Trip Prediction Algorithm for Dynamic Ride Sharing Allocation
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Solution Overview
Problem
Existing trip sharing services are cumbersome and inefficient, requiring users to manually input trip details and search for matches, which is time-consuming and often not worth the effort for short trips, leading to low adoption rates among commuters, and are hindered by an imbalance of passengers to drivers.
Innovation Solution
A computer-implemented method that uses a trip prediction algorithm to automatically predict future trips based on user history and input parameters, allowing for semi-automated submission of service requests and dynamic allocation of driver and passenger roles, improving the efficiency of trip sharing by reducing manual interactions and optimizing car usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual entry of trip parameters and searching for matches is implemented, then trip sharing service functionality is provided, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically predicting future trips based on historical data and patterns before the user needs to specify them. The trip prediction algorithm analyzes past trip behavior, calendar events, and transportation preferences to pre-generate trip specifications, eliminating the need for manual entry at the moment of need.
Solution Approach 2:
The system enables self-service by allowing the trip prediction algorithm to autonomously generate trip specifications without requiring user intervention. The algorithm learns from user behavior patterns and automatically creates trip entries, matching them with potential companions, and managing the entire process without manual input from the user.
2Loss of time
If automated trip prediction is implemented, then time for trip specification is reduced, but system complexity increases
Solution Approach 1:
The trip prediction algorithm serves multiple functions: it analyzes historical trip data, predicts future trips, generates trip specifications, matches users with potential companions, and manages trip scheduling. This multi-functionality consolidates what would otherwise require separate systems into a single integrated algorithm, managing complexity through consolidation rather than proliferation of components.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters such as prediction confidence thresholds, matching criteria weights, and data freshness requirements based on user behavior patterns and service conditions. This allows the system to adapt its computational intensity and complexity levels to match actual needs, reducing unnecessary processing while maintaining accuracy.
3Productivity
If dynamic role allocation is implemented, then efficiency of car usage is improved, but complexity of matching process increases
Solution Approach 1:
The system implements dynamic role allocation where driver and passenger roles are not fixed but can be exchanged based on trip characteristics, user preferences, and availability. The matching algorithm dynamically assigns roles by analyzing factors such as trip direction, user driving history, and stated preferences, allowing flexible optimization of car usage without requiring complex predefined role structures.
Data Source
AI summary
A method and system for allocating users as trip accompanies provides for: creating a first trip prediction algorithm, collecting input parameters, predicting by the first trip prediction algorithm using the collected input parameters as input, at least one first trip, executing a matching method, the matching method comparing attributes of the predicted at least one first trip with attributes of at least one second trip, and allocating the first and the second user to each other as trip accompanies in dependence of the matching score of the first and second potential trip data object.