Adaptive Route Proposals Using Historical Ride Data
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Solution Overview
Problem
Existing navigation and map services often fail to account for experienced drivers' preferred routes, leading to inconveniences such as mismatched pick-up and drop-off times for carpool participants, as they do not consider the actual routes taken by drivers which may differ from computed routes in terms of time and distance.
Innovation Solution
A system that captures and stores location and travel time information from completed routes, allowing for the generation of optimized route proposals for future rides by matching user input with stored route data, including start and end locations, and stopover points, using web-based and mobile applications connected to a backend server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If navigation systems use computed routes based on raw map data, then route planning is automated and efficient, but the routes may not match experienced drivers' actual travel patterns and preferences
Solution Approach 1:
The system captures actual route data from completed rides and uses this feedback to generate improved route proposals for future rides. The backend server stores completed route information and uses it to create optimized route proposals that reflect actual driver behavior patterns rather than relying solely on static map data.
Solution Approach 2:
The system performs preliminary capture and storage of completed route information before generating future route proposals. By storing actual route data from past rides in the backend server, the system prepares reference data that will be used to generate more accurate future route recommendations.
2Stability of the object's composition
If navigation systems use standard computed routes, then consistency is maintained, but pick-up and drop-off time calculations become inaccurate for carpool participants
Solution Approach 1:
The system changes the parameters used for route calculation by incorporating actual travel time data from completed rides. Instead of using only static map data, the system uses real-world travel times captured during completed routes to generate more accurate time estimates for future carpool rides.
Solution Approach 2:
The system uses feedback from actual completed ride times to adjust and improve future time calculations. The backend server stores actual travel time information and uses this feedback to generate more accurate pick-up and drop-off time proposals for future rides.
3Device complexity
If navigation systems rely on raw map data, then implementation is simple, but the system cannot adapt to experienced drivers' actual routing behaviors
Solution Approach 1:
The system serves itself by automatically capturing and storing completed route information without requiring manual input. The mobile application automatically captures location and time data during completed rides and transmits it to the backend server, which then uses this self-collected data to generate adaptive route proposals.
Solution Approach 2:
The system performs preliminary capture and storage of actual route data before generating adaptive proposals. By proactively collecting completed route information in the backend server, the system prepares the foundation for generating personalized route recommendations that adapt to individual driver behaviors.
Data Source
AI summary
User input including a start location and an end location of a desired ride may be received. A database may be searched for completed routes matching the user input. The completed routes may include location information and travel time information associated with the completed routes. Upon identifying one or more matched completed routes, a route proposal for the desired ride may be generated based on the one or more matched completed routes.