Predictive Model for Spontaneous Carpooling Match Locations
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Solution Overview
Problem
Current ridesharing services face challenges in efficiently matching drivers and passengers without prearranged agreements, particularly for short and frequent rides, and there is a need for a predictive model to estimate the probability of encounters at specific locations to eliminate the requirement for prior agreements.
Innovation Solution
A computer-implemented system that uses a predictive model to estimate the number of drivers or passengers at a given location and time, transmitting coordinates to user devices for potential ride matches, allowing for spontaneous connections between drivers and passengers based on trajectory data and NFC technology for secure transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prearranged agreements are required for ridesharing, then ride matching reliability is improved, but ease of operation deteriorates due to the hassle of making agreements beforehand
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimal ride match locations and trajectories in a database before users need them. When a user requests a ride, the system quickly retrieves pre-computed match locations rather than calculating agreements in real-time, thus maintaining reliability without requiring users to make prearranged agreements manually.
Solution Approach 2:
The system enables self-service by automatically computing ride match locations and trajectories without requiring users to negotiate or make agreements. The predictive model autonomously determines optimal pickup locations and routes based on historical data, allowing users to simply request rides without the hassle of prearranged agreements while maintaining reliable matching.
2Ease of operation
If predictive modeling is implemented to eliminate prearranged agreements, then ease of operation is improved, but measurement precision deteriorates in estimating encounter probabilities
Solution Approach 1:
The system performs preliminary actions by pre-calculating ride match locations and trajectories using historical trajectory data before users need them. This allows the system to provide precise predictions without requiring complex real-time calculations, maintaining measurement precision while improving ease of operation by eliminating the need for users to make prearranged agreements.
Solution Approach 2:
The system uses feedback from historical trajectory data and actual ride outcomes to continuously improve the accuracy of its predictive model. By learning from past patterns of successful rides and encounters, the system refines its probability estimates for future predictions, maintaining measurement precision even as it eliminates prearranged agreements and improves ease of operation.
3Productivity
If spontaneous ride matching is enabled without agreements, then productivity is improved through faster matching, but reliability deteriorates due to risk of failed rides
Solution Approach 1:
The system performs preliminary actions by pre-computing optimal ride match locations and trajectories using historical data before spontaneous matching occurs. This allows the system to enable fast, spontaneous ride matching while maintaining reliability, as the pre-calculated match locations are based on proven successful patterns from historical trajectories rather than random assignments.
Solution Approach 2:
The system provides beforehand cushioning by using the predictive model to estimate encounter probabilities and select only high-probability match locations. This cushions against the risk of failed rides by ensuring that spontaneous matches are made at locations and times where encounters are statistically likely to succeed, based on historical data, thus maintaining reliability while enabling fast productivity.
Data Source
AI summary
A system for generating a carpooling prediction includes a computer that performs the operations of receiving a query for a ride match location from a user computing device. The query includes a given destination location and target arrival time for reaching the destination. The computer is operative to transmit coordinates to the user computing device of a location where a user can find other users willing to rideshare. For a number of passengers that are looking for a ride to a given destination during a given time, the computer is operative to predict a number of drivers that will pass or end at the given destination during a given time interval that includes the target time. For a number of drivers that are looking to offer a ride to a given destination during a given time, the computer is further operative to predict a number of passengers that will pass or wait at a given node, for example, along the drivers' route or alternate route, for reaching the destination. Based on the prediction, the computer is further operative to transmit coordinates to the user computing device corresponding to a location where the at least one driver or passenger is predicted to pass or be located. The computer is further operative to process a transaction between two users in response to receiving a communication from a remote user device authenticating the transaction between the two users.