovation Solution
A priority management system that identifies candidate provider devices based on estimated times of arrival (ETAs) to offer prioritized transportation options, allowing for dynamic reassignment of providers to ensure the quickest pickup for requestors, even if it means rerouting or reassigning already assigned vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional pooling time window algorithms are used to match providers with requestors, then system stability and uniform processing are maintained, but system latency increases and wait times are excessive
Solution Approach 1:
The system transitions from static pooling time windows to dynamic real-time matching. The matching algorithm continuously evaluates provider availability and requestor needs without fixed time constraints, allowing immediate pairing when conditions are met. This dynamic approach eliminates the inherent delay of waiting for pooling windows to expire while maintaining systematic processing through continuous algorithmic evaluation.
Solution Approach 2:
The system performs preliminary identification of candidate providers before a requestor's request is fully processed. By pre-identifying and pre-evaluating available providers based on their current status and historical performance, the system reduces matching latency when requests come in, as the evaluation work has already been partially completed.
2Adaptability or versatility
If rigid pairing algorithms are used to limit the set of providers, then processing simplicity is maintained, but adaptability to different requestor needs is reduced
Solution Approach 1:
The provider selection process is segmented into multiple independent evaluation criteria: geographic proximity, vehicle availability, provider rating, historical performance, and real-time status. Each criterion can be evaluated separately and combined to form the overall matching score. This segmentation allows the system to consider diverse factors without creating an intractably complex algorithm, as each segment remains relatively simple to compute.
Solution Approach 2:
The matching algorithm serves multiple functions simultaneously: it identifies the closest providers, evaluates their availability, assesses their historical performance, and ranks them according to requestor preferences. This multi-functional approach consolidates what would otherwise require multiple separate algorithms into a single unified system, managing complexity while providing comprehensive adaptability.
3Productivity
If conventional algorithms treat all transportation requests uniformly, then processing consistency is maintained, but ability to prioritize time-sensitive requests is lost
Solution Approach 1:
The system applies different processing qualities to different request types. Time-sensitive requests receive priority processing with expanded provider search parameters and relaxed matching criteria, while standard requests follow the conventional uniform process. This local differentiation allows efficient handling of urgent cases without compromising the consistency and reliability of standard requests, as each category receives appropriately tailored processing.
Solution Approach 2:
The system dynamically changes matching parameters based on request characteristics. For time-sensitive requests, parameters such as provider search radius, matching stringency, and evaluation time windows are adjusted to enable faster processing. For standard requests, parameters remain at conventional settings. This parameter adaptability allows the system to optimize processing efficiency for different request types while maintaining overall system consistency.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for identifying provider devices and corresponding vehicles as candidates to transport time-sensitive (or otherwise prioritized) requestors and providing prioritized transportation options as fast passes for display on such requestors' devices. To provide such a prioritized transportation option, the disclosed systems can identify provider devices either matched or unmatched with requestors as candidates for prioritized transport based on estimated times of arrivals (ETAs) of vehicles for the candidate provider devices at the requestor's pickup location. Based on the ETAs at the pickup location, the disclosed systems can select a closest provider device from among the candidate provider devices to transport a prioritized requestor. After matching the prioritized requestor to the closest provider device, the disclosed systems can further search for providers with sooner ETAs.