Pre-request Transportation Matching Using Real-time Provider Data
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Solution Overview
Problem
Existing dynamic transportation matching systems struggle to provide accurate pre-request transportation matching results due to reliance on historical data, which fails to account for real-time transportation network conditions and personal preferences, leading to inaccurate estimates of ETA, ETD, and price.
Innovation Solution
The implementation of a pre-request matching system that uses real-time information about transportation provider resource availability, location, and conditions to determine potential matches based on various constraints and user preferences, thereby improving the accuracy of pre-request match information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If historical data is used for transportation matching, then system complexity is reduced, but measurement precision of ETA, ETD, and price estimates deteriorates
Solution Approach 1:
The system performs preliminary matching computations before the actual transportation request is made. The pre-request matching system calculates potential matches, ETA, ETD, and price estimates in advance using real-time provider availability data, so that when the request is made, the matching is already determined or near-determined, reducing post-request computation while maintaining high accuracy.
Solution Approach 2:
The system dynamically adjusts matching computations based on the request stage. During pre-request phase, comprehensive real-time matching is performed with full provider availability data. During active request phase, the system uses the pre-computed matches and only performs minimal adjustments, dynamically adapting computational depth to the operational context.
2Measurement precision
If real-time information about transportation provider resource availability is used, then accuracy of pre-request match information is improved, but loss of time for computation increases
Solution Approach 1:
The system performs matching computations in advance during the pre-request phase when real-time provider availability data is already available. By computing matches, ETA, ETD, and price estimates before the actual request is made, the system eliminates the need for time-consuming computations during the critical request moment, reducing perceived computation time while maintaining accuracy.
Solution Approach 2:
The system performs matching computations for multiple potential providers in advance, even though not all will be selected. This excessive pre-computation of candidate matches allows the system to quickly present accurate options to users without needing to perform exhaustive real-time computations when the request is actually made.
3Ease of operation
If pre-request matching is implemented, then user experience is improved through accurate information, but device complexity of the matching system increases
Solution Approach 1:
The matching system is segmented into distinct functional modules: pre-request matching module that handles advance computations, request processing module that handles active requests, and result presentation module that displays matches to users. This segmentation allows each module to be optimized independently and managed separately, reducing overall system complexity while enabling sophisticated pre-request functionality.
Solution Approach 2:
The system introduces an intermediary pre-request matching layer between the user and the actual transportation request. This intermediary performs computations using real-time provider data and presents accurate match information to users before they commit to a request, improving user experience while isolating the complexity of real-time matching computations from the request processing flow.
4Reliability
If accurate pre-request matching is achieved, then post-request cancellations are reduced, but productivity requirements of the matching system increase
Solution Approach 1:
The system performs comprehensive matching computations in advance during the pre-request phase, determining accurate matches, ETA, ETD, and price estimates before users commit to requests. This preliminary action ensures high reliability of match information, reducing cancellations, while distributing computational load over time rather than concentrating it during peak request periods.
Solution Approach 2:
The pre-request matching system operates continuously in the background, maintaining up-to-date match information as provider availability changes. This continuous computation ensures that when users make requests, accurate matching information is already available, improving reliability without requiring sudden spikes in processing capacity during request moments.
Data Source
AI summary
The disclosed computer-implemented method may match transportation requestor devices to transportation provider devices pre-request by using the same matching process employed by a matching engine that is capable of predicting transportation swaps and walks. Multiple matches may be showcased on user devices, and offline transportation providers may see an exact match to a requestor, with an ability to go online and accept the pre-request match. Additional techniques disclosed include the curation and presentation of offers using constraint space partitioning and adjustment of price and/or the presentation of offers based on real-time information to improve the efficiency and/or utilization of transportation provider resources.