Route Recommendation Using Achieved Travel Data
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Solution Overview
Problem
Conventional route creation technologies, such as Dijkstra's algorithm and road learning models, fail to reflect individual preferences and often result in poor road connections due to reliance on local-area scored roads, neglecting factors like ease of travelability, safety, and memorability.
Innovation Solution
An information processing method that acquires past travel route data to create recommendable routes by connecting actual routes selected by experienced individuals, reflecting personal preferences and ensuring good road connections, while displaying route specifics like user experience and traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional route creation technologies (Dijkstra's algorithm, road learning models) are used, then optimal travel routes can be determined based on topology data or skilled driver history, but the routes fail to reflect individual user preferences and have poor road connection
Solution Approach 1:
The system performs preliminary action by acquiring and storing achievement data (past travel routes with start points, finish points, and achieved routes) before route creation. This pre-collected data serves as a foundation for generating recommendable routes that reflect actual user preferences and behaviors, rather than relying solely on theoretical algorithms or aggregated skilled driver history.
Solution Approach 2:
The system creates recommendable routes by copying and connecting actual achieved routes from the achievement data. Instead of generating entirely new routes through algorithms, the system replicates proven successful routes that users have actually taken, ensuring they reflect real user preferences and have good road connections.
2Productivity
If local-area scored roads are used for route creation, then route optimization can be performed, but the resulting routes have poor road connection and fail to reflect personal preferences
Solution Approach 1:
The system copies actual achieved routes from the achievement data to create recommendable routes. By replicating routes that users have successfully traveled before, the system ensures good road connection quality while maintaining efficient route creation, avoiding the need to re-optimize routes that have already been proven to work well.
Solution Approach 2:
The system uses achievement data as feedback from actual user travel experiences. This feedback loop allows the system to continuously improve route recommendations by learning from what routes users have actually taken and completed successfully, rather than relying on theoretical road scoring that may not reflect real-world usability.
Data Source
AI summary
A server acquires, from an achieved record database, achieved route information associating a start point, a finish point, and an achieved route being a past travel route between the start point and the finish point with one another, acquires a query including a start point and a finish point, creates one or more recommendable routes each having a start point and a finish point respectively agreeing with the start point and the finish point of the query by connecting one or more achieved routes recorded in the achieved data group, and outputs the one or more recommendable routes.