Alternate Route Selection Using Learned Rider and Driver Preferences
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Solution Overview
Problem
Existing navigation systems lack the ability to dynamically determine and present routes based on user-specific, machine-learned preferences, particularly in ride-sharing and delivery services, leading to incongruence between driver and rider preferences.
Innovation Solution
A networked system that identifies user preferences through past route selections and dynamically determines alternate routes, reconciling conflicts between driver and rider preferences to provide tailored navigation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If routes are determined based on estimated time of arrival (ETA), then navigation efficiency is improved, but user satisfaction deteriorates because routes do not align with user-specific preferences
Solution Approach 1:
The system changes the parameters used for route determination from generic ETA-based metrics to user-specific parameters derived from machine-learned preferences. By analyzing historical route selections and adjusting to individual user patterns, the system optimizes routes according to each user's unique preferences while maintaining efficiency
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing user route selections and using this information to refine and update user preference models. This iterative feedback loop enables the system to learn from actual user behavior and improve future route recommendations, aligning them more closely with user expectations
2Ease of operation
If manually indicated preferences are used (e.g., avoid highways, tolls, ferries), then user control is improved, but route options are limited to a fixed number of preferences
Solution Approach 1:
The system enables self-service by automatically learning and inferring user preferences from historical route selection data without requiring manual configuration. The machine learning models autonomously analyze patterns and generate personalized preference profiles, freeing users from manual preference setting while expanding route option flexibility through adaptive, data-driven preference inference
3Adaptability or versatility
If driver and rider preferences are independently determined, then individual needs are met, but conflicts arise between driver and rider route selections
Solution Approach 1:
The system introduces an intermediary mechanism in the form of a conflict resolution module that mediates between driver and rider preferences. This intermediary analyzes both sets of preferences, identifies conflicts, and reconciles them by selecting routes that satisfy both parties or determining when driver preferences should take precedence, thereby managing the complexity of multi-stakeholder route selection
Data Source
AI summary
Systems and methods for providing user control of alternate routes are provided. In example embodiments, a networked system receives a ride request from a user that indicates a drop-off location. The networked system identifies a current location of a user (e.g., a rider) and determines a plurality of routes from the current location of the user to a drop-off location. The plurality of routes is displayed on a user interface of a device of the user. In response, a selection of a route from the plurality of routes is received by the networked system. The networked system then causes presentation of a driving route corresponding to the selected route on a device of a driver and the device of the user.