Navigation System Predicting Destinations via Travel Pattern Analysis
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Solution Overview
Problem
Current mapping systems and navigation technologies lack the ability to predict events or popular destinations based on user travel patterns and real-time data, failing to provide timely and relevant information to users about potential events or points of interest along their route.
Innovation Solution
A system that analyzes mapping queries and travel patterns to predict destinations and events by accessing historical and real-time data, including location information from GPS systems, to suggest stops and provide information about events, traffic conditions, and alternative routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mapping systems only provide basic route navigation based on origin and destination, then the system complexity remains low, but the system cannot predict events or popular destinations based on user travel patterns
Solution Approach 1:
The system pre-processes and stores travel pattern data from multiple users in advance, creating a database of common routes and destinations. When a user requests navigation, the system queries this pre-existing data to predict popular destinations and events, rather than analyzing raw data in real-time. This preliminary action enables rich information delivery without requiring complex real-time computation.
Solution Approach 2:
The patent introduces an intermediary processing layer between basic navigation requests and the complex travel pattern data. This intermediary analyzes aggregated anonymous travel data from multiple users, identifies patterns and popular destinations, and presents this processed information to individual users. The intermediary transforms raw data into actionable insights without requiring the navigation system itself to become overly complex.
2Measurement precision
If the system analyzes detailed travel patterns and real-time data to predict destinations and events, then the information quality improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs heavy computational analysis of travel patterns during off-peak times or in batch processes, pre-calculating popular destinations, events, and route preferences. This pre-computed information is stored and quickly retrieved during actual navigation requests, achieving high prediction accuracy without real-time processing delays.
Solution Approach 2:
The system analyzes a subset of relevant travel pattern data rather than all available data. It focuses on recently collected patterns and highly relevant features, sacrificing some completeness for faster processing. This partial analysis approach maintains sufficient prediction accuracy while significantly reducing computational time and resources.
3Adaptability or versatility
If the system provides comprehensive information about events, traffic conditions, and alternative routes, then the user experience improves, but the information overload may overwhelm the user
Solution Approach 1:
The system provides different levels and types of information based on the specific context and user needs. Rather than uniformly presenting all available data, it tailors the information display to the local situation - highlighting events near the user's route, showing traffic conditions when relevant, and presenting alternative routes only when necessary. This localized information delivery maintains completeness while avoiding overwhelming the user.
Solution Approach 2:
The system selectively presents a subset of the most relevant information rather than all available data. It filters and prioritizes information based on user location, destination, time of day, and predicted interests, providing just enough detail to be useful without creating information overload. This partial presentation approach maintains versatility while improving ease of operation.
Data Source
AI summary
Techniques are provided for predicting a destination for a ground-based vehicle. Travel patterns are accessed and a series of locations of a ground-based vehicle are received. At least some of the received locations are related to at least one accessed travel pattern, and a destination of the ground-based vehicle is predicted based on relating the received locations with the travel patterns. An existence of an event may be predicted using the predicted destinations of a ground-based vehicle.