Alternative Destination Recommendation for Ridesharing Supply Balance
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Solution Overview
Problem
Ridesharing platforms face challenges in balancing demand and supply, leading to inefficiencies and potential losses due to surge pricing, which can result in riders canceling trips.
Innovation Solution
A method for determining alternative destination recommendations on ridesharing platforms using a classifier trained on historical trip data, including trip purpose categories, to suggest destinations with similar features, estimated costs, and service levels, while considering supply-demand imbalances and carpool matching probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If surge pricing is applied to balance demand and supply, then driver supply is improved, but rider satisfaction deteriorates leading to trip cancellations
Solution Approach 1:
The system changes the parameter of destination location to alternative destinations with better supply-demand balance, avoiding areas with excessive surge pricing while maintaining service availability
Solution Approach 2:
The platform acts as an intermediary by introducing alternative destinations as a mediator between rider demand and driver supply, finding compromise locations that satisfy both parties
2Productivity
If alternative destinations are recommended, then demand distribution is improved, but system complexity increases
Solution Approach 1:
The system uses historical trip data and automated classification to self-determine trip purposes and generate recommendations without requiring manual intervention or complex real-time analysis
Solution Approach 2:
The system pre-classifies trip purposes using historical data and pre-identifies alternative destinations before riders make decisions, reducing real-time computational complexity
Data Source
AI summary
Methods, systems, and apparatus for recommending alternative destinations in ride-sharing services are provided. A computing device implementing the method may start with receiving a trip request from a user device. The trip request may include an origin and a destination. Then the computing device classifies the trip request into one of a plurality of trip purpose categories based at least on the origin and the destination of the trip request, the rider's information, and a machine-learning classifier trained to predict the one trip purpose category of the trip request. In response to the one trip purpose category belonging to a preset group of trip purpose categories, the computing device determines one or more alternative destinations for the trip request, and sends to the user device, the one or more alternative destinations.