Navigation Device Predicting Trip Destinations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current navigation systems are inefficient for short trips and daily commutes due to the tedious process of manual destination entry, leading to underutilization of trip sharing services and lack of relevant information for familiar routes.
Innovation Solution
A navigation device that predicts destinations based on a set of starting parameters using a machine-learning algorithm, allowing for automatic detection of regular trips and providing relevant information only, thereby eliminating the need for manual entry and enhancing user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual destination entry is used in navigation systems, then the system can provide detailed routing information, but the process becomes tedious and time-consuming
Solution Approach 1:
The system performs preliminary action by automatically determining the destination based on trip history data before the user needs to enter it manually. The destination prediction module uses stored trip information to pre-identify likely destinations, eliminating the need for manual input while maintaining routing information accuracy.
Solution Approach 2:
The navigation system performs self-service by automatically predicting and determining destinations using its own stored trip history. The system serves itself by using its internal data resources to fill in destination information without requiring user intervention, thus saving time while preserving routing details.
2Adaptability or versatility
If manual destination entry is required, then the system can adapt to user needs, but the effort required increases for short trips and daily commutes
Solution Approach 1:
The system uses feedback from trip history data to automatically determine destinations. By continuously analyzing past trips stored in the database, the system learns user patterns and automatically predicts destinations for similar trips, maintaining adaptability while significantly reducing entry effort for routine journeys.
Solution Approach 2:
The system changes the parameter of destination determination from manual input to automated prediction based on trip history parameters. By transforming the determination method from requiring user input to using stored historical data, the system maintains versatility while easing the operational burden for regular trips.
3Ease of operation
If navigation device is turned off for familiar routes, then manual entry effort is avoided, but traffic information is lost
Solution Approach 1:
The system performs preliminary action by automatically determining destinations and retrieving relevant traffic information before the user needs to operate the device. The destination prediction module pre-identifies destinations and the information retrieval module pre-fetches traffic data, ensuring both ease of operation and information availability without manual intervention.
Solution Approach 2:
The navigation system maintains continuity of useful action by continuously monitoring trip history and automatically determining destinations for familiar routes. The system remains active and informative for routine trips, continuously providing traffic information without requiring the user to manually input destinations or turn the device off.
4Adaptability or versatility
If trip sharing services are used, then collaboration is enabled, but participation is limited to a fraction of users
Solution Approach 1:
The system enables self-service trip sharing by automatically determining destinations and preparing trip data for sharing without requiring user intervention. The automated destination determination and information retrieval processes prepare trip data that can be automatically shared, increasing participation and capacity utilization by eliminating the manual effort barrier.
Solution Approach 2:
The system performs preliminary action by pre-determining destinations and preparing trip information for sharing before users need to participate. By automatically identifying trips suitable for sharing and preparing the necessary data, the system increases the fraction of users who can participate in trip sharing services, thereby improving overall capacity utilization.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
A navigation device (200) and computer implemented method (317) for predicting the destination of a trip, the method being executed by a navigation device, the method comprising the steps of: determining starting parameters, the starting parameters comprising at least the starting point, starting time and date of the trip, executing a destination prediction algorithm, the destination prediction algorithm taking the starting parameters as input and predicting a destination, wherein the destination prediction algorithm is generated by using information of a trip history; determining, upon arrival at the predicted or another destination, the actual destination.