Probabilistic Destination Prediction Using Bayes Rule
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Solution Overview
Problem
Conventional GPS navigation systems require users to manually input their destination, which can lead to missed alerts and irrelevant information, especially for frequent travel locations like work or home, as users often fail to enter these destinations, resulting in a lack of relevant notifications during trips.
Innovation Solution
A system that probabilistically predicts a user's destination based on historical data, ground cover data, efficient route analysis, and trip time distributions, using Bayes rule to combine priors and likelihoods, such as personal destinations prior, ground cover prior, efficient driving likelihood, and trip time likelihood, to provide relevant information and route guidance without manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS navigation systems require manual destination input, then users can provide exact destination information, but users often fail to enter destinations for frequent locations, resulting in missed alerts and irrelevant information
Solution Approach 1:
The system automatically detects and predicts user destinations without requiring manual input. By analyzing GPS trajectory data, historical travel patterns, and contextual information, the system infers destinations autonomously, allowing users to benefit from destination-based alerts and information without the burden of manual entry
Solution Approach 2:
The system performs preliminary destination prediction by analyzing partial trajectories and travel patterns before the user completes their journey. This advance prediction enables the system to prepare and deliver relevant alerts and information about upcoming destinations, such as traffic conditions, parking availability, and point of interest recommendations
2Quantity of substance
If the system provides comprehensive information about all possible destinations, then users receive complete information, but users are overwhelmed with irrelevant data and cannot find useful information quickly
Solution Approach 1:
The system tailors information delivery to the specific predicted destination and user context. Instead of providing uniform information about all possible destinations, it selectively presents relevant alerts and data based on the inferred destination type, user preferences, and current trip characteristics, making information both complete and contextually appropriate
Solution Approach 2:
The system provides information selectively based on prediction confidence levels. When destination prediction confidence is high, it delivers comprehensive destination-specific information. When confidence is lower, it provides more general or preliminary information, avoiding overwhelming users with uncertain predictions while maintaining information completeness where appropriate
3Adaptability or versatility
If the system uses probabilistic prediction methods, then it can handle uncertainty in destination prediction, but the prediction accuracy may be reduced compared to exact matching methods
Solution Approach 1:
The system dynamically adjusts prediction confidence levels and information delivery based on trajectory completeness, historical pattern strength, and contextual evidence. As more data becomes available during the trip, the system refines its predictions and increases confidence, allowing it to adapt between probabilistic and near-certain prediction modes to optimize both flexibility and accuracy
Data Source
AI summary
The claimed subject matter provides systems and/or methods that facilitate inferring probability distributions over the destinations and/or routes of a user, from observations about context and partial trajectories of a trip. Destinations of a trip are based on at least one of a prior and a likelihood based at least in part on the received input data. The destination estimator component can use one or more of a personal destinations prior, time of day and day of week, a ground cover prior, driving efficiency associated with candidate locations, and a trip time likelihood to probabilistically predict the destination. In addition, data gathered from a population about the likelihood of visiting previously unvisited locations and the spatial configuration of such locations may be used to enhance the predictions of destinations and routes.