Alternate Route Selection Using Learned Rider and Driver Preferences
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Solution Overview
Problem
Conventional 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 suboptimal route selection that may conflict with driver or rider preferences.
Innovation Solution
A networked system that learns user preferences from past route selections and dynamically determines alternate routes, reconciling potential conflicts between driver and rider preferences to provide tailored navigation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If routes are determined based on estimated time of arrival (ETA), then routing speed is improved, but route suitability deteriorates because it does not account for user-specific preferences
Solution Approach 1:
The system performs preliminary actions by pre-computing multiple candidate routes and pre-learning user preferences from historical data before the actual routing request. This allows the system to quickly present pre-sorted route options based on ETA while also having pre-identified user-specific preferences ready for filtering, thus maintaining both speed and suitability.
Solution Approach 2:
The routing system dynamically adapts between two modes: a fast ETA-based routing mode for general queries and a preference-aware routing mode for users with learned preferences. The system dynamically selects which routing strategy to apply based on user profile availability and preference strength, allowing flexible adaptation between speed and suitability without compromising either extreme.
2Measurement precision
If multiple user preferences are considered simultaneously, then route precision is improved, but system complexity increases
Solution Approach 1:
The system segments the preference handling into distinct modules: preference learning module, preference filtering module, and route determination module. Each module processes preferences independently and passes results to the next stage, breaking down the complex task of handling multiple simultaneous preferences into manageable segments that reduce overall system complexity.
Solution Approach 2:
The system introduces preference vectors as intermediary representations that capture user preferences in a compressed, structured format. These preference vectors act as mediators between the raw preference data and the route determination algorithm, simplifying the interaction between multiple preferences and the routing system while maintaining precision in route selection.
3Ease of operation
If driver preferences are prioritized in route selection, then driver satisfaction is improved, but rider control is reduced leading to conflicts
Solution Approach 1:
The system merges driver and rider preferences into a unified route selection process by combining both preference sets and finding routes that satisfy both. The preference vectors from both driver and rider are combined and processed together, creating a collaborative routing approach that respects both parties' needs while resolving conflicts through weighted preference evaluation.
Solution Approach 2:
The system changes the parameter of preference weighting dynamically based on the specific route segment and user context. For certain route segments critical to rider safety or comfort, rider preferences are weighted higher; for segments where driver convenience is more important, driver preferences take precedence. This dynamic parameter adjustment allows both preferences to be satisfied without fixed prioritization.
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.