Pre-dispatching Provider Devices Using Forward and Backward Queue Filters
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Solution Overview
Problem
Conventional on-demand transportation matching systems face challenges in accuracy, efficiency, and flexibility due to their reliance on static models that fail to accurately predict and respond to real-time fluctuations in transportation requests, leading to inefficiencies and increased computational resources.
Innovation Solution
The implementation of a flex forecasting pre-dispatch system that utilizes a requestor device forecasting model in conjunction with forward and backward looking queue filters to intelligently pre-dispatch provider devices, predicting future requestor and provider device queues and adjusting dispatches accordingly to optimize availability and reduce conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static models are used to manage transportation requests, then system simplicity is maintained, but accuracy and responsiveness to real-time fluctuations deteriorate
Solution Approach 1:
The patent transitions from static models to dynamic forecasting models that continuously update predictions based on real-time data. The system uses historical data, current conditions, and predicted future conditions to dynamically adjust provider device dispatch decisions, enabling the system to adapt to real-time fluctuations in transportation demand while maintaining computational efficiency through structured prediction frameworks.
2Productivity
If real-time dispatch decisions are made without forecasting, then system responsiveness is improved, but computational resources are increased and efficiency deteriorates
Solution Approach 1:
The system performs preliminary forecasting actions by predicting future transportation demand and provider device needs before actual dispatch decisions are made. This advance planning allows the system to pre-position provider devices at strategic locations, reducing the need for reactive computational intensive real-time optimization and lowering overall computational resource consumption while maintaining high system efficiency.
3Measurement precision
If provider devices are dispatched without forward-looking queue filters, then dispatch speed is maintained, but accuracy of predicting future queues deteriorates
Solution Approach 1:
The system implements feedback mechanisms where actual queue data from historical and current periods is fed back into the forecasting model to refine future predictions. The model continuously learns from actual versus predicted queue behaviors, improving prediction accuracy over time while maintaining fast dispatch decisions through optimized prediction algorithms that balance computational requirements with real-time responsiveness.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for intelligently pre-dispatching candidate provider devices to a geographic area by utilizing a pre-dispatch model accounting for forecasted transportation requests. For example, the disclosed systems can determine how many candidate provider devices to pre-dispatch to a geographic area based on a variety of inputs, including a projected requestor device queue, a projected provider device queue, and/or a queue capacity. In particular, the disclosed systems can utilize a look-ahead model and a look-behind model to compare a queue capacity with projected provider device queues corresponding to estimated times of arrival for one or more candidate provider devices. If the projected provider device queues comport with the queue capacity according to both the look-ahead model and the look-behind model, the disclosed systems can proceed with pre-dispatching the candidate provider device to the geographic area.