Ride Assignment Optimizing Expected Arrival Time
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Solution Overview
Problem
Conventional transportation systems face inefficiencies and inaccuracies due to driver response issues, leading to increased request-to-pick-up times, inefficient resource utilization, and computational burdens, as they fail to account for the probability of driver rejection in ride assignments.
Innovation Solution
A system that determines the expected time-to-arrival for ride requests by calculating the probability of driver acceptance and associated delays, optimizing assignments based on this probability to minimize rejections and optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the system assigns ride requests to drivers based on closest distance, then the initial assignment minimizes travel time, but driver rejection causes reassignment delays that increase request-to-pick-up time
Solution Approach 1:
The system performs preliminary actions by calculating expected time-to-arrival that incorporates probability of driver acceptance before making the assignment. This allows the system to anticipate potential rejections and account for associated delays in advance, rather than reacting after rejection occurs. The expected time-to-arrival is computed using the formula: E[T] = p × t + (1-p) × (t + Δ), where p is probability of acceptance, t is estimated time-to-arrival, and Δ is average delay from rejection processing.
Solution Approach 2:
The system uses feedback from historical driver behavior data to continuously refine probability of acceptance estimates. By monitoring actual acceptance/rejection patterns and comparing them with predicted probabilities, the system improves its assignment accuracy over time. This feedback mechanism allows the system to adapt to changing driver behaviors and improve assignment reliability.
2Reliability
If the system waits for driver response before assignment, then driver acceptance can be confirmed, but request-to-pick-up time increases due to waiting period
Solution Approach 1:
The system performs preliminary calculation of expected time-to-arrival incorporating rejection probability before waiting for driver response. This allows the system to make informed assignments proactively rather than reactively, reducing the effective waiting time by accounting for potential delays in advance.
Solution Approach 2:
The system dynamically adjusts assignment strategies based on real-time conditions including driver response patterns. By monitoring actual response times and acceptance probabilities, the system can adapt its assignment algorithm to optimize between confirmation reliability and waiting time reduction.
3Measurement precision
If the system provides estimated time-to-arrival based on assumed acceptance, then the estimation is simple, but accuracy decreases when drivers reject requests
Solution Approach 1:
The system changes the parameter used for estimation from simple estimated time-to-arrival to expected time-to-arrival that incorporates probability of acceptance. This parameter transformation allows the system to maintain simplicity while improving accuracy, as the expected time formula E[T] = p × t + (1-p) × (t + Δ) remains computationally manageable while accounting for rejection scenarios.
4Reliability
If the system recalculation assignment after each rejection, then the correct driver can be assigned, but computational resources are doubled
Solution Approach 1:
The system performs preliminary assignment based on expected time-to-arrival that anticipates potential rejections. By pre-calculating which drivers are most likely to accept and accounting for rejection delays in advance, the system reduces the need for iterative recalculation after rejections occur, thereby improving computational efficiency while maintaining assignment correctness.
Data Source
AI summary
The present disclosure relates assigning ride requests to providers based on the probability that a provider will accept the request. For example, one or more embodiments identify a first provider to assign the ride request. The system then generates a probability of acceptance for that provider. The system then determines an estimated time-to-arrival for the first provider and an alternate estimated time-to-arrival associated with a re-assigned provider. Based on the acceptance probability, the estimated time-to-arrival, and the alternate estimated time-to-arrival, the system then determines an expected time-to-arrival associated with the first provider. The system assigns ride requests to providers based on optimizing the expected time-to-arrival across multiple potential providers for a ride request.