Carpool Dual-Pricing System Using ML Matching Probability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current ridesharing platforms face inefficiencies in pricing strategies for carpool trips, as upfront discounts become unnecessary when carpool trips fail to match, leading to unrealized savings and inefficient pricing.
Innovation Solution
Implementing a carpool dual-pricing system using machine-learning models to determine optimal price adjustment multipliers based on historical data, distinguishing between matched and unmatched carpool requests, and applying different price adjustment multipliers to ensure optimal Key Performance Indicators (KPIs) across spatial-temporal pricing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If upfront discounts are applied to carpool trips, then rider incentives are provided, but pricing efficiency deteriorates when matches fail to occur
Solution Approach 1:
The patent implements dynamic pricing by adjusting carpool discounts in real-time based on predicted matching probabilities. The system transitions from static upfront discounts to dynamic pricing that adapts to current demand conditions, supply conditions, and historical matching patterns, thereby maintaining pricing efficiency while still providing rider incentives when appropriate.
Solution Approach 2:
The system changes the discount parameter based on multiple factors including predicted matching probability, current demand levels, supply conditions, and rider preferences. By adjusting the discount parameter dynamically rather than applying a fixed discount, the system optimizes pricing efficiency while maintaining effective rider incentives.
2Measurement precision
If machine-learning models are implemented for dual-pricing, then pricing precision is improved, but system complexity increases
Solution Approach 1:
The patent segments the pricing system into distinct components: a machine-learning model for predicting matching probabilities, a dual-pricing calculation module, and an implementation layer. This segmentation allows the complex ML functionality to be isolated and managed separately, reducing overall system complexity while maintaining high pricing precision.
Solution Approach 2:
The patent introduces an intermediary pricing calculation layer that translates machine-learning predictions into actual pricing decisions. This intermediary module processes the probabilistic outputs from the ML model and converts them into concrete discount values, simplifying the integration between complex ML algorithms and the pricing system.
3Productivity
If differential pricing is applied based on matching probability, then revenue optimization is achieved, but calculation complexity increases
Solution Approach 1:
The patent performs preliminary calculations by pre-computing matching probabilities using machine-learning models before final pricing decisions are made. By preparing these probability estimates in advance and caching relevant features, the system reduces real-time calculation complexity while maintaining revenue optimization capabilities.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: determining an expected trip count based on a plurality of carpool requests in a pricing unit and a pair of price adjustment multipliers applied to the pricing unit; for each of the plurality of carpool requests, generating a carpool matching probability of the carpool request by a second machine learning model based on the first expected trip count; constructing one or more Key Performance Indicator (KPI) models based on the plurality of carpool matching probabilities and the pair of price adjustment multipliers; and determining optimal values of the pair of price adjustment multipliers based on an optimization model maximizing an aggregated value of the one or more KPI models.